MOBILITY 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 household 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 with more pollution). Our results show

poor/minority households are more likely to make this trade-off and that wealth taken 

from appreciating housing stocks increases their ability to avoid the conventional 

sorting induced-exposure story. 

Keywords: Mobility, air pollution, household sorting, environmental justice

Corresponding Author: Brooks Depro, RTI International, 3040 Cornwallis Road, Research Triangle Park, NC 

27709-2194 e-mail: bmd@rti.org. We would like to thank 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. Introduction

A variety of studies suggest that minority and low-income households often live in 

areas with poor environmental 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 

determining 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 direct way that avoids many of the modeling assumptions that are typically required 

without these data (i.e., locational equilibrium models). As a result, we build on existing 

analyses that draw conclusions about sorting-induced exposure.

Specifically, we used a repeat-sales regression analysis for houses that sold two or 

three times during the years 1990 to 2004 to recover a hedonic price function controlling for 

time invariant unobservables. We found evidence that more ozone and particle pollution 

standard exceedences reduce 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, and 
3

minorities are more likely to be poor. This provides a compelling explanation for observed 

racial correlations with criteria pollutants.

Specifically, since changes in common air pollutants appear to be reflected in housing 

prices, we looked 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 injustice used indirect evidence (i.e., do the percentages of 

poor and minority residents rise when pollution increases?). There are potential alternative 

explanations 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-offs they make between 

housing services, pollution, and other determinants of neighborhood quality.

One interesting avenue we 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 household’s economic 

circumstances—annual household income. To enhance our understanding of the role of 

changing wealth in pollution exposure, we considered whether large and small gains (or 

losses) from the previous home sales influence the housing trade-offs people make. We were 

able to calculate this amount by observing not only what Bay Area homeowners sold their 

previous houses for, but also what they had originally paid for them. The difference between 
4

these values is the amount of capital gain that they take with them into the house purchase 

that we examined. Do poor/minorities use large gains from a housing sale to buy more 

housing services and cleaner air when deciding to move? Are homeowners who move from 

declining neighborhoods more constrained in the housing services/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 

environmental equity questions. They do so from three perspectives. The first group of 

studies documents 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, 2000; Houston et al., 2004). Fisher, Kelly, and Romm (2006) and 

Pastor, Saad, and Morello-Frosch (2007) are notable recent examples of typical EJ analyses,

and they also focus on a similar 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 Environmental 

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 environmental 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]) is 
5

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), their analysis finds that 

census tracts with lower per capita incomes and home ownership rates were more likely to be 

in close proximity (i.e., within 1 mile) to stationary TRI facilities 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 

coefficients 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 

Hispanic populations were more likely to live within a mile of a TRI facility with air releases 

after controlling for income and other tract-level characteristics.

The second data set used in the report (1999 National Air Toxics Assessment 

[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. Regressing these tract 

level estimates of cancer and respiratory risk on income and share of homeownership shows 

that after controlling for race, population density, and percentage of industrial/ 

commercial/transportation land use, census tracts with lower incomes and home ownership 

rates appear to be at a higher risk for cancer and other respiratory hazards.
6

The results in Pastor, Saad, and Morello-Frosch (2007) correspond to six Bay area 

county visual patterns of the correlations between minority status (Figures 1 and 2) and high 

levels of criteria pollutants. As shown in Figures 3 and 4, we mapped air pollution 

exceedances (ozone and PM10) reported by air quality monitors along with census-tract 

minority population share.

The second group of papers in the EJ literature investigated the siting decisions of 

polluting firms. For example, Hamilton (1995) used contemporaneous community attributes 

to explain the planning decisions of commercial hazardous waste facilities. He tested three 

theories: (i) pure discrimination, (ii) Coasian bargaining (i.e., that plants are sited in places 

where the potential costs of compensating affected residents are low because their demand 

for environmental quality is weak), and (iii) collective action/political economy (i.e., that

firms 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 plans 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 caused by 1993 TRI emissions). They performed tests similar to Hamilton’s and found 

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 by Census Tract: 2000
8

Figure 2. Share of Hispanics by Census Tract: 2000
9

Figure 3. Minority Ozone Exposure (Days Exceeded) by Census Tract: 2000
10

Figure 4. Minority PM10 Exposure (Days Exceeded) by Census Tract: 2000
11

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. For 

example, exposure patterns could be driven by a complex sequence of housing market 

changes and migration decisions that occur over time (e.g., households “vote” for the mix of 

environmental quality, housing stock, and local communities using their feet). In one of 

several versions of this migration story, declines 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” 

(Oakes, Anderton, and Anderson, 1996), and “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 (Oakes, Anderton, and Anderson, 1996; Been and Gupta, 1997; and Pastor, 

Saad, and Hipp, 2001).

Recently, Banzhaf and Walsh (2008) published one of the most direct tests of 

migratory responses with the entry/exit of polluting facilities and emissions of air toxics. 

Using difference-in-difference and matching program evaluation methods, they found 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 communities where 

the air becomes dirtier experience population declines. In addition, they also found evidence 

of environmental gentrification similar to that found in Sieg et al.’s (2004) counterfactual 
12

simulations of household responses to air quality changes. Increases 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 unobserved local factors that determine residential location decisions. 

Many previous studies have not considered the role these amenities play in household sorting 

because numerous factors need to be considered; even if one was successful in developing a 

comprehensive and agreeable list, complete data would be to difficult and costly to collect. 

To overcome this challenge yet still address this issue, they used school district and zip codes 

fixed effects in addition to other demographic controls. We followed their lead and used 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 and consequently may help explain 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 addressed 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-offs 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 to environmental quality 

choices (Bullard, 2000, p. 6). Mobility constraint questions (in particular, wealth effects 
13

associated with a previous home sale) have not been addressed to date in this empirical 

literature. This is the primary focus of our analysis.

3. Data

The new and unique data set we used to examine environmental justice questions 

combines information from three data sets: 

• Housing transactions: Purchased from a national real estate company, these data 

provide actual transaction (instead of self-reported) prices and include key mortgage

information such as loan amount and lender’s name.

• Home Mortgage Disclosure Act (HMDA): The HMDA data provide key 

demographic information about people (race and income) as well as mortgage 

information. Census tract, mortgage loan amount, and lender’s name allow HMDA 

data matches with the housing transactions data set and links 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 provides each monitor’s geographic 

coordinates, which allowed us to compute a house-specific exposure measure using 

all air quality monitors in the area. 

3.1 Housing Transactions

The data purchased from DataQuick included real estate transactions for 1990–2004 

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 included

a unique parcel identifier, transfer value (sale price), sales date, and geographic information 

(census tract, latitude, longitude). DataQuick also provides several useful housing characters 
14

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 code 

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 during the sample 

period. Within 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 transaction date that is prior to year built. 

To compute distances between houses and air quality monitors, we needed the property's 

geographic coordinates. Therefore, we dropped properties where latitude and longitude were 

missing. We also eliminated transactions without a sales price and dropped 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 attributes: 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 is very similar to the full sample reported in Bayer et al. (2008).
15

Table 1. Housing Variable Descriptions

DataQuick Variable Data Set Name Description

SA_PROPERTY_ID Idp Primary key. Unique 

parcel identifier

SA_DATE Date Document date for the 

transaction

SA_VAL_TRANSFER Price Transfer value of the 

property, AKA sale 

amount or sale value

SA_CENSUS_TRACT Tract Census tract

SA_X_COORD Longitude Longitude coordinate

SA_Y_COORD Latitude Latitude coordinate

SA_LOTSIZE Lotsize Lot size expressed in 

square feet

SA_SQFT Sqft Total living and/or 

heated and/or air 

condition area square 

feet

SA_NBR_BATH Baths Number of bathrooms

SA_NBR_BEDRMS Bedrooms Number of bedrooms

Table 2. Housing Variables: Subsample of Houses with 2 Sales

Variable Mean

Standard 

Deviation Minimum Maximum

2 Sales

price($1,000) $361 $214 $15 $1,500

lotsize 6,258 3,526 1,000 30,000

sqft 1,616 497 800 3,000

baths 2.0 0.64 1 5

bedrooms 3.2 0.82 1 8

Number of Observations: 195,426 (97,713 houses)

3 Sales

price($1,000) $342 $210 $15 $1,500

lotsize 5,885 3,339 1,000 30,000

sqft 1,560 481 800 3,000

baths 2.0 0.63 1 5

bedrooms 3.1 0.80 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 Data

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 Valley and South 

Coast air basins (CARB, 2007). However, the region continues to deal with air quality issues 

associated with ground-level ozone and particle pollution. Between 1990 and 2004, Bay 

Area pollution levels exceeded state and federal air quality standards (see Figure 5). The 

number of ozone excess days reached a peak in 1996 but has gradually declined since that

year. In 2004, monitors measured ozone concentrations that exceeded the air quality 

standards on fewer than 15 days. Currently, California designates the Bay Area as a 

nonattainment area for ground-level ozone and coarse (PM10) and fine (PM2.5) particle 

pollution. Using federal standards, the Bay Area is a nonattainment area for ozone 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

Year

Days

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

Year

Days

Calc Days Above State 24-Hr Std

Calc Days Above Nat 24-Hr Std

Source: CARB (2007). Table 4-18 and Table 4-21
18

The Bay Area air quality monitors are part of a statewide system of over 250 common 

pollutant monitors. Each year, CARB uses these monitors 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 provided air 

pollution measurements related to ozone and particle pollution (PM10). 

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 counties 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 provides a variety of air quality measures for ozone. For this study, we used a 

simple 3-year moving average of annual days exceeding California’s 1-hour ozone standard 

and annual days exceeding California’s 24-hour PM10 standard.

2

 We used a moving average 

because pollution levels tend to fluctuate from year to year (especially in the case of ozone); 

when making purchase decisions, homebuyers may recognize this and take 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 dropped monitors with less than 60 percent coverage 

for a given year.
19

With the geographic information (latitude and longitude), we computed a housespecific exposure measure using a weighted average of all monitors’ exceedances with one 

over distance as the weight. The distance in kilometers between each house and monitor is 

calculated using the “Great Circle” estimator. Table 4 provides 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

PM10

(24 hour)

17.7 11.0 2.5 59.7

Number of Observations: 195,426 (97,713 houses)

3 Sales

Ozone 

(1 hour)

3.1 1.5 0.1 14.1

PM10

(24 hour)

17.7 10.8 2.3 60.2

Number of Observations: 76,563 (25,521 houses)

Note: Pollution measured as 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 algorithm 

that compares characteristics 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 estate 
20

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 buyers 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 (or was missing), we used the 

reported race in the second observation or replaced 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 statistics 

are reported in Table 5 and are compared to the 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: 4,889
21

Table 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 specification of the hedonic price equation for a sample observation (house 

i located in zip code j selling in year t) is as follows:

(1) Pi, j,t 0 Z j Hi Yt Ai, j,t i, j,tln = β + ′φ + ′λ + ′θ + ′ α + ε

(2) i, j,t = ωi + ui, j,tε

where Zj is a vector of zip code indicators, Hi is a vector of variables describing housing 

attributes, Yt is a vector of year indicators, and Ai,j,t is a vector of variables describing the air 

quality associated with house i. We assumed that the error term ( i, j,tε ) can be decomposed 

into a fixed component that is specific to house i (ωi) and a time-varying component ( ui, j,t). 

Year indicators are included to control for the effects of unusually rapid growth in Bay Areawide housing prices while overall air quality improved. Failure to control 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 houses that sold at least twice during the period between 

1990 and 2004. We found strong evidence that more ozone and PM10 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 Pi, j,t Pi j t Yt Yt Ai j t Ai j t + ui j t − ui j t − = − θ + − ′α

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

ωi’s) are differenced away in Equation (3). Assuming that only fixed house and 

neighborhood attributes might have been 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 that 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 PM10 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 PM10. For example, houses 

located near major roadways may be attractive because it is easier 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 

(Differences)

Dependent Variable: ln(price) ln(pricet+1) - ln(pricet)

Explanatory Variables:

Lot size <0.01*

<(0.001)

NA

Square feet <0.01*

<(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 271,989 148,755

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 models show that holding other factors 

constant, increases in housing services increase housing prices. Lot size, square feet, and 

number of bathrooms coefficients are generally small but positive and statistically 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.

4
24

These hedonic results constitute our first two pieces of empirical evidence: (i) a tradeoff does exist between house price 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 neighborhood 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 

when 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 constructed 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 specified the log of housing price as a function of 

neighborhood (i.e., zip code) dummies, housing characteristics, air pollution, and other 

unobserved factors. Importantly, we allowed all the parameters in this regression to vary by 

year; this allowed for the most flexible calculation of neighborhood and housing services 

indices possible. We estimated the following model with ordinary least-squares:

(4) Pi, j,t 0,t Z j t Hi t Ai, j,t t i, j,tln = β + ′φ + ′λ + ′ α +η
25

For each house, we then used the estimated housing and zip code coefficients (i.e., λt and 

φt, respectively) to compute a housing services ( Hiλt

′ ) and neighborhood quality index for 

each home in the sample in each year. Next, we computed the differences in housing 

services index, house-specific air pollution (measured using the 3-year moving average 

number of days exceeded for ozone and PM10), and other neighborhood services (i.e., zip

code fixed effects) associated with the move from a first home to a second home. As a last 

step, we computed the correlation coefficients across these variables for different groups.

5.2 Results

Arguments based on sorting-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 neighborhood with better overall quality). Initially, we 

looked for patterns in correlation coefficients that are consistent with this story. We paid

particular attention to differences across racial groups, income levels, and wealth derived

from the previous home sale. 

As shown in Table 8, minorities bought bigger homes that had more air pollution

exposure. Compared with white and Asian homeowners, minorities also took on more 

pollution (both ozone and PM10) to get more housing services; evidence that is consistent 

with the simple sorting story. The minority household’s housing service/ozone correlation 

coefficient is nearly two times higher than white and Asian 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

Minority 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 looked more closely at the differences between low- and high-income 

groups to see what role that distinction plays in mobility-induced exposure patterns. As 

shown in Table 8, the racial difference in house-ozone correlation disappears for highincome homeowners. However, the house-PM10 correlation persists. In contrast, lowincome Asians and minorities take on significantly more pollution to get more housing 

services. Therefore, the observed correlation between race and air pollution exposure can 

really be explained by income differences. We also note that income does not seem to 

influence the trade-offs white homeowners make (i.e., poor white homeowners do not seem 

to have to give up cleaner air to get more housing services). In contrast, Asians and 

minorities do.
27

Environmental justice stories emphasize the role annual household income plays in 

determining exposure. To enhance our understanding of the relationship between economic 

circumstances and pollution exposure, we also considered whether information about 

changes in homeowner wealth that have not been typically observed in the data (e.g., large 

and small percentage gains [or losses] from the previous home sale) influences the housing 

trade-offs people make. Intuitively, households that make more money 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 minorities avoid the 

ozone trade-offs. The difference between low- and high-income with low gain correlation 

coefficients is 0.19 for ozone. In contrast, the difference between low- and high-income is

smaller (0.15) for high-gain households. This suggests poor minorities do use housing 

wealth to “buy” their way out of high pollution when they move to a bigger house (difference 

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 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 

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 Correlation 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 services

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 

avoid the trade-off (see Table 11). Instead, it appears that low income households can escape 

pollution just as easily as high income households. 

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 estate 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 price and pollution, and (ii) increased housing 

services do indeed result in a higher housing price. An individual can, therefore, get more 
29

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

Race, Income, and Housing Wealth Change

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 Groups: 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 the correlation patterns between changes in housing service indices 

and house-specific ozone and PM10 pollution suggests that poor/minority households are 

more likely to make this trade-off. Moreover, it provides additional support for the sorting 
30

(versus discriminatory 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 house price 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 neighborhood 

with more pollution. Analysis of differences in the correlation patterns between changes in 

housing service indices and house-specific ozone and PM10 pollution suggests that 

poor/minority households are more likely to make this trade-off. Moreover, it provides 

additional support for the sorting (versus discriminatory siting) explanation for observed 

patterns of race, wealth, and air pollution exposure.

Our analysis also finds that wealth taken from appreciating housing stocks can 

increase the ability of poor/minority individuals to avoid the conventional sorting story. This 

has two implications. First, poor 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 appreciate by as much as a house in an improving neighborhood. If 

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. 

Poor minority households are likely 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, 

policies that help minorities gain access to credit may have an indirect benefit that enhances 
31

their ability to move to neighborhoods with cleaner air. Policy makers could consider this 

factor as they 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 “OZEX1HST.” For PM10, CARB’s variable name is 

“PM10CX1S.”

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 assumption 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 presumably more people.