by Andrew A. Friestedt B.S., Construction Management, 1996 ...

THE CORRELATION BETWEEN MARKET FUNDAMENTALSAND APARTMENT REIT PERFORMANCE

by

Andrew A. FriestedtB.S., Construction Management, 1996

Arizona State University

and

Brian J. TusaB.S., Accounting, 1994

Boston College

Submitted to the Department of Urban Studies and Planning in Partial Fulfillment of theRequirements for the Degree of Master of Science in Real Estate Development

at the

Massachusetts Institute of Technology

September 2001

© Andrew A. Friestedt and Brian J. TusaAll rights reserved

The authors hereby grant to MIT permission to reproduce and to distribute publicly thispaper and electronic copies of this thesis document in whole or in part.

Author:.. Department of Urban Studies and Planning

August 3, 2001

Author:Department of frbin Studies and Planning

August 3, 2001

Certified By:William C. Wheaton

Professor of EconomicsThesis Supervisor

Accepted by:William C. Wheaton

Chairman, Interdepartmental Degree Program in Real Estate Development

THE CORRELATION BETWEEN MARKET FUNDAMENTALSAND APARTMENT REIT PERFORMANCE,

by

Andrew A. Friestedtand

Brian J. Tusa

Submitted to the Department of Urban Studies and Planningon August 3, 2001 in partial fulfillment of the requirements forthe Degree of Master of Science in Real Estate Development

ABSTRACT

This paper empirically examines the correlation between apartment REIT performance (asmeasured by Funds from Operations, Net Operating Income, Gross Rental Revenue, Net Income,Market Capitalization and CAP Rate) and market fundamentals (as measured by weightedaverage rent growth, weighted average employment growth, weighted average stock growth andweighted average excess demand). The objective of this paper is to explain the variance inhistorical apartment REIT performance based on historical market fundamentals.

Market fundamentals are broadly defined as the employment growth, population growth, stockgrowth and rent growth. More detailed definitions of market fundamentals are provided withinthe paper. Independent variables are developed from market data collected from 57 MSAs.Using these data, weighted averages are generated in order to isolate geographical effects. Theseindependent variables are regressed against measures of financial performance of apartmentREITs as of December 31, 2000.

The results show that weighted average rent growth (given NREI rent data) and growth inapartment units explain 37.1% of the variance in the percent change in FFO per unit and 37.8% ofthe variance in the percent change in market capitalization per unit across the sample of selectedapartment REITs. Furthermore, weighted average rent growth (given government rent data) doesa relatively poor job of explaining the variance in the percent change in FFO per unit.

Thesis Supervisor: William C. Wheaton

Title: Chairman, Interdepartmental Degree Program in Real Estate Development

Acknowledgements

We would like to thank our wives for providing endless support and understandingthroughout the project in the past school year. Their help made the difficult times seembearable. We would also like to thank our classmates for their availability to answer ournumerous questions.

We would also like to thank Professor Wheaton for his assistance in data collection aswell as providing leadership and guidance for the research.

The Correlation Between Market Fundamentals and Apartment REIT Performance 3

Table of Contents

1. INTRODUCTION ......................................................................................................... 06

1.1. Context of Research ...................................................................................... 06

1.2. Supply and Demand Issues Affecting Market Fundamentals ........................ 10

1.3. Research Issue ............................................................................................... 11

2. RESEARCH M ETHODOLOGY ........................................................................................ 13

2.1. Literature Review.......................................................................................... 14

2.2. Empirical Data Sources ................................................................................. 29

2.3. Inform ational Interviews ............................................................................... 31

3. STATISTICAL RESEARCH . ........................................................................................... 33

3.1. M arket Fundamentals .................................................................................... 33

3.2. REIT Financial Performance ......................................................................... 37

4. STATISTICAL EXAMINATION OF CORRELATION .......................................................... 42

4.1. Statistical Sample - REITs ............................................................................ 42

4.2. Statistical Sample - M arkets ......................................................................... 44

4.3. Descriptive Statistics - Dependent Variables ................................................ 49

4.4. Descriptive Statistics - Independent Variables .............................................. 50

5. REGRESSIONRESULTS ................................................................................................ 52

5.1. Additional Research ...................................................................................... 60

6. CONCLUSIONS. ........................................................................................................... 62

7. BIBLIOGRAPHY. .......................................................................................................... 64


Table of Exhibits

State Distribution of Apartments ................................................................. 06No. of Units on Property ............................................................................. 07Apartment Ownership ................................................................................. 08Apartment REIT Sample ............................................................................. 44MSAData Coverage ................................................................................... 45Statistical Summary - Dependent Variables ................................................ 49Statistical Summary - Independent Variables ............................................ 51WARG, WAEG, WASG Panel Data Regression Results ............................ 52Panel Data Regression Results (excluding apartments growth) .................. 54Panel Data Regression Results .................................................................... 55Cross Sectional Data Regression Results .................................................... 56

Table of Appendices

Appendix 1:Appendix 2:Appendix 3:Appendix 4:Appendix 5:Appendix 6:Appendix 7:Appendix 8:Appendix 9:Appendix 10:Appendix 11:Appendix 12:Appendix 13:Appendix 14:Appendix 15:Appendix 16:Appendix 17:

M arket Data .............................................................................................Apartments, Rental Revenue, NOI, FFO and NI .....................................M arket Cap ..............................................................................................W eighted Averages .................................................................................Cross-Sectional W eighted Averages ........................................................Geographic Concentration- 2000 ...........................................................Geographic Concentration- 1999 ...........................................................Geographic Concentration - 1998 ...........................................................Geographic Concentration- 1997 ...........................................................Geographic Concentration - 1996 ...........................................................W ARG Panel Data Results ......................................................................Panel Data Results (excluding apartment growth) ...................................Panel Data Results (including apartment growth) ....................................Cross Sectional Data Results ...................................................................Percent of Portfolio in M SA with Rent Data ...........................................1 0-REIT Index .........................................................................................10-REIT Data Results .............................................................................

The Correlation Between Market Fundamentals and Apartment REIT Performance

Exhibit 1:Exhibit 2:Exhibit 3:Exhibit 4:Exhibit 5:Exhibit 6:Exhibit 7:Exhibit 8:Exhibit 9:Exhibit 10:Exhibit 11:

67777879808185899397

101103116134144145146

5

1.0 Introduction

1.1 Context of Research

As of March 2000, there were approximately 17 million apartments in the US.' Of

these, approximately 54% are located in major metropolitan cities and another 40%

are located within the suburban ring of these metropolitan markets.2 Additionally,

construction started on 331,600 apartment units in 1999, and another $24.1 billion

worth of apartment building permits were issued.3 Below is a distribution breakdown

by state.

Exhibit

State Distribution of ApartmentsState # of Apts. Rank State # of Apts. RankAlabama 178,616 26 Montana 29,725 49Alaska 30,609 48 Nebraska 98,503 34Arizona 349,676 15 Nevada 189,801 24Arkansas 84,421 38 New Hampshire 63,719 40California 2,437,646 1 New Jersey 486,500 8Colorado 306,010 18 New Mexico 66,862 39Connecticut 188,261 25 New York 1,943,860 2Delaware 39,092 45 North Carolina 348,516 16Dist. of Columbia 107,852 33 North Dakota 49,836 42Florida 1,146,079 4 Ohio 620,066 6Georgia 453,548 10 Oklahoma 149,228 30Hawaii 93,071 35 Oregon 205,068 23Idaho 34,077 47 Pennsylvania 531,758 7Illinois 808,124 5 Rhode Island 57,175 41Indiana 286,892 20 South Carolina 158,402 29Iowa 130,681 31 South Dakota 37,883 46Kansas 118,836 32 Tennessee 274,060 21Kentucky 168,371 28 Texas 1,460,445 3Louisiana 171,738 27 Utah 92,343 36Maine 48,736 43 Vermont 20,208 50Maryland 358,454 14 Virginia 415,990 12Massachusetts 416,025 11 Washington 403,049 13Michigan 485,375 9 West Virginia 48,573 44Minnesota 313,112 17 Wisconsin 298,640 19Mississippi 88,021 37 Wyoming 15,538 51

'NMHC estimates based on U.S. Census Bureau data.2 NMHC tabulations of data from the U.S. Census Bureau's Current Population Survey for March 20003 U. S. Bureau of the Census, Current Construction Survey 1999


Missouri 243,994 22Total U.S. 17,153,065

Source. NMHC estimates based on U.S. Census Bureau data.Note: The apartment stock estimates include both occupied and vacant units. Forthese estimates, only units in structures with at least 5 or more units are counted.

Interestingly, the majority of apartments in the US are located in smaller structures,

not the large garden-style or high rise properties so commonly associated with

apartments.

Exhibit 2

# of Units on % ofProperty Total

2 - 99 units 64%100 - 199 units 14%200 - 299 units 9%300 - 399 units 5%400 - 499 units 3%500+ units 4%

Source: NMHC tabulation of unpublished data from the US. Census Bureau's 1995-1996Property Owners andManagers Survey.Note: Statistics refer to privately owned housing and do not include the 13,493 publichousing projects or their 1,326,000 apartments (HUD estimates for 1995-1996).

One reason that the apartment unit per property breakdown favors small properties is

the current U.S. ownership structure. In the aggregate, individuals and partnerships

owned 67.2% of all apartment units.4 Smaller units per property are characteristic of

individual or small partnership ownership, whereas institutional ownership tends to

own larger properties.

4 NMHC tabulations of unpublished data from the U.S. Census Bureau's1995-1996 Property Owners and Managers Survey.


Exhibit 3

Apartment Ownership 2-4 unit 5-49 unit 50+ unitPro es perties Properties

Individuals 84.80% 57.40% 19.20%Partnerships 3.90% 14.90% 32.70%Real Estate Investment Trusts 0.60% 1.10% 3.40%Real Estate Corporations 1.00% 4.00% 9.60%Other Corporations 0.90% 4.00% 4.60%Non-Profits/Co-Ops 0.60% 2.50% 6.00%Other 3.70% 4.60% 4.90%Not Reported 4.50% 11.50% 19.60%

TOTAL 100.00% 100.00% 100.00%Source. NMHC tabulations of unpublished datafrom the U.S. Census Bureau's1995-1996 PropertyOwners and Managers Survey.

As of 3rd quarter 2000, there were nineteen equity REITs whose portfolio is at least

90% comprised of apartments.5 This represents approximately 10% of the entire

REIT industry. These nineteen REITs comprise a market capitalization of

approximately $30 billion, or approximately 18% of the entire REIT industry market

capitalization. 6 These statistics speak to the importance and size of the apartment

sector in the REIT industry. Surprisingly, however, REITs only owned 2.1% of all

apartments in the United States as of December 31, 1999. 7 Yet many real estate

observers foresee a shift in the way real estate is owned, declaring that "the future of

commercial real estate is in securities, not direct ownership."8 If REITs truly become

the preferred method of ownership in the US, then apartment REITs certainly have

significant market share yet to obtain.

5 Constituent Companies and Relative Weights in the NAREIT Real-Time Index for July 1, 20016 Ibid7 NMHC tabulations of unpublished data from the U.S. Census Bureau's1995-1996 Property Owners and Managers Survey.8 Richard Schoninger, Prudential Securities, as quoted by Maria Wood, "Cash-Rich for the Next BuyingSpree"


With a growing trend in public ownership of real estate and apartments, coupled with

a huge market share still available to apartment REITs, a question then becomes, 'is it

possible to predict how apartment REITs will perform, given a certain geographic

concentration for their respective apartment portfolio?'

Prior academic research has touched on this topic, namely "The Determinants of

REIT Franchise Value" by Jim Young and a reprise of that paper by Rosanna Santos-

Wuest. In these papers, regional economic growth opportunities were examined as a

determinant of franchise value, which is the premium/discount to NAV or the value

of the company in relation to its net asset value. These papers, however, included

both apartment and commercial REITs and focused more on the NAV aspect of

REITs.

The purpose of this paper is to examine the correlation between apartment REIT

financial performance (proxied by FFO, rental revenue, rental net operating income,

net income, market capitalization and CAP rate) and market fundamentals (proxied

by weighted average rent growth, weighted average employment growth, weighted

average stock growth and weighted average excess demand). Apartment REIT

performance is empirically analyzed in conjunction with market fundamentals using

regression analysis in an attempt to explain the variation in historical performance of

apartment REITs.


1.2 Supply and Demand Issues Affecting Market Fundamentals

Some previous academic research has focused on supply-demand factors affecting the

investment demand for rental housing and accordingly, the apartment REIT industry.

Kenneth Rosen's paper (1996), "The Economics of the Apartment Market in the

1990s," succinctly explains how the market fundamentals discussed in Chapter 3.0

are affected by supply and demand issues, thus affecting fundamental and investment

demand for rental apartments. Rosen states that, "demand [for rental housing] will be

strong for areas with high in-migration, due to the young age characteristics of

movers, and the high costs of homeownership in many regions. Compounding this

effect is the continued growth in nontraditional households, which tend to be younger

and more likely to rent."9 The size, age distribution and growth rate by age group of

the population are critical factors in determining rental-housing demand. So too is the

increase in household formation relative to population, strong regional in-migration

due to job growth, and the increase in the relative affordability of rental housing on

the East and West coasts because of the sharp rise in prices of single family homes. ' 0

New household formation is being greatly affected by divorce, couples delaying

marriage, surviving elderly spouses' desire to remain in their own living quarters and

very young people living with the opposite sex. Rosen writes that "these dramatic

socioeconomic changes affecting all age groups [and increase in non-traditional

households] have led to a substantial increase in the demand for rental housing units

9 Rosen, "The Economics of the Apartment Market in the 1990's" [1996]o Ibid


because individual and nontraditional households are more than twice as likely as

family households to occupy rental housing units."'I

Rosen's paper illustrates the increase in the demand side for apartments, highlighting

apartments' increased importance in the coming years. The supply side should follow

suit as long as vacancies remain moderate and the capital supply for apartments

remains in-check. The combination of the increasing demand for apartments and

increased demand for public ownership for REITs helps to set forth the importance of

apartments in the U.S.

1.3 Research Issue

Market research firms, such as Torto Wheaton Research, have demonstrated an

ability to predict MSA rent growth, employment growth and stock growth with

reasonable accuracy. Also predictable, based on US Census Data, is age distribution

and group size. Since it is possible to predict market fundamentals with reasonable

accuracy, is it then possible to explain how apartment REITs perform based on

geographic portfolio concentration? In order to make this jump, three hypotheses are

set forth and tested empirically and/or through the academic research of others. They

are:

1. weighted average employment growth and weighted average stock growth

effectively measure demand and supply growth, which should be a proxy for

weighted average rent growth;


" Ibid

11

2. weighted average rent growth should determine net income and FFO / NOI

growth; and

3. FFO growth should determine a REIT's market capitalization growth.

The purpose of this paper is to explore these three hypotheses and determine if such a

correlation exists between market fundamentals and apartment REIT performance.

This correlation will be tested empirically through market data collected for

numerous MSAs and apartment REIT performance data collected on 19 REITs.

The following Chapter describes the research methodology and literature used to

review the correlation between apartment REIT performance and market

fundamentals. Chapter Three provides a more detailed account of the research

methodology and defines the dependent and independent variables. Chapter Four

describes the statistical sample pool used to empirically test the research findings, and

Chapter Five summarizes the results of the empirical tests. Conclusions are presented

in Chapter Six.


2.0 Research Methodology

This study defines the different measures of apartment REIT financial performance

and market fundamentals and quantifies a relationship between the two through

empirical analysis. Numerous explanatory (independent) variables were utilized to

empirically examine and statistically explain an apartment REIT's performance using

regression analysis. Regression analysis measures the relationship between one

economic variable, the "dependent variable" and one or more explanatory variables,

the "independent variables".

The investigation into the determinants of apartment REIT performance began as a

theory proposed by Professor William Wheaton of the MIT Center for Real Estate

and Economics Department. The research was initiated by a review of academic and

industry literature in real estate and finance to establish how a REIT's financial

performance is measured, as well as the appropriate components of market

fundamentals. This work was complemented by informational interviews with

academic practitioners to establish a scope of components for the regression analysis,

particularly with respect to market fundamental data. Through the literature reviews

and informational interviews, the appropriate dependent and independent variables

were established in order to complete the regression analysis.

It is important to note that this paper's research is limited to REITs focusing solely on

apartments and excludes manufactured home REITs. Apartment REITs were selected

because:


1. The U.S government publishes reliable market data on MSAs

2. Apartment properties should better reflect market fundamentals due to the

apartment lease structure. When compared to office or industrial leases, the

apartment lease is generally shorter term (6 - 18 months) versus an office lease,

which can be as long as 20 years (or even longer). Therefore, the office data is

smoothed over a longer period and doesn't move as quickly as apartment data.

The shorter-term nature of the apartment lease can more accurately reflect the

current market conditions when compared to the longer-term office leases signed

many years ago. Apartments essentially can re-price immediately. Additionally,

office or industrial leases are affected by tenant quality, amount of space leased,

concessions, and/or tenant improvements. Thus in some instances, it can be

difficult to determine the effective lease rate, which can skew the market data.

2.1 Literature Review

A review of literature pertaining to apartment housing, apartment REITs and the

REIT industry as a whole, yielded useful information, but also revealed very little

research to date on the correlation of market fundamentals and apartment REIT

performance. Over the last several years, considerably more has been written about

the REIT industry. The National Association of Real Estate Investment Trusts

(NAREIT), an industry group, as well as publications from independent research

firms specializing in the REIT sector, such as Green Street Advisors and The

Penobscot Group, sponsored much of this research. These publications provided the

most useful insight into REIT performance measures. Academic research and


research sponsored by the National Multi Housing Council (NMHC) provided the

greatest information regarding the apartment industry, particularly economic

information utilized in generating market fundamental data. The NMHC and

NAREIT websites, which provided "one-stop shopping" for apartment and REIT

literature, were a tremendous resource for providing information. A large volume of

writing is in non-academic publications covering current topical issues, particularly

supply and demand issues that may affect the apartment industry, and therefore

apartment REITs, as well as issues concerning REIT performance.

Publicationsfrom Trade Organizations

The NMHC and NAREIT organizations have published several papers relevant to

the apartment industry or apartment REIT industry. Among the papers reviewed,

one of the more notable is NMHC's "Performance Across Local Markets" by Jack

Goodman, which discusses the geographic correlation across local apartment

markets. Specifically, the paper seeks to aid investors in maximizing the

geographic diversification of an apartment portfolio, utilizing rent increases and

vacancy rates among the measures. Two key results in this paper were: 1) some

apartment markets are easier to forecast than others and 2) growing apartment

markets are not always profitable markets. 12 Goodman's research concludes that

rent predictability in some apartment markets is possible while in others it is very

difficult. The reason is that annual percentage increases in rents over the last ten

years is highly correlated in some markets with annual job growth, multi-family

12 Goodman, "Performance Across Local Apartment Markets", [1999].


construction and vacancy rates, while in others there is no correlation at all.

Based on this study, there is no obvious pattern. 13

Goodman's research is important because this thesis attempts to explain

apartment REIT performance, which is predicated upon the ability to predict

market fundamentals, such as rent growth, job growth and stock growth, by

establishing a correlation between performance and fundamentals.

NAREIT has published several reports that were useful in gathering information

on REIT return measures as well as understanding the industry's position on

Funds from Operation (FFO), a topic of much discussion. According to

NAREIT's "Investing in Real Estate Investment Trusts", there are many factors

affecting REIT returns, such as real estate fundamentals, earnings and dividends,

and company fundamentals. An important conclusion of this research is that

although real estate fundamentals may affect the commercial real estate business

as a whole, understanding a REIT's geographic concentration may impact a

certain stock's price more than others, because the economy is not equally strong

in all geographic regions, and economic demand may not increase the demand for

all property types at the same time.' 4 This is important in this thesis as it supports

the use of geographic concentration weights in the empirical analysis discussed in

Chapter Five. By appropriately weighting the geographic concentration of a


13 Ibid14 Ibid

16

REIT's portfolio, it is possible to compare different apartment REITs based on the

relative strength or weakness of the real estate markets that exists in that portfolio.

The NAREIT "White Papers on FFO " were informative in defining and

calculating FFO (discussed in greater detail in chapter 3.2). Moreover, the

publication helped explain the history and intended purpose of the financial

measure. Since FFO has been the subject of much debate among industry

observers, this publication set out to clarify the industry's position. That is, FFO

is intended to be a supplemental financial measure of a REIT's performance

which specifically addresses the issue that historical cost accounting, in particular

depreciation, can be misleading, as historical real estate values have risen and

fallen with market conditions.' 5 Therefore, FFO excludes historical cost

depreciation in its calculation. Understanding FFO is important since it is one of

the dependent variables used in this paper's empirical analysis.

Publications from Independent Research Firms

Widely regarded as one of the key financial measures of a REIT, FFO was

initially considered the most important dependent variable in the regression

analysis. Green Street Advisors, however, has issued research reports that

indicate net income may be just as important as FFO. Green Street's "The High

Cost of Owning Real Estate" states that FFO dramatically overstates performance

for most REITs while net income dramatically understates performance for most

REITs. Although it is unclear which measure comes closer to the actual


economic truth, Green Street feels that net income is a conceptually superior

performance measure because real estate does indeed depreciate (contrary to what

FFO calculates) and net income properly matches revenues with expenses (which

FFO does not do). 16

A change in analyst sentiment also changed the initial importance given to FFO.

Three major Wall Street firms, Merrill Lynch, Morgan Stanley and Citigroup's

Salomon Smith Barney, recently announced that REIT analysts would add an

additional forecast for financial results using net income per share. 17 The reason

for the change is that FFO does not conform to Generally Accepted Accounting

Principles (see definition of FFO above). Many analysts feel that FFO represents

a pro forma number. "As with other pro formas, the issue with FFO is there is no

common definition, and people pick and choose the numbers that they use to

calculate FFO.' 8 A major advantage to using net income per share is that gains

and losses on asset sales, a large part of REITs' earnings, are included in the

calculation, which is contrary to the FFO calculation.

As a result of Wall Street's increased emphasis on net income as well as

independent research reports such as Green Street's support of net income, a net

income dependent variable was added for the empirical analysis.

15 NAREIT "White Paper on Funds from Operations", [1999]16 Green Street Advisors, "The High Cost of Owning Real Estate", [1999].17 Starkman, Weil, "Three Firms to Emphasize Different Metric for REITs", [2001].18 Green Street Advisors, "The High Cost of Owning Real Estate", [1999].


Academic Research

Much of the REIT academic research to date has focused on the relationships

between publicly traded equity REITs and the larger stock market forces for

publicly traded equities. Han and Liang (1995) studied the historical performance

of REITs by determining: 1) whether REITs performed differently from the

market portfolio, on a risk-adjusted basis, over the period 1970 - 1993; 2)

whether REIT performance varies significantly over time; and 3) whether the

outcomes of REIT performance studies are sensitive to the choice of performance

benchmarks and REIT samples. Han and Liang concluded that over the 1970 -

1993 period, REIT performance was similar to that of a passively managed

portfolio of three-month treasury bills and stock market portfolio. Also, REIT

performance was not stable over the sample period, concluding that studies that

focus on short time periods may lead to varying conclusions. Most importantly

though, they found that the use of the S&P 500 index lead to results that

overstated the performance of the REIT industry portfolios, relative to the stock

market portfolio. 9

More recent research on the same topic has yielded similar results. Ziering, Liang

and McIntosh (1995) note that "the total return correlation between REITs and the

S&P 500 Index, as well as other standard stock market indexes, spiked

dramatically during the second half of 1998, reversing a trend toward decreasing

19 Han and Liang, "The Historical Performance of Real Estate Investment Trusts", [1995].


correlation over the past three years."20 Ziering, Liang and McIntosh believe

though that this trend is temporary and that there will be a continuation of the

gradual disconnect between the performance of the REIT sector and other capital

market indexes.2 '

Sanders (1997) found that equity REIT returns have a high correlation to the

Wilshire Small Value index and the high-yield corporate bond index. Sanders,

however, notes that there is still a considerable amount of unexplained variation

in REIT returns that cannot be diversified away with major stock and bond

indexes, particularly since 1991.22

Hartzell and Mengden (1986) concluded that equity REIT prices track the stock

market, mirroring the volatility, but have income characteristics that resemble

unsecuritized real estate.23 Similarly, Giliberto found that equity REITs

correlation with the stock market has declined over time and correlation with

bond returns has increased. 24 Intuitively, this makes sense since many investors

view the cash flow stream of leases similar to the cash flow stream of bonds.

20Ziering, Liang, and McIntosh, "REIT Correlations with Capital Market Indexes: Separating Signal FromNoise", [1999].21 Ibid.22 Anthony B. Sanders, "The Historical Behavior of REIT Returns", in Real Estate Investment Trusts,Garrigan and Parsons, [1997].23 Hartzell and Mengden, "Equity Real Estate Investment Trusts - Are They Stocks or Real Estate?",Solomon Brothers, Inc., [1986].24 S. Michael Giliberto, "Equity Real Estate Investment Trusts and Portfolio Diversification", SalomonBrothers, Inc., [1989].


Chen and Peiser examine the risk and return characteristics of REITs and their

effects on returns. Specifically, they examined (1) the performance of"new"

versus "old" REITs, (2) the risk-return trade-off of different REIT sectors, and (3)

how certain characteristics of REITs such as size and portfolio diversity affect

performance. Their research indicated that REITs are more highly correlated to

the S&P Mid-Cap 400 index than with the S&P 500 index. This is not surprising

since REITs have smaller capitalizations than the large companies included in the

S&P 500 index. However, the correlation between REITs and the S&P Mid-Cap

400 index is still not very strong.25

Their results also indicate that diversified REITs (multiple property types)

performed worse than non-diversified REITs, meaning the market did not value

diversification by property type as much as it valued more focused investment

strategies. Other results of their study indicate that small REITs ($20 - 100

million in market cap) had higher returns than the large REITs, but also had a

higher standard deviation. Geographically concentrated REITs (investments in

only one state) showed significantly higher returns but also significantly higher

standard deviations than geographically diversified REITs (investments in four or

more states.)

Since investors often perceive investment in equity REITs as comparable to direct

investing in unsecuritized real estate, academic research has attempted to explain

the correlation between equity REIT returns and unsecuritized real estate returns.

25 Chen and Peiser, "The Risk and Return Characteristics of REITs - 1943 - 1997", [1999].


The basic premise to these studies is that equity REIT returns share some

unspecified factor or factors that also affect more traditional unsecuritized real

estate. However, the correlation between indexes of equity REITs and real estate

returns is conflicting, which casts doubt on this assumption. Giliberto's research

(1990) concludes, "the correlation between indexes of equity REITs and real

.,26estate returns is zero."26

Follow-up research by Giliberto finds that the residuals from regressions of both

real estate series on financial asset returns are significantly correlated. After

removing financial asset market influences, the co-movement between equity

REIT returns and the NCREIF Property Index is significant. Giliberto states that

there is a common factor (or factors) associated with real estate that affects both

sets of returns. This may be the pure real estate market fundamentals that are not

shared with financial asset markets but that influence both equity REITs and the

NCREIF Index (private, institutional real estate returns). Additionally, lagged

values of the equity REIT residuals help explain variation in the conventional

unsecuritized real estate return residuals. Giliberto concludes that investors do

capture some portion of real estate market returns by investing in REITs, although

they must accept volatility that approaches that of stocks.27

Lieblich, Pagliari and Webb (1997) conducted similar research to Giliberto's.

Their research was motivated by the theory that long-run behavior of REITs

26 S. Michael Giliberto, "Equity Real Estate Investment Trusts & Real Estate Returns", [1990]27 Ibid


should generally follow the behavior of the underlying real estate assets.

Accordingly, the NAREIT Index (historical public REIT returns) is compared to

the NCREIF Index. Unlike previous research, Lieblich, Pagliari and Webb

focused on dividends, investment values and dividend yields to compare the

indexes. 28 As with previous research, a weak statistical relationship between total

returns for securitized and unsecuritized real estate was found. In the short-term,

dividends, investment value and changes in dividend yields between secured and

unsecured real estate were statistically weak even when lags of up to two years

were examined. They concluded that the long-term path of prices for securitized

and unsecuritized real estate exhibited the strongest relationship and that the weak

relationship for explaining total returns may be more attributable to the volatility,.~~~~~~~~2

of dividends and/or changes in dividend yields.29

Nelling and Gyourko (1998) examined the predictability of monthly returns on

equity REITs over the period 1975-1995 and compared them with small and mid-

cap firms. Using a time series approach, their study indicated statistically

significant evidence of predictability of monthly returns. The average monthly

return, however, was insufficient to cover transactions costs necessary to exploit

the prediction. 30

28 Lieblich, Pagliari and Webb, "Historical Behavior of REIT Returns: A Real Estate Perspective", in RealEstate Investment Trusts, Garrigan & Parsons, [19971.29 Ibid.30 Nelling and Gyourko, "The Predictability of Equity REIT Returns", [1998].


Myer and Webb (1994) examined the return properties of equity REITs, common

stocks and unsecuritized commercial real estate with the retail industry used as the

common thread. Again, one of the goals of their research was to explore the long-

term relationship between securitized and unsecuritized real estate. The results of

Myer and Webb's research found evidence that a positive contemporaneous

relationship exists between common stocks and equity REITs. Thus a common

factor (or factors) affects the returns of common stocks and equity REITs that are

unrelated to the general stock market. Their results, however, were inconclusive

in drawing a correlation between commercial unsecuritized real estate and either

equity REITs or common stocks.31

Chan, et al (1990), using a multifactor arbitrage pricing model for the period 1973

- 1987, found that four factors (unexpected inflation, changes in the risk and term

structure of interest rates, and the percentage change in the discount on closed -

end stock funds) consistently drive equity REIT returns. The impact of these

variables is approximately 60 percent of that for common stocks.32

Liu and Mei (1992) examined the predictability of equity REIT returns and their

co-movement with other assets. They found that expected excess returns are

more predictable for equity REITs than for bonds and small-cap and value-

31 Myer and Webb, "Retail Stocks, Retail REITs and Retail Real Estate", [1994].32 Chan, Hendershot and Sanders, "Risk and Return on Real Estate: Evidence from Equity REITs", [19921.


weighted stocks. They also found that equity returns move more closely with

small-cap stocks than with large-cap stocks.33

Similar to previous research, Goldstein and Nelling (1999) examined the

diversification potential of REITs by comparing their return behavior over the

period 1972 - 1998 to the returns on common stocks, small stocks, treasury bills,

long-term government bonds, corporate bonds and the inflation rate. The major

difference in Goldstein and Nelling's work is that the behavior of REIT returns

are investigated in advancing and declining stock markets separately to examine

the claim that REITs provide a good hedge against general stock market declines.

Additionally, both equity and mortgage REITs are studied. The results of the

study indicated that REITs "do not have symmetric hedging properties." In

particular, both equity and mortgage REITs are more highly correlated with

stocks when the market is declining than when it is advancing. 34

Clayton and MacKinnon's (2001) research is very similar to Goldstein and

Nelling in that Clayton and MacKinnon also study the links between REITs and

financial asset returns. However, Clayton and MacKinnon include unsecuritized

real estate returns as one of the factors in order to evaluate the claim that REITs

are more highly linked with direct property markets. Accordingly, Clayton and

MacKinnon examine the correlation between NAREIT and an "unsmoothed" or

"de-lagged" NCREIF index. The NCREIF index is subject to a number of

33 Liu, Hartzell, Grissom, Greig and Mei, "The Predictability of Returns on Equity REITs and Their Co-movement with Other Assets", [1992].


limitations, most notably NCREIF returns lag true market returns as a result of

appraisal smoothing at the individual property level and the inclusion of outdated

information. Clayton and McKinnon counteract these limitations by employing

the Transaction Value Index (TVI) created by Fisher and Geltner (2000). The

TVI aims to undo the lag-induced distortions in the NCREIF index and produce a

more realistic index of property returns. The results of Clayton's and

MacKinnon's work are consistent with previous studies in that REIT market

returns are highly correlated with small cap stocks and uncorrelated with direct

real estate returns over the 1978-1998 time period. However, since 1992, the

equity NAREIT returns were positively correlated and statistically significant

with the de-lagged NCREIF index while the correlation between REITs and

stocks in general fell by a large amount. Clayton and MacKinnon conclude that

"with growth and maturation in the market, the performance of REITs has

become less like the performance of stocks and more like that of the underlying

real estate since the REIT boom of 1992 or 1993." 35

The work of Liang et al. (1996), which relates specifically to apartment REITs

and apartment real estate, is very applicable to this thesis. Their research

examines the possibility that equity apartment REITs provide a proxy for the

ownership of apartment sticks and bricks. For this purpose, a hedged apartment

REIT index was constructed by removing the return components of stocks in

general and non-apartment equity REITs from returns of equity REITs that invest


34 Goldstein and Nelling, "REIT Return Behavior In Advancing and Declining Stock Markets", [1999].

26

in apartment real estate. The resulting "double-hedged" apartment REIT index

was found to satisfactorily track the performance of appraisal-based apartment

real estate. Also, the hedged apartment REIT index does not suffer from appraisal

smoothing or seasonality issues. Therefore, the hedged apartment REIT index

can be used as a proxy for apartment real estate.36 They determined this was

significant in making portfolio diversification decisions.

A fair amount of research has been published on REIT pricing, most of which is

in the context of REIT performance relative to the stock market or indices. A

major impact on REIT pricing, the Revenue Reconciliation Act of 1993 paved the

way for greater institutional ownership of REITs. Prior to this law, REITs had to

abide by strict rules in order to qualify for the significant tax advantages that

REITs enjoy. One of the rules that REITs had to comply with was the "five or

fewer" rule. This rule disqualifies a REIT from advantageous tax status if more

than 50% of its shares are held by five or fewer shareholders. This rule restricts

the sources of income and retention of earnings, as well as limited institutional

investment interest in REITs. Since REITs traditionally have small market

capitalizations, institutional buyers found it difficult to accumulate a sufficient

position in an individual REIT without jeopardizing the REIT's tax status.

The 1993 Act modified the "five or fewer" rule, allowing each institutional

beneficiary, rather than the fund itself, to be considered an individual REIT


35 Clayton and MacKinnon, "The Time-Varying Nature of the Link between REIT, Real Estate andFinancial Asset Returns", [2000].

27

shareholder. Therefore, institutional investors can now take sizable positions in

REITs without jeopardizing the REITs status. The result has been an increase in

institutional ownership of REIT securities. Chan, Leung and Wang (1998) found

institutional ownership in REITs ranged from 12% to 14% between 1986 and

1992. Institutional ownership increased to 17% in 1993, 26% in 1994 and 30% in

1995.37

Crain, Cudd and Brown (2000) studied the increased REIT ownership by

institutional investors specifically as the increased ownership relates to

unsystematic risk. Their theory is that unsystematic (idiosyncratic) risk, which

can be removed through diversification, will become less important due to the

highly diversified nature of pension funds.38 Since systematic risk can be

removed through diversification, it should become less important in the pricing of

equity REITs. They found strong empirical evidence that after enactment of the

1993 Act, the risk structure of equity REIT pricing changed significantly.

Unsystematic risk represents variations in equity REIT returns unexplained by

movement in the market index (S&P 500). Their study results indicate that since

the 1993 Act the role of unsystematic risk in explaining equity REIT returns

declined significantly over time,39 the same corresponding time period as the

increase in institutional ownership.

36 Liang, Chatrath and McIntosh, "Apartment REITs and Apartment Real Estate", [1996].37 Chan, Leung and Wang, "Institutional Investment in REITs: Evidence and Implications", [1998].38 Crain, Cudd and Brown, "The Impact of Revenue Reconciliation Act of 1993 on the Pricing Structure ofEquity REITs", [2000].


Fields, Rangan and Thiagarjan (1998) conducted research directly applicable to

FFO growth as it relates to market capitalization. Fields et al. compared net

income and FFO to help explain contemporaneous REIT pricing and annual stock

returns. Since previous research has indicated that price should be expressed as a

linear function of net income, book value and dividends, Fields et al. used end of

year book value of equity and dividends, in addition to net income and FFO, as

explanatory variables for stock prices. Using regression analysis, Fields et al.

found that net income has more explanatory power for stock returns than does

FFO. Moreover, net income explained 61% of the variation in price compared to

FFO, which explained approximately 57.8% of the variation in price. However,

the results were not statistically significant. They concluded that net income only

marginally explains equity REIT prices better than FF. 40

2.2 Empirical Data Sources

The U.S. Census Bureau was the primary source for market data, such as rent growth,

employment growth and stock growth (which was calculated from permit data).

These data are considered extremely reliable because they are comparable across

markets and comprehensive of the conditions within the market. The figures

comprise the entire rental stock, not just one structure type or quality grade. Housing

construction data, as measured by building permits by size of structure, are also

available from the Census Bureau. Since over 80% of all multi-family, construction

is built for rental occupancy, multi-family permits are a good indicator of additions to

39 Ibid.


the apartment stock.4 1 The estimates of rents, vacancies, and construction are

generated using large, scientifically drawn samples and therefore considered very

accurate.42

The great extent to which publicly available rental housing data exists is both a

strength and a weakness. Metropolitan areas are comprised of various, diverse sub-

markets, and metro averages smooth through that diversity. Additionally, rental

housing includes not only large properties, but also single-family home rentals and

small multi-family properties. These smaller properties often perform differently

than larger, institutional properties, which can affect the data and essentially

"average" out the diversity. 43

Data was also obtained from National Real Estate Index ("NREI"), a provider of real

estate/economic research. NREI's publications analyze commercial real estate trends

and demographic changes in over 50 metropolitan markets throughout North

America. NREI's proprietary database also provides quarterly prices, rents and cap

rates for the office, industrial, retail, and apartment sectors at the metropolitan,

regional and national levels.

NREI's apartment rent data is gathered directly from apartment owners and managers

in 58 metropolitan markets, as well as appraisers, brokers and institutional advisors.

The Correlation Between Market Fundamentals and Apartment REIT PerformanceI

40 Fields, Rangan and Thiagarajan, "An Empirical Evaluation of the Usefulness of Non-GAAP AccountingMeasures in the Real Estate Investment Trust Industry", [1998].41 Goodman, "Performance Across Local Apartment Markets", [1999].42 Ibid.

30

The rents include both Class A and B-rated properties. NREI defines Class B

properties as those built or substantively renovated between 1980 and 1988.

Additionally, the prototypical apartments NREI tracks are typically garden-style or

campus-style, ranging from 100-300 units per property and maintaining a certain

standard of appearance and amenities appropriate for the geographic region.

Much of the REIT-specific data (geographic concentration, revenue, NOI, FFO, units

owned, interest expense, market share, etc.) were obtained from SNL DataSource, a

fee-based service provider that tracks and researches corporate information in

financial services, real estate and energy industries. Through an MIT subscription

agreement, the SNL DataSource Real Estate Securities Module provided detailed

geographic information on all owned-properties in which an applicable apartment

REIT has an equity interest as well as all pertinent financial data, such as rental

revenue, rental NOI, FFO, net income, market capitalization and CAP rate. SNL

obtains all REIT data from either SEC public documents or directly from a REIT's

published literature.

2.3 Informational Interviews

Having completed the initial literature review, interviews with academics were

conducted to better understand the optimal dependent and independent variables

required for the regression analysis. Moreover, these interviews helped to better

organize the databases for regression analyses, resulting in the most efficient method

for empirical analysis. Once initial regression analyses were produced, additional

43 Ibid.


academic interviews yielded more independent variables, in hopes of generating

stronger results. These additional interviews were critical as they strengthened the

thesis core. Perhaps the main benefit of the academic interviews was the additional

data source suggestions, which facilitated the data collection efforts. Through these

data source suggestions, contacts at source providers were gained, providing access to

data that was otherwise thought to be unattainable within the time constraints of this

thesis.


3.0 Discussion of Variables

3.1 Components of Market Fundamentals

From the literature review and academic interviews, the important components of

market fundamentals and REIT financial performance were determined for the

empirical analysis. The components of market fundamentals are:

1. Weighted average rent growth

2. Weighted average stock growth

3. Weighted average employment growth

4. Weighted average excess demand growth

5. Percent change in apartments

6. Percent of portfolio in MSA

A short explanation of each component follows:

Weighted Average Rent Growth (WARG)

Rental growth represents the annual percent change in rental units for U.S. markets.

Weighted average rent growth was calculated by multiplying each REIT's portfolio

concentration (number of units in each MSA divided by the total number of units in

the portfolio) by the percent change in rent growth for that MSA from the previous

year. Summing up the results for each REIT equals the weighted average rent

growth. WARG was calculated with two different sets of market rent data -

government and NREI data - in order to fill gaps where data were missing.

Government - Weighted Average Rent Growth


Government - WARG was calculated by multiplying the REIT's portfolio

concentration by the percent change in rent for a given MSA (given government

rent data). If the government data lacked information on the MSA, NREI rent

data were used. If the NREI rent data lacked information on the MSA, the

national rent average was used. As previously discussed, the government data

includes all units rented in the U.S. regardless of size. It is obtained through U.S.

Census Bureau Survey.

NREI- WeightedAverage Rent Growth

NREI - WARG was calculated by multiplying the REIT's portfolio concentration

by the percent change in rent for a given MSA (given NREI rent data) If the

NREI data lacked information about the MSA, government rent data were used.

If the government data lacked information on the MSA, the national rent average

was used. Again, NREI data focuses on larger, institutionally owned class A and

B properties and obtains data directly from property owners, appraisers and

pension funds.

Weighted Average Stock Growth (WASG)

Stock growth represents the annual change in units available for rent. Weighted

average stock growth was calculated by first constructing the historical stock growth

for 57 MSAs (identified in Appendix 1) by taking the number of"renter-occupied

housing units" as reported by the U.S. Census Bureau in 1990 and adding permits

(1990 - 1999) to the respective MSA's stock number. For example, the number of


renter-occupied housing units for a certain MSA in 1991 was calculated by adding the

number of renter-occupied housing units in 1990 to the number of multi-family

permits issued in 1990. This process was repeated through 2000. After calculating

the percent change in stock growth from year to year, that number was multiplied by

each REIT's portfolio concentration. Where stock growth was not available for an

MSA, the national stock growth minus the sum of the stock growth for the 57 MSAs

that had stock growth information was used. Summing up the product of stock

growth and portfolio concentration for each REIT produced the WASG.

Weighted Average Employment Growth (WAEG)

Employment growth measures the change in number ofjobs for a specific MSA.

Weighted average employment growth was calculated by multiplying the REIT's

portfolio concentration by the growth in employment for each MSA. Where

employment growth was missing for an MSA, the national employment growth minus

the sum of the 57 MSAs that had employment growth information was used.

Summing the product of employment and portfolio concentration for each REIT

produced the WAEG.

Weighted Average Excess Demand (WAED)

This variable measures the extent to which employment is growing faster or slower

than stock in each MSA where market data was available. Weighted average excess

demand was calculated by taking the difference between the weighted average

employment growth and weighted average stock growth.


Percent Change in Apartments

As discussed in Chapter 3.2, the dependent variables were calculated using per unit

growth. Per unit values were used to compare REITs on an "apples to apples" basis.

One problem with the per unit calculations though is the accounting method

employed by REITs for newly acquired or disposed properties. For example,

properties that were acquired in the second half of the calendar year were included in

the per unit calculation if the units were still in the portfolio at year-end. However,

the operating results associated with newly acquired units were only for a short stub

period, therefore understating that REITs' data, particularly during a high acquisition

period.

To help smooth the volatility caused by this accounting issue, the independent

variable "Percent Change in Apartments" was added. This variable was added to all

regressions in order help offset the effects of the per unit data aberrations. The

percent change in apartments variable was calculated by taking the difference in the

number of apartments each REIT held in its portfolio between year 2 and year 1 and

dividing that result by the number of apartments in year 1. This was done for five

years, 1996 - 2000.

Percent of Portfolio in MSA

As indicated in Chapter 4.2, Exhibit 5, government and NREI rent data was obtained

for 57 MSAs for a five-year period. However, rent data were unavailable for markets


outside the 57 MSAs in certain years. In those instances, national rent data were used

as a substitution. In order to offset the possible effects of too many national data

entries, the independent variable "Percent of Portfolio in MSA" was calculated. For

each apartment REIT portfolio in the pool (see Chapter 4.1 Exhibit 4) the total

number of apartments in the 57 MSAs (Exhibit 5) was calculated. This number was

then divided by the total number of apartments in the corresponding REIT's portfolio.

The result is the concentration of a REIT's portfolio in MSAs where rent data were

available.

An additional measure was undertaken to negate the possible influence of national

data. The ten REITs with the greatest concentration of units in MSAs for which we

had government data were calculated and used in a regression analysis to verify the

construction accuracy of the government data. This was used as a test to try and

eliminate the noise of REITs with high concentrations of units in MSAs where

government rent data did not exist.

3.2 Components of REIT Financial Performance

The measures of REIT financial performance are:

1. Percent change in rental revenue per unit

2. Percent change in rental net operating income per unit

3. Percent change in funds from operations per unit

4. Percent change in net income per unit

5. Percent change in market capitalization per unit

6. CAP rate


Percentage Change in Rental Revenue per Unit

Rental revenue data was collected from SNL DataSource, which obtained the data

from company SEC filing documents. The percentage change was calculated year-to-

year for a five-year period (1996 - 2000). Rental revenue is a significant financial

measure of apartment REITs because it represents the revenue generated solely by

real estate assets and excludes "other income" such as furniture rental, utility bill

backs, laundry, etc. By excluding all other non-real estate revenue, the real estate

revenue is isolated, providing a better analysis when compared to rent growth data.

In order to properly compare the REITs on an equal basis, percentage change in rental

revenue per unit was used as a component of REIT financial performance.

Comparing REIT performance on an absolute basis (i.e., not on a per unit basis) is

misleading and non-informative because the results of highly acquisitive REITs will

show abnormal absolute growth. Note that for all per unit calculations, unit

information was obtained from SNL DataSource and crosschecked against annual

reports. Immaterial differences existed in certain instances, which can be explained

by the criteria for determining actual units in a REIT's portfolio for a given year.

These criteria are defined as follows: in order for a unit to be considered part of a

REIT's portfolio, the REIT had to own the unit as of December 31 t of a given year.

This implies that units under construction as of December 3 1st were included while

assets sold anytime before December 3 t were excluded.


Percentage Change in Rental Net Operating Income per Unit

Rental net operating income was also collected from SNL DataSource. The

percentage change was calculated in a similar method to rental revenue, over the

same five-year period. Rental net operating income ("Rental NOI") is defined as

rental revenue from real estate assets less all real estate-related expenses, including

tenant improvements, leasing commissions and revenues but excludes financing

costs. Rental NOI is the cash flow available to service the debt. This is a significant

financial measure because many real estate properties are valued by applying a

capitalization rate to the rental NOI to derive a property valuation. Additionally,

since the NOI reflects the cash flow available for financing which is property specific,

it can be a useful measure to compare properties, particularly on a per unit basis. As

was the case with rental revenue, it is necessary to compare the REITs on an "apples

to apples basis". Therefore, percentage change in NOI is calculated on a per unit

basis.

FFO per Unit

Funds from operations ("FFO") is perhaps one of the most widely used measures of

financial performance by industry analysts. FFO is net income (as measured by

Generally Accepted Accounting Principles), excluding gains or losses from sales of

properties, plus depreciation and amortization and after adjustments for

unconsolidated partnerships and joint ventures.44 Heavily endorsed by NAREIT,

FFO was created to become a "standard supplemental measure of REIT operating

performance that excluded historical cost depreciation from - or "added it back" - to


GAAP net income."4 5 FFO was intended to allow the prices of various REIT stocks

to be compared with each other and in terms of the relationship between REIT stock

prices and FFO. Thus, FFO was to be used for determining a capitalization multiple

similar to a P/E ratio.46 Perhaps the biggest argument for using FFO as a performance

measure is that real estate, unlike other assets, doesn't depreciate predictably over

time and certainly not as quickly as GAAP accounting implies. A building might

take 70 or 80 years to become fully obsolete, not the 30 or 40 years allowed by

GAAP.47 Due to the importance placed on FFO by industry observers, FFO is an

integral measure in the empirical analysis. FFO data were obtained from SNL

DataSource.

The percentage change is used since it is the growth in FFO that is being isolated.

This percentage change is calculated on a per unit basis in order to properly compare

each REIT on a "level playing field". Additionally, calculations on a per unit basis

help negate the effects of mergers or industry consolidation (discussed in chapter 4.1).

Net Income per Unit

As discussed in Chapter 2.1, a shift among industry analysts has occurred favoring

the use of net income over FFO. Due to this shift in analyst sentiment by major Wall

Street research firms, net income is included in the empirical analysis as a dependent

variable. Net income figures were obtained from SNL DataSource for five years

(1996 - 2000). Many industry observers favor net income because it represents

44 NAREIT, "White Paper on Funds from Operations", [1999].45 Ibid.46 Ibid.47 Ibid.


Generally Accepted Accounting Principles and properly matches income and

expenses, a fundamental accounting rule. The net income used in this analysis is after

extraordinary items and gains on sales.

As previously discussed, a per unit basis of net income is the appropriate measure

used to compare REITs in this study because it "levels the playing field".

Market Capitalization per unit

Market capitalization equals the share price multiplied by the number of shares

outstanding, for a respective year. Using SNL DataSource, historical market

capitalization information was obtained. Using six years of data, the percent change

in market capitalization was calculated for five years (with the exception of Roberts

Realty and Cornerstone Income Realty Trust, in which only three years of data was

available so only two years of percentage change information was calculated.)

Market capitalization per unit is used because it reflects how the stock market values

the equity of the company.

Cap Rate

As a measure of a REITs portfolio's yield potential, cap rate was calculated by

dividing rental NOI by the market capitalization for each respective REIT. The

objective of this test was to measure a REITs weighted average rent growth relative to

cap rate to determine the correlation.


4.0 Discussion of Statistical Sample and Markets

4.1 Statistical Sample- REITs

The initial database included all equity residential REITs in existence as of December

31, 2000. From this sample, several REITs were excluded because dependent

variable data were not available or consistent with apartment data. For example, all

manufactured home REITs were excluded from the sample since manufactured home

rental/revenue characteristics are different from apartment characteristics and data is

difficult to obtain for the product. Presidential Realty Corp. was also excluded since

it is a hybrid REIT (not exclusively equity).

It is important to note that the apartment industry has experienced significant

consolidation over the past few years. Among the REITs studied that have

experienced consolidation over the last five years are:

* Avalon Communities and Bay Apartment Communities, consolidated into

AvalonBay (AVB) in June 1998;

* Merry Land Investments Inc. was acquired by Equity Residential Trust in 1999;

* Security Capital Pacific Trust and Security Capital Atlantic Inc. merged into

Archstone Communities Trust in 1999.

Where two apartment REITs merged in the middle of a calendar year, the target's

operating results (Rental Revenue, Rental NOI, FFO, Net Income, and Market Cap)

and total number of apartments held were added to those of the acquirer's in that

calendar year, providing for seamless results over the five year period (1996 - 2000).


Several REITs have also de-REITed recently due to either low stock valuations, the

inflexibility of the REIT tax structure, increasing liquidity of the private real estate

markets or a strong desire to return to private status to avoid the pressures of being a

public company. Some examples of these in the apartment industry which were

excluded from the pool were:

* Irvine Apartment Communities Inc. was privatized in 1999.

* Berkshire Realty Company was taken private in 1999 by an alliance of its

management team, a Goldman Sachs real estate equity fund, and the Blackstone

Group, a financial advisor and global real estate investor.

Where apartment REITs broke-up or de-REITed in the middle of a calendar year, the

target's operating results (Rental Revenue, Rental NOI, FFO, Net Income, and

Market Cap) and total number of apartments held were subtracted from those of the

acquirer's in that calendar year, providing for seamless results over the five year

period (1996 - 2000).

In May 2001, Archstone Communities and Charles E. Smith announced a merger

creating one of the largest apartment REITs in the U.S.. Due to the timing of the

merger (post December 31, 2000), the two REITs remain in the sample pool as

separate entities.

The final REIT sample consists of nineteen equity apartment REITs listed below,

whose portfolios contain at least 90% apartments:


Exhibit 4

Apartment REIT Ticker1 Associated Estates Realty Corporation AEC2 Apartment Investment & Mgmt. Co. AIV3 AMLI Residential Properties Trust AML4 Archstone Communities Trust ASN5 AvalonBay Communities Inc. AVB6 BRE Properties, Inc. BRE7 Camden Property Trust CPT8 Equity Residential Properties Trust EQR9 Essex Property Trust, Inc. ESS10 Gables Residential Trust GBP11 Home Properties of New York, Inc. HME12 Mid-America Apartment Communities, Inc. MAA13 Post Properties, Inc. PPS14 Roberts Realty Investors, Inc. RPI15 Summit Properties Inc. SMT16 Charles E. Smith Residential Realty SRW17 Cornerstone Realty Income Trust TCR18 Town and Country Trust, The TCT19 United Dominion Realty Trust, Inc. UDRSource: Constituent Companies and Relative Weights in theNAREIT Real-Time Index for July 7, 2001

4.2 Statistical Data - Markets

Torto Wheaton Research provided market data (i.e. employment growth, permits, and

rent growth) on 57 MSAs (see Appendix 1). Employment growth, in MSAs where

data were not readily available, was calculated by taking the difference between the

National employment growth and sum of employment growth in the 57 MSAs. These

calculated data were used as a proxy for employment growth for all MSAs outside of

the 57 where Torto Wheaton Research provided information. Permit data for MSAs

outside of the 57 MSAs were calculated in the same fashion.

Rent growth was calculated following two different procedures as outlined in Chapter

3.1. The following table highlights the MSAs where government and NREI data

were used. For markets that lacked both government and NREI rent data, the


National average was used as a rent growth proxy. Note that an "x" denotes data

obtained.

Exhibit 5

MSA YearI ALBUQUERQUE 1995

19961997199819992000

2 ATLANTA 199519961997199819992000

3 AUSTIN 199519961997199819992000

4 BAKERSFIELD 199519961997199819992000

5 BALTIMORE 199519961997199819992000

6 BOSTON 199519961997199819992000

7 CHICAGO 199519961997199819992000

8 CHARLOTTE 199519961997199819992000

GOV'TRENT

XXXX

XXXXXX

XXXX

XXXXXXXXXXXXXXXXXX

XXXX .X

NREIRENT

XXXXX

XXXXX

XxXXX

XXXXX

XXXXX

XXXXx

XXXXX

MSA Year9 CINCINNATI 1995

19961997199819992000

10 CLEVELAND 199519961997199819992000

11 COLUMBUS 199519961997199819992000

12 DALLAS 199519961997199819992000

13 DENVER 199519961997199819992000

14 DETROIT 199519961997199819992000

15 FT. LAUDERDALE 199519961997199819992000

16 FT. WORTH 199519961997199819992000


NREIRENT

XXXXX

XXXXX

XXXXX

xXXXX

XXXXX

XXXXX

XXXXX

GOV'TRENT

XXXXXXXXXxXX

XXXx

XXXXXXXXXXXxXXXXXXXXXXXXXXXxXX

45

MSA Year17 FRESNO 1995

19961997199819992000

18 HARTFORD 199519961997199819992000

19 HONOLULU 199519961997199819992000

20 HOUSTON 199519961997199819992000

21 INDIANAPOLIS 199519961997199819992000

22 JACKSON 199519961997199819992000

23 JERSEY CITY 199519961997199819992000

24 KANSAS CITY 199519961997199819992000

GOV'TRENT

XxXxxxxxxxxxx

xxxx

xxxx

xxxxxxxxxxx

xXXXXXXXXXX

XXXX

XXXX

XXXXXXXXXXXX

NREIRENT

XXx

Xxxxxx

xxxxx

xxxxx

xxxx

xXXXX

XXXXX

XXXXX

XXXXX

MSA Year25 LOS ANGELES 1995

19961997199819992000

26 LONG ISLAND 199519961997199819992000

27 LAS VEGAS 199519961997199819992000

28 MEMPHIS 199519961997199819992000

29 MIAMI 199519961997199819992000

30 MILWAUKEE 199519961997199819992000

31 MINNEAPOLIS 199519961997199819992000

32 NASHVILLE 199519961997199819992000


GOV'TRENT

XXXXXXXXXXXX

XXXX

XXXX

XXXXXXXXXXX

XXXXXX

XXXX

NREIRENT

XXXXx

XXXXX

XXXXX

XXXXX

XXXXX

XXXXX

XXXXX

iI

i 1

46

MSA Year33 NEW YORK 1995

19961997199819992000

34 OAKLAND 199519961997199819992000

35 OKLAHOMA CITrY 199519961997199819992000

36 ORANGE CNTY. 199519961997199819992000

37 ORLANDO 199519961997199819992000

38 OXNARD 199519961997199819992000

39 PHILADELPHIA 199519961997199819992000

40 PHOENIX 199519961997199819992000

GOV'TRENT

XXXXXXXXXXXX

XXXXXX

XXXX

XXXXXXXXXXXX

XXXXX

NREIRENT

xXXXXX

XXXXX

XXXXX

XXXX

XXXX

XXX

MSA Year41 PITTSBURGH 1995

19961997199819992000

42 PORTLAND 199519961997199819992000

43 RALEIGH/DURHAM 199519961997199819992000

44 RIVERSIDE 199519961997199819992000

45 SACRAMENTO 199519961997199819992000

46 SALT LAKE CITY 199519961997199819992000

47 SAN DIEGO 199519961997199819992000

48 SEATTLE 199519961997199819992000

The Correlation Between Market Fundamentals and Apartment REIT PerformanceI

GOV'TRENT

XXXXX

XXXXXX

XXXX

XXXXXX

XXXX

XXXX

XXXXXXXXXXXX

NREIRENT

XXXXX

XXXXX

XXXXX

XXXXX

XXXXX

XXXXX

XXXXX

XXXXX

47

MSA Year49 SAN FRANCISCO 1995

19961997199819992000

50 SAN JOSE 199519961997199819992000

51 ST LOUIS 199519961997199819992000

52 STAMFORD 199519961997199819992000

53 TAMPA BAY 199519961997199819992000

54 TUCSON 199519961997199819992000

55 WASHINGTON D.C 199519961997199819992000

56 W. PLAM BEACH 199519961997199819992000

GOVTRENT

XXXXXXXXXXXxxxxxxx

xxxxxx

xxxx

xxxxxxxxxxx

xX ,,

XXXXXX

XXXXXX

XXXX

XXXXXXXXXXXX

NREIRENT

XxXxxx

xxxxx

xxxxx

xxxxx

xxxxx

xxxx

xXXX

XXXXX

XXXX

XXXX

X

XXXX

X

XXXX

MSA Year57 WILMINGTON 1995

19961997199819992000

58 NON 57 MSAs 199519961997199819992000

59 NATION 199519961997199819992000

GOV'TRENT

XXXXXXXXXXXXXXXXXX

xxxxx

xxxx

xXXXXX

XXXXX


|

_I_

Ii

I

INREIRENT

48

4.3 Descriptive Statistics - Dependent Variables

The following exhibit highlights the dependent variables used to proxy for apartment

REIT returns. Of note in Exhibit 6 is AvalonBay's Rental Revenue per Unit, Rental

NOI per Unit, Percentage Change FFO per Unit and Percentage Change Net Income,

which is significantly higher relative to the other 18 apartment REITs. AvalonBay's

portfolio is significantly concentrated in markets considered to have high barrier

entries and high costs of living. Specifically, AvalonBay's 2000 portfolio is

concentrated in San Jose (12%), San Francisco (5%), Boston (5%), New York (5%),

Seattle (7%) and Washington D.C (14%). This provides anecdotal evidence that

these markets are high rent growth markets, supported by Exhibit 6, which shows that

AvalonBay has the second highest WARG given both Government and NREI data.

Exhibit 6

Statistical Summary -Dependent Vxiable

Avr s OverA Five Ye Period (1996. 2000)

% A Re l Rev per % RealNOIper % ANen Inome M .rCapper % A MrkeCapUnit Unit % A %O fpN INaer Uni per Un Ne I e Uni Ru

# REIT Ticker A SDen Av SdDev A SIDev AvA [ S dDev D- A S A SdD A SdD

I AnIoc iaedEsaresRe.icComoration AEC 3.28% 6.38% 2.09% 5.05% -11.9% 8.96% -1.4% 42.83% 6.2% 4463S 400% 0.38% -2.72% 7.30' 8.86% 1.14e

2 Ap men nvesment& Mm Co. AIV 1103% 46.39% 15.40% 46.63% 1663% 364% -0.49% 37.31 517 60.09 0.9 19.66% 4513% 5.15% 0.96%

3Am Residental Propenies AML .9.74% 11.84% -8.0% 12 -.1.46% 7.89% 17.84% 2523% 4299 26.14% 323% 0.41% -5.96% 6.51% 7.24% 0.71

4Archsione CommunidesTnu ASN 10.82% 23.75% 1330% 2457% 1437% 234% 2121% 61.91% 42.42% 80.37% 6.57% 1.55% 1004% 131% 6.69% 0.87%

5 AvanBay Communites Inc. AVB 4471% 6180% 42.37% 56.61% 40.13% 36.90% 509% 63.8B% 89.72% 77.06% 8.41% 3.79% 37.34% 41.47% 6.04% 1.08%

6BRErpenies, Inc. BRE 4.46% 16,58% 5.14% 17.23% -2.44% 2596% 1.49% 65.51% 4459% 134.37% 8.27% 0.65% 11.64% 19,88% 6.25% 1.185i

7 CamdcenPropey Trus CPT 31.86% 3071% 38.84% 38.70% 36.52% 3413% 7.09% 144. 77.85% 149.88 3.72% 1.46% 42% 351% 7.80% 0.84%

8 Equiy ResidntialPropeies Tu EOR 17.21% 30.04% 18.71% 2816% 2402% 33.89% 25.89% 21.14% 542% 12.91 .02% 1.28% 16.82% 15.88% 6.91% 1.37%

9Es-sxPrpen Tn. nc. ESS 30.08% 59.48% 31.35% 59.18 39.02% 73.32% 30.97% 48.72% 5419% 103.59 8.21% 0.82% 57.20% 119.58% 6.52% 1.01

10 Gables sdendri Trnsi GBP 6.92% 6.77% 8.60% 8.11 6.30% 7.97 19.92% 22.62% 39.09% 2099% 6.01% 0.44% 3.41% 13.10 7.69% 1.04%

1I Home Propedesof New Yori In. H-M 7.23% 17.14% 10.5% 17.57% 1034% 1.35% 19.67% 33.29 78.65% 63.95% 4.90% 0.32% 9.00% 1061% 7.06% 1.32%

12 Ml.Amei aAparmen Communies Inc. MAA 7.04% 2210% 8.18% 23.82% 3.04% 20.40 2336% 62.54% 3532% 6.59% 4.14% 0.20% 3.73% 8.22% 8.87% 1.55%

13 Pos Propenl Ile. PPS 3.99% 22.91% 438% 22.66% 4.75% 22.64% 8.53% 23.21% 30.26% 26.06% 8.32% 0.95% 2.10% 20.10% 6.56% 1.00%

14 RobensR .e ylnveios lnc. RPI 3.04% 27.94% 6.51% .30 3.07% 31.3% 4.3 6 453% 94.60% 56.51% 216.71% 5.76% 0.19 .9.99% 15.09% 8.60% 0.34%

15 SummiPropres Inc. SMT 8.10% 3.46% 1008% 3.66% 7.52% 4.43% 31.99% 41.59% 46.41% 45.50% 7.06% 0.85% 7.90% 12.41% 7.40% 0.81%

16 Charls .Smith R.sidential Rady SRW 3.80% 9.75% 5.36% 10.92% 8.43% 1Z.73 52.39% 426% 79.00% 5449% 8.60% 1.27% 7.97% 1476% 7.39% 0.40%

17 Cometon Reiy nom Trusl TCR 8.79% 35.49% 9.74% 35.76% -7.84% 47.84% -80.37% 213.37 -119.42% 267.32% 413% 0.914 Z.86% 25.08% 9.09% 0.97%

18 Tn and CoumryTs The TCT 444% 5.31% 467% 7.23 3.28% 6.01% 4471% 57.22% 49.34% 6.97% 4.70% 0.48% 7.06% 4.57% 9.01% 0.41

19UnldDominion Ray Tru, Inc. UDR 16.59% 19.92% 17.84% 21.13% 15.08% 2401% 1.35% 3641% 22.74% 373% 453% 0.48 177% 19.8% 845% 1.99%ToiS AdD 11.45% 12.87% 10.99% 20.67% 40.72% 5.73% 12.326% 7.43%Total S De 12.51% 12.62% 14.90% 32.63% 44.74% 1.8(0% 15.98% 1.13%


4.4 Descriptive Statistics - Independent Variables

The following exhibit highlights the independent variables used to proxy for

apartment REIT returns. Exhibit 6 illustrates the effects of market fundamentals on

REIT performance. Of note in the Exhibit is that Essex Property Trust experienced

the highest WARG in both the Government and NREI data. Essex's portfolio

concentration is primarily located on the West Coast (Long Beach and Los Angeles

20%, Seattle 20%, Oakland 15%, Orange County 15%). AvalonBay also experienced

high WARG. Similarly, AvalonBay's portfolio is heavily weighted on the West

Coast (San Jose 12%, Los Angeles 6%, San Francisco 5%, Oakland 5%, Orange

County 5%). Camden Property Trust experienced the highest employment growth

based on market concentration. The three largest markets in Camden's portfolio are

Las Vegas (20%), Dallas (15%) and Houston (13%). Charles E. Smith experienced

the highest stock growth relative to the other REITs, which contributed to it also

having the lowest WAED. Charles E. Smith is significantly weighted in the

Washington D.C./Virginia/Maryland markets (64%).

The Correlation Between Market Fundamentals and Apartment REIT Performance 50a

Exhibit 7

Statistical Summay -Independmt VariablesAvemies Over A Five Year Perid (1996 -200)

Oovt WARG NREI WARG WAEG WASG WAED % Totl in MSA % D # f Apts

REIT . .Ticker Aiv{I i{~ Ii~i~er iI~ An SidDe. uA, $ AnvREff Sieker Aer | Std~ev Avr | St~ev A v StDev' StdDv Sa D- A $Dev Av dDev

I AzrciatedEstaRealtyCorporaftion AEC 2.82% 0.70% 3.96% 0.76% 1.92% 0.26% 1.17% 023%< 0.75%Y 0.43% 72.43% 2.24% 12.32% 8.92%

2 Apearnetl esment&MgmLCo. AIV 3.15% 0.40% 3.89% 0.56 6% 3.00% 0.60% 1.86°%. 0.10% 1.14% 0.60% 70.96% 5.11% 76.25% 103.65%

3 AmliR.etentlalPrtpetties. Trt AML 3.62% 0.46% 3.30% 0.74% 3.57% 021% 2.47% 0.22% 1.10% 0.34% 96.65% 1.17% 22.30% 10.26%

4 Archtne C mm..unitis Trut ASN 3.53% 0.4OA6% 3.74% 0.49Y 3.62% 0.29% 2.20% 0.13% 1.42% 0.37% 5.77% 5.14% 13.47% 23.97%

5 Aval.onBevy Commuies Inc. AVB 3.82% 0.90M 5.76% 1.26% 2.4S% 0.44% 1.51% 0.12% 0.97% 0.49% 81.68% 2.05% 20.81% 22.29%

6 BRE P wd.ertez Ic. BRE 3.55% 1.20% 4.51% 1.09% 3 5% 0.22% 1.68% 0.13% 2.17% 0.19% 96.91% 1.02% 29.46% 30.02%

7 C aden PrertyTrcat CPT 3.13% 0.51% 3.37% 0.79% 4.37% 0A.49% 2.92% 0.28%Y. 1.44% 0.4A5% 91.00% 0.69% 3.40 6.97%

8 EqnityResienti Properties Trust EQR 337%/ 0.37% 3.88°/0. OAI% 3.29% 0.34% 2.0% 0.09% 1.20%. 0.41% 78.980% 2.24% 24.56% 27.63%

9 EtexPropertyTrut Inc. ESS 4.11% 1.02% 6.22Y. 2.21% 3.16% 0.79% 124% 0.19% 1.92% 0.80% 87.73% 1.08% 16.76% 45.65%

10 OGbe, ResidentalTrust GBP 3.41% 0.32%A 3.04% . 1.1 3.78%/. 0A.45% 2.41% 0.35% 1.37%/ 0.586 97.99% 1.21% 17.75% 15.60%Y

1 Ho e Properties. fNewYork Inc. HME 3.03% 0.39% 4.37% 1.01% 2.01% 0.44% 1.02% 0.16% 0.99/. 0.57% 42.08% 20.59y 50.81% 33.51%

12 Mid.-AerAicAparmentCommunite,. Inc. MAA 3.40% 0.19% 4.03% 0.61% 2.56% 0.42% 1.57% 0.1IS% 1.00%/ 0.50% 52.80% 3.12% 14.98% 24.46%

13 Pot..Prpcetie, Inc. PPS 3.87% 0.50% 2.87% 0A.47%/ 4.15% 0.21% 2.77% 0.24"% 1.38% 0.24% 97.18% 1.66% 21.71% 2413%

14 Robert Realty nvetn, Inc. RPI! 3.43% 0.21% 2.11% 0.80% 4.18% 0.63% 2.70% 0.17% 1.47%. 0A.49 100.0% 0.00% .85%, 22.53%

15 SummitProperti Inc. SMT 3.3% 0.45 32Y 0.47 3.26% 0.33% 2.65%, 0.45Y 0.61% 0.34% 85.71% 7.08%e 11.36% 6.99Ye

16 Chades E.Smith Reidential Realy SRW 2.42% 1.13% 4.08% 1.55% 2.05% 1.07% 3.57% 0.37% -1.52% 1.14% 1I0.00% 0.00%. 17.89% 8.09%

17 Corerstne.RealtylncomeTr-st TCR 3.76% 0.45% 3.92% 0.62% 3.17% 0.46% 2.14% 0.24% 1.03%' 0.55% 62.20% 6.92% 91.17% 17421%

18 Town andCountyTrus The TCT 2.54%Y 1.01% 3.77% 0.991% 2.06% 0.89% 1.70% 0.38 % 0.36% 0.73% 77.80% 2.37% 2.36% 5.37%

19 UnidDminion RealityTrust Inc. UDR 3.32Y, 0.35% 3.87% 0.65% 3.07% 0.39% 2.00% 0.22/, 1.07% 0.53% 65.07% 3.92% 9.41% 9.10%Total Avg 3.35%Total Std Dev 0.43%

3.89% 3.13% 2.09% 1.05% S1.25%0.93% 0.7S% 0.67% 0.75% 16.73%

24.51%23.51%

51The Correlation Between Market Fundamentals and Apartment REIT Performance

5.0 Regression Results

Hypothesis #1 - weighted average employment growth and weighted average stock

growth effectively measure demand and supply growth, which should be a proxy for

weighted average rent growth.

Results: A regression analysis of WARG (given NREI and Gov't rent data) versus

WAEG and WASG resulted in statistical models that explain (see Exhibit 8):

1. 55.1% of the variance in WARG (given Gov't rent data) using a cross-sectional

data approach with 19 observations;

2. 10.5% of the variance in the WARG (given Gov't rent data) using a panel data

approach with 95 observations; however the t-stat on the WASG variable is not

significant;

3. the regression results using WARG (given NREI rent data) were not significant.

Exhibit 8

Panel Data Regression Results

Dependent Variable # of Obs Indep. Variable & Coef t-Stat R SquareWAEG WASG WAEG WASG

Govt WARG 95 0.295090251 -0.21395749 3.244325084 -1.81772392 0.105450901NREI WARG 95 -0.07772166 -0.92457699 -0.5661638 -5.2044401 0.278205674

Cross-Section Data Regression Results

Dependent Variable # of Obs Indep. Variable & Coef t-Stat R SquareWAEG WASG WAEG WASG

Govt WARG 19 0.479364224 -0.33222551 4.39489386 -2.61276007 0.551425904NREI WARG 19 -0.27577776 -0.69669975 -1.0480718 -2.27123058 0.407624388

Discussion: While the results indicate WAEG and WASG are highly correlated with

WARG, given the cross-sectional data, the results are inconclusive because the

sample size is less than 30. The Gov't WARG results also show there is a positive

correlation between WARG and WAEG and a negative correlation between WARG

and WASG. Interestingly, when using NREI rent data, the t-stats are insignificant


and the sign on the WAEG variable, in both result summaries above, is negative.

Intuition tells us that as employment grows in a market, rent growth in that market

should be positive, not negative.

The results do not show conclusive evidence to support hypothesis#l.

Hypothesis #2 - weighted average rent growth should determine Net Income and FFO /

NOI growth.

Results: A regression analysis of the variables described in Chapter 3.0 resulted in

statistical models (see Exhibits 9, 10, 11), with significant t-stats, which explain:

1. 37.1% of the variance in the percent change in FFO per unit across the sample of

selected apartment REITs using a panel data approach given NREI WARG and

apartment growth;

2. 26.4% of the variance in the percent change in FFO per unit across the sample of

selected apartment REITs using a cross-sectional data approach given NREI

WARG;

3. 32.1% of the variance in the percent change in Rental Revenue per unit across the

sample of selected apartment REITs using a panel data approach given NREI

WARG and apartment growth;

4. 3 7.0% of the variance in the percent change in Rental Revenue per unit across the

sample of selected apartment REITs using a cross-sectional data approach NREI

WARG;

The Correlation Between Market Fundamentals and Apartment REIT Performance___53

5. 31.2% of the variance in the percent change in Rental Net Operating Income per

unit across the sample of selected apartment REITs using a panel data approach

given NREI WARG and apartment growth;

6. 29.0% of the variance in the percent change in Rental Net Operating Income per

unit across the sample of selected apartment REITs using a cross-sectional data

approach given NREI WARG;

Exhibit 9


Panel Data Regression Results Excludiog % Change in Apartments

Dependent Variable # of Obs I ndependent Variable & Coefficient t-Stat R SquareWARG WARG WARG WARG(Govt) (NREI) WAEG WASG WAED (Govt) (NREI) WAEG WASG WAED

% A in FFO per Unit 94 0.589 0.135 0.0002% A in FFO per Unit 94 7241 2.983 0.0882% A in FFO per Unit 94 3.767 1.057 0.0120% A in FFO per Unit 94 -1.203 -0.259 0.0007% A in FFO per Unit 94 4.513 1.266 0.0171% A in Rental Revenue per Unit 94 3.394 0.810 0.0071% A in Rental Revenue per Unit 94 6.420 2.728 0.0748% A in Rental Revenue per Unit 94 1.987 0.577 0.0036% A in Rental Revenue per Unit 94 -3.945 -0.888 0.0085% A in Rental Revenue per Unit 94 4.371 1.275 0.0174% A in NOI per Unit 94 3.233 0.772 0.0064% A in NOI per Unit 94 5.823 2.460 - 0.0617% A in NOI per Unit 94 2.760 0.804 0.0070% A in NOI per Unit 94 -2.809 -0.632 0.0043% A in NOI per Unit 94 4.467 1.304 0.0182% A in Net Income per Unit 93 -14.853 -1.335 0.0192% A in Net Income per Unit 93 7.642 1.159 0.0145% A in Net Income per Unit 93 6.007 0.653 0.0047% in Net Income per Unit 93 15.776 1.334 0.0192% A in Net Income per Unit 93 -3.428 -0.371 0.0015Net Income per Unit 93 49.728 3.225 0.1026Net Income per Unit 93 7.642 0.797 0.0069Net Inome per Unit 93 22.510 1.711 0.0312Net Income per Unit 93 28.979 1.702 0.0309Net Income per Unit 93 5.230 0.391 0.0017% A in Market Cop per Unit 91 -2.881 -0.566 0.0036% A in Market Cap per Unit 91 13.800 5.303 02401% A in Market Cap per Unit 91 2.563 0.621 0.0043% A in Mttket Cap per Unit 91 -7.731 -1.463 0.0235% A in Mirket CaP per Unit 91 7.220 1.772 0.0341Market Cap per Unit 91 0.945 3.146 0.1001Market Cap per Unit 91 0.254 1.383 0.0210Maket Cap per Unit 91 0.121 0.473 0.0025Murket Cap per Unit 91 0.499 1.522 0.0254Market Cap per Unit 91 -0.178 -0.693 0.0054Cap Rate 91 -0.222 -1.055 0.0124Cap Rate 91 -0.412 -3.556 0.1244Cap Rate 91 -0.388 -2.329 0.0574

Cap Rate 91 0.230 1.044 0.0121Cap Rte 91 -0.528 -3.248 0.1060

54

Exhibit 10

.... ..l... . ... ... I. . . . . .. . . . . ... . . .. . . . . .. . . I. . . .. .. .. . . . ............. I. . . . . . .. . .. . . . . .. . . . . .. . . . . . .. . . . ..s. I. . . . . .. . . . .. . . . ... . .P...I D.. R........ R... t. -ciur . Ch.. i, Ap-tment.roAb. - i : .pd- Vmbl. & cmT t S:., P

WAJOG WAR D V.Dit %Pontin WARG WARG : Dn #:%P.rtnW(Gln) (IeiS WAR: WASG WARD Al. MSA (G-nt) R EI): WAEG WASG WAED Apt MSA

'~.4.~ .. 9ooo~.=....~ .............. 94 0 07306 : 0.280607: 0 004216 .523044 029551.................. .............. ' ...................... . . . ..... ....... ........... . . . . . ...............................................................................-........ 6. . 5~5%A~~~p~~Un .94 05509197 0291218 0069762 0046855 44920 0492324 023267554~~~~~:.514428:-0 0234 Dg~??A~....... .......... . /i io ' ......... ......................'6i g s ''.......... ......... ~9~~ ............................ ~ ~9i ..... ' 6' 6%~ Al FF0 pe Un94 9.321918068 .06104973 -342 308'~~~~~~~~~~~~~~~ ~~~~ ~~./:. .. ... . . . . .. ... .......,. ~... ...: ....... .......... ........ .

%A FP0 per Unit 94 {9 3610855: -0.315202 0.0592776 4.469442 -6.12182: 0.379227 0.37188575

% A. i~n FF. ~er Uit 94 : 6: g. 0642680-7.509092: ,[ - 0 300090: 0 : ,~ . .7900991 1 720373 -55364; 0.26733029%Ain FF9 pnn Unit 94 60470205287160913 0.26611

. 'i; ................. 4 ......... . ..... 0 1 - . . ........ - 02702":363, .0. .......... 7 'F: .ARNIM%A nFFO p~~~~~y0,inil 94 ~~~~10,977743Ap 6.82003.265943 03297 .473799.244711.46933 61566402' 041445106

[ % b in FFO -= Unit 94 11 567921 00738564 5 5895405 -0.337327 0246314 4992123 20276030 1 001105 667708 -1.23346 0.4246806610/ i Rm66 Rnnnnunp p! nt.9 4 321194165:0530.0353.0103 020674% A n Kenti Revenuenper Unit 94 37367317: -0.274184i -021041 0 9917177i -5.001851 1.319851 022311459

% A In Rental Reenue J~et Unit 94 ; 0197360 : .0286930 3996867 .- 742786 0.3209424% A in 9ett~~~~ Rnnnntunpet Unit 94 79703246 0293029 0094307 30695939 14460 -0604100 0323604009-4--0-7. :-3 895939

..4. 4040144.......... . .... :-0275492 .... 1 4676862 26096 ............5 7 0

~'X'~G, ii'i/;~,~[;b'.i; .............. ~[ ........... ! ........... i .......... i . . . . . .. ..!' ....... :.........'.'...'...:.................... ............inn. p&Uiiil . ~~~~~~~~~94 .167376. 2600 2.0147004' .5106537: 0.23002083%Ain9.nit~~~~l~nnnnun p~~~Uno . 94 10243203 45580555 9100332 0274847 0261171 03073314 2069244 5140202 0245899.O.19.603P. .0.27047 6

94 13074952: 5 2505763 *7.145037: -0.28393 0161205 0350226 1452063 1370502 .5.174041i -0.747642 0.25009649001 . 94 00~~~~~8 3411455354 849 091 62971520 -63904 56698 0343020

;,A i [. R.;,.n'i J',it 94 . .. 3802804 70183202 2.6027061 . 0,361683 -0.311466 :3.9172069i21664576 04071759 -6.092957 1543772 036120196% A i NOi pi Unt 94 3.0486319i -0260412. 0 14421. .4 9D6431. 021429325

%.n.in ...OI.p.... . ...nt .................3.516036 : -I0276306.0 194701 00361916 -50.296.10472 022627325An.569!.p99.Uatt 94 76106610 -60288804 .68923 i~:. 47003: 0.3163100v:..a.J.".N.°!.P.*.r..U**n!! . . . ~ ~ ~ ~ ~ ~ ~ ~~ ~ ~ ........... .. ....... ' ......... .. iiii ...................... ..... ........ ~ ~ii'................. "'i'' ....................... ............ i ~ ~i~............. 6 ii&NI2I pet Unit 94 74214592 ~~~~~~~~~~~~~~-.294509: 0079007 35435 620 502874 031349900

.AnN.O.I Unit 94.6299954.0273366.......... 20686673.5.200 .567 0.24419209......... ~........... ~ ............. ,....... ....90.¥.. -.D,73.1.6i

......V.6 in Noipno~~~~nit .94 07540400 51505000 0400437 0277377 00920676 148193 1099446 5213103898 024993522292554979

%,';, in,'i01 pet Uit~... ...... 94 ..... 219i232} 59293276' 6i270794-. ..... -0'2933........ ' 34964932~ 1:9005065' 0-o200654.....i :5:86873 ...... 06276V.Ai inNO)!penrUnt 94 73693409:03102309 096035071 02763607. 0 40281 26133900i23799010 01665910, 50651066 -107015 039180602

'.:mNp Unit . .........9 150137: ... ... .... .0.37921 .. ...-1392241 6.:.........'.63.~.9 , .. 006103279

% i Nt nm cp it93 1651049: : : -033152:060450721 1526033 -2.095503 1.261733 0.09799177:::::::::::::::::::::::::::::::::: ::::" :'¢:~ ¥::: ... :.::...:.........T.:/ .:..:....::.::::.:::.:......:::.I..::.::::... :: : :::..:..:....:::::.:.....'..'....~(.%..in.iN.nmcp.Unit. .... 93 : 11516368 ' , .036269; 0 702478 0 17698240: : - ,92297 13930089 0 10521060

................................. . . . . . . . . , ........ .94 0........... . ..................... .....................

.. ... . .... ... . .. J. ......... ~..u..... ..... ..... .. .. .P5 P~~~~~A ..... ... . 9. : !.... .e.. ......... .0 I. '- 8611 ...~...~. ..~..~. .~. . ... .... . ... -.:o...~. . ..... ..... . 1.:L,~5. .. ....... o./.?........ . ....... I I.-.2.1~ ?.......o.....! '% A NOI U~~~~k 94 .21 2015.9357 604786382 -378 208902091 2032 0032% An* ~.l~oapR~p~o.V~n~i0, . 93 0950974a037555 000 0023548 .. 71 3006 26¶4.A~~io~~inietmepto~~~nir 93 1 749 2694 75 129 09 63505 0376 0 40 1 4579 1 380723723438 04066245909807

in N n pUni 93: 177651 0.605636 6.1679586 -034237904596393 1531132 03653 0.3732622 -2.1261110.7230523 0.60030223V.6. ...NerIn~m~c~p..et. n~ .... . ... .. 93 08039035 41004203 26.1809412i . 0307508i i24507749:0.4315970 168721073 .2.519520 0.13224093

0-Jet Innome per Uit 93 49 42009 !: -049046; 32019207: i!-.24319: 0,1533807593 10710398 0539908 11381962 75244 ~~~~~~~~~~~~~~~~~~~1,61330,0971817

,et lnininnpnUnit . .... i ..... 93' 0 ' ' ! ii472991 32056~l~303i ..... - 04i76017! 000..... } ....... .O. .. 3371 ' 1.'009970 2609'172 0090094..... .4 7

N~.eo m.pUnit......... 93 506355 : -27.20153:1435236! : -0445970 3.2072399 0....521708 1,8109543 2.07076 0.10532273

I~et lnmmc~er Un i t 93 : 240176767 3570789:55527924i

0517177i 2.278292: 0177452 27267297 ; 23323016 0 14079778,,~~~~~~~~~~~~~~~~~~~~~ ~~~~~~~~~.4........ ..~.. .p........ i: ........... ..........

.p.p....91 3075057 : 0340059: 0100 .0625052 71603 0.515648 016. .. 0, ...0.~.,,::,0 p.,r.: . 96:.. ... ........ 14904906 : :.00944' 6715134 . .0.7.7..~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~.~...1:....... ... i i ~.mM.in~ tCa p.n!t........ 91 : 15 09044 5 0396130 0530024 : 6 2132~522

i: - . 96700; 03409974 037858457

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~..A..ig..M.~k.~..C.?p.[~.9..ni7... ...91 7.772056 .9 .3982321 -1624673 1 : 390899: 1 50026: 021714249 2.977594 -3.51010: 0.19568324

........... ........... 163276 82753 023141 -0.40215 6.055725 236 409 0.04456 4. 6507 041975

M IIkt3Cap pin Uni t 90 019256737 : 7 0.012336 36.2622: i - 2.714627 001 360653i C p' ni 91082690067.................. . i ... 2 1105 ....... 27823 40107994 0270 5

MaikinCap'Un6. . ........ 91 ........ !'012956495' . . . ... i ' ' 0'014223' 16320.......562 14183.056634559..................... ...................:Mikne Cap per Unt 91 0428723 0010156 0058225: 26042489 : 6078 47032306 026041094

t Ut 91 0011204 03906602 001624 0045104 1.103690 1684547 0 05633642M(nker Ca~pe Unit 91 : i : 0105215 :0.002580 .0460260 1: -4: 1.3148 004694948/,;; ;:;';'i ............. i,~~i ...... ::'/¢"''i~!..... . . . ' ;/i' 3'/".ii'.......... ~:i½i.'.¥ii ........ 9 572 0.5117 282... 1!'7452 Y72 297 'i~ ' 2.'3~¥i~~i;;~ E;~.';J.............. ..... ......... ......... .. /.;$B.'" i i '.'di' ........... :/i '.: ...... ...... ......... : / ' B8' i ;/ ............ ~ .'i' ........... ''i'

tC,~i.~ tc ......................... ['' 1 0250112i ! ? i -0.021405i ' .300018. '} ' [ -552409 0025491906

a. i c 91 :-0353980 : 2 1 -0019773! : 3.446502 003 27 1592:48 0.3291'0?796 -0604079 -0021300 .6021939 .0042736 68426 2100923~~~~~~ 0.19413935

ap:::::::::::::::::::::::::::::::::::::. :":'::::":":-6 . 29740594489.....8 ............. 4..0.:307.::3821..Cap.90~~~~~.......... ...... ..041579-. -01949.204845.90206.0306493

Casp~e 0. 91 0099356 :-0434363;02759316: - 002005 : - 0524974 : -2609652 12953345 :-5030407, 020979397'a;. ... . ........... . ..........91... .0037749:; i 0305;,,": 0552495i.........':'.e -00209:8 0016.'626 -020106...,2~8 203427' 22403479~ 5307503... 311195.:/4" 0344;....06.

Ca Ai. .e aCpp ni 901 435 7.01809 1004.571 j .00351999 0033 380 1 754556670390 3755 457 1 744383


Exhibit 11

Discussion: While the strength of these relationships supports the notion that

apartment REIT performance and market fundamentals are linked, other non-market

factors seem to play a more important role.

Surprisingly, NREI does a much better job of predicting apartment REIT performance

over the last five years than does the government's, whether using a cross section or

panel data approach. In fact, none of the regression results using the government rent

index yielded results that were significant. Simply using the difference between

current weighted average job growth and weighted average stock growth works better

than the government index and about half as well as the NREI. The results from the

10-REIT sample (see Appendix 16 & 17), constructed from the 10 apartment REITs

with the greatest portfolio concentration in MSAs where government rent data were


Cross-Section-l Data Regre-sion Resuls Inciudiar . Chang in

Depndent Variable # fObf Idepndnnt Variable & Ceffiele t-Sta .R Sguare

Averg Average: Average Averg:WARG WARG Aver g Avera Aver .... ge %A#of WARG WARG Averg A.ver... g 'Av.rage i Afr(Gv,) (NREI) WAEr WASG WAZD Apts (Go-) (NREI) WAEG WASG WAZD Ap

Ave% n FOperUfit " 19.28894 0 1.1590649: : . 0.07312066

Avege%A i F0 perUvit 19 10.70475i .0055549 13188946 -1.033601 0.131662513;v;R;'%' g ,iA'~fn&' FF,9, L t , ,,,,,.................. ........ !' ........... ....................... ...... ................ ........... ....... ..

i.A .v.ae.%.. e.U 19.8..........,.r. .......................... . .... ........... 2... .......... .......... 63 4

Av'~~ e~ A~i F'*O' L-rLl't'

...................... . . ..... ...... :. . .... I . ........ . .[35 '51 ;. .... 2' .......... . 0{ ~ 2 .. ......... . .. .. ....... ! 0 {2 7 ;.'1..2 ... .. ......;i 1'7 0{5r$ 18Arerg V..inFF9.pr.Unit . t9 362933 3.049902: 06780335 .0 48835. . .0087

.Av.enA...in.FFO Enr.U i t...........19 4226658 0,7190787[ 0.0295183~Aege%A On pUnit 19 ' 3.57ts485 .3913124: -0.131624 0 660207 -0.611624 , .0.819147 007.61183sv re~l~eFF~o~p~u..... .. *----- ............................. '.,............ 2........... -........................ ,A ' PY '........... 'M ! ;" ;, :Avere.% A i PFO per Unit . 19 3.6900497 0.130005 0.7672427 .0.49727 0.07142247Avetg~sainSentI~rvenu ,, , ,,,,, A !.9~ os . e 9.02066s: : 13052639 : :1: 0.09108199A,~?.~ ~ ~.i3...mr..R.?.?: u ..(......... ......... ... ?..~ 2.a !........... .................. ...........1 . 0 9 : i " : : . 0 ' 18Average .A R.enta RRevenue per Urit 19 20 662 0374204

i8 0 0'0 i ........... ............ !'i i 6' il.................. .......... ............ ! ............... .0.... 4'.:..Avnese % A itn Rental Renue per Unit 19 2 : 24270493 542683 ' 05501246 -1.05422i 0.061454

AveraRge% A Rentl Rvenur pet Unt 19 3.4970428' 0.8815719 0.04371725

Avere% A in O peU Unit . . .t9 2537087::- 12201616: : .052414Average .% A NOI par Unit 19 73266137: 2.6360821: 0.2904666

!Average';';.'&'n~oI'per'Unit........... . i .......... '.~26~........... ...... i......?2 ~ 8 1 2

............ i..:...... 0 94i6AYte % nN9 ,tUnit , 9 j 7 3.3808647 46512 , 0.7261 0891098 0.0554171

Av... .% n N01 per Unit 19 3 8339669 962127 0.65164019;,';~¥~'i';'~ [;;;~,~ ............. ........ .... :"~............ ........... ..... .......... "...:''a' ........:i"'..... 'i..........' ........ 1 "'...... .''' qp~N.... .. ;i'aAap nAn~,,eruNet 1nme Uni 1w .... t 9 -17.,673,8 09980e3: : 0.05522427.*x~,Jmsi?.~..~n.NL.m..~p..u~n.it 1 i~~6+, ....... , ..l~-..?ai ......... ............... ......... i ................... :° ~~.!- ..... ........... ............. o...iss4..i?AveageV.A mA Net I.ne p Un.t 19 1203966 0.1396044: 0 00114512

Av er% n Netn epe ,Un~it,,,,,,,,,,,,, , ,19 , - ,,,,,,,j,3,290065 15042947 , ,,, ,,,,,,, -0293734 11269567 0.0778622Avaze At ANe ine. pr Unit 19 7224762 0699336 0.02796435Average Nt Income per Unit .,,,,, 19 85949595 : 1.9652478! 0.18512905

Aveagrk~rtlneamaper Unit 19 30156269 14402167 010874488A, lte 'e''n tn¢p Ini...................... 9...... . . '11' 29' ......... .... .. :I.......... ........... ........ ?i4t2 6:/i... ......... ..... ........ 01 '74 8

AveangeNettnome per Unit 19 0 it8809603 70979659 : : 0620488 001964773 0.0421890

Av.a.eNat I...ty p Uvit 19 10 1361 0 3656983 0.007B0538A nv. Aa ge% inMtket~sE ppeU n ,,,,,,,,,, 19 3033t,;,,;,; ,1 572570; --TT-- 0.12699544;,';~;,,;'~:~i~_a;'~'~'~;; ~~~~~~~~~~~~~~~~~............. ........ ~~.... .'ii ;;;''......... a;Average % A n Market Cap per Unit 19 13514141 .00374 , 09559911

,veae% IA in Maet Cap pa Unit 19 [26721842 -802562 04905755 -1239369 0.0883275

AvrtagrV. A in Market CapperUnit 09 ' - i ii447926 0896679 0.04506296Ayrra eMakC pr tt. 19 13031238: 0.. 2631.i894 , ' ' 008580758Aveage Mkt CC p Un.t 19 :0 7514677 '16052377 0.13162466Avtage Urket UE p09 U0t1 9 1 : 0 !15389 0443399 ' : 0 0171912 05556749 0.0259222i';, ~; ;;,~ ~ ~,':,;., ~(i: .................. ..... " ~:. .........:. ........... ........... ............ ':':a' ................. : ........... : ............. ........... f:::'''":... ::a::;~vRae Merke CaP PD Unit 19 -0142028 , 0.233259 0.00319037

Avt~~pete 9 .0 940082: 0 -158872: 0.12647413.. .. . .... .. . .. . I. . . .. .. . I~ . .. . . .... . . .'::.'',.t,, ................... |..o..........'...'.'...... ........... t ":6:,' ....................... ' ..................................... ..................................................Ava.ge C.p Ra .te 19 0 4 592 -1 726632 0.04920279

Ayrecse,,C~p,~tr , ,,,,0,,,,,,, ,,,,,,, ,, ,,,,,,,t9 ,,, ,,, ,,, , -0375496 0 3276645 .0956913 0 7023919 ' 00584684q CrgeCap Rt 19 '03593052 0.05771951

56

available, show that the failure of the government index to work has more to do with

the index than the coverage.

A Review of Statistically Significant Variables

NREI - Weighted Average Rent Growth (WARG)

The regression results show that the NREI - WARG, when using either a cross

section or panel data approach, does the best job of predicting FFO, Rental

Revenue, Net Operating Income, and Market Capitalization.

The intuition behind these results is straightforward. FFO, Rental Revenue, and

NOI all grow according to what rents are doing in markets where REITs own

properties. REITs that have a high portfolio concentration in markets where rents

are growing rapidly will show strong FFO, Rental Revenue, and NOI growth. We

postulate that the NREI data does a better job of predicting apartment REIT

performance over the last five years than does the government's because of the

differences in how indices are constructed.

The government rental index includes not only larger multi-family rental

properties of interest to institutional investors, but also single-family rentals and

small multi-family properties. These smaller properties may perform differently

from the institutional segment of the market. The NREI, however, excludes the

smaller properties and looks only at class A and B properties, those most likely to

be held by apartment REITs or institutional owners.


Weighted Average Excess Demand (WAED)

Weighted Average Excess Demand, the difference between employment growth

and stock growth, was statistically significant in some models. Interestingly,

WAED does a better job of predicting REIT returns than the government index

and works about half as well as the NREI.

Percentage Change in the Number of Apartments

Because of the noise created by apartment unit accounting discussed in Chapter

3.1, this-variable helped reduce the volatility in REIT returns, resulting in higher

R-Squares across the board. For example, the statistical model which represents

FFO growth per unit goes from an R-Squared of 8.82% to 37.1% when apartment

growth is included in the regression.

A Review of Non-Statistically Significant Variables

Government - Weighted Average Rent Growth (WARG)

In all but a few cases the Government - WARG variable resulted in insignificant

t-stats (i.e. 2> t-stat >-2). As previously discussed, we believe noise inherent in

the construction of the government index is diluting its effectiveness. The

inclusion of apartment growth in the analysis, however, did improve the R-

Squared in each case, further supporting our theory that apartment unit accounting

is skewing per unit growth rates.


Weighted Average Employment Growth (WAEG) & Stock Growth (WASG)

While WAEG and WASG were not predictive measures of apartment REIT

returns, the signs of these variables indicate: (1) As MSA employment grows in

areas where apartment REITs are heavily concentrated, their returns are positively

impacted, and (2) Stock growth and apartment REIT returns are inversely

correlated.

The results show conclusive evidence that FFO growth and marketfundamentals are

correlated, albeit the relationship is not as strong as anticipated

Hypothesis #3 - FFO growth should determine a REIT's market capitalization growth.

Results: A regression analysis of the variables described in Chapter 3.0 resulted in

statistical models, with significant t-stats, which explain:

1. 37.8% of the variance in the percent change in Market Capitalization per unit

across the sample of selected apartment REITs using a panel data approach given

NREI WARG and apartment growth;

2. 59.6% of the variance in the percent change in Market Capitalization per unit

across the sample of selected apartment REITs using a cross-sectional data

approach given NREI WARG; and

3. 39.0% of the variance in the Capitalization Rate across the sample of selected

apartment REITs using a panel data approach given NREI WARG and apartment

growth


Discussion: While the empirical results presented above do not directly address the

issue of FFO and stock price correlation, previous academic research does. As

previously stated, Fields et al. (1998) showed that FFO explains 57.8% of the

variation in price, which is less than the 61.3% explained by the model including net

income. "However, the Vuong test statistic is 1.55 with an associated two-tailed p-

value of 0. 12, suggesting that the explanatory power of the two models is not very

different."

Previous academic research does show conclusive evidence that FFO and market

capitalization are correlated. The cumulative effect of this thesis' results tends to

validate the notion of reasonable REIT pricing.

1. weighted average rent growth explains 37% of FFO growth, and

2. FFO explains 58% of stock price.

Our empirical analysis supports these findings: weighted average rent growth

explains 3 8% of the percent change in market capitalization (stock price). The

model, when using a cross-sectional data approach, explains 60% of the variance, but

lacks sufficient observations, with only nineteen.

5.1 Additional Research

Overall, a statistical model explaining 37.1% of apartment REIT per unit FFO growth

is intriguing. The fact that the NREI does a better job than the government index at

predicting REIT returns, however, is more enlightening. Additional study on a larger

sample with better apartment unit accounting would provide important insight into


our results. Furthermore, more empirical analysis on the direct correlation between

market fundamentals and stock price appreciation / yield could support the

conclusions and highlight a more global perspective on reasonable REIT pricing.


6. Conclusion

This paper examines the correlation between apartment REIT performance and

market fundamentals. Through a review of relevant literature and informational

interviews with academics, the components of REIT performance were defined as: (1)

FFO growth, (2) Rental Revenue growth, (3) Rental NOI growth, (4) Market

Capitalization growth, and (5) Cap Rate. Market fundamentals were defined as: (1)

Rent Growth, (2) Employment Growth, (3) Stock Growth, and (4) Excess Demand.

Components of market fundamentals were then empirically examined to explore and

quantify their impact on REIT performance. Independent variables were developed

as proxies for market fundamentals, such as weighted average rent growth, weighted

average employment growth, weighted average stock growth, and weighted average

excess demand. Dependent variables were calculated based on data obtained by SNL

DataSource.

The analysis revealed that weighted average rent growth (given NREI data) and

growth in apartment units explained 37. 1% of the variance in the percent change in

FFO per unit across the sample of selected apartment REITs. The results also show

that NREI data does a much better job of predicting apartment REIT performance

over the last five years than does government data, whether using a cross section or

panel data approach. The failure of the government index to work has more to do

with the way the index is constructed than the coverage. Finally, weighted average

excess demand (WAEG - WASG) works better at predicting FFO growth than


weighted average rent growth (given government rent data) and about half as well as

the weighted average rent growth (given NREI data).

The market capitalization results tend to validate reasonable REIT pricing. The

results show that weighted average rent growth and apartment growth explain 38% of

the variation in the percent change in market capitalization when using a panel data

approach, and 60% when using a cross sectional data approach. These results, when

taken in the context of previous research done by Fields et al., confirm the rational

link between market fundamentals and REIT stock price returns.


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~~9.411% *1)a~~~~, $1)5~~ V.0/60 06115 %"'W 91115 OMI*.C. 601 9910 tZ 9,101 0661 Vi1J.1V8/.o0- 50001 V.91 00801 '4* 511 1511 SIM1 1661 981.TVS

0t569 019 01 *105 10911 6861 9O1.1.79 SZ10 W 0090f KDS Frol~~~~~~~16 810 6861 NOIJ,ISg

8691 10111 8861I 1q1.17V81/.o' o0 .o'0 0 , 01 61 000,; 01dYV

'*1)fl ~~~~tfflo '169k'? 4000.OO' 000 V/.000 00.0 St.. 109 44W 9-981 6661 SI16V8~~t~t 44W?~~~ 4*Wt .. V.000%GO' 000 V.000 0 DW ..'*t 11151 98 BS4~. 0181 8661 0608>98f

.~~tttr* *4t~~~~~~t 4000' 000 V.00'0 000 *49 ' 8*1 08L&t? 51 L661 S613>9~~I41* 4S~~'t.. ~~~A~aM .~~. /4.0 000 V.0 00TL*011 11I11 9661 01(3)9

'4t~~k"" ~~~4t2%* ~~4*'& V000O' 001G V.000 0 4..O' 999*1 99111 1661 0103)198

~~4$~~9~~ 4*9*t V.0006' 000 V.~000 000OV l 19091 180 S. 8-691 9661 063)19"' ~~"9~~$ 'tid?!? ' V000' 00V 00 000 44*GOT 1*11 911 1ZU ' 001 1661 0113)198

*1)9* ~~~~~~~~~ ~~~ ~ 1.08 000O V/.00 00''16f 01011 515 si 4It 111 0661 S113)198~~~4+~~~~~~ ~~~~ V.~~~/000 00 V/.000 0010 MU~" *0 160 it 1911 1661 0103g)198

.. '.. ~~~~~~~~~4' V.~~~~/00-0 000 V.000o 000 ..%* IRE 160 I 11 06 11)9

0 6510 181 8,191 6861 0103)19809811 *60 8111 8861 0S13)198

V.000 800O V.000' 00-0 "X6. 900161 1*1 L9 0000 NIJ0fl44*99 $dt* 4**~~~~~~~ ' 01~819o 11*9 V/.609 60050S %t)1c" 11*81 61 "'4'-.. 910V9 6661 NI.s0fl.~~e14*'t iS~~~~~.*.44~~~9 .9- 19'661 V.019 510 .A~St. 561111L 8195 %) ' 088 8661 munv.~l

i'j'% ' "1*~'6t 191 0911 01901s 1119 *tt) 9111 1911 M** ~ 0S 1661 NI.Lnfl%lZ..''tk.... VIoD 1915 V.000 19059 t?'? 05959 0869 *kk.$..~0111 9661 NI.1.5fl

W*'*'. *tttt..... ~~~~~~ V 119 11901 V.000 ~~~C900 ME&P. *811 85 9019 9661 NI.10flA09 4IW~~~~~9'*t~~~~tt'~%60199 09969 V.000 800 Z 0191 9110 S~'M 89 661 NiLsnvl~~$? t~~~94it ~~~sW* ~~~V16119 519s V0010 00 0 KM'"" 091 011 a *W* 91 0661 NIJ.0fl .. i ... :... 0 10 4...... 09 6

Y~~tt'? %$*~~'~ ''*t~~*'~~' 1.01£o 1P90 1 V000 0 SWzvIII ..%l ~ 18 0661 NO.1.01W i:...:iiia:{'1 0:' : 9111 00ot 111 661 Nr.1.'fl

1/6011 L9,89C 1/6(0'o OO'16011 101 80 801 NI.1.Ofv / ~6'~ '.. . ...0 000 01901 0111 ,1 6001. .''$.?.'4:.'" 91100 0000 .NON WS I LZE 8,C9C ~ ~~~~~ o e61 iLn oo'o~~~~~~~EC1 6~,Zc L0 Saalv

*1U~~ k~~0t.'~~ ~"44t't~~~" V.081l 01'619 V61 89601 ST=6f 111 "tt 11 6661 J'.1.V..t~~?'* (4*93 44~~~~~~L* 4~98'1 09'809 1111T 10189 14P 0:. 689 11001 .'.'.*t 119 8661 N.L.1..

Ita. stC" 41 1 0189 11Z99 C191 U61 I : ... ':!'!i' {.~-.... 09161 1661 .'1~V* ~~#j1':ssss g~ss~~*X:~.ss VIZ'l 18,985 V.6011 98'609 ~I? 81I6919 51101 V....LLS... E08 9661 .1NV't1V

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::.:,tM~*k":::4Etflk~ 9i9'* .V.1911 00110 01051 18105 4Kkfl~~ 981 0851i'..:!s' 8665::.:.:.'61 661 .1

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109t 60101S 80118 11111 16191 6861 .1.N911. LOME 10091 S~~.:¥'":.D.?:'iii I dkM"~·.-'% " : / 80'0 000 V00'0 000 9069l'

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:*fl$$$10 4449ff * 4000 000 1000. 00-0 ..... ::'.; 0 .......* 1110, S 9,6Z 0661 nbnwiv "~~~~~~~~~~~~~~~O' i :,' ° /.tz'o nEC N PS~m n n*04?O'O'"k't0 0 GOV %000 00 01011 49 0990 1661 ntfnlv

v/O.'o oo'o /V0 0 0 000 WmW 09 6191 0661 flbno

0 0 19101 199, 6'550 6861 floflgl

P11-1-M ~ ~ ~ ~ ~ ~ ~~~601 81 69 86 f:f11

.1.N131 .1NH36 ±9131 .19 ~-#S9&ZI IDO.LS S.IlU~d d003~~ I 13)091~~~~~ME 03689 .A.00 .1.400 '9

E lF utAr] Pu~nz~uz1 -.....-.. 0.,~ .. .]4V 0

putruwaa ssaox 'uoj 'poos otuodw -13Cl 3l1aWI xvpuaddv

Appendix 1Market Data -Employment, Stock, Rent, Excess Demand

·i --~:,:_:~..'.~...:~'..j?.:i GOV'T GOpT NREI NREI [ EMP PERMITS STOCK RENT RENT RENT RENT

MSA Yer Gien 8ivu Gave. Calejard *' ki~a6~ ivd~ Gwen Caul.ed Giv.: Cakleltd 1 L

CHICAG 1988 3558.4 10188CHICAG 1989 36395 7862 910142 463.31 579.25

7 CHICAG 1990 3707.6 6162 18 48700 5.11% 598.50 3.32% CHICAG 1991 36725 0 3233 924166 50934 4.59% 600.25 0.29% *

CHICAG 1992 3631.9 416? 927399 525.24 3.12% 634.08 5.64%CHICAG 1993 3684.6 5588 931566 537.43 232% 654.50 3.22%. CHICAG 5596 ~~~~~~~~~~~~~~~~~~931566 ;:PW~:~ :~"-..t524 : . ' - ii'':ii/;ii'i~':;i'!:ii:.";iii

CHICAG 1994 3763.3 7007 937162 554.69 3.21% 680.75 4.01%,CHICAG 1995 3890.8 " 8069 944169 57770 4.15% 721.58 6.00%4$CHICAG 1996 3947.1 6N 9489 952238 '59496 299% 721.00 -0.08%./.CIC~ 1W7 I 00.7 ':ii: ~.'"~ L":!!ii ~. CHICAG 1997 4006~7 ~8850 961727 618.99 4.04% 733.25 1.70% ', ""."ii ' . (":5ii! iCHICAG 1998 4093.2 M:{..". 8406 970577 N 643.35 3.94% 80208 9.39% .CHICAG 1999 4164.3 11130 978977 .66840 3.89% 829.50 3.42%CHI C AG {i~~;... ' . : i:'...ii~j] .8.aoCHICAG 2000 4209.2 .. :'". 10942 990107 ii! .'. 69243 3.59% 0.00 0.00%,

CHRLTE 1988 591.3 4430CHRLTE 1989 611.6 6031 124364 0 391.42

8 CHRLTE 1990 627.2 3077 000 0.00%/. 390.83 .0.15% "CHRLTE 1991 614.2 .. 1534 133472 .0.00 0.00% 387.33 -0.90%,4CHRLTE 1992 6154 : : 15 135006 0.00 0.0/. 400.17 3.31%CHRLTE 1993 639.1 . 1422 136159 0.00 0.00% 423.50 5.83% CHRLTE 1994 6724 3573 137581 *~: 0.00 0.00%/ 44&00 5.79% . .CHRLTE 1995 ] 704.3 3:21 :'...:.:~''. i"MI-~ 372~~~CHRLTE 1995 704~3 4A 3721 ~ 141154 0.00 0.00% 481.25 7.42%5CHRLTE 1996 7210 5943 144875 46430 0.00 524.42 8.97%

CHRLTE 1997 [ 7462 $itA!.'L.'}'".. 5134 150818 51264 10.41% 533.17 1.67% .... CHRLTE 1998 765.1 . 4308 155952 52964 3.32% 527.92 .0.98% .

CHRLTE 1999 807.1 ' " 6531 160260 545.07 291% 541.33 254%,4 $CHRLTE i'~jili~,,~_ 7CHRLTE 2000 825.0 ". 7400 166791 0.00 0.00% 0.00 0.00/.

CINCIN 1988 702.6 3579CINCIN 1989 7323 2192 161870 359.33 421.17

9 CINCIN 1990 7523 2457 1640692 36700 213% 421.17 0.00% *CINCIN 1991 754.1 4. 2092 30166519 .: 38097 3.81% 427.58 1.52% .....CINCIN 1992 7526 ~*4- 2936 168611 · 393.57 3.31% 42292 .1.09..

CINCIN 1993 759.6 j: 2619 171547 399.87 1.60% 438.08 3.59% CINCIN 1994 778.5 .. 2572 174166 409.73 2.47/ 450.92 293'/. CINCIN 1995 796.3 2714 176738 423.15 3.28% 465.50 3.23% .. ..CINCIN 1996 8164 t 179452 431.09 1.88% 494.08 6.14%

CINCIN 1997 837.5 2702 181500 43848 1.71% 541.92 9.68% CINCIN 1998 854.9 : 2159 184202 t 448.07 219% 544.83 0.54% ' 'CINCIN 1999 868.9 i 3172 186361 461.21 293% 553.58 1.61%CINCI: 2000 890.3 i] 2406 189533 75.73 3.15% 0.00 0.00.

CLEVEL 1988 1017.2 2025CLEVEL 1989 1050.1 2958 240918 39222 499.33

10 CLEVEL 1990 1067.1 1450 NN 2' 40000 3.51% 481.25 -3.62 A CLEVEL 1991 10465 3 1163 245326 431.69 6.33% 494.08 267% 4 CLEVEL 1992 1037.1 .f .. ~ 2773 246489 44579 3.27% 400.08 -283%CLEVE 1993 1 1003 29262 7.38 260% 509.83 6.20'0 '24 '21993~ ~ ~~~~ n", 12 25026.5 :::'..CLEVEL 1994 1068.0 :::::*: 1120 0265 1 46521 1.71% 541.33 6.18% CLEVE. 1995 1097.1 1863 251385 478.68 290% 536.67 -0.86% " : iCLEVE. 1996 1114.2 1353 253248 4246 288% 566.42 5.54.,CLEVEL 1997 1277 1123 254601 509.07 3.37% 592.08 4.53%CLEVEL 1998 1149.3 .. ! 1168 255724 533.19 4.74% 599.08 1. CLEVEL 1999 1165.3 1077 256892 54259 1.76% 637.00 6.33/.CLEVEL 2000 11763 : 1252 257969 ::: 560.13 3.23% 0.00 000%

COI5.0 1988 655.3 5611COLUMB 1989 679.9 4223 201101 0 398.42

11 COL0B 1990 697.1 , 3653 20532, 000 0.00% 396.67 044%COLIJME 1901 7022 iiiiii{W{i:.i ,. ~iii~i~i~i~ 2252 208977 { .00 0.00% 408.92 3.09% 0.00COLUMB 1991 7OZ2 20897CULUNB i~~~~~PZ m5.2 ~~~~~ 22fZ ZCBP77 QOO ~~~~~~ 0.0~,4 40.2 ,.09./ . ZCOLUME 1992 705.2 2939 211229 000 0.00% 405.42 -0.86%

COLUMB 1993 7209 '': 3808 214168 "X 0.00 0.0/. 42292 4.32% .COLUIMB 1994 7453 3890 217976 000 0.00% 447.42 5.79./,~CC~LUO: 1995 777:7 :3906 221866 D000 0.00% 455.00 1.69%.COLUMB 1996 7922 ::: 4502 225772 5228 0.00/ 469.00 3.08%COLUMB 1997 [ 807.2 ..:!°".' 26 °:i.:: 99 230274 ! 52587 0.55%/ 484.17 273.23%/ COUM ,997 802 4411CIL0MB 1998 8321 " . 3383 233973 53875 245% 494.08 205%COLUW 1998~~~~~~~~~~~~~~~~~~~~~~~~~~z5, 8.18 zo5.,COUna4 1999 854.1 · 4i91 . 6899 237356 I " 539,94 0.22% 517.42 4.72% .

COLUMB 2000 8652 : ... 5282 244255 : : .0,00 0.00% 0.00 0.0

DALLAS 1988 1349.0 1399DALLAS 1989 1373.4 538 424877 438.3 407.75

12 DALLAS 1990 1403.0 .. 4901 2 .. 45384 .55% 40 5.584901 241 ::i:i....:::i 453.84 3.55% 430.505.8DALLAS 1991 14180 :::.:::::: 3124 430316 473.36 4.30% 441.58 257% :::i41:::i DALLAS 1992 14196 ''N 1862 433440 48293 202% 453.25 264% LrDALLAS 1993 14525 4386 435302 49309 223% 478.92 5.60% DALLAS 1994 15029 10166 439688 51241 3.79% 498.17 4.02% DALLAS 1995 1579.7 '': 11475 449844 531.54 3.73/ 50.83 234% J .DALLAS 1996 1633.6 10612 461319 54788 3.07 534.33 4.81% .DALLAS 1997 17225 ~::::;':':.: 14045 471931 .56939 3.93% 565.83 5.9%,DALLAS 1998 1808.4 17136 485976 597.28 4.90/,. 581.58 278% "DALLAS 1999 18895 12716 503112 62239 4.20% 585.58 241%,DALLAS 2000 1947.4 : 6395 515828 2 646.69 3.90/% 0.00 0.00/.



GOV'T GOV'T N WRr NEIEMP F*4 PERMITS STOCK RENT RENT RENT RENTd

M SA Year GI GivnG'de ' I - Ca.ulte tSdl Given Calculted Gwen Calculted Wit ddhed vlou

DENVER 1988 904.8 2068DENVER 1989 923.7 1075 246690 430 403.67

13 DENVER 1990 942.8 a .. 113 277 43000 0.00/. 425.25 5.35% :,l'. ... "' l . %..~3.8 ~~~~~~~23 24787B8ZDENVER 1991 9638 ~440% 238 247878 .4fl....... 44203 2.80% 433.42 1.92%DENVER 1992 985.4 1553 248116 A4'' 459,68 3.99% 487.08 1238% ~ 11 I0

DENVER 1993 1023.85.4 49298 7.24% 530.83 8.98DENVER 1994 1065.7 'W"%f 4618 251805 51985 5.45% 56.25 10.44% DENVER 1995 11144 5199 256418 5455 4.94% 593.25 1.19% * v :

DENVER 1996 1145 .5*1*'*~. 440 261608 w..4...'577.21 5.81% 603.75 1.77% A# .j .DENVER_19' 16 4 ......DENVER :':':::8i '1':3.0 . . . 21 27200:.9i 63.57i ii.4?.' 65.0,i0iiii :i276i' %!.

DENVER 1999 1291.0 6192 280330 .67468 6.32% 691.25 6.18%DENVER 290 13409..'$*t 39 28652 '.*2~S 717.60 6.36% 0.08 oar,'.6

DETROI 1988 1858.4 7955moran;r~r 10ORO iRQO 5 618 474985 437.3 495.83

FORTIA 1988 476,1 8067

FORTLA 1. 5007 7564 160824 561.76 457.33

15 FORTLA 1990 5136 iii..i*i! *:

' 5292 14113 : i!i 575.00 236% 449.17 -1.79% Ng..FORTLA 1991 507.0 1701 173680 ;"':::::: 595.10 3.50% 479.50 6.75% "'.. : 12" 4.:FORTA 1992 505.7 2024 175381 .. $ .... 61225 288% 498.75 4.01% 4J5% " 4flf . . : ' *PORTLA 1903 5304 3156 177405 637.25 4. 08% 548.92 10.06% $414 )Q 1%

PoRTLA 1994 5539 <%?"'~l }[?.i5l 5061 180561 67206 5.46% 57225 4.25% :.:':x.:'.4i :*.FORTA 1995 5779 4682 185922 691.67 Z92% 560.58 -204%

FORTLA 1996 5998 4835 190304 71373 3.19% 583.33 4.06%

PORTIA 1997 617.2 5487 195139 72843 206% 597.92 259(/.

PORTI.A 1998 6350 3730 20D626 .x:43:.. 741.18 1.75% 597.33 .0.10% f9* .~p ,$FORTLA 1999 650.9 ?":i? 4.l" 3439 204356 749.51 1,12% 609.58 205%

FORTLA 2000 669.9 2559 207795 762.75 1.77% 0.00 0.0(9/.

FORTWO 1988 548.4 220

FORTWO 1989 561.7 109 194724 414.14

16 POR TWO 1900 I 5900 o iAi:::d::$*.w~.] 328 11 42e.82 3.54% o 0.00 0.09': . .. . .. ".FORTWO 1991 595.8 4.. ... 904 195161 447.27 4.30% 0.00 0% ... ,$834 : .:::FORTWO 1992 589.7 -m 24 196065 .. '4'' 459.30 202% 0.00 0.099. :yW*:: :::*SA *PORTWO 1993 6060 :[i~iii~: . 401 196089 466.47 2.23/6 0.00 0.00%

FORTWO 1993 606.0 196089 .......... %~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~:'~t:""! 'iiiii:i[.".[...FORTWO 1994 6228 1478 196490 . .48416 3179% 0.00 0.0". .FORTWO 1995 645.9 ~~~3042 197968 502z24 3.73./ o. oo 0.oo%/

PORTWO 1996 671.5 ... 2675 201010 *4$ 517.67 1.07% 0.00 OO. 0 *lhFORTWO 1997 593.9 : 3683 203685 5{ 38.00 3.93% 0.00 0.08% $4. A! ?".. . 1.%FORTWO 1998 7299 5285 207368 59426 4.9WI. 0.00 00k%

FORTWO 1999 757.8 2393 212753 588.07 4.20/ 000 0.60%FORTWO 2000 7832 3:;!;'. 2216 215146 611.04 3.91% 0.00 0

FRESNO 1988 220.6 889

FRESNO 1989 227.9 2274 94891 017 FRESNO 1990 238.4 .'.481 4 0 0.o% .2. 0 0.60% '~!ii~ l .....'..iai!~ii!:.'i."i.. :.'..::. f " :i~iii

FRESNO 1991 2503 872 98646 000 0.099k 0.00 0.00%FRESNO 1992 248.6 835 99518 0.00 % 0.00 0.00%PRESNO 1993 2 '.' i 509 100353 ,OM.000 0.00% 0.00 0.00% , .5 ...'!ii 'i.-: ..'~ii~. 'i:Ai:..:!..:.,,.~:i'FRESNO 1994 257.2 i40'v' 1053 100862 . .00 0.09%/ 0.00 0.00%FRESNO 1995 263.5 $f.> 1243 101915 0...14%. .... 000 0.00%A 000D 0.00% 2 f $.3......<..%3FRESNO 1996 270.3 .....4f3l 371 103158 , .000 000% 0.00 0.099 %o o % : . i[ AFRESNO 1997 273.1 : ::-. 13 103529 0.00 0.099k 0.00 0.00%FRESNO 1998 275.6 .' j:"'~ , 448 103842 . 00 0.00% 0.00 0.00%FRESNO 1999 284.0 .. *40 / 490 104290 oM.o. 0 8.00% 0.0 .0 : W .

FRESNO 2000 295.5 ....... : . 346 104780 o.O 0099. 0.00 0.00%

HARTFO 1988 658.4 1179

HARTFO 19809 6579 1725 98719 018 NARTPo 1990 6456 o...'.'40% 415 1k656000 0.00% 000i.0a¢. .t..:.::::1~~~~~~~~~~~~~(:¢ '"8 '.'TF '1990 415 1-01 o1o

HARTFO 1991 6201 :: .': 215 100859 . 00 0.00% 0.0 000% : ¢0:' 444.fl~.- .....HARTPO 1992 600.1 439 350 101074 $,~ 0.00 0.099k 0.00 0.00% 41i* .. 44$... ...- fl$HARTFO 1993 597iiii!:: t...%}RT 1993 5970 4...424 {.{..:. ' . 0.oo222 101424 OM..".'.0 0.00% 0.00 M{.-'/.:.::' HARTPO 1994 5874 4*' 325 101646 . 4% 0.00 0.009k O 0. 0 Qak M* 49]ARTFO 1995 5910 ..4:.. 54 101971 0.00 0.00% 0.00 0.009 ' 47111 '$" ' : i-

HARTFO 1996 58%, 4 260 102025 9,00 000% 0.00 oMk TFO 1997 601,0' 544 102285. 0.00 00k 0.00 0.00%...'

HARTFO 1998 605:9 *94 102829 '000 0.099k 0.00 0k * .HARTFO 1999 616.6 '285 103777 'f 00 0.09 0.08 0.00%HARTPO 2OO 619.0 RMS.""'' 199 104072 o 000 0.00% 0.00 0. M

69

14 DETROI 1990 1905.0 4673 8 44 45600 4.28% 497.00 0.24%

DETROI 1991 18498 MIIKW'::. 3352 486476 4 '.,' .:I 463.68 1.68% 508.61 235%DETRO! 1992 18412 i

2916 489828 465.69 0.43% 501.08 1.49DETROI 1993 18749 , >. 3263 492744 . 477.05 Z44% 508.67 1.51%DETROI 1994 1930.7 . 3707 4 7 486.07 1.89% 550.67 8.26%DETROI 1995 1985.1 . .. 3477 499714 494.42 1.72% 57.25 3.92x"'i" '"""~' ''~iDETROI 1996 2033.3 " .4:ir 37 : 03191 '",0** 510.45 3.24% 589.75 3.06%DETROI 1997 2073.4 i . 2998 507068 522.15 2Z29% 592.08 0.40%

DETROI 1998 2106.5 ' 4399 510066 53818 3.07% 613.08 3.55%

DETROI 1999 21298 .8 4 % 3399 514465 . 554.22 Z98% 626.50 2.19%DETROI 2000 21703 ' : ' . 3437 517864 S 577.27 4.16% 0.00 0.00%



P# Ma MSA V nPERMITS STOCK :* O RENTi.S-1·"I"·. ·. ..... ·

%AGOV'TRENT

*''~n*.4 umtS LJCtlearted ~:Xtd.~C~i~flS/.l: Given Calculated Gien Clcultd 4tdwkntC HONOLU 1988 3727 1341HONOLU 1989 388.3 1876 124863 606.81

19 HQNOLU 2890 404.4 * 1453 323p "*" 663.00 9.26% 0.00 0.0% .P ' . aHONOU 1991 416.1 2915 12812 720.94 8.74 0.00iHONOL 1992 ~417 5 Z M 2795 231107 7 6.22 % 0. 00 0.00/.HONOI- 1993 416 :-5 - 1495 133902 790.20 3.1 000 0.00HONOLI 1994 411.8 2172 135397 '809.80 2489% 0.00 0.00% 1::.'HONOLU 1995 410.9 2454 137569 814.59 0.59 0.00 0.00

HONOLL 1996 406.1 45 875 140023 " 815.00 0.16% 0.00 r : * ... fHONOL 1997 4039 894 140898 808.93 -0.85% 0.00 0.OOHONOL 1998 4012 419 141792 799.5 -1. 1016.75 0.00%HONOLU 1999 396.6 479 142121 791.51 .098% 1020.83 0.40% .HONOL 2000 4023 .237 142600 : 785;41 0.77% 0.00 % 0.00/.

HOUSTO 1988 14255 741HOUSTO 1989 1480.1 2118 529373 378.04 37333

HOUSTO 1994 16873 ::: 5" 55 .' 4::19HOUSTO 17499667 44. . 2% 46550 431':':'' . ..:" .W n'-KA(t ~ n

HOUSTO ..'.. SSIOIS S~~~~~~~~~~~~~~~~~~~~~~~.. 51292 262% 487.25 0 38% ;$3flqt::/ 1.::s.HOUSTO 1996 1791 339 556355 524.02 2 16% 497.58 649, 1IJt :9*E::i*$.:HOUSTO 1997 18603 11157 560749 : 536.72 23% 534.92 7 % ....HOUSTO 1998 19592 20713 571916 :*5*..556.15 3.6rh 56:.058 4<HOUSTO 1999 20237 10337 592629 578.77 4.07% 542.50 .

HOUSTO 206 . 7672 602966 595.43 0.00

INDIAN 1988 662.8 2383 875 ~ ~ ~ ~ ~ ~ ~ ~ 5INDIAN 1989 693. 2r750 4 167S97 0 395.5021 INDIAN 1990 718.0 2044 127 **M00 .0 9.4 3/~INDIAN 1991 1. $ 93 17291 000 0.00551 38128 138% :k

HOUSTO 2000 2064.3 " 7672 M]2W6 ~~j~ D 595.935 17239 0.00 0.001/ 310INDIAN 1992 7229 1355 173326 Ri 122%INDIAN 1993 739.4 149S 17468 : 0.00 0.00% 40250 4 07%/:

INDIAN 1994 764.7 $$~UX 1945 1716~'$. 0.00 0.00%/ 448.58 11.45% -i4E~i~4*fM$~INDIAN 1995 791.9 3352 178121 C"..p 0.00 0.00%/ 45208 0785' B% .-I - 'INDIAN 1996 8,5772HINDIAN 1996 8..72 !13033 181473 53330 0.00% 470.17 400% INDIAN 1998 84.2

54413 03

INDIAN 1999 87.8 3621 18710S 5860 266% 498.17 4.79%

INDIAN 199 8586 190726 " 52968 .5n 51.67 3515' % ' " '~INDIAN 6 2 3M2649 193815! 0.00 0.00% 0.00 0.00% .'

JACICSO 1980 4025 2777INDIAN ~~~~~~~~:i·:is~' "~ilHACSOL 199 4108 1899 118322 0 396.67

22 JACICSO 1990 421.4 :~!~ij~ji~ : ~ 2177 -0.00% 414.17 4.41'! % o: . ~:'JACICSO 1991 419.6 ......46.1298..'~AJO 0.00 0.00%/ 408.25 0.99'! '?1/6 ~ ' ~ ~ ...49i:~JACICSO 199 420.3 545 $$~ 0.00 0.00%/ 421.75 0.84'! ...$u*.%x :x.~q $JACIUSO 1993 431.5 491 124405 .44$........ .000 0.06% 422.33 0. 14.!. .i.... -. "..."":.-.-!JACOSO 1994 1248 000 0.00% 424.08 041%JACKSD 1995 474.0 22 136280 C 0 0.00 %OD/ 443.33 4.545'$%:s::.,i -

JACKSO 1996 491.2 3831 18402 50670 0.00% 44 .33 4JACICSO 1897 501.8 6 *~j~it4 ~ 1595 132233 ~ 53041 4.68% 49292 6.15 -/JACICED 1998 523.1 6 1794 133828 . 54120 205% 495.83 0.595%JACK 1999 532 i 3552 135622 56302 4.01% 504.58 1. ...... ::::.::: ....... .......ACKSO

139174 000 000% 0.00

JERSEY 1988 2948.8 8137IERSEY 1989 3011.5 6970 133789 564.79

23 JERSEY 199 2980.4 1 4PW' 3196 14059 54I~ 59694 5.69% 0.00 0. nov.

JERSEY 1991 2845.0 1532 143955 61758 346% 0.00 000 rJERSEY 1992 2785.0 " '.. 9 2107 145487 . 637.42 3.215 0, 00% ".!':t: $ i X02JERSEY 1993 2801.6 :::-""' : 307 35 ':: 14.009 2% 0.00 oo% ODi1'/.

JERSEY 1094 2824.8 *$~ % 3221 150631 *i0- 670.37 2 ..49-- -.-:-0. ....JERSEY 1995 2894.0 3678 15296 0X 88.2 249! 0.00 0.001%JERSEY 1996 2904.3 20 156630 ......... 43' 707.68 283% 000 000% DD/.JERSEY 1997 29. ...................... .................JERSEY 1997 2967.2 $ % 3436 159133 "'*:1 731.89 3.42% 0.00 0 005' VJERSEY 1998 3034.2 4724 162569 ' 75689 3.42%/ 0.00 0o -- . ..JERSEY 1999 30924 ...-- % 5271 167293 .5'"'N$.----' 7810 3.25% 0.00 0 00! 't/

J~ERSEY 2000 3147.5 * -. 7117 172564 815,24 4.32.: 0.00 0.0, :

KANSAS 1988 755.4 3543KANSAS 1989 771.6 905 7127 419. 628 41067,-,ourroY 19 20.7 ~Qi~i;ii!"-_~ i.:~ 31035 59~2~ ~I~ 58.,4 .07/1 .0 5,~5 Po-24 KANSAS 1999 73 !!i2!!.'. 72 1333s s 4269.400 1.52% 395.50 3-695'%i: .:..'A.'.:::

KANSAS 1991 777.4 ~~~818 73525 ~ Ih$..... 443.79 4.189/ 410.67 3.835'KANSAS 1992 786.9 744748 083%/ 425.83 369% JI/ $%$7KANSAS 1993 798207

475421 1.95% 44275 3.97!%:

KANSAS 1994 828.1 1673 76049 OR 46336 1.55% 464.33 4.875%KANSAS 1995 8569.7 2917 77722 '47870 233% 479.50 3.27'! KANSAS 1996 871.2 2649 80639 4960 3.72% 506.82 572 ::;KANSAS 1997 906.5 4116 .83288 5.. 64 4.06% 523.25 3' 22%

KANSAS 1998 934.4 3685 87404 , 540.81 4.68% 53958 3 1 .=1~ ~ ~ ~ ~ ~ ~ ~~~8.80 32·, 0.0 OM/KANSAS 199 97.14

270%KANSAS 1999 9571 ; 5060 91089 560.95 3.72% 554.17 *W %' -

KANSAS 03642 -58546 4.37% 0.00 0. 00': :134 ,0,,7 0~42.00 150m 39.2 -1.·0 3%,ANDA 1992 [. 7=9~~iiKANSAS era 73525 ~~~~~~~135KANS" 1993 7981 907 7514~~~~2 ~ 6.oo 0.~,,o 475 1M.s~%,ANDA 14 I2. 17 760 41 .5·,7 4--.3.'".'"'.0iiKANSAS~~~~~~~~~~~~~~~19 ,,19 i8i. .1~. ~ii !i o:"':: 2917 m22 478.70 3~~~~~~o. /. 47 .50 4027./.KANSAS 1996 871~~~~~~~ ~ ~~~~~~ ~~~~~~ ~~~~~~~. ..... 83 " 9.5 .72 0.9 ..... . . ....... ~JiD."96249 ···· ·; .oo . m 4.58 1. 4 5%.O·/

N ;NE . iii.:*- f'.! ' ........NREI NREIRET RENT :I.'6~ 1

, 70The Correlation Between Market Fundamentals and Apartment REIT Performance

:2:·:·:·;f·�·:·�f:·�:·:·:·:·I·I

Appendix 1Market Data - Employment, Stock, Rent, Excess Demand

\ *~~$!$~i~$~.1 56 A 56 A -- c~

GOV'T GOWT WE WE! qr5 $EMP i PERMITS STOCK RENT RENT RENT RENT ,rina

LANGEL 1988 4013.2 32313LANGEL 1989 4103.5 24765 1515729 628.89 76242

2 LANCEL 1990 4163.8 16090 00 4! .4:": 657.00 4.47% 744.92 -2 30% 4LANGEL 1991 40178 ..:"/,$.!,'i 8505 1556584 675.88 z87% 737.33 -1.02% .:!. ! --t4$$9----.. '---4:.

LANGE!. 1992 38392 *(sI~~W 5038 . 156509 .• ....... 684.23 1.24%,. 728.58 -1.19% i .LANGE!. 1993 37425 2909 1570127 . .. 685.55 0.19 747.83 264%

LANGE!. 1994 3690.0 .- '.4* 2876 1573036 686.86 0.19% 77233 3.22% -z8;96 ~ -'419-- -

30~~~~~~~~~~~'. 7 67.024068LANGE!. 1995 3737.5 -.469 293 1575912 ::~:..p::::: 693.79 -0.45% 76.0 -68 .94$ LAGL 1996 3760.3 K' ~$ 02 1578842 690.82 1.03% 821.33 7.07% "I* /.469. .~LANGE!. 1997 2838.1 3476 1501874 ~$~~ 700.92 1.46% 864.50 5.26% I* .NLANGE!. 1998 3919 ''**-- 4803 1585350 ii*4*. 720.24 276% 945.00 9.31% -N--- -e* LANGE!. 1999 39705 .... :j5$9: 6977 1590153 !iPq> 746.15 3.60% 989.33 4.69%1.AINGEL. 2000 - 4058.2 -' ;f"R''. .8523 1597130 776.45 4.06% 0.00 0.00/.

LISLAN 1988 1134.8 1168LISLAN 1989 1146.4 912 166420 733.72

26 LISLAN 1990 1132.4 446 167332 * ' 775,49 5.69% 0.00 0.00% !."'" 'LISLAN 199.1103 176 1678 8020 .40% 0.00 0.0% LISLAN 1992 1053.5 38 167954 828.08 3.21/. 0.00 0.00% JC /LISLAN 4481993 1049 .2 446 167992 :v...o .. 849.74 262% 0.00 0.00% ' 9 '- -' " "LISLAI4 1994 10618 942 168440 870.88 249% 0.00 0.00%

LISLAN 1995 1... 169382 ;: 894.0. 266% 0.00 .0 4:1089.9 ~ ~ 1 196971LIS!AN, 1996 100999 ,19 169718 . , 9.35 2.83% 0.00 0.00% ALISLAN 1997 11120 1018 170977 950.80 3.42% 0.00 0.00%LISL.A1 1999 1139.5 *? 689 171995 '.*-..983.28 3.42% 0.00 0.00% #1/ M% .bI

LILN 1999 11804 4. 4 7090 172684 049 112 25 00 2.00% $41 *0LISLAN 101::.2:5. 3:.25. :6

LISLAN 20-O 12126 1513 179774 105908 4.32% 0.00 0.00.

LVEGAS 198 329.8 16967

L'VEGAS 1989 3588 11818 125904 0 457.9227 LVEGAS 1990 4023 *4A 9502 0922 .'4l .000 00%' 46250 1.02% 'N/¶.

LVEGAS 1991 421.8B 4-' 5301 147224 W$9 0.00 0.00%/ 466.08 0276% 41 1kt 'AA!.VEGAS 1992 420.3 .44b 3365 152525 '.' . .00 0.00%/ 449.17 -3.63% 4At 4I% mL'VEGAS 1993 441.3 &PZ- 3561 155890 .000 0.O0/6 476.58 6.10%LVEGAS 1994 491.5 7513 859451 . 0 . 516.25 8.32%..,.w~~~~~~~~~~~~~~~~~~53 9 .o~ 9,{0!.VEGAS 1995 529.1 .-.-.... ... 9483 166964 ' 0.00 0.00% 524.42 1.58% ! 49:

'.5;>_ ..=~.:: :-!'.

LEGAS 1996 573.0 1J.:' 11868 176447 637.52 0.00% 525.0o 0.11%.

L.VEGAS 1997 6224 10321 169315 650.20 1.99% 53200 1.33%LVEGAS 1998 640.6 ......*i~41 10977 198636 647.42 .0.43. 539.00 1.3 2%LVEGAS 1999 695.7 & "4.. 6234 209613 661.78 2.22% 549.50 1.95%LVEGAS200 76 '-"f* 5092 215047 '-D41 00 0.00% 0.00 0.00%/

MEMPHI 1900 450.1 2339

MEMP-i 1989 468.9 2820 122589 0 0

28 HEMPH 1990 461.2 ! . 1242 126699 51 : i 00 0.00% 0.00 0.00% . : ' .'[$.2MEMHI 1991 478 4 15 l 0 .00% 0.00 0.00.,MEMPH-!! 1992 4637 2367 1000 0.00% 0.00 0.00% .

.6MPHI 1993 486.7 379 127322 .00 0.00% 375.08 0.00%*

MEMPHI 1994 5029 621 127701 41242 9.95%MEMPI 1995 530.4 M961%"-,1856 100i0 0.00% 434.00 5.23%LMMlvPHI 1996 543.3 '...:194/.-!!. 3567 30178 ":'*i1:' 45435 0.00% 456.75 5.24% 4 /*MEMPHI 1997 556.6 .!i-'""4fl 1594 133745 473.68 4.25% 470.75 3.07%

MEMPHI 1998 5679 '891 135339 '49439 4.37% 466.67 .87% i5.0EMPH! 1999 583.7 : .: 2514 136230 501.46 1.43% 4z42 238%.., ,MEMPHI 300 599.4 --'-"1:. 5092 138744 o .oo ' 000 0.00% 0.: 2.00%.

MAvII 1988 843.3 8786MIAMI 1989 8726 9997 304635 481.66 49233

29 MIAMI 1990 8828 t.$ 5782 1402 "-$$- 493 00 235% 516.83 4.98% 1$t -ir"MIAMI 1991 858.9 ---49$....- 2865 328414 98% 510.23 3.40%/ 522.33 2.68% --.-..~p1-----.....gs..... ..... ssMIAM 1992 849.5 ..... .. . 2797 323279 .-4,14' 524.94 288% 571.08 9.75% "".:$~-'" ......MIAMI 1993 878.2 3233 336076 54628 4.08% 699.09 6.64%MIAMI 1994 903.3 -"5231 329309 ~: 57622 5.46% 646.92 6.23% fMIAMv 1995 923.8 $94.-. 7370 334540 ..~ 593.03 2925'. 635.25 -1.00% '<if'" ~~-im 0#MIAMI 1996 933.2 -crif4-.. 249 341910 "...$04...6194 3.19% 637.58 2.37% .... 91 . *0 .alMAMI 1997 953.7 tA 4592 344659 624.55 206% 644.58 1.0 $S9 4*-- -- 9*MIAMI 1998 969.7 344~- 5592 349251 -*41'-'635.46 1.75% 647.50 24% .*W -n-''--<stMIAMI 1999 981.6 946* 22 354843 --38% 64292 1.12%A 67200 378 $M 9*-. 4$I

MIAM 2000 - 1003.8 .t*GS-'----- 6145 356105 65397 1.77% 0.00 00%

MILWAU 1988 710.6 3538MI!WAU 1999 737.7 5040 211250 425.67 469.42~, mo~ 5.,.,~~~~~~~44.0 .54 2 0.83.

36 MILWAU 1990 755.6 49--.' 4513 216290 s'$9*.--459 .4 485.33 -03% ""*1l."

MI.WAU 1991 7534 2547 203 46270 298% 500.50 213%'..WVLWAU 1992 7527 --t435-' 2986 223350. 4$'475.15 269%. 506.33 1.17%. 41 4% S5~91,~~~~~ ~ ~ ~~~~~~~~~~~ ~.~ ~. .5.~ 4.~ .4 .......MI!.WAU 1993 765.7 39-- 4476 226336 194* 487.60 262% 512.50 242% .49$- -. *1 .

MIWAUI 2000 1994 3784 i]{~{~iiii'~' '-' '~ j.':~:i.~ 1102559 230812 .750464 3.49% 5542 6.52% #': !

LAL 1995 0011 4.74% 557.08 284% .... .MILWAU 1096 811.0 2753 236015 53511 1.24% 574.00 204% 2MII..WAU 1997 819.2 78 238768 54527 1.90% 570.50 -.61%

,LWAU 1998 8406 3297 238846 556.74 210/. 595.58 4.40%MILWAU 1999 855.1 ::4%" :, 2973 242143 ' 567.23 1.89/ 609.50 2%.)5'/.

MILWAU 20001 875.0 !---t; . 2240 245116 ! f0l00 0.00:.: 200 o.0*/.



I .':."<....<.::. ,' *:;!ii'1 GOV'T GOV'T NREI NREI :. .Year P P ERMITS STOCK 4f~Clfl RENT RENT RENT RENT ..... :

# MSA , Gien { ' f. s.t 'G:v. Cl.ed GiJfdih .Gie Caculatd Gic.. Calculaed

MNEA 1988 1320.7 6746MINNEA 1989 1355.5 5039 280899 467.19 464.92

31 INNEA 1990 1385.4 iil.'= 385 : .'.'.' 47900 253% 486.50 4.64% 31~~~~~~ ~~~~~~~~~~~~~~ 3385ii' 2859ii8 479.oo40 . ii0!igiai~~¢'~~~~~i/~ ...!:~.8IZNRA 1991 13824 ~ ~ 1671 289323 · 487.86 1.85% 487,67 0'24 V

MINNEA 1992 1397.4 2159 290994 49931 235% 511.58 4.80 MI*vIIINEA 1993 14344 4R1 2633 293153 i!m 507.07 1.55% 533.75 4.33% .

WkS: 978 i . 5519 160/ 540.17 L2(P/.,IvNEA 1994 1483.1 *~4 2718 2957.86 51. 19 1.% 5.17 1LINNEA 1995 15328 3809 298504 52886 265% 568.75 5.29% 2MINNEA 1996 1570.8 M 308 32313 4$ 547.69 3.56%1 59298 4.10/% *)INNEA 1997 15968 56.903024 305321 . l%.0 662 3.51% 614.25 3.74% MIs^E 1998I 1643.0 3~ t~345MIC~~~~~~~~~~~~~NI4RNE~~A 1998 1643.~~~~~~~~0 i"'3923 308345 583.15 287% 655.08 6.65%.MINNEA 1999 1684.0 . 4851 312268 605.67 3.86% 679.58 3.74% .. : *MINNEA 2000 1727.8 ii:.'..: 5335 317119 s 641,13 5.85% 0.00 0.00/%;

NASI-VI 1988 484.4 2970NASHVI 1989 496.7 1117 133806 0 396.67

32 NASVI 1990 503.6 9 ' : : 980 I ."::: 0 .00 0.00% 407.17 Z65%NASHVI 1991 4957 454 135903 0 00% 41242 1.29% NASHVI 1992 508.2 0 229 136357 0.00 0.90% 427.00 3.54,A .is." " w [."oNASHVI 1993 531.5 , 516 136586 .00 0.0o o% 4413.33 3.83% .4ASJVI 1994 557.4 1735 137102 000 O % 487.08 9.87%.....NASH-VI 1995 5903 A 2447 13837 , 000 0.005. 509.83 4.67%. .138837 ~ ~~o o.W ooT/ ,0., 9.67,NASH-VI 1996 603.8 5668 141294 501.56 0.00% 520.33 206/, :NASHVI 1997 6210 . 3096 146952 4% 52751 5.17% 515.08 -1.01% ::' '.NASHVI 1998 6459 2129 1 531.76 0.81% 53258 3.40%/r .. .NASHVI 1999 661.9 2660 152176 546.35 274% 53258 0,00% :: ' .:: :iINASHVI 2000 678.6 1923 154836 000 0.00% 0.00 0.00/.

NYORK 1988 4139.3 9295NYORK 1989 4155.1 9576 2155686 50.08

33 NYORK 1990 4138.6 4:! i 6210 216526 : ' 537.00 5.69% 0.00 0.00% "' - ' ii' .NYORK 1991 39420 : " 3634 2171472 4 55557 3.46%, 0.00 0.00... .a~~~ ···5 364 555NYORC 1992 37972 3136 2175106 57342 221% 250 o ,3136 573~~~~~.42 321% 0.oo MNYORK 1993..~:.... 3765.9..':ti.ii "AW~l~i i~i;NYORK 1993 3765.9 .. ~g~4<, 4487 2378242 UI 58841 261% 0.00 2.00% Y

NYORK 1994 3798.7 : '.. 3903 2182729 '603.05 249% 0.00 2.80 : '. : NYORK 1995 3833.6 : .4545 2186632 !: 619.12 266% 0.00 00% $.,YORK 1996 3845.1 M, 8467 2191177 6 03662 283% 0.00 O.o% A

NYORK 1997 2914.7 :-.~8653 2199664 . 65.40 3.42/. 2.00 0.0%.': ¢i*-i;mWYORK 1998 4013.7 :'I 10342 2208317 680.89 3.42% 0.00 0.0% ': ..: :5 ii-WORK 1999 4129.1 , , 11495 2228659 703.03 3.25% 0.00 o0.0%o.

NYORK 2000 42128 I.'I. 14514 2230154 733.38 4.32% 0.00 0.00%

OAKLAN 1988 823.3 4903OAKLAN 1989 854.2 4924 315098 611.95 709.33

34 OAKLAN 1990 86618 2223 322 ' "*9 .6400 4.71%. 710.50 .16%.,.OAKLAN 1991 8848 : 277 3224 ....... 663..2 35 737.92 3.86%.'. ...... ... :i

,OAKLAN 1992 871.1 46A 1329 325022 67942 237% 73267 -0.71% 1011 Z 6 9%~~~~~~~~~~23,, 739.67 .0.91%OAKLAN 1993 873.1 1011 326351 697.67 269% 739.67 296% 4 iOAKLAN 1994 874.3 , 1064 32762 710.82 1.88% 759.50 268 :' * A LAN 1995 890.4 ' ... 826 328426 721.43 1.49%. 790.42 4.07% !

OAKLAN 1996 907.8 . 1202 329262 -'. -" 740.53 265% 926.92 17.27.% , .!..:tlOAKLAN 1997 9363 2533 330464 , 785.51 6.07% 1020.83 1213% 9A9

OAKLA 1998 964.4 .* 3034 332997 <'.t 84704 7.83% 1059.33 3.77% "OAKLAN 1999 995.7 1958 336631 906.46 7.02% 1107.17 4.52% 4 * ~ f$OAKLAN 2000 1024.4 ! %."8 2054 337989 970.11 7.02% 0.00 o .00 .

OKLA'O 1988 405.2 125OKLAHO 1989 419.7 566 129146 0

35 OKL^AO 1990 4267 "--'% l 34 123212 97 ' -... 000 0.00% 0.00 0.00% .

OKLAHO 1991 4318 9 6 129740 000 2. 00 00% 1992 433~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~.00,,OKLAHO 1992 433.4 224 129752 '. 0 00% 0.000 0. 2001% . * OKLAHO 1993 445.5 *& 28 129976 "'* 000 2.00 0.00 2 00% 249OKLAHO 1994 4530 ... . 435 .130004 .': 0 0.00% 200 0.00% : :.-"*

OKLAHO 195 71 . 650 130439 0.00 0.00% 20 0.0%-OKLAO-O 1996 486.3 ." 857 131089 08 200% 0.00 2005' 'l " 0.0(' 4OKLAHO 1997 495.7 1 1801 131946 0o. .00 .00 200 0.00%1

OKLAHO 1998 5,08.1 2180 133747 o-4$14' 000 0.0/. 0.00 oo00% MOKLAIO 1999 25.3 :..:. ... 983 135927 o. o. 00oo% o.oo o. WAOKIA4HO 2000o 539.1 .';. 1218 136910 .t::t::'/-.:: o. o.00 Oo% 0.00 o.00o%

ORANGE 1988 1105.7 11941ORANGE 1989 1157.4 8745 1532749 756.21 730.33

26 ORANGE 1990 11774 7605 Il41494 79900 4.47% 748.42 248%

ORANGE 1991 1150.9 . 2968 1549099 'j 14*$~ 81271 287% 765.33 226% ./'4""..."4iiORANGE 1992 1127.3 494W... 2247 1552067 82274 1.23% 746.67 -244/.ORANGE 1993 11161 .,.... 1903 1554314 . 2432 0.19% 744.92 -0.23%ORANGE 1994 1119.3 '2 4882 1556217 . :-'" 825.91 0.19/ 733.25 -1.57% ..ORANGE 1995 11432 2245 1561099 82221 -0.45 75250 263 " . . .":"ORANGE 1990 11739 IflM- 3197 1563344 *9A 830:66 1.03% 790.42 5.04%. < . .i . :

ORANGE 1997 1206~6 & 4055 1566541 $ 84281 1.46% 849.33 7.45%-ORANGE 1998 1278.7 i' 2419 1570596 f.-' 866.04 276% 914.08 7.62% 4 .:ORANGE 1999 13304 -*49 4560 1573015 S 897.20 3.605'. 951.42 4.08%ORANGE 2000 13750 ' " 5671 1577575 " 4 933.64 4.6% . 2005.



GOVT GOVT NREI NREIEMP a PERMITS STOCK RENT RENT RENT RENT

8 MSA G-e tfL4.X Gwen Clcued Gwen Calculated Given Cl1ulated

ORLAND 1988 5325 8220ORLAND 1989 568.4 7604 144357 0 435.75

37 ORLAND 1990 605.5 * * 7471 11 fr~ 000 0.00%/6 451.50 3.61% .37 ORAN 1990 ~57471 laORLAND 1991 5993J r~r 4362 159432 4P*' 000 0.00% 439.83 -ZS* 4OR AN 1991 599.3 -3- ...ORLAND 1992 6100 276 163794.00 0.00 .o 451.50 265` . ' ..ORLAND 1993 635.1 4121 166170 O.0 0.00%/ 463.17 258%/.ORLAND 1994 670.2 " ... 400 170291 000 0.00% 477.17 3.0% .ORLAND 1995 703.2 :, 5032 174591 0.00 2.00% 476.58 -0.o .' '0~WW 199 7327 '""'""";l'!iORLAD 1996 7327 ~3689 179623 56087 0.00% 501.08 5.14%ORLAND 1997 778.2 7695 183312 607.86 138% 528.50 5.47%ORLAND 1998 822.6 . .4*.' 10734 191007 " " 65235 7.32%` 550.08 4.08%OR.AIND 1999 80.7 7.".:. 13225 201741 67204 3.02% 561.75 212% .ORLAND 2000 911.6 i:: ." : 9538 214966 o .( 0 o 0.00% 0.0 o 0.00`

OXNARD 1988 211.1 1479

OXNARD 1989 217.1 1731 72107 724.62

38 OXNARD 1990 2280 .. t ·18'" 1252 7 757.00 4.47% 0.00 0.00%OXNARI 19913 232.3 75090 0.00 0.00%? iii..\e9:V :.IOXNARD 1991 1147 778i IB76 2 8 7 1/ W O.WOXNARD 1992 226.4 554 76237 ' 1 788.37 1.23% 0.00 0.00%1OXNARD 1992 226~~~~~/~i. "':~..OXNARD 1993 226.7 *'--4~ 206 76791 78989 0.19% 0.00 0.00%*OXNARD 1994 230.1 $: 3t: . 809 76997 "4$'79141 019%/ 0.00 0.00 ":'..:

~iii:'i~: '.s?.:' .,:°x~ 787,87 -0.45% 0.oo 0.00O/° ::;:i:!:::':': '~::-:i:':: .':~:':':"~ '76::::::::1":~::': ·':0 : : : : : iOXNARD 1995 2368 :,2D7 77806 78787 -0.45% 2.00 0.00%OXNARD 1996 2376 246 78013 7c*$~ 795.96 1.03% 0.00 o.00 - i!OXNARD 1997 240.1 4 232 78259 007.60 1.46% .00 0.00% '...".w ':y 4:.OXNARD 1998 248,3 368 78491 829.87 276% 0.00 2.00 4/OXNARD 1999 258.9 '[[? 782 78859 >¶ 859.72 3.60/ 0 .00 % '. ': : i: OXNARD 2000 270,6 i 976 7964.1 894.64 4.06% 0.00 0.

PHILAD 1988 2194.2 4038PHILAD 1999 2227.1 2874 427579 493,16 653.33

39 PHILA 1990 22414 1959 6"51500 4.43% 670.83 28% o , . ..

PHILAD 1991 2176,0 1332 432412 535.48 3.98% 678.42 1.13%PHILAD 1992 .21250 : .':; t\ 756 433744 550.15 274% 651.58 -3,96%PHILAD 1993 21368 1386 434500 : 558.68 1.55% 684.25 5.01% ....PHILAD 1994 2151.8 913 435886 .... 6585 1.2% 721.00 37 .PHILAD 1995 21822 1311 436799 574.04 1.45% 736.17 210%&WPHIWAD 1,,~ I 2193.2 D"9; 3 . . : '~P81L.AD 1996 293 " 2098 438110 :..' .. 5.99 208%/. 737.33 0.16% PHILAD 1997 2247.0 '.°, 3374 440208 60032 245% 771.17 4.59% $PHILAD 1998 2303.5 !! ' 2475 443582 615.68 256% 814.92 5.67%`4PHILAD 1999 23493 ' 2506 446057 631.72 261% 868.00 6.51%

PHILAD 2000o 2397.6 t::'e" 2946 448563 651.51 3.13%, 0.00 o.o/ .

PHOENI 1988 958.2 6526PHOEN! 1969 985.0 1714 293121 0 376.25

40 PHOEWI 1990 10007 2041 29435 - - . 00 0.00 369.83 -1.71% PHOENI 1991 10149 1070 296876 0.00 2.00% 395.50 6.94%PHOENI 1992 10101 1541 297946 *4* 0.00 2.00% 389.67 -1.47%t

0PHOEN 1993 1036.2 :: 2257 299487 0.00 : .00%: 412.67 5.39%.. : :,PHOENI 1994 11006 6501 301744 . .000 00% 461.42 1236% PHOENI 1995 1195.3 8953 308295 o o 0.. 502.83 8.9:.,,2 12L8.7 103 . 3172,.4.8."--._.PHONI 1996 1287 13 1728 56272 0.00%:: 517.42 2 ""90% " :I z 'PHONI 1997 13572 W 11014 327381 0 5.10 4.87% 53550 349%P808W! 1995 11953 ZWA ~~~8953 300295 000o 0.00%/ 50283 8.98%°/,,*$0~-.. PHOENI 1998 1429.5 '.'. 1 11240 338395 . .588.93 -0.20% 568.17 6.10%.`PHONI 1999 1497.3 * 9265 349635 - 597,65 1.48% 568.75 0.10%/ $1¶PHO 2000 1565.0 .'.. : 1i0981 358900 .* o . 0.00 O.0.'. 0.00 2.00%. :

PITTSB 1988 9655 1705PITTSB 1989 990,7 872 247906 364.11 56292

41 PITTSB 1990 1016.9 0v2. 9 783 26228 ' 37500 2% 567.00oo 0.73% : ". .PI1TTSB 1991 10147 : 932 249561 385.89 290% 60958 7.51% .... ........PITTSB 19925 1006,8 """'':"'' L',:i-/3. 522 250493 5. $ .399.14 3.43% 621.25 1.91%PITSB 1993 10208 - 44999' 5720 251015 t: (0W 409.73 265% 595.08 -5.82% .P!?TTO 1994 10345 41M4~ 782 256743 " 4499%.~j~iib~l~~ 420.92 273% 604.50 11.86% L4755PITTSB 1995 105o08 : 641 257525 40.. 273% 635.25 .%PIVISB 1996 10530 4J$ 911 258166 440.64 1.91% 667.92 5.14%

PITTSB 1997 1071.6 .":':' -' 1632 259077 445.94 1.20%' 683.67 236% PITTSB 1998 1004.4 :" 1557 260709 46213 3.63% 686.58 0.43%-*P11B 1999 109 $49 1817 262266 472.66 1.85% 693.58 1.02%PITTSB 2000 11112 ' . " 1336 264083 . '_:::.:'.,,P4-::: .:: 000 o .00% 0.00 0.00%

PORTIA 1988 6438 3358PORTLA 1989 681.8 9220 179595 417.48 513.33

42 PORTLA 1990 713.8 " 5692 leg15 -.-... 441.00 5.63% 5 519.75 1.25% '.:::: :: .:.:.:.PORTIA 1991 723.2 $ 2331 194507 :44W 470.58 6.71% 507.50 .236%. *N-PORTIA 1992 7324 - 196 195838 493.77 4.93%/ 50283 .092% .PORTIA 1993 7543 3092 196944 · $3 511.59 261% 491.17 -232/. .PORTLA 1994 787.4 5814 200o36 535.45 4.66% 524.42 6.77% ....PORTIA 1995 824.5 7081 205850 551.92 3.08/. 543.08 3.56% *PORTLA 1996 867.2 "' 7055 212931 %"' 566.71 268% 541.33 -0.32% * -PORTIA 1997 904.3 -9 8387 219986 591.92 4.45% 554.17 237%"PORTIA 1998 931.0 6575 228373 $ 613.43 3,63% 575.17 3.79% a.PORTIA 1999 9376 4482 234948 627.21 225% 572.50 -0.81% 4*PORTLA 2000 9660 3614 239430 63696 1.55% .00 00%


EMP : PERMITS STOCK ' RENT RENT RENT RENTMSA YeaTZ -- GGG

Appendix IMarket Data -Employment, Stock, Rent, Excess Demand

RALEIG 1988 425.7 1198RALEIG 1989 447.8 1254 114870 0 0

43 RALEIG 1990 464.9 I'm:l:: 16490.00 O0% 0.00RAEG 191 47 99 117773 0.00 0.00%/ 0.00 0.00% /61992 481.7 775 11871 . 0.00 0.00% 0.00 0.00% RALEIG 1993 503. / 107 119546 99$!. :io 0.00% 49467 0.0 *

~L~,i~ ~~~~~~~~~...'!..~ii:¥.~ii..:~ii'. 'RLG 194 53480.00 0.00% 5300 7.55%::::::::::;i~~~~~~~~ ~~~~~~.~.....~..'*..:::.: ';~iiiiii

RALE G 1995 5469 **'~*~~'.~ 3946 125496 0.00 0.00% 55z42 3.84%RALEIG 1996 572.8 3326 129442 4.~~4~:i 5024 0.00% 546.00 .1% ~ 4:! RALEIG 1997 6007 ... '1~ 4720 132768 566.72 4.90% 564.67 3.42%RALEIG 1998 6353 374...ji8 4945 17488RALEIG 1999 659.4 6857 142433 598.93 1.81% 581.00 205%RALEIG 2000 6805 . ":: 6315 149290 0.00 0.0% 0.00 0.00

RIVERS 1988 610.9 9922RIVERS 1989 648.8 7237 291308 535.09

44 RIVERS 1990 701.5 : 5486 ::M. 4: 5. 559.00 4.47", 533.17 2.:1"'RIVERS 1991 719.8 I~ 3252 53317 2031RIVE2S1992 7249601 575.07 287% 507.50 4.81%RIVER ,1992 7268 " ~ 1960 307283 i i:. 582.17 1.23% 4992 287%RIVERS 1993 730.5 752 3243 583.29 0.19% 49875 1 18%RIVERS 1994 728.2 '510: 309995 584.41 0.19% 510.42 234., '::::.. . ::: :,IERS ,,5s 78 1? .~!~ 94 ,0RIVERS 1995 7708 .'pt4~....... 194 310505 /ii : 5 581.79 -0.% 48358 526..RIVERS 1996 796.6 649 310699RIVERS 587~~~~~!i;t'':--77i~ ,1o~ 7.03, 49,.42 65RIES 1997 827.7 $ ~~~1324 314 59.7 146% 518.58 4.47%' ::::.. RIVERS 1348 5967 1Si"fii] ~9 17RIVE 1998 8007 ~2395 312672 . 61281 276% 550.08 607 ./..:..:. .. ':i::.RIVERS 1999 913.4 1903 3107634.85 3.60%.' 577.50 4.98%.. ..:A:RIVERS 200.968 . 2416 3190660.64 4.06% 0.0 00.'

SACRAM 1988 5021 4605SACR Al 1989 523.9 4168 221134 0

45 SACRA 1900 549.4 2889!p'.1.*.00..0.00%i523.83 2,,16 0.00 0.00/ 51.83 30%

SACRAM ,193 154.0 '. 557 21535 i..{.~ . 000ii 0 .00SACRAM .....~~~~~~~~~~~~~~~~~0 ., s53.17. %

SACRAM 1994 5629 X 575 232092 0 0.00% 524.42 3.81:: 0.00SACRAMv 1995 581.1 *$ 47 237 0.00 00% 585 8 . ~ ..SACRAM 1996 ~~~600.2 755 233134 . .... . 601.57 0.00%/ 557.08 5.41% ~ .. :SACRAv 1997 615.4 1234 233889 678.36 1276%/ 588.00 5.55% 1.. SACRAMv 1998 639.4 ~ .. 2853 235123 671 .4.61% 617.75 506%DV SACRAM 1999 676.3 ,2744 237976 " 0 . ,

SACRAM 2D.' 3318 2407000 0.00% 04.0 030A *87%..~,,::

SALTLA 1988 448.2 152SALTLA 198 465.1 116 112810 0 30846 SALTLA 1999 486.5 285 I= . ' .1 25 j 000 0.00% 322.58 055514 63.4 2850*0SALTLA 19912 0. 9 131 0 0.00'.'. 364.58 13 02%.~S3LTLA 199 514.4567 113410 000 0.001% 397.25 8.96%SALTI.A 1993 535.8 1756 113777 0.00 0O0% 418.25 5.29'. . ~SALTLA 1994 567.3 7 1636 115533 0 0.00% 448.00 7115SALTLA 1995 001.6 117169 1.1SALTLA 1990 6 325~ 0.00 0,005': 473.6 5.73 o :.. 6325 ~~3689 120175 ... 434.40 0.00% 003.42 6.28% SALTLA 1997 661.7 882710

23964 46851 7.85% 51292 2095SALTLA 1998 680.1 2391 1 4829.0 37 9SALTLA 1999 71.X:~.*!. 95 186 484.79 0. 39%' 558.25 3.575' 6 714TLA 2800 1442 1309400.00 0.00

,DIEGO 198 884.5 13656SDIEG^ 1989 924.7 7854 399467 58549 710.047 SDIEGo 1990 900.4 9~~$~ 080 60721i 611.00 4.36% 722.75 1.72% /. SDIEGO 1991 9688 .. :..

693.00 12.'8 4.40

ODEO 1992 949.5 *:.i4$: 2259 4191~626.39 0.72% 683.09 -1.435'Y.j~:SDIEGO 1993 946.0 1521 421100 52112 -0.84% 65 1SAIEGO 19949560.96795 300617.SDIE 1994 9509 1 0707 422701 ~ . 609.50 -0.26% 690.67 16.3% ii .. .:! ' '" '*:* :':'SDIEGG 1995 965.0 ..

: 1868 424408

! 6231'. 7 1 5ODIEGO 1996 995.2 ~~~~~~~~AW.. 1017 426276 .. 61.6.13 6% 757.17 765 $ATA 199 631.65 368 17563SOIEGO 1997 1034,5 2i*4-.WA 2903 427283 ~ 647.04 244% 804.42 6.24%/SDIEGO 1998 1084.5 2878 430196 6 4.94% 69.17 8.05%SDIEGO 1999 1136.4 6225 433075 72 .56% 893.67 2825/

SEATTLA 1989 1038.2 1 21347963 567.0048 SEATTL 1990 1107.9 ~ 12939 J309, 6 *I0'9$ 51800 8.00% 574.58 134%SEATTL 1991 119.7 32.2540 552.53 6.67 557.67 94%SEATT 8 84.5 136

SEATT'L 1992 1137.3 S~:42 220 .4 1) 73.25 3.75% 555.33 -0.42% :::.. .~::SIEATT 199 1141.5 ~i'~~i:~:: 4299 331012 58515 298% 557.67SEATTL 1994 1148.8 : i 3981 335311 59781 2 1 .67 2SEATTL 1995 1176.3 :-4711 339292 60855 1.0% 59267 3675'

SEAT"1L 1996 1204.0 6703 344003 625.44 278% 634.88 6.995'%*...SEA1oL 1997 1265.1 ' { 7795 350706 3 6496 3.86% 69242 928%.SEATTL 1996 1334.2 *~ o 11814 358501 684/14 5.32% 718.08SEATTL 199 1377.6 10220 370315 71369 4.32.' 7425 3. : SEATTL 2000 1396.9 380535 74016 3.1%: .0 0.'::::

The Correlation Between Market Fundamentals and Apartment REIT Performance74

II - I . A .. . . -

"'Mm.......0

7 a

GOf T"'. A rl., 1.I. OV'T

Appendix 1Market Data -Employment, Stock, Rent, Excess Dermand

:....&.'.:: ¥" :: GOVT ..".'-. O'. GOV T NREI NREI : " 'EMP K PRMTS STOCK RENT RENT RENT RENT .\I8j

# MSA Y Gear Jv."': Glven Cllated <'""'""~"'e~ : Given Calculd Ghien Cal'ulated

SPRANC 19 918.9 3137SFRANC 1989 930.5 3234 327507 672.98 749.58

49 SFI9ANC 1990 944.0 .156 ~74 . 704.72 4.72,. 769.42 265% ...,.....

SFRANC 1991 943.6 1296 332302 729.92 3.58. 819.58 6.52% 1.

SFRANC 1992 9196 1198 3358~747.19 2.3 7%/ 829.50 1.21% 4S' \41SFRAN4C 1993 910.4 I 193 334796 ~'*.K 67.26 2.697. 882 00 6.337, *P1i ::::. :;%SFRAN4C 1994 902.2 'v 1176 335889 .. 781.7 1.89% 898.33 1.85% /6

SPF.ANC 1995 908.9 49A~ 1271 3305~793.39 1. 4 9% 1023.17 13.90% ** :WSFRANC 1996 939.5 1848 338336 814.39 265% 1131.0B 10.55% *SF9.ANC 1997 970.6 '~$j$~ 2346 340184 863.87 6.08% 1295.00 14.49%/,SFRAI4C 1999 998.5 *~'*~*~~ 3497 342530 931.54 7.83% 1363.25 5.27% ,SFRANC 1999 1033.3 ~'"'"'"''~ 2728 346027 996.88 7.01% 144200 5.78%6SFRANC 200 10028 3553 348755 106.88 7.02% 0.90 0.00oo6

SJOSE 1988 7925 2794

S$OSE 1989 810.0 2311 209088 74201 766.5050 SJOSE 1990 813.3 3223 21199 777.00 4.72% 70,0 0.84%1W

SJOSO 1091 814.8 ~ 2118 2146232 . 84.79 3.587% 754.83 .69%. .SJOSE 1992 7899 9" "'" 1301 216740 : .:.:.:.. 8233 237% 755.42 0.08% Ai1t . . ' ..

SJOSE 1993 795.0 : 1628 218041 i 845.95 2.69% 751.33 -0.54% . 44:.' !* ~}~:..SJS 1994 795,6 : : 1:" 9

SJOSE~~~ ~~~~~ ~~ ...94..7956... 1817 21966i9 ~:f'e:..i''~i;: 8610~0 1.89',, 828.33 10.25% SJOSE 1995 811.4 . 1213 221486 { $ .- . 874.77 1.49% 945.00 14.0% : ' .SJOSO 1996 8660 3542 222699 897.92 265% 1067.50 1296% '** '

SJOSE 1997 906.2 .':' : ' 4053 226241 95247 6.08% 1178.33 10.38%sJOSE 1998 954.2 31 230294 1027.00 7.83% 1258.83 6.83%

OJOSE 1999 961,9 4t..~... 3557 233910 . 1099.12 7.01% 1315.42 4.49% ~ .*..~~~~

SJOSE 2000 . 9825 38 237467 1176.31 7.02% O.00 0.00% . ':. ,

SLOUIS 1988 1143.5 3975SL.OUIS 1989 1176.9 2185 220313 404.82 43225

51 SLOuIS 1990 1193.5 291.018 41500 251% 441,58 216% . ......SLOUIS 1991 1172.2 /994 223589 41882 0.92% 442.17 0.13%SLOUIS 1992 1166.2 W 6 4 224583 415.95 0.69% 44217 0.00 . .. ' .

SLOU 1993 1176.3 92 2522 I' 422.63 1.61% 464.33 5.01% ·

SLOUWS 1994 1207.9 1267 226199 .. '.'. 437.26 3.46% 499.33 7.54% ~ $I~

SI.OUIS 1995 12395 ~ .. 145 227566 ~ 447.75 240% 520.33 4.21%

S.OWS 1996 1255.8 .2...a 2129 229051 457.61 220% 557.67 7.17%

SL.OWIS 1997 1285.3 : 1738 231180 468.74 2.43% 547.75 -1.78%

sI.OIJIs 19,i 13051 6 91954 232918 $::< 479.24 2.4% 5725. 4.47% 4 .

SLOWJIS 1999 1311.2 ~2130 234872 485.28 1.26% 582.17 1.73%/

SLOUIS 200 13304 ' 2764 237002 498.32 269/" 0.00 0.00% OD', .:: ' . .

STAMFO 1988 426.2 326STAMFO 1989 430.7 74 26802 0

52 STAMFO 1990 49.6 471 OE.DO~00 0.007. 0.00 0.007. STAMO 1991 40.4 = 201 27347 000 0.00% 0.00 o.o0.o

STAMFO 1992 389.6 4:::. ."" 43 27548 :- 4 00 0.00% 0.0 0.00%STAMPO 1993 390.2 585 27591 000 OSO1e 0.00 0.00.$6STAMFO 1994 39.8 ' 154 28176 0.00 o.o0. 0.00 0.00%/W

g'w~~~~~~~~~~~~~~~O 0OO 0.00%/ 0.0 0. OO%STAMFO 1995 400'3 '567 28330 :: ' 0 0.00% 0.00 0.00%"'"J 1 STAMFO 1996 4024 * .' 269 28897 0.00 0.007. 0.00 0.00% %./16 STAMFO 1997 4127 : 158 29166 000 0.00% 0.00 0.00.STAMFO 1998 427 1 29316 : 0 00% 0.00 0.007. STAMFO 1999 427.9 : 439 29498 000 0.00% 0.0O 0.00%STAMPO 2000 43z iif "' 554 29937 ,00 0.00% 0.00 0.007.

TAMPA 1988 024,8 7481TAMPA 1999 855.7 4461 261110 428.46 43225

53 TAMPA 1990 877.3 N=25 S22 * 448.00 4.56% 431,67 .0.13%TAMPA 1991 863.3 3296 27046102 291% 45.25 .1.49% .....TAMPA 1992 859.2 1291 27412 47 2.12% 427.00 0.40% :.:".."...

TAMPA 1993 8851 2132 275413 48381 2.77% 441.00 3.28%

TAMPA 1994 938.9 3740 277545 : 50099 3.53% 4TAMPA 1995 9825 .48 4214 281285 513.51 2 52%/ 481.25 0.24% . ** .TAMPA 1996 I09 . " ':.4'' 4674 285499 : 525.31 2.30% 490.58 1.94% -. .: " % : . . :&TAMPA 1997 18 6241 290173 3 505.17

TAMPA 1998 1102.2 7286 296414 558.68 2.47% 537.25 6.35% TAMPA 1999 1138.8 ~ 9671 303700 ~ 4~*9~ 572.51 246% 556.00 2.58%

TAMPA 2000 1192.5 5II .. 5919 313371 591.23 3,27% 0.00 0,007/

TUCSON 1988 248.0 1942TUCSON 1989 250.0 482 101007 0

54 TUCSON 1990 251 8 101.8 471 ,o $,o, o 0.o 0.00%o. 0.00 0.007 ::..:i".:.:./-TUCSON 1891 28.5 3 . 30 101960 0.00 0.00% 0.00 0.:00 W/ ........ .

-2 ~~~~~~~~~~~~~~~~~~~~~~~~~~. .... .TUCSON 1992 74 1000 0.00% 0.00 0.00/6TUCSON 1993 269.8 840 102864 0.00 0.007.6 0.00 0.00%/ .......TUCSON 1994 287.5 / ".:{ 1954 102904 ...... .... 000 0.00ooo 0.00 0.00/ " :.'i' 'TUCSON 1995 30o .2341 104858 .00 0.007. 0.00 0.00%TUCSON 1996 304.4 ' 63 107199 ' 487.66 0.00/. 0.00 0.00'. -.. t::!TUCSON 1997 3103. 125 , 1073 511.07 4.80% 0.00 o.0D/"TUCSON 1998 322.4 ?? :'1028 109048 $'4".3 490.38 -4.05% 0.00 0.00% oTUCSON 1999 328.2 '".'~ . 1500 110076 500.18 20O% 0.00 0.00%

TUCSON 2800 349.4 ..:.' t 682 111576 0.00 0.00",. 0.00 0.007%



.~~~~~~~ ~~%A %A - wFF-.' .. ...... .:~rqGOWT GOVT NREI NEI r1 ~ ~ 4*

EMP PERMITS STOCK RENT RENT RENT RENT ~ JI ~ 1!4 1~# MSA Year Giv Gin.. C: : ated G{,m- ~ta Give. Calculted Given Ckulated

WASraN 1988 2231.2 11668WASHIN 1989 2313.0 12821 135732 641.69 684.25

55 WASIN 1990 2348 4" 57 IM $*r' 670.00 4.41% 695.33 1.62% 4WASI11[N 1999 2304.2 ~02 157126 68.990 297% 70350 1.17%j

4

WASHIN 1992 22798 :::m ' " 2916 159928 699.63 141% 676.67 -3.81%WASHIN' 1993 236 $ ...... 3983 162844 703.61 0.57% 706.42 4.40% $91 &i#WASHIN 1994 23501 5334 166827 716.00 1.76% 713.42 0.99% %

WASI-IIN 1995 24027 i..5i ZW&i.i . 6159 172161 ... 0 204%H 730.33 3AWASHIN 1996 24070 7824 1783 ' 747.39 2 30% 760.67 4.15% ." WASHIN 1997 2465.6 7189 186144 749.60 0.30% 774.08 1.76%WASHIN 1998 2520.5 8795 193333 ' 768.28 249% 813.17 5.05%0/WASHIN 1999 2599.5 9322 202128 79220 3.1% 859.25 5.67%.WASHIN 2000 2685.5 8973 211450 9 21.35 3.61% 0.00 0.O

WBEACH 1988 335.8 6563WBEACH 1989 350.0 7501 94784 573.48 501.67

56 WBEACH 1990 3591 $ 4196 58700 236% 51217 209%WBEACH 1991 3557 2363 106481 j 60752 250% 503.42 :1.71%. W/BEACH 1992 353.7 .'.~~'.i.*c~6% 1876 108744 4 62503 z28/. 494.08 .1.85% .$$*$'4R%::.:: : #. .:: : jW/BEACH 1993 3620 ~ 1861 1106280 65055 4.08% 518.00 4.84% **k'W/BEACH 1994 3805 ~3060 112481 $449~.686.08 5.46% 541.92 4.62%'HWBEACH 1995 3928 :2872 115541 70610 292% 54109 0.22% WBEACH 1996 410.6 : 1643 118413 72862 3.19% 565.83 4.19% ..

V/BEACH 1997 428.2 .2794 120056 7.4$* 4 3.63 206% 574.00 1.44% .... ..... ' . . ...

WBEACH 1998 4528 3:2346 122850 'o 756.64 1.75% 594.42 3.56%V/BEACH 1999 465.1 *j4* 3580 1269 7 615 1.12%/ 611.92 294% ... *Wr'..zf 4V/BEACH 200 99 ;zY 3567 1287 S,*t 778.68 1.77%/ 0.00 0.DD0%

WILMIN 1988 259.2 1265WILMIN 1989 2727 960 50617 50169 0

57 WILMN 1990 276 4 589 526.00 4.43% 0.90 .'6...ILMN 1991 2697 :::::::::::::: 176 52166 546.91 3.98% 0.00 o!.

WILMN 1982 26349 *4 431 5242 ~. 561.90 Z74% 0.00 0.00% . ....WIMN 1992 [263.4 431"...:¢1 8 WILMIN 1993 267.7 85 527 ',73 % 570.62 1.55% 0,00 0.00'HVA1Av88N 1994 273.4 ::::::: z.. 208 52858 • 577.94 1.2r/8 0.00 0.00%: . :::.'At...-:' WILMIN 1995 2816 4P 87 53066 58630 1.45% 0.00 0.9O% . 4* .N :':.:WILMIN 1996 284.5 :.:, 152 53153 2o~.H 598.50 208/ 0.00 0.00%:<...: &$..WILMIN 1997 297.5 .IWA 348 53305 613.14 245% aoo 0.oo/ . : " .:.:.:....... i .WILMIN 1998 308.2 894 52653 620.83 256% 0.08 0 Ai t ..WILMIN 1999 317.7 :. ' 355 54547 645.21 260% 0.00 0.00%WILMIN41W 20o 325.0 i '. 791 54902 665.43 3.13% 0.00 0o.% .

NON57 1989 49227.9 146340 15360424 428.91 54250NONs7 1990 48836.8 . 122393 IM74 .447.00 4.22%. 551.83 12% , . :NCN57 1991 48739.0 89324 15628157 ! 462.83 354 555.33 0.63%NONS7 1992 503494 ...... 93539 15718491 .:' ":..a 474.45 251% 564.08 1.58I o

1993 523175 .. ':... 98166 15812020 48543 231% 58192 2.2% .;::.i!4i::.: :a...- .NONS7 1994 548309 4i*P8~ ..' :::::::::::137289 15910186 497.38 246% 611.33 4.70'.NON57 1995 55337.1 ~ 144818 16047475 509.66 247 647.50 5.924 tNON57 1996 567721 15494 1 52322 266% 6 0N0N57 1997 58231.7 154393 16347241 ~.$ 5 3 58.40 290%/ 71225 4.27% 4q NONS7 1998 526 4%. 164503 16501634 I*9 555894 3.4 5.3 6.68% 4 Ap i7W

N0N57 1999 602154 16666137 573.20 3.14% 793.33 4.41%WOWS7 2000 604294 *W.. 136792 16821 .6.0:. . 593.95 3.61% 0.00 0.00,.

NAT/IS 1999 1085177 406761 32515838 4891 54250NAT/US 1990 108104.3 #NY 316842 U53ZM2.44*t" 44700 422% 551.83 1.72% Yflk*M14I.~ :f:'fl~'' :::i: ::: ".NATUiS 1991 100148,3 4*4 195257 23239441 14% 46283 3254% 555.33 0.63% 34 W% -69NATUiS 1992 109108.7 *6% 184254 33434698 ~ 74 5% 564.08 1.58%Ar/4* NATLNS 1993 1117797 212514 3618952 . 4543 231% 583.82 352%:..NAT/iS 1994 1590 303176 33831466 siW'497.38 246% 611.33 4.70% * .NATUiS 1995 117951.0 335281 34134642 1t*~p% 06 47% 647.50 5.2 'tir~' Ams *M.oNAT/iS 1996 1206797 356144 34469923 523.22 266% 683.08 5.50%.NATUiS 1997 1239557 *i* ,378748 34826067 538.40 290% 71225 4.27%NAT/S 1998 126967'3 '424658 35204807 *:. 555.84 3.24% 759.83 6.68%.NAT7S 1999 128783.0 ,416868 3562465 57328 3.14% 793.33 4.41% ,NATUS 2000 131830.7 394200 36846333 8 59395 3.61% 00 .0 %

76

S BMP %. D EMP S PERMITS S STOCK

.YAR C.alculated Clculated Calculated Calculted

57MSA 1989 59289,8 260421 1715541457MSA 1990 60267.5 v .a 194449 1741583557MSA 1991 594093 . " 105933 1761028457MSA 1992 587593 S>..47. , v': 90715 1771621757MSA 1993 594622 i.4 :, 114348 17806932S7MSA 1994 60767.1 . Wi 165887 1792128057MOA 1995 62613.9 :*' " 's ,> 190463 18087167S7MSA 1996 63907.6 .::I:. 201196 1827763057MSA 1997 85724 a 224347 184782657MSA 1998 67750 6 * y 260155 1870317357MSA 1999 695676 fi% 261845 18963328

57MSA 2000 71401,3 * 1": 4 . : 257408 19225173


Appendix 2Apartment Growth, Rental Revenue Growth, NOT Growth, FFO Growth

a8 1;IT 'li" Ym1 A.o.iab.d Estes R Ly Copo.aibo AEC 1996

199719981999200O

2 Apotmsnt Invos'en t & Mgst. Co. AIV 19961997199919992000.

3 A.1i P, id.ni Properties Trust AhL 1996199719991999

4 Arth.ton. Communities Trust ASN 19961997199919992000

5 AalonBay Comuonities I. AVB 19961997199819992000

6 ORE Prop-sti., In BRE 19961997199919992000

7 Camden ?foprr7Trust CPT 199619971999199920D0

8 Equity RidniA Propdties Trust EQR 1996199719981999

20009 E.exProeosT-t4tn. ESS 1996

199719981999

Gble R.esideil Trust GHP 1996199719981999

11 -o e Iropesties of New York, In. 1'-IME 199619971998199920D0

12 Mid-Amsesico Apattrnent Commuities, lonc MAA 19961997

19992000

13 Post opsstts, I. PPS 19961997199619992090

14 Robetst R tly Isrstrs, lt RPI 1996!997199819992000

15 Sit Proprties Ic SMT 19961997199819992099

16 Chodes F. Smith Rsidetiul Relty SRW 19961997199819992000

17 Corr.stose R.esiy Iom Tust TCR 19961997199819992000

18 Tow-andCottoyTrut,Th TCT 19961997199819992000

19 UniedDomin io eoltyTrusot,In DR 199619971999

199920O0

%A#AM~-

16.73%181..6%20.61%0.0/0,5.05%

47.99%43.79%.

259.49%19.47%.10.52%/31.05%35.69%16.06%14.80%13.915t4.48%13.21%A22.99%.7,3YA-9,97°433.68A52.05%17.02%A-2.41%A3.75"4

8.835%30.90%14.19%7.20%

14.10%&,55%3.79%A

11.32%0.01%

-6.25%5332%54.77%h14.64%2.66%

-2.59%.50.82%77.90%A15.84%21.49'%19.38%26.21%14.77%37.37%150..4.622%26.29%94.22%76`66%/41.44%15.46%

5.82%58.12/9.36%.3.32%

.1.74%63.04%12.46%9.901,

21.331.80%/

#DRV/0(-171 e0

37.68%5.03%9.86%

19.87%11.55%11.64%.13.27%0.4Y.

23.31%18.815%27.50%12.23%7.55%

400.82%39.65%21.82%,7.31%

.13.7r/.0.010.00%8.38%7.52

-4.07%21.201

5.81%10.34%13.1 %,-3.42%

%ARen RH'Sotr 1Uk

-2.3P/0

8&10/7.99%

.4.899~-9.37%3334%

-45.6494

78.101/o

.20.52%-169.4

9.54%.7.149

-13.799-17.39

17.58442.0519,91%15.399)-0.28%A

159.41%39.79%9.28%

-14.3945.48

29.859616CA%

-5.2f.5.62

79.8345.01%413.9814.84%

.20,4340.63

59.43%A

17324133.31%-5.96%28.94.5.37/:0.5514.17910.3949.04

.2.859/

4.49%-21.14O.20.67.,12.42%A19.71%1125%

-22.06%41.94%3.05".1.01%

-27.304.12%

35.09%.4.7P.412.78%

#DIV/939.07%

635/.5.25%5.28'/0

11,17%412.47%/5.18%,6`8%

2.62%.1.91%

7.34%18.29%

'50.58%24.71%3.97%

27.1 4%12.77%A

1.85%1.49%3.016.31%6`325.4

12.89%2.32%

56.93.412.20%14/29%3.202

%ARental NO!

Uni.6.19%-..084.846.24%

-3,730%-5.64%A29.11%.44.3%,16.34%

.2132%-1121,11.3%-5.14%

-13.81%-1729.3.43%19.19.45.93%2225%16.74%-0.47%

139.63A43.O2%12.94%.

-152.A3.39%

31.38%9.11%

.3,97,0

4.950,96.1 1%61.40%15.14f16.62'0.17.3%

5.901Y.57.83,31.68%15.4504

134.099,-5.76'A29.96%A-3,44%41.92%

18. 1 4%A12.4911.1 ,.2.71%3.88%

20.54%17.12,022.08%14.630,-22.01%43.59%3.2O%,1.59%

-2639%4.68,0

35.49,0.3.7',12.09..

#DIV/053.15"/.3162,8.3%.3.79,5.50%A

13.71%13.959,030,0

.8,07%A

022%9.32%,

2060,.50.00/26.02%5.A6,46,

25.73,%41.40,0.71%5.27%,

.4.64%7.36%

14.63-0.29A53.49%13.49i17.66%4.80,

%AFF0

o-e Ui

1.25%.11.,295

-21.0.,.19.49%-5,96%60.41%

.33250,

38.12,23,'3%

-10.87%.2.907,0

4.0,1%

.631%-20.45,16.45%27.34,30.351.17/30.90,720,0

10331%35.31%23.92,

.44.70G%2.13

25.989%6.5~/

.2.19,16.65%6Z.4A63.64%10.41%9.41%

-13.5P9.15"A77.750

29.37,A17.40%A

169.1P.414.59,

_5.75,1.03%

18&46O/7.16.4

-3.61%

-},87%-6.4%30.81%1.51%

29.57.16`85,-2021%30.35,0-9.69%..2.0O9%

-19.86%4.71°.4

41.75A-1.5..27%

#DIV/0141.32,.35.36%

0.8505.4%

9.00%1425,03.92%/7.18%A326%-9.59%.9990

5.31%24.06%

.80.0,%.28.39%

3.73%,26.099k39.42%4.34%4.010%

-529,01096%

11.48%,.5.530,/56.44%7.670,0

12.13%4.69%

%ANe Is.a-Uh

223%-9235%

-29.89%35.32

.90.30

.34.40,5327%-37.36%

5.11%1084%

-15.1 ,17.69%

5.0,051.74%29.060k

4.41%-50.72%12.74.24.43,27.20%2.11%30.49.0

171.1 1%42.91%17.81

11o.92%-3535,-30.30%12,62%

-50.56%.34.90%3256A33.99,07.47%

2830-2.12A12.270,27.54A48.6(%43.199,70.29.85.55,-22.47%3621%.14.7.30.81%32.56%-19.40%20.79%k34384ol17,45,

-20.66%65.55%.0.57,36.61%37.36-50.20%117.930.021.44%.9.70,-.44%122547.50-3.16%-5.52%

#DIV/91649.71%-119.71%

-3.71%260.03o%19.6343.43%86.23,

-28.36,39,01%1820%103.84O.421.3(7.

106.42.,12.17%

.115.92%-430.168,0

-0.91%20.60,0

124.51%29.18,

.27.39%40.71%

132.01%49.03,.5.7,74.50°,0-6.55%14.4 400

.15.27,t

~ Ne Mas% %AIwOnlt CaP Miis CaPaerUaj a-Oaio - ais

1263 5 0.043 S*945 1.18 0.045 3.21%5 0.83 0.039 -11.41%5 1.12 $ 0.038 .3,43.

022 o 0.035 .795 0.35 0.0281 28.41%5 0.53 0.049 75.46%5 0.33 5 0.025 -49.10,05 0.35 5 0.029 1129%1; 09 1 0.036 32205 0,09 5 0.037 .29C05 1.04 0 9.034 .8.14,

1,10 0.034 -1.31%5 1.66 5 0.028 .16.42%

2.16 e.29 -.04,02.20 0 0.049 .12.85

5 1.08 5 0,054 10.76%

5 2.39 5 0065 2034,5 2.98 0.074 13.93,0/I .....79 0 .. 087_ 17.90,05 0.82 $ 0.943 38.14%$ 1.07 0.047 9.44%5 2.89 5 0.097 0.14O.

4.13 5 0.105 7,4',0} 4.87 ! 0.29 23.505 5.90 5 0.074 39.505 3.02 0.090 22.095 2.66 5 6.084 -699,5 3.00 5 0.078 -6.94,

1.46 ! D.080 16.05%5 0.18 5 0.015 9.5P5 0.76 5 0.031 100.53%5 1.02 $ 0.041 35.34,

1.09 0 0.046 10.69,5 1.41 0 6.053 14.91%5 0.0 5 0.032 0.79,$ 0.90 5 .04 42.14%

1.15 5 0.051 10.63A5 1.70 5 0.055 9.454

2.4 ! 0267 21.07,$ 1.32 5 o,04 269.86%

2.44 5 0.080 -.449%$ 1.89 5 0.076 -5,50,05 2.58 0. 076 .002%5 220 j 0.95 2624

129 0061 17.581.71 5 o64 5.09,

5 1.3 5 o.04 .. 15,1.67 5 0.053 .16.78',

5 2.25 5 0.059 112A

0.59 5 0.044 25.59%5 0.47 0.049 12.02,

5 0.78 0.049 - 1.2.,5 0.78 $ 0.050 1230,05 1.06 5 0.053 7.26,0

0.74 5 0.039 10.65,0.37 5 0.044 12.89%

5 0.0 0.043 -2.735"5 0.97 5 o .040 .5.95,

! 0.86 ! 0.042 3.59%1.815 0 0.67 -19.56%

5 2.08 0.0o0 34.0,A0

5 3.07 0 .0.005 2.97 0.082 - .72%{ 2.81 5 0.086 4.82%$ (o.y 5 . # DrV/05 0.5 0.079 #DIV/0

0o0 5 0.058 .26.61%S 029 5 0.059 2.81k{ 1.05 o o .056 -6.24%

1.19 5 0,063 6.18.,1.71 $ 0,066 5.6,03 3.181 0.072 6.95,0

S 2.28 5 0.067 -7.98%I 3.17 5 0,084 26.72.

0.66 S 0.072 -.,59,5 1.35 5 0.000 21.999,5 1.64 0 6.077 -11.59%

5 3.38 5 0.008 13.96%5 3.79 5 0.105 19.07,S ~,36) S nDIV/0

1.20 0.036 #DIV/0$ 1.19 5 0,031 .13.030,05 1.43 5 043 37,030,

3.22 0.049 14.59,05 0.41 5 0.041 5.97,0

0.30 5 0.046 11.98%5 0.42 5 0.046 0.56%S 0.98 5 0.049 5.92,0 1,46 6 0.054 10.88%

5 0,63 5 0.0 M.9A1.10 5 0.042 4.07,01.03 5 o .053 25.85,0

5 1.17 0.046 .13.C4! 0.99 ! 0.047 1.968%

C.. Ito7.93%7.49%096%975,0

10205.70%

4.75%~6`546.74%6.51%7.34%0.34,0

-7260k6.71°.5.055.79*,7.42%7.6945.3?.4.89562,7.485634%5.53.,4,670k6.5?*.7.70A6.79,8,99%

8.14%8.47%kS.,59,0

6.51%4.050,0

7,93,

5.71%5.6.P.7.740/07.48,6.04°.46`4506.91/67.69%8&99,0829%6.94%521%

7.71%8.78,

9.01%

9.1P.,10.07%

9.088%8,37,04.99%6750,7.11%7.61%

#DIV/08.848.241.48.670,08.907k6.52%7.02,07.34%.69,07.43,7.79,06.74,7.64%7.3.7. 42%

#DIV/CO7.399.08.94%8.21%

-10.130k9.65,9.078,60,8.72,9.01%567?/,8.36%7.54/6

10.19%10.48%


Appendix 3Market Cap Growth

REIT 'rk. ¥VI Aodtced Ettes Relty Copocion AEC 1996

1997199819992000

2 Ap.rmen Inuo nto & Mgmt. Co. AIV 19961997199819992000

3 Amli Rtoidontie Proprcnti Trut M . 199619971998199920O0

4 Archtone Communitie Tnrut ASN 19961997199819992000

5 Aolny Commuiic inc AiVB 19961997199819992000

6 BRE Propoei, Inc BRE 19961997t99819992000

7 iGmdenPro PeryTet CPT 19961997199819992000

8 Equity Rrideontil Propeirdc Trust EQR 19961997199819992000

9 Essc Per T rust Inc ESS 19961997199819992000

11 Gble Rsidentil Trult GBP 19961997199819992000

11i Home Popeties uo Ne Yord, In c 19961997199819992000

12 Mid.Americ Aprtment Communiiet, nc MAA 19961997199819992000

13 Post Popertin I.c PPS 19961997199819992000

14 Robrt Reliy In tor Inc RPI 19961997199819992000

15 Summit Properties nc SMT 19961997199819992000

16 Chrnu E. Sith Reddeni, l Ri lty SRW 19961997

19992000

17 Coen toneelul. InoeomoTr st TCR 19961997199819992000

18 Tote d CUOt tyTrs., The TCT 19961997199819992000

19 UOited Dominion R.ly Trut Inc.. UDR 19961997199519992000

M-s rMbcMv c2

S 63.0 2A$ 7799 2224'$ 833.3 6.85'$ 804.9 -3.41%$ 784.5 -2.5 5 1,041t3 9.02'

2,627.1 152J294,807.4 82.99'

$ ,391.7 32.965 9,340.3 46.13

641.1 27.25%1 799.1 24.65'6

915.3 14.54'hS 878.2 .4.051 990.0 12.73'I Z8912 29.39'1 3,6253 25.396$ 5,365.5 480o%{ 5,666.6 5.61%1 6,018.3 6.211 1,026.7 84.66'h1 1,708.5 66.41'h

4,1612 143.56%$ 4,362.3 4.83'h

5,5E9.6 28.13'h1,125E 152.36'

; 1,800.3 59.91,h; 1,920.6 6.6V00$ 1,916.0 .0.24AS 2405.8 . 25.565 744.8 18.94%| 1,550.1 10,.12'h5 2,335.5 50.67$ Z585.6 10.71%1 2785.3 7.72'h1 4,081. 54.54'hI 8,979.4 120.0P/

11,388.9 26.83h/S t1796.6 1236'

t 5,093.5 17.95A565.7 81.90'h

$ 961.2 69.91$ 1,052.0 9.45%$ 1,277.7 21.4565 1.924.9 50.65A,

t1,O52. 48.41%$ 1,269.9 20.62%

1,741.9 37.17%5 1,666.9 .431%

1,769.7 6.17%t 30.0 5.A00%$ 670.1 117.56D$ 1,169.6 74.54%$ 1,676.2 43.31%

2,075.9 23,85'4,1 749.7 17.31%

1,3383 78.51%{ 1,423.5 6.37%$ 1,383.3 .2.82'S 1,408.0 1.79'$ 1,5755 31.15.

2,374.2 50.710'8 2608.8 9.88%

2,889.4 10.76%/1 3,083.1 6.70'1,

S #DIV/0l1 134.1 #DIV/05 135.5 1.04%'A$ 146.4 8.041'

150.0 3.011t 894.0 27.28'65 1,054.3 17.83%$ 1,282.3 21.63%$ 1,336.6 423%h

I 1,701.4 27.29h$ 1,190.0 18.899%$ 1,7252 44.970A5 1,944.7 12772%

2Z487.2 27.90'I 185.0 28.06%I - #DIV/091 500.0 #DIV/0S 614.5 5.95%$ 903,6 47.05%5 092.7 .121%S 555.8 5.97'5 622.4 11.99,6

678.2 8.97P772.4 13.19,821.6 637%,

$ 2,422.6 63.59/2667.8 10.121,',704.6 38.86%3,643.9 -1.64A

5 3,588.4 _- 1.52',j

Mkse % ACp hI Cp

,m.r uI. - .1t

$ 0.043 5.899[ 0.045 3.289S 0.039 -11.41'A5 0.038 -3.43,

o0.035 -7.925 0.028 28.415$ 0.049 75.46'$ 0.025 .49.10$ O028 11.2995 0.036 32.2291 0.037 -2.909

0.034 .8.14"A5 0.034 -.1,31$ 0028 .16.420A1 0.028 .1.04'A$ 0.049 -12.851 0.054 10.765.

0.065 20.34°[ 0.074 13.9,7I .087 17.975$ 0.043 38.149,1 0.047 9.445S 0.097 10.14A$ 0.105 7.4YJ

0.129 23.53',0.074 39.50A

$ 0.090 22.09'5 0.84 .6S95 0.078 .6.94

0.086 10O.05'S 0.015 9.58'

0.031 100.53'A$ 0.041 35.34S 0.046 10.69A

I 0.055 14.91$ 0032 0,79'A$ 0.046 42.149

0.051 10. 63'5 0.055 9.45'

0.067 21.07/5 0084 269.8

0.080 -4.49'.OD76 .5.52A

1 0.076 -0.029A0.095 26.20'0.061 17.5/

{ 0.064 5.09$ 0.64 -0.15$ 0.053 -16.78%

0.059 11.32'0.044 25.59A0.049 12.02'

$ 0.049 .120'30.050 1.33"

$ 0.053 7.260.039 10.85'h

$ 0.044 12z8940,043 -2.7y

5 0.040 .5.9501 0.042 3.59A

0.067 -19.56A0.090 34.0010.090 -0.021o

5 0.082 -8.721o 0.086 4.82'S - #DIV/05 0.079 #DIV/9$ 0058 -26.61A

0.059 2.870.o05 -624'h0.063 6.18

$ 0.066 5.63'/a5 0.072 8.95,5 0.067 .7.98'h1 0.084 26.72'5 0.072 .3.58'h

0.088 21.9895 0.077 -11.5P,65 0.08 13.96'A! 0.105 19.07%6

- # DIV/090.036 #DIV/01

5 0.031 -13.0 '1 0.043 37.O3'

0.049 14.58"/1 0.041 5.97AS 0.046 11.98'I 0.046 0.56

0.049 5.92', 0.054 10.8B%$ 0.040 34.9,'A

0042 4.07%0.053 25.85%

$ 046 -13.04'A! 0.047 1.96,h

Cv R

7.49A8.86%9.750,

1020A5704.204.5554.75'A6.54'A6.7496.51%,7.348.34'97.2696.7195.855.797.42'7.69'5,374B.0,5.62'7.48'i6.8495.52A4.67'6377.70'6.79A6.98'A6.83'8.1498.47PA8.59%6.51'4.8596.92'A8.32'A7.93'A5.71U5.63'7.74A7.48'

6.45%36.9194

8.99PA8.3906.94A521ff6.6747.71%A8.7A9.010A6.22'9.18ff

10.07'A9.88'A6.371A4.99A6.75'h7.11%7.61

#DIV/OI.849f824ff8.67PA8.90'6.52'A7.02734'A8.69',7.43f7.79%6.74%7.64%733'7.42,A

#DIV/O7.38'h.94

10.13A9.65A9.07,6

8.72'/9.01%5.67,8.36%7.54%/

10.19%10.48h,


Appendix 4Weighted Averages

REIT Ti.ker YeerI Aesoeited Estate Realty Crpoqotion AEC 1996

1997199819992000

2 Apatment Investment & MgmL Co. AIV 19961997199819992000

3 Ai Residetial Propetia Trust AML 1996199719981999200

4 Archstone Communitie Trust ASN 1996199719981999

2000AlonBay Communitie Inc AVB 1996

1997199B19992000

6 BRE Ptoperti Inc. BRE 19961997199819992000

7 Cmden ropes Trust CPT 19961997199819992000

8 Equi Reidenta Propesfies Trust EQR 19961997199819992000

9 Es- Ppert Trust., Inc ESS 1996199719981999

200010 Gbles Reidential Tust GBP 1996

199719981999

200011 Home Propeti orNew Yor , Inc RE 1996

199719991999200o

12 Mid-Ami Aparme nt Communitie . Inc. MAA 1996199719991999

200013 Post .Propeti, Inc PS 1996

1997199819992000

14 Rober Real ty Inveto, Inc RPI 1996199719981999200O

1I Summit PNopertcs Inc SMT 19961997

199819992000

16 Chales E. Smith Resldental Re ty SRW 19961997199819992000

17 Cotnest.nc Real I.m Trust TCR 19961997199819992000

18 T-~n sd CounU7 Trust, The TCT 19961997199819992000

19 United Dominion Raq Trust. Inc UDR 19961997I99319992DO

GOVT NREIWARG WARG2944' 4.94%2.51% 3.92%3.50% 3.06181% 4.44%3.42/ 3.42%2.81% 4.14%3.57% 3.79%2.96% 4.70%2.82'h 324%3.58% 3.5'8%3.47 2.48%3.74 3.28%425%h 4.33%2.97% 2.74%

3.68% 3.68%2.97% 35%

3.88% 4.12%329% 3,7(P/3.42% 2.93%4.10% 4.10%2.59% 7.74%320%h 5.77%422Y 5.944.31% 4.54%4.79% 4.79%2.64%' 5.50%4.800/ 5.58F1.98% 4.04%3.4% 297/04.48% 4.48%2.32. 3.65/3.45% 3.87%2.r97% 3.73%

328% 1.97r/

3.63% 363%3.02% 4.01%M3.65 3.98%3.18% 4.34%2.12A 3.22%

2.I5 3.85%2.49%' 923'h3.76% 7.64%5S9h 5.78%04.57/ 3.1'h

4.64' 4.64%3.61% 3.78A3.58P 423%3.72Oh 3.06%3.16% 1.14%2.98% 2.98%2.70% 529%'2.67" 329%

3.0% 5.56A3.05% 4.068%3.64% 3.4%3.32/ 4.94%3.52% , 4.10A3.51% 4.16'h3.9 3.39%3.56A 3.56A4.6% 2,63%3.79/ 3.12%4.00% 2.87%/3.41% 225%3.49 3.49'5.36% 1.69%3.48% 1.79%3.71% 1.4P%324% 1.91%329'h 329'h3.63' 4.09P3.93' 2.94%3.33A 2.98%A2.71% 3.09%3.32 3.32'h2.30'% 4.15%0.54'% 1.6%/2.79/ 5.84%3.06A 5.14'h3.42/ 3.42%3.41% 5.00%4.50O' 3.74'h3.851% 3.77°03.37% 3.41%3.69P 3.69'h2.18% 5.22%0.99/ 2.43'287N 3.81%

3.09% 3.91/3.50% 3.50%2.96'h 4.5SO3.75P 41/324%' 4.32%3.04'h Z889%3.62'h 3.62'

WAEG WASG WAED1.899h 0.97/ 0.92%1.3% 0.99% 0.84%/2.29% 1.10% 1.20%h2.01% 126% 0.75%/1.58'h 1.53% 0.05%3.59% 1.87% 1.72/D3.51% 1.90o/ 1.61%3.07% 1.72% 1.35'b2.64A 1.99% 0.65/2.18% 1.81% 0.37h3.522 Z43% 1.09%3.49% 2.16% 1.33%/1.92 2.38% 1.5I4%3.579 2.73h 0.84h3.37% 2.64% 0.72%/3.78 2,05% 1.73%3.79A 2.07" 1.72%A

3.87/ 2.27% 1.60%3.49%/ 2.36% 1.13%3.16% 224% 0.93%2.13%/ 1.35% 0.78%2.85 1.46/ 1.40/h3.04% 1.46/ 1.58%2.33' 1.65'h 0.68%2.04% 1.61% 0.44%3.88%/ 1.55Y 2.33%3.91% 1.65% 2.26/4.13% 1.80% 2.33%3.80TA 1.85/ 1.95'h3.53% 1.57% 1.96%/4.42%/ 2.85/. 1.57%

5.05/ 2.91% 2.15%/4.31% 2.93' 1.38%4.38A 3.35Y 1.02°A3.68% 2.58h% .L0MA

3.60% 2.07/ 1.53h3.49% 1.98% 1.52A3.48% 2.03%b 1.45/3.06% 2.2% 0.86%2.81% 2.14% 0.67%3.25% 0.95Y 2.30%3.83% 1.26/ 2.58%3.98% 1.36. Z602.53% 1.42'h 1.11%2.19'h 1.19%/ 1.00A3.37% 2.11% 1.26%3.71% 2.0A 1.62%/"4.56% 2.36% 2.20%3.62% 2.93. 0.69/3.65Y 2.59A 1.06%2.53%/ 0.98/6 1.55%

2.39% 0.84 1.55%1.85% 0.90/ 0.95%/1.79% 1.16% 0.63%A1.49%/. 122%/ 0.27/

2.77% 1.39%/" 1.39P2.86% 1.79t 1.06%2.84% 1.46 1.39/2.46% 1.471/ 0.99%1.87% 1.72% 0.15%

3.90/ 2.80 9 1.19%

3.96% 2.40A 1.56%436A 2.67A 1.68%4.21% 3.00% 1.21%4.32% 2.96'/. 1.36%4.3P 2.91% 1.39'h3.42% 2.51% 0.90/3.91% 2.69%/ 1.21%4.59% 2.69% 1.904.79% 2.92% 1.86%/2.71% 2.18% 0.53%326% 2.37%/ 0.89h3.36% 2.50% 0.86%3.59% 2.88% 0.71%3.35% 329% 0.06'h0.18% 3.589% -3.40/2.39% 4.16%/ -1.77%222eh 3.25Y -1.02%A2.70/ 3.56/ -0.97%

2.75% 328% -0.53%2.97% 1.78% 1.189%

3.53% 2.01% 1.53%3.47 2.19A 129%3.43h 2.35A 1.08/h2.45/ 2.36% 0.09%0.49A 126% -0.77%2.66% 1.44% 1.22%229/ 1.61% 0.67/2.39% 2.00% 039'h2.49% 2.18% 0.31%2.95Y 1.75% 120%3.42A 1.81% 1.61%3.48% 2.01% 1.47/3.01% 222% 0.79%/2.51% 2.21% 0.301


Appendix 51996 - 2000 Average - Weighted Averages

# REIT Ticker1 Associated Estates Realty Corporation AEC2 Apartment Investment & Mgmt. Co. AIV3 Amli Residential Properties Trust AML4 Archstone Communities Trust ASN5 AvalonBay Communities Inc. AVB6 BRE Properties, Inc. BRE7 Camden Property Trust CPT8 Equity Residential Properties Trust EQR9 Essex Property Trust, Inc. ESS

10 Gables Residential Trust GBP11 Home Properties of New York, Inc. HME12 Mid-America Apartment Communities, Inc. MAA13 Post Properties, Inc. PPS14 Roberts Realty Investors, Inc. RPI15 Summit Properties Inc. SMT16 Charles E. Smith Residential Realty SRW17 Cornerstone Realty Income Trust TCR18 Town and Country Trust, The TCT19 United Dominion Realty Trust, Inc. UDR

AveraPe 1996 - 2000

GOVT NREIWARG WARG

2.82% 3.96%3.15% 3.89°/03.62% 3.30%3.53% 3.74%3.82% 5.76%3.55% 4.51%3.13% 3.37%3.37% 3.88%4.11% 6.22%3.41% 3.04%3.03% 4.37%3.40% 4.03%3.87% 2.87%3.81% 2.03%3.38% 3.28%2.42% 4.08%3.76% 3.92%2.54% 3.77%3.32% 3.87%

WAEG WASG WAED1.92% 1.17% 0.75%3.00% 1.86% 1.14%3.57% 2.47% 1.10%3.62% 2.20/0 1.42%2.48% 1.51% 0.97%3.85% 1.68% 2.17%4.37% 2.92% 1.44%3.29% 2.08% 1.20%3.16% 1.24% 1.92%3.78% 2.41% 1.37%

'2.01% 1.02% 0.99%2.56% 1.57% 1.00%4.15% 2.77% 1.38%4.20% 2.75% 1.45%3.26% 2.65% 0.61%2.05% 3.57% -1.52%3.17% 2.14%/o 1.03%2.06% 1.700/o 0.360/a3.07% 2.00% 1.07%


I -_____ __ __ ____ _____

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Appendix 7Geographic Concetratibons -1999

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Append 7Geog.phic Concentiation -1999

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000.0 00o% :::::: 000o oo.'.'.'.. 00:% *'.'0o% 000% :::::::::: 0ooo.000. 000% . ..... 0.000% 00% '~~" 00% """"": 00 o.' 0:00. :.:ii:? 000% :''04.. 00% ooo% .j. :020?:~:o.0000% "*50'.'. 040 """::"< 000% :!:'.".::.:!:.: 00. . 00 o.,...': 000 o.!:?::i:.'.:?:0:o oo.,(00 ..... '' 00

0 ...... .000% .:?:i:?:?:?:i:? 00% : .0 % : 00 . 0 . 00000% "'205*'' 2:0 0o% :'::' : 0000 ? o00 00% : " 00o.% ""' : : ?:::"" 20% 00 . 00o.

000% ""202*4.'. 300% ..'4%01<' 000% 000/~..'........ 4-0..'". 00 "4*'' 33 '''''''" 00% o.4'' 05'0120*~~~~~~~~ o05.0%. 0:::0%:: o o o'000 00 1% " . 000000:::::: .0o.0 43% 51.oo./000% . 000%"""~~: 000. :?:?:??:?:: 00ooo ''<.<00.~:o.oo.. :000:::% 000% 00 00

00 o.oo%.:.:' :. : : i:. 00% 000 . 00% . %:002%0 000 :0I0I00 . 000.0'~ ~~~ 000% ::!:?: : "":!:! : : %.'o%."':"oo00%"'.".:::::%i000% o000

:: o oo% : : : : : : : .': :.. -:o0%::::::':':0 0.o'00°% "..:!:':' ?:oo' 0 .,, 00::0 0% 0 0.000% """""."' 00: % '." , 000 : : : 00:% ":"""" 000: '00°% .'": 0: %?:.:..::'':'. 0:0oo0o.,00

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*422~~~~~~ ~~~ 070 0000 0000 ?':?::: 000% oo.5..20 .':.'..'::::"~ 00% ""0.'00 "."... 00% 0. 2/

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00/ 000% 0000 000%2".'.""'. 00 " .. 000'.00.

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App 7endi G-ogir'cCnetei -19PP

-A U.:: S

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i

.v ..... ~ - s .- -.- oo. ,., ; ~ :; A"/ .: " : 1,/ e : 0."/::.


Appendix 8Geographic Conenttions. -1998

2 . ITX o 0 00 o0.0 00 .20000 '74%~m 0T0% i 0% 00 0% 00 00% ~ 07 0.00%

3 OH o.4, .

6 ~ ~ ~ ~ ~ OW DdtOulqu N)(ow .1* O. O.00'~

0 Ab00oq0 0J210m- 0.00% 0~1.49%~o FA o. .ool. 30 0%~ .5 .0% ~ 00 007 Altoo o 400 0.% 0 0 00 00 00 0.00%

X;~:0 400000 N. 0,.'A .5 :'..00 0% 0W/ 0% 00% .~ 0% 00 .0IA~utw.-Bdlrb~m-1 ~~Io ~5i % o n Ok:...'..' 000 .P 00% 000 ow 0.00 ~ ~ 0.00l .~ 130 0.00% ~ 0.00

I1t m1Ae ~ MIw o ~eolA w ;: oo· .. "

07 AP.idooAL 000%9.00% ~:~ji 000 1 CU 0/ . 000 .B 1k 005%13 Atlefi NC 1o.A B, ~-A ...'A V%

17 AulOtLkm SC o. ooC-Ai

21 Bs~l-Ymnouh MbA .00*&'A -2 :a o] LA .'A .jin o.o.,o B.~w

2 euo- Po Am TX o..o.%.-%~N

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3Bde-onot CO M oo.'%.A-~4 Br~~~~~to~, 'nr :: o ool,!,~~~~~~~I'A

37 B-Coe TX' o . ooTX38 B. B.-%~~~0.01 Bu o-Niqtr Fdhr ,· oD 0.0 . :. O%

30 ..- CmtC-fidln --- B.%4 oC.lOllO41-1oh C.qorlof 0.00S.!: .0 00 0% 00 .*C .0% ~ 00% 00 .0 0.00%-x~~~~~~~-

42 SC..c-'O - lS C oo2. .0 0404000040.0 Chb'tottervi~lyAL ::.X.~ .00% 0.33% 0.00% 0% 0.00% .00% 0.00% .00% 00%090000000049000~IT .: :0 ow o(%% ·000 00% < 0.00% :: .0090 .~5:X: 009%

42 C-b.. .I.L ..MO-......O.B%.

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39 OoIstvi-Io pkinl 'n0o.4%o oFoi 305 00% 000% 000% 000% 0Q 0 00 .00% 0.00% 00 * %

41!;:o' 05 0. 004 00% 00. ~0;%'~'~$ ~.$~:!!i:;:0.00%:::! ::::::::::::.:::.: .0 .$% 0%~500 ::: 0;.'.'0;!;41 CodtO CSuboi0 $C 0. :n 00 ::~: 0.0 9;4I2.03% .0% oo00%

5 C0otbulOA-A o.Ook0 CO 0 w'6. 0 . 00.. 6~.3 . 0% 00 ~5~2333 : .5 22t .0 :$ 0/

44 C0. otuO..bu, OH 00% ...j.~..* 000% .* . 000% ........ 000 000 '~~'::~:: 0.00% ~ 0.00% ~ 0.00% ::::~ 00090 22 -22X 0004B..A ,..c:*.~.. . B. .. 6 o.

03 C.-.O.%0% 0 0.% ...'e 3% 00 000%40 Dlon&Be.Oopich j 75-Y 0M. o2962~04 :2$$: .0 99$$$ .0 'o00o0/. 00 $:k::00% :::$:'00 . 0%

58 .OH '5-S 30 0 0 0 00H .. 7%00X.50C59 D ,GAA 00 2:2::: 00.2:$$: .0 ::::$$ .0% 00%AL'. 000 00 ::k0$ .0% ::'2 00 0/

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70 CMmP... NC o%.0 2 S.:.04*: . . ' $:.0 .1% : 0 ,

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94 :.H.Ix -00013 S. ......h..~q0 r30oo0 0090 .000%...·" 0 %'.2$ 0009 o. 000%96~~~~~~~~~~~~~~~~~~~~~~1 I H.Lebmon -.-Ad pA 0. o~%

H9 - -( CT B . .A B o 4o %4 ?? 0 79 .. 09. 000 0....0%. ..... 00 o 0I.,::::::::::::::::::::::::::::: ~ ~ ~ ~ ~ ~ ~ ~ 00t ::::::::::::::: 0-' 0.000opp~~~l ~ ~ ~ 000~~~0: B.'A --- -'

tot Hu~~~~~~~~~~~~~~~~~~l~~~14-Adl .7-¥-O O..%%102 l~kunlwme . .oo%

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Kd--B I-A n. g O.- B .- o902 ...000000o.%500000.Sol.00.900g4.P0.oIWC -2'--"-2 b~*::::::::tt~""'~5s54:::::::::.......... . ..........o:9%$::$::::::~15lt~::::::]~ ~ ~ ~ ~~~~07 r~o~w )m o o.oo% ~ DO90001 30C 00%tlb :2'9::$ 000 ::::2' 000 000/. ~:::::l:: 0.00% ::::::> P U W i '-. .... . 00% 00046

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13 Oldld~~~U O.O~n ~~ 0.13Y b:DOD o 0 O.W *""":·OO 00/ :2.:::: 100% -22:2.22:.:

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000 0.0000090004 ..... 7i'~ ~D.P* S;~~~i 000 i~~:~ wn OW· ~.~ D.01 .OY·· I ODf9) Clla~~lr~eutulbur~AadaoaS C ~ 1;1; aooX l~~lr~ 0.0016 O~wn .04 ::$::'ooo% > 3:'ol) ,. io

·55~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~'::::2 0.0' 6.99::9:9: 000% 02..'. 000% .W .O~~f'pp+ :b


Appendix 8

Geogrphc Conct=atlns - 998

# MOA NAMg T-,*!.

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I2 m nt ~ .OO

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t~.11~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ O~fd.de 0o1 o000 ' ~ ~ :: o-. . .. 00 000I. 000 ~.......... 0.0%~...... 00% . . . .0~f............ 0001 Mredoo SCA O 0 . 0

143 I., fidemo me-ulre WA oo142 ..Ok-o' T. 0 2 0 0.000000% .4..e 0..0040 Moroe LA . 00%

10100.Ol.. !AOC.000%...~.... 00. 20.......... 00. 00 00k 0%. . o~. . 0....... 00

022OO.ooo-/,i1 2o240000 . .. 7% 00%'W*20 '":00 00 . 0% *4' My00% Bec 0C000% '~ .47

Y.2 NYle O O

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102* 0.00.oAA 0.0% ..lt~t.. 020% .'~... 000% ~ 202% ~k~*k 000% ~lt. 409% 000k ..%S0. 0.0% . o** 1472%.O00O

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141 o..*id.oA 0 M 0 0% 0 . 00%10 Ob- -. ,Y CA0 .. " S% 6�9%

1~40dp.~* ..X. . 000% I. 000% 000%0070 .00% ..O:::::n00% I040:; . ' A

172 p.robPo~ 10 o0 o o .0% 10 ..'. 0%17 Pk-Pckln .1 .. . O M . ... O 000% H0% . 000%~ ~~'2 FL O~~%1; 02~ :::... 0002 A* 00% . 00 0002. 0O

170 OO~~ol~ogA PA 21% 14U*~~~ 070% 000% . 000% ~~:::~~:: 100% ::.~~ 000% ~~ 0 00% ~~:t:::~ 121% ~~ oo% .0O0k

071 . .0% 0.0% .00% %%177 .0 d0 .. ' O .O 0.00 0

P P0 % 0 .... 00

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1811 Richmo~~~~~~d4PeterlburII VA ~ ~ ~ O 0 O3%.00%UT O..~~~~~~~~~.. 002 :~::007 ~ 00 0% :7. .:~00% O.000% .~.."A00 ,8 . 47. C. .00%4 O.0% o0% O.. ..17 Ok.ooo~oo.4ok.7o=oo WA 00%N.*...'." 000% ~ 000% ".' 000% ....... 000% C>~ 000%. .000% 000%.O.. .00..%.O O-. O0%100 'IV0.0k.-t0 .. A .00 7On : O.24 . ~ '00 2% :A: .% % .-00% O::14: 0,79 .00 V::::0.0109 -ik -o 000400 VA ~40 .. : O0% 1440 29.0.. ~0. 2 j*4 . 07 7*44..7 09% <- 00%:A:~0

00RIA.002 f~ L5:-A 4 . m.0% '00%~ ~ ~0004 002% 0 ......... 000% 100......... .. 00....::.~.<.. . .:::..~. 107....~: .019. Rocky M-n NO..% .0

19 Szmnt CA O . 0

024 000le OO0 3 0.00 ~00 ~..'00 0 :~00% 01. .9 ...>.00 %40~02107 000 Aonio UT 0% * 2% 0 402% .. 9*00 O..0

III . Alloli, TI .... A.~.... ..41 ::.:~:: 10% M00 O 00 **. 2.00 ' 0.30 1 >. *4141k I..%'**.'.'.'30 .00%'~ U .01 N7 0 'O .'. 2 '.*' .'.'2.*9% '" 70% ". 00% '" 11% '' 2' * ' 018sm EM CA "CA''" :O": *"'****" ~:*" .

099 S07 3 00 .......... 000

204 000000 CA .00%~o vdle 1004 o~X 04 70~:44~.32 ~ 0 . 00 44 .320 . 07 oo%.................

237 $crlnlono./Ofikes-Btr*o uleton PA o 000% :::*:.oo 000% ~. 0004 . 000% 0.00% 000k 0 .000

209 A o. 04 0ok

212 0.40ia2.1eld 00% 0o:o%0% . 00 . .. 0217 7oloo LA 0070% 00%... 00 0%::::M00% ::: %0%O..444..10% . 00 .

M SO.S0.1d M O~~000 3.41141... 20 0% 004. 2 2 " 00% .'A 0 000 4*.927 44 20% 000% O .'0214 ..1.. ......6.. ..O .o. o .21 Sy...0e 0Y0 .':0 07 . O O., 'A

22opld .2. r,:::::S!:: 00.oo% .::::::.:0:0:..o*4~::::..: 70...% :*::::::.:::ioo :::~::: 0.oo% :::::: oo. ~:::3.::::: ok. oo%~ ::::';:: 0'..~::::::o o o ::::::0::: o .000%

12 O 0 07 004 X.4 . 'A0..A00 . 0M O 4'.. 4$4 .... 44 007 00

220 7-.04o.Iloo-..-.,Ao- : . 00:.000:: . ::..... 0% o.0%.0%...o%

222 T...'::.0.0.oo% O:.,::.:::: 0 ,% :... 000% ..3 0.:: ::::: 0.oo ::::: 0004 .::::. o. ::: o.00 00.% : ::.. :::.:. 0oo%

231 TXuh~o OC-JDV-% O

2......... O . * ** .... O. -............ *. .*..**. 0 .. ...... .. . 00..70

220 '00I00opooo.Oho..A00.000 . 000k~~ ~:*4::: 040 :::::::: o.04 .::: 000% :::: o.00% :::..::::::::.07 ::::: 0...0% :::::::~ 0.004 :::::"::oo* :~::/'::,':oo%:::',,: :: .oot~'~::',' .o 0''':,:ooo0::':.''%.o.,227 V...10 CA N- 00.0 R:43:00%:::.m003 33:. 0% ::3: 00 0%"*.' 0 '.. " A00020 CA 000% 0~ . ...,.... 000k ,:%:~ 0:0:0%~:~:~:~:~ :~:~:~:~:~:~ :~:~:~::: O

241 702100........ .~t . 0*k 0*1.4___ __ ___d _ __ __t__ __ ;:' ..:.oo(/,;: .':!oo00%,;;.." ;;x0o%:: .'....;: 00o*/,:"3 0.DO00%*; ";,.:..::... 00% .:;: ...000%;- ';100%;:"';~00%: ...3~. 000% 1oo00o%

0S1 ..04 T t170440, 00041304:: 00100 OW : 0.0.~44t 04344004:0014::l%7ooo Oo :44444: 00407 %1 .-0000


Appendix 8Geographic Concentrations -1998

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Appendix 8Geog.phlc Concnmtioo -1998

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Appendix 9Geographic Concentraions -1997

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App 9endx G-Waphi C-ne~t.oins -1927

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Ap

pendix 10

Geogrphic Concenrtions -1996

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Appendlx 10Geographi Concentrlaon -1996

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Appendix 10Geographic Concentations - 96

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I

I

Appendix 11WARG v. WAEG WASG Results

Govt WARG v. WAEG, WASG

Resion StatirtiesMultiple R 0.324732045R Square 0.105450901Adjusted R Square 0.086004181Standard Error 0.007218946Observations 95

ANOVA

df SS MS F Sinifiance FRegression 2 0.000565173 0.000282587 5.422554713 0.005940082Residual 92 0.004794412 5.21132E-05Total 94 0.005359586

Coeffidients Standard Error t Stat P-value Lower 95% Upper95% Lowtr 95.0% Upper95.0%Intercept 0.028927222 0.003017706 9.585832784 1.69733E-15 0.022933798 0.034920646 0.022933798 0.034920646WAEG 0.295090251 0.090955821 3.244325084 0.001641762 0.11444414 0.475736362 0.11444414 0.475736362WASG -0.213957486 0.117706261 -1.817723919 0.072361243 -0.44773228 0.019817308 -0.44773228 0.019817308

NREI WARG v. WAEG, WASG

Reion StatiSti.sMultiple R 0.527452059R Square 0.278205674Adjusted R Square 0.262514493Standard Error 0.010895403Observations 95

ANOVA

df SS MS F Signifiance FRegression 2 0.004209465 0.002104733 17.73006598 3.06916E-07Residual 92 0.010921301 0.00011871Total 94 0.015130767

Coeffidients Standard Errr t Stat P-value Lower 95% Upper95% Lower95.0% Upper 95.0%Intercept 0.060642803 0.00455456 13.31474574 3.56402E-23 0.051597054 0.069688551 0.051597054 0.069688551WAEG -0.077721664 0.137277701 -0.566163797 0.572660907 -0.350367028 0.194923699 -0.350367028 0.194923699WASG -0.924576985 0.177651576 -5.204440103 1.17635E-06 -1.277408349 -0.571745621 -1.277408349 -0.571745621

Average Govt WARG v. Average WAEG & WASG

Rtgosion StatisticsMultiple R 0.742580571R Square 0.551425904Adjusted R Square 0.495354142Standard Error 0.003172582Observations 19

ANOVA

df SS MS F Ssnificance FRegression 2 0.00019797 9.89849E-05 9.834288862 0.001639357Residual 16 0.000161044 1.00653E-05Total 18 0.000359014

Coeffaents StandardErnmr tStat P-value Lower95% Upper95% Lower95.0% Upper95.0%Intercept 0.025620884 0.003261855 7.854697291 7.02843E-07 0.018706061 0.032535706 0.018706061 0.032535706WAEG 0.479364224 0.109072992 4.39489386 0.000452009 0.248139862 0.710588585 0.248139862 0.710588585WASG -0.332225506 0.127155 -2.612760069 0.018845707 -0.601782003 -0.062669009 -0.601782003 -0.062669009


Appendix 11WARG v. WAEG WASG Results

Average NREI WARG v. Average WAEG & WASG

Rignsson StatistisMultiple R 0.638454687R Square 0.407624388Adjusted R Square 0.333577436Standard Error 0.007653567Observations 19

ANOVA

df SS MS F Significana FRegression 2 0.000644927 0.000322464 5.504944899 0.015162729Residual 16 0.000937233 5.85771E-05Total 18 0.001582161

Coeffldents Standard Enor t Stat P-valae Lower 95% Upper 95% Lower95.0% Upper 95.0%

Intercept 0.062091581 0.007868929 7.890728177 6.62564E-07 0.0454102 0.078772962 0.0454102 0.078772962WAEG -0.275777764 0.263128695 -1.048071798 0.310180375 -0.833585553 0.282030024 -0.833585553 0.282030024WASG -0.696699748 0.306749897 -2.271230585 0.037292493 -1.346980334 -0.046419163 -1.346980334 -0.046419163


Appendix 12Panel Data Results without Apartment Growth

% Change in FFO per Unit v. Weighted Average Rent Growth (Govt Data)

Reission StatitmsMultiple R 0.014068772R Square 0.00019793Adjusted R Square -0.010669483Standard Error 0.307616636Observations 94

ANOVAdf SS MS F Siqnfwance F

Regression 1 0.001723478 0.001723478 0.018213197 0.892941268Residual 92 8.705775513 0.094627995Total 93 8.707498991

Coeffwients Standard Error tStat P-Value Loer 95% Upper95% Loer 95.0% Upper95.0%Intercept 0.091000127 0.149712822 0.607831219 0.544796963 -0.206342461 0.388342715 -0,206342461 0.388342715WARG 0.589475334 4.36789861 0.134956277 0.892941268 -8.085548332 9.264499 -8.085548332 9.264499

% Change in FFO per Unit v. Weighted Average Rent Growth (NREI Data)

Pagtcesiyn Statirti sMultiple R 0.296930873R Square 0.088167943Adjusted R Square 0.078256725Standard Error 0.293771886Observations 94

ANOVAdf SS MS F Significance F


Ceff-icfts Standard Ervr IStat P-vale Loer 95% Upper 95% Loar 95.0% Upper 95.0%Intercept -0.172569871 0.099705799 -1.730790714 0.086841753 -0.370594194 0.025454452 -0.370594194 0.025454452WARG 7.240973122 2.42775633 2.982578207 0.003658711 2.419239492 12.06270675 2.419239492 12.06270675

% Change in FFO per Unit v. Weighted Average Employment Growth

Rerssion StatisticsMultiple R 0.109580441R Square 0.012007873Adjusted R Square 0.001268828Standard Error 0.305794412Observations 94

ANOVAdf SS MS F Sienifsncr F


Coefficients Standard Eror t Stat P-vae Loner 95% Upper95% Lower95.0% Upper950%Intercept -0.006899343 0.115640485 -0.059662 0.952554245 -0.236571327 0.222772642 -0.236571327 0.222772642WAEG 3.767689882 3.563074736 1.057426566 0.293085625 -3.308884101 10.84426386 -3.308884101 10.84426386



% Change in FFO per Unit v. Weighted Average Stock Growth

Rereion Stairti

Multiple R 0.02708513

R Square 0.000733604

Adjusted R Square -0.010127987Standard Error 0.307534217

Observations 94

ANOVA

df SS MS F Signifxnce F

Regression 1 0.006387859 0.006387859 0.067541143 0.795531821Residual 92 8.701111133 0.094577295

Total 93 8.707498991

Coeffientsr StandardError tStat P-luse Loer95% Upper 95% Lower95.0% Upper 95.0%

Intercept 0.135775414 0.101398437 1.339028663 0.18386043 -0.065610635 0.337161463 -0.065610635 0.337161463WASG -1.203275306 4.629997963 -0.25988679 0.795531821 -10.39885091 7.9923003 -10.39885091 7.9923003

% Change in FFO per Unit v. Weighted Average Excess Demand

Rensrrion Sakirc


R Square 0.017133393

Adjusted R Square 0.00645006Standard Error 0.305000178Observations 94

ANOVA

df SS MS F Snifricnce F

Regression 1 0.149189002 0.149189002 1.603749826 0.208569659

Residual 92 8.55830999 0.093025109

Total 93 8.707498991

Coefficwient Standard Ermr t Stal P-vaue Lorwr 95% Upper95% Loer95.0% Upper950%

Intercept 0.063707768 0.048675045 1.308838406 0.19384906 -0.032965072 0.160380608 -0.032965072 0.160380608

WAED 4.512661369 3.563398839 1.266392445 0.208569659 -2.564556309 11.58987905 -2.564556309 11.58987905

% Change in FFO per Unit v. % Change in Rental Revenue per Unit

Reclrsion StatirtitMultiple R 0.878280019

R Square 0.771375791

Adjusted R Square 0.768890745Standard Error 0.147100419

Observations 94

ANOVA

df SS MS F Sinifgcan F

Regression 1 6.716753924 6.716753924 310.4070788 3.11527E-31Residual 92 1.990745067 0.021638533Total 93 8.707498991

Coeffirntr Standard Eror tStat P-Vale Lower 95% Upper95% Lower95.0% Upper95.0%

Intercept 0.00540772 0.016307795 0.331603365 0.740942985 -0.026980969 0.037796408 -0.026980969 0.037796408

% D Rent Rev per Unit 0.912534709 0.051794493 17.61837333 3.11527E-31 0.809666376 1.015403042 0.809666376 1.015403042



% Change in FFO per Unit v. % Change in Rental NOI per Unit

Reression StaiticsMultiple R 0.891236439

R Square 0.79430239


Observations 94

ANOVA

df SS MS F Sign4ance F


Coeffcient Standard Emr Stat P-value Lwer95% Upper 95% Lowr 95 0% Upper 95. 0%

Intercept -0.009207134 0.015735786 -0.585107977 0.559907482 -0.040459763 0.022045495 -0.040459763 0.022045495% D Rental NOI per Unit 0.926996028 0.049181939 18.84830175 2.3781E-33 0.829316452 1.024675605 0.829316452 1.024675605

% Change in FFO v. % Change in Number of Apartments

Reegssion Statistics


R Square 0.064986228


ANOVA

df SS MS F Sgnificarn F


Total 93 9.999699151

Coefficents StandardErrr tStat P-alPe Lwer 95% Upper 95% Loaer95.0% Upper 95.0%

Intercept 0.269816587 0.036496578 7.39292832 6.4605E-11 0.197331232 0.342301942 0.197331232 0.342301942# Apts 0.162295427 0.064181625 2.528689909 0.013152117 0.03482518 0.289765674 0.03482518 0.289765674

% Change in Rental Revenue per Unit v. Weighted Average Rent Growth (Govt Data)

Renssion Statitic


R Square 0.007083001

Adjusted R Square -0.003709575Standard Error 0.295048143Observations 94

ANOVA

df SS MS F Sifzancr F


Total 93 8.066045235

Coeffientr Standard Ewr tStat P-Yalae Laer 95% Upper 95% Lower95.0% Upper 95.0%

Intercept 0.001747998 0.143595908 0.012173038 0.99031392 -0.28344587 0.286941867 -0.28344587 0.286941867WARG 3.393920888 4.189436539 0.810113927 0.419965221 -4.926661701 11.71450348 -4.926661701 11.71450348



% Change in Rental Revenue per Unit v. Weighted Average Rent Growth (NREI Data)

ReiRresrrion StatirtiiMultiple R 0.273574716

R Square 0.074843125


ANOVA

df SS MS F Srnifcn F


Total 93 8.066045235

Ceffcient Standard Error Sta P-Vae Lor 95% Upper95% Lower950% Upper95.0%

Intercept -0.135797299 0.096661669 -1.404872279 0.163426777 -0.327775717 0.056181119 -0.327775717 0.056181119WARG 6.420977501 2.353634207 2.728111906 0.007630005 1.746456806 11.0954982 1.746456806 11.0954982

% Change in Rental Revenue per Unit v. Weighted Average Employment Growth

Regrnion Stairficr


R Square 0.003603362


Observations 94

ANOVA

d/ SS MS F Signifance F


Ceeffwientr Sbandard Ener t Sa P-Vwale Lewr 95% Upper 95% Lwer 95.0% Upper 95.0%

Intercept 0.053407967 0.111771968 0.47782971 0.633904792 -0.16858081 0.275396744 -0.16858081 0.275396744WAEG 1.986459196 3.443879339 0.576808593 0.565477467 -4.85338244 8.826300831 -4.85338244 8.826300831

% Change in Rental Revenue per Unit v. Weighted Average Stock Growth

Reorision Slailicr


R Square 0.008501947


Observations 94

ANOVA

df SS MS F Sinnifance F


CoeffWcintr Standard Errr Sta P-value Laoer 95% Upper95% Laewr95.0% Upper95.0%

Intercept 0.197444295 0.097212064 2.031067822 0.045133793 0.004372745 0.390515846 0.004372745 0.390515846WASG -3.942546915 4.438842154 -0.888192636 0.376753427 -12.75847058 4.873376754 -12.75847058 4.873376754



% Change in Rental Revenue per Unit v. Weighted Average Excess Demand

Rrtsrion Statiri


R Square 0.017356526


ANOVA

df SS MS F Significance FRegression 1 0.139998527 0.139998527 1.625004866 0.2056055Residual 92 7.926046708 0.086152682

Total 93 8.066045235

Coeffzientr Stndard Enrr tS tat P-value LoaWr95% Upper 95% Lwr 95.0% Upper 95.0%Intercept 0.069868446 0.046842565 1.491558934 0.139235874 -0.023164931 0.162901823 -0.023164931 0.162901823WAED 4.371455625 3.429246794 1.274756787 0.2056055 -2.439324513 11.18223576 -2.439324513 11.18223576

% Change in Rental NOI per Unit v. Weighted Average Rent Growth (Govt Data)

Refrion SttirtsMultiple R 0.080268703

R Square 0.006443065


ANOVA

4/ SS MS F Signi[ane FRegression 1 0.051858034 0.051858034 0.596605927 0.441855712Residual 92 7.996801473 0.086921755Total 93 8.048659507

Coeffientr Sfndrd Enmr Stat P-Vale Lower95% Upper 95% Lwer 95.0% Upper 95.0%Intercept 0.021087185 0.143487286 0.146962043 0.883483554 -0.263890952 0.306065321 -0.263890952 0.306065321WARG 3.233484292 4.186267479 0.772402697 0.441855712 -5.080804269 11.54777285 -5.080804269 11.54777285

% Change in Rental NOI per Unit v. Weighted Average Rent Growth (NREI Data)

ReMion Siaftitir


R Square 0.06169879


ANOVA

df SS MS F Sni*cance FRegression 1 0.496592557 0.496592557 6.049537895 0.015778731Residual 92 7.552066951 0.082087684

Total 93 8.048659507

Caeffrients Standard Error Stat P-value Loer 95% Upper 95% Loer950% Upper 95. 0%Intercept -0.098460641 0.097240949 -1.012542991 0.31393409 -0.29158956 0.094668277 -0.29158956 0.094668277WARG 5.82364599 2.367739211 2.459580837 0.015778731 1.12111154 10.52618044 1.12111154 10.52618044



% Change in Rental NOI per Unit v. Weighted Average Employment Growth

Reftnuion Statrtics


R Square 0.006971927


ANOVA

df SS MS F Siniwnc F


Coeffident Standar Errr t Stat P-vale Lover95% Upper 95% Loaer95.0% Upper95.0%Intercept 0.043214497 0,111462553 0.387704174 0.699130526 -0.178159754 0.264588749 -0.178159754 0.264588749WAEG 2.760155683 3.434345743 0.80369185 0.423646671 -4.060751407 9.581062772 -4.060751407 9.581062772

% Change in Rental NOI per Unit v. Weighted Average Stock Growth

Refresnon Statitics


R Square 0.004324903Adjusted R Square -0.006497652Standard Error 0.295139058Observations 94

ANOVA

df SS MS F Sianircasa FRegression 1 0.034809671 0.034809671 0.399619389 0.528853757Residual 92 8.013849836 0.087107063Total 93 8.048659507

Coeffientr Standard Err tSlat P-vale Lower 95% Upper 95% Lower 95.0% Upper 95.0/Intercept 0.187828263 0.097311576 1.93017389 0.056665551 -0.005440926 0.381097452 -0.005440926 0.381097452WASG -2.808906726 4.443386 -0.632154561 0.528853757 ' -11.63385486 6.016041412 -11.63385486 6.016041412

% Change in Rental NOI per Unit v. Weighted Average Excess Demand

ReArion Statirics

Multiple R 0.134757766R Square 0.018159656


ANOVA

df SS MS F Sinifcan FRegression 1 0.146160885 0.146160885 1,701588578 0.19533424Residual 92 7.902498623 0.085896724Total 93 8.048659507

Coficintr Standard Enrror Stat P-value Lor 95% Upper95% Loer 95.0% Upper95.0%Intercept 0.082841411 0.046772929 1.771140125 0.079849082 -0.010053663 0.175736485 -0.010053663 0.175736485WAED 4.466629428 3.424148902 1.304449531 0.19533424 -2.334025855 11.26728471 -2.334025855 11.26728471

% Change in Net Income per Unit v. Weighted Average Rent Growth (Govt Data)

RcWrusion Stadrdi

Multiple R 0.138561413R Square 0.019199265Adjusted R Square 0.008421235Standard Error 0.783637583Observations 93



ANOVA

df SS MS F Srnifwnt FRegression 1 1.093895224 1.093895224 1.781333411 0.185315499

Residual 91 55.88199535 0.614087861Total 92 56.97589057

Coeffzcients Standar Error IStat P-te Lover 95% Upper95% Lomer 95,0% Upper 950%

Intercept 0.698715128 0.381389048 1.832027245 0.0702185 -0.058867442 1.456297697 -0.058867442 1.456297697

WARG -14.85329009 11.12884404 -1.33466603 0.185315499 -36.95937389 7.252793708 -36.95937389 7.252793708



% Change in Net Income per Unit v. Weighted Average Rent Growth (NREI Data)

Reemsion Statistcs


R Square 0.014541556

Adjusted R Square 0.003712343

Standard Error 0.785496081Observations 93

ANOVA

df SS MS F Sinifmanc F

Regression 1 0.828518127 0.828518127 1.342808155 0.249571236

Residual 91 56.14737244 0.617004093

Total 92 56.97589057

Coffint Standard Error t fSta P-value Lower 95% Upper 95% Lovr 95,0% Upper95.0%

Intercept -0.099378804 0.272021831 -0.365333928 0.715709937 -0.639716809 0.440959201 -0.639716809 0.440959201

WARG 7.64208542 6.594849708 1.158795994 0.249571236 -5.457774706 20.74194555 -5.457774706 20.74194555

% Change in Net Income per Unit v. Weighted Average Employment Growth

Raexresion StatitcsMultiple R 0.068263648

R Square 0.004659926


ANOVA

df SS MS F SirnifiWanc F

Regression 1 0.265503409 0.265503409 0.426038535 0.515584772

Residual 91 56.71038716 0.623191068Total 92 56.97589057

Coeffciens Standard Error t Stat P-value Louver95% Upper 95% Lower 95.0% Upper 95 0%

Intercept 0.01398557 0.298535647 0.046847237 0.962737639 -0.579018879 0.606990019 -0.579018879 0.606990019

WAEG 6.007433211 9.203743567 0.652716274 0.515584772 -12.2746746 24.28954102 -12.2746746 24.28954102

% Change in Net Income per Unit v. Weighted Average Stock Growth

Remcrssion Stamtitic


R Square 0.01918972


ANOVA

lf SS MS F Signiofane F

Regression 1 1.09335138 1.09335138 1.780430471 0.185425874

Residual 91 55.88253919 0.614093837

Total 92 56.97589057

Coeffucint StndardError t Stat P-value Loer 95% Upper 95% Lower 95.0% Upper 95.0%

Intercept -0.126054862 0.258494659 -0.487649772 0.626970731 -0.6395228 0.387413076 -0.6395228 0.387413076

WASG 15.77640694 11.82348735 1.334327722 0.185425874 -7.70950059 39.26231447 -7.70950059 39.26231447



% Change in Net Income per Unit v. Weighted Average Excess Demand

Rerrston StStiricrMultiple R 0.038866412R Square 0.001510598Adjusted R Square -0.009461813Standard Error 0.790672428Observations 93

ANOVA

df SS MS F Significanr FRegression 1 0.086067665 0.086067665 0.137672384 0.711468432Residual 91 56.8898229 0.625162889Total 92 56.97589057

CoeffzentXr Sandard Ewr ISaW P-z'alu LosJer9/5 Upper 95% Loiar95.0% Upper 95. 0%Intercept 0.237159849 0.126582447 1.873560311 0.064200434 -0.014280658 0.488600355 -0.014280658 0.488600355WAED -3.428017651 9.238886522 -0.371042294 0.711468432 -21.77993263 14.92389733 -21.77993263 14.92389733



% Change in Market Cap per Unit v. Weighted Average Rent Growth (Govt Data)

Regsersion StatitsMultiple R 0.059894539

R Square 0.003587356


ANOVA

df SS MS F SiniFana FRegression 1 0.04003211 0.04003211 0.320424138 0.572777408Residual 89 11.11919281 0.124934751Total 90 11.15922492

Coeffiientr Standard Ervr tStat P-valse Lowr95% Upper 95% Lowar95.0% Upper95. 0%Intercept 0.223472325 0.173709915 1.286468452 0.201616041 -0.121685388 0.568630039 -0.121685388 0.568630039WARG -2.880530302 5.088735011 -0.566060189 0.572777408 -12.9917329 .7.230672291 -12.9917329 7.230672291

% Change in Market Cap per Unit v. Weighted Average Rent Growth (NREI Data)

Rtrxssion StatirisrMultiple R 0.490005653R Square 0.24010554


ANOVA

df SS MS F Sinifianrc FRegression 1 2.679391729 2.679391729 28.1215277 8.22903E-07Residual 89 8.479833187 0.095279025Total 90 11.15922492

Coefficnrs Standard Enrr t Stat P-valae Lower 95% Upper 95% Loar95.0% Upper 95.0%Intercept -0.414391089 0.10716994 -3.86667277 0.000209497 -0.627335362 -0.201446816 -0.627335362 -0.201446816WARG 13.8003871 2.602386597 5.302973477 8.22903E-07 8.629503168 18.97127104 8.629503168 18.97127104

% Change in Market Cap per Unit v. Weighted Average Employment Growth

Rersfion StatirircMultiple R 0.065715223

R Square 0.004318491


ANOVA

df SS MS F SOnicanc FRegression 1 0.048191007 0.048191007 0.386012652 0.535990906Residual 89 11.11103391 0.124843078Total 90 11.15922492

Coeffiecntr Standard E"rr ISltat P-value Lo"wer 95% Upper 95% LoWr 95.0% Upper 95.0%Intercept 0.047536617 0.13378029 0.355333488 0.723180518 -0.218281825 0.313355059 -0.218281825 0.313355059WAEG 2.562810612 4.12492203 0.621299165 0.535990906 -5.633317157 10.75893838 -5.633317157 10.75893838



% Change in Market Cap per Unit v. Weighted Average Stock Growth

Rerssion StaterMultiple R 0.153234478R Square 0.023480805Adjusted R Square 0.01250868Standard Error 0.349914875Observations 91

ANOVA

df SS MS F SiRnicance FRegression 1 0.262027588 0.262027588 2.140041578 0.147020611Residual 89 10.89719733 0.122440419Total 90 11.15922492

CoeffWentr Standard Errr iStat P-value Lorer 95% Upper95% Loerr95.0% Uppr 95. 0%

Intercept 0.28816999 0.115855701 2.487318159 0.01473506 0.057967304 0.518372676 0.057967304 0.518372676WASG -7.731302289 5.284958102 -1.462888095 0.147020611 -18.23239577 2.769791196 -18.23239577 2.769791196

% Change in Market Cap per Unit v. Weighted Average Excess Demand

Ressrrion Statistics


R Square 0.034094827


ANOVA

df SS MS F SiOnicane F


Total 90 11.15922492

Coeffiernts StandadErr tStat P-Vawle Lowr 95% Upper95/o Lowr95.0% Upper 95.0%

Intercept 0.052529461 0.055816259 0.94111396 0.349193907 -0.058376198 0.163435119 -0.058376198 0.163435119WAED 7.220162382 4.073567903 1.772441887 0.079743088 -0.873925885 15.31425065 -0,873925885 15.31425065

Cap Rate v. Weighted Average Rent Growth (Govt Data)

Regrersion Statiticr


R Square 0.012357619


Observations 91

ANOVA

df SS MS F SiniFncance F


Coefficenftr Standanrd Err tStat P-value Lowr 95% Upper 95% Loer95.0% Upper 95. 0%

Intercept 0.081297853 0.007178733 11.32481935 6.16923B-19 0.067033872 0.095561835 0.067033872 0.095561835WARG -0.221919553 0.210296974 -1.055267453 0.294159271 -0.639774935 0.19593583 -0.639774935 0.19593583



Cap Rate v. Weighted Average Rent Growth (NREI Data)

Rcrsimon Stnatstic


R Square 0.124426061


Observations 91

ANOVA

df SS MS F S~iniicanc F


Total 90 0.019227372

Coefflient Standard Eror tStat P-value Lower95% Upper95 Lower95.0% Upper 95.0%/o

Intercept 0.09008621 0.004775136 18.86568637 7.46225E-33 0.080598123 0.099574298 0.080598123 0.099574298WARG -0.412371648 0.115953679 -3.556348116 0.000605036 -0.642769013 -0.181974282 -0.642769013 -0.181974282

Cap Rate v. Weighted Average Employment Growth

eruession Slatirtia


R Square 0.057439632

Adjusted R Square 0,046849066Standard Error 0.014269854

Observations 91

ANOVA

df SS MS F SInifcnce F


Total 90 0.019227372

Coefficient Standad Eror Stat P-value Lover 95% Upper 95% Lower 95.0% Upper 95. 0%

Intercept 0.085987587 0.005402931 15.91498809 9.0624E-28 0.075252083 0.09672309 0.075252083 0.09672309WAEG -0.387971027 0.166591583 -2.328875324 0.022130773 -0.718984774 -0.056957279 -0.718984774 -0.056957279

Cap Rate v. Weighted Average Stock Growth

Regr.rion Statirtics


R Square 0.012087811


ANOVA

df SS MS F SOniFance F


Coeffmients. StandardEror tStat P-value Lower 95% Upper 95% Lorr 95.0% Upper95.0%/Intercept 0.069108738 0.004837034 14.28742052 9.25109E-25 0.05949766 0.078719816 0.05949766 0.078719816WASG 0.230257047 0.220649665 1.043541339 0.299523882 -0.2081689 0.668682994 -0.2081689 0.668682994



Cap Rate v. Weighted Average Excess Demand

RIruion StafirtwrMultiple R 0.325510351

R Square 0.105956988


ANOVA

df SS MS F Sinifcwan FRegression 1 0.002037274 0.002037274 10.54778332 0.001641329

Residual 89 0.017190097 0.000193147Total 90 0.019227372

CoeffwentS Standard Eemr St Da P-alue Lorr95% Upper 95% Loaer95.0%/ Upper 95.0%

Intercept 0.07937574 0.002229029 35.61001454 1.93834E-54 0.07494671 0.083804769 0.07494671 0.083804769WAED -0.528336317 0.16267839 -3.247735106 0.001641329 -0.851574638 -0.205097997 -0.851574638 -0.205097997


Appendix 13Panel Data Results

% Change in FFO per Unit v. WARG (Govt), % Change in # of Apts

RerMssin Statutics


R Square 0.231295512

Adjusted K Square 0.214400908Standard Error 0.271210019

Observations 94

ANOVA

df SS MS F Sinecance F

Regression 2 2.014005434 1.007002717 13.69049607 6.33948E-06

Residual 91 6.693493557 0.073554874Total 93 8.707498991

Coefficientr Standard Enr t Stat P-lcue Lower95% Upper 95% Lower95.0% Upper 95.0%

Intercept 0.168269944 0.132818363 1.266917766 0.208417612 -0.095557445 0.432097334 -0.095557445 0.432097334

Govt WARG 0.386738625 3.851150199 0,10042159 0.920230545 -7.263098925 8.036576175 -7.263098925 8.036576175

% D # of Apts -0.285606814 0.054604681 -5.230445579 1.07371E-06 -0.394072316 -0.177141311 -0.394072316 -0.177141311

% Change in FFO per Unit v. WARG (Govt), % Change in # of Apts, % in MSA

Reeomsion Statisti


R Square 0.232675543


ANOVA

df SS MS F Sinificane F

Regression 3 2.026022059 0.675340686 9.096890161 2.54039E-05

Residual 90 6.681476932 0.074238633

Total 93 8.707498991

Coeffcsents StandardEvr t Stat P-Value Lower95% Upper95% Lowr95.0% Upper95.0%

Intercept 0.219853332 0.185049705 1.18807718 0.237928823 -0.14777991 0.587486574 -0.14777991 0.587486574

Govt WARG 0.55191972 3.890731793 0.14185499 0.88751161 -7.177691856 8.281531295 -7.177691856 8.281531295

% D # ofApts -0.291217725 0.056602881 -5.144927617 1.55512E-06 -0.403669137 -0.178766313 -0.403669137 -0.178766313

% Total in MSA -0.068761794 0.170911362 -0.4023243 0.688399321 -0.408306772 0.270783184 -0.408306772 0.270783184

% Change in FFO per Unit v. WARG (NREI), % Change in # of Apts

RtsOn StaistirsMultiple R 0.609000884

R Square 0.370882076


ANOVA

df SS MS F Signiancea F


Ceffiientr Standard Emr I Stat P-value Lower 95% Upper 95% Lower95.0% Upper 950%

Intercept -0.171119462 0.08327293 -2.054923033 0.042753233 -0.336530925 -0.005707999 -0.336530925 -0.005707999

NREI WARG 9.219180468 2.05108359 4.494785347 2.04482E-05 5.144954469 13.29340647 5.144954469 13.29340647

% D # of Apts -0.319535469 0.049967848 -6.394821505 6.78966E-09 -0.418790471 -0.220280467 -0.418790471 -0.220280467



% Change in FFO per Unit v. WARG (NREI), % Change in # of Apts, % in MSA

Regrwion Statistis


R Square 0.371885755



ANOVA

df SS MS F Sinifrancn FSs k~~~~~~~~~~~~~inificanot Regression 3 3.238194833 1.079398278 17.7620118 3.86691E-09

Residual 90 5.469304158 0.060770046

Total 93 8.707498991

Coeffoaients Standard Error t Stat P-sare Lowr 95% Upper95% Lower95.0% Upper95.0/

Intercept -0.225817183 0.166745071 -1.354266018 0.179042033 -0.557085102 0.105450737 -0,557085102 0.105450737

NREI WARG 9.361885466 2.094876355 4.468944166 2.28128E-05 5.200051295 13.52371964 5.200051295 13.52371964

% D # of Apts -0.315202472 0.051488356 -6.121820465 2.37293E-08 -0.417492995 -0.212911948 -0.417492995 -0.212911948% Total in MSA 0.059277606 0.156311688 0.379226955 0.705412596 -0.251262596 0.369817807 -0.251262596 0.369817807

% Change in FFO per Unit v. WAEG, WASG, % Change in # of Apts

Regrsson Statrtics


R Square 0.267336288



ANOVA

df SS MS F Signifcance FRegression 3 2.327830462 0.775943487 10.94647998 3.38301E-06

Residual 90 6.379668529 0.070885206Total 93 8.707498991

Coeffzaclts Standard Error tStat P.-value LoWer95% Upper 95% Loer 95.0% Upper 95.0%

Intercept 0.153434738 0.114217594 1.34335466 0.182534681 -0.073478266 0.380347742 -0.073478266 0.380347742

WAEG 6.064926759 3,376533686 1.796199097 0.075818211 -0.643141352 12.77299487 -0.643141352 12.77299487

WASG -7.589092059 4,411305147 -1.720373405 0.088801221 -16.35291263 1.174728515 -16.35291263 1,174728515

% D # of Apts -0.300697858 0.054308329 -5.536864495 3.01495E-07 -0.408590746 -0.192804971 -0.408590746 -0.192804971

% Change in FFO per Unit v. WAED, % Change in # of Apts

Rersion Statirtics


R Square 0.266346109



ANOVA

df SS MS F Sienifiance FRegression 2 2.319208475 1.159604238 16.51834483 7.58317E-07

Residual 91 6.388290516 0.070200995Total 93 8.707498991

Coefftcierti Standard Errr t Stat P-Malue Loaer95% Upper 95% Loer 95.0% Upper 95 0%

Intercept 0.116609875 0.043341535 2.690487892 0.008488934 0.030517231 0.202702519 0.030517231 0.202702519WAED 6.505457509 3.116218748 2.087612595 0.039627788 0.315471151 12.69544387 0.315471151 12.69544387% D # ofApts -0.298556085 0.053698944 -5.559812975 2.67743E-07 -0.405222452 -0.191889719 -0.405222452 -0.191889719



% Change in FFO per Unit v. WARG (Govt), WAEG, WASG, % Change in # of Apts

ReMsrion StatlrtiasMultiple R 0.519771761

R Square 0.270162684


ANOVA

df SS MS F Significanc FRegression 4 2.352441298 0.588110324 8.236246059 1.0672E-05Residual 89 6.355057693 0.071405143

Total 93 8.707498991

Coeffeienx Standardar for tSa P-value Lower 95% Upper 95% Lowe r95.5.0%Intercept 0.224421194 0.16661805 1.346920059 0.181425705 -0.106645143 0.555487531 -0.106645143 0.555487531Govt WARG -2.353873163 4.009448313 -0.587081558 0.558635317 -10.32055724 5.612810912 -10.32055724 5.612810912

WAEG 6.713165449 3.564238977 1.88347793 0.062901153 -0.368897639 13.79522854 -0.368897639 13.79522854

WASG -8.166658258 4.535437636 -1.800632908 0.075148178 -17.17847135 0.845154832 -17.17847135 0.845154832

% D # of Apts -0.30217881 0.054565479 -5.537911817 3.06562E-07 -0.410599195 -0.193758424 -0.410599195 -0.193758424

% Change in FFO per Unit v. WARG (NREI), WAEG, WASG, % Change in # of Apts, % in MSA

ReRrresion Statirts


R Square 0.270363872


ANOVA

df SS MS F Significancn FRegression 5 2.354193145 0.470838629 6.521612583 3.33279E-05Residual 88 6.353305846 0.072196657Total 93 8.707498991

Coeffments Standard iEr t Sat P-valrie Lower95% Upper 95% Lower 95. 0% Upper 95.0%Intercept 0.236556717 0.184766332 1.280302064 0.203803444 -0.130627826 0.60374126 -0.130627826 0.60374126Govt WARG -2.276284708 4.062261126 -0.560349184 0.57666509 -10.3491813 5.796611886 -10.3491813 5.796611886

WAEG 6.861115981 3.707656627 1.850526268 0.067593052 -0.50707814 14.2293101 -0.50707814 14.2293101

WASG -7.732007623 5.346398957 -1.446208501 0.151671098 -18.35686028 2.892845035 -18.35686028 2.892845035

% D # of Apts -0.304130632 0.056279624 -5.403920833 5.50494E-07 -0.415974645 -0.19228662 -0.415974645 -0.19228662% Total in MSA -0.03443998 0.221092168 -0.15577205 0.876569477 -0.473814545 0.404934584 -0.473814545 0.404934584

% Change in FFO per Unit v. WARG (NREI), WAEG, WASG, % Change in # of Apts

Rtreszon Stas:ticsMultiple R 0.643937156

R Square 0.414655061


ANOVA

df SS MS F SiRnifran FRegression 4 3.610608529 0.902652132 15.76177482 8.68552E-10Residual 89 5.096890462 0.057268432Total 93 8.707498991

Coeffsicntr Standard Eror tStat P-alse Lower95% Upper 95% Lower 95.0% Upper 95.0%Intercept -0.495961528 0.17136725 -2.89414417 0.004780869 -0.83646442 -0.155458637 -0.83646442 -0.155458637NREI WARG 10.97774262 2.319502944 4.732799605 8.29177E-06 6.368942172 15.58654306 6.368942172 15.58654306WAEG 6.822002339 3.039158435 2.244701119 0.027266035 0.783262537 12.86074214 0.783262537 12.86074214WASG 2.09593434 4.461961296 0.469733868 0.639694053 -6.769882907 10.96175159 -6.769882907 10.96175159

% D # of Apts -0.321877929 0.049018919 -6.566402022 3.34904E-09 -0.419277424 -0.224478434 -0.419277424 -0.224478434



% Change in FFO per Unit v. WARG (NREI), WAEG, WASG, % Change in # of Apts, % in MSA

ReRsion Stamirtr


R Square 0.424680659


Observations 94

ANOVA

df SS MS F S -nifiwcna F

Regression 5 3.697906412 0.739581282 12.99170578 1.8085E-09Residual 88 5.009592579 0.056927188

Total 93 8.707498991

Caeflerent StandardE r tStat P-svalue Loair95% Upper 95% Loawr95.00/ Upper 95,0%

Intercept -0.427329641 0.179620073 -2.379075087 0.019517894 -0.784287068 -0.070372215 -0.784287068 -0.070372215

NREI WARG 11.56792106 2.361179788 4.899212302 4.34513E-06 6.875568783 16.26027335 6.875568783 16.26027335

WAEG 8.073856409 3.194273344 2.527603476 0.013269833 1.725904469 14.42180835 1.725904469 14.42180835

WASG 5.589548542 5.267789152 1.061080537 0.29155679 -4.879083527 16.05818061 -4.879083527 16.05818061


% Change in Rental Revenue per Unit v. WARG (Govt), % Change in # of Apts

Rctsivn Statirtict


R Square 0.208077482


ANOVA

df SS MS F Sifwance F


Total 93 8.066045235

Coeffictnr Standard Error t Stat P-valwe Lawr 95% ULouwer95.0% Upper 95.0%

Intercept 0.071104609 0.129748807 0.548017439 0.585021737 -0.186625483 0.3288347 -0.186625483 0.3288347Govt WARG 3.211946457 3.762146514 0.853753687 0.395483292 -4.261096188 10.6849891 -4.261096188 10.6849891

% D # of Apts -0.256357803 0.053342716 -4.805863329 6.05084E-06 -0.362316566 -0.15039904 -0.362316566 -0.15039904

% Change in Rental Revenue per Unit v. WARG (Govt), % Change in # of Apts, % in MSA

Rirxrion Statirtic


R Square 0.223114593


ANOVA

df SS MS F SiO niasceF


Coeffirientr Standard Errr tStat P-vmale Lower 95% Upper 95% Lowr950% Upper9.0%

Intercept 0.23498657 0.179209476 1.311239647 0.193112368 -0.121044047 0.591017186 -0.121044047 0.591017186Govt WARG 3.736731722 3.767939019 0.991717675 0.323994093 -3.748930773 11.22239422 -3.748930773 11.22239422




% Change in Rental Revenue per Unit v. WARG (NREI), % Change in # of Apts

R4Rnmnn Staftitrs


R Square 0.320942143


ANOVA

df SS MS F Snifignance FRegression 2 2.588733839 1.29436692 21.50459983 2.2478B-08Residual 91 5.477311396 0.060190235Total 93 8.066045235

Ceefficnt Standarmd rro t Stat P-value Lanaer95% Upper 95% Loawr95.o Upper 95./o .

Intercept -0.134494867 0.083267364 -1.615217059 0.109725752 -0.299895274 0.03090554 -0.299895274 0.03090554NREI WARG 8.197360469 2.050946494 3.996867053 0.000130238 4.123406795 12.27131414 4.123406795 12.27131414

% D # of Apts -0.286935222 0.049964508 -5.742780888 1.21808E-07 -0.38618359 -0.187686855 -0.38618359 -0.187686855

% Change in Rental Revenue per Unit v. WARG (NREI), % Change in # of Apts, % in MSA

Reiasen Statitir


R Square 0.323684588


ANOVA

df SS MS F Sinfcane F


Coefficients Standard E r I Stat P-value Lvwer95% Upper95% Lower 95.0% Upper95.0%

Intercept -0.047473766 0.16652979 -0.285076719 0.776240357 -0.378313994 0.283366462 -0.378313994 0.283366462NREI WARG 7.970324572 2.092171712 3.809593891 0.000254111 3.813863643 12.1267855 3.813863643 12.1267855


% Change in Rental Revenue per Unit v. WAEG, WASG, % Change in # of Apts

R eysson Stalirics


R Square 0.24552337


Observations 94

ANOVA

df SS MS F Sjnifcanew FRegression 3 1.980402605 0.660134202 9.762663001 1.21789E-05Residual 90 6.08564263 0.067618251

Total 93 8.066045235

Ceffsient Standard Error tSta P-value Lowr 95% Upper 95% Lowr 95.0% Upper 95,0%

Intercept 0.228474773 0.111554521 2.048099633 0.043463913 0.006852425 0.45009712 0.006852425 0.45009712WAEG 4.840146013 3.297807127 1.4676862 0.145675609 -1.711518174 11.3918102 -1.711518174 11.3918102WASG -9.431668777 4.308452072 -2.189108436 0.031176579 -17.99115393 -0.872183624 -17.99115393 -0.872183624

% D # of Apts -0.275491938 0.053042087 -5.193836681 1.27113E-06 -0.380869217 -0.170114659 -0.380869217 -0.170114659



% Change in Rental Revenue per Unit v. WAED, % Change in # of Apts

Ragmsion StatuitsMultiple R 0.485615925

R Square 0.235822827


Observations 94

ANOVA

df SS MS F Si£nifancr FRegression 2 1.902157586 0.951078793 14.04116608 4.8454E-06

Residual 91 6.163887649 0.067735029

Total 93 8.066045235

Cocfficns Standard En t Stat P-vale Lonr95% Upper 95% Lo)e r 95.0% Upper 95.0%Intercept 0.117540483 0.042573496 2.760883966 0.006971971 0.032973454 0.202107511 0.032973454 0.202107511WAED 6.167237613 3.060997424 2.014780399 0.046879037 0.086941642 12.24753358 0.086941642 12.24753358

% D # of Apts -0.269039883 0.052747365 -5.100536886 1.83651E-06 -0.373816055 -0.164263711 -0.373816055 -0.164263711

% Change in NOI per Unit v. WARG (Govt), % Change in # of Apts

RParsion StatutrexMultiple R 0.462918193

R Square 0.214293253



ANOVA

df SS MS F Sginifcanc FRegression 2 1.724773429 0.862386715 12.40964655 1.71535E-05Residual 91 6.323886078 0.069493254

Total 93 8.048659507

Coeffscntr Standard Errr t Stat P-vale Loar 95% Upper95% Lower 95.0% Upper95.0%/oIntercept 0.091540666 0.12909925 0.70907202 0.480092494 -0.164899161 0.347980492 -0.164899161 0.347980492Govt WARG 3.048631947 3.743312211 0.814420966 0.41753001 -4.386998665 10.48426256 -4.386998665 10.48426256

% D # ofApts -0.260412086 0.053075668 -4.906430667 4.04444E-06 -0.365840392 -0.154983781 -0.365840392 -0.154983781

% Change in NOI per Unit v. WARG (Govt), % Change in # of Apts, % in MSA

Reession SttisticsMultiple R 0.475681881

R Square 0.226273251


Observations 94

ANOVA

df SS MS F Signifcanc FRegression 3 1.821196357 0.607065452 8.773378402 3.64542E-05Residual 90 6.227463151 0.069194035Total 93 8.048659507

Coeffcients StandardEmrr tStat P-vale Lowr95% Upper95% Lowr95,0% Upper 95.0%Intercept 0.237660366 0.178651943 1.330298241 0.186781154 -0.117262616 0.592583347 -0.117262616 0.592583347

Govt WARG 3.516538633 3.756216709 0.936191627 0.351679867 -3.945835466 10.97891273 -3.945835466 10.97891273




% Change in NOI per Unit v. WARG (NREI), % Change in # of Apts

Re, rMiWn ShtirtaMultiple R 0.558184621

R Square 0.311570071


ANOVA

df SS MS F Siniana F


Coeffcntr StandaidEramr IStat P-valee Lower9 Uppe95% L % Uer 95 .0%

Intercept -0.09714968 0.083749602 -1.160001699 0.24908253 -0.263507993 0.069208632 -0.263507993 0.069208632NREI WARG 7.611661506 2.062824416 3.68992215 0.000381715 3.514113796 11.70920922 3.514113796 11.70920922% D # of Apts -0.288814202 0.050253874 -5.747103211 1.19547E-07 -0.38863736 -0.188991044 -0.38863736 -0.188991044

% Change in NOI per Unit v. WARG (NREI), % Change in # of Apts, % in MSA

Re-sion Stairtiax


R Square 0.313499007


ANOVA

df SS MS F SIni*an F


Total 93 8.048659507

Coeffients Standard Errvr t Stat P-value Lomre 5 Upper 9595/ Loser 95. 0/ Upper 95. 0%

Intercept -0.0242466 0.167598192 -0.14467101 0.88529411 -0.357209392 0.308716193 -0.357209392 0.308716193NREI WARG 7.421459175 2.1055944i2 3.524638521 0.000669006 3.23833173 11.60458662 3.23833173 11.60458662% D # of Apts -0.294589376 0.051751787 -5.692351779 1.54969E-07 -0.397403251 -0.191775502 -0.397403251 -0.191775502% Total in MSA -0.079007315 0.157111429 -0.502874399 0.616280979 -0.39113634 0.233121709 -0.39113634 0.233121709

% Change in NOI per Unit v. WAEG, WASG, % Change in # of Apts

Re.rrsion Statirtict


R Square 0.248915698


Observations 94

ANOVA

df SS MS F Sknifwnc FRegression 3 2.003437702 0.667812567 9.942254062 1.00064E-05Residual 90 6.045221806 0.067169131Total 93 8.048659507

Coeffirents StandadE r IStat P-valuc Lorr 95% Upper 95% Loaer95.0% Upper95.0%

Intercept 0.209244638 0.111183431 1.881976804 0.063070765 -0.011640476 0.430129752 -0.011640476 0.430129752WAEG 5.358168071 3.286836867 1.630189841 0.106556413 -1.171701797 11.88803794 -1.171701797 11.88803794WASG -8.585456962 4.29411987 -1.999351956 0.048586303 -17.11646872 -0.054445209 -17.11646872 -0.054445209% D # of Apts -0.277851091 0.052865641 -5.25579726 9.83289E-07 -0.38287783 -0.172824353 -0.38287783 -0.172824353



% Change in NOI per Unit v. WAED, % Change in # of Apts

Reresrion StatirticsMultiple R 0.494077817

R Square 0.244112889


ANOVA

df SS MS F Sinicane F


Coeffients StandardEr tStat P-vale Lower 95% Upper95% Lower95.0% Upper95.0%

Intercept 0.13127116 0.042296283 3.103609806 0.002547457 0.04725478 0.215287539 0.04725478 0.215287539WAED 6.290954033 3.041066064 2.068667336 0.041414218 0.250249267 12.3316588 0.250249267 12.3316588% D # ofApts -0.273316072 0.052403907 -5.215566731 1.14216E-06 -0.377410005 -0.169222138 -0.377410005 -0.169222138

% Change in NOI per Unit v. WARG (Govt), WAEG, WASG, % Change in # of Apts

Rcnsrion Statitics


R Square 0.249229486


Observations 94

ANOVA

df SS MS F Sifnifw-n FRegression 4 2.00596327 0.501490818 7.386219829 3.48757E-05Residual 89 6.042696237 0.067895463Total 93 8.048659507

Ceffsentr Standard Err tStat . P-vale Lower95% Upper 95% Lower 95.0% Upper95.0%Intercept 0.186504563 0.162471683 1.147920418 0.25407762 -0.136323036 0.509332162 -0.136323036 0.509332162Govt WARG 0.754048813 3.909671351 0.19286757 0.847502122 -7.014380673 8.522478299 -7.014380673 8.522478299WAEG 5.150508788 3.475541254 1.481929982 0.141891036 -1.75531391 12.05633148 -1.75531391 12.05633148WASG -8.400437177 4.422571188 -1.899446458 0.060744531 -17.18798716 0.387112808 -17.18798716 0.387112808% D # ofApts -0.277376677 0.053207592 -5.213103393 1.19437E-06 -0.383098972 -0.171654383 -0.383098972 -0.171654383

% Change in NOI per Unit v. WARG (NREI), WAEG, WASG, % Change in # of Apts

Remtesion Statistics


R Square 0.339627601


ANOVA

df SS MS F Sinitanc FRegression 4 2.733546919 0.68338673 11.44311017 1.54097E-07Residual 89 5.315112589 0.059720366Total 93 8.048659507

Coeffintr Standard Errvr I Stat P-vale Louer 95% Upper 95% Lowr 950% Upper 95.0%Intercept -0.280678488 0.174997326 -1.60390158 0.112278803 -0.628394258 0.067037283 -0.628394258 0.067037283NREI WARG 8.281923159 2.368637017 3.496493172 0.000737587 3.57549441 12.98835191 3.57549441 12.98835191WAEG 5.929327553 3.103536984 1.910506491 0.059287623 -0.237330986 12.09598609 -0.237330986 12.09598609WASG -1.278795909 4.556479104 -0.2806544 0.779626859 -10.33241793 7.774826108 -10.33241793 7.774826108% D # ofApts -0.293829943 0.050057288 -5.869873423 7.36604E-08 -0.393292653 -0.194367232 -0.393292653 -0.194367232



% Change in N.I. per Unit v. WARG (Govt), % Change in # of Apts

Retsmiion Stattic


R Square 0.081032785


ANOVA

df SS MS F Srnfiwnar FRegression 2 4.616915106 2.308457553 3.968014613 0.022310063Residual 90 52.35897546 0.581766394Total 92 56.97589057

Coeffient S tStat P-ale Lowr 95% Upper95% Lor95.0% Upper95.0%

Intercept 0.801646175 0.373565581 2.145931574 0.034569701 0.059493544 1.543798806 0.059493544 1.543798806Govt WARG -15.08132014 10.83240791 -1.392240789 0.167280093 -36.60177183 6.439131553 -36.60177183 6.439131553

% D # Apts -0.379261172 0.154118634 -2.460839175 0.015769403 -0.685444491 -0.073077852 -0.685444491 -0.073077852

% Change in N.I. per Unit v. WARG (Govt), % Change in # of Apts, % in MSA

Rovnsirrn StatisMultiple R 0.311739907R Square 0.09718177


Observations 93

ANOVA

df SS MS F Signifwan- F


Total 92 56.97589057

Coeffints Standard Enr t Stat P-value Loaer 95% Upper95% Lower 95.0% Upper95.0%

Intercept 0.348778534 0.517171716 0.674395995 0.501807599 '0.678830093 1.376387161 -0.678830093 1.376387161Govt WARG -16.51048694 10.85621079 -1.520833305 0.131846633 -38.08153485 5.060560965 -38.08153485 5.060560965

% D # Apts -0.331520359 0.158205622 -2.09550302 0.038966492 -0.645871391 -0.017169328 -0.645871391 -0.017169328% Total in MSA 0.604507195 0.479108664 1.261732964 0.210341671 -0.347471 1.55648539 -0.347471 1.55648539

% Change in N.I. per Unit v. WARG (NREI), % Change in # of Apts

ReResjion StatistisMultiple R 0.292739102

R Square 0.085696182


ANOVA

df SS MS F Sinifwn- FRegression 2 4.882616274 2.441308137 4.217775429 0.017744918Residual 90 52.0932743 0.578814159Total 92 56.97589057

Coefficients Standard Err t Stat P-value Lowr 95% Upper95% Louwr 95,0% Upper 95.0%

Intercept -0.089229329 0.26349678 -0.338635368 0.735673201 -0.612711252 0.434252594 -0.612711252 0.434252594NREI WARG 10.00693098 6.449691844 1.551536293 0.124282749 -2.806497291 22.82035925 -2.806497291 22.82035925% D # Apts -0.410790684 0.155218338 -2.646534478 0.009599047 -0.719158757 -0.102422611 -0.719158757 -0.102422611



% Change in N.I. per Unit v. WARG (NREI), % Change in # of Apts, % in MSA

Ression Statidis


R Square 0.105210665



ANOVA

df SS MS F Sinificana F

Regression 3 5.994471321 1.998157107 3.488250918 0.019007536

Residual 89 50.98141925 0.572824935

Total 92 56.97589057

Coff Snd ntr ss d Stat P-vale Loer 95% Upper95% Lower 95.0% Upper9.0%

Intercept -0.70259208 0.512383223 -1.371223818 0.173752795 -1.720686079 0.315501919 -1.720686079 0.315501919

NREI WARG 11.51636759 6.507066434 1.769824806 0.080181188 -1.413027805 24.44576298 -1.413027805 24.44576298

% D # Apts -0.362697913 0.15822466 -2.29229699 0.024246814 -0.677086772 -0.048309053 -0.677086772 -0.048309053

% Total in MSA 0.670247843 0.481085532 1.393198918 0.167029603 -0.285658344 1.62615403 -0.285658344 1.62615403

% Change in N.I. per Unit v. WAEG, WASG, % Change in # of Apts

Ression StatisicrMultiple R 0.270225609

R Square 0.07302188


ANOVA

df SS MS F SiOnifmac F

Regression 3 4.16048663 1.386828877 2.33696537 0.07904243

Residual 89 52.81540394 0.593431505

Total 92 56.97589057

Cooffsentfs StandardErrr t Stat P-valse Lower95% Upper 95% Loner95.0% Upper95.0%

Intercept -0.017450519 0.330511824 -0.05279847 0.95801081 -0.674170117 0.63926908 -0.674170117 0.63926908

WAEG 2.919357936 9.77066916 0.298787922 0.765798402 -16.49474304 22.33345891 -16.49474304 22.33345891

WASG 10.48385163 12.77597571 0.820591074 0.41407171 -14.90172621 35.86942947 -14.90172621 35.86942947

% D # Apts -0.357484294 0.157599922 -2.268302482 0.025729948 -0.670631813 -0.044336776 -0.670631813 -0.044336776

% Change in N.I. per Unit v. WAED, % Change in # of Apts

Rerssion Statirties


R Square 0.061355683


Observations 93

ANOVA

df SS MS F StiniFRanc F

Regression 2 3.495794679 1.74789734 2.941482396 0.057882526

Residual 90 53.48009589 0.594223288Total 92 56.97589057

Coeffientsr StandasEdr IStat P-lc Loner 95% Upper 95% Loner95.0% Upper 95.0%

Intercept 0.305666265 0.126680741 2.41288663 0.017854518 0.053993066 0.557339464 0.053993066 0.557339464

WAED -0.950974032 9.066529657 -0.104888427 0.916697748 -18.96320318 17.06125511 -18.96320318 17.06125511

% D # Apts -0.375550171 0.156777232 -2.395438202 0.018672124 -0.687015257 -0.064085086 -0.687015257 -0.064085086



% Change in N.I. per Unit v. WARG (Govt), WAEG, WASG, % Change in # of Apts

Regrrion StatitirMultiple R 0.308032069

R Square 0.094883755


Observations 93

ANOVA

df SS MS F Sirnifwance F


Coeffient-r StandErr I Stat P-valse Laxer 95% Upper 95% Lower95.00

/o Upper 95.00%Intercept 0.487789421 0.477462202 1.021629394 0.309756716 -0.461067107 1.436645948 -0.461067107 1.436645948Govt WARG -16.74902059 11.48832067 -1.457917225. 0.148423039 -39.57966143 6.081620255 -39.57966143 6.081620255WAEG 7.527960758 10.21108363 0.737234267 0.462941052 -12.76443791 27.82035943 -12.76443791 27.82035943WASG 6.358535096 13.00744955 0.48883796 0.626172515 -19.4910575 32.20812769 -19.4910575 32.20812769% D # Apts -0.367685177 0.156768966 -2.345395187 0.021255556 -0.679230794 -0.056139561 -0.679230794 -0.056139561

% Change in N.I. per Unit v. WARG (Govt), WAEG, WASG, % Change in # of Apts, % in MSA

RcrtWnen StatirticrMultiple R 0.316705279

R Square 0.100302234


Observations 93

ANOVA

df SS MS F Sinificanr FRegression 5 5.714809081 1.142961816 1.939827938 0.095832298Residual 87 51.26108149 0.589207833

Total 92 56.97589057

Coefficint Standard Ervr IStat P-value Lwr95V% Upper 95% La r 95.0% Upper95.0%Intercept 0.325517215 0.528645553 0.615757029 0.539662493 -0.725223826 1.376258256 -0.725223826 1.376258256Govt WARG -17.76950716 11.60546794 -1.531132329 0.129364941 -40.83664806 5.297633739 -40.83664806 5.297633739WAED -0.605635836 15.27345928 -0.039652827 0.968460764 -30.96331199 29.75204031 -30.96331199 29.75204031WAEG 6.167958617 16.52446567 0.373262212 0.709861247 -26.67622999 39.01214723 -26.67622999 39.01214723% D # Apts -0.342379154 0.161035383 -2.126111346 0.036327081 -0.662454647 -0.022303662 -0.662454647 -0.022303662% Total in MSA 0.458639346 0.633609011 0.723852309 0.471098331 -0.800728085 1.718006776 -0.800728085 1.718006776

% Change in N.I. per Unit v. WARG (NREI), WAEG, WASG, % Change in # of Apts

R.r.rsion Staitic


R Square 0.132248832


ANOVA

d¥ SS MS F Sinian-e~ FRegression 4 7.534994994 1.883748748 3.352890111 0.013277201Residual 88 49.44089558 0,561828359Total 92 56.97589057

Coeffientr Standard Errr t Sta P-value Lower 95% Upper 95% Lar95, 0% Upper95.0%Intercept -1.082686755 0.540688153 -2.002423669 0.048317696 -2.157191671 -0.008181839 -2.157191671 -0.008181839NREI WARG 18.03903464 7.360543208 2.450774913 0.016232981 3.411490434 32.66657884 3.411490434 32.66657884WAEG 4.108420261 9.519314099 0.431587845 0.667096531 -14.80923076 23.02607128 -14.80923076 23.02607128WASG 26.18094234 13.98414731 1.872187254 0.064501954 -1.609633313 53.971518 -1.609633313 53.971518% D # Apts -0.387597908 0.153837524 -2.519527739 0.013556643 -0.693317896 -0.081877921 -0.693317896 -0.081877921



% Change in N.I. per Unit v. WARG (NREI), WAEG, WASG, % Change in # of Apts, % in MSA

RErmstrion Satitics



ANOVA

df SS MS F Sinifancr FRegression 5 7.535222624 1.507044525 2.651923591 0.027978548Residual 87 49.44066794 0.56828354Total 92 56.97589057

Coeffientx Standad Enr t Stat P-Wdce Lover 95% Upper95% Lorr95.0% Upper 95.0%Intercept -1.08606074 0.569317415 -1.907654167 0.05973484 -2.217641568 0.045520089 -2.217641568 0.045520089NREI WARG 18.00587515 7.585850022 2.373613385 0.019816298 2.928165899 33.08358439 2.928165899 33.08358439WAED -26.00013739 16.71572125 -1.555430185 0.123474072 -59.22446743 7.224192661 -59.22446743 7.224192661WAEG 30.04432644 18.74850731 1.602491652 0.112671677 -7.220388936 67.30904182 -7.220388936 67.30904182% D # Apts -0.386826446 0.159448133 -2.426033085 0.017330005 -0.703747105 -0.069905787 -0.703747105 -0.069905787% Total in MSA 0.012667418 0.632929077 0.020013962 0.984078065 -1.245348568 1.270683404 -1.245348568 1.270683404

% Change in Market Cap per Unit v. WARG (Govt), % Change in # of Apts

Rernion Statistis


R Square 0.104135377


ANOVA

df SS MS F Sinifancr FRegression 2 1.162070099 0.58103505 5.114563623 0.007918903Residual 88 9.997154817 0.113604032Total 90 11.15922492

Coeffientts Standard E r Stat P- e Loer 95% Upper95-/o Loer 95.0% Upper95.0%Intercept 0.309827468 0.167909159 1.845208867 0.068370533 -0.023856958 0.643511895 -0.023856958 0.643511895GOVT WARG -3.395374461 4.85525935 -0.699318866 0.486197135 -13.0441896 6.253440681 -13.0441896 6.253440681% D # Apts -0.332003899 0.105641918 -3.142728818 0.00228132 -0.541945174 -0.122062625 -0.541945174 -0.122062625

% Change in Market Cap per Unit v. WARG (Govt), % Change in # of Apts, % in MSA

Resrnon Statistrr


R Square 0.106865013


ANOVA

df SS MS F SOnifanr FRegression 3 1.192530711 0.397510237 3.469895825 0.019540472Residual 87 9.966694205 0.114559704Total 90 11.15922492

Coe9tintis Sanrd Error t Stat P-wale Lower 95% Upper 95% Laer 95.0% Upper 95, 0%Intercept 0.391312642 0.231089794 1.693335894 0.093969407 -0.068003678 0.850628963 -0.068003678 0.850628963GOVT WARG -3.075956624 4.914831495 -0.625851899 0.533050558 -12.84472348 6.692810235 -12.84472348 6.692810235% D # Apts -0.340559461 0.107374987 -3.171683376 0.002094049 -0.553979029 -0.127139893 -0.553979029 -0.127139893% Total in MSA -0.111028021 0.215317451 -0.515648037 0.607408176 -0.538995081 0.316939039 -0.538995081 0.316939039



% Change in Market Cap per Unit v. WARG (NREI), % Change in # of Apts

Regsrion StisfisMultiple R 0.614585298

R Square 0.377715088



ANOVA

df SS MS F Sinifwann FRegression 2 4.215007624 2.107503812 26.7071619 8.62041E-10Residual 88 6.944217292 0.07891156Total 90 11.15922492

Coeffiiens Standand Err tStat P-value Lower 95% Upper 95% Lower 95.0% Upper95.0%Intercept -0.378333748 0.097873352 -3.865543992 0.000211715 -0.572836619 -0.183830877 -0.572836619 -0.183830877NREI WARG 14.95491561 2.382753848 6.276315796 1.27086E-08 10.21968939 19.69014184 10.21968939 19.69014184

% D # Apts -0.390543176 0.08853155 -4.411344598 2.89914E-05 -0.566481165 -0.214605188 -0.566481165 -0.214605188

% Change in Market Cap per Unit v. WARG (NREI), % Change in # of Apts, % in MSA

Retression StatirticsMultiple R 0.61529226

R Square 0.378584566


Observations 91

ANOVA

edf SS MS F Snificanc FRegression 3 4.224710317 1.408236772 17.66765322 4.83523E-09Residual 87 6.934514599 0.079707064

Total 90 11.15922492

Coefficents StandardErnr tStat P-Male L.0mr95% Upper 95% Loer 95, 0% Upper 95.0%Intercept -0.436250889 0.192955759 -2.260885564 0.026261082 -0.81977163 -0.052730148 -0.81977163 -0.052730148NREI WARG 15.09914383 2.43015146 6.213252167 1.73416E-08 10.26895116 19.92933649 10.26895116 19.92933649

% D # Apts -0.386137562 0.089868213 -4.296709015 4.50009E-05 -0.564760496 -0.207514628 -0.564760496 -0.207514628% Total in MSA 0.063082423 0.180805083 0.348897398 0.728009319 -0.296287518 0.422452364 -0.296287518 0.422452364

% Change in Market Cap per Unit v. WAEG, WASG, % Change in # of Apts

Rc,-msion StatiticsMultiple R 0.41513231

R Square 0.172334835


ANOVA

df SS MS F Stnificnce FRegression 3 1.923123185 0.641041062 6.03832374 0.00087514Residual 87 9.236101731 0.106162089

Total 90 11.15922492

Coeffsants StandardErrr t Stat P-vae Lower95% Upper 95% Loer 95.0% Upper 95.0%Intercept 0.283191322 0.14101794 2.008193577 0.047724589 0.002902693 0.563479952 0.002902693 0.563479952WAEG 7.281688197 4.150871027 1.754255468 0.082907917 -0.968623408 15.5319998 -0.968623408 15.5319998WASG -14.54571228 5.447183114 -2.670318214 0.009042494 -25.37258639 -3.718838173 -25.37258639 -3.718838173

% D # Apts -0.385110904 0.104062784 -3.700755328 0.000376194 -0.591947105 -0.178274702 -0.591947105 -0.178274702



% Change in Market Cap per Unit v. WAED, % Change in # of Apts

ReRnrsion StaticsMultiple R 0.393599278R Square 0.154920392


ANOVA

df SS MS F Sinifsance FRegression 2 1.728791495 0.864395748 8.066100717 0.000607429Residual 88 9.430433421 0.107164016Total 90 11.15922492

Coeffentr Standani Ervr tStat Pvalue Lower 95% Upper 95% Lower 95. 0o Upper 95.0%Intercept 0.107202864 0.054720147 1.959111404 0.053265606 -0.001542013 0.215947742 -0.001542013 0.215947742WAED 9.346012126 3.878450257 2.409728501 0.018047317 1.638401292 17.05362296 1.638401292 17.05362296% D # Apts -0.368159929 0.103792166 -3.547087821 0.000627026 -0.574425208 -0.16189465 -0.574425208 -0.16189465

% Change in Market Cap per Unit v. WARG (Govt), WAEG, WASG, % Change in # of Apts

Regmrsion Statistics



ANOVA

df SS MS F Signifwance FRegression 4 2.183673341 0.545918335 5.230762303 0.000807528Residual 86 8.975551575 0.104366879Total 90 11.15922492

Coeffient Standard Errr tStat P-value Loer95% Upper 95% Loer 95.0% Upper 95.0%Intercept 0.519922825 0.204934344 2.537021451 0.012984411 0.112527165 0.927318486 0.112527165 0.927318486GOVT WARG -7.772905536 4.919478518 -1.580026319 0.117772034 -17.55249722 2.006686145 -17.55249722 2.006686145WAEG 9.39823206 4.328140652 2.171424825 0.03265308 0.794180195 18.00228392 0.794180195 18.00228392WASG -16.49672728 5.540286978 -2.977594364 0.003773529 -27.51044433 -5.483010228 -27.51044433 -5.483010228% D # Apts -0.398989103 0.103552368 -3.85301765 0.00022418 -0.604844231 -0.193133975 -0.604844231 -0.193133975

% Change in Market Cap per Unit v. WARG (Govt), WAEG, WASG, % Change in # of Apts, % in MSA

Refsion StatirtinMultiple R 0.44580177

R Square 0.198739218


ANOVA

df SS MS F Sinfiance FRegression 5 2.217775634 0.443555127 4.216563176 0.001796645Residual 85 8.941449282 0.105193521Total 90 11.15922492

Coeffwcjnt Standard Eror tStat P-wlre Lower95% Upper95% Lower95.0% Upper 95.0/oIntercept 0.468568779 0.224645677 2.08581258 0.039993691 0.02191272 0.915224838 0.02191272 0.915224838GOVT WARG -8.214835806 4.999539458 -1.643118506 0.104052765 -18.15526565 1.725594038 -18.15526565 1.725594038WAEG 8.744150962 4.494536165 1.945506865 0.05501911 -0.192196435 17.68049836 -0.192196435 17.68049836WASG -18.47887689 6.56180002 -2.816129238 0.006041126 -31.52550114 -5.43225264 -31.52550114 -5.43225264% D # Apts -0.393546427 0.104400198 -3.769594651 0.000301068 -0.601122115 -0.185970739 -0.601122115 -0.185970739% Total in MSA 0.155515781 0.273134883 0.569373562 0.57060427 -0.387549868 0.69858143 -0.387549868 0.69858143



% Change in Market Cap per Unit v. WARG (NREI), WAEG, WASG, % Change in # of Apts

Rensiomn Stadres

Multiple K 0.64788819

K Square 0.419759106



ANOVA

df Ss MS F Sinifzwce F

Regression 4 4.684186279 1.17104657 15.55357592 1.3033E-09

Residual 86 6.475038637 0.075291147Total 90 11.15922492

Coeffinntft Standad Eevr tStat Pvalue Loner95% Upper95% LonUer 95 .00%%

Intercept -0.683278441 0.198932827 -3.434719398 0.000915124 -1.078743492 -0.28781339 -1.078743492 -0.28781339

NREI WARG 16.32760045 2.69622515 6.055725893 3.56565E-08 10.9676866 21.6875143 10.9676866 21.6875143

WAEG 8.2878593 3.499584612 2.368240868 0.020114465 1.330920992 15,24479761 1.330920992 15.24479761WASG -0.231480915 5.16050795 -0.044856227 0.964325899 -10.49022286 10.02726103 -10.49022286 10.02726103% D # Apts -0.402150335 0.087681185 -4.586506616 1.5208E-05 -0.57645462 -0.22784605 -0.57645462 -0.22784605

% Change in Market Cap per Unit v. WARG (NREI), WAEG, WASG, % Change in # of Apts, % in MSA

Regrmsion Satti


R Square 0.426163179



ANOVA

df SS MS F Sivifane FRegression 5 4.755650767 0.951130153 12.6251467 3.55372E-09

Residual 85 6.403574149 0.075336166Total 90 11.15922492

Coeflfcientr Standard E"r IStat P-lsae Lower 95% Upper95% Loner95.0% Upper 95.0%

Intercept -0.623194336 0.20833534 -2.991303995 0,003634222 -1.037421057 -0.208967615 -1.037421057 -0.208967615NREI WARG 16,91738672 2.76417615 6.12022744 2.7763E-08 11.42146068 22.41331276 11.42146068 22.41331276

WAEG 9.459242717 3.70147159 2.55553568 0.012381849 2.099721112 16.81876432 2.099721112 16.81876432

WASG 3.028170039 6.152050918 0.492221225 0.623831891 -9.203762732 15,26010281 -9.203762732 15.26010281

% D # Apts -0.411899466 0,088276735 -4.666002507 1.13361E-05 - -0.587417371 -0.236381562 -0.587417371 -0,236381562% Total in MSA -0.227934357 0.234027237 -0.97396508 0.332837524 -0.693243482 0.237374768 -0.693243482 0.237374768

Cap Rate v. WARG (Govt), % Change in # of Apts

Repsrnon Sttri

Multiple R 0.504894105R Square 0,254918058


Observations 91

ANOVA

df Ss MS F S~.mfcansn FRegression 2 0.004901404 0.002450702 15.05390736 2.3822E-06Residual 88 0.014325967 0.000162795

Total 90 0.019227372

Coefficients Standard Err I Sat P-value Lower95% Upper 95% Lowr 95, 0% Upper 95 0%Intercept 0.086865278 0.006356209 13.66620908 1.79048E-23 0.074233639 0.099496918 0.074233639 0.099496918GOVT WARG -0.255112205 0.183796063 -1.38801779 0.168635822 -0.620368539 0.11014413 -0.620368539 0,11014413% D # Apts -0,02140471 0.00399908 -5.352409116 6.8264E-07 -0.029352046 -0.013457374 -0.029352046 -0.013457374



Cap Rate v. WARG (Govt), % Change in # of Apts, % in MSA

etmssion StatistiwMultiple R 0.535215534R Square 0.286455668Adjusted R Square 0.261850691Standard Error 0.012557721Observations 91

ANOVAdf SS MS F Sienificane F


Coeffwientr Standard Enmr IStat P-alue Lower 95% Upper95% Lower95.0% Upper 95.0/%Intercept 0.098362255 0.008573845 11.47236187 4.27516E-19 0.081320797 0.115403713 0.081320797 0.115403713GOVT WARG -0.210044628 0.18234905 -1.151882214 0.252525035 -0.572483373 0.152394117 -0.572483373 0.152394117% D # Apts -0.022611839 0.003983804 -5.675941109 1.78331E-07 -0.030530087 -0.01469359 -0.030530087 -0.01469359% Total in MSA -0.015665261 0.007988663 -1.960936523 0.05308523 -0.031543606 0.000213083 -0.031543606 0.000213083

Cap Rate v. WARG (NREI), % Change in # of Apts

ReRnxsion StattisMultiple R 0.573717449R Square 0.329151711Adjusted R Square 0.313905159Standard Error 0.012106839Observations 91

ANOVAdf SS MS F Sirnificance F


Coeffiients Standard Entror tat P-alue Lower 95% Upper95% Lower95.0% Uppr 950%Intercept 0.09191178 0.004218178 21.78944896 3.21242E-37 0.083529031 0.100294529 0.083529031 0.100294529NREI WARG -0.353918291 0.102692718 -3.446381593 0.000873082 -0.557998646 -0.149837937 -0.557998646 -0.149837937% D # Apts -0.019773058 0.003815562 -5.182213579 1.3804E-06 -0.027355692 -0.012190424 -0.027355692 -0.012190424

Cap Rate v. WARG (NREI), % Change in # of Apts, % in MSA

Rerersion StatisticsMultiple R 0.624650742R Square 0.39018855Adjusted R Square 0.369160569Standard Error 0.011609088Observations 91

ANOVAdf SS MS F Signi/cance F


Coeffients Standard Enr t Stat P-Value Lower 95% Upper95% Lower95,0% Upper95.0%Intercept 0.112054446 0.007934279 14.12282686 3.12334E-24 0.096284196 0.127824696 0.096284196 0.127824696NREI WARG -0.404078584 0.099927048 -4.043735848 0.000113397 -0.602694556 -0.205462613 -0.602694556 -0.205462613% D # Apts -0.021305261 0.003695352 -5.765421246 1.21648E-07 -0.028650179 -0.013960344 -0.028650179 -0.013960344% Total in MSA -0.021939069 0.007434647 -2.950922838 0.004070648 -0.036716246 -0.007161893 -0.036716246 -0.007161893



Cap Rate v. WAEG, WASG, % Change in # of Apts

Rternion StatiticsMultiple R 0.55460087

R Square 0.307582125


ANOVA

df SS MS F SiRnificane FRegression 3 0.005913996 0.001971332 12.88222324 4.85306E-07Residual 87 0.013313376 0.000153027

Total 90 0.019227372

Coeffientr Standard Enmr tStat P-tae Lower95%/ Upper 95% Lower 95.0/ Uppr 95.0%Intercept 0.086159225 0.005353948 16.09265408 8.18324E-28 0.075517667 0.096800783 0.075517667 0.096800783WAEG -0.461353746 0.157593747 -2.927487644 0.004360079 -0.774588609 -0.148118883 -0.774588609 -0.148118883WASG 0.300877623 0.206810086 1.454849848 0.149309464 -0.110180115 0.711935361 -0.110180115 0.711935361

% D # Apts -0.019872647 0.003950892 -5.02991381 2.61031E-06 -0.027725479 -0.012019816 -0.027725479 -0.012019816

Cap Rate v. WAED, % Change in # of Apts

Reresion StatiticrMultiple R 0.55013577

R Square 0.302649365


Observations 91

ANOVA

df SS MS F SuniFnc F


Coeffcientr StandardEmr t Stat P-vale Lowr95% Upper 95% Loer95.0% Upper 95.0%Intercept 0.082271305 0.002063322 39.87322812 4.05247E-58 0.078170883 0.086371727 0.078170883 0.086371727WAED -0.4157489 0.146243967 -2.842844791 0.005557572 -0.706378278 -0.125119522 -0.706378278 -0.125119522

% D # Apts -0.019498168 0.003913671 -4.982066267 3.11618E-06 -0.027275773 -0.011720563 -0.027275773 -0.011720563

Cap Rate v. WARG (Govt), WAEG, WASG, % Change in # of Apts

RcXrrion StatitirMultiple R 0.556591388

R Square 0.309793973


ANOVA

df SS MS F Sinicance FRegression 4 0.005956524 0.001489131 9.650119185 1.70633E-06Residual 86 0.013270848 0.000154312Total 90 0.019227372

Coeffcients StandardE ror t Stat P-value Louwr 95% Upper95% Lowr 95 00

Upper 95.0%Intercept 0.089183686 0.007880132 11.31753675 1.02282E-18 0.073518514 0.104848858 0.073518514 0.104848858GOVT WARG -0.099305956 0.189163712 -0.524973604 0.600952306 -0.475350665 0.276738753 -0.475350665 0.276738753WAEG -0.434312968 0.166425598 -2.609652445 0.010688874 -0.765155839 -0.103470098 -0.765155839 -0.103470098WASG 0.275951627 0.21303503 1.295334516 0.198671209 -0.147547652 0.699450906 -0.147547652 0.699450906% D # Apts -0.020049954 0.003981794 -5.035407066 2.59545E-06 -0.027965492 -0.012134416 -0.027965492 -0.012134416



Cap Rate v. WARG (Govt), WAEG, WASG, % Change in # of Apts, % in MSA

Rearon StatirtirMultiple R 0.586691167

R Square 0.344206525


Standard Error 0.012179629

Observations 91

ANOVA

df SS MS F SiROWran FRegression 5 0.006618187 0,001323637 8.92279529 7.65312E-07Residual 85 0.012609185 0.000148343Total 90 0.019227372

Ceeffwi-rt StandardEror i Stat P-vdale Lower95% Upper95% Lower95 0% Upper 95. 0%Intercept 0.096336901 0.00843602 11.4197096 7.55544E-19 0.079563823 0.113109979 0.079563823 0.113109979GOVT WARG -0.037748543 0.187745505 -0.201062301 0.841130209 -0.41103713 0.335540043 -0.41103713 0.335540043WAEG -0.343204613 0.168781338 -2.033421492 0.045128797 -0.678787334 -0.007621893 -0.678787334 -0.007621893WASG 0.552049486 0.246412388 2,240347943 0.027676696 0.062115348 1.041983624 0.062115348 1.041983624% D # Apts -0.020808076 0.003920495 -5.307512875 8.72448E-07 -0.028603074 -0.013013078 -0.028603074 -0.013013078% Total in MSA -0.021662126 0.010256914 -2.111953531 0.037625506 -0.042055631 -0.001268621 -0.042055631 -0.001268621

Cap Rate v. WARG (NREI), WAEG, WASG, % Change in # of Apts

Rctrnion StairMultiple R 0.653072652R Square 0.426503889Adjusted R Square 0.399829651


ANOVA

df SS MS F SW7ificanc FRegression 4 0.008200549 0.002050137 15.9893562 8.00312E-10Residual 86 0.011026823 0.000128219Total 90 0.019227372

Ceffsdert Standanilrd r tStat P-vasc Lower95% Upper95% Lower 95,0% Upper 95.0%Intercept 0.113971771 0.008209382 13.88311188 1.12506E-23 0.097652072 0.130291469 0.097652072 0.130291469NREI WARG -0.469866875 0.11126541 -4.22293753 5.96653E-05 -0.691055009 -0.24867874 -0.691055009 -0.24867874WAEG -0.490308795 0.14441773 -3.395073408 0.001040101 -0.777401508 -0.203216083 -0.777401508 -0.203216083WASG -0.111049603 0.21295923 -0.52145945 0.603387072 -0.534398197 0.312298991 -0.534398197 0.312298991% D # Apts -0.019382296 0.003618349 -5.356668746 6.97873E-07 -0.026575329 -0.012189262 -0.026575329 -0.012189262

Cap Rate v. WARG (NREI), WAEG, WASG, % Change in # of Apts, % in MSA

RP.rion StatirtzrMultiple R 0.663625179

R Square 0.440398379


ANOVA

df SS MS F Sienifanc FRegression 5 0.008467703 0.001693541 13.37875402 1.28032E-09Residual 85 0.010759668 0.000126584Total 90 0.019227372

Coefsacentr Standard Errvr Stat P-vawe Lower95%5 Upper 95% Lwer95.0% Upper 95.0%Intercept 0.117645403 0.008539868 13.77602096 2.27842E-23 0.100665847 0.134624959 0.100665847 0.134624959NREI WARG -0.433806455 0.113306268 -3.828618329 0.00024567 -0.659089808 -0.208523103 -0.659089808 -0.208523103WAEG -0.418688651 0.15172692 -2.759488241 0.007088851 -0.720362598 -0.117014704 -0.720362598 -0.117014704WASG 0.088250361 0.252178549 0.349951893 0.727240439 -0.413148458 0.589649179 -0.413148458 0.589649179% D # Apts -0.019978372 0.003618549 -5.521100107 3.59398E-07 -0.027173022 -0.012783723 -0.027173022 -0.012783723% Total in MSA -0.01393625 0.009593004 -1.452751349 0.149974549 -0.033009723 0.005137224 -0.033009723 0.005137224


Appendix 14Cross-Section Panel Data Results

Average % Change in FFO per Unit v. Average WARG (Govt)

Resrar/aon Smtsrfe#Multiple R 0.270408318RSquare 0.073120659Adjusted R Square 0.018598345Standard Error 0.147623186Observations 19

ANOVA

df SS MS F Sipnoanc FRegression 1 0.029226375 0.029226375 1.341114364 0.262848895Residual 17 0.370474284 0.021792605Total 18 0.399700659

Co ffinent StondardE-rrr IStat P-value LUpper 95% Lai 95.0% Upper 95.0%Intercept -0.20125835 0.270816791 -0.743153146 0.46753559 -0.772632632 0.370115932 -0.772632632 0.370115932Govt WARG Average 9.286894726 8.019321326 1.158064922 0.262848895 -7.632417961 26.20620741 -7.632417961 26.20620741

Average % Change in FFO per Unit v. Average WARG (Govt), % Change in # of Apts

Ruerr.ion StatiuigrMultiple R 0.362853286R Square 0.131662507Adjusted R Square 0.023120321Standard Error 0.147282693Observations 19

ANOVA

lf SS MS F SiniinreF,, Regression 2 0.052625591 0.026312795 1.213007691 0.323227591Residual 16 0.347075068 0.021692192Total 18 0.399700659

Caffianst Standard Error t Stt P-vale Lawar 95% Upper 95% Lowr 95, 0% Upper 95 0%Intercept -0.210638815 0.270343066 -0.779153754 0.447260935 -0.783740384 0.362462754 -0.783740384 0.362462754Govt WARG Average 10.70474952 8.116455692 1.318894592 0.205770501 -6.50136403 27.91086307 -6.50136403 27.91086307% D # ofApts -0.155548667 0.149767425 -1.038601464 0.314431946 -0.473041352 0.161944019 -0.473041352 0.161944019

Average % Change in FFO per Unit v. Average WARG (NREI)

RArarion StatirtirMultiple R 0.513385622R Square 0.263564797Adjusted R Square 0.22024508Standard Error 0.131586169Observations 19

ANOVA

dl SS MS F SignifaneFRegression 1 0.105347023 0.105347023 6.084176231 0.024567585Residual 17 0.294353636 0.01731492Total 18 0.399700659

Coaeentzr SoandSardError Sat P-valu La 95% Upper 95% Lwr 95.0% Upper 95.0%Intercept -0.210967524 0.133542346 -1.579779968 0.132582792 -0.49271764 0.070782593 -0.49271764 0.070782593NREI WARG Average 8.240655189 3.340879795 2.466612298 0.024567585 1.192005111 15.28930527 1.192005111 15.28930527



Average % Change in FFO per Unit v. Average WARG (NREI), % Change in # of Apts

Rererion StatirtarMultiple R 0.576240525RSquare 0.332053143Adjusted RSquare 0.248559785Standard Error 0.129174978Observations 19

ANOVA

df SS MS F SinifnneFRegression 2 0.13272186 0.06636093 3.977000733 0.039621899Residual 16 0.266978799 0.016686175Total 18 0.399700659

Cffodirienrs StandrEr-r tStot P-cvl Loer 95/. Upper 95% Loer 95.0% Upper 95.0%Intercept -0.191331416 0.131988661 -1.449604953 0.166486883 -0.471134815 0.088471982 -0.471134815 0.088471982NREI WARG Average 8.78919347 3.30750466 2.657348779 0.017209189 1.777598398 15.80078854 1.777598398 15.80078854% D # ofApts -0.16725581 0.130582157 -1.280847346 0.218502764 -0.444077554 0.109565935 -0.444077554 0.109565935

Average % Change in FFO per Unit v. Average WAEG, WASG

IRrernior StatirtarMultiple R 0.173441244RSquare 0.030081865Adjusted R Square -0.091157902Standard Error 0.155659267Observations 19

ANOVA

If SS MS F Si fnwae FRegression 2 0.012023741 0.006011871 0.248118798 0.78321435Residual 16 0.387676918 0.024229807Total 18 0.399700659

Cosie-r St-anrdError tStat P-value Lonr 95/t Uppr 95% L-r 95.0% Upper 95.0%Intercept 0.059817946 0.160522648 0.3726449 0.714302968 -0.28047479 0.400110682 -0.28047479 0.400110682WAEG Average 3.629329955 5.352729219 0.678033543 0.507438337 -7.71794652 14.97660643 -7.71794652 14.97660643WASG Average -3.049901723 6.245320129 -0.488349942 0.631927312 -16.28938597 10.18958252 -16.28938597 10.18958252

Average % Change in FFO per Unit v. Average WAEG, WASG, % Change in # of Apts

Rgrrion StatrticrMultiple R 0.26760386R Square 0.071611826Adjusted R Square -0.114065809Standard Error 0.157284745Observations 19

ANOVA

d SS MS F Sienifxan FRegression 3 0.028623294 0.009541098 0.385678253 0.764898055Residual 15 0.371077365 0.024738491Total 18 0.399700659

Coeffriaer SlmdardError St P-vale LoaWr 95% Upper 9% Lor 95, 0% Upper95.0%Intercept 0.111902631 0.1742166 0.642318996 0.53036148 -0.259431489 0.483236752 -0.259431489 0.483236752WAEG Average 3.571548514 5.409085267 0.660286969 0.519081377 -7.957650907 15.10074794 -7.957650907 15.10074794WASG Average -3.913123631 6.397920524 -0.611624295 0.549944687 -17.54997681 9.723729545 -17.54997681 9.723729545% D # of Apts -0.131623637 0.160683876 -0.819146517 0.425526819 -0.474113422 0.210866148 -0.474113422 0.210866148



Average % Change in FFO per Unit v. Average WAED

Rerrien StlbrtiarMultiple R 0.171808896R Square 0.029518297Adjusted R Square -0.027568862Standard Error 0.151055536Observations 19

ANOVA

df SS MS F Sipifirac FRegression 1 0.011798483 0.011798483 0.517074196 0.481860831Residual 17 0.387902177 0.022817775Total 18 0.399700659

CaJiaWrtr Stard Error I Stl P-vale Lr 95% UPPtr 95% Laer 95.0%/ Upper 95.0%Intercept 0.074072327 0.060694575 1.220411013 0.238968074 -0.053982211 0.202126865 -0.053982211 0.202126865WAED Average 3.422665778 4.759792934 0.719078713 0.481860831 -6.619633525 13.46496508 -6.619633525 13.46496508

Average % Change in FFO per Unit v. Average WAED, % Change in # of Apts

Raresion Statir.rMultiple R 0.267249826RSquare 0.07142247Adjusted R Square -0.044649722Standard Error 0.152305829Observations 19

ANOVA

Idf 55 MS F Siifaa FRegression 2 0.028547608 0.014273804 0.615328003 0.552771144Residual 16 0.371153051 0.023197066Total 18 0.399700659

CoaSffitr StandardError tStat P-vale Loaer 95% Uppr 95% Ler 95.0% Uppr95.0%/oIntrcept 0.103137841 0.070107746 1.471133324 0.160648767 -0.045483907 0.25175959 -0.045483907 0.25175959WAED Average 3.69004969 4.809494928 0.767242662 0.454110955 -6.505621793 13.88572117 -6.505621793 13.88572117%D # of Apt. -0.130005457 0.152996692 -0.849727242 0.408012163 -0.454343883 0.194332968 -0.454343883 0.194332968

Average % Change in NOI per Unit v. Average WARG (Govt)

Rercan StatirtaaMultiple R 0.283767761R Square 0.080524142Adjusted R Square 0.026437327Standard Error 0.124522762Observations 19

ANOVA

df SS MS P Signf-anm FRegression 1 0.023085123 0.023085123 1.488794306 0.239060181Residual 17 0.26360061 0.015505918Total 18 0.286685733

Coefaier StandardError It Stat P-lr L r 95% Upper 95% Lmer 95. 0% Uppr 95. 0%Intercept -0.147821514 0.228438741 -0.647094765 0.5262126 -0.629785802 0.334142775 -0.629785802 0.334142775Govt WARG Average 8.253708694 6.764439039 1.22016159 0.239060181 -6.018030097 22.52544749 -6.018030097 22.52544749



Average % Change in NOI per Unit v. Average WARG (NREI)

Rgrrion StairtrcMultiple R 0.538951447R Square 0.290468663Adjusted R Squre 0.248731525Standard Error 0.109386662Observations 19

ANOVA

df SS MS F SisnifaceFRegression 1 0.083273221 0.083273221 6.959477341 0.017262697Residual 17 0.203412511 0.011965442Total 18 0.286685733

cGausr1 StAndardError I Slat P-vae Loer 95% Uppr 95%/ Lwer 95.0% Upper 95.0%Intercept -0.15655753 0.111012819 -1.410265334 0.176496367 -0.390774432 0.077659372 -0.390774432 0.077659372NREI WARG Average 7.326613651 2.777250018 2.638082133 0.017262697 1.467120117 13.18610718 1.467120117 13.18610718

Average % Change in NOI per Unit v. Average WAEG, WASG

Rerrn SaIt/drtMultiple R 0.235408377R Square 0.055417104Adjusted R Square -0.062655758Standard Error 0.130095743Observations 19

ANOVA

df SS MS F Siniran FRegression 2 0.015887293 0.007943647 0.469346665 0.633753862Residual 16 0.27079844 0.016924902Total 18 0.286685733

Cfw-nr StandardErrr I Slat P-vala, Le 9ow5 Upper 9595 Ler 95.0% Upper 95.0%Intercept 0.119838155 0.134160424 0.893245203 0.38496456 -0.164569175 0.404245485 -0.164569175 0.404245485WAEG Average 3.380864725 4.473664185 0.755726086 0.46079538 -6.102877546 12864607 -6.102877546 12.864607WASG Average -4.651235809 5.219667171 -0.891098159 0.386080848 -15.71643341 6.413961789 -15.71643341 6.413961789

Average % Change in NOI per Unit v. Average WAED

Rsiruion StatintsaMultiple R 0.227244775R Square 0.051640188Adjusted R Square -0.004145683Standard Error 0.126463486Observations 19

ANOVA

df SS MS F Sini/can FRegression 1 0.014804505 0.014804505 0.925685783 0.349473155Residual 17 0.271881227 0.015993013Total 18 0.286685733

Cosicir StandrdErrvr tSeat P-va Lower 95% Upper 95% Lower 95.0% Upper 95.0%Intercept 0.088586048 0.050813414 1.743359496 0.099326585 -0.018621034 0.19579313 -0.018621034 0.19579313WAED Average 3.833966903 3.984892065 0.962125659 0.349473155 -4.573432193 12.241366 -4.573432193 12.241366



Average % Change in Rental Revenue per Unit v. Average WARG (Govt)

Rorernso StatuisaMultiple R 0.301797926RSquare 0.091081988Adjusted R Square 0.037616223Standard Error 0.122732905Observations 19

ANOVAdf SS MS F S gniarn F


tContir Sled.rd Error tStmt P-vale, Lower 95% Upper 95% Lo r95.0% Upper 95.0%/Intercept -0.17702669 0.225155224 -0.786242872 0.442547627 -0.652063351 0.298009972 -0.652063351 0.298009972Govt WARG Average 8.702066787 6.667208789 1.305203881 0.209213911 -5.364533821 22.7686674 -5.364533821 22.7686674

Average % Change in Rental Revenue per Unit v. Average WARG (NREI)

Rewtr/on StaarrMultiple K 0.608622001RSquare 0.37042074Adjusted R Square 0.333386666Standard Error 0.102146588Observations 19

ANOVA

df Ss MS F SinifismreP FRegression 1 0.104361793 0.104361793 10.00216015 0.005686736Residual 17 0.177376732 0.010433925Total 18 0.281738526

Coof-ieotr StandardError tSt P-vle. Lower 95% Upper95/ Lower95.0% Upper95.0%Interept -0.204826822 0.103665113 -1.97585105 0.064632747 -0.423541398 0.013887753 -0.423541398 0.013887753NREI WARG Average 8.202030397 2.593429655 3.162619191 0.005686736 2.73036447 13.67369632 2.73036447 13.67369632

Average % Change in Rental Revenue per Unit v. Average WAEG, WASG

Ratorusion StatitticrMultiple R 0.255236092RSquare 0.065145463Adjusted RSquare -0.051711354Standard Error 0.128302508Observations 19

ANOVAdf SS MS F Sipyfrec F


Co~ffesos St-ndardError Stat P-volvo Lower95% Upper 95% Lowr95.0%/ Upper 95.0%Intercept 0.151724899 0.132311162 1.146727886 0.268354209 -0.128762171 0.432211968 -0.128762171 0.432211968WAEG Average 2.42714929 4.41199936 0.550124579 0.589831963 -6.925869421 11.780168 -6.925869421 11.780168WASG Average -5,426829758 5.147719469 -1.054220183 0.307442501 -16.33950507 5.485845558 -16.33950507 5.485845558



Average % Change in Rental Revenue per Unit v. Average WAED

Rrana StautriaMultiple R 0.209086703RSquare 0.043717249Adjusted R Square -0.012534677Standard Error 0.12589017Observations 19

ANOVA

df SS MS F Signfrasnr FRegression 1 0.012316833 0.012316833 0.777168927 0.390302876Residual 17 0.269421692 0.015848335Total 18 0.281738526

Coeff-eatrE S-ndrd Eor Stat P-velo Lower 95% UPPr 95 Lar 95 0/% Upper 95, 0%Intercept 0.077930448 0.050583054 1.5406434 0.141810769 -0.028790616 0.184651511 -0.028790616 0.184651511WAED Average 3.497042783 3.966826733 0.88157185 0.390302876 -4.872241742 11.86632731 -4.872241742 11.86632731

Average % Change in N.I. per Unit v. Average WARG (Govt)

Rgrraion StatlrtimMultiple R 0.234998443R. Square 0.055224268Adjusted R Square -0.000350775Standard Error 0.326334636Observations 19

ANOVA

df SS MS F S ificranc FRegression 1 0.105822131 0.105822131 0.993688264 0.332820877Residual 17 1.810403009 0.106494295Total 18 1.91622514

Coi SanrError t tdard Error iSat P-vae Lwr 95%/ Upper 95%/ Lowr95.0/ Upper95.0%Intercept 0.798859056 0.59871335 1.334293039 0.199706708 -0.464317461 2.062035573 -0.464317461 2.062035573GOVT WARG Average -17.67738152 17.73343449 -0.996839137 0.332820877 -55.0917101 19.73694705 -55.0917101 19.73694705

Average % Change in N.I. pet Unit v. Average WARG (NREI)

Rgrn.on Statirtr

Multiple R 0.033839653RSquare 0.001145122Adjusted R Square -0.057611047Standard Error 0.335544406Observations 19

ANOVA

df SS MS F SiifmaneFRegression 1 0.002194312 0.002194312 0.019489394 0.890613153Residual 17 1.914030828 0.112590049Total 18 1.91622514

Coeffimentr StandardError tStat P-vae Lower 95/ Uppfr 95% Ler 95.0% Uper 95.0%Intercept 0.159774465 0.344991893 0.463125275 0.649148257 -0.568095821 0.887644751 -0.568095821 0.887644751NREI WARG Average 1.203963559 8.624107824 0.139604419 0.890613153 -16.99133889 19.399266 -16.99133889 19.399266



Average % Change in N.I. per Unit v. Average WAEG, WASG

Re-rerr/on StatcrMultiple R 0.279037984R Square 0.077862197Adjusted R Square -0.037405029Stndard Error 0.332323604Observations 19

ANOVA

df SS MS F Sinifane FRegression 2 0.149201499 0.074600749 0.675492937 0.522837358Residual 16 1.767023641 0.110438978Total 18 1.91622514

Coesffimatr SdnadardError tStat P-valuP Loer 95%/o Upper95% r95.0% Upper95.0%Intercept -0.004145431 0.336323909 -0.012325711 0.990318138 -0.717120107 0.708829245 -0.717120107 0.708829245WAEG Average -3.290064574 11.20083517 -0.293733862 0.772737315 -27.03476905 20.4546399 -27.03476905 20.4546399WASG Average 15.04294674 13.34829145 1.126956719 0.276379146 -13.25416065 43.34005414 -13.25416065 43.34005414

Average % Change in N.I. per Unit v. Average WAED

Rrerrion StatiriaMultiple R 0.167225451R Square 0.027964352Adjusted R Square -0.029214216Standard Error 0.331009076Observations 19

ANOVA

df SS MS F Sigifan FRegression 1 0.053585994 0.053585994 0.489070517 0.493800694Residual 17 1.862639146 0.109567009Total 18 1.91622514

Cewffieir Stad-rdError tSta P-ahlue Lover 95%/. Uppr 95% Ler 95.0% Upper 95.0%Intercept 0.283076383 0.132992694 2.128510786 0.048217367 0.002485934 0.563666832 0.002485934 0.563666832WAED Average -7.224761778 10.33089126 -0.699335769 0.493800694 -29.02106752 14.57154397 -29.02106752 14.57154397



Average Cap Rate v. Average WARG (Govt)

Renrio$n StatrtirMultiple R 0.355632017RK Square 0.126474131Adjusted RSquare 0.075090257Standard Error 0.010892044Observations 19

ANOVA

df SS MS F Signifcans FRegression 1 0.000292007 0.000292007 2.461358401 0.135102536Residual 17 0.002016823 0.000118637Total 18 0.00230883

Coosdaotrl StaonrdError t Stat P-vota Lawr 95% Upper 95% l. -r 95.0% Upper 95.0%Intercept 0.105971853 0.020208369 5.243958791 6.59863E-05 0.063335862 0.148607843 0.063335862 0.148607843GOVT WARG Average -0.941081951 0.599846344 -1.568871697 0.135102536 -2206648879 0.324484977 -2.206648879 0.324484977

Average Cap Rate v. Average WARG (NREI)

Rorrion SthrstriaMultiple R 0.386267765R Square 0.149202786Adjusted R Square 0.099155891Standard Error 0.010749408Observations 19

ANOVA

df SS MS F Sinfian FRegression 1 0.000344484 0.000344484 2.981259601 0.102358805Residual 17 0.001964346 0.00011555Total 18 0.00230883

Coffaitr StandardError tStoa P-Nvso Lwer 95% Upper95% Lwer95.0o Uppr95.0%Intercept 0.09299432 0.010985321 8.465325746 1.67624E-07 0.069817286 0.116171353 0.069817286 0.116171353NREI WARG Average -0.475919969 0.275634789 -1.726632445 0.102358805 -1.057459352 0.105619414 -1.057459352 0.105619414

Average Cap Rate v. Average WAEG, WASG

R4rrerrian StatirticrMultiple R 0.241675076RSquare 0058406843Adjusted R Square -0.059292302Standard Error 0.011656485Observations 19

ANOVA

sdf SS MS F SinfcnrsceFRegression 2 0.000134851 6.74257E-05 0.496238462 0.617883148Residual 16 0.002173978 0.000135874Total 18 0.00230883

Coeffri Standard.Error tStat P-vale Loler 95% Uppr 95% Lower 95.0% Uper95.0%Intercept 0.079440532 0.011800826 6.731777257 4.81842E-06 0.054423904 0.104457161 0.054423904 0.104457161WAEG Average -0.375495616 0.392403034 -0.956913131 0.352847695 -1.207352699 0.456361467 -1.207352699 0.456361467WASG Averaage 0.327664464 0.466498037 0.702391946 0.492527778 -0.661266973 1.316595902 -0.661266973 1.316595902



Average Cap Rate v. Average WAED

R mrrnon StarMultiple R 0.240248842R Square 0.057719506Adjusted R Square 0.002291242Standard Error 0,011312579Observations 19

ANOVAdf Ss s MF Simfaim F


caffi-etr Sadar.d Brew tSt P-v Low 95% Upper 95%/ Lonr 95 0% Uppr 95.0%/Intercept 0.078267986 0.004504596 17.37513814 Z94276E-12 0.068764105 0.087771867 0.068764105 0.087771867WAED Average -0.359349973 0.352145346 -1.020459242 0.321814175 -1.102312748 0.383612801 -1.102312748 0.383612801

Average % Change in Market Cap per Unit v. Average WARG (Govt)

Rarcrion StatirtaicMultiple R 0.3563642R Square 0.126995443Adjusted R Square 0.075642234Standard Error 0.153610234Observations 19

ANOVAdf s s F Si.pifwan.e F


Caeffinssnt Standard Error I Sta P-va4e Lowr 95 Upper 95% Loer 95.0% Upper 950%C-Oid-~~~~~~~~~~~~~~~~~~~~pe 9A U ppr 95, 0%Intercept -0.322095968 0.284998132 -1.130168698 0.274099255 -0.923390306 0.279198371 -0.923390306 0.279198371GOVT WARG Average 13.3033509 8.459618382 1.572571043 0.134243526 -4.544908668 31.15161047 -4.544908668 31.15161047

Average % Change in Market Cap per Unit v. Average WARG (NREI)

R orersion StatuittMultiple R 0.77175068R Square 0.595599111Adjusted R Square 0.571810824Standard Error 0.104548533Observations 19

ANOVA

If Ss MS F Sipsisn FRegression 1 0.273669722 0.273669722 25.03749417 0.000108722Residual 17 0.185816729 0.010930396Total 18 0.459486451

Cafli-m# Standard Error t Sta P-vae Lower 95% Upper 95% Low 95.0% Uper 95.0%/Intercept -0.398326012 0.10684302 -3.72814258 0.001672446 -0.623745395 -0.172906629 -0.623745395 -0.172906629NRBI WARG Average 13.41414104 2.680818659 5.003748012 0.000108722 7.758100169 19.0701819 7.758100169 19.0701819



Average % Change in Market Cap per Unit v. Average WAEG, WASG

Rgrarion SatiriaMultiple R 0.297199433RSquare 0.088327503Adjusted R Square -0.025631559Standard Error 0.161806435Observations 19

ANOVA

d/ SS MS F Significane FRegression 2 0.040585291 0.020292645 0.775080991 0.477211419Residual 16 0.41890116 0.026181322Total 18 0.459486451

Coesimtr StadrdError tSat P-valr Lamr 95% Upper95% Lo. 95.0% Upper95.0%/%Intercept 0.207047354 0.163810062 1.263947717 0.224353623 -0.140214386 0.554309094 -0.140214386 0.554309094WAEG Average 2.672184248 5.447039472 0.490575525 0.630386542 -8.875020986 14.21938948 -8.875020986 14.21938948WASG Average -8.02561996 6.475569766 -1.239368928 0.233081136 -21.75321152 5.701971602 -21.75321152 5.701971602

Average % Change in Market Cap per Unit v. Average WAED

Reerrion StairtiaMultiple R 0.212280385R Square 0.045062962Adjusted RSquare -0.011109805Standard Error 0.160656856Observations 19

ANOVA

sdf SS MS F Sinifan FRegression 1 0.02070582 0.02070582 0.802220799 0.382937659Residual 17 0.43878063 0.025810625Total 18 0.459486451

Coeffsiatr Standard Error t Stat P-vaL La- r 95% Upr 95% LIcr 95,0/ Uppr 95.0/Intercept 0.075811703 0.063972533 1.185066462 0.252293758 -0.059158732 0.210782139 -0.059158732 0.210782139WABD Average 4.479263043 5.001031707 0.895667795 0.382937659 -6.072006293 15.03053238 -6.072006293 15.03053238


Appendix 15% of Portfolio in MSA with Rent Data (Govt and/or NREI)

REIT Tnkr.. YuI Asodnted Eset' Reiy Coportidon AEC 1996

1997199819992000

2 Apartment Inrtment & Mgmt Co. AIV 19961997199819992000

3 Ali Reidentil Ptoprties Trust AML 19961997199819992000

4 Achstone Communitie Tust ASN 1996199719919992000

5 AvalonBy Communities Inc AVB 19961997199819992000

6 BRE Properdi , In. BRE 19961997199819992000

7 Cmden Pp.t Tru st CPT 19961997199819992000

B Equiy Rnsidntil Prpeties Tust EQR 19961997199819992000

9 EtProperqTrut, Inc ESS 19961997199819992000

10 Gable ResidenEil Tust GBP 1996199719981992000

11 Home Propcrtie oFNe York, Inc. HME 19961997199819992000

12 MiAmuic Ap tment Communities. Inc MAA 19961997199819992000

13 Pt Propneti, lne PPS 19961997199819992000

14 Robcrts Realt Instor Inc. RPI 19961997199819992000

15 Summit P:pdi Inc. SMT 19961997199819992000

16 Chles E. Smith Ridentil Relty SRW 19961997199819992000

17 Cornston Rlty IncomeTrust TCR 19961997199819992000

18 Ton nd Couny Tst, The TCT 19961997199819992000

19 United Dominion Rly Trut, Inc. UDR 19961997199819992000

% Toto i MSAw/Rent BD

72.79%71.27%76.18%70.65%71.25%74.92%77.73%68.70%67.76%65.69%95.60%A95.90%i96.47%%96.72%98.58%.80.166%81.86%84.83%89.73%92.27%78.72%84.42%81.14%81.92%82.17'98.65%.16%928%96.53%96.96%89.84%91.19'h91.38%91.61%.91.00%78.62%7624%77.84%B0.12%h82.11%8621%88.64%87.55%A87.36%88.89%96.86%9726%98.01%97,79%100.00%8.66%39.14%4521%56.75S%60.65%5821%N.76%52.76%

5121%51.08%95.81%9627%A96.61%97.20'100.00%88.72%1000n100.00%100.00%100.09%A

7e.6s%80.88%82.44%92.06'%94.48%

100.00'100.00%10.9O100.00%51.9%61.18%62.58%64.01%7127%76.77%76.77%78.56%75.3P/81.53%61.82'%63.58%63.84%69.6070.52'%


# REIT Ticker3 Amli Residential Properties Trust AML4 Archstone Communities Trust ASN6 BRE Properties, Inc. BRE7 Camden Property Trust CPT9 Essex Property Trust, Inc. ESS10 Gables Residential Trust GBP13 Post Properties, Inc. PPS14 Roberts Realty Investors, Inc. RPI15 Summit Properties Inc. SMT16 Charles E. Smith Residential Realty SRW

* 10 Apartment REITs with the greatest percent portfolio concentration in MSA with Govt Rent Data


Appendix 1610-REIT Index

5 YearAverage

Govt MSA*96.65%85.77%96.91%91.00%87.73%97.99%97.18%97.74%85.71%

100.00%

145

Appendix 1710-REIT Data Results

% Change in FFO per Unit (10 REITs) v. WARG (Govt), % Change in # of Apts

Regnssion StarstarsMultiple R 0.654180838R Square 0.427952569


Observations 49

ANOVA

df SS MS F Sgnfianae FRegression 2 1.980704533 0.990352267 17.20645625 2.63595E-06Residual 46 2.647622706 0.057557015Total 48 4.628327239

Coeffentr Standard Enrr tStat P-valre LoAger 95% Upper 95% L Law 95.0% Upper95.0%Intercept 0.402014989 0.15673632 2.564912766 0.013647723 0.086521441 0.717508537 0.086521441 0.717508537Govt WARG -3.903298981 4.464599183 -0.874277582 0.386508899 -12.89006243 5.083464472 -12.89006243' 5.083464472%D # of Apts -0.909623826 0.160902375 -5.653265388 9.5519E-07 -1.233503198 -0.585744454 -1.233503198 -0.585744454

Average % Change in FFO per Unit (10 REITs) v. Average WARG (Govt), % Change in # of Apts

Rf,mssion Statirfcs

Multiple R 0.680057482R Square 0.462478178Adjusted R Square 0.308900515Standard Error 0.119307024Observations 10

ANOVA

df SS MS F Sinfwanm F


Cefzieant Standard Etnr t Stat P-Vale Loaer95% Upper 95%/ Loar 95.0% Upper 95.0%/o

Intercept 0.003049407 0.307369022 0.009920995 0.992361147 -0.723762316 0.729861129 -0.723762316 0.729861129ovt WARG Average 9.755105589 9.140374128 1.067254519 0.321275692 -11.85842928 31.36864046 -11.85842928 31.36864046% D # of Apts Average -1.333045114 0.553766607 -2.407232751 0.046964489 -2.642494126 -0.023596101 -2.642494126 -0.023596101


by Andrew A. Friestedt B.S., Construction Management, 1996 ...

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