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Urban Studies

DOI: 10.1080/0042098042000227028 2004; 41; 1587 Urban Stud

N. Edward Coulson and Robin M. Leichenko Historic Preservation and Neighbourhood Change

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Urban Studies, Vol. 41, No. 8, 1587–1600, July 2004

Historic Preservation and Neighbourhood Change

N. Edward Coulson and Robin M. Leichenko

[Paper first received, August 2003; in final form, November 2003]

Summary. Historical designation has become an important tool in efforts to revitalise central-city neighbourhoods. Yet designation has also come under scrutiny because of its presumedassociation with gentrification and displacement of lower-income residents. Using Fort Worth,Texas, as a case study, the paper asks whether historical designation is associated with demo-graphic change in neighbourhoods. It is found that historically designated areas started out withslightly worse neighbourhood indicators than those without designation—a finding that isconsistent with the idea that preservation efforts are targeted to areas in ‘need’ of revitalisation.However, we find no evidence that preservation efforts altered the demographic composition ofneighbourhoods. This finding runs counter to the notion that historic preservation is a precursorto gentrification.

1. Introduction

Historical designation is a device that be-stows recognition on particular properties be-cause of their importance, in some great orsmall way, to the history of the city or regionin which they are located. While historicaldesignation takes place at the local, state andnational levels, the putative goal in all casesis the preservation of properties with histori-cal and/or aesthetic appeal that would other-wise be neglected or even demolished.Designation has both positive and negativeimpacts on the owner of the building. Whileit confers a certain prestige on the property,the certification can be costly to attain andoften imposes substantial restrictions on howthe property may be maintained and altered.It also—and usually typically—preventsdemolition of the unit. The effect of desig-nation can therefore be dramatic and it is

often thought to be one of the most draconianof land use policies.

Since by its very nature historical desig-nation focuses upon old buildings, its impli-cations for land use are in turn primarilydirected towards old neighbourhoods. Sincethe experience of many urban areas is thattheir oldest neighbourhoods are also thosemost in need of exogenous stimulus of somesort, designation and preservation of historicproperties and historic districts has becomean important tool in efforts to preserve cen-tral-city neighbourhoods and to promoteeconomic development in blighted urban ar-eas (Newman, 2001; Listokin et al., 1998;Slaughter, 1997; Rypkema, 1995; Wonjo,1991). A number of recent studies havefound that, despite the costs and restrictionsthat are associated with historical desig-

N. Edward Coulson is in the Department of Economics, Penn State University, University Park, PA 16802, USA. Fax: 814 8634775. E-mail: fyj@psu.edu. Robin M. Leichenko is in the Department of Geography, Rutgers University, New Brunswick, NJ08901, USA. Fax: 732 445 0006. E-mail: rleichen@rci.rutgers.edu. The paper was presented at the conference on ‘Analysis ofLand Markets and the Impact of Land Market Regulation’, Lincoln Institute of Land Policy, July 2002. The authors thank AustinTroy and the other conference participants for insightful comments, an anonymous referee for helpful comments and SachiyoTakata for research assistance.

0042-0980 Print/1360-063X On-line/04/081587–14 2004 The Editors of Urban StudiesDOI: 10.1080/0042098042000227028 at RUTGERS UNIV on June 24, 2009 http://usj.sagepub.comDownloaded from

N. EDWARD COULSON AND ROBIN M. LEICHENKO1588

nation, it is in fact associated with increasesin housing values within designated districts(Leichenko et al., 2001; Clark and Herrin,1997; Schaeffer and Millerick, 1991) andthat designation has a positive external effecton the prices of properties which are in thesame neighbourhood as designated units butwhich themselves are not so identified (Coul-son and Leichenko, 2001). Thus historicaldesignation may have important effects onthe broader neighbourhood.

Much of the interest in the broader, exter-nal effects of designation is related not to theprice implications of designation, per se, butto questions concerning the impacts of desig-nation on the demographic composition ofurban neighbourhoods (Schneider, 2001;Smith, 1998). Does historical designationslow or halt income and racial ‘tipping’ ofneighbourhoods, a process which entails atransition in the demographic composition ofa neighbourhood from higher-income andpredominantly White households to lower-income and predominantly minority ones?By the same token, does designation promotegentrification, whereby a neighbourhood oc-cupied by lower-income, often minority resi-dents, undergoes an upward shift in averageincome and a change in demographic compo-sition towards predominantly upper-middle-class, White urban professionals? Both ofthese questions have important implicationsfor the use of historical designation as a localdevelopment strategy, yet there has been lit-tle systematic evidence addressing them.This paper represents a first effort to fill thisgap.

In this paper, we examine the effects ofhistorical designation on the demographicand housing characteristics of urban neigh-bourhoods, using Fort Worth, Texas, as a testcase. Fort Worth is ideal for such a purposebecause of the extent to which historicaldesignation has been implemented, either ex-plicitly for aesthetic purposes, or implicitlyas a development tool. Using census tracts asthe unit of observation, we examine the im-pact of the existence and extent of historicalpreservation on tract demographic and hous-ing characteristics between 1990 and 2000.Five demographic and housing indicators are

examined: diversity of population as mea-sured by the Simpson index of diversity,1

growth rate of population, change in the res-idential vacancy rate, percentage change inmedian income and change in the owner-oc-cupancy rate.

Our primary results concern the changesthat take place in the housing and demo-graphic characteristics of tracts between1990 and 2000. Our central result is thatnothing happens. The changes which occurin the tracts between the two Censuses arenot correlated, either conditionally or uncon-ditionally, with the existence or extent ofhistorical designation.

The remainder of the paper is organised asfollows. In section 2, we review the econ-omic literature on neighbourhood transitionand the role that historical designation mightplay within these models that describe tran-sition. Section 3 describes our data and Sec-tion 4 presents the econometric analysis ofthat data. Section 5 concludes.

2. Neighbourhood Transition

The filtering model—an early reference isMuth (1972)—begins with the assumptionthat high-income people have a higher ‘inter-nal’ depreciation rate for housing. That is,while high-income households offer bid rentsfor new property that are higher than bidrents from households with lower income,the high-income bid rents for any given unitof property decline faster over time, becausethe decline in quality that occurs as unitsdepreciate has higher (in absolute value)value to high-income households. In themodel of Bond and Coulson (1989), a two-income neighbourhood is considered. In sucha case, the bids are as in Figure 1, where �H

and �L denote the bids of households of highand low income. Note that the slope of theformer is steeper than that of the latter, indi-cating a greater decline in value. The bids oflow-income households exceed those ofhigh-income households at turnover age, T,and so the housing changes hands at thepoint in time when the unit reaches that age.With more than two income groups, a unitturns over a number of times, until such point

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HISTORIC PRESERVATION AND NEIGHBOURHOOD CHANGE 1589

Figure 1. Filtering and neighbourhood transition.

as the lowest-income group’s bid for the unitbecomes zero, at which point it is demol-ished or otherwise removed from the housingstock.

New construction can occur at that, orsome earlier, point (see Bond and Coulson,1989), but in the absence of this, the incomeof a unit’s occupants will inexorably declineover time and so neighbourhood income willdecline just as inexorably as more and moreunits reach the turnover age. Studies offiltering (Brueckner, 1977 and Phillips, 1981)have relied on this relationship between theage distribution of the housing stock and theincome composition of the neighbourhood totest the filtering model.

As a theory of neighbourhood change, thefiltering model is somewhat limited, relyingexclusively as it does on the housing unit asthe object of interest. There is no role forneighbourhoods in this framework except asa collection of individually filtering resi-dences.

On the other hand, the tipping model(Schelling, 1978; other references includeMiyao, 1978) relies on neighbourhood exter-nalities to generate neighbourhood transition,without reference to the condition of housingunits. For example, if Whites dislike living inthe same neighbourhood as Blacks and/or ifall households prefer to live with neighbours

of the same race, then an exogenous influx ofnew Black residents will lower Whites’ and/or raise Blacks’ willingness-to-pay for adjac-ent units, and cause a turnover of units fromWhite to Black households. The increasedproportion of Blacks in the area may increasethe willingness of White households to moveout, or Blacks to move into the neighbour-hood, and the changing racial composition ofthe neighborhood encourages yet further in-and out- migration. Quite rapid shifts in thedemographic composition of the neighbour-hood are therefore possible; the neighbour-hood ‘tips’. The turnover can also beincome-oriented, rather than race-oriented, asfound in Coulson and Bond (1990). While itmight be the case that all income-groups likehaving high-income neighbours, as long asthe bids for high-income neighbourhoods arehigher among high-income households, anexogenous increase in low-income house-holds could trigger the same sort of ‘tip’from high- to low-income residents in agiven neighbourhood.

A lacuna in the traditional tipping litera-ture is the source of the influx of residentswho alter the demographic composition ofthe neighbourhood and begins the tippingprocess. Bond and Coulson (1989) combinethe filtering and tipping models, so that thefiltering of the oldest housing in the area

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N. EDWARD COULSON AND ROBIN M. LEICHENKO1590

provides the source of the ‘exogenous’ shockto the neighbourhood composition. Theyidentify ‘tipping’ with the outcome of ashock to an unstable mixed-neighborhoodequilibrium and ‘filtering’ with the case ofstable mixed-neighborhood equilibria.Whether or not the mixed equilbrium is sta-ble depends on the number of houses at theturning-point, T, and the relative dislike oflow-income neighbourhoods by low- andhigh-income households. In cases with un-stable mixed neighbourhoods, there are sta-ble, but segregated, equilibria involving allhigh- or all low-income residents in the area.

Thus in Figure 1 the tipping age, T, hasbeen lowered from T to T* because bothtypes of household lower their bids over timeas some housing has filtered from high- tolow-income residents. However, the effect onhigh-income households is greater and sothis generates the decline in tipping age. Theprocess continues, until all residents are low-income. Such an outcome is stable. In theabsence of further exogenous shocks, theneighbourhood remains in this state. How-ever, if demolition and redevelopment occur,the neighbourhood could either ‘tip’ upwardsor simply remain in an all-high-income state.

Another extension of the tipping modelhas been proposed recently by Frankel andPauzner (2002). In Bond and Coulson(1989), the neighbourhood transition is gov-erned by history—more specifically, by theinitial conditions which are captured by thedistribution of vintages of the housing stock.The more heterogeneous the housing stock,the less likely it is that tipping will occur. Onthe other hand, it is possible that neighbour-hood transitions are more often motivated byexpectations of future events. One then ob-tains the usual multiple equilibria: if (say)Whites expect neighbourhoods to turn overto minority households, they sell—and theexpectation of turnover creates the turnoveritself. If Whites do not hold such expecta-tions, then turnover need not occur.

The innovation of Frankel and Pauzner isto provide conditions under which a uniqueequilibrium could arise. One of the ways inwhich this could happen is if there is a

deterministic trend that favours (say) Blacks,in the sense that it increases the desirabilityof the neighbour to one group over another.In such a case, the neighbourhood can reacha point of inevitability, where everybodyknows that the neighbourhood is going toturn over at some point, and a kind of back-ward induction leads all (say) Whites tomove out immediately. Thus turnover canhappen very rapidly.

So, what is the role of historical desig-nation in neighbourhood turnover in light ofthese models? In the view of those whowould see preservation efforts as a develop-ment tool, the starting-point for such analysismust be as configured in Figure 2. In Figure2, let age H be the age at which designationoccurs. Figure 2 assumes a change in tastethat manifests itself as a discrete jump in bidsby high-income people, leading to an in-crease in the price of the unit, as demon-strated in Leichenko et al. (2001). (Althoughthere is little published evidence on the slopeof the price path after designation, we havedrawn it as positive.)2

In the ‘pure filtering’ version of residentialsuccession and neighbourhood turnover, thisis the end of the story. By bestowing desig-nation on a property, the property turns overfrom low-income households to high-incomehouseholds and a limited form of gen-trification occurs. Thus we would expect

Figure 2. Filtering and neighbourhood transitionwith historical designation.

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HISTORIC PRESERVATION AND NEIGHBOURHOOD CHANGE 1591

only minor changes to the neighbourhoodand only to the extent that designation occurswithin its borders.

However, advocates of preservation be-lieve in the ability of historical designation toeffect broader changes in neighbourhoods.Wagner (1993) and Rypkema (1994) de-scribe the external effects that can take placeafter preservation efforts occur. Wagner(1993) describes preservation as a ‘catalyst’for investment in other properties in blightedor depressed areas. Rypkema notes that

one renovation supports another … asmore properties are rehabilitated, lendersare more interested in making loans … andterms become more attractive (Rypkema,1994, p. 68).

However, Listokin et al. (1998) note that theevidence for these external effects is anecdo-tal rather than systematic.

The model of Bond and Coulson (1989)can be used to describe how such ‘reversefiltering’ or broader neighbourhood changecan occur. At H, the designated housing turnsover from low to high income; this increasesthe desirability of the neighbourhood to bothrich and poor households but, under the as-sumptions of Bond and Coulson (1989), theeffect is greater on the bids of rich house-holds and so (as in the case with no desig-nation) the age at which the original turnover(from rich to poor) occurs increases and theaverage income of the neighbourhood in-creases as well. Something like the catalysteffect then occurs, because the neighbour-hood becomes even more desirable to high-income individuals, increasing T, and so on(as if redevelopment were occurring).Whether this is a ‘stable’ neighbourhoodtransition, or an example of ‘reverse tipping’(i.e. immediate transition) depends on theprecise age distribution of the housing stockand the desirability of having high-incomeneighbours.

If this model is correct, we would expectto see neighbourhood demographics andhousing characteristics conditionally corre-lated in a positive manner with the existenceof historical designation. If the externality

effects are strong enough, any neighbour-hood with designated properties would con-tribute to such correlations, regardless of theextent to which designation occurs, althoughthe definition of neighbourhoods would haveto be fine enough not to blunt the measure-ment of the spatial impact of this catalysteffect.

An intriguing possibility is that, followingthe model of Frankel and Pauzner (2002),historical designation acts as the determinis-tic force which engenders the ‘zone of in-evitability’ which in turn generatesimmediate neighbourhood transitions. Thatis, the designation of historical propertieswithin a neighbourhood generates a deter-ministic outcome of all White or high-in-come residents which is observed by allresidents, creating the immediate turnover ofthe neighbourhood. In this case, we shouldobserve an immediate impact; neighbour-hoods with designated properties should im-mediately have different demographic andhousing characteristics at the point in time ofdesignation, or soon after.

To summarise, if historical designation hasimpacts, then

1. the ‘pure’ filtering model predicts thatonly minor neighbourhood effects will beobserved as a result of designation; suchchanges should be soon after designationoccurs, but then only to the extent thatdesignation permeates the neighbourhood;

2. the Bond–Coulson filtering/tipping modelsuggests that major neighbourhood effectswill happen in any neighbourhood wheredesignation occurs, but will perhaps occuronly over some longer time-period;

3. the Frankel–Pauzner model suggests thatthe upward mobility of whole neighbour-hoods with designation will occur verysoon after designation.

We turn now to the empirical evidence aboutthe relationship between historical desig-nation and neighbourhoods, and considerwhether the actual Fort Worth experiencecoincides with any of these models.

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Figure 3. Concentration of designated property in Fort Worth.

3. Data

We use the city of Fort Worth, Texas, as ourlaboratory to test assertions about the effectof historical preservation on neighbourhoodsand their transition. Our original database,which came from the Tarrant County Ap-praisal District as part of a broader study ofthe economic impact of historical preser-vation in Texas (see Leichenko et al., 2001,for further information), consists of data onover 100 000 residential properties in the cityof Fort Worth. The database contains infor-mation on the historical status of each of theproperties as well as a number of housingcharacteristics such as would typically beavailable on a property assessment form.These data on individual housing units weregeocoded and linked to the census tract inwhich the housing unit is located, of which236 are in the final data-set. The tract-aver-age data are then linked to demographic in-formation on the tract from the 1990 and2000 Censuses.3

Our database includes 1338 residentialproperties in Fort Worth that have been des-

ignated as historic either by placement on theNational Register of Historic Places or byrecognition from the Texas Historical Com-mission or by local historical commissions.So far as we have been able to determine,most of these properties received their desig-nation in the 1980s through to 1990, al-though the earliest recognition date we canfind is 1979. The substantial majority ofthese properties (1245) are in one of threelarge historic districts: Elizabeth Avenue,Grand Avenue and Fairmount–Southside.The remaining 93 historic properties are des-ignated individually as historic, but are notlocated in one of these three historical dis-tricts.

This concentration of historically desig-nated properties within historical districtsdoes not imply that Fort Worth historicalproperties are concentrated in just a few par-ticular census tracts. As illustrated in Figure3, which identifies census tracts containinghistoric properties, many census tracts lo-cated throughout the city contain either his-toric districts or individual historically

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HISTORIC PRESERVATION AND NEIGHBOURHOOD CHANGE 1593

Table 1. Means and standard deviations for Fort Worth census tracts

Tracts without Tracts withAll tracts designation designation

Mean StandardMeanMean Standard StandarddeviationVariable deviation deviation

0.13 0.06Vacancy rate, 1990 0.11 0.06 0.11 0.06Ownership rate, 1990 0.60 0.22 0.61 0.23 0.58 0.19Population. 1990 4 312.98 2 581.972 284.30 4 310.524 313.70 2 196.14Percentage Black, 1990 0.15 0.25 0.130.16 0.220.25Percentage Hispanic, 1990 0.13 0.17 0.11 0.13 0.19 0.24Simpson index, 1990 0.69 0.170.17 0.680.69 0.17Median income, 1989 32 905.48 16 281.15 33 520.79 16 424.05 30 831.69 15 761.81Average year built 1 952.43 16.3815.98 1 947.051 954.03 15.56Average living area 1 505.56 1 600.76537.00 528.581 477.31 537.67Number of designated 0.00 60.335.57 24.3330.43 0.00

homesHistorical designation 0.000.23 1.000.42 0.000.00

dummyChange in vacancy rate � 0.05 0.05 � 0.05 0.05 0.06� 0.04Change in ownership rate 0.02 0.090.08 0.010.02 0.08Change in Hispanic 0.080.09 0.090.08 0.090.09

percentageChange in Black 0.000.01 0.07 0.01 0.07 0.07

percentageChange in Simpson 0.11 0.12� 0.12 � 0.100.11 � 0.12665

indexPopulation growth rate 0.33 1.86 0.35 2.08 0.26 0.71Percentage change 0.290.41 0.430.36 0.290.41

median income

designated properties. In total, 54 of the 236tracts included in our database contain his-torically designated properties. As Figure 3also indicates, there is gratifying variation inthe geographical scope and intensity of his-torical designation.

Designation in Fort Worth is not aphenomenon only of the inner city, but alsohas presence in more suburbanised tracts.Moreover, tracts that overlap with historicdistricts, and therefore have a more substan-tial connection to historic properties, are alsonot confined to the central city—although, ascan be seen on Figure 3, the most concen-trated placement of designated properties isindeed in the centre of the city. This set oftracts is the centre of the Elizabeth Avenueand Fairmount–Southside Historic Districts.

Table 1 presents the means and standarddeviations of the tract-level variables we will

use in our analysis and then presentsstratified means and standard deviations fortracts without and with any historical desig-nation. The row labelled ‘Historical Desig-nated Dummy’ provides information aboutthe indicator variable which equals one if anysuch property exists in the tract. As can beseen from Table 1, 23 per cent of the tracts inthe sample have at least one historically des-ignated property. From the following row,we find that across all tracts there is anaverage of 5.57 properties per tract, but inthose tracts with designated property the av-erage is 24.33.

The columns in Table 1 provide compari-son between tracts that have designation andthose that do not. As can be observed, thetwo types of tract differ slightly, but onlyslightly, in their housing and demographiccharacteristics. While the average popula-

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N. EDWARD COULSON AND ROBIN M. LEICHENKO1594

Table 2. Determinants of the existence and extent of historic designation

Independent variable DependentDependent Dependentvariable � HDUM variable � ND variable � ND

(Poisson) � 100 (Poisson)(probit)

2.59 10� 4 1.55 10� 4Population, 1990 5.82 10� 4

(2.47)(18.16)(1.30)3.32Ownership rate, 1990 2.671.27

(3.07)(12.88)(1.95)18.65 8.48Vacancy rate, 1990 4.09

(3.26)(26.58)(1.92)� 7.97Percentage Black, 1990 � 1.050� 0.554

( � 1.54)( � 16.79)( � 1.06)1.35Percentage Hispanic, 1990 0.2071.20

(0.30)(10.16)(1.84)� 2.26 10� 5Median income, 1989 � 1.91 10� 5� 1.13 10� 4

( � 1.47)( � 6.27)( � 1.14)� 0.0645 � 0.0575Average year built � 0.0245

( � 5.73)( � 22.62)( � 3.18)0.00211Average living area 0.00149.09 10� 4

(5.78)(28.34)(3.91)Pseudo R2 0.47550.0044 0.1572

Note: The above table provides the coefficients and t-statistics (in parentheses) for regressions of theindicated dependent variable on the independent variables in the first column.

tions of designated and undesignated tractsare nearly identical, the designated tractshave rather greater percentages of their popu-lation claiming Hispanic heritage, while thepercentage of the population that is Black islower. Tracts with historical designation alsoseem to have slightly more depressed econ-omic indicators; they have a slightly highervacancy rate, lower home-ownership rate andlower median income. The variables aboveare all derived from the 1990 Census databut, as noted, we have fairly complete dataon single-family properties in these tractsfrom our appraisal data. Although we con-structed a number of indicators of ‘tracthousing quality’, we confine ourselves to twoin this analysis: average building age andinterior square feet. These two seem ad-equate for instrumenting building quality andthe difference between tracts with and with-out designation. Buildings in historic tractsare (naturally enough) somewhat older, butthe difference in average ages is only aboutseven years. A larger and more interestingdifference is that of building size; designatedtracts have buildings that are on average over

1000 square feet bigger than buildings inundesignated areas.

4. Model and Results

Our first regressions are those where thedependent variable is an indicator of theextent to which historical designation existsin a census tract in 1990. Table 2 presentsthese results. In the first column, the depen-dent variable is the dummy variable for theexistence of any historical property and werun probit regressions to model this. As canbe seen there, several of the indicators arecorrelated with the existence of designation.Obviously the average age of the tract isimportant, but so is the size of units. This isof interest if only because age and size arenegatively correlated. Given two tracts withthe same average age (and other characteris-tics), the one with the larger housing units ismore likely to have some historical desig-nation. This is presumably the result of his-torical designation being more likely to occurin places that have been historically wealth-ier than others of the same vintage. However,

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HISTORIC PRESERVATION AND NEIGHBOURHOOD CHANGE 1595

while this may have been the case in the past,it is not so today; the income levels of thedesignated tracts do not have conditionallyhigher income. The coefficient on 1989 in-come is negative and insignificant.

However, there are some demographic dif-ferences. The coefficients indicate that, atstandard levels of significance, tracts withdesignated properties in 1990 were, on aver-age, slightly more Hispanic than others.Significantly, the vacancy rate is higher inthose neighbourhoods as well, which is con-sistent with the idea that designation is di-rected at those areas with more depressedhousing markets. However, it is also the casethat neighbourhoods with designation havehigher home-ownership rates (and recall thatwe have controlled for building size); thus, ifit is the case that home-owners are morepolitically aware (as suggested byDiPasquale and Glaeser, 1999), thiscoefficient is consistent with a picture ofhistorical designation as the result of neigh-bourhood activism.

We explore this topic in a slightly differentway in column 2, which uses a Poisson countregression model to examine the determi-nants of the number of designated propertiesin a tract. The Poisson was used because ofthe integer nature of the variable and becausethe unconditional distribution actually some-what resembles a Poisson.

The results of this regression are, in aword, incredible. The goodness-of-fit mea-sure is 45 per cent, and all of the coefficientsare highly significant, and of the signs thatare to be expected (and the same as in theprevious probit). One of the problems withthis regression is that it may be influencedunduly by the few tract observations withlarge numbers of historical properties. As acheck on these results, we run the sameregression, but eliminating from the sampleany tract with more than 100 designatedproperties, and these results are rather morehumbling. The goodness-of-fit measure is re-duced to a bit under 16 per cent and all of thet-statistics are reduced in magnitude. Themajor difference between these results andthe probit of column (1) is that the popu-

lation of the tract is now significant, which issensible given that we are trying to explaincounts of homes in a tract. But the rest of thecoefficients reflect the results in column (1):tracts with greater vacancy rates, proportionsof owner-occupied houses and buildings witholder vintages or more space have more des-ignated properties. However, income differ-ences are still not significant.

How do we interpret these results? Theresults do not support the theories of neigh-bourhood change described above; if such‘upward tipping’ theories were true, wewould expect that in 1990 the indicatorswould be significantly better for tracts withdesignation than those without. One wouldhave expected indicators such as (and per-haps especially) vacancy rates to have beenlower in those areas in which designation hasoccurred. It would seem rather that we aremodelling the choices made by historicalcommissions (or neighbourhood activists) todesignate properties in the tract. There isclearly an element of choice involved. Lis-tokin et al. (1998, p. 460) quote Rose (1981):“The phrase ‘historical preservation’ is soelastic that any sort of project can bejustified”, although Listokin et al. (p. 461)say that most preservation efforts are“judicious, so that historic designationsreflect legitimate concerns to protect a com-munity’s historical resources”. However, ifhistorical designation is indeed a develop-ment tool, then it is presumably aimed atlocations that would receive more benefitfrom place-based development. These regres-sions are therefore not assessing the causaleffects of historical designation, but the re-verse.4

Therefore, we turn now to the second setof regressions that attempt to estimate thechanges between 1990 and 2000 that mightbe ascribed to historical designation. Theseregressions are now meant to be causal sinceall of the historical designation is prior to theyear 1990. They are of the form

90–00 yi � f(x90)

where, the left-hand side is the change from1990 to 2000 of some tract characteristic of

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N. EDWARD COULSON AND ROBIN M. LEICHENKO1596

Figure 4. Change in ownership rate (orc) vs 1990 ownership rate (or90). Note: 1 indicates a tract withhistorically designated properties.

interest, yi, and x90 is a set of regressors dated1990 (or perhaps earlier) among which willalways be an indicator for the extent of his-torical designation in the tract. The depen-dent variable will alternately be: populationgrowth rate; the change in the owner-occu-pation rate; the change in the vacancy rate;the percentage change in median tract in-come; and the change in the Simpson indexof (ethnic) diversity. The Simpson index ofdiversity is measured as the sum of squaredshares of each population group within thetract and is used as a measure of ethnicchange in the tract. We use this, as opposedto changes in population shares of variousethnic groups, because it was felt that this ismore what planners, and others, wish to seeemerge from a process of neighbourhoodrevitalisation.5

For each dependent variable, four regres-sions were run. Bivariate regressions withHD and NUMDESG as the sole independentvariable were calculated, as were multipleregressions with 1990 measures of popu-lation, vacancy rates, ownership rates, Blackand Hispanic percentages and size and age ofthe housing stock, along with 1989 medianincome.

The results are presented in Table 3 and it

is straightforward to summarise the lesson ofthat table. We obtain no significant outcomesfor neighbourhoods that are the result ofeconomic development that arises from his-torical designation. Both HD andNUMDESG are insignificant in both the con-ditional and unconditional regressions. Thesole exception is in the multivariate regres-sions with the vacancy rate as the dependentvariable and HD as the designation measure.But even then the coefficient on HD is posi-tive, indicating that, after controlling for theother attributes of a neighbourhood, thosetracts with historical designations had in-creases in residential building vacancy rateshigher than those without. This case aside,the t-statistics for the preservation variablesare almost always less than one. Thus, ourinterpretation is straightforward. Historicaldesignation and preservation have no impacton neighbourhood composition.

What one does observe from this set ofregressions is convergence. In several of theregressions, the only significant variable isthe ‘own’ variable—the 1990 ‘starting-point’for that particular dependent variable. This isan indication of convergence of tracts to-wards the mean. For example, tracts withhigh ownership rates experience a relative

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1597HISTORIC PRESERVATION AND NEIGHBOURHOOD CHANGE

Tab

le3.

Impa

cts

ofhi

stor

icpr

eser

vatio

non

dem

ogra

phic

and

hous

ing

char

acte

rist

ics

ow

nersh

ipPe

rcen

tage

Perc

enta

gePe

rcen

tage

ow

nersh

ipPe

rcen

tage

ow

nersh

ip

owne

rship

Si

mps

onPo

pulat

ion

rate

m

edia

nra

te

vaca

ncy

Si

mps

onPo

pulat

ion

va

canc

y

med

ian

m

edia

n

vaca

ncy

Si

mps

on

med

ian

va

canc

yra

tePo

pulat

ion

Si

mps

onPo

pulat

ion

rate

rate

rate

inde

xra

te19

90–2

000

inde

x19

90–2

000

1990

–200

0in

com

e19

90–2

000

grow

thra

tein

com

egr

owth

rate

grow

thra

tegr

owth

rate

inco

me

inde

xra

tein

dex

inco

me

0.34

8–0

.339

70.

333

–0.1

223

0.22

4–0

.357

0–0

.011

0–0

.126

60.

020

0.46

531.

220.

0171

0.35

501.

321

–0.0

445

Inte

rcep

t0.

413

1.25

0.41

4(3

.48)

(–14

.81)

(0.0

0)(–

0.41

)(0

.02)

(–15

.57)

(2.7

0)(2

.52)

(–0.

039)

(–13

.51)

(1.7

3)(1

4.48

)(1

.10)

(0.3

7)(3

.28)

(16.

39)

(1.4

4)(0

.37)

–0.0

866

–0.0

93–0

.009

8–0

.011

–0.0

024

HD

UM

0.01

220.

250

–0.0

100.

0162

0.02

13(–

0.31

)(–

0.71

)(–

0.31

)(–

0.86

)(2

.24)

(0.2

8)(–

0.81

)(0

.32)

(–0.

30)

(1.4

0)–8

.34

10–4

2.16

10

–4–0

.149

10

–34.

29

10–4

1.83

10

–41.

82

10–4

–1.4

610

–4N

UM

DES

G6.

93

10–5

0.00

052.

54

10–4

(–0.

037)

(1.0

9)(1

.07)

(–1.

36)

(–0.

021)

(0.9

3)(0

.78)

(1.0

7)(–

1.14

)(0

.59)

–2.2

610

–60.

0002

443

.000

2447

–3.2

10

–5Po

pula

tion,

–2.5

710

–6–3

.91

10–6

–3.7

710

–6–1

.97

10–6

–2.0

510

–6–3

.2

10–5

1990

(–0.

61)

(–0.

94)

(–4.

41)

(–0.

64)

(–3.

84)

(–4.

39)

(–2.

59)

(–3.

68)

(–1.

07)

(–2.

60)

0.03

04–0

.830

0–0

.841

–0.0

225

–0.0

715

Ow

ners

hip

–0.0

254

–0.1

66–0

.077

10.

0289

–0.1

66(0

.81)

(–2.

42)

(–1.

87)

(–2.

28)

(0.7

7)(–

1.65

)(–

1.14

)(–

1.17

)(–

1.12

)ra

te,1

990

(–1.

17)

–0.1

278

4.08

2–1

.33

4.10

–0.0

671

–0.1

229

–0.0

958

Vac

ancy

–0.7

272

–1.3

3–0

.719

4(1

.54)

(1.5

5)(–

0.59

)(–

0.84

)ra

te,1

990

(–0.

95)

(–15

.01)

(–2.

72)

(–0.

99)

(–14

.70)

(–2.

75)

–1.0

40.

0771

–1.0

5Pe

rcen

tage

–0.0

071

–0.0

030

0.04

350.

195

0.04

260.

194

0.07

85(–

1.52

)(–

1.70

)(–

1.70

)(–

0.27

)(–

0.11

)B

lack

,(3

.86)

(2.6

2)(3

.75)

(2.6

6)(–

1.53

)19

900.

046

0.49

11–0

.793

–0.7

81–0

.024

30.

4939

–0.0

351

0.05

3(0

.33)

–0.0

183

–0.0

157

Perc

enta

ge(0

.29)

(–0.

95)

(–0.

93)

(–0.

67)

His

pani

c,(–

0.97

)(–

1.01

)(–

1.20

)(1

2.33

)(1

2.24

)19

90–1

.27

10–7

1.38

10

–5–1

.24

10–5

1.40

10

–5M

edia

n–9

.31

10–8

–1.8

810

–7–2

.15

10–7

1.21

10

–61.

23

10–6

–1.2

310

–5

inco

me,

1989

(2.0

7)(–

0.25

)(1

.19)

(2.1

3)(–

0.87

)(1

.17)

(–4.

61)

(–0.

99)

(–0.

18)

(–4.

64)

5.20

10

–56.

12

10–4

1.90

010

–77.

31

10–4

–0.0

0062

4.27

10

–5–0

.000

48A

vera

ge–1

.56

10–4

1.44

10

–5–2

.15

10–4

(0.0

7)(0

.08)

(–1.

57)

(–1.

24)

year

built

(0.1

0)(–

0.94

)(0

.00)

(0.1

1)(–

1.29

)(0

.01)

1.57

10

–5–1

.12

10–4

1.51

10

–5–1

.19

10–4

–1.6

210

–5A

vera

ge–2

.11

10–5

–2.9

910

–41.

12

10–5

–3.1

210

–41.

35

10–5

(–0.

38)

(–0.

41)

(1.0

6)(1

.06)

livin

gar

ea(2

.54)

(–1.

27)

(–0.

57)

(2.0

7)(–

1.69

)(–

0.56

)0.

0004

0.00

020.

1230

0.12

310.

0028

R20.

0862

0.08

760.

0076

0.59

710.

5897

0.00

950.

4560

0.00

560.

4581

0.24

40.

245

Not

e:Th

ista

ble

prov

ides

the

coef

ficie

nts

and

t–st

atis

tics

(inpa

rent

hese

s)fo

rre

gres

sion

sof

the

indi

cate

dde

pend

entv

aria

ble

onth

ein

depe

nden

tvar

iabl

esin

the

first

colu

mn.

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N. EDWARD COULSON AND ROBIN M. LEICHENKO1598

decline in ownership rates during the 1990sand those with low ownership rates see anincrease. Observe Figure 4, which gives thegraph of ownership rate change (orc) againstinitial ownership rate (or90). The symbol ‘1’indicates a tract with designated properties.The ticks fan inwards and are roughly nega-tively sloped as would be expected given theconvergence of ownership rates, but no his-torical tract is associated with any large in-crease in home-ownership.

There are a few other significant t-statis-tics scattered through Table 3. Tracts withlarge Black populations are associated withincreases in the vacancy rates and changes inthe diversity index are associated with largeinitial Black and Hispanic populations. Thisis rather like a convergence result in itself.Also, tracts with larger populations andlower vacancy rates are associated withsmaller growth rates in median tract income.

However, the effect of historical desig-nation is not entirely neutral. As noted in theintroduction, our previous work has foundthat historical designation increased the levelof housing prices for individual homes (Le-ichenko et al., 2001) and the level of pricesin census tracts that contained historicalhomes or districts (Coulson and Leichenko,2001). Although housing prices are some-what outside the scope of the present paper,we performed similar regressions (i.e. withidentical conditioning variables) to those re-ported in Table 3. Using the percentage in-crease (over the period 1990–2000) ofmedian housing price in the tract as thedependent variable, we found that thosetracts with historically designated homes hada significantly higher rate of increase in prop-erty values.6 Thus it would seem that theeffects of historical designation are not toonoisy to blunt totally the measurement of anyeffects. However, the only observable effectis that of the increased desirability of theneighbourhood; whether that is due to theincreased cachet of historical designation, ordue to the strictures placed on the owners ofsuch property, remains an area of research.What seems clear from the evidence pre-sented in Table 3 is that designation is not

associated with neighbourhood turnover ofany type.

We experimented with other measures, in-cluding growth in the stock of housing, otherdiversity indexes and simple growth in theminority populations, and used other mea-sures of the extent of historical designation.(Moreover, in view of the absence ofspillovers within tracts, we eschew examin-ation of intertract spillovers and other spatialconsideration.) The overall results remainthat no significant change in tract demo-graphic composition is associated with anymeasure of historical designation.

5. Conclusion

Designation of historic properties and his-toric districts is a popular, yet somewhatcontroversial, tool for revitalisation of oldercentral-city areas. In this paper, we explorethe effects of historical preservation on thecharacteristics of neighbourhoods in FortWorth, Texas, during the period from 1990to 2000. In particular, we examined the im-pact of the existence and extent of historicalpreservation on tract demographic and hous-ing characteristics between 1990 and 2000.

Our overriding conclusion is that historicaldesignation does not lead to gentrification, orany other kind of neighbourhood turnover.We found that, while there is some evidencethat areas are chosen for preservation effortswith neighbourhood revitalisation in mind,the decade or so following designation pro-duced no significant change in neighbour-hood demographic composition. Those whofear that designation will lead to displace-ment of lower-income residents may bemollified on that account. Concerning theeffects of designation on local economic con-ditions, we found evidence that historic pres-ervation increases property values, but haslittle effect on other measures such as va-cancy rates and rates of owner-occupancy.Those who hope that designation will lead todramatic economic development benefitsmay be disappointed on that account.

Our findings have several implications forurban development policy. Our results sug-

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HISTORIC PRESERVATION AND NEIGHBOURHOOD CHANGE 1599

gest that designation has direct benefits interms of property values—a finding thatreaffirms the conclusions of our prior re-search (Leichenko et al., 2001). Higher prop-erty values, in turn, are likely to have indirectbenefits for cities in the form of higher prop-erty tax revenues. With regard to neighbour-hood revitalisation efforts, however, ourresults suggest that historical preservationmay not be as effective a tool as is some-times thought. If the aim of urban policy is torevitalise deteriorating, older neighbour-hoods, then historic districting representsonly a partial solution. More direct measuresin central-city areas—such as incentive pro-grammes to promote the purchase of vacanthousing by owner-occupants—are also re-quired.

Notes

1. The Simpson index is defined in section 3.2. The bids by low-income people may rise as

well, although for simplicity we eschew thatpossibility in Figure 2.

3. As is typical, there were some 1990 tractsthat were split into two tracts for the 2000Census. These tracts were recombined into asingle tract to preserve comparability be-tween the two Censuses. There were no otherborder changes or other issues with tractcomparability. In view of the results we re-port in the next section, it might be thoughtthat census tracts are too large an area toobserve the spillover results of historical des-ignation. We believe, however, that tracts arein fact an appropriate level of observation,first because there are some impacts of pres-ervation we have observed—particularly,price changes—in other studies (as describedabove). Furthermore, part of our interest is inwhether the extent of preservation mattersand it is easier to obtain sample variation ofthis when tracts are used.

4. We did not find any causal impact of priordesignation on any of the 1990 indicators.Since these results are so similar to the re-gressions presented in Table 3, we omit themfor convenience.

5. However, letting y be population proportionof Whites, Blacks or Hispanics had similar,insubstantial results.

6. Using HDUM as the measure of historicaldesignation yielded a coefficient on HDUMof 0.088 and a t-statistic of 1.98 in the fullregression and a coefficient of 0.085 and a

t-statistic of 2.20 in the binary regression.Slightly weaker t-statistics were observedwhen the number of designated properties inthe tract was used instead.

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