Electronic copy available at: http://ssrn.com/abstract=1717030

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ARE CLEANUPS A WASTE? HOUSING MARKET RESPONSES TO BROWNFIELD CLEANUPS

3 DECEMBER 2010

PRELIMINARY DRAFT. PLEASE DO NOT CITE WITHOUT PERMISSION. ALL COMMENTS ARE WELCOME.

Douglas S. Noonan Associate Professor Georgia Institute of Technology douglas.noonan@pubpolicy.gatech.edu Douglas J. Krupka Institute for Research on Labor, Employment and the Economy (IRLEE) and Ford School of Public Policy, University of Michigan, and IZA * This material is based upon work supported by the National Science Foundation under Grant No. 0433165. "This document contains demographic data from GeoLytics, E. Brunswick, NJ."

Electronic copy available at: http://ssrn.com/abstract=1717030

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Abstract: Cleaning up brownfields such as Superfund sites is expected to catalyze neighborhood change in the vicinity. Several previous studies have estimated changes in housing prices associated with Superfund sites, with mixed results. This paper provides new estimates of price effects associated with cleanups of these brownfields and assesses alternative explanations for previous results. Issues such as stigma, sorting and Tiebout bias, and housing supply conditions motivate the analysis. Prominent models of housing market responses to brownfield cleanups (e.g., Greenstone and Gallagher 2008) raise important issues of how timing and location are handled in the research design. This analysis shows the sensitivity of results to alternative specifications using different (and arguably more robust) definitions of neighborhoods and different milestones in the cleanup process. Housing price effects are estimated using first-differenced models with Census data aggregated at the block-group level for all metro areas in the nation (from 1990 to 2000). The remediation treatment variable is defined based on three different phases in Superfund cleanup process, and proximity is defined based on alternative spatial proximity measures. An instrumental variables estimator, where lagged geographic and political factors instrument for Superfund site cleanups is used. The results show substantial policy endogeneity and significant variation across alternative proximity definitions, as expected. The variation in results by cleanup phase offer insights into the timing of market responses to amenity improvements. The initial results show a generally positive causal impact of progress towards cleaning up Superfund sites, although completion of the high-visibility “construction” phase attenuates some of that positive effect. The presence of significant indirect price effects would suggest that Superfund site cleanups can trigger changes in other aspects of the neighborhood – transforming the composition of the housing stock and neighbor mix in the area. Our results point to a small indirect effect of improved environmental quality, indicating that these brownfield cleanups catalyze minimal neighborhood change. Thus, policy endogeneity, timing, and spatial definitions matter greatly to identifying price impacts of brownfield cleanups. Transforming neighborhoods more generally, however, does not appear to play an important role in brownfield redevelopment.

Electronic copy available at: http://ssrn.com/abstract=1717030

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I. Introduction

This paper assesses the impacts of a major environmental and urban initiative to clean up

the roughly 1,500 worst derelict hazardous waste sites in the nation. This list of

“Superfund” sites contains both government and private facilities, includes urban and

rural sites, and covers a diversity of chemical contaminants and risk pathways. After 25

years, the program incurred costs exceeding $30 billion, and arguably the job is not even

halfway done.

As the cleanup progresses, one might reasonably expect to observe several things. We

might expect to see the quality of life nearby the site to be noticeably enhanced due to

improved environmental quality. We might expect the new “greenness” of the area to

attract newcomers, at least at the margins. These newcomers might push up prices for

housing in the area as they buy their way into the area. After the clean-up, we might

expect to see some residents priced out of the area, along with some former land uses.

The shift in prices and influx of newcomers and new investment might spur additional

adjustments in local land and labor markets. The simple shock to environmental quality

from remediating the longstanding hazardous waste site might be expected to ripple out

and affect several connected markets and systems.

This article examines a program such as this and seeks to identify its impacts on price

through many of these other dimensions. Specifically, our estimates of the price effects

of the Superfund are sensitive to (1) the spatial extent of the treatment, (2) the progress of

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the clean-up process, (3) the heterogeneous effects of clean-ups that an average treatment

effect might mask, and (4) the direct and indirect effects the remediation on other

neighborhood characteristics. In measuring the impacts of the Superfund program, we

estimate how clean-ups affect the equilibrium distributions of prices when the housing

stock and mix of local residents are held constant and when those other price

determinants are allowed to vary with the clean-up. This allows us to identify the direct

effects of environmental quality improvements on land values from the indirect ones. In

addition to addressing policy endogeneity, the special care paid to the remediation

process and its spatial impact in a national-scale analysis extends the literature in

important ways. Cleanups occur over time and following a particular sequence of events

– from site discovery to the cleanup conclusion – and the price impacts of sites may vary

over that process. Measuring spatial impact and policy endogeneity prove crucial to

consistent estimates of price impacts.

II. Policy background

The Comprehensive Environmental Response, Compensation and Liability Act

(CERCLA) was enacted in 1980 to address what was perceived as a growing problem of

a nation’s toxic legacy of industrialization. High-profile crises, such as Love Canal, put

abandoned waste sites into the national spotlight, and Congress responded with CERCLA

as a policy mechanism for remediating these preexisting hazardous and toxic sites. To

finance these clean-ups, Congress established joint and several liability rules to help

regulators identify “potentially responsible parties” (PRPs) and extract payments from

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them. When such efforts failed -- sometimes because no PRPs were to be found -- the

EPA could tap the Superfund, a fund established from a tax on petroleum and chemical

industries.

The process through which a site is remediated was also standardized under the

Superfund program. To remediate these sites, Congress empowered EPA to establish a

National Priorities List (NPL). Greenstone and Gallagher (2005) offer a useful and quick

overview of the listing process. As sites received preliminary investigations, they

received scores under the Hazard Ranking System (HRS). The EPA then rank ordered

the nominated sites using the HRS scores. The worst ranked sites (plus one chosen by

each state) were then placed on the NPL. Over time, the list has grown and listing rules

have changed somewhat.

After listing on the NPL, several distinct steps follow in the timeline of a site. These

steps out detailed elsewhere (e.g., US EPA 2007, Hird 1994), but we offer a brief

summary. First, a remedial investigation/feasibility study is undertaken. Following the

remedial investigation, a remedial plan is proposed and subject to public comment.

Afterwards, the EPA makes its final “Record of Decision” (ROD) regarding the clean-up

plan for that site. The project is then implemented, which involves various remedial

action and construction efforts that depend on the site. Once the construction is

complete, the site is monitored for some time afterwards and this leads to eventual

deletion from the NPL. For our purposes, we take “deletion” from the NPL as the sign

that a clean-up is complete and environmental quality has been demonstrably improved.

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How clean is the deleted site? Risk standards vary widely across NPL sites. The

variation in clean-up standards has been examined by Gayer et al. (2000), Viscusi and

Hamilton (1999) and others. The Superfund program has received much criticism for

applying unrealistic and overly costly risk thresholds (see, e.g., Breyer 1993, Greenstone

and Gallagher 2005). These costs and high clean-up standards combine with sluggish

progress to make Superfund one of the more maligned federal policies. After 25 years,

1,240 sites remain on the NPL. Only 317 sites have been deleted from the NPL, despite

in excess of $30 billion having been spent to date. With the average site expected to cost

$43 million to remediate (Greenstone and Gallagher 2005), there is little doubt of the

substantial costs incurred by the program.

III. Literature

Assessing the benefits of Superfund projects has also attracted a sizable literature from

economists. Much of the economic research on Superfund clean-ups applies valuation

techniques such as hedonic price analysis to identify values associated with Superfund

activities. Kiel (2006) provides a recent summary of much of this hedonic research.

Many of these studies find values of the disamenity effects of proximity to one or a few

NPL sites (e.g., Kohlhase 1991, Michaels and Smith 1990). Other studies estimate values

associated with changes in the environmental conditions associated with those sites (e.g.,

Kiel 1995, Kiel and Zabel 2001, McCluskey and Rausser 2003, Mendelsohn et al. 1992)

or information about those sites (e.g., Gayer et al. 2000). In general, these studies serve

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as case studies of the price impacts of particular NPL sites. None of these earlier studies

examined price effects of the larger system of NPL sites.

Recent work by Kiel and Williams (2007) calls into question whether this handful of sites

previously examined are at all representative of other NPL sites. The meta-analysis

employed by Kiel and Williams helps move forward the literature on estimating price

impacts associated with Superfund sites by looking beyond a few case studies. The

analysis that follows carries on the spirit of this inquiry by estimating price effects of

deletions from, rather than just listings on, the NPL across the entire NPL system. The

heterogeneity in price effects found by Kiel and Williams – some sites even appear as

amenities rather than disamenities – is consistent with recent findings by Greenstone and

Gallagher (2008), who fail to find significant effects on land value, on average, for listing

of sites on the NPL. Vigdor (2009) offers a model to explain this variation in outcomes

based on the slackness in the housing markets. This leads to a series of model extensions

to test the theory and explain the effect heterogeneity. It also allows for non-price effects

of clean-ups, an issue of particular interest to Cameron and McConnaha’s (2006) recent

work and Noonan et al. (2007). Allowing for these richer impacts, across a large sample

of NPL sites, contributes to this “next wave” of research on environmental migration and

robust estimates of Superfund policy impacts.

IV. Empirical model

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Suppose that the supply and demand for housing are in equilibrium near a hazardous

waste site. As a disamenity, we might reasonably expect that buyers will pay a premium

to live at greater distances from the site. This should be reflected in a standard hedonic

price surface.

As a disamenity, we might also reasonably expect that relatively “insensitive” buyers will

tend to live closer to the site. This sorting over tastes or other characteristics is described

in various contexts by Parsons (1991), Hoyt and Rosenthal (1997), Ioannides (2004),

Bayer et al. (2006), Sieg et al. (2004), Kahn 2004, Blomquist, Berger and Hoehn (1988)

and others. In the context of hazardous waste sites, the “sensitive” residents are those

willing to pay more for distance, such as the wealthy (as environmental quality is a

normal good), those with high-risk household members (such as children), those who are

informed (rationally or otherwise) about more of the risks or who perceive higher risks

relative to other would-be residents, or those who simply have a stronger distaste for

proximity to that site than others. While the hedonic price surface captures the premium

paid for distance from the site, it does not also reveal the residents’ characteristics over

which they sort with respect to the site. These characteristics remain hidden in a hedonic

price analysis.

After remediating the site, the disamenity vanishes and we might expect several changes.

First, prices change to reflect the absence of the disamenity. Yet the prices, observed

through transactions, are unlikely to noticeably shift without some in-migration to the

cleaner area. When the previously discounted properties lose their discount and see

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prices bid up, the exchange that takes place reveals a new market price and also entails a

new resident coming in to take the place (literally) of an old resident.1 The ability for

prices to adjust that is essential to the hedonic framework actually also implies that

neighborhood composition is changing. The marginal consumer, whose marginal

willingness-to-pay (WTP) the implicit price is commonly thought to represent, is not the

same person or household before and after the clean-up.

The shift in price and neighborhood composition following the remediation might not end

there. Given new prices and new demanders, the suppliers may also adjust the attributes

of housing stock provided. The different types of residents attracted to the area might

express different demands for housing services, and for other local services for that

matter. Consequently, to equilibrate the housing market, we might expect prices,

neighbors, and housing stock to all shift in response to a remediation.

If we assume that a new equilibrium is reached after the remediation, these neighborhood

dynamics thus involve the simultaneous determination of prices, housing characteristics,

and neighborhood characteristics. While this complex system operates in the

background, it does nothing to undermine the existence and interpretation of the hedonic

price surface. The observed prices, P, can be described in the standard hedonic

framework:

(1) P = P(E, X, G),

1 Of course, for non-longitudinal studies of property values, other possibilities exist, such as vacancies and new construction. In general, there are many possibilities other than just location-swapping by residents.

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where E represents environmental quality, X represents the structural characteristics of

the housing and the characteristics of the property’s neighbors and neighborhood, and G

represents a variety of other geographic variables that affect price.

An empirical model might take the common semilog functional form:

(2)

Here, the i indexes observations or properties, while the t indexes the time of the

observation or sale. Pit is the log of the property value of unit i in period t. ε1it is a noise

term.

In such a model, βE represents the implicit price of environmental quality conditional on

the other attributes X and G. This interpretation as a conditional mean is crucial here.

The implicit prices from hedonic models is often taken to represent a change in economic

value (e.g., MWTP) associated with a change in environmental quality, ceteris paribus.

A βE estimate of 0.05 suggests here that prices on average are 5% higher, for given levels

of X and G, for a unit increase in E.

But what if the ceteris paribus restriction is inappropriate and changing E will also entail

adjustments in X? If βE represents the “direct” effects of E on price, then estimating the

full or total price effects of E might involve modeling how E influences the other

characteristics (X). A simpler alternative would be to simply estimate equation (2) in a

restricted form. In equation (3):

itiGitXitEtit GXEP 10 εββββ ++++=

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(3)

by restricting βX to be equal to zero, β'E now estimates the price effect of environmental

quality unconditional on levels of X. Insofar as the effects of E on prices operate through

induced changes in X, the two estimates of βE and β'E will differ. The former captures the

direct effect, the latter captures the full effect.

Complications

Before estimating (2) and (3) for the context of NPL remediations, several complications

must be addressed. First, estimating average price effects of clean-ups across the entire

Superfund program requires using nationwide data. Hedonic practitioners have

historically frowned on combining data from multiple housing markets in the same

analysis (see, Ekeland et al. 2002), and not just for reasons of practical data limitations.

Their concerns notwithstanding, our research question necessitates the use of data from

across many markets if we are to obtain average treatment effects of NPL deletions.

Similar problems face Greenstone and Gallagher (2008), Bayer et al. (2006), and Hanna

(2007) in their analyses. Yet they all include observations from markets spread out

across large regions.

To address some of these concerns, we include lagged prices and two vectors of control

variables. Lagged prices give the “hedonic” model a partial-adjustment model feature.

The geographic control variables, G, include MSA-specific distance gradients as well as

MSA-specific fixed effects (essentially giving each MSA its own rent gradient and

intercept). The model also includes a vector of place-level attributes, M, designed to

itiGitEtit GEP 10 '''' εβββ +++=

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proxy for municipal amenities such as crime and school quality. Data availability limits

the quality of these proxies, unfortunately. We have little reason to suspect, however,

that community-level amenities like crime and school quality systematically correlate

with Superfund site remediations.

Second, it is possible that individual observations have some unobserved attributes that

correlate with E. To allow for observation-specific unobservables, we leverage the panel

structure of our data to estimate the model in first differences. Like a “repeat sales”

hedonic model (e.g., Mendelsohn et al. 1992), this estimation approach controls for time-

invariant observation-specific characteristics (unobserved or observed). For θi as the

observation-specific effect, equation (2) becomes:

Taking first-differences, we obtain

(4)

Here, variables with a dot over them indicate the variables is measured in first-

differences. Notice that taking first-differences has preserved the coefficients βE and βX

and their interpretations as marginal prices. Time-invariant attributes, like G and θ, on

the other hand, drop out of the model and cannot be identified in the hedonic regression.

In the present application, unobserved and time-invariant measures of environmental

quality will be differenced out of the model. With respect to the presence of NPL sites,

observations’ proximity to a hazardous waste site will appear to have E& = 0 if the site’s

status is unchanging. For other sites with E& ≠ 0, perhaps because they were added to or

itiiGitMitXitPitEtit GMXPEP 110 εθββββββ +++++++= −

ititMitXitPitEit MXPEP 110 εβββββ &&&&&& +++++= −

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removed from the NPL, βE can capture the effect of a change in environmental quality.

In this study, we simplify matters and assign E& = 1 for sites that completed a phase of

remediation under CERCLA and E& = 0 otherwise (including sites that were never on the

NPL).

Although estimating in first-differences offers important advantages2 in controlling for

unobservables, it does come with some shortcomings. The first-differenced estimator

assumes time-invariant parameters, which might not be the case. To address this, we

estimate the model with the G vector interacted with a time dummy variable. This

effectively allows for βG to be varying over time and, while the level effect of G on price

cannot be recovered, the coefficients on G represent the changes in marginal prices of G

between periods.

Third, a complication arises if E& is endogenous in the price equation in (4). It is possible

that the likelihood of a policy treatment (e.g., a deletion from the NPL) is endogenous,

which would make the estimator OLS estimator of (4) inconsistent. In particular, we are

concerned with the possibility that the error in (4) – the unexplained portion of housing

price appreciation – somehow correlates with the incidence of deletions or other steps in

the remediation process. Although it seems unlikely that the EPA is able to deliberately

and systematically complete clean-ups in areas with above (or below) average

appreciation rates, after controlling for the observables in (4), it may seem more plausible

that something unobservable about observations nearby clean-ups all share some

2 By controlling for time-invariant unobservables, the permanent components of relatively stable attributes like school quality and crime will be soaked up in this first-differenced approach.

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unobserved attribute that predicts appreciation rates and belongs in (4). For example, if

NPL deletions tended to be in areas of local blight (perhaps because all NPL sites,

regardless of status, tend to be in such areas), and if the implicit price of proximity

became less negative over time (perhaps because it became fashionable to live in post-

industrial wastelands), then the OLS estimate of βE will be biased upwards as it attributes

a price effect to E& that was rightly due to the omitted factor that determined treatment in

the first place.

To address this problem of policy endogeneity, we employ an instrumental variables (IV)

regression where clean-ups are predicted using a set of instruments that do not belong in

equation (4). Tests of the validity of these exclusion restrictions are also performed. The

excluded instruments, based on Noonan et al. (2007), used to estimate the clean-up

variable, E& , are described below.3

Fourth, the Superfund remediation process entails several phases, and different price and

non-price effects might be observed to follow each of them. For instance, Greenstone

and Gallagher (2008) examine the listing of sites. They model the “discovery” of sites

for the NPL as potentially beneficial, as listing brings the promise of future remediation.

Alternatively, Noonan et al. (2007) investigate the final stage, deletions, also expecting

positive impacts on property values following the completion of the clean-up. Other

researchers have looked at intermediary phases in the remediation process. The approach

3 Noonan et al. (2007) discuss alternative approaches to handling the endogeneity of E& . In short, their findings do not show a dramatic difference in the estimated direct effects of deletions on prices between a model that assumes deletions are exogenous and one that instruments for them. Significant differences in the indirect price effects, however, were evident.

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taken here remains agnostic about the timing and direction of price effects arising from

site remediation. The change in environmental quality indicator, E& , is a vector of

dummy variables that each take a value of unity when that phase is complete. The

analysis initially included five different phases in the clean-up (discovery, removal,

remedial action, construction, deletion). The EPA offers further information about these

phases (e.g., US EPA 2007). If prices rise following discovery, deletion, or another

phase, these price effects will be separately estimated. They can then be compared.4

Multicollinearity concerns, however, lead us to report a smaller vector E& , with only three

major clean-up milestones: discovery, construction complete, and deletion. Arguably the

‘construction complete’ phase should bring the biggest positive price gains, because

completion of this phase typically indicates that the EPA has completed work on the site,

remediation standards have been met, and only longer-term monitoring remains. This

phase should signal an improvement in environmental quality, with the ‘deletion’ phase

perhaps only affecting stigma or uncertainty.

Fifth, alternative definitions of proximity might affect the results substantially. Similar

studies using aggregate data, such as Noonan et al. (2007) and Greenstone and Gallagher

(2008), must ultimately define proximity in determining which observations (i.e., block

groups, tracts) are “nearby” the site and experience the change in environmental quality.

There are numerous ways to define proximate observations, such as those observations

containing the site or those observations with part of their area within a fixed radius the

4 This approach, estimating the models separately for different definitions of E& rather than nesting a richer set of E& variables in a single model, has its limitations. Many sites had multiple phases completed in between time periods, which may bias the phase-specific implicit price estimates.

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site. As Mohai and Saha (2006) emphasize, alternative definitions of proximity to

noxious facilities can have profound effects on multivariate relationships between

socioeconomic and proximity variables. Their useful discussion of the strengths and

weaknesses of various proximity definitions are applied to environmental justice

research, but might have equally important implications in this context. To address this,

a sensitivity analysis is performed for five different definitions of proximity: (1) the

observation contains the site or abuts an observation that does [BGadjacent], (2) the

center of the observation is within one mile of the site [Center1], (3) 50% of the

observation’s area is within one mile of the site [Half1], (4) the center of the observation

is within six miles of the site [Center6], and (5) 50% of the observation’s area is within

six miles of the site [Half6]. The one- and six-mile cut-offs were selected based on

previous hedonic studies of NPL sites (see Noonan et al. 2009) as distances likely to

capture most and all affected properties. Mohai and Saha favor the percentage-based

definitions (3) and (5), while the containment approaches like (1) have been used

previously. Even without measurement bias, the effects of proximity are likely to vary

across different definitions if the site’s remediation has nonmonotonic or more complex

impacts (e.g., donut patterns) on surrounding neighborhoods.5

Sixth, and finally, the possibility of heterogenous treatment effects – inspired by Vigdor

(2009) – is addressed simply by interacting a neighborhood descriptor variable with the

E& vector. The interaction model thus allows for clean-up price effects to vary across

5 Differences at the 1- and 6-mile distances are also expected. The 6-mile distance may just obtain an attenuated effect as the site’s intensity is diluted by include less proximate observations. This is ultimately an empirical question. As Anderton et al. (1994) find in their research involving noxious facilities, the labor and housing market conditions may differ systematically between neighborhoods immediately adjacent and those less nearby the site. They showed a sort of “donut” effect, which may arise here as well.

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different types of neighborhoods. Of primary interest is a variable measuring housing

market slackness: the percent of housing units that are vacant in 1990. The interaction

terms are also considered endogenous whenever the main E& vector is endogenous.

Additionally, lagged household income and population density are separately considered

for inclusion as interaction terms in the model. This allows for clean-up effects to

possibly differ by neighborhood income (consistent with an environmental quality

demand story) or by density (consistent with an environmental harm story).

To summarize, the basic model (4) will be estimated in several different ways. An IV

estimator will include the three different phases, each as an endogenous treatment

variable. This model is estimated separately for each of the five different definitions of

proximity. An OLS estimator for each of those five models is also estimated and

reported in the Appendix. For a preferred model (Half6), the analysis is then repeated for

interaction models. Finally, the results for these unrestricted models with direct price

effects are compared to the results for corresponding restricted models with full price

effects.

V. Data

Several sources contribute the data for this analysis. The approach to identifying impacts

of changes in environmental quality taken here requires panel data encompassing the

nation. The national sample provides control groups, observations not receiving clean-

ups, as well as the treatment groups. The first-differenced estimator requires observing at

least two time periods. The U.S. Census data is used to obtain such a broad sample of

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property values (and other variables) at multiple time periods (e.g., 2000, 1990).6

Addressing possible endogenous NPL clean-ups involves using instruments from 1980

and other geographic measures. This places still further demands on the data, as time-

series stretching back to 1980 are employed.

The demographic and socio-economic variables are provided by the Neighborhood

Change Database of Geolytics™. A major concern with using Census data as a panel is

that geographic units may not be consistent over time as Census geography frequently

changes. The Geolytics data product enables observations to be consistently defined over

such a long timespan by using block-level information to convert 1980 and 1990 census

data to the 2000 geographies. We use block-group level data because it is the smallest

geographic unit for which the variables of interest are available. In total, the continental

U.S. has approximately 200,000 block groups that serve as our units of observation.

Other data sources complement the Census data. Data on the sites in the NPL are

provided by the EPA. This data includes site-specific information and a detailed timeline

of events (e.g., deletion, listing). The Economic Research Service of the USDA provides

another environmental measure, their Natural Amenities Index, which is measured at the

6 While this is not ideal, data limitations preclude the use of transaction-level observations. There are many implications of using aggregate data such as this. This shifts the attention and, strictly speaking, the interpretation of the results to “neighborhood”-level aggregates (rather than individual property-level interpretations). This may actually be an advantage in our context, as our interest is in neighborhood-level dynamics. Nonetheless, the problem of ecological inference remains. Moreover, the aggregated measures may understate many changes at the margins or in individual properties which might not affect the central tendency of the measures, especially for median measures. The median property value measure, unlike transaction level data, may suffer from biases as a self-reported estimate by homeowners (Kiel and Zabel 1997). On the other hand, it does not suffer from the selection bias in most hedonic studies that observe only sold property values and nothing about the majority of properties (which are unsold during the study timeframe).

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county level. Although the Census TIGER files provide most of the GIS data for the

analysis, the National Atlas provides an important map of the historic city centers of the

thousands of U.S. cities.

The definitions of the variables (P, E, X, M, G) are presented in Table 1. The table also

shows the descriptive statistics of many of the variables used. Except as noted, these

variables are already first-differenced, representing the change in that measure from 1990

to 2000. One highlight of Table 1 is that 5.4% of the observations had an NPL site in

1990. Of those observations, about 10% of them received deletions during the 1990s.

Other highlights include an average 36% increase in home values, a 33% increase in

median incomes, and a two minute increase in average commute times.

[Insert Table 1 here.]

The IV regression makes use of several variables to construct the predicted remediation

phase complete variables, E& . Many variables were initially considered (e.g., 1980 levels

of P, X, and M; and differences from 1980 to 1990 in M). Ultimately, a smaller set was

chosen for parsimony and to avoid lagged variables that might correlate with omitted

variables in the outcome equation. The most important instruments are two measures of

political representation.7 In addition, several other site-specific characteristics are

7 Political representation variables are the count of Representatives and the count of Senators on their respective CERCLA oversight committees. (Also considered were the count of Representative and the count of Senators on their respective Appropriations committees, but these fit the data too poorly to help.) These two variables, measured at the block-group level, refer to the district or state boundaries and committee memberships circa 1980, the year that CERCLA was passed. District maps from the 103rd

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employed as instruments. The HRS score of the closest site predicts the likelihood of that

site’s clean-up, although it ought not describe the environmental quality of the

observation and thus belong in the price equation (4). This is so because every

observation (i.e., block group) has a “closest site,” so every observation has an “HRS

score of closest site” value that is high enough to get listed on the NPL in the first place

and thus hardly indicative of the environmental quality in that observation. Rather, it

should merely correlate with whether that site got deleted from the list (Hird 1990, Daley

and Layton 2004). And, if it proxies for likelihood of deletion of the closest site, then it

may predict remediation progress. Also, proxying for likelihood of the closest site being

cleaned-up should also be correlated with likelihood that a “closest site” inside the

observation or in a neighboring block-group also gets remediated, thus making it a

possible predictor of ( E& ). (A dummy variable for whether the HRS score is missing or

unknown for that closest site is also used as an instrument.) Similarly, a variable

measuring the years elapsed since the closest site was first “discovered” for inclusion on

the NPL is included. A similar logic applies to the validity of the exclusion restriction for

this variable, and time elapsed since discovery is expected to affect the likelihood of

clean-up. Two final instruments, the square of the population density in 1980 and the

MSA’s population interacted with the log of the distance to downtown, are also included

in an attempt to capture aspects of the city size and density that might affect likely clean-

up progress but not directly belong in a price equation.8

Congress were used to match block groups to jurisdictions because these were the oldest available GIS map files. 8 The logic for these variables’ inclusion is fairly straightforward. In a first-differenced price model that controls for population density, the square of the twice-lagged levels of population density does not belong in the main price equation and yet may still be correlated with historic development intensity in that neighborhood which might affect how likely it is to host an NPL site or receive clean-up efforts. Similarly,

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VI. Results

The results of the IV estimations of equation (4), with Huber-White robust standard

errors, are presented in Table 2 below. In this table, the phases refer to the completion of

a phase in a proximate sites, where proximate sites are defined alternatively in each

column (i.e., BGadjacent, Center1, Half 1, Center 6, Half6).

[Insert Table 2 here.]

The control variables, X, appear to have coefficients of the expected sign generally. Most

of them are significant at the 1% level. Moreover, they never change signs across

models, except for the insignificant school expenditure variable. While the control

variables in X are not the focus of this paper, they do offer some tests of the face validity

of the “hedonic” model in first differences using nationwide, aggregated Census data.

Prices rise with the newness of the housing stock, number of rooms, and the prevalence

of rental units. Prices fall with the prevalence of vacant units. Apparently, appreciation

rates are higher in areas with a higher proportion of multi-unit housing. The results for

the demographic variables also confirm expectations. Prices rise in neighborhoods with

greater incomes and share of college educated. Prices fall in neighborhoods with a

greater proportion of poor residents and of families with children.

the interaction of distance from downtown and MSA population avoids variables that could plausibly be included in the main equation while capturing a sense of bigger MSAs may locate NPL sites or clean-ups farther from downtown than smaller MSAs.

22

Table 2 shows an interesting story of the price effects of Superfund clean-ups. First, the

coefficients for the three different phases all differ markedly across the different

definitions of “proximate sites” shown in each column. The containment approach (“BG

plus adjacent”) in column (1) is most comparable to previous approaches like Greenstone

and Gallagher (2008) and Noonan et al. (2007). These results show positive price effect

for listing, a larger negative effect from completing construction, and a negative but

statistically insignificant effect from deletion. Columns (2) and (3), on the other hand,

should offer an improvement in defining nearby sites by avoiding some instances where

sites are at the periphery of a large block-group or may be nearby but not technically

contained by an observation. At shorter distances, the positive price effect of discovery

remains, the negative price effect of construction completion fades, and an extremely

large and negative price effect of deletions appears. Columns (4) and (5) use the

preferred proximity definition by identifying nearness based on the mass of a block-

group’s area within a distance of a site. Comparing columns (4) to (2) shows a dramatic

change: price effects for discoveries and construction completion are much stronger,

while the deletion effect is now positive and a more reasonable magnitude. The areal

concentration method shows much strong price effects than the containment method in

column (1), likely avoiding some serious measurement error. As Mohai and Saha (2006)

discuss, the containment definitions of proximity may poorly reflect actual exposure,

especially with a national sample of heterogenous census units. Comparing columns (3)

and (5) to columns (2) and (4) suggests that expanding the “footprint” of these sites to the

upper limit of what Superfund sites’ price effects have been shown in previous hedonic

literature can go a very long way to attenuating the large price effects observed at the

23

shorter distances. This is as expected as the larger footprints dilute a price effect that is

likely decreasing with distance. The larger z-statistics (in absolute value) for the

discovery and construction complete phases suggests a better statistical fit at the larger

radius buffer . The results are clearly sensitive to how nearby sites are defined.

Second, the price effects differ dramatically across phases. It appears that discoveries

enhance property values, statistically significantly so. This is unlike Greenstone and

Gallagher’s finding but consistent with expectations that the promise of a clean-up is

capitalized into housing values. Completing the construction for remediation on the site,

however, is consistently shown to have a negative price effect in Table 2. In the

preferred models based on areal concentration measures (columns 4 and 5), the price

boost from discovery is only partially undone when the EPA determines its clean-up

efforts are finished. This might result from the remediated sites consistently serving as

less of local amenity than was initially expected at the time of listing, because of negative

externalities or stigma associated with the clean-up process itself, or possibly other

explanations. The price effect of deletions is particularly sensitive to proximity

definitions. In the preferred areal concentration models, its price effect appears as

positive, consistent with a story that deletions reduce the stigma associated with

Superfund sites.

Overall, the magnitude of these price effects associated with different remediation phases

are much larger than observed in previous literature like Greenstone and Gallagher

(2008) or Noonan et al. (2007). This might arise due to somewhat different samples and

24

estimation methods. Moreover, the large coefficients should be interpreted in light of

correlated E& or “treatment” variables. Many observations that have a discovery during

the 1990s also experience construction completion, for example. Viewed in this light,

The cumulative impact on home values of the clean-up process in many neighborhoods is

not so dramatic as the large coefficients in Table 2 would indicate.9

All of these results in Table 2 should be interpreted with caution, however, because

diagnostic tests reveal concerns about the validity of the instruments. The difference-in-

Sargan C-statistics across each model are all large and, with the exception of column (2),

are significant at the 1%. There is strong evidence that, regardless of the proximity

definition, progress toward clean-up ( E& ) is endogenous in the equation (4). The

rejection of exogeneity is based on valid exclusion restrictions for the instrument set,

which might not be the case. The Hansen’s J statistic offers a diagnostic tool and, as

reported in Table 2, shows large values that (regardless of proximity definition) suggest

that the exclusion restrictions may not hold. Hansen’s J statistic, however, lacks power

against the null of valid instruments and tends to overreject the null. The models in Table

2 nonetheless should be interpetted with some caution given the suspect instruments.

Issues with these IVs likely arises from their being correlated with the error terms rather

than their weakness. These are typically strong IVs, with first-stage F-statistics (Staiger

and Stock 19997) in the double-digits for two or three of the three first-stage equations.

9 OLS models for each of the models in Table 2 are estimated under the assumption of exogenous E& variables. These are presented in the Appendix. The E& coefficients are all much smaller in absolute value than those in Table 2. Several signs have changed, notably for column (5), and many of the price effects are not statistically significant. The positive price effects are consistent with much of the case-study hedonic literature.

25

The endogenous variable that they predict least well, in general, is the deletions phase.

The concern that the IVs predict the deletions phase weakly is reflected in the relatively

noisy and inconsistent parameter estimates for the deletion phase in Table 2.

Interestingly, the IVs are quite strong in columns (3) and (5). The strength of the IVs in

predicting clean-ups around sites using the larger, 6-mile buffer leads to the better fit and

larger z-statistics for the phases in the price equation. Given this better fit and more

conservative estimates of price effects, the areal concentration 6-mile model in column

(5) is the preferred approach for subsequent analyses.

Interpreting Table 2’s coefficients for the phase variables as average treatment effects

overlooks the possibility of hetergeneous effects from remediation activities. A few

types of effect heterogeneity may be likely. First and foremost, the expected increase in

home prices due to an exogenous improvement in local amenities (due to the clean-up)

may not occur if the housing market is especially slack. Slackness in the housing market

may lead to in-migration to occupy previously underutilized supply of housing stock

without an upward pressure on prices. This type of effect heterogeneity is investigated in

column (1) of Table 3 via interaction terms with the E& variables and the share of vacant

housing units in the block-group. (Now, the model includes six endogenous variables.)

The second and third columns include two more similar tests for effect heterogeneity,

considering income and population density, respectively. The former case is motivated

by a hypothesis that the amenity values of remediated Superfund sites accrue

predominantly to wealthier residents with greater demands for environmental quality.

The latter case is motivated by a hypothesis that information about or exposure to a site

26

may be greater in areas with greater density, and thus price responses may differ in high

density areas. The model from column (5) in Table 2 is estimated three separate times

are reported in Table 3.

[Insert Table 3 here.]

The results indicate a good model fit (i.e., strong instruments and valid exclusion

restrictions) judging by the diagnostic statistics reported in Table 3. Arguably, the model

fits are stronger here than in column (5) from Table 2, in particular noting the small

values of Hansen’s J. The endogenous variables – the phase indicators and their

interaction terms – prove to be jointly statistically significant in the models with income

and density interactions, although individually they are often insignificant in all models.10

The effects for the Vacancy (%) interaction model exhibit some heterogeneity, although

the estimates are imprecise. The positive (but not significant) average price effect of

deletion in column (5) of Table 2 is replaced now with a negative effect at the mean

(which, given that the variables are all de-meaned at the metro level, is nearly zero) that,

importantly, grows more positive as vacancy rates rise. In other words, areas with

vacancy rates of at least 16% experience a positive price effect of deletion. There is a

great deal of uncertainty around this effect, making it difficult to reject the alternative

hypothesis that there is no significant price effect (as seen in Table 2). Still, this result, 10 Specifically, the hypothesis that all six endogenous variables in the vacancy model are jointly equal to zero cannot be rejected (F < 5.8). The same is true for the joint significance test for just the three interaction terms (F = 2.9) and for the three uninteracted terms (F=2.3). Even though the first-stage models exhibited strong fits for the IVs, the price effects of cleanups appear negligible on average and this does not appear to mask differential effects across market slackness. For the income and the density interaction models, on the other hand, the joint significance tests for the all six endogenous variables, for only the interaction terms, and for only the uninteracted terms all let us reject the null (at the 2% level) that there is no price effect.

27

and insignificant interaction terms for discovery and construction complete phases, offers

little confirmation of the hypothesis that price effects will be different when housing

markets are slack.

The other interaction models in Table 3 present somewhat stronger results. The

endogenous phase variables are jointly statistically significant in both models. As block-

group median wealth increases, the positive price effect of a deletion decreases. That

poorer neighborhoods experienced a greater price increase is contrary to our expectations

that follow informally from remediations being a normal good. Similarly, as

neighborhood density increases, the negative price effects of completing the construction

phase fade. If the unexpected negative price effect of the construction complete reflects a

stigmatization or a congestion (from remediation action) effect, then price effects are fade

with density suggests that high-density neighborhoods are less sensitive to site

stigmatization or associated congestion.

VII. Discussion

Overview of the results

Several conclusions arise from the results in Table 2. First, Superfund clean-up activity

is a boon to local property values – declining in distance from the site. The benefit fades

as clean-up work is completed, but the benefits of Superfund clean-ups return after the

site is deleted from the NPL. Second, the method of defining proximity to an NPL site

has enormous influence over the estimated implicit prices. This is more than just diluting

price effects with larger buffer areas; different definitions for the same buffer sizes yield

28

contrasting results. Third, endogeneity of clean-ups appears to be a serious problem.

Reliable instruments are hard to find in this context, but there is some consistency in

results even for models that have worrisome diagnostics.

If proximity was properly determined by areal apportionment and the buffer size with

more valid instruments is used (i.e., the Half6 definition), several observations follow

from Table 3. First, the price effects from clean-up activity do not appear to vary

significantly or systematically with vacancy rates. This finding fails to confirm the

prediction that price effects from remediations would be lesser in neighborhoods with

slack housing markets. Price effect heterogeneity is observed, however, for income and

density measures. Deletions’ positive price effect is moderated by high-income

neighborhoods. Construction completions’ negative price effect is moderated by high-

density neighborhoods. Both of these unexpected (and difficult to explain) effects follow

from models with instruments that appear to be strongest and most valid. Further work is

needed to explain these unexpected results.

Another aspect of Superfund’s impacts on property values is to identify indirect price

effects (i.e., those arising from dynamics in neighborhood composition). The direct price

effects of NPL clean-ups, conditional upon unchanging neighbor or structural

characteristics of the neighborhood, may be quite different from the total price effects

after accounting for the neighborhood dynamics induced by the policy. This process

involves environmental migration (Banzhaf and Walsh 2006, Cameron and McConnaha

2006) and changes in the housing stock and other aspects of neighborhood transition

29

often associated with brownfield remediation projects. To identify these effects, the

model in column (5) of Table 2 is estimated while omitting the X variables describing the

housing stock and neighborhood quality. This is equivalent to estimating equation (4)

with βX restricted to equal zero.

The evidence presented in Table 4 suggests only very modest indirect price effects.

Assuming that the instruments are valid here, the price effect from discovery is somewhat

stronger (6%) and from deletion is somewhat weaker (8%). Given the size of the

standard errors for these estimates, the differences between the βE estimated in the

restricted and the unrestricted models is negligible. The positive (negative) average

indirect effect from discoveries (deletions) points to changes in X following listing on the

NPL that enhance (depress) property values. These net effects are small and imprecisely

estimated here. The minor indirect effects observed in Table 4 might be the result of

offsetting and significant changes in X, or it might truly reflect insignificant changes in X.

A 3SLS model like Noonan et al. (2007) can illustrate the pathways through which the

Superfund program catalyzes neighborhood change.

Conclusions

The price effects estimated here, for the preferred models at any rate, are quite large.

They are large absolutely (e.g., over 30% price increase from discovery in column (5) of

Table 2) and relative to previous research. This is due largely to difference in methods,

including the simultaneous inclusion of multiple milestone indicators for the clean-up

30

process and the use of instrumental variables. Multicollinearity and weakness in the

instruments may account for some of the rather large coefficients here. Alternatively,

when the model specifications seem best (e.g., Table 3), the results still indicate rather

large main price effects for some phases and insignificant impacts for others. The large

price effects may be plausible, but the substantial uncertainty surrounding their estimates

is undeniable.

These models also clearly rejection of the hypothesis that the price effects of progress

through the Superfund program differ (linearly) with housing vacancy rates. They do

show price effect heterogeneity for income and for density. The absence of a role of

housing market slackness might be a result of misspecification (perhaps a nonlinear

relationship with vacancy is needed). Or it might simply be the case that the housing

market fails to react to remediations differently when vacancy rates are high. The

wealthier and denser neighborhoods may experience less of a price shock following

clean-up progress poses an interesting empirical finding in its own right. Further research

is needed to explain this.

In addition, care should be taken in using alternative definitions of proximity when using

aggregated Census data. Table 2 shows how sensitive the results are to alternative

definitions of proximity. This applies at all phases of Superfund remediations. The

results for the BGadjacent proximity – perhaps the closest to a Census tract definition

used by Greenstone and Gallagher (2008) and others – differ in magnitude and size from

the effects estimated at other proximities. This casts doubt on both the robustness of

31

empirical findings such as Greenstone and Gallagher (2008) and Noonan et al. (2007).

As Mohai and Saha (2006) also show, other, arguably superior definitions for proximity

can yield different results.

The results here offer some guidance for the conduct of future studies of price effects of

remediation efforts. The limited generalizability of case study evidence is well

established, and the results here add evidence of differing effects of clean-up efforts.

This evidence is reinforced by both Kiel and Williams (2007) in their showing the

heterogeneity of price effects across many sites and in Greenstone and Gallagher (2008)

who demonstrate how Superfund listings are endogenous.

Finally, the approach here raises questions about the interpretation of hedonic models

estimated in differences when there is sorting. This endogenous sorting introduces a

“Tiebout bias” (Hoyt and Rosenthal 1997). Given that some turnover in residents seems

necessary for new prices to be observed in market transactions, the influx of new

residents with different tastes or incomes who outbid the natives will likely affect the

neighborhood composition. The influx of groups whose presence affects neighborhood

quality can also affect implicit prices. While this may matter more in for non-marginal

shocks to environmental quality, it has implications for the use in policy assessments of

MWTP measures derived from hedonics. When the implicit price of an environmental

condition (or an environmental change in the case of repeat-sales models) is taken as a

measure of welfare change, the conditionality of this implicit price merits scrutiny from

the perspective of policy. Measures of welfare changes arising from policy interventions

32

may be biased in this framework. If policy effects environmental change, marginal or

otherwise, and also affects other attributes in the hedonic model, then parsing a direct

effect of the policy from its full effect tells only part of the story of its welfare effects. In

practice, policymakers cannot affect environmental quality while also holding

neighborhood composition fixed.

The jury is still out on whether approaches like this can identify whether Superfund

activities are beneficial or harmful to local quality of life. Robust evidence is not easy to

find. While the promise (the “discovery” phase) or the conclusion (the “deletion” phase)

of remediation might both be expected to improve local quality of life and be reflected in

housing values, alternative hypotheses exist. Listing a site might bring notoriety and

stigma (as well as genuine disincentives to redevelop), adversely affecting property

values. The stigma and decay may persist long after official delisting, yet the pipeline to

federal or other external redevelopment funds might dry up following delisting, leading

to a decline in local quality of life. Either way, these amenity or disamenity effects of

remediating Superfund sites should appear as direct price effects. The total price effects

of remediations are realized after neighborhood composition reaches a new equilibrium.

This might entail substantial changes in neighborhood attributes. Analyses seeking to

explain the effects of a policy intervention like Superfund would do well to address these

non-price effects and how they, in turn, affect housing markets.

33

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36

Table 1: Variable Names, descriptions and descriptive statistics.

Vector Name Descriptiona Mean Standard Deviation

P Price Difference in log of median value, owner-occupied housing 0.3589 0.322

E

Discovery Own or adjacent block group has an NPL site discovered or added to list 0.0025 0.050

Construction complete

Own or adjacent block group has an NPL site with construction completed 0.0173 0.130

Deletion Own or adjacent block group has an NPL site deleted from list 0.0053 0.073

X

Income Difference in log of median household income 0.3343 0.231

Poor Difference in percent population with income under 1.5 poverty line -0.0031 0.099

Population density Difference in people per mile2 199.2871 2117.182

Renter Difference in percent occupied housing units that are renter-occupied -0.0053 0.096

Children Difference in percent population aged 18 or younger -0.0023 0.062

College Difference in percent population age 25+ with at least college degree 0.0486 0.079

Rooms Difference in average number of rooms in housing units 0.1058 0.457

Vacancy Difference in percent of housing units vacant -0.0233 0.189

New construction Difference in percent of housing units built in last 10 years -0.0094 -0.013

M

PlaceHouseholds Difference in place-level number of households 18019.47 42207.37

PlaceValue Difference in place-level median housing value 34706.75 34466.24

PlaceIncome Difference in place-level median household income 10366.28 4852.253

SchoolExpend’s Difference {1992-2002} in district’s (unified

and elementary) school expenditures per pupil ($1000s)

3.6289 2.070

G Distance log of distance to historic city center 2.8742 0.818

MSA × Distance MSA-specific log of distance to historic city center

a All variables entered into model differenced from MSA-level means. Variables measured at the block-group level and enter the model as a difference between 2000 and 1990 levels, unless otherwise noted.

37

Table 2: IV results for different proximity definitions (1) (2) (3) (4) (5)

Proximity: BGadjacent Center1 Center6 Half1 Half6N 107842 107842 107842 107842 107842R2 -0.15 0.07 0.26 0.18 0.23MSE 0.28 p-val 0.25 p-val 0.22 p-val 0.23 p-val 0.23 p-valendogeneity test 25.201 <0.01 13.294 0.03 14.454 <0.01 19.353 <0.01 32.743 <0.01Hansen’s J 15.334 <0.01 26.815 <0.01 32.098 <0.01 20.596 <0.01 16.566 <0.01 variable coefficient z coefficient z coefficient z coefficient z coefficient z discovery 0.525 2.2 0.540 1.7 0.140 2.0 1.743 3.1 0.311 4.0construction complete -1.098 -2.3 -0.665 -1.4 -0.086 -2.4 -1.216 -2.5 -0.177 -3.7deletion -0.692 -0.5 -4.349 -1.7 -0.035 -0.3 1.229 1.7 0.235 1.6ln(Price1990) -0.306 -28.0 -0.309 -29.1 -0.308 -28.8 -0.312 -29.4 -0.310 -28.8ln(Income) 0.141 14.1 0.138 14.6 0.135 15.0 0.131 14.2 0.131 13.5Poor (%) -0.033 -1.9 -0.022 -1.4 -0.033 -2.4 -0.032 -2.2 -0.034 -2.4Population density 9E-07 1.1 5E-07 0.6 6E-07 0.8 7E-07 0.9 1E-06 1.5Renter (%) 0.106 6.3 0.100 6.8 0.093 6.9 0.090 6.2 0.085 6.0Children (%) -0.089 -4.3 -0.082 -4.5 -0.084 -5.3 -0.079 -4.5 -0.087 -5.3College (%) 0.494 27.6 0.497 34.4 0.513 36.3 0.515 32.3 0.528 32.7Rooms 0.080 22.8 0.078 25.2 0.076 27.1 0.076 26.7 0.074 26.4Vacancy (%) -0.084 -3.2 -0.088 -3.9 -0.074 -3.8 -0.070 -3.5 -0.074 -3.7New construction (%) 0.185 22.8 0.177 33.0 0.176 31.2 0.176 31.5 0.175 34.0ln(PlaceHouseholds) -0.038 -2.6 -0.078 -7.6 -0.075 -4.9 -0.068 -5.8 -0.066 -4.0ln(PlaceValue) 0.498 31.0 0.490 36.0 0.512 33.3 0.483 32.1 0.483 18.5ln(PlaceIncome) 0.104 4.5 0.111 6.2 0.084 4.4 0.117 6.7 0.139 5.5ln(SchoolExpend’s) 0.011 1.5 0.006 0.9 0.000 0.0 -0.002 -0.3 0.006 1.1controls included G G G G GAll variables first-differenced and de-meaned at the metro level. Estimated with robust standard errors.

38

Table 3: IV results with interactions (Half6) Z= % Vacant Income Density

Proximity: Half6 Half6 Half6 N 107247 107247 107247 R2 -7.02 -0.76 0.04 MSE 0.73 p-val 0.34 p-val 0.25 p-valendogeneity test 48.357 <0.01 48.118 <0.01 48.802 <0.01Hansen’s J 0.321 0.57 0.177 0.67 1.246 0.26 variable coefficient z coefficient z coefficient z discovery 2.246 1.4 -26.989 -1.1 0.686 1.5construction complete -0.946 -1.2 1.418 0.9 -0.580 -2.8deletion -3.527 -0.8 17.600 3.3 0.261 0.8discovery × Z -11.920 -0.7 2.716 1.1 0.000 -0.2construction complete × Z 23.579 0.6 -0.185 -1.2 0.000 2.0deletion × Z 24.439 1.4 -1.725 -3.3 0.000 -0.2ln(Price1990) -0.332 -13.2 -0.293 -17.7 -0.302 -26.2ln(Income) -0.143 -1.5 -0.147 -3.1 -0.047 -2.2Poor (%) -0.028 -0.2 -0.121 -2.8 -0.177 -14.6Population density 0.000 -0.3 0.000 3.8 0.000 2.6Renter (%) 0.028 0.5 0.065 3.7 0.079 4.8Children (%) -0.067 -0.9 -0.038 -1.2 -0.050 -2.9College (%) 0.716 7.1 0.669 14.4 0.608 35.6Rooms 0.064 3.0 0.083 20.6 0.094 25.1Vacancy (%) 0.210 6.0 0.190 12.5 0.183 28.3New construction (%) 0.040 0.4 0.004 0.1 -0.010 -0.4ln(PlaceHouseholds) 0.616 1.5 0.412 8.8 0.408 8.8ln(PlaceValue) -0.154 -0.3 0.179 4.9 0.208 7.1ln(PlaceIncome) -0.017 -0.3 -0.023 -0.6 0.012 1.8ln(SchoolExpend’s) -0.444 -0.5 0.120 1.1 0.128 1.1controls included G G G All variables first-differenced and de-meaned at the metro level. Estimated with robust standard errors.

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Table 5: IV restricted model (full effects) (1) (5)

Proximity: Half6reprinted from

Table 2 N 107899R2 0.12MSE 0.24endogeneity test 21.56 <0.01Hansen’s J 16.76 <0.01 variable coefficient z discovery 0.254 1.9 0.311 4.0construction complete -0.173 -2.8 -0.177 -3.7deletion 0.319 2.0 0.235 1.6ln(Price1990) -0.282 -25.2ln(PlaceHouseholds) -0.050 -3.9ln(PlaceValue) 0.516 17.5ln(PlaceIncome) 0.306 14.4ln(SchoolExpend’s) 0.014 2.5Controls included GAll variables first-differenced and de-meaned at the metro level. Estimated with robust standard errors.

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Appendix

Table 2a: OLS results for different proximity definitions (1) (2) (3) (4) (5)Proximity: BGadjacent Center1 Center6 Half1 Half6N 107842 107842 107842 107842 107842R2 0.28 0.28 0.28 0.28 0.28MSE 0.22 0.22 0.22 0.22 0.22Hansen’s J 20.04 0 31.90 0 28.67 0 25.47 0 17.25 0Difference-in-Sargan C-statistic 25.20 0 13.29 0.03 19.10 0 19.35 0 32.74 0 variable coefficient z coefficient z coefficient z coefficient z coefficient z discovery 0.044 2.2 0.042 1.3 0.000 -0.1 0.042 1.5 -0.030 -4.7construction complete 0.010 1.8 -0.010 -1.2 -0.002 -0.9 -0.032 -3.6 -0.004 -1.6deletion 0.010 1.0 -0.001 0.0 0.022 3.6 0.029 1.4 -0.002 -0.3ln(Price1990) -0.313 -29.3 -0.313 -29.3 -0.313 -29.3 -0.313 -29.3 -0.313 -29.3ln(Income) 0.130 14.7 0.130 14.7 0.130 14.7 0.130 14.7 0.131 14.7Poor (%) -0.037 -2.7 -0.037 -2.7 -0.037 -2.7 -0.037 -2.7 -0.037 -2.7Population density 9E-07 1.4 9E-07 1.4 9E-07 1.4 9E-07 1.4 9E-07 1.3Renter (%) 0.088 6.8 0.088 6.9 0.088 6.9 0.088 6.9 0.088 6.8Children (%) -0.089 -5.9 -0.089 -5.9 -0.088 -5.9 -0.089 -5.9 -0.089 -5.9College (%) 0.513 38.9 0.513 38.9 0.512 38.9 0.513 38.9 0.512 38.8Rooms 0.076 29.1 0.077 29.1 0.077 29.2 0.076 29.1 0.076 29.1Vacancy (%) -0.071 -3.9 -0.071 -3.9 -0.070 -3.8 -0.071 -3.9 -0.071 -3.9New construction (%) 0.175 35.8 0.175 35.9 0.175 35.8 0.175 35.9 0.175 35.9ln(PlaceHouseholds) -0.069 -9.7 -0.069 -9.7 -0.067 -9.5 -0.069 -9.7 -0.070 -9.9ln(PlaceValue) 0.479 41.2 0.479 41.2 0.479 41.1 0.479 41.2 0.480 41.3ln(PlaceIncome) 0.117 7.8 0.117 7.8 0.116 7.7 0.117 7.8 0.115 7.7ln(SchoolExpend’s) -0.004 -1.1 -0.004 -1.1 -0.004 -1.0 -0.004 -1.1 -0.004 -1.1controls included G G G G GAll variables first-differenced and de-meaned at the metro level. Estimated with robust standard errors.