### Working Paper Series

U.S. Environmental Protection Agency National Center for Environmental Economics 1200 Pennsylvania Avenue, NW (MC 1809) Washington, DC 20460 http://www.epa.gov/economics Moving Beyond Cleanup: Identifying the Crucibles of Environmental Gentrification

H. Spencer Banzhaf and Eleanor McCormick

Working Paper # 07-02 January, 2007Moving Beyond Cleanup: Identifying the Crucibles of Environmental Gentrification

H. Spencer Banzhaf and Eleanor McCormick

NCEE Working Paper Series
Working Paper # 07-02 January, 2007

DISCLAIMER The views expressed in this paper are those of the author(s) and do not necessarily represent those of the U.S. Environmental Protection Agen cy. In addition, although the research described in this paper may have been funded entirely or in part by the U.S. Environmental Protection Agency, it has not been subjected to the Agency's required peer and policy review. No official Agency endorsement should be inferred. Correspondence:

Spencer Banzhaf Department of Economics Georgia State University P.O. Box 3992 Atlanta, GA30302 (404) 651-6981, hsbanzhaf@gsu.eduMoving Beyond Cleanup: Identifying the Crucibles of Environmental Gentrifi cation

H. Spencer Banzhaf Georgia State University

Eleanor McCormick Resources for the Future

Correspondence: Spencer Banzhaf Department of Economics, Georgia State University PO Box 3992, Atlanta, GA 30302 404-651-6981, hsbanzhaf@gsu.edu
Report Prepared for the National Center for Environ mental Economics (EPA) December 2006

This paper reviews the distributional impacts assoc iated with "environmental gentrification" following the cleanup and reuse of Superfund sites, brownfields, and other locally undesirable land uses (LULUs). By making a neighborhood more attractive, cleanup and reuse of LULUs may drive up local real estate prices. Renters in the neighborhood would have to pay higher rents. Altho ugh they would also reap a capital gain, homeowners too would face higher housing cost s, including perhaps higher tax bills. Moreover, existing residents may not value the removal of the disamenity as much as other households, creating a mismatch between th eir priorities and the new character of the neighborhood. Thus, even if they do not mov e, existing residents, especially renters, may be harmed by the gentrification effect s of cleanup. If many former residents do move, to be replaced by wealthier households, th e character of the neighborhood would change further, feeding the gentrification. Such environmental gentrification is a key concern of local stakeholders. Nevertheless, t he extent of environmental gentrification, if any, following cleanup and reuse of LULUs has not been solidly confirmed in the empirical literature. This paper seeks to fill that void by reviewing the evidence to date. We find limited evidence for rising real estate pr ices following cleanup of LULUs. We find stronger evidence for increased hou sing density and increasing incomes, but no evidence for racial impacts. Our r eview also uncovers a variety of factors that are likely to temper the adverse conse quences of gentrification for residents.
Key words: land cleanup, reuse, gentrification, bro wnfields Subject Area Classifications: Hazardous Waste, Land Use, Distributional Effects1 Moving Beyond Cleanup: Identifying the Crucibles of Environmental Gentrifi cation

H. Spencer Banzhaf 1 Eleanor McCormick

I. Introduction
The protection of land and the cleanup of Superfund sites, brownfields, and other locally undesirable land uses (LULUs) is a core part of the mission of the Environmental Protection Agency. To achieve the full benefits of cleanup, “reuse,” the next step of restoring these sites to productive uses, is increa singly an emphasis as well (Vitulli et al. 2004). In evaluating the success of such programs, benefit-cost analysis naturally can play a central role, and accordingly EPA has recent ly reviewed the benefits of cleanup and reuse of Superfund sites (E2 Inc. 2005, US EPA 2006a,b). However, net economic benefits cannot be the only criterion for planning. Over 40 years ago, writing in the context of water programs, Maass et al. (1962) note d that there might be other important policy objectives, such as the distribution of net benefits. They suggested that multiple objectives, rather than a single measure such as ne t benefits, be given weight in policymaking. In some cases multiple objectives may be c ombined (as traditional benefit-cost analysis would seek to do), but in other cases inco mmensurable impacts are best left separate. 2
In that spirit, this paper reviews the distribution al impacts associated with "environmental gentrification" (Sieg et al. 2004) f ollowing the cleanup and reuse of LULUs. By making a neighborhood more attractive, c leanup and reuse of LULUs may drive up local real estate prices. Renters in the neighborhood would have to pay higher rents. Moreover, existing residents may not value the removal of the disamenity as much

1 Spencer Banzhaf is an Associate Professor in the D epartment of Economics, the Andrew Young School of Policy Studies, Georgia State University. Eleanor McCormick is a Research Assistant at Resources for the Future. We thank Marie Howland, Robin Jenkins, Eli zabeth Kopits, Sarah Beth Link, Brian Morrison, Kate Probst, David Simpson, Jeffrey Zabel, and espe cially Kris Wernstedt for comments and suggestions. We also thank participants at an EPA/NCEE seminar. The results in this paper reflect the views of the author and do not necessarily reflect the views of the U.S. Environmental Protection Agency. 2 The same consensus was recently reached by members of a workshop on reuse of contaminated properties (Probst and Wernstedt 2004).2

as other households, creating a mismatch between th eir priorities and the new character of the neighborhood. Thus, even if they do not mov e, existing residents, especially renters, may be harmed by the gentrification effect s of cleanup. 3 If many former residents do move, to be replaced by wealthier hous eholds, the character of the neighborhood would change further, feeding the gent rification. Such environmental gentrification is a key concern of local stakeholde rs, as emphasized in a recent report from the National Environmental Justice Advisory Co uncil (NEJAC) on "Unintended Impacts of Redevelopment and Revitalization Efforts " (2006). Nevertheless, the extent of environmental gentrification, if any, following cleanup and reuse of LULUs has not been solidly confirmed in the empirical literature. This paper seeks to fill that void by reviewing the evidence to date.
Wernstedt (2004) has recently identified a range of such impacts that might arise from cleanup and reuse of LULUs, but which would no t be classified as standard economic benefits, and has carefully reviewed the s tudies and data that speak to them. Unfortunately, as he notes, most studies to date ha ve not distinguished between cleanup and reuse. Moreover, few have provided insights in to the distribution of the costs and benefits of cleanup and reuse. Indeed, Wernstedt w rites in his conclusion that, of fourteen studies on land reuse, none
examines in any great detail the distributional eff ects of reuse with respect to different segments of a community. Yet, many co ntaminated properties lie in disadvantaged areas, where site reuse has th e potential to address long-standing issues of local environmental and eco nomic inequities. Future work to clearly identify the communities and subpopulations that are supposed to benefit from the reuse of contamina ted land—and to assess where the beneficial effects of reuse actual ly go—could both make explicit what often appears to be an implicit objec tive of site reuse as well as improve program initiatives to accomplish this o bjective. Such work would require baseline documentation of the socio-e conomic composition of the communities hosting reuses and systematic tr acking of changes in this composition and general community well-being.

3 Viewed in these terms, the logic of environmental gentrification is somewhat the reverse of the logic of the “environmental justice” literature. If househo lds chose to live in dirtier neighborhoods because they prioritized cheap housing over environmental amenit ies, cleaning up adverse land uses and causing pric es to increase may work against their greatest needs.3

Although the situation is not quite as bleak, much the same could be said of our state of understanding of the distributional effect s of cleanup as well.
To overcome some of these limitations, in this pape r we review a wider literature that allows us to garner insights into potential im pacts. We first take a closer look at what sociological and economic theories say we migh t expect too happen under various conditions. We then look to empirical studies of t he correlation of both land prices and demographics—key aspects of gentrification—with lan d uses. We also review a more recent economic literature that, while not necessar ily connected to land use per se, has begun to quantify the connection between land price s and demographics with other social
forces such as crime and education.
We first find that even a simple economic model doe s not yield clear predictions on neighborhood effects following cleanup, except f or the initial effect of rising housing values. In the empirical literature, we actually f ind conflicting evidence of rising real estate prices following cleanup of LULUs. We find somewhat stronger evidence for increased housing density and increasing incomes, b ut no evidence for racial impacts. Our review also uncovers a variety of factors that are likely to minimize the likelihood of gentrification or temper its adverse consequences.
This report is divided into six sections. Section II explores what, exactly, is meant by the term "gentrification" as understood fr om diverse perspectives and academic disciplines. Section III encapsulates some of thes e insights into an economic model that yields predictions about the most likely impacts fr om local land use changes associated with cleanup and reuse. (A more formal version of the model is provided in an appendix.) Section IV begins a review of the relev ant empirical literature, starting with “partial equilibrium studies” which capture one asp ect of gentrification at a time. Section V extends the review to “general equilibriu m” studies, which have tried to capture multiple aspects of gentrification simultan eously. Section VI concludes.4

II. What is Gentrification?
Ruth Glass, a sociologist, is credited with coining the term “gentrification.” Describing the metropolitan center of London in the 1960s, Gla ss (1964) portrayed gentrification as a process of invasion whereby
one by one, many of the working class quarters of L ondon have been invaded by the middle classes-upper and lower. Shabby, modest mews and cottages—two rooms up and two down— have been taken over, when their leases have expired, an d have become elegant, expensive residences. Larger Victorian ho uses, downgraded in an earlier or recent period...have been upgraded once again....Once this process of 'gentrification' s tarts in a district it goes on rapidly until all or most of the origina l working class occupiers are displaced, and the whole social chara cter of the district is changed. (xviii-xix)
Thus, class and class change are at the center of G lass’s work and remain so in contemporary definitions and studies of gentrificat ion. Nevertheless, contemporary definitions and popular understandings have evolved and expanded over the years.
Hallmarks of Gentrification
Today, the concept of gentrification has spread int o many disciplines and into popular discourse. Writers across disciplines, and even within them, would not agree on everything that is meant by the term "gentrificatio n." Nevertheless, most would agree on three hallmarks: rising property values and rental costs; new construction or renovation upgrading the housing stock and perhaps converting it from rental to owner-occupied units; and a turnover in the local population, brin ging in residents with a higher
socioeconomic status. 4 Their new economic status also may bring changes in the racial or ethnic composition of the neighborhood, sometime s creating tensions along these lines (Levy et al. 2006).
Economists have generally stressed the market force s that link many of these characteristics together. As a neighborhood become s more desirable for some reason, more households, including wealthier ones, desire t o live there. This increased demand

4 This may be accompany abnormal rates of turnover i n a rapidly changing neighborhood, but not necessarily. Nationally, almost half of all househ olds move within a five-year period. Such a baseli ne rate of mobility may be sufficient to bring about a chan ging character.5

for space increases real estate prices to a level t hat the wealthier households can afford, but that previous residents might not. The wealthi er households, in turn, can also afford to improve their living environments in both size a nd appearance, and so they renovate their property. This renovation then further incre ases housing costs, reinforcing the
trend.
Sociologists emphasize these market forces as well, but also describe an aesthetic process. Sharon Zukin (1987), in an excellent intr oduction to the topic, notes that while gentrification can involve new development, often i t involves the renovation or rehabilitation of historic buildings. As with Glas s's Victorian mansions, this renovation reflects the preferences of specific demographic gr oups for the architecture in the gentrifying neighborhood. Furthermore, historic pr eservation ordinances may solidify the process.
Where various depictions of gentrification differ t he most is in their understanding and description of the indirect effec ts that new demographic groups, with their new aesthetic tastes, have on the characteris tics of the neighborhood. At the simplest level, the demographic changes themselves may represent a new aesthetic. If racial or socioeconomic groups have differing prefe rences for the racial or socioeconomic makeup of the neighborhood (perhaps preferring to l ive with people like themselves), initial demographic changes would fuel further chan ges in the demographic composition. (See the economic literature on “endogenous” racial amenities and sorting, e.g. Bayer et al. 2003, 2005, Sidon 2005, Schelling 1969.)
Sociologists tend to stress the character of the co mmunity that different groups create, giving more content to the economists' mode l. Some highlight gentrification as the arrival of yuppies. Others stress the arrival of a more bohemian culture. For instance, Zukin (1987) describes gentrification as a radical break with suburbia, “a movement away from child-centered households toward the soci al diversity and aesthetic promiscuity of city life” (p. 131).
These new groups may have tastes for different priv ate goods. Thus, another aesthetic aspect of gentrification is the change in the character of local retail and other services. O’Sullivan (2005) develops an economic m odel in which low- and high-income6

residents consume distinctive private goods. As mo re high-income residents move in, local provision of their good increases (more coffe e shops, florists, book stores), making the neighborhood more attractive to those household s and again reinforcing the demographic shift. 5 O’Sullivan calls this the "Starbucks effect."
Other indirect affects of demographic changes in th e community may include changes in the crime rate or quality of local publi c schools. Crime may first increase following gentrification, as increased inequality c reates more tension (associated with violent crime) and new targets for property crime. The former effect in particular appears strong in the data (Blau and Blau 1982, Morgan 2000 ). Consistent with these findings, some have suggested that the first wave of gentrifi ers has a high tolerance for crime (Skogan 1986, Zukin 1987). 6 In the longer run, as the gentrification process continues, crime would be expected to decrease. Likewise, loc al school quality might improve, via the peer-group effect in which the higher education al attainment of wealthier families spill over to other children as well. 7
A Case Study of Portland, Oregon 1990-2000 highligh ts many of these changes, including changes in racial composition, income com position, and housing prices. Specifically, the average number of blacks per cent ral city tract decreased, while elsewhere the number of black persons increased; si multaneously, households with at least twice the poverty income increased. Median h ousing prices increased by a factor of 3.4 in the inner city and 2.6 in the rest of the ci ty. In addition, crime decreased by 49% in the inner city but only 5% in the rest of the city (O’Sullivan, 2005). A case study of Boston from 1970 to 1998 found influxes of college- educated households in gentrifying areas, but found city-wide increases in land values . It concluded that the effects had

5 Although these are private goods for all practical purposes, technically small economies of scale in production induce some publicness, with the goods o nly provided if local demand is high enough. 6 This finding is confirmed by information on reside nts’ reactions and opinions to their newly resettle d homes and neighborhoods as solicited in both the Mo unt Pleasant and Capitol Hill neighborhoods in Gale ’s (1976-77) survey of Washington DC. Even with a maj ority of respondents, within each of these area studies, reporting personal victimization, in or re lated to a criminal incident and unease pertaining to the level of criminal activity, “most saw it as a neces sary price to pay in order to enjoy the attributes of innercity living” (Laska and Spain, 1980, p. 102). 7 But, as Vigdor (2002) notes, if gentrifying househ olds do not have children they would have little im pact on schools.7

more to do with metro-wide changes in the income di stribution than with local amenities (Vigdor 2002).
While this study has examined a generally gentrifyi ng city, one final part of gentrification is the fate of other areas that do n ot gentrify. Gentrification’s contribution to increased homelessness and trends in displacemen t has long been a contentious area of study (Zukin 1987). One of these displacement phen omena is termed the mismatch hypothesis, in which a mismatch of skills of previo us blue-collar residents and the requirements of new white-collar jobs leads to unem ployment or migration (Frey, 1979). Zukin (1987) emphasizes that gentrification can tak e place alongside areas that continue to deteriorate. Glass (1964) similarly saw, in 196 0s London, “pockets of blight” (p. xx) growing more dense and areas where “change and stag nation exist side by side” (p. xxv). On this reading, as low-cost neighborhoods gentrify , poorer residents have fewer options, and so pack into remaining poor neighborhoods more densely.
Theories of Neighborhood Change
These various hallmarks of gentrification receive g reater or less weight, or are described in different terms, according to differen t theories of neighborhood change. In addition to neoclassical economics, five social the ories of neighborhood change serve as paradigms for descriptions of gentrification (Liu 1 997 and Zukin 1987). The five theories are the invasion-succession model, the nei ghborhood life-cycle model, the pushpull model, the institutional theory of neighborhoo d change, and a Marxist-materialist perspective. Most of these theories have component s that resonate with neoclassical economics as well; accordingly we discuss economic theories along side the sociological.
Associated with the Chicago school of human ecology , the classical invasionsuccession model is based on an ecological process in which a new plant invades a habitat and replaces previous plants as the dominan t species, only to be itself replaced by a later invasion. This process, according to the t heory, is mirrored in human society, with succeeding demographic waves cascaded over a neighb orhood. This may well be what Glass (1964) had in mind when describing gentrifica tion as an "invasion." In Liu’s (1997) analysis of the theory, the invasion-success ion model implies that relationships between races will be characterized by competition, conflict, and accommodation.8 The neighborhood life-cycle model, originally formu lated by Hoover and Vernon (1959), as cited in Liu, also views neighborhood ch ange as a natural process. Here, the analogy is to generational succession rather than e cological succession. As housing stock ages, it becomes less desirable, leading to success ive in-migration of lower socioeconomic groups that are willing to occupy it at reduced costs. Eventually, when it deteriorates to a critical point, the housing stock is recycled and renewed. Economists will recognize in this perspective a “filtering” mo del or a model of the optimal timing of investment (e.g. Brueckner and Rosenthal 2005, Rose nthal 2006, Somerville and Holmes 2001). As a young housing stock first begins to ag e, renovating it requires high fixed costs and only modest returns. Eventually, as it c ontinues to age, it reaches a point at which investment is economical. This process has b een confirmed in empirical work by Helms (2003), who finds age a significant factor in the prediction of housing renovation.
This life-cycle perspective is important for unders tanding the potential consequences of decontamination and reuse of LULUs. If local land prices and the
socioeconomic status of local residents are correla ted with LULUs primarily through mutual correlation with the age of a neighborhood, removal of the LULU would not reverse the basic phenomenon, and gentrification wo uld be less likely to occur.
A third theory, the so-called push-pull model, focu ses on two related forces. The pushing force includes neighborhood disamenities th at make a neighborhood less desirable for residence, such as existing (or plann ed) LULUs. The pulling forces are amenities, those things that might draw a person to reside in a given neighborhood, for example parks or employment opportunities. The pus h force motivates households to relocate, and they then choose a new neighborhood b ased on its pulling factors (e.g. Frey 1979). Applied to land uses, this theory interpret s the cleanup of a LULU as pulling in
those who are attracted to the new set of amenities and pushing out those who cannot afford to stay (see Liu 1997). Clearly, the sociol ogists' push-pull model is quite close to neoclassical economic models of neighborhood choice , in which households choose where to live based on the balancing of prices and desirable and undesirable features. One difference, however, is that economists would t ell a story of simultaneous push and pull forces. Households may decide to relocate bas ed on changing pull factors elsewhere, even without new push factors in their current neig hborhood. This increases the9

prospects for environmental gentrification, since c leanup and reuse will be a pull factor for many residents.
Fourth, the institutional theory indicates that ins titutions, including universities, banks, and insurance companies, play a large role i n the status of a neighborhood as they make their location, economic, and political decisi ons. Banks, for example, can consign a neighborhood to decline through the practice of “ redlining," in which they refuse to finance mortgages or home equity loans in a neighbo rhood, a practice emphasized by NEJAC (2006). According to the institutional theor y, amenities or push-pull factors have only a minor role to play. Thus, LULUs would not n ecessarily be obstacles to neighborhood stability and do not necessarily lead to neighborhood decline. Reuse, on the other hand, would not be expected to occur in r edlined areas, as it is often connected to large investments, in some cases backed by publi c subsidies or insurance (Wernstedt, Meyer, and Yount 2003).
A fifth and final perspective comes from Marxism, w here gentrification is a manifestation of the propensity for capital to repr oduce itself. Zukin (1987) writes that, according to this perspective,
in our time, capital expansion has no new territory left to explore, so it redevelops, or internally dedifferentiates, urban s pace. Just as the frontier thesis in US history legitimized an economic push t hrough ‘uncivilized’ lands, so the urban frontier thesis legitimizes the corporate reclamation of the inner city from racial ghettos and marginal bus iness uses. (Zukin, 1987, p. 141)
From the materialist viewpoint, such expansion migh t also partly explain conversion of rental units to owner-occupied units. Again Zukin:
As a form of homeownership, gentrified dwellings ar e both a means of accumulation and a means of social reproduction for part of the highly educated middle class. Moreover, as a reference to specific building types in the center of the city, gentrification connotes both a mode of high status cultural consumption and the colonization of an exp anding terrain by economic institutions [and employment] associated w ith the service sector. (Zukin, 1987, p. 144)10

Thus, the expansion of capital into the neighborhoo d may represent the accumulation of wealth, or it may represent the social expansion of capitalists. In any case capitalists are simply doing what capitalists do.
Although there are many perspectives and ways of de scribing gentrification, most authors appear to agree on the main signs and indic ators. Thus, an operational and widely accepted definition of gentrification might read as follows. Gentrification is a phenomenon with many reinforcing characteristics, i ncorporating three hallmarks of community change: rising property values and renta l costs; renewal or creation of housing stock corresponding to the appreciation of housing values; and changes in demographic composition, especially economic status but perhaps affecting race, education, and household size as well. A fourth se t of issues, differing more widely from case to case and writer to writer, is the formation of new amenities, including the aesthetic feel of a neighborhood, its crime rate, a nd its school quality.

III. A Conceptual Framework for Gentrification
Many of the most salient features of gentrification can be captured in an economic model of household choice for communities. In particular , models that follow the insight of Tiebout (1956), in which households "vote with thei r feet" and select communities providing their preferred level of amenities and ho using costs, can predict community impacts following changes in public goods, such as the cleanup and reuse of LULUs. Of this class of models, we use one introduced by Eppl e, Filimon, and Romer (1984), a more general version of which has been estimated econome trically by Epple and Sieg (1999) and applied to environmental improvements by Sieg e t al. (2004) and Walsh (2004). Banzhaf and Walsh (2006) and Vigdor (2006) use this model to gain insights into environmental gentrification. The results presente d here are derived in Banzhaf and Walsh. In the appendix to this paper, we represent this model formally and derive mathematically the key insights discussed here. 8

8 This section and the appendix are written in paral lel. Thus, readers wishing to skip the mathematica l details may safely read this section and skip the a ppendix; those interested in the mathematical versi on may do the opposite.11 In this model, households differ by their income an d by their tastes for public goods/bads, including contamination or visual disam enities like brownfields. Consequently, they also differ by their willingness to pay, in terms of higher housing prices, for bundles of public goods. However, they do not differ in how they value one particular

public good (like parks and green spaces, say) rel ative to another (like public safety). Because of the latter simplifying assumpt ion, households agree on their rankings
of “most desirable” communities. Because of the hi gher demand for housing there, housing prices are highest in the most desirable co mmunities; likewise, they are lowest in
the least desirable. And because households do dif fer in their willingness to trade off housing prices for public goods, these price differ entials lead households to select different communities. Holding tastes constant, ri cher households will be in the nicer communities and poorer households in the less desir able communities.
Figure 1 illustrates the situation. The figure sho ws two demographic types (differing by race, for example, or educational att ainment) with different income distributions. The two curves in the figure are th eir respective density functions, with Type 2 being wealthier on average than Type 1. Typ e 2 households may be white for example and Type 1 minority; or Type 2 may be colle ge-educated and Type 1 not.
Holding tastes constant, the income threshold Y separates them into two different
communities: all those with incomes above Y are in the more desirable of two communities, all those with incomes below Y are in the less desirable. In the case depicted, the more desirable of the two communities also has more Type 2 households. This follows logically in the case of symmetric dis tributions and will be true in practice in most cases. However, it is not guaranteed. If the Type II pdf had a thicker tail and Y were moved to the left, one could imagine the less desirable community, while still poorer, having more Type II people. This case woul d represent an upper-middle class minority neighborhood for example.
The question we seek to answer with this model is w hat happens to a community, and to other communities, when its public goods imp rove, as when LULUs are cleaned up and returned to productive uses. Consider an im provement in Community 1. After the improvement, households who were somewhat indif ferent before will now clearly12

prefer Community 1. As depicted in Figure 2, Y will shift to the right. All the people who preferred Community 1 will continue to prefer i t, but now additional people (represented by the shaded area in the figure) will as well. In other words, the demand for housing in Community 1 will increase and the de mand for housing in Community 2 will fall. This will have the effect of increasing the population of Community 1 and decreasing the population of Community 2. 9 Other things equal, it will also have the effect of increasing housing prices in Community 1 and lowering them in Community 2. We thus have two clear hypotheses: Housing prices and population will both increase following cleanup and reuse of LULUs. The former, at least, is one hallmark of gentrification.
What about the demographic composition of the commu nities? Note that the people in the shaded area, those who previously pre ferred Community 2 but now prefer Community 1, are richer on average than the origina l residents of Community 1. When they move in following the cleanup of the LULU, the y will thus increase the average income of Community 1. However, note that these pe ople were poorer on average than those who remain in Community 2. Accordingly, the average income of Community 2
will increase as well! 10 While we can thus hypothesize an increase in inco me following cleanup and reuse of LULUs, another hallmark of gen trification, this hypothesis is harder to test in the data, since the process involves mov ement of supposedly “control” communities in the same direction. However, as dis cussed by Banzhaf and Walsh (2006), it should be possible to detect this effect in some special cases.
Finally, we cannot say anything about the change in the composition of Community 1 with respect to other demographic varia bles. Common sense seemingly suggests that as income increases, demographic grou ps associated with higher incomes should increase as well. However, that need not be the case. If the ratio of Type 1 households to Type 2 households among those moving into Community 1 (the gray area) is greater than the ratio among those previously li ving in Community 1 (to the left of the

9 This increase in population density could follow t he development of undeveloped land, rezoning developments for more dense uses, and/or changes in vacancy rates. To the extent these mechanisms are restricted, changes in population density likewise would be restricted and price effects would be grea ter. 10 If Community 2 were improved, so it becomes even m ore desirable than before, all the opposite effects would occur. Both communities would get richer on average.13

original Y ), then Community 1’s share of Type 1 households co uld increase, even while getting richer. Thus, this additional hallmark of gentrification—where it is associated with certain demographic groups, whether it be whit es, more educated individuals, “urban” sophisticates, Yuppies, or something else—i s the least likely to result from cleanup and reuse of LULUs and the least likely to be found in empirical studies.
To summarize this section, even a fairly simple Tie bout model does not yield clear predictions about the effects on a neighborho od of cleanup and reuse, except for rising population densities and housing prices and, to a lesser extent, rising incomes if the cleanup occurs in poor neighborhoods. More complic ated models are likely to be even less definitive. For example, in reality, househol ds do differ in their tastes for different public goods. Some dislike brownfields more than o thers, others fear crime more, others value good schools more. Intuitively, cleaning up and reusing brownfields and other LULUs would then make the neighborhood more attract ive to those who most disliked
them and who are most attracted to the new use. Of course, just who these demographic groups are is an empirical question. Second, as we discuss below in Section V, models in which groups have preferences for the endogenous ma keup of the neighborhood (i.e., where people prefer to live with other people like themselves) can lead to some surprising outcomes through the indirect effects of these pref erences. Before turning to those more complicated issues, ho wever, we first review empirical studies of the basic issues, in particula r studies connecting changes in land uses to one of two of the hallmarks of gentrification: increases in real estate prices and changes in demographic composition. We then turn t o studies that look at more than one hallmark, including the indirect effects on other p ublic goods, aesthetics, and feedbacks through group preferences.

IV. Empirical Evidence of Gentrification: Direct Effects

As described in Section II and in the model of Sect ion III, gentrification is a multifaceted phenomenon featuring housing price increases, demog raphic changes, and other aspects. Very few empirical papers have looked at even two o f these faces simultaneously, still fewer more of them. However, in many cases one asp ect or another has been separately14

studied in a distinct literature, often without a s elf-conscious connection to gentrification. To see how these literatures connect to the larger whole, consider the illustration in Figure 3. This figure shows a pyramid, with each o f its four points representing one salient feature of environmental gentrification. A t the top is the urban environment itself, which can be improved by cleaning up a LULU and con verting it to a healthier and more aesthetic use (public open space, for example). Th is improvement would be the starting point in the process of environmental gentrificatio n. Directly underneath this point is a second point that represents the demographic compos ition of the neighborhood. During gentrification, the neighborhood might become wealt hier, more white, more educated, or younger. To the right is a third point representin g real estate prices. As the community
becomes more desirable first because of the cleanin g up of the LULU, and perhaps later because of other indirect effects, rental prices an d real estate prices would increase, another component of gentrification. Finally, and most speculatively, to the left is a fourth point representing other, endogenous urban a menities and public goods. These may include the aesthetics of the gentrified housin g, public safety, local school quality, and the sets of private goods offered in the neighb orhood. These are additional changes in public and private goods responding to the clean up of the LULU.
Each line segment in the pyramid represents a corre lation between two of these aspects. When accompanied by a theory of a causal direction between the two, they
become theories tested in various literatures. The arrows drawn in the figure represent some of the most important cases. The hedonic lite rature connects both environmental and other amenities to real estate prices, viewing the amenities as influencing prices. The environmental justice literature connects demograph ics to environmental quality, viewing the demographics as a potential determinant of poll ution. Some economic critiques of this literature, on the other hand, would point to an indirect effect of environmental quality on housing prices and housing prices on dem ographics. This latter line segment, between housing prices and demographics might be de scribed simply as differing housing demand elasticities held by differing group s. The connection between demographics and other amenities includes those cas es where the new populations change the aesthetic character of the housing stock , are associated with lower crime rates, bring a new peer group to the public schools, or at tract new retail (the "Starbucks effect").15

Last, and most speculatively, LULUs might have some direct connection to amenities
such as crime if there is a "broken windows" effect , wherein the decay represented by a brownfield engenders social disconnectedness and di sregard (e.g., Kelling and Coles 1996).
Again, although environmental gentrification is a c omplex process involving the interaction of all these factors, insights can stil l be obtained from the literature exploring some of these links independently. In particular, there is a fairly large literature documenting the consequences of land uses on both h ousing prices and demographics, probably the two most important aspects of gentrifi cation. As price increases and demographic changes are each two key features of ge ntrification, evidence of these effects taken separately is a necessary, though not sufficient, condition for the presence of gentrification.
The ideal empirical evidence of these partial conne ctions would be observed effects on each following randomized cleanup and re use of LULUs. Such randomized treatments being rare or nonexistent in social scie nce, however, the next-best evidence comes from comparisons of changes before and after cleanup and reuse (or before and after the announcement of cleanup) with changes ove r a similar time frame with a control group. A third tier of evidence comes from simpler cross-section regressions comparing areas with and without brownfields. Although these regressions do not document the effects of land use changes per se, they do provide important evidence about the likely effects of reuse. Cleanup and reuse of a LULU chan ges an area so that it looks like other areas that have never had a brownfield. However, t here may still be the memory or stigma of the old LULU to hold back gentrification. In addition, as we discuss below, the existing demographic makeup and aesthetic feel of t he community may be selfreinforcing, acting as a force for stability. Thus , the largest possible gentrification effects of cleanup would be to elevate prices and incomes t o the level of control communities. Consequently, if cross-sectional regressions show n o substantial difference between neighborhoods with LULUs and control communities, w e would have no reason to16

expect changes after cleanup. 11 In this way, even cross section regressions provi de important empirical tests of gentrification.

We review studies that fall under each of these thr ee categories in the remainder of this section, looking first at evidence of effec ts on land prices and second at effects on income and other demographic variables. We briefly consider other amenities as well.

Land Prices
There is a large literature regressing real estate prices on the proximity of LULUs, or changes in the status of LULUs (Boyle and Kiel 2 001, EPA 2006, E2 Inc. 2005, Farber 1998, Kiel and Williams 2007). Typically, this "he donic" literature is used in policy analyses to evaluate the benefits of cleanup and re use. Such an application to benefits requires additional assumptions about the structure of markets and household behavior, so that the difference in prices with or without LU LUs, ceteris paribus, equals the willingness to pay to be without a nearby LULU. Th is "benefits" interpretation is not required for our present purpose, however, which is only to show the effect on prices.
As past literature reviews of these studies have co ncluded, the clear consensus in the literature is that, looking cross-sectionally, LULUs are associated with lower housing prices (Boyle and Kiel 2001, EPA 2006, E2 Inc. 2005 , Farber 1998). More recently, Kiel and Williams (2007) have looked at 57 Superfund sit es and found what they interpret as evidence of heterogeneity in effects. Of the 57 si tes, 18 had significantly lower prices nearby, 32 had insignificant effects, and 7 had sig nificantly higher prices. In a metaanalysis, they found that bigger sites and sites wi th more data (and where estimates are more precise) were more likely to have statisticall y negative impacts, as one might expect. We thus view these findings as consistent with the conventional wisdom. 12

The existence of cross-sectional differences, while consistent with such an outcome, does not necessarily imply that gentrifica tion would follow from cleanup. Thus, signs of changes following discovery and, esp ecially, cleanup are especially relevant. The largest literature in this area is a pplied to Superfund and other hazardous

11 The reuse of the land could elevate prices higher if it involves an especially attractive use, one ma king the community even more attractive than the compari son communities. 12 Moreover, Cameron (2006) has recently demonstrated that the failure of studies to date to account for directional effects (stronger effects downwind, for example) can bias results downward.17

waste sites. 13 In one of the first such studies, Kohlhase (1991) finds that, in Houston, land prices were no lower nearer Superfund sites un til after sites were listed on the National Priorities List (NPL) (1986, as compared t o 1980 and 1976). One site was cleaned up by 1986, and it does not show the same r elationship, suggesting cleanup can raise housing prices. In a similar study of hazard ous waste sites in Boston, Michaels and Smith (1990) find a positive effect of distance on prices, but again that it is only significant after discovery and publication. Inter estingly, they find these effects in all but the lowest tiered market, as defined by local real estate agents. In a study designed to further explore these inform ation effects, Gayer, Hamilton, and Viscusi (2000) look at properties surrounding s even Superfund sites in Grand Rapids, Michigan. They find that housing prices increase a bout 1 percent for every mile in distance from the closest Superfund site. They fin d, however, that these effects decline after the US EPA released information on objective health risks, illustrating that the markets react to changes in perceptions (see also G ayer, Hamilton, and Viscusi 2002). This suggests that they might react to objective ch anges from cleanup and reuse as well.
Two sets of researchers have studied housing prices around the RSR lead smelter in Dallas. After many years of operation, the plan t closed in 1984. During cleanup, lead
was abated from the soil at homes within a half-mil e of the facility. A court ruled in 1986 that cleanup was complete, but new concerns la ter arose and the site was finally placed on the Superfund NPL in 1993. A second roun d of cleanup was completed in
2002.
In the first study of this site, Dale et al. (1999) find that during operation and through the initial cleanup, housing prices were si gnificantly lower near the smelter, by about 2% per mile. This trend reversed itself in t he period following the first cleanup, becoming higher near the smelter by 4 to 5% per mil e, suggesting cleanup of such sites can reverse previous depressing effects on housing prices. McCluskey and Rausser

13 Others have looked at sites that might be viewed a s sources of air pollutants or themselves adverse l and uses. Kiel and McClain (1995) look at the effect o f the siting of an incinerator in North Andover, MA

. They find that housing appreciation slowed in North Andover during construction of the incinerator and during operation, compared to the Boston metro area . However, they do not find that it had a systemat ic relationship with distance to the facility. Bui an d Mayer (2003) and Oberholzer-Gee and Mitsunari (20 06) find conflicting evidence of the effect of informat ion disclosure about polluting facilities (the TRI program) on nearby property values.18

(2003a) further explored subtleties in the relevant area around the site and extend their work into the period of new concerns. Using linear splines (a flexible functional form), they find a significant price gradient up to 1.2 mi les, and find that this gradient is lowest in the 1979-80 period before initial identification and in the 1987-90 period following the first clean up. The gradient is highest in the 198 1-86 period of initial concern and the 1991-5 period of subsequent concern. This is preci sely what one would expect, and again suggests that housing prices can rebound and increa se following cleanups. But in an extension of this work, McCluskey and Rausser (2003 b) find that housing appreciation rates continue to lag nearest the facility.
In an earlier study of three Superfund Sites in Wob urn, Massachusetts, Kiel (1995) also finds that housing prices increase with distance from the nearest sites, finding a somewhat higher gradient of 2 to 5 percent. Unli ke the studies reviewed above, however, she does not find an obvious pattern in th ese effects from pre-discovery, through several phases from discovery to cleanup. Messer et al. (2006) have also recently cast doubt on the hypothesis that housing prices rebound after cleanup. They study neighborhoods around Superfund sites in Monte ry Park, CA, northern New Jersey, and Woburn. At least in these three cases, where c leanup at the sites was delayed for 10 to even 20 years, property prices do not seem to re bound relative to more distant controls. Messer et al. suggest this may be because, during t he period of delay, the sites became increasingly stigmatized and that this stigma was n ot reversed by cleanup and its related
activities.
Ihlanfeldt and Taylor (2004), meanwhile, have found somewhat mixed evidence of the effects of cleanup. Instead of looking at r esidential property, they explored the
effects of hazardous waste sites on five classes of commercial and industrial property in Atlanta: apartment, office, retail, industrial, an d vacant. 14 They find that price gradients with distance increased after discovery and listing of a hazardous waste site. After listing, they find that prices continue to rise to a distance of at least 2 miles, and are generally 10-25 percent higher at that distance tha n at 0.5 miles, and as much as

14 Howland (2000, 2004) similarly studied industrial sites in Baltimore, looking at sales of actual brownfield sites. She finds that such sites sold f or 55% less per acre than clean sites, but that the contamination and associated regulatory uncertainty did not otherwise limit the operation of land mark ets. Contaminated properties continued to sell, but at l ower prices.19

36 percent higher for office space but only 3 perce nt higher for industrial sites. However, looking at sites that were subsequently delisted, t hey find no statistically significant effect of this delisting. Moreover, surprisingly, these s ites did not show the same systematic effect at the time of discovery and listing as the other sites.
Throughout this literature, distance and time effec ts are crucial. In a study of property values surrounding a former copper smelter in Tacoma, WA, McMillen and Thorsnes (2003) refine the treatment of these dimen sions by modeling them nonparametrically. In particular, they estimate a smo oth but otherwise arbitrarily varying derivative of price with respect to distance from t he smelter. Moreover, they allow this derivative to vary with time, which runs from 1977 to 1998. On average, they find that there is a positive effect of distance on price in the early years, but an effect that is steadily declining in time. At about 1986 or 1987, after the smelter was closed, the effect
reverses itself and distance to the smelter—correla ted with distance to the city center and to the bay—is given a premium. This effect occurs well before cleanup began in 1995.
These conflicting results raise several questions a bout conventional hedonic techniques. For example, are areas further from ad verse land uses systematically
different in terms of other factors that cannot be observed or controlled in multivariate regression? Are sites that are cleaned up relative ly quickly (as in the Ihlanfeldt and Taylor study) smaller or less dirty than other site s? And likewise, are sites that take many years to clean up (as in Messer et al.) system atically worse?
To address these problems, Greenstone and Gallagher (2005) employ a natural experimental design that allows cleanup of Superfun d sites to be treated as an exogenous variable. They also improve upon earlier work by e mploying a national sample. This sample includes all census tracts near the 690 haza rdous waste sites that were finalists for Superfund's National Priority List. These sites we re given a score using a Hazardous Ranking System, with a score of 28.5 in this system being the original cut-off for listing. Although other sites were later added, the score re mains a major factor in the pattern of listings. By using the score as an econometric “in strument,” Greenstone and Gallagher remove the effect that land values may have had on listing via a political process for listing. Moreover, by focusing only on sites with scores just above or just below the20

threshold of 28.5, sites with similar objective ris ks but with different policy treatments can be compared.
By using these strategies, Greenstone and Gallagher provide the strongest empirical design to date in this literature. 15 They find little evidence that NPL listing affects housing prices relative to other CERCLA sit es that are not listed. However, note that they studied the effect of listings, including any subsequent cleanup, but not the isolated effect of cleanup or reuse. The effect of listing is ambiguous in theory. On the one hand, it might have been expected to increase p rices, as cleanup follows listing. On the other, it might be expected to lower prices, as it is an informational signal to households about risks and may perhaps create a per manent stigma. To help distinguish these effects, Greenstone and Gallagher also analyz e housing rents , with differential effects for listing and for actual cleanup. Again, there is little consistent effect.
Taken as a whole, this literature yields conflictin g findings for a property value effect following cleanup. As a final comment, we n ote that most of these studies, and our discussion of them to this point, have essentially been framed in terms of the presence or absence of a LULU. However, the reuse of a cleaned up LULU, that is, the land use that replaces it, may be just as important an issue. Fo r example, one-third of reused Superfund sites have been converted to green spaces (Vitulli et al. 2004), as have many brownfields (IEDC 2001, De Sousa 2003). Hedonic st udies have generally confirmed that such land uses increase nearby property values (see McConnell and Walls 2005 for a recent review, and Irwin and Bockstael 2001 and Irw in 2002 for excellent examples.)
Areas where land reuse involves conversion to ameni ties like parks or other open spaces, then, might be especially prone to gentrification. On the other hand, if the land is used for residential developments, the increase supply o f housing may actually lower housing prices. While this possibility should be kept in m ind, most studies appear to support the idea that cleanup increases property values.

15 Although one limitation is that they are constrain ed to analyze data at the census tract level. It s hould also be noted that a crucial assumption in their de sign is that non-NPL sites are not cleaned up under other, state programs. If they were, the comparison would be merely between sites cleaned up under one progr am vs. sites cleaned up another program.21

Demographics
Just as a large literature has analyzed the direct connection between LULUs and real estate values, a separate (and for the most pa rt quite distinct) literature has focused
on the direct connection between LULUs and local de mographics. In particular, the socalled "environmental justice" literature has explo red correlations in the presence of LULUs and poor or minority populations. See Pastor (2002), Ringquist (2003), Bowen (2002), and Noonan (2005) for recent reviews and Ri ngquist (2005) for a meta-analysis. 16
Most of this literature has addressed the question of whether there is racism or other injustice in the siting of hazardous faciliti es. However, just as we were not primarily concerned with the benefits interpretatio n of hedonics, but merely the correlation and the causal connections, so too here our concern is merely with evidence for or against demographic gentrification—changing populations that are richer and perhaps more white—following cleanup and reuse of a site.
Since early work by the United Church of Christ (19 87), most researchers have found that LULUs are located in poor and minority n eighborhoods. 17 In a recent metaanalysis, Ringquist (2005) finds the location of no xious facilities and/or Superfund sites had a statistically significant relationship with m inority populations across 33 studies, even controlling for various levels of geographic a ggregation and measures of study quality. 18 A weak correlation is present for income and pove rty, but it is not very robust to study details. A number of individual studies h ave found an inverted u-shape, with LULUs present near low-middle class neighborhoods, so the interpretation of Ringuist's
results is less clear in this case.
There are two non-mutually exclusive hypotheses exp laining these correlations. One possibility, stressed by environmental justice activists, is that firms and local

16 Engel (1997-98) discusses the potential conflict b etween the goals of the environmental justice movement and the movement to reuse brownfields. In conflict with the former, the latter often involve s easing cleanup requirements. See also The Environm ental Law Institute (2002). 17 Anderton et al. (1994) is one exception. 18 Although they are more numerous, less work has exp lored such correlation for brownfields. The Counci l for Urban Economic Development (1999) found that a sample of 107 brownfield projects had higher concentrations of minority populations and poverty levels within a 1-mile radius than statewide averag es. However, this may simply be because they are in mor e urban areas than the rest of the state. Heberle and Wernstedt (2005) found in a sample of nearly 50 dev elopers that their brownfield projects were in cens us tracts where the median income was 25 percent less than the median income of the wider metropolitan ar ea.22

governments are discriminatory in their siting prac tices. An alternative possibility is that the siting, or perhaps later revelations of hazards at the site, triggers neighborhood decay. Under this scenario, demand for land in the neighbo rhood declines, richer households leave, and poorer (perhaps minority) households att racted to lower housing costs move
in. 19 Essentially, this process following siting is the reverse of the gentrification story following cleanup.
Several papers have tested these dynamics in variou s settings, with conflicting findings. Been (1994, 1997) was the first to artic ulate such a reverse gentrification story in the environmental justice literature. In her mo st extensive empirical analysis, Been (1997) studies a national sample of census tracts w ith Toxic Storage and Disposal Facilities (TSDFs) compared to a control group of a ll other tracts. She finds that, in 1990, Black and Hispanic populations are correlated with the presence of a TSDF. When she looks at the demographics of those tracts at the time the TSDF was sited , she continues to find some evidence of a correlation, e specially for Hispanics. The probability of hosting a TSDF is also an inverted-U shape function of income. Moreover, when she looks at changes in demographics between 1 970 and 1990 at those tracts that hosted new TSDFs in the 1970s, she finds that their share of minorities grew less than control tracts. Thus, despite having introduced it , Been's findings suggest that gentrification processes have not been important in shaping existing correlations between demographics and TSDFs.
Pastor et al. (2001) similarly study TSDFs in Los A ngeles. They give greater
attention to issues associated with the scale of im pacts than did Been, looking at all tracts within 1⁄4 mile or 1 mile of a TSDF. They also look only at tracts without a TSDF as of 1970. They find that the probability of a tract ha ving a facility located within these distances between 1970 and 1990 is a positive and s ignificant function of 1970 minorities. Again, it is also an inverted-U shaped function of income. Like Been, Pastor et al. also find that 1980-90 changes in minority p opulations is not a function of TSDF sitings in the 1970s.

19 For a discussion of this process, see Been (1994, 1997), Baden and Coursey (2002), Bowen (2002), and Banzhaf and Walsh (2006).23 Those two papers thus find no evidence of reverse-g entrification following siting of a LULU. Other papers have found more evidence o f reverse gentrification. Baden and Coursey (2002) studied environmental hazards (C ERCLA and large RCRA sites) in Chicago. They found that although these LULUs were correlated with Hispanic populations in 1990, there was no correlation with minority groups as of 1960, presumably before they were understood as hazards. This pattern holds up under a variety of specifications and hazard types.
Wolverton (2002) improves upon the econometrics of these papers by explicitly modeling firm decisions using a conditional logit m odel. Specifically, she models the siting of polluting plants listed in the Toxic Rele ase Inventory in Texas as a function of land prices, local wage rates, the existence of oth er facilities, and local demographics. Like Baden and Coursey (2002), she does not find ev idence that minority populations contributed to firm location decisions, although li ke Been (1997) and Pastor et al. (2001) she finds evidence for the inverted-U shape with re spect to income.
Thus, studies thus provide conflicting evidence abo ut the historical pattern after siting. Recently, Cameron and Crawford (2004) and Cameron and McConnaha (2006)
have conducted careful and focused evaluations of a small number of case studies, over periods which include the cleanup of some sites. C ameron and McConnaha look at four Superfund sites: the Old Bethpage Landfill on Long Island, Tacoma's copper smelter, the RSR lead smelter in Dallas, and Love Canal. Camero n and Crawford (2004) study seven Superfund sites.
Both studies report the evolution, from 1970 to 200 0, of a number of socioeconomic, demographic, and mobility indicators around these sites as a function of distance to the site. They find heterogeneous patt erns. In some cases, the share of whites decreased closer to the sites, in other cases it in creased. Interestingly, given the role of childless households in some of the gentrification literature, they also consider a variety of household structures, although, again, consisten t patterns do not emerge. They also find no clear patterns in mobility (as proxied by t he number share of people in the same house as five years ago). They do find, however, t hat the percent of households above the regional median income either declined or was s tatistically insignificant over the three24

decades. They also find some evidence of rebound e ffects in some indicators. In particular, Cameron and McConnaha (2006) note that the Bethpage landfill, which was listed early and which was cleaned up in 1993 (thou gh not de-listed) had the strongest signal of a rebound. Households with income above the regional median, for example, increased in the 1990 to 2000 period nearer to the site, after declining from 1970 to 1980. The fact that the strongest evidence for this aspec t of gentrification occurred near this site, where there was the most time post-cleanup, s uggests that gentrification may occur with some lag, and that it may still occur at the o ther sites.
Banzhaf and Walsh (2006) have focused on gentrifica tion-type responses to the entry and exit of polluting facilities in Californi a, as recorded in the Toxic Release Inventory (TRI). Although these facilities are act ive polluters, they point out that households may be reacting more to a locally undesi rable land use than to local air quality. They find strong evidence for decreasing population density from 1990 to 2000 near both old and new (post-1990) facilities, and i ncreasing density following closure. They also find weak evidence of decreasing incomes near both older and new facilities. They find that both affects occur at one-half mile from the TRI sites, but at a weaker level than one-quarter mile. Greenstone and Gallagher (2 005), discussed above in the context of property values, find similar results in looking at demographic responses to Superfund cleanups. In particular, they find that in 2000, t racts near cleaned up NPL sites had increasing population density over 1980, relative t o other hazardous waste sites. They find some weak evidence of increasing incomes after cleanup, but no meaningful evidence of other demographic effects.
In summary, the evidence of demographic effects ass ociated with gentrification is roughly consistent with the theoretical model descr ibed above. There is preliminary
evidence of an increase in the number of households and the population density of a cleaned up neighborhood. Such revitalization repre sents an increase in the demand for real estate, and in this sense is connected to the hedonic literature reviewed above. There
is also some weak evidence of an increase in the sh are of wealthier and non-minority households in Banzhaf and Walsh (2006), Greenstone and Gallagher (2005), and perhaps in Cameron and McConnaha (2006) (at the Bethpage la ndfill). Studies have not found consistent racial, family, or other group effects.25

Crime and other amenities
As noted in the earlier discussion, decreasing crim e and increasing changes in the set of urban amenities, including the type of publi c spaces, the type of retail (the "Starbucks effect") or the feel of the community (" aesthetic promiscuity"), are all hallmarks of gentrification. O'Sullivan (2005) fou nd that in Portland, changes in crime, housing prices, and the income and educational atta inment of the population moved together.
Most of these types of effects of cleanup, if prese nt at all, are likely to be the indirect result of changes in demographics (lines 2 and 5 in Figure 3). Reuse, while also having these indirect effects, might be more likely to directly affect some other amenities, but this would be highly case specific. As a rule, then, we do not expect to see many
direct effects on other amenities. The one reasona ble exception is crime. As brownfields and other LULUs are located in high-crime areas, cr ime reduction is often cited as one goal of cleanup and reuse.
One plausible mechanism for cleanup of LULUs to red uce crime is the “broken windows” effect (Wilson and Kelling 1982, Kelling a nd Coles 1996). The broken windows theory is that small changes in the physica l environment, such as fixing broken windows and removing graffiti, can reduce the rates of more serious violent and property crimes. Cleaning up pollution, removing abandoned buildings, and so forth may fit into this category. An intriguing potential second mech anism has been suggested by Kuo and Sullivan (2001). They point to psychological evide nce that brain activity differs when people are in green spaces, in a way that is more r estful and reduces stress. They hypothesize that this psychological effect, as well as the congregation of people around green spaces, may reduce crime. In support of this theory, they find that crime rates are lower in greener sections of a major Chicago public housing development, even though residents were randomly assigned to apartments. If brownfields and other LULUs have the opposite effect, cleaning them up may reduce cr ime rates. If the land is reused as a park or other green space, the effect would be more direct.26 These effects remain speculative. Apart from a sma ll number of qualitative case studies, 20 we have not found any documentation of improved cr ime rates following cleanup and reuse of LULUs.

V. General Equilibrium Effects
So far, we have discussed direct connections betwee n land uses and the character of nearby communities, with an emphasis on land prices and demographics. Although these are probably the two most important features, vario us perspectives on gentrification all describe a cascade of effects that interact, perhap s reinforcing, perhaps dampening, these direct effects. As a starting point, changes in la nd prices and changes in demographics occur simultaneously. If different groups have dif fering demands for land, then changes in land prices will further influence demographic c omposition. At the same time, if people have preferences for the demographic composi tion of their neighborhood, demographic changes can affect the demand for land and hence prices.
In addition, these changes may bring about other ch anges in the character of the neighborhood. Wealthier households may replace or rejuvenate the housing stock, further affecting housing prices. New residents ma y vote for, or organize to create, different levels of public goods (or just different kinds of public goods). They may be associated with lower crime rates. They may also b ring differing levels of educational attainment to the school system and otherwise alter the nature of the student peer group. They may have differing tastes for private goods, a nd so new retail may move in to cater to those tastes (the “Starbucks” effect). And so o n. Recently a family of new "equilibrium sorting model s" has emerged in the economics literature, which model some of these sim ultaneous effects through economic structures (preferences, public good "production fu nctions," and so on). Very few of these models address environmental issues at all, l et alone cleanup and reuse of LULUs. Nevertheless, we can gain insights on the importanc e of these indirect effects for gentrification processes, even if a change in local land uses is not the precipitating factor
in the studies.

20 See, e.g., http://www.epa.gov/brownfields/success/ bridgeport_wentfield.pdf.27 In the first application of this literature to an e nvironmental issue, Sieg et al. (2004) and Smith et al. (2004) study the simultaneo us effects of changing populations and changing land prices in response to changes in air quality in Los Angeles. They estimate an empirical version of the model introduc ed in Section III, in which households differ by income and tastes for public goods. They find that in general equilibrium, although average ozone improved by 6 to 20 percent across the 5 counties, the equilibrating change in average prices was -6% to 2 0%.
For our purposes, one of the most important take-ho me messages from these papers is that local residents can be made worse of f after an improvement in neighborhood amenities, even though they have some value for it. Because of belowaverage tastes for public goods and/or below averag e incomes, the households who live in an initially dirty community have lower (though still positive) willingness-to-pay for the amenity. 21 When the community is cleaned up and amenities im prove, other, outside residents compete to live there, bidding up the pri ce of land to their own, higher willingness to pay for the amenities. Poorer resid ents are forced to pay these higher rents, or move. Even if they move elsewhere, they are not likely to find as good an alternative as their original community, which they had chosen over other alternatives. Moreover, in reality they would have to pay both th e out-of-pocket and the psychological costs of moving, an issue overlooked in the model. However, note that housing prices did fall in some communities where air quality impr oved, though by less than other areas. These residents are likely to be made better off, a s housing prices fall and public goods improve.
In an application closer to the cleanup and reuse i ssues addressed in this paper, Walsh (2004) uses the same basic model to study the provision of new open space in Raleigh, NC. In his model, households have prefere nces for fixed green spaces like parks, but also for the population density, which s erves as a proxy for “greenness” insofar as larger lots help maintain a more open and rural character. When new open space is provided in a neighborhood, increased demand for li ving in the neighborhood drives up population density, along with new construction, de creasing that aspect of open space and

21 In the Tiebout logic of the model, this is reveale d by their choice to live in that community in the first place. Relatively speaking, they weight the advant ages of affordable housing over those amenities.28

thus counter-acting some of the effects of the orig inal policy. Again, Walsh finds that local residents can be made worse off after the pro vision of green space in their community. Even though they have some value for gr een spaces, their value is not as high as others, who, with a relative high willingne ss to pay for the amenity, bid up rents high enough to offset the direct gain of the green spaces to the initial residents.
Although these papers offer important insights into the distributional effects of public good improvements, two limitations suggest r eason for caution before applying their empirical findings too literally. First, mob ility is costless in the models, and
households have no tastes for home and no connectio n to a specific job location. In short, they have no connection to "place" beyond its descr iption by a vector of public goods and prices. As a result, the models predict an unreali stic level of turnover from changes in public goods. 22 This high turnover, in turn, allows prices to cha nge more than they otherwise might in some communities. On the other hand, as noted above, abstracting from moving costs also implies that the negative we lfare impacts on those initial residents who do move are understated (see Vigdor 2 006 for discussion).
Second, all households are renters in the model, wi th increases in rents being captured by absentee landlords outside the model. Homeownership will have different implications for the general equilibrium welfare ef fects, with homeowners reaping the capital gain in their house rather than paying out higher rents. However, allowing for homeownership probably would only increase the ineq uity of the welfare impacts. On average, poorer households would be the renters in a neighborhood experiencing cleanup and reuse of a LULU, so it is the poorer households who would pay out the higher rents, while (on average) richer homeowners would capture the gains in their property assets. 23
A second strand of this equilibrium sorting literat ure accounts for households’ preferences for racial homogeneity or diversity and more generally socioeconomic

22 For example, Sieg et al. (2004) find in their poli cy simulation a 97% turnover in one community following a 9% improvement in ozone concentrations. Seventy-five percent of the new residents came from two communities that were close substitutes. 23 Of course, along with their higher housing values homeowners would also face higher tax assessments. If new residents have a higher demand for public go ods, they might also raise tax rates (e.g. Epple et al. 2001). However, homeowners should still be better off, unless they are highly immobile (so they canno t cash out and move) and

liquidity constrained (so they cannot tap into hig her values to pay current taxes with, e.g., a reverse mortgage).29

composition of communities, which naturally develop endogenously. In a recent theoretical paper, Sidon (2005) explores conditions under which two neighborhoods, one clean and one dirty, are segregated by race or inco me. Generally speaking, Sidon finds that when environmental preferences are strong and racial preferences are weak, the communities will be segregated by income, with rich er households outbidding poorer households for the clean community. Wealthier mino rities will join wealthy whites in the clean community. As racial tastes increase in inte nsity, segregation is more likely to occur along racial lines, with the wealthy minoriti es now joining their poorer peers in the dirty community. As those tastes strengthen even f urther, it is even possible to support an equilibrium in which the minorities are in the c lean community and whites in the dirty community.
Sidon's model is particularly relevant to this revi ew because he analyzes the effect of cleaning up the dirty community, such as by deco ntamination and reuse of a LULU.
The effect of this shift is similar to the effect o f lowering the relative weight on environmental preferences. Income sorting becomes less likely, because rich minorities, previously attracted to the clean community (or rep elled by the dirty community), need no longer sacrifice environmental quality in order to join their peers. Racial sorting then
becomes more likely. Since the dirty community had an initiall y higher level of minorities in the model, as observed empirically fo r communities near LULUs, we would consequently predict clean up and reuse to increase the proportion of minorities in the neighborhood. 24
In recent empirical work, Bayer and McMillan (2005) have confirmed Sidon's intuition. Using a sorting model of locational cho ice in San Francisco, they estimate households' preferences for the racial, income, and educational composition of a community, preferences that individually vary by th ose same socioeconomic factors. They then simulate a counterfactual in which such s ociodemographic tastes are not present. Although they do not look at land uses or other environmental amenities, they

24 Note that this reinforces the message from Banzhaf and Walsh (2006), presented in Section III, where increasing incomes was predicted more strongly than changes in racial composition. It is also consist ent with their empirical findings as well as those of G reenstone and Gallagher (2005).30

find that the black-white gap in other public goods such as school quality and crime falls by about one-half.
Others have argued that school quality and crime ar e themselves endogenous functions of local sociodemographics (e.g. O’Sulliv an 2005). Bayer, Ferreira, and McMillan (2003) allow for this possibility in addit ional simulations using a similar model. Again, they do not look at environmental am enities, but they do consider an exogenous change in a local public good, namely sch ool quality. When racial preferences are ignored, the average school zone ex periences a $1,000 increase in mean income when its test score is increased by one stan dard deviation. When racial preferences are accounted for, income increases $1, 600. And when further endogenous increases in school quality are allowed to follow f rom the new peer group in the school, and when crime rates are similarly allowed to fall, the effect rises to $1,800.
In the simulation of Bayer et al., the presence of racial preferences accelerated the gentrification process. However, this need not nec essarily be the case. The most fundamental insight of these models and simulations is that the introduction of racial and other group-preferences creates the possibility for a “tipping point,” above which gentrification may be accelerated but below which i t may be slowed (Schelling 1969, 1972). That is, one important implication of these models and simulations is that, when multiple equilibria are possible, cleanup of a LULU may not automatically transition a community to look like communities that never had o ne. In some cases, demographic preferences may be a force keeping the previous dem ographic composition relatively stable. Absent any history of a LULU, a neighborho od may be just as likely to be predominantly white as it is to be predominantly mi nority. With the introduction of a LULU, it is more likely to become a predominantly m inority community. With the removal of LULU, either outcome is supportable in t heory as an economic equilibrium. However, the existing equilibrium of predominant mi nority makeup is an obvious focal point and so may remain stable and even become more dominated by the minority group. If they are directly or indirectly related to any o f these demographic effects, housing prices would be expected to reflect them concomitan tly.
Cameron and McConnaha (2006) make this precise poin t when they note that31 Studies that have failed to find that housing price s rebound to precontamination levels may be assuming, implicitly, t hat socio-demographic characteristics of the affected neighborhoods were unaffected by events surrounding the discovery and remediation of an env ironmental hazard. If households were immobile, if the environmental risk was transitory, and if there was no permanent taint associated with a Supe rfund site, then restoration of prior housing prices might be expect ed. However, if the character of the neighborhood is fundamentally chan ged by these events, and reverse migration is asymmetric compared to out -migration, we might see prolonged effects upon housing prices. (p. 286 )
Thus, the importance of other aesthetic amenities a nd public goods created by neighborhoods may make them somewhat more resilient to gentrification. This may be one reason why before-and-after comparisons of clea nup of LULUs have found weaker demographic responses than cross-sectional comparis ons, and that Cameron and McConnaha (2006) found the strongest evidence in th e case where there was the most time for a response.
We conclude this section on general equilibrium eff ects with a paper that takes a very different modeling approach, but that applies it to a policy question of direct relevance to this review. Noonan et al. (2006) mod el the change in local housing prices following the cleanup of Superfund sites, allowing for endogenous changes in the housing stock and in local demographics. They look at changes in mean housing values as reported in the 1990 to 2000 census years in a n ational sample of census block-groups, with the variable of interest being an indicator va riable for the presence of a Superfund
site that has been deleted from the National Priori ty List, either in the block-group or an adjacent block-group.
Changes in housing prices are also a function of ch anges in endogenous housing quality (e.g. age, number of rooms, utilities, dens ity) and neighborhood demographics (e.g. income, percent white, percent college gradua tes, percent poor, and percent blue collar workers.) These in turn are modeled as func tions of changes in one another and housing prices, as well as the deletion from the NP L. Noonan et al. take a traditional reduced-form simultaneous equations approach, with lagged levels and changes of each of these factors serving as instruments.32 In their preliminary results, Noonan et al. find li ttle evidence for a direct effect of housing appreciation following cleanup, except for with the most restrictive models. They do find evidence for upgrade in the housing st ock, with younger and bigger houses appearing after cleanup. They do not find demograp hic effects. Taking the joint effects of all the relevant interactions, Noonan et al. fin d slightly positive effects (on the order of 1% to 5%) on housing values (pure price effects and quality upgrades). 25 Taken as a whole, there is thus some suggestive evidence of ge ntrification, but as with the other empirical work not definitive.

VI. Summary and Conclusions
Unfortunately, there are really no rigorous empiric al studies that have looked at the full picture of gentrification in the context of the app earance, cleanup, or reuse of LULUs. In this paper, we have tried to gain insights into the issue by looking at theoretical models and empirical studies that have looked at at least one aspect of gentrification at a time, plus others that have looked at multiple aspects in settings that are similar to the cleanup
of LULUs. In this final section we summarize our f indings from the literature by responding to a series of questions about environme ntal gentrification.
What are the characteristics of gentrification?
There are three principle hallmarks of gentrificati on in virtually every perspective on the subject: rising property values and rental c osts; new construction or renovation upgrading the housing stock; and a turnover in the local population bringing in residents with a higher socioeconomic status. Various perspe ctives emphasize other features as well, but most involve some sense of the creation o f a new amenity, based on architecture, retail, lifestyle, or just common pee r groups. These new endogenous amenities further drive gentrification.
Do cleanup of LULUs and/or reuse trigger gentrifica tion?
The literature yields conflicting signals on whethe r cleanup is likely to trigger gentrification. A simple Tiebout model predicts pr ice effects following an increase in demand for living in the neighborhood. It also pre dicts richer households under

25 As of the time of this writing standard errors are not available for this estimate.33

restrictive circumstances, but has no predictions f or other demographic groups, even those differing by mean income. Introducing group preferences can lead to counterintuitive effects, where populations of mino rities or other groups with lower average incomes can increase following cleanup. If housing prices are also a function of these demographics, one would expect attendant indi rect effects on real estate values as
well.
Consistent with these surprisingly vague theoretica l predictions, the empirical literature has found only weak or conflicting evide nce of gentrification. First consider land prices. Cross-sectional work has consistently found lower housing values near a variety of LULUs. In addition, early work found lo wer housing prices near Superfund sites after discovery, and rebounding prices follow ing cleanup. But more recently, Messer et al. (2006) suggest this need not happen i n every case, especially where prolonged cleanup activities have created a potenti al stigma. Greenstone and Gallagher (2005) likewise find no effect in a national sample of NPL sites compared to other
CERCLA sites.
With respect to demographics, again, cross-sectiona l studies consistently find poorer or minority households near LULUs. Studies are conflicted as to whether these are the result of reverse-gentrification following siting of the LULU, with Been (1997) and Pastor et al. (2001) suggesting not, but Baden and Coursey (2002) and Wolverton (2002) suggesting so. After cleanup, the evidence is again mixed. Banzhaf and Walsh (2006) and Greenstone and Gallagher (2005) both fin d evidence of increased density following improved land uses, weak evidence for inc reases in income, and little to no
evidence for a decrease in minorities. Cameron and McConnaha (2006) find little effect on income, except perhaps at the first site to be c leaned up (in 1993), and again no effect on either racial or family composition.
Finally, recent work by Noonan et al. (2006) lookin g at a set of simultaneous effects of Superfund cleanup finds no direct effect on prices, no consistent effect on demographics, but some effect on the quality of the housing stock, which increases housing values. These joint effects are consistent with the partial effects found in other
studies.34 We find no papers looking at the further effect of specific reuses of land following cleanup. However, evidence that some con verted land uses such as greenfields are associated with higher neighborhood land prices (Walls and McConnell 2005) is suggestive. In addition, the sociological descript ion of gentrification as a new aesthetic emphasizing recent construction or renovated proper ties and urban culture hints at another way that reuse might trigger gentrification . In particular, if cleanup, and especially a particular reuse, of a LULU changes th e aesthetic "feel" of a community, it may be more likely to trigger gentrification by att racting a new type of resident. Yet this is mere speculation.
What is the potential geographic extent of gentrifi cation?
If gentrification occurs, its geographic extent is likely to be very local. Glass (1964) and Zukin (1987) stress that gentrified neig hborhoods can exist side-by-side with deteriorating ones. Most hedonic price studies of Superfund sites have focused on very local areas, with effects extending to 2 miles or s o at most. Studies with the most flexible functional forms (using rings around a LULU or spli nes, for example) find the strongest effects within about a mile (e.g. Ihlanfeldt and Ta ylor 2004, McCluskey and Rausser 2003).
With respect to demographics, Banzhaf and Walsh fin d that income responses to TRI sites are still present one-half mile away, but weaker than one-quarter mile. The cleanup and reuse of smaller LULUs, such as brownfi elds, are likely to have even more
local effects.

To what extent do residents and businesses turn ove r following decontamination or
reuse?
As emphasized earlier, turnover is not necessary fo r pre-existing residents to be harmed by cleanup and reuse of LULUs. Housing pric es may still increase, and immobile households may simply have to pay these hi gher costs. On the other hand, when they are mobile and have good substitutes to m ove to, households may escape these costs (see Banzhaf and Walsh 2006, Vigdor 2002, 200 6). Moreover, if baseline turnover rates are high enough, gentrification and demograph ic changes may occur even without
an increase in the turnover rate.35 Simulation models of general equilibrium responses to changes like cleanup of a LULU have generally predicted quite high—indeed, un realistic—turnover (e.g. Sieg et al. 2004). However, these models impose no monetary or psychological costs of moving, and incorporate no sense of place (a “home” or job location). Empirical studies of mobility have generally found no evidence of increa se in turnover following cleanup. In particular, Cameron and McConnaha (2006) find no pa ttern to the change in the percentage of households at the same house as five years ago following either discovery or cleanup of four Superfund sites. Somewhat remov ed from these land use and environmental applications, Vigdor (2006) finds tha t renters are no more likely to move in neighborhoods experiencing lower rates of abando ned housing and barred windows.
Although more work would be needed before reaching definitive conclusions, these two studies suggest that turnover is not like ly to increase following cleanup and reuse of LULUs. However, this does not imply that there is no gentrification. The
studies do not show a decrease in turnover either. Even if it occurs at an ordinary, typical pace, if the turnover involves replacing one demogr aphic group with another, gentrification might still occur. As discussed abo ve, there is some evidence supporting increases in income following cleanup, although lit tle evidence for changes in other demographic groups.
What factors and policies can mitigate gentrificati on, and what role can they play?
The literature suggests several factors that might minimize the probability of any gentrification occurring or mitigate its effects. First, given the importance of aesthetic amenities, reuse projects that fit the existing cha racter of the community are less likely to trigger gentrification. Consistent with this point , the NEJAC (2006) report on unintended consequences stresses the importance of community i nvolvement in planning reuse projects.
Second, given the role of racial and other demograp hic preferences in maintaining an equilibrium, targeting more homogenous communiti es may be less likely to trigger gentrification as well. This is because these comm unities are probably further from a "tipping point" at which they would switch over to a new demographic composition.36 Third, the work of Sieg et al. (2004) and Smith et al. (2004) suggests that when large policies target several communities within a metro area, gentrification effects are likely to be smaller. While improving a single com munity is likely to increase its attractiveness relative to all other communities, c reating incentives for other households to move in and driving up its housing prices, impro ving many communities would neutralize this effect, lifting all boats equally.
Finally, we have also noted that communities with o wner-occupied housing are less likely to suffer from the adverse effects of r ising land prices, although taxes remain a concern. A recent report from the Urban Institute (Levy, Comey, Padilla 2006) suggests three strategies that can be used to help low-incom e residents afford housing when gentrification threatens. Strategies that have bee n observed to be successful for providing or assuring affordable housing include: production of affordable units, retention of existing affordable housing units, and asset buildi ng for current residents and neighborhood families. Strategies for the producti on of affordable units include creating housing trust funds, inclusionary zoning ordinances , and the federal Low-Income Housing Tax Credit, in addition to split-rate tax s tructure and tax-increment financing. In the studies completed, it was found that retention of the affordable housing stock can be encouraged through the enforcement of code, rent co ntrol, and the preservation of federally subsidized affordable housing. Thirdly, strategies to build resident assets may be effective in mitigating displacement. As used b y Levy, Comey, and Padilla, asset building includes individual development accounts, homeownership education and counseling, limited equity housing co-ops, communit y land trusts, location efficient mortgages, and the Section 8 homeownership program. These three strategies were
found to be effective in six case studies examined by Levy et al. (see also Kennedy and Leonard 2001).
Given these empirical findings, and given these pol icy options, there seems ample hope for cleanup and reuse of contaminated properti es that is a true Pareto improvement for all households—not just a "potential" improveme nt.37

Appendix: A formal model of gentrification
As noted in the text, to gain insight into gentrifi cation following cleanup and reuse of LULUs, we employ a model of vertically differentiat ed communities introduced by Epple, Filimon, and Romer (1984), a more general ve rsion of which has been estimated econometrically by Epple and Sieg (1999) and applie d to environmental improvements by Sieg et al. (2004) and Walsh (2004). Banzhaf an d Walsh (2006) and Vigdor (2006) use this model to gain insights into environmental gentrification. This appendix is based
on Banzhaf and Walsh.

Consider a continuum of households that are charact erized by their income y and demographic group t . The joint distribution of types and income is gi ven by f(y,t). The marginal distribution of income is given by f y (y) and the distribution of income conditional on type t

is given by )

( y f t y . Household preferences are defined over housing with price P , a numeraire whose price is normalized to 1, and e nvironmental quality G . Household i ’s preferences are represented by the indirect util ity function

V i = V ( y i ,P,G ). (1)
Each household chooses to live in a community J j ∈ and, conditional on community choice, chooses a quantity of housing D i . Each community is characterized by its supply of housing S j and level of environmental quality G j , both of which are exogenously determined. To facilitate a characteri zation of the equilibrium sorting of households across communities, we further assume th at household preferences satisfy the "single crossing" property. This condition require s that the slope of an indirect indifference curve in the ( G,P ) plane be increasing in y

. 26 Although household demand for public goods in this simple model is differenti ated only by differences in income, the model can be extended to include heterogeneity in t astes without altering the key insights
derived here.
Given the assumption of single crossing, equilibriu m can be characterized by an ordering of communities that is increasing in both P and G . That is, there is a clear ordering of communities from low price, low quality communities to high price, high

26 For a discussion of the single crossing property i n this context see Epple & Sieg (1999).38

quality communities. Further, for each pair of "ne ighboring" communities (as sorted by this ranking), there will exist a set of boundary h ouseholds (defined by an income level)
that are indifferent between the two communities. H ouseholds whose income is below the boundary income will prefer the lower ordered commu nity and those whose income is above the boundary income will prefer the higher or dered community. This leads to perfect income stratification of households across communities. 27 Equilibrium prices P j and boundary incomes 1 , + j j Y are implicitly defined by the equilibrium condition s of equation (2):
} 1 ,..., 1 { ) , , ( ) , , ( 1 1 1 , 1 , − ∈ ∀ = + + + + J j G P Y V G P Y V j j j j j j j j (2)

}
,..., 1 { ) ( ) , , ( J j S dy y f G P y D M j C y y J j

j ∈ ∀ = 

## ∫

∈ ,

where M is the total mass of households, D (.) is housing demand, and C j is the set of incomes locating in community j . These equations formalize the J -1 boundary indifference conditions and the requirement that th e land markets clear in each of the J communities, yielding 2 J -1 equations to identify the 2 J -1 endogenous variables.
We use the model to consider two issues important f or the analysis of migration and environmental gentrification. First, we consid er the implied distribution of households across communities for demographic group s with different income distributions, )

( y f t y . Second, we evaluate how the predicted demographi c composition of communities changes, in response to changes in e nvironmental quality. Consider two demographic groups, Type 1 and Type 2. Assume that their conditional income distributions )

( 1 y f y and )

( 2 y f y are such that the mean income for Type 1 individua ls is less than the mean income for Type 2 individuals. Figure 1 provides a graphic representation of the distribution of these demogra phic types in a system of two communities, with Community 1 having the lower ( P, G ) pair and Community 2 the higher. All households to the left of 2,1 Y sort into Community 1, all to the right sort into

27 It is straightforward to relax this assumption by introducing heterogeneity in tastes, so that there is heterogeneity of income within each community, but perfect stratification by tastes for each income (s ee Epple and Sieg 1999 and Sieg et al. 2004). Accordi ngly, this assumption is not critical for the follo wing implications of the model.39

Community 2. Obviously, Community 1 has a lower av erage income than Community 2. As shown in the figure, in this example Community 2 will have a much higher concentration of Type 2 individuals and Community 1 a much higher concentration of Type 1 individuals. Thus, Tiebout sorting with het erogeneity in income can induce correlations between other demographic groups (like race) and pollution. (Nevertheless, while this correlation is guaranteed under the cent ral case where the two distributions have identical higher moments, it need not occur in general.)
The above results are completely expected given the model. However, the comparative statics associated with a change in env ironmental quality in one of the communities is more subtle. Consider the impact of cleaning up and reusing LULUs in the lowest G community in a system of two communiti es. Evaluating the resulting demographic responses requires identifying the shif t in the income boundary,

1 2 , 1 dG Y d . To evaluate this shift, we assume that housing d emand is separable from

G and apply the implicit function theorem to the bou ndary indifference condition and two market clearing conditions from equation (2). This yields the following comparative static relationship:

    

 

    

 

+

− − −
=


## ∫

∫ ∞

2,1 2 2
2,1

1 1 1

)

( ) , ( ) , (

) ( ) , ( ) , ( ) ( ) (

2 2 2,1 2

0 1 1

2,1 1 2,1 2 1 1

1 2,1

Y P P
Y

P P
y y y G

dy y f y P D V Y P D
dy y f y P D V Y P D Y f V V V dG Y d , (3)

where
) , , ( 1

1 2,1 1 G

P Y V V = ,

) , , ( 2

2 2,1 2 G

P Y V V =
.

D (.) is the household demand function, and subscript s denote partial derivatives

.40 The key to signing the derivative in equation (3) i s to recognize that the single crossing property implies that 0 ) ( 2 1 <

− y y V V implying that 1 2 , 1 dG Y d is positive. 28
Figure 2 illustrates the impact of an increase in G 1 on the equilibrium sorting. In response to the change, the indifference boundary 2,1 Y moves to the right and the set of households in the shaded region A relocate from Com munity 2 to Community 1. If G 1 were to fall instead, or if G 2 were to increase instead of G 1 , an opposite shift would occur. At the aggregate level this change leads to an incr ease in population for Community 1 and a decrease in population for Community 2 (and t o an increase in prices in Community 1 and decrease in Community 2).
What is the change in community 1's composition rel ative to community 2? Surprisingly, the model does not offer clear predic tions. Consider first the effect on income distributions. As the bordering households move from Community 2 to Community 1, Community 1 gets richer. (This follow s from the fact that the moving households, shaded in Figure 2, are richer on avera ge that the original residents of Community 1.) We call this an absolute composition effect. But meanwhile Community 2 loses its lowest income residents and t herefore also experiences an increase in average income. Thus, we have the counter-intui tive result that increasing the level of
G 1 leads to an increase in average income for both communities! So the relative composition effect is indeterminate. Not surprisin gly, the effect on relative racial composition is also indeterminate. In fact, so is the absolute effect: As Community 1 increases in average income, it does so by gaining the richer type I individuals as well as poorer type II individuals. In general, the ratio of new type II to new type I individuals can be either greater or lesser than the existing r atio, so that the percent of type I
individuals can increase or decrease. 29
We thus have three propositions.

28 By the definition of Y
, ) , , ( 1

1 2 , 1 G P Y V = ) , , ( 2

2 2 , 1 G P Y V . Since all those with incomes higher than Y prefer Community 2, ) , , ( 1

1 2 , 1 G P Y V ε + < 0 ), , , ( 2

2 2,1 >

∀ + ε ε G P Y V

. 29 As an example, consider a set of three minorities with incomes {10k, 30k, 50k} and a set of Whites wi th incomes {40k, 60k, 80k}. These are two symmetric d istributions differing only in the location paramet er, with a higher mean for Whites. Yet if 2,1 Y is initially at $45k and shifts to $55k, the compo sition of Community 1 will change from two-thirds minorities to three-quarters minorities.41 Proposition 1 (Scale Effect) . For any two communities, a marginal increase in public goods in one community relative to the other will cause population to rise in the community experiencing the improvement and to fall in the other community.
Proposition 2 (Absolute Composition Effect). Ceteris paribus, a marginal increase in public goods in any community will increase its average income. The effect on the share of racial or other demographic groups is inde terminate.
Proposition 3 (Relative Composition Effect) . The change in the average income, or mean share of demographic groups positively corr elated with income, in a community experiencing a marginal increase in G, relative to another community, is indeterminate.
These results suggest that for small changes in Com munity 1’s environmental quality, there are no clear predictions for the rel ative change in community compositions. The interpretation is even more complicated by the possibility that sometimes it is the nicer community, Community 2 in this example, which improves. In that case, both communities would become poorer on average.
These negative conclusions are mitigated by three f actors. First, in most cases it will be the less desirable communities (like Commun ity 1) which host LULUs. Second, if we consider a larger change—one that raises the level of environmental quality in Community 1 above that of Community 2—clearer predi ctions arise. Such a change will cause the populations in Communities 1 and 2 to swi tch places, resulting in an increase in average income in Community 1, while average income drops in Community 2. Third, when we consider many communities instead of just t wo, the affect of a change in public goods in Community 1 will intuitively have the larg est effects on the composition of close substitutes, with effects on other communitie s dampening out in the rank ordering (a pattern we have confirmed in simulations). If t hese more distant communities act as a control group, we would expect to find a relative c omposition effect. Thus, despite our inability to predict relative composition effects i n general, there remain plausible reasons for expecting an increase in income among communiti es experiencing an exogenous improvement in public goods. One might also expect a similar increase in the share of demographic groups correlated with income, such as race, but this remains purely an empirical matter.42

Figure 1. Density of income for two household types and community income

boundary.

y Type 2 PDF Type 1 PDF

Y 1,243

Figure 2. Shift in community income boundary after improvement in G 1 .

A

y

Type 2 PDF Type 1 PDF

Y 1,244

Figure 3. The Four Sides of Environmental Gentrifi cation45

Works Cited Anderton, Douglas L., Andy B. Anderson, John Michae l Oakes, and Michel R. Fraser. 1994. "Environmental Equity: The Demographics of Dumping." Demography 31(2): 229-48. Baden, Brett M., and Don L. Coursey. 2002. "The L ocality of Waste Sites within the City of Chicago: A Demographic, Social, and Econom ic Analysis." Resource and Energy Economics 24: 53-93.
Banzhaf, H. Spencer, and Randall P. Walsh. 2006. "Do People Vote with their Feet? An Empirical Test of Environmental Gentrification." R FF Discussion Paper 06-10. Washington, DC: Resources for the Future.
Bayer, Patrick, Fernando Ferreira, and Robert McMil lan. 2003. "A Unified Framework for Measuring Preferences for Schools and Neighborh oods." Mimeo. New Haven, CT: Yale University. Bayer, Patrick, and Robert McMillan. 2005. "Racia l Sorting and Neighborhood Quality." Mimeo. New Haven, CT: Yale University.
Been, Vicki. 1994. "Locally Undesirable Land Uses in Minority Neighborhoods: Disproportionate Siting or Market Dynamics?" Yale Law Journal 103: 1383- 1422. Been, Vicki. 1997. "Coming to the Nuisance or Goi ng to the Barrios? A Longitudinal Analysis of Environmental Justice Claims." With Fr ancis Gupta. Ecology Law Quarterly 34: 3-56.
Blau, Judith R., and Peter M. Blau. 1982. “The Co st of Inequality: Metropolitan Structure and Violent Crime.” American Sociological Review 47: 114-129. Bowen, William. 2002. "An Analytical Review of En vironmental Justice Research: What Do We Really Know?" Environmental Management 29: 3-15.
Boyle, Melissa A., and Katherine A. Kiel. 2001. “ A Survey of House Price Hedonic Studies of the Impact of Environmental Externalitie s.” Journal of Real Estate Literature 9(2): 117-44.
Brueckner, Jan K., and Stuart S. Rosenthal. 2005. “Gentrification and Neighborhood Housing Cycles: Will America’s Future Downtowns be Rich?” Mimeo. Accessed at www.econ.uconn.edu/brueckner.pdf

. Bui, Linda T.M., and Christopher J. Mayer. 2003. "Regulation and Capitalization of Environmental Amenities: Evidence from the Toxic Re lease Inventory in Massachusetts." Review of Economics and Statistics 85: 693-708.46

Cameron, Trudy Ann. 2006. “Directional Heterogene ity in Distance Profiles in Hedonic Property Value Models.” Journal of Environmental Economics and Management 51: 26-45.
Cameron, Trudy Ann, and Graham D. Crawford. 2004. "Superfund Taint and Neighborhood Change: Ethnicity, Age Distributions, and Household Structure." Mimeo. Cameron, Trudy Ann, and Ian McConnaha. 2006. “Evi dence of Environmental Migration.” Land Economics 82: 273-290. Clay, Phillip L. 1979. “Neighborhood Renewal.” Le xington, MA: Lexington Books.
Council for Urban Economic Development. 1999. Brownfields Redevelopment: Performance Evaluation . Washington, DC.
Dale, Larry, James C. Murdoch, Mark A. Thayer, and Paul A. Waddell. 1999. "Do Property Values Rebound from Environmental Stigmas? " Land Economics 75: 311-26.
De Sousa, Christopher A. 2003. "The Greening of B rownfields in American Cities." Journal of Environmental Planning and Management 47: 579-600.

E2 Inc. 2005. Superfund Benefits Analysis . Draft report prepared for the US EPA. http://www.epa.gov/superfund/news/benefits.pdf

. Environmental Law Institute. 2002. “A Citizen’s Gu ide to Using Federal Environmental Laws to Secure Environmental Justice.” Washington, DC: ELI. Engel, Kirsten H. 1997-1998. Brownfield initiative s and environmental justice: “Second class cleanups or market based equity?” Journal of Natural Resources and Environmental Law 13(2): 317-337. Epple, Dennis, Radu Filimon, and Thomas Romer. 198 4. Journal of Public Economics 24: 281-308. Epple, Dennis, and Holger Sieg. 1999. "Estimating Equilibrium Models of Local Jurisdictions." Journal of Political Economy 107: 645-681.
Epple, Dennis, Thomas Romer, and Holger Sieg. 2001 " Interjurisdictional Sorting and Majority Rule: An Empirical Analysis. " Econometrica 69: 1437-66.
Farber, Stephen. 1998. “Undesirable Facilities and Property Values: A Summary of Empirical Studies.” Ecological Economics 24: 1-14. Frey, W.H. 1979. “Central city white flight: racial and non-racial causes.” American Sociological Review 44: 425-488.47

Gale, Dennis E. 1984. “Neighborhood Revitalization and the Postindustrial City: A Multinational Perspective.” Toronto: Lexington Boo ks of D.C. Heath and Company. Gayer, Ted, James T. Hamilton, and W. Kip Viscusi. 2000. "Private Values of Risk Tradeoffs at Superfund Sites." Review of Economics and Statistics 82: 439-51.
Gayer, Ted, James T. Hamilton, and W. Kip Viscusi. 2002. "The Market Value of Reducing Cancer Risk." Southern Economic Journal 69: 266-89. Glass, Ruth. 1964. “Introduction: Aspects of Change .” In London: Aspects of Change , ed. by Ruth Glass et al. London: MacGibbon & Kee. Greenstone, Michael, and Justin Gallagher. 2005. "Does Hazardous Waste Matter? Evidence from the Housing Market and the Superfund Program." MIT Working Paper 05-27, December.
Heberle, Lauren, and Kris Wernstedt. 2005. “Under standing Brownfields Regeneration in the U.S.” Mimeo. Washington, DC: Resources for the Future.
Helms, Andrew C. 2003. "Understanding Gentrificat ion: An Empirical Analysis of the Determinants of Urban Housing Renovation." Journal of Urban Economics 54(3): 474-98.
Howland, Marie. 2000. "The Impact of Contaminatio n on the Canton/Southeast Baltimore Land Market." Journal of the American Planning Association 66: 411- 20. Howland, Marie. 2004. "Central City Decline: Is Con tamination Responsible?" Economic Development Quarterly August 18(3): 207-19. Ihlanfeldt, Keith R., and Laura O. Taylor. 2004. "Externality Effects of Small-Scale Hazardous Waste Sites." Journal of Environmental Economics and Management 47: 117-39. International Economic Development Council [IEDC]. 2001. Converting Brownfields to Green Spaces . Washington, DC.
Irwin, E. 2002. "The Effects of Open Space on Resid ential Property Values." Land Economics 78(4): 465-80.
Irwin, E.G., and N.E. Bockstael. 2001. "The Problem of Identifying Land Use Spillovers: Measuring the Effects of Open Space on Residential Property Values." American Journal of Agricultural Economics 83(3):698-704. Kelling, George L., and Catherine M. Coles. 1996. Fixing Broken Windows . New York: Touchstone.48

Kennedy, Maureen, and Paul Leonard. 2001. “Dealin g with Neighborhood Change: A Primer on Gentrification and Policy Choices.” Disc ussion Paper prepared for the Brookings Institution Center on Urban and Metropoli tan Policy. Kiel, Katherine A. 1995. "Measuring the Impact of the Discovery and Cleaning of Identified Hazardous Waste Sites on House Values." Land Economics 71: 428- 35.
Kiel, Katherine A., and Katherine T. McClain. 1995. “The Effect of an Incinerator Siting on Housing Appreciation Rates.” Journal of Urban Economics 37: 311-323.
Kiel, Katherine A., and Michael Williams. 2007. “ The Impact of Superfund Sites on Local Property Values: Are all Sites the same?” Journal of Urban Economics 61: 170-92.
Kolhase, Janet E. 1991. "The Impact of Toxic Wast e Sites on Housing Values." Journal of Urban Economics 30: 1-26.
Kuo, Frances E., and William C. Sullivan. 2001. “ Environment and Crime in the Inner City: Does Vegetation Reduce Crime?” Environment and Behavior 33: 343-367.
Laska, Shirley Bradway and Daphne Spain eds. 1980. “Back to the City: Issues in Neighborhood Renovation.” New York: Pergamon Press

. Levy, Diane K., Jennifer Comey, and Sandra Padilla. 2006. “In the Face of Gentrification: Case Studies of Local Efforts to M itigate Displacement.” Washington, DC: The Urban Institute.
Liu, F. 1997. “Dynamics and causation of environme ntal equity, locally unwanted land uses, and neighborhood changes.” Environmental Management 21: 643-656.
Maass, Arthur, Maynard M. Hufschmidt, Robert Dorfma n, Harold A. Thomas, Stephen A. Marglin, and Gordon M. Fair. 1962. Design of Water Resource Systems

. Cambridge, MA: Harvard University Press.
McCluskey, Jill J., and Gordon Rausser. 2003a. “S tigmatized Asset Value: Is it Temporary or Long-term?” Review of Economics and Statistics 85(2): 276–85.
McCluskey, Jill J., and Gordon Rausser. 2003b. “Ha zardous Waste Sites and Housing Appreciation Rates.” Journal of Environmental Economics and Management 45:166-76. McConnell, V., and M. Walls. 2005. "The Value of Op en Space: Evidence from Studies of Nonmarket Benefits." Washington, DC: Resources for the Future.

McDonald. Scott C. 1986. “Does Gentrification Aff ect Crime Rates.” Crime and Justice 8:163-201.49

McMillen, Daniel P., and Paul Thorsnes. 2003. “The aroma of Tacoma: Time varying average derivatives and the effect of a superfund s ite on house prices.” Journal of Business & Economic Statistics 21: 237-246.
Messer, Kent D., William D. Schulze, Katherine F. H ackett, Trudy A. Cameron, and Gary H. McClelland. 2006. "Can Stigma Explain Larg e Property Value Losses? The Psychology and Economics of Superfund," Environment and Resource Economics 33: 299-324. Michaels, R. Gregory, and V. Kerry Smith. 1990. " Market Segmentation and Valuing Amenities with Hedonic Models: The Case of Hazardou s Waste Sites." Journal of Urban Economics 28: 223-42. Morgan, Kelly. 2000. “Inequality and Crime.” The Review of Economics and Statistics 82: 530-539.
National Environmental Justice Advisory Council. 2 006. Unintended Impacts of Redevelopment and Revitalization Efforts in Five En vironmental Justice Communities . Draft Report. Noonan, Douglas S. 2005. “Defining Environmental Justice: Policy Design Lessons From the Practice of EJ Research.” Paper Presented at the 2005 Annual APPAM Conference.
Noonan, Douglas, Douglas Krupka, and Brett Baden. 2006. "Neighborhood Dynamics and Price Effects of Superfund Site Clean-Up." Mim eo. Atlanta, GA: Georgia State University.
O’Sullivan, Arthur. 2005. “Gentrification and Crime .” Journal of Urban Economics 57: 73-85.
Oberholzer-Gee, Feliz, and Miki Mitsunari. 2006. "Information Regulation: Do the Victims of Externalities Pay Attention?” Journal of Regulatory Economics
, forthcoming.
Pastor, Manuel, Jim Sadd, and John Hipp. 2001. “Wh ich came first? Toxic facilities, minority move-in, and environmental justice.” Journal of Urban Affairs 23: 1-21.
Pastor, Manuel. 2002. "Environmental Justice: Re flections from the United States." Conference Paper No. 1, Political Economy Research Institute, University of Massachusetts at Amherst.
Probst, Katherine N., and Kris Wernstedt. 2004. " Estimating Community Economic Impacts from the Reuse of Contaminated Properties: Workshop Summary and Highlights." http://www.rff.org/rff/Events/loader. cfm?url=/commonspot/security/ getfile.cfm&PageID=13661.50

Ringquist, Evan J. 2003. “Environmental Justice: Normative Concerns, Empirical Evidence, and Government Action.” In Environmental Policy: New Directions for the Twenty-First Century , ed. by Norman J. Vig and Michael E. Kraft. Washington, DC: CQ Press. Ringquist, Evan J. 2005. “Assessing Evidence of E nvironmental Inequities: A MetaAnalysis.” Journal of Policy Analysis and Management 24(2): 223-247.
Rosenthal, Stuart S. 2006. “Old Homes, Externalit ies, and Poor Neighborhoods: A Model of Urban Decline and Renewal.” Mimeo. Syrac use, NY: Syracuse University. Schelling, Thomas C. 1969. “Models of Segregation s.” American Economic Review 59: 488-92. Schelling, Thomas C. 1972. “A Process of Resident ial Segregation: Neighborhood Tipping.” In Racial Discrimination in Economic Life , ed. by A. Pascal. Lexington, MA: Lexington Books. Sidon, Joshua. 2005. "Environmental Injustice and Racial Segregation: Investigating the Link." Mimeo. Boulder, CO: University of Col orado.
Sieg, Holger, V. Kerry Smith, H. Spencer Banzhaf, a nd Randy Walsh. 2004. "Estimating the General Equilibrium Benefits of Lar ge Changes in Spatially Delineated Public Goods." International Economic Review 45(4): 1047-1077.
Skogan, Wesley. 1986. “Fear of Crime and Neighborho od Change.” Crime and Justice 8: 203-229 Smith, V. Kerry, Holger Sieg, H. Spencer Banzhaf, a nd Randy Walsh. 2004. “General Equilibrium Benefits for Environmental Improvements : Projected Ozone Reductions Under EPA’s Prospective Analysis for the Los Angeles Air Basin.” Journal of Environmental Economics and Management 47(3): 559-584.
Somerville, C. Tsuriel, and Cynthia Holmes. 2001. “Dynamics of the Affordable Housing Stock: Microdata Analysis of Filtering.” Journal of Housing Research 12: 115-140. Tiebout, Charles. 1956. "A Pure Theory of Local E xpenditures." Journal of Political Economy 64: 416-424.

United Church of Christ. 1987. “Toxic Wastes and Race in the United States.” Report of the Commission for Racial Justice.

US EPA. 2006a. What Does the Evidence Say about NPL Listing and Ho me Prices? www.epa.gov/superfund/programs/recycle/property.htm

.51

US EPA. 2006b. Superfund Creates Benefits Worth more than $4 Billi on per Year. http://www.epa.gov/superfund/programs/recycle/news/ benefits.htm

. Vigdor, Jacob L. 2002. “Does Gentrification Harm the Poor?” Brookings-Wharton Papers on Urban Affairs : 133-182. Vigdor, Jacob L. 2006. “Does Urban Decay Harm the Poor?” Mimeo. Durham, NC: Duke University.
Vitulli, Angela, Charlotte Dougherty, and Kimberly Bosworth. 2004. "Characterization of Reuse Activities at Contaminated Sites." NCEE W orking Paper 04-03. Washington, DC: National Center for Environmental Economics, US EPA.
Walsh, Randall P. 2004. "Analyzing Open Space Pol icies in a Locational Equilibrium Model with Endogenous Landscape Amenities." Mimeo. Boulder, CO: University of Colorado.
Wernstedt, Kris. 2004. "Overview of Existing Stud ies on Community Impacts of Land Reuse." NCEE Working Paper 04-06. Washington, DC: National Center for Environmental Economics, US EPA.
Wernstedt, Kris, Peter B. Meyer, and Kristen R. You nt. 2003. “Insuring Redevelopment at Contaminated Urban Properties.” Public Works Management & Policy 8: 85- 98. Wilson, James Q, and George E. Kelling. 1982. “Br oken Windows: The Police and Neighborhood Safety.” Atlantic Monthly , March.
Wolverton, Ann. 2002. "Does Race Matter? An Exam ination of a Polluting Plant's Location Decision." Mimeo. Washington, DC: Natio nal Center for Environmental Economics, US EPA.
Zukin, Sharon. 1987. “Gentrification: Culture and Capital in the Urban Core.” Annual Review of Sociology 113: 129-147. Zukin, Sharon. 1998. “Urban Lifestyles: Diversity and Standardisation in Spaces of Consumption.” Urban Studies 35(5-6): 825-839.