MOBI LITY AND ENVIRONMENTAL EQUITY: DO HOUSING CHOICES DETERMINE EXPOSURE TO AIR POLLUTION ?
Brooks Depro North Carolina State University and RTI International and Christopher Timmins Duke University

and NBER
August 2008

Abstract U.S. Census data show that approximately 40 million Americans move each year , raising questions about the role of mobility in determining observed environmental risk exposure patterns. The literature in this area continues to be contested , and the relationship between ho usehold sorting and exposure is still not well understood. We offer a new assessment of this question with respect to the criteria air pollutants (focusing on ozone and particulate matter) using a unique data set that combines information from repeat real estate transactions by the same San Francisco Bay area homebuyers. Our hedonic results suggest a trade

- off does exist between housing services and pollution (i.e., households can get more housing services for the same price by moving to a neighborhood wit h more pollution )

. Our results show p oor/ minority households are more likely to make this trade

- off and that wealth taken from ap preciating housing stocks increase s their ability to avoid the conventional sorting induced

- exposure story

. Keywords: Mobi lity, air p ollution, household s orting , environmental j ustice

Corresponding Author: Brooks Depro, RTI Internationa l , 3040 Cornwallis Road

, Research Triangle Park, NC 27709

- 2194 e - mail: bmd@rti.org. We would like to th ank the Micro Incentives Research Center and the Social Sciences Research Institute at Duke for making the data available

. All errors and omissions are our own.2

1. I ntroduction A variety of studie s suggest that minority and low

- income households often live in areas with poor environment al quality. Annual data also show that 14 percent of the U

. S

. population, or 40 million people, move to a new residence in each year ( U.S. Census Bureau, 2004). Together, these facts raise questions about the role that moving decisions play in determinin g environmental risk exposure patterns. This is in contrast to previous research, which has posited that this exposure has been the result of the siting decisions of polluting firms. The literature in this area continues to be contested , and the role of mobility in exposure patterns is still not well understood. We offer a new assessment of this question with respect to the criteria air pollutants (focusing on ozone and particulate matter) using a data set that combines individual real estate transactions with buyer attribute information for San Francisco Bay area homebuyers. Since we can observe individual choices and homebuyer economic circumstances on multiple occasions, we can test selected environmental justice (EJ) hypotheses in a new and more direc t way that avoids many of the modeling assumptions that are typically required without th ese data (i.e. , lo cational equilibrium models). As a resu lt, we build on existing analyse s that draw conclusions about sorting

- induced exposure. Specifically, we use d a repeat

- sales regression analysis for houses that sold two o r three times during the years 1990 to 2004 to recover a hedonic price function controlling for time invariant unobservables. We f ou nd evidence that more ozone and particle pollution standard exceedence s r educe Bay Area housing prices. This finding suggests that optimizing home buyers may indeed face an important trade

- off

— more housing services at the expense of lower environmental quality. The poor may be more likely to make this trade

- off , and3

minori ties are more likely to be poor. This provides a compelling explanation for observed racial correlation s with criteria pollutants. Specifically, s ince changes in common air pollutants appear to be reflected in housing prices, we look ed for direct evidence that poor/minority homeowners trade more pollution exposure for cheaper housing and/or other desirable neighborhood attributes when they move, whereas other homeowners do not. This is an important addition to the environmental justice literature, because previous analyses that have looked for verification of the sorting explanation for environmental inju stice used indirect evidence ( i.e., do the percentages of poor and minority residents rise when pollution increases ? ). There are potential alternative

ex planations for such a finding. For example, do individuals actually move nearer to the pollution not because of cheaper housing but because of proximate job opportunities? We cannot answer this question without seeing both the house the individual bought and the house they sold. With a unique data set, we are able to follow buyers as they move from their old house to their new house, observing directly the trade

- off s they make between housing services, pollution, and other determinants of neighborhood qu ality.
O ne interesting avenue w e explore is the role that changes in wealth (as opposed to income) play in housing decisions of poor and minorities. To date , existing environmental justice stories have emphasized only one aspect of the hou se hold’s economi c circumstances

— annual household income. To enhance our understanding of the role of changing wealth in pol lution exposure, we consider ed whether large and small gains (or losses) from the previous home sale s influence the housing trade

- off s people make. We we re able to calculate this amount by observing not only what Bay Area homeowners sold their previous house s for, but also what they had originally paid for them . The difference between4

these values is the amount of capital gain that they take with th em into the house purchase that we examine d

. Do poor/minorities use large gains from a housing sale to buy mo re housing services and

cleaner air when deciding to move? Are homeowners who m ov e from declining neighborhoods more constrain ed in the housing s ervices /pollution trade

- off ? Are environmental justice benefits associated with improving minority homeowners’ access to credit ?

2. Related Literature
A large number of papers in the environmental justice literature examine environ mental equity questio ns. T hey do so from three perspectives. The first group of studies document s the correlation between pollution and community characteristics ( e.g. , Freeman, 1972 ; Asch and Seneca, 1978 ; UCC, 1987; GAO , 1983 ; GAO, 1995; Brooks and Sethi, 1997; Bullard, 20 00; Houston et al., 2004 )

. Fisher, Kelly, and Romm (2006) and
Pastor , Saad, and Morello

- Frosch (2007) are notable recent example s of typ ical EJ analyses , and they also focus on a simi la r geographic area of interest

— the San Francisco Bay Area. The authors of the latter study were motivated to perform the analysis after finding that no existing empirical studies had addressed the overall distribution of air pollution exposure in this region. Entitled “Still Toxic After All These Years: Air Quality and Env ironmental Justice in the San Francisco Bay Area,” the report uses a single

- year cross - sectional design, a common method that is used to support claims of e nvironmental injustice. The authors leverage two data sets to compute census tract

– level measures of hazardous air pollutant exposure and compare these to contemporaneous socioeconomic characteristics of the tracts. The first data set (EPA’s Toxic Release Inventory [TRI]) is5

commonly used in the environmental justice literature and includes the location and emissions information on large industrial facilities. Using this data set, the authors specify a binary logit model where the dependent variable describes a census tract’s proximity to a TRI facility (= 1 if less than 1 mile, 0 if greater than 1 mile ). After controlling for selected factors (race, population density, and share of manufacturing employment), the ir analysis finds that census tracts with lower per capita incomes and home ownership rates were more likely to be in close proximity (i.e. , wi thin 1 mile) to stationary TRI facilit ies with air releases. Although the income and homeownership coefficients have intuitive (negative) signs, their magnitudes and standard errors are not reported; therefore , it is not possible to assess whether the

coe fficients are large or small. However, the authors are able to reject the hypothesis that these coefficients were zero at the 5 percent level. In addition to examining the influence of economic resources and proximity to toxic releases, the authors also found that black and
H ispanic populations we re more likely to live within a mile of a TRI facility with air releases after controlling for income and othe r tract

- level characteristics. The second data set used in the report (1999 National Air Toxics Assess ment [NATA]) is unique because it considers mobile source emissions as well as large industrial facilities covered by TRI. In addition, procedures can be applied to the NATA data to describe a census tract’s potential cancer and respiratory hazards. Regr essing these tract level estimates of cancer and respiratory risk on income and share of homeownership shows that after controlling for race, population density, and percent age of industrial/ commercial/transportation land use, census tracts with lower inc omes and home ownership rates appear to be at a higher risk for cancer a nd other respiratory hazards.6 The re sults in Pastor , Saad, and Morello

- Frosch (2007) correspond to six Bay area county visual patterns of the correlation s between minority status (Figu res 1 and 2 ) and high levels of criteria pollutants

. As shown in Figure s 3 and 4 , we map ped air pollution exceedances (ozone and PM 10 ) reported by air quality monitors along with census

- tract minority population share

. The second group of papers in the EJ literature investigate d the siting decisions of polluting firms. For example, Hamilton (1995 ) use d contemporaneous community attributes to explain the planning decisions of commercial hazardous waste facilities . He test ed three theories: (i) pure discri mination, (ii) Co asian bargaining ( i.e., that plants are sited in places where the potential costs of compensating affected residents a re low because their demand for environmental quality is weak ), and (iii) collective action/political economy (i.e., that
fir ms site plants in communities that are less likely to organize to collect compensation)

. Hamilton found that commercial hazardous waste facilities did avoid sites where potential compensation costs were high and areas more likely to mobilize against p lans for expansion

. Arora and Cason (1999) compare 1993 TRI data to 1990 neighborhood attributes in an attempt to limit reverse causality in correlation (i.e., 1990 neighborhood attributes could not be ca used by 1993 TRI emissions). They perform ed test s similar to Hamilton ’s and f ou nd that race, income levels, and unemployment influence release patterns from TRI facilities

. Community mobilization variables also influenced the level of TRI releases. Although these papers offer interesting hypothesis and empirical tests , siting explanations for exposure inequities are less relevant for criteria pollutants because mobile sources , rather than specific sites (e.g., TRI plants ) , are a substantial contributor to these air quality problems.7

Figure 1 . Share of Non
- Hispanic Blacks b y Census Tract: 20008 Figure 2 . Share of Hispanics b y Census Tract: 20009 Figure 3 . Minority Ozone Exposure (Days Exceeded) b y Census Tract: 200010 Figure 4 . Minority PM 10 Exposure (Days Exceeded) b y Census Tract: 200011 Only a few empirical studies have departed from documenting correlation or testing the polluting firm’s siting decision exposure hypothesis and looked at the influence of household mobility in generating the pollution exposure patterns observed in the data. F or example, exposure patterns could be driven by a complex sequence of housing market changes and migration decisions that occur over time (e.g. , household s “vote” for the mix of environmental quality, housing stock, and local communities using their feet) . In one of several versions of this migration story, declin es in environmental quality cause households to leave and property values to fall. In response, low

- income minority households may find these communities attractive because they are more willing to trade higher rates of exposure in exchange for a bigger (and now less expensive) house. This process has been referred to in the EJ literature as “housing market dynamics” (Been and Gupta, 1997), “white

- flight” (Oake s , Anderton, and Anderson , 1996), a nd “minority

- move in” (Morello

- Frosch, et al., 2002). However, three early longitudinal studies examining this question found limited or no evidence of community demographic changes after the siting of hazardous waste storage and disposal facilities (Oake s , Anderton, and Anderson, 1996; Been and Gupta, 1997; and Pastor , Saad, and Hipp , 2001). Recently, B anzhaf and Walsh (2008) published one of the most direct tests of migratory responses with the entry/exit of polluting facilities and emissions of air toxi cs. Using difference

- in

- difference and matching program evaluation methods, they f ou nd strong evidence of migration patterns that are consistent with the earlier work of Kahn (2000); communities where the air becomes cleaner see population gains , while co mmunities where the air becomes dirtier experience population declines. In addition, they also f ou nd evidence of environmental gentrification similar to that found in Sieg et al

. ’s (2004) counterfactual12

simulations of household res ponses to air quality ch anges. I ncreases in air pollution levels
appear to encourage rich households to exit a community , while poor households are more likely to enter. One of Banzhaf and Walsh’s important contributions is their attempt to better control for time

- invariant u nobserved local factors that determine residential location decisions. Many previous studies have not consider ed the role these amenities play in household sorting because numerous factors nee d to be considered; even if one was successful in developing a comprehensive and agreeable list, complete data would be to difficult and costly to coll ect. To overcome this challenge yet still address this issue, they use d school district and zip codes fixed effects in addition to other demographic controls. We foll ow ed their lead and use d zip code fixed effects to control for unobserved spatially distributed amenities. The evidence presented by Banzaf and Walsh suggests that people migrate in response to environmental quality changes an d consequently may help ex plain pollution exposure patterns that emerge over time. From a policy perspective, it suggests very different responses than would, for example, evidence of disproportionate siting. However, research to date has not address ed an important question about what types of constraints

movers face, the consequences these constraints may have in terms of pollution exposure, and differences in the trade

- off s they make in return for dirtier air (bigger houses, more other local amenities). Well

- known environmental justice advocate Robert Bullard argues these mobility constraints are an important concern and that “poor whites and poor blacks do not have the same opportunities to ‘vote with their feet ’ ” when it comes t o environmental quality choices (Bullard, 2000, p

. 6 )

. M obility constraint questions (in particular, wealth effects13

associated with a previous home sale ) have not been addressed to dat e in this empirical literature. This is the primary focus of our analysis. 3. Data The new and unique data set we use d to examine environmental justice questions

com bines information from three data sets:

• Housing transactions : P urchased from a national real estate company, th ese data provide actual transacti on (instead of self

- reported ) prices and include key mortgage information such as loan amount and lender ’ s name .

• Home Mortgage Disclosure Ac t (HMDA) : The HMDA data provide key demographic information about people (race and income) as well as mortgage information

. C ensus tract, mortgage loan amount, and lender ’ s n ame allow HMDA data matches with the housing transactions data set and link s demographic information about the people who bought and sold the homes.

• California Air Resources Board (CARB) Air Quality Data : CARB provides the latest 27 years of air quality data (1980

– 2006). It also provide s each monitor ’ s geographi c coordinates , which allow ed us to com pute a house

- specific exposure measure using all air quality monitors in the area.
3.1 Housing Transactions The data purchased from DataQuick include d real estate transactions for 1990

– 200 4 covering six key counties of the San Francisco Bay Area (Alameda, Contra Costa, Marin, San Francisco, San Mateo, and Santa Clara). Transaction variables for this analysis include d a unique parcel id entifier , transfer valu e (sale price), sales date, and geographic information (census tract, latitude, longitude). DataQuick also provides several useful housing characters14

observed at the last transaction: lot size, square footage, number of baths, and number of bedrooms. In addition to these data, we used GIS software to assign a 5

- digit U.S. zip c ode to each house

. 1 We reviewed the data set and dropped observations meeting the following criteria. First, we restricted our analysis to houses that sold two or three times durin g the sample period. With in this group, we dropped properties sold multiple times on the same day or the
same year . Next, we screened properties for land

- only sales or rebuilds and dropped all transactions where year built is missing or with a transactio n date that is prior to year built.
T o compute distances between houses and air quality monitors, we need ed the property's geographic coordinates. Therefore, we dropped properties where latitude and longitude we re missing. We also eliminate d transaction s without a sale s price and drop ped 1 percent of observations from each tail of the price distribution to minimize the effect of outliers. Finally, we restricted the sample to include only houses with identifiable zip codes and the following ranges of att ributes: lot size (1,000 to 30,000 square feet), square feet (800 to 3,000 square feet), bathrooms (1 to 5), and bedrooms (1 to 8). Housing variable descriptions are included in Table 1 , and the resulting sample statistics are reported in Table 2. This sample i s very similar to th e full sample reported in Bayer et al

. (2008).15 Table 1. Housing Variable Descriptions DataQuick Variable Data Set Name Description SA_PROPERTY_ID I dp Primary key. Unique

parcel identifier SA_DATE D ate Document date for th e transaction SA_VAL_TRANSFER P rice Transfer value of the property, AKA sale amount or sale value SA_CENSUS_TRACT T ract Census t ract SA_X_COORD L ongitude Longitude coordinate SA_Y_COORD L atitude Latitude coordinate SA_LOTSIZE L otsize Lot size expresse d in square feet SA_SQFT S qft Total living and/or heated and/or air condition area square feet SA_NBR_BATH B aths Number of bathrooms SA_NBR_BEDRMS B edrooms Number of bedrooms

Table 2 . Housing Variable s : Subsample of Houses with 2 Sales

Variable Me an Standard Deviation Minimum Maximum 2 Sales price($1,000) $3 61 $2 1 4 $15 $1,500 lotsize 6,2 58 3, 5 26 1,000 30,000 s qft 1,61 6 49 7 800 3,000 baths 2.0 0.6 4 1 5 bedrooms 3.2 0.8 2 1 8 Number of Observati ons: 195,426 (97,713 houses) 3 Sales price($ 1,000) $3 4 2 $2 10 $15 $1,500 lotsize 5, 885 3, 3 39 1,000 30,000 s qft 1, 5 60 4 8 1 800 3,000 baths 2.0 0.6 3 1 5 bedrooms 3. 1 0.8 0 1 8 Number of Observations: 76,563 ( 25,521 houses)16 Table 3 . Housing Variables: Full Sample

Variable Observations Mean Standard Deviation Minimum Maximum price($1,000) 1,232,575 $352 $222 $16 $1,505 Lotsize 1,105,557 6,884 11,385 0 199,940 Sqft 1,106,305 1,647 720 400 10,000 Bedrooms 1,106,360 2.9 1.13 0 8 Source: Bayer et al. 2008. Table 2.

3.2 Air Quality Da ta Currently, the San Francisco Bay Air Basin remains one of the cleanest of California’s air basins. Its coastal climate (cooler temperatures and better ventilation) makes its air quality better relative to other inland regions such as the San Joaquin Va lley and South Coast air basins (CARB, 2007). However, the region continues to deal with air quality issues associated with ground

- level ozone and particle pollut ion. Between 1990 and 2004, Bay
A rea pollution levels exceeded state and federal air quality standards (see Figure 5 ). The number of ozone excess days reached a peak in 1996 but ha s gradually declined since that year. In 2004, monitors measured ozone concentrations that exceeded the air quality standards on fewer than 15 days. Currently, Calif ornia d esignates the Bay Area as a

non attainment area for ground

- level ozone and coarse (PM 10 ) and fine (PM 2.5 ) particle pollution. Using federal standards, the Bay Are a is a non attainment area for o zone only.17

Figure 5 . Bay Area Air Quality Has Improved but Pollution Levels Continue to Exceed State Air Quality Standards

SF Bay Area Air Basin Air Quality Trends: Ozone

Source: California Air Almanac 2007 (Table 4-18)

0 5 10 15 20 25 30 35 40

1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 YearDays

Days Above State 8-Hr. Std. Days Above Nat. 8-Hr. Std. Days Above State 1-Hr. Std. SF Bay Area Air Basin Air Quality Trends: PM10

Source: California Air Almanac 2007 (Table 4-21)

0 10 20 30 40 50 60 70 80 90 100

1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 YearDays

Calc Days Above State 24-Hr Std Calc Days Above Nat 24-Hr Std Source: CARB (2007). Table 4

- 18 and Table 4

- 2118 The Bay Area air quali ty monitors are part of a state wide system of over 250 common pollutant monitors. Each year, CARB uses these m onitors to collect millions of measurement observations. CARB is also responsible for ensuring data quality and reporting and storing these results. For this study, 37 monitors were identified and provide d air pollution measurements related to ozone and particle pollution (PM 10 ). In February 2008, the California Environmental Protection Agency Air Resources Board (CARB, 2008) provided the latest DVD

- ROM with 27 years of air quality data (1980

– 2006). The data set covers all monitors located in the six cou nties covered by the real estate transactions data (Alameda, Contra Costa, Marin, San Francisco, San Mateo, and Santa Clara) and the three counties on the boundaries of this area (Napa, Solano, and Sonoma). The data set provide s a variety of air quality m easures for ozone. For this study, we use d a simple 3

- year moving average of annual days exceeding California ’s 1

- hour o zone standard and annual days exceeding California’s 24

- hour PM 10 standard

. 2 We use d a moving average because pollution levels tend to fluctuate from year to year (especi ally in the case of ozone); when making purchase decisions, homebuyers may recognize this and tak e into account where the pollution level had been in the previous year and where it is likely to go in the following year. In addition to these pollution readings, CARB provides information on each monitor’s coverage. This variable ranges from 0 to 100 and indicates the extent of monitoring performed during months where high pollution concentrations are expected. For example , a coverage number of 50 indicates that monitoring occurred 50 percent of the time during high

- concentration months. We drop ped monitors with less than 60 percent coverage for a given year.19 With the geographic information (latitude and longitude), we com puted a house

specific exposure measure using a weighted average of all m onitor s ’ exceed a nces with on e over distance as the weight. The distance in kilometers between each house and monitor is calculated using the “Great Circle” estimator. Table 4 provide s summary air quality data for houses included in our housing sample. Table 4 . Housing
- Specific Pollution Measures Summary Statistics Days Exceeding California Standard Variable Mean Standard Deviation Minimum Maximum 2 Sales Ozone (1 hour) 3.1 1.4 0.1 14.1
PM 10 (24 hour) 17.7 1 1.0 2.5 59.7 Number of Observati ons: 195,426 (97,713 houses) 3 Sales Ozone (1 hour) 3.1 1.5 0.1 14.1
PM 10 (24 hour) 17.7 10.8 2.3 60.2 Number of Observations: 76,563 (25,521 houses) Note: Pollution measured a s 3

- year moving average (t, t

- 1, t

- 2)

.

3.3 Home Buyer Characteristics DataQuick data and HMDA data were merged on the basis of census tract, loan amount, date, and lender ’s name. People were then linked across time using an al gorithm that compares char acteristics of names and dates and is described in Bayer et al. (2008) and Bishop and Timmins (2008). Bayer et al

. (2008) validated the algorithm by comparing the matched data set to public access Census micro data from IPUMS and the original real estate20

transaction data set. The matched buyers are representative of all the buyers and the housing attributes as well. We reviewed the data set and dropped observations meeting the following criteria. First, we restricted the sample to people who appear as bu yers twice. We restricted the sample to white, black, Hispanic , and Asian households. In cases where conflicting race information was provided in the first and second observations (o r was missing), we used the reported race in the second observation or rep laced missing information with the race of the first sale. We dropped observations where no race information was available, where income was missing, or when we could not compute the capital gain buyer. Buyer sample sta tistics are reported in Table 5 and are compared to th e full sample reported in Bayer et al

. (2008) (see Table 6)

. Table 5 . Buyer Characteristics: Subsample

Variable Mean Standard Deviation Minimum Maximum Income ($1,000) $138 $88 $3 $1,527
Share White 0.63 0.42 0 1 Share Asian 0. 23 0.15 0 1 Share Black 0.02 0.32 0 1 Share Hispanic 0.12 0.35 0 1 Number of Observations: 4,88921 T able 6 . Buyer Characteristics : Full Sample

Variable Mean Standard Deviation Minimum Maximum Income ($1,000) $119 $129 0 11,200,000 Share White 0.60 0.49 0 1 Share Asian 0.24 0.43 0 1 Share Black 0.03 0.18 0 1 Share Hispanic 0.11 0.31 0 1 Source: Bayer et al. 2008. Table 3

.

4. Housing Prices and Pollution: The Hedonic Gradient Our basic s pecification of the hedo ni c price equation for a sample observation ( house
i located in zip code j

selling in year t ) is as follows :

(1) t j i t j i t i j t j i A

Y H Z P

, , , , 0 , , ln ε α θ λ φ β + ′ + ′ + ′ + ′ + = (2) t j i i t j i u

,

, , , + = ω ε

w here Z

j is a vector of zip code indicators, H i is a vector of variables des cribing housing attributes, Y

t is a vector of year indicators , and A i,j,t is a vector of variables describing the air quality associated with house i

. We assume d that the error term ( t j i , , ε ) can be decomposed into a fixed component that is specific to ho use i ( i ω ) and a time

- varying component ( t j i u

, , ).
Y ear indicators are included to control for the effects of unusually rapid growth in Bay Area

wide housing prices while overall air quality improved. Failure to contr o l for these effects would lead us to overestimate the positive effect that air quality improvements might have on housing prices.22 One of the strengths of our data is that we can match houses over time. As such, we estimate a differenced regression for hou ses that sold at least twice during the period between 1990 and 2004 . We fou nd strong evidence that more ozone and PM 10 exceedences reduce Bay Area housing prices (see Table 7 )

. Using superscripts to denote either the first or second sale of house i :

(3 ) ) ( ) ( ) ( ln ln 1

, , 2

, , 1

, , 2

, , 1 2 1

, , 2

, , t j i t j i t j i t j i t t t j i t j i u u A A Y Y P P

− + ′

− +

− = − α

θ

All house and neighbor hood attributes (including the zip code fixed effects and unobserved

i ω ’s) are differenced away in E quation (3 ). Assuming that only fixed house and neighborhood attributes might have be en correlated with pollution , this eliminates any potential source of bias in our estimate of a . To the extent that neighborhood attributes change over time in a manner correlated with our pollution measures, we still have a potential source of bias. 3 To highlight the advantages of the differencing approach, we estimate one other specification tha t does not leverage house fixed effects. In this regression, we include house and neighborhood attributes in the hedonic price equation instead of differencing them away. As shown in Table 7, more PM 10 exceedances are associated with a small increase in housing prices in that specification. One might argue that this result is driven by the omission of unobserved location attributes that are positively correlated with PM 10 . For example, houses located near major roadways may be attractive because it is e asier to commute to work or access public transit; however, these roadways would also be more likely to be used by vehicles emitting diesel exhaust.23 Table 7 . Hedonic Prices and Pollution: Bay Area Results
No House Fixed Effects House Fixed Effects

(Di fferences) Dependent Variable: ln(price) ln(price t+1 )

- ln(price t ) Explanatory Variables: Lot size <0.0 1* <(0.001 ) NA Square feet <0.0 1* <(0.001 ) NA
Bathrooms 0.05 * (0.003 ) NA
Bedrooms -0.09 * (0.002 ) NA Days exceeded state 1 hour ozone standard (3 year moving average) -0.05 * (0.001 ) -0.09 * (0.002) Days exceeded state 24 hours PM10 standard (3 year moving average) 0.03 * <(0.001 ) -0.01 * <(0.001 )
Constant 10 .2 * (0.026 ) -0.08 * (0.017 ) Year indicators Yes Yes Observations 271,989 1 48,755 R

- Squared 0.38 0. 07 *Statistically different than zero at a =0.01

. Note : Quantities in parenthesis below estimates are the panel robust standard errors

.

With respect to housing services, both of these model s show that holding other factors
constant, increases in housing services increase housing prices. Lot siz e, squa re feet, and number of bathrooms coefficients are generally small but positive and s tatistically different from zero . The one sign exception is the bedroom coefficient . Since a homeowner may not consider the partial effect of an additional bedroom (holding the total number of rooms fixed) desirable , this makes sense

. 424 These hedonic results constitute our first tw o pieces of empirical evidence: (i) a trade

off does exist between house price and pollution , and (ii) increased housing services do indee d result in a higher housing price. An individual can, therefore, get more housing services for the same price by moving to a neig hborhood with more pollution. Whether poor/minority individuals are more likely to make this trade

- off is the subject of the remainder of our analysis.

5. Following the Decisions of Bay Area Home Buyers
In this section, we look for direct evidence that poor/minority homeowners trade more pollution exposure for cheaper housing and/or other desirable neighborhood attributes whe n they move, whereas other homeowners do not. In addition, we leverage the fact that we can see homeowners on multiple purchase occasions to explore the role of housing wealth in the purchase decisions of the poor and minorities.
5.1 Method
We construct e d year - specific housing service and neighborhood quality indices using a regression model similar to that used above to document the capitalization of pollution into housing prices. In particular, we specif ied the log of housing price as a function of nei ghborhood (i.e., zip code) dummies , housing characteristics, air pollution , and other unobserved factors

. Importantly, we allow ed all the parameters in this regression to vary by year; this allow ed for the most flexible calculation of neighborhood and hou sing services indices possible. We estimate d the following model with ordinary least

- squares :

(4 ) t j i t t j i t i t j t t j i A

H Z P

, , , , , 0 , , ln η α λ φ β +

′ + ′ + ′ + =25

For each house, we then use d t he estimated housing and zip code coefficients ( i.e., t λ and

t φ , respectively ) to compute a housi ng service s ( t i H λ ′ ) and neighborhood quality index for each home in the sample in each year

. Next, we compute d the differences in housing services index, house

- specific air pollution (measured using the 3

- year moving average number of days exceeded for ozone and PM 10 ) , and other neighborhood services (i.e., zip code fixed effect s) associate d with the move from a first home to a second home. As a last step, we compute d the correlation coefficients acr oss these v ariables for different groups.
5
. 2 Results Arguments based on s orting

- induced exposure suggest that poor and minority households are willing to live in a house with dirtier air in exchange for receiving more housing services ( and possibly a neighbor hood with better overall quality ) . Initially, we look ed for patterns in correlation coefficients that are consistent with this story. We pa id particular attention to diff erence s across racial groups, income levels , and wealth derived from the previous ho me sale

. As shown in Table 8 , minorities bought bigger homes that had more air pollution
exposure . Compare d with white and A sian homeowners, minorities also took on more pollution (b oth ozone and PM 10 ) to get more housing services ; evidence that is consi stent with the simple sorting story. The minority household’s housing service/ozone correlation coefficient is nearly two times higher than white and A sian groups.26

Table 8 . Housing, Pollution, and Neighborhood Correlation Coefficients by Race and Income
Ozone PM10 Neighborhood Quality Minority 0.22 0.04 0.02 High Income 0.10 0.08

- 0.08 Low Income 0.27 0.03 0.02 White 0.09

- 0.07

- 0.10 High Income 0.11

- 0.09

- 0.17 Low Income 0.09

- 0.05

- 0.07 Asian 0.11

- 0.09

- 0.05 High Income 0.08

- 0.10

- 0.08 Low Income 0.14

- 0.07

- 0.08 Minority White Diff: High Income

- 0.01 0.17 0.08 Minori ty White Diff: Low Income 0.18 0.08 0.09 Double Difference:

- 0.19 0.09

- 0.01 Asian White Diff: High Income

- 0.03 0.00 0.08 Asian White Diff: Low Income 0.05

- 0.02

- 0.01 Double Difference:

- 0.08 0.02 0.09 Note: Correlation coefficients with respect to housing services

Next, we look ed more close ly at the difference s between low

- and high

- income
groups to see what role that distinction plays in mobility

- induced exposur e patterns. As shown in Table 8 , the racial difference in h ouse - ozone correl ation dis appears for high

income homeowners . However, the house

- PM 10 correlation persists

. In contrast, l ow income A sians and minorities take on significantly more pollution to get more housing services

. Therefore , the observed correlation between race and air p ollution exposure can really be explained by income differences

. We also note that income does n o t seem to influence the trade

- off

s white homeowners make (i.e., p oor white homeowners do n o t seem to have to give up cleaner air to get more housing services ) . In contr ast, Asians and minorities do.27 Environmental justice stories emphasize the role annual household income plays in determining exposure. To enhance ou r understanding of the relationship between economic circumstances and pollution exposure, we a lso considered whether information about changes in homeowner wealth that have not been typically observed in the data (e.g., large and small percentage gains [ o r losses ] from the previous home sale ) influence s the housing trade

- off s people make. Intuitiv ely, households that make more mo ney from the previous home sale relative to the initial purchase price may be better positioned to avoid the housing services/ pollution trade

- off

. As shown in Table 9 , a big relative gain does seem to help poor minor ities avoid the

ozone trade

- off

s . T he difference between low

- and hi gh

- income with low gain correlation coefficients is 0.19 for ozone . In contrast, the difference b etween low

- and high

- income is smaller ( 0.15 ) for high

- gain households

. This suggests poor mi norities do use housing wealth to “ buy ” their way out of high pollution when they move to a bigger house (d ifference of -0.04 )

. Similar effects occur for PM , but the difference is more than twice as large ( -0.10 ). For Asians (see Table 10), the wealth effect suggests they are more likely to make the ozone trade

- off; the ozone difference between low and high in come for low gain households is -0.02, while it is 0.16 for high gain households, a difference of 0.17. However, housing wealth does appear to help low

- income households escape higher levels of PM pollution. For white homeowners, housing wealth doesn't se em necessary for low income households to avoid the trade

- off (see Table 11). Instead, it appears that low income households can escape pollution just as easily as high income households.28

Table 9 . Minority Housing, Pollution, and Neighborhood Correlat ion Coefficients by

Race, Income, and Housing Wealth Change
Ozone PM10 Neighborhood Quality Minority 0.22 0.04 0.02 Low Income 0.27 0.03 0.02 High % Gain 0.27 0.04 0.00 Low % Gain 0.27 0.03 0.00 High Income 0.10 0.08

- 0.08 High % Gain 0.12 0.12

- 0.06 Low % Gain 0.08 0.01

- 0.10 Diff Income Groups: High % Gain 0.15

- 0.09 0.05 Diff Income Groups: Low % Gain 0.19 0.02 0.10 Double Difference:

- 0.04

- 0.10

- 0.05 Note: Correlation coefficients with respect to housing se rvices

For Asians (see Table 10), the wealth effect suggests they are more likely to make the ozone trade

- off; the ozone difference between low and high income for low gain households is -0.02, while it is 0.16 for high gain households, a difference of 0. 17. However, housing wealth does appear to help low

- income households escape higher levels of PM pollution. For white homeowners, housing wealth doesn't seem necessary for low income households to

a void the trade

- off (see Table 11 ). Instead, it appears t hat low income households can escape pollution just as easily as high income hous eholds. 6. Conclusion
We offer a new assessment of environmental equity questions in the San Francisco Bay area using a unique data set that combines individual real es tate transactions with homebuyer mortgage information . The key advantage of this data is that we observe Our hedonic results document two pieces of empirical evidence necessary for a sorting

- induced exposure story: (i) a trade

- off does exist between house pri ce and pollution, and (ii) increased housing services do indeed result in a higher housing price. An individual can, therefore, get more29

Table 10 . Asian Housing, Pollution, and Neighborhood Correlation Coefficients by

Race, Income, and Housing Wealth Ch ange Ozone PM10 Neighborhood Quality Asian 0.11

- 0.09

- 0.05 Low Income 0.14

- 0.07

- 0.08 High % Gain 0.19

- 0.05

- 0.11 Low % Gain 0.10

- 0.09

- 0.06 High Income 0.08

- 0.10

- 0.08 High % Gain 0.02

- 0.05

- 0.09 Low % Gain 0.12

- 0.14

- 0.08 Diff Income Gro ups: High % Gain 0.17 0.00

- 0.02 Diff Income Groups: Low % Gain

- 0.02 0.06 0.02 Double Difference: 0.19

- 0.06

- 0.04 Note: Correlation coefficients with respect to housing services

Table 11 . White Housing, Pollution, and Neighborhood by Race, Income, and Housing Wealth Change

Ozone PM10 Neighborhood Quality White 0.09

- 0.07

- 0.10 Low Income 0.09

- 0.05

- 0.07 High % Gain 0.08

- 0.06

- 0.07 Low % Gain 0.10

- 0.04

- 0.08 High Income 0.11

- 0.09

- 0.17 High % Gain 0.10

- 0.11

- 0.20 Low % Gain 0.12

- 0.08

- 0.13 Diff Income Groups: High % Gain

- 0.02 0.05 0.13 Diff Income Groups: Low % Gain

- 0.02 0.04 0.05 Double Difference: 0.01 0.01 0.09

housing services for the same price by moving to a neighborhood with more pollution. Analysis of differences in th e correlation patterns between changes in housing service indices and house

- specific ozone and PM 10 pollution suggests that poor/minority households are more likely to make this trade

- off. Moreover, it provides additional support for the sorting30

(versus d iscriminatory siting) explanation for observed patterns of race, wealth, and air pollution exposure. Our hedonic results document two pieces of empirical evidence necessary for a sorting

- induced exposure story: (i) a trade

- off does exist between hous e pric e and pollution, and (ii) increased housing services do indeed result in a higher housing price. An individual can, therefore, get more housing services for the same price by moving to a nei ghborhood with more pollution. Analysis of difference s in the co rrelation patterns between changes in housing service indices and house

- specific ozone and PM 10 pollution suggest s that poor/minority households are more likely to make this trade

- off . Moreover, it provides additional support for the sorting ( versus discr iminatory siting ) explanation for observed patterns of race, wealth, and air pollution exposure. Our anal ysis also finds that w ealth taken from appreciating housing stocks can increase the ability of poor/minorit y individuals to avoid the conventional sort ing story

. This has two implications

. First, p oor minority households living in a declining neighborhood that
want to improve their housing situation could be at a significant disadvantage because they

own a house that will not appreciat e by as much as a house in an improving neighborhood . I f an individual is a high

- income minority, this effect appears to be diminished and if an individual is white, it seems to go away altogether. This finding has dynamic implications

. P oor minority households are like ly to be constrained in their housing choices by wealth effects, which will force them to buy houses in polluted neighborhoods . If those neighborhoods continue to deteriorate, those homeowners are going to be even further constrained when it comes time to buy a house with even greater housing services. Second,
p olicies t hat help minoriti es gain access to credit may have an indirect benefit that enhances31

their ability to move to neighborhoods with cleaner air . Policy makers could consider this factor as th ey weigh the many other benefits and costs of relaxing credit constraints.

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1 ESRI Data: U.S. Zip Code Areas: 2000.

2 The CARB DVD variable name for ozone is “O ZEX1HST .” For PM 10 , CARB’s variable name is

“ PM10CX 1S .”

3 Our identification assumption would be violated if, for example, improving employment opportunities in a neighborhood led to increased pollution and higher housing prices. Whereas this might be the case with TRI pollutants, we believe that this ass umption is a reasonable one in the case of criteria pollutants. These pollutants tend to be produced by automobiles traveling on highways to jobs in other parts of the city, and the distribution of their ambient concentrations is driven, in large part, by wind patterns and geography.

4 For a given size of home (square feet) and bathrooms, adding an additional bedroom means the average size of these rooms will necessarily be smaller

. In addition, the same number of bathrooms would now have to be used by pr esumably more people.