Residential Mobility and Ozone Exposure in the San Francisco Bay Area

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ABSTRACT

DEPRO, BROOKS MATTHEW. Residential Mobility and Ozone Exposure in the San Francisco Bay Area. (Under the direction of Raymond B. Palmquist).

Although a large social science literature has focused on pollution’s influence on property values, less emphasis has been placed on residential mobility responses to pollution. However, the literature has not addressed two questions. First, when homeowners move and buy bigger homes, do they expose themselves to more ozone pollution? Second, do homeowners try to avoid extended ozone exposure by reducing the time between moves? If we can address each question using

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Residential Mobility and Ozone Exposure in the San Francisco Bay Area

by

Brooks Matthew Depro

A dissertation submitted to the Graduate Faculty of North Carolina State University

in partial fulfillment of the requirements for the Degree of

Doctor of Philosophy

Economics

Raleigh, North Carolina 2009

APPROVED BY:

_______________________________ ______________________________

Raymond B. Palmquist Christopher Timmins

Committee Chair

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BIOGRAPHY

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ACKNOWLEDGMENTS

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TABLE OF CONTENTS

LIST OF TABLES...vii

LIST OF FIGURES... x

CHAPTER ONE INTRODUCTION ...1

CHAPTER TWO THE ECONOMICS OF URBAN RESIDENTIAL MOBILITY: EXAMPLES OF THEORY AND PRACTICE...4

A Representative Model...8

Considering Changes in Neighborhood Quality ...12

Other Residential Mobility Barriers ...14

Conclusions...22

References ...23

CHAPTER THREE SAN FRANCISCO BAY AREA HOUSING SALES DATA ...26

Overview ...27

Housing Sales Sample ...28

Air Quality ...40

Conclusions...55

References ...56

CHAPTER FOUR RESIDENTIAL MOVES, BIGGER HOUSES, AND OZONE EXPOSURE: CHALLENGES FOR ENVIRONMENTAL JUSTICE POLICY ...58

Related Literature ...61

Housing Prices and Pollution: The Hedonic Gradient ...73

Bay Area Home Buyers, Housing Services, and Ozone Exposure ...79

Results...81

Conclusions...87

References ...90

APPENDICES ...94

Appendix A. Results For Alternative Hedonic Regression Models ...95

Appendix B. Procedures for Correlation Analysis...99

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Conclusions...134 References ...136 APPENDICES ...138

Appendix C. Control Variable Descriptions and

Results for Alternative Models ...139 Appendix D. Discrete-Time Hazard Model Results

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LIST OF TABLES

Table 3.1 San Francisco Bay housing sales variables and descriptions...29

Table 3.2 San Francisco Bay housing sales sample statistics, 1990 to 2006...31

Table 3.3 San Francisco Bay housing sales with buyer race and income, 1990 to2006 ...35

Table 3.4 Buyer-panel comparison with IPUMS 2000 5% sample ...38

Table 3.5 Buyer-panel sample statistics by race/ethnicity...39

Table 3.6 Federal and State Air Quality Standards for Common Pollutants ...41

Table 3.7 San Francisco Air Basin monitors ...50

Table 3.8 One hour maximum ozone concentrations (ppm) by monitor, 1990–1996...53

Table 3.9 One hour maximum ozone concentrations (ppm) by monitor, 1997–2006...54

Table 4.1 Hedonic prices and pollution: Bay Area results ...75

Table 4.2 Correlation coefficients (conditional on buying more housing services) by race/ethnicity ...82

Table 4.3 Correlation coefficients (conditional on buying more housing services) by income group: Black/Hispanic and white ...84

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Table 4.6 Correlation coefficients (conditional on buying more housing services) by income and home appreciation

rate: black/Hispanic ...86 Table 4.7 Correlation coefficients (conditional on buying more

housing services) by income and home appreciation

rate: Asian ...87 Table A.1 Hedonic regressions: No House Fixed Effects...97 Table A.2 Hedonic regressions: House Fixed Effects ...98 Table 5.1 Example house-spell data set for 3 houses that sell

one, two, or three times...107 Table 5.2 Example house-period level data set...108 Table 5.3 Life table describing all housing spells ...111 Table 5.4 Real estate price index for San Francisco Bay using a

repeat sales regression...124 Table 5.5 Logit discrete-time hazard model of ozone effects ...129 Table 5.6 Goodness-of-fit comparison ...129

Table 5.7 Fitted baseline hazard estimates (logit) ...130 Table 5.8 Odds ratios for control variables ...133 Table 5.9 Tests for race and ozone interaction effects...134 Table C.1 Control variables and descriptions for discrete-time hazard

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Table C.3 Odds ratios for control variables...142 Table C.4 Logit discrete-time hazard model of ozone effects ...143 Table C.5 Odds ratios for control variables...144 Table D.1 Complementary log-log discrete-time hazard model of

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LIST OF FIGURES

Figure 2.1 Annual mobility rates, 1947 to 2007...5

Figure 2.2 Types of moves: 1990 to 2000...5

Figure 2.3 Reason for moving: 1999–2000 ...6

Figure 2.4 Main reason for choice of present neighborhood (MSAs): 2001...6

Figure 2.5 Conventional 30-year fixed home mortgage rates: 1987 to 2007 ...16

Figure 2.6 Monthly Housing Price Trends in San Francisco: 1987 to 2008 ...19

Figure 3.1 Distribution of housing observations by lot size ...32

Figure 3.2 Distribution of housing observations by square feet ...32

Figure 3.3 Distribution of housing observations by number of bathrooms ...33

Figure 3.4 Distribution of housing observations by number of bedrooms ...33

Figure 3.5 Distribution of housing observations by sales price (2000$)...34

Figure 3.6 Distribution of housing observations by annual maximum 1 hr ozone concentration (ppm)...34

Figure 3.7 California Air Basins ...42

Figure 3.8 California air basin Ozone attainment status ...43

Figure 3.9 San Francisco Air Basin ground-level ozone pollution, 1990–2006...44

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Figure 3.12 Bay Area air quality index (AQI) measures by county,

1998–2004...49 Figure 4.1 Locations of TRI facilities relative to neighborhood

demographics (people of color) ...65 Figure 4.2 Bay Area Hispanic or Latino population share by

2000 census tract...66 Figure 4.3 Bay Area Asian population share by 2000 census tract ...67 Figure 4.4 Bay Area non-Hispanic black population share by

2000 census tract...67 Figure 4.5 Ozone spatial distribution, 2006...68 Figure 4.6 Bay Area median household income by 2000 census

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CHAPTER ONE INTRODUCTION

Although a large social science literature has focused on pollution’s influence on property values, less emphasis has been placed on residential mobility responses

to pollution. There is evidence that people seek neighborhoods with lower ozone levels and also reduce outside activities to avoid short-term exposure to ozone pollution. However, the literature has not explicitly addressed the following two questions. First, when homeowners move and buy bigger homes, do they also

expose themselves to more ozone pollution? Second, do homeowners try to avoid extended ozone exposure by reducing the time between moves? If we can address each question using information about individual moving decisions, we can better understand the distributional consequences of environmental policy and learn more

about the extent of ozone avoidance behavior.

Overview

Chapter 2 presents national statistics on urban residential mobility and discusses the aspects of the residential mobility literature. I review a well-known

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Chapter 3 describes the newly developed micro data set that is used to

address the research questions. I focus on a California study area that covers the several counties within the San Francisco Bay Air Basin. Commercial and public sources of information were combined to provide an extensive set of real estate transactions with homebuyer characteristics and monitor-level ozone pollution

measures. Therefore, I am able to follow an individual buyer as they move from their old house to a new house. I can also track an individual house that sells multiple times, the race and income characteristics of each buyer, and the house’s

year-by-year ozone concentration.

Chapter 4 focuses on the connections between buying more housing services and ozone exposure. Using information on a sample of homebuyers who chose to buy more housing services, I find evidence that ozone exposure goes up as a result

of the move. The positive relationship between housing services and ozone

exposure was also stronger for low income black/Hispanic homeowners than it was for low income white homeowners. As a result, blacks/Hispanics who bought more housing services tend to pay for it by taking on more ozone pollution; this may

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Chapter 5 examines whether homeowners use the move/stay decision to

avoid ozone exposure. A discrete-time hazard model provides evidence that after a move, homeowners do reduce the time between moves when ozone levels get worse relative to other homes. In addition, minorities are more likely to reduce time

between moves than white homeowners.

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CHAPTER TWO

THE ECONOMICS OF URBAN RESIDENTIAL MOBILITY: EXAMPLES OF THEORY AND PRACTICE

Although American mobility rates have declined since 1950, national

statistics show that 15 to 20 percent of Americans move in a given year (Figure 2.1).

Most people choose short distance moves; of the 40 million people that leave their homes each year, sixty-four percent relocated within the same county (Figure 2.2). Between county moves also have a local flavor; in 2003, forty-three percent of between county moves were within 100 miles (U.S. Bureau of the Census 2004).

So why do people move? A standard source of residential mobility statistics, the Current Population Survey (CPS), suggests that the majority of 1999 to 2000 moves were made for housing related reasons (U.S. Bureau of the Census 2001)1. This is especially true for local within county moves where 65 percent of people

cited housing reasons (Figure 2.3). In this group, most wanted more housing services or better neighborhoods (44 percent) 2. Similar information from the American Housing Survey (U.S. Bureau of the Census 2002) suggests that housing

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0% 5% 10% 15% 20% 25% 1947 -194 8 1950 -195 1 1953 -195 4 1956 -195 7 1959 -196 0 1962 -196 3 1965 -196 6 1968 -196 9 1975 -197 6 1982 -198 3 1985 -198 6 1988 -198 9 1991 -199 2 1994 -199 5 1997 -199 8 2000 -200 1 2003 -200 4 2006 -200 7 Years M o b il it y r at e

Figure 2.1. Annual mobility rates, 1947 to 2007

Source: Data from U.S. Bureau of Census 2008, Table A-1

0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%

Same County Different County, Same State Different State

Location P er ce n t

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0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%

Housing-related Work-related Family-related Other

Reason P er ce n t

Within County Between County Figure 2.3. Reason for moving: 1999–2000

Source: Data from U.S. Bureau of Census 2001, Table 1.

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Together, housing and neighborhoods reasons accounted for 41 percent of

within MSA movers. Convenience to job and family and good schools were also popular reasons.

In addition to the government provided mobility statistics, a large social science literature has examined determinants of residential mobility. 3 Although early work advanced research in this area, the existing theory was considered weak because it did not provide testable claims. For example, Quigley and Weinberg (1977) concluded

“…theoretical statements provide a rich taxonomy of the household decision-making process, and they do present a complex description of the calculus of

household choice. However, these theories provide little in the way of specific hypotheses or verifiable proposition.” (Quigley and Weinberg 1977, 48–49).

To address the perceived gap and other emerging mobility questions,

additional research and statistical models were developed. A Social Science Citation Index search shows that a wide range of journals published over 100 residential mobility articles since Quigley and Weinberg’s critique. A notable example in this group casts the mobility decision within a simple single-period model where

mobility is a response to housing consumption “disequilibrium” (i.e., the difference

3

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in preferred and actual housing consumption) (Hanushek and Quigley 1978, 414). 4 The paper is a good starting point for two reasons. First, the framework is well-known in the urban mobility literature and continues to motivate research today (e.g., Ferreira et al. 2008). Second, other researchers have extended the theory to consider neighborhood quality issues (Boehm and Ihlanfeldt 1986). This extension

makes it easier to connect the model with other studies that have examined environmentally motivated residential mobility.

A Representative Model

Hanushek and Quigley’s model begins with a housing demand curve for demographic group (A) that is determined by: income(y), housing prices (ph), and all other goods prices (px):

Hd = fA(y, ph, px) Eq. 2.1

As time passes, the household’s current housing consumption choice may no longer be the preferred choice. For example, income may increase and homeowners may want a bigger house; children grow up and move away and parents may decide

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keep up the maintenance for a house with a big yard. Hanushek and Quigley argue

that the difference between the preferred and current housing consumption provides a research question that can be tested:

“The model hypothesizes that the strength of the incentive to relocate varies with the gap between actual and equilibrium housing consumption.”

(Hanushek and Quigley 1978, 426).

In a world without moving costs, households would close the gap between

preferred and actual consumption quickly. However, with barriers such as search, transaction, and other moving costs, the gap can persist and people end up staying

in their homes longer even if they would prefer to move (Hanushek and Quigley 1978, 412–415).

The basic version of the model proposed a relationship between residential mobility (M), the absolute value of the difference between preferred

consumption

( )

d t

H+1 and actual consumption

( )

d t

H and transaction and moving costs

(Z). The claim was that the probability of moving increases as the gap in housing consumption grows and the probability of moving decreases as transaction and

moving costs become larger (Hanushek and Quigley 1978, 416).

(

H H Z

)

f

M t

d t

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To test the claim, the authors needed to quantify the gap in housing

consumption. To do this, Hanushek and Quigley estimated a housing demand model using data from the Housing Allowance Demand Experiment (HADE). The sample was restricted to recent movers under the assumption that they were more likely to represent preferred housing consumption levels. The dependent variable in

the regression was self-reported monthly rent and the explanatory variables included income, assets, education, household size, race and age. In the next step, Hanushek and Quigley used the demand models to predict the household’s

preferred housing consumption (Hd). The difference between the predicted and actual consumption was the model’s key explanatory variable. Since data on

transaction cost (Z) were not available, the authors assumed the transaction costs are distributed normally and independent of the disequilibrium measure; this

assumption about transaction costs, they argue, motivates their choice of a probit estimation method (Hanushek and Quigley 1978, 418). Two variants of the model were estimated. The first was a binary probit of the move or no move decision; the second was an ordered probit model of the move, search without move, or no move

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For example, a 10 percent increase in preferred consumption increased the

likelihood of moving by 9 to 15 percent (Hanushek and Quigley 1978, 420).

A subsequent study by Weinberg, Friedman, and Mayo (1981) extended the empirical implementation in three ways. First, hedonic price methods were used to construct the dependant variable for the housing demand equation (versus reported

monthly rental expenditures). This approach takes into account variations in the home’s structural characteristics and location when estimating preferred housing consumption. In addition, the authors used the estimated demand functions to

calculate a consumer surplus measure of disequilibrium. This measure was designed as a proxy for the additional income that would be required to make the household indifferent between the preferred and actual housing consumption. Moving costs, search costs, and psychological costs were also included as

explanatory variables. Predicted moving costs were estimated using in a regression of reported moving costs on household demographics. Predicted search costs were derived in a similar manner. Psychological costs were included in the model by adding information on length of residence.

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influenced mobility more than the gap in housing consumption. They speculated

that the results were driven by the use of a single measure of the housing gap. Future work, they argued, should consider focusing on differences between preferred and actual consumption of individual elements of the housing bundle. The subsequent literature moved in this direction and went a step further—it

abandoned attempts to directly measure disequilibrium altogether. Considering Changes in Neighborhood Quality

Boehm and Ihlanfeldt (1986) used the Hanushek and Quigley model to study

the effects of perceived neighborhood quality on the probability of moving. They placed emphasis on changes in housing services supplied by the house in addition to changes in housing demand variables (e.g., income, age, preferences).

Housing services were classified into the following categories: structural

characteristics (S), accessibility to job and shopping (A), and the neighborhood quality (N).

(

S A N

)

H

H= , , Eq. 2.3

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variables that caused gaps: changes in the services supplied by the location (∆S,

∆A, and ∆N) or changes in household demand (e.g., change in income [∆Y] and

relative prices ∆P).

(

)

Z N A S P Y h

M= ∆ ,∆ ,∆ ,∆ ,∆ , Eq. 2.4

They argued this empirical approach was more relevant in policy settings

where stakeholders were interested in learning about what causes differences between preferred and actual housing consumption (Boehm and Ihlanfeldt 1986, 414). It also overcame questions about how to properly measure housing

consumption disequilibrium.

The authors also used an alternative housing data set (the Neighborhood Housing Services [NHS] project) that expands the type of households considered. In contrast with HADE, the NHS included information about homeowners in addition

to renters. The survey was also important for the analysis because it recorded the household’s perception of neighborhood quality (i.e., 1= poor and 5=excellent) in addition to socioeconomic characteristics.

Absent data on transaction costs, Boehm and Ihlanfelt included level

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discrimination and length of residence and perception of neighborhood would

proxy any psychic costs associated with attachment (1986, 416).

Using the answers to the perceived neighborhood quality questions, the probability of moving was estimated using a binary outcome model. Model results suggested declines in perceived neighborhood quality increased the

probability of moving; people living in what they perceived as better neighborhoods were less likely to move; and the longer a household stayed in a home, the less likely they moved (Boehm and Ihlanfeldt 1986, 419–422).

Other Residential Mobility Barriers

When considering barriers to mobility, the early literature tended to emphasize direct financial costs associated with moving (e.g., out-of-pocket

expenses for moving possessions, loan origination fees, closing costs), the time costs

associated with a housing search, and the psychological costs associated with leaving the home (Quigley and Weinberg 1977; Venti and Wise 1984; Smith, Rosen, and Fallis 1988). However, mobility studies have also considered other indirect barriers caused by external trends, such as mortgage/housing market fluctuations

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discussed below with emphasis on those studies that focus on the California housing

market.

Quigley (1987) considered whether mortgage rate fluctuations significantly influence homeowner mobility. The basic idea is that changes in mortgage interest rates can make new home loans more or less attractive than current loan terms. In

periods with rising interest rates, homeowners could be “locked-in” their current home because moving would require them to give up a favorable interest rate (Quigley 1987, 636) .

To measure this effect, a continuous time hazard model was applied to three samples of homeowners from the Panel Study of Income Dynamics. The hazard rate was expressed as a function of several time-invariant explanatory variables; the key variable was the value of the mortgage premium. Proportional and

non-proportional models were the primary focus; however, the panel nature of the data set also allowed him to track some of the homeowner sample across time. As a result, he explored a model that controlled for unobserved individual-specific heterogeneity. To do this, the explanatory variables used in the hazard function

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positive mortgage premiums significantly reduced mobility rates (1987, 639–641).

As shown in Figure 2.5, conventional 30-year mortgage rates have fallen since 1990. As a result, the interest rate lock-in constraint identified by Quigley has likely relaxed since his original study.

0 2 4 6 8 10 12

1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007

Year

In

te

re

st

R

at

e

(p

er

ce

n

t)

Figure 2.5. Conventional 30-year fixed home mortgage rates: 1987 to 2007

Source: Data from U.S. Bureau of Census 2008, Table 1157.

Chan (2001) used a similar empirical model, but applied the approach to a different indirect barrier: falling home prices. Chan suggested that falling home

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Even in cases where declining home values did not prevent a mortgage payoff,

selling the home under these circumstances reduced the size of a down payment available for a subsequent home purchase. As a result, housing market fluctuations could influence residential mobility in similar ways that mortgage market

fluctuations did.

To measure the significance of the constraint, Chan selected a continuous time hazard model and applied it to a sample of residential single family mortgages issued by a large commercial bank. The mortgage duration was assumed to be a

good proxy of housing duration for a sample of homeowners choosing adjustable rate mortgages. This sample restriction, he argued, allowed him to screen out mortgages that were simply refinanced (e.g., common with fixed-rate mortgages) versus those that represented actual moves.

In contrast with Quigley (1987), the hazard rate was specified as a function of several time-varying explanatory variables; the most important for the analysis was contemporaneous loan-to-value ratio. To compute this measure, Chan used county-level repeat sales housing price indices to proxy trends in changes in the value of the

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example, an average homeowner who had lived in a home for 4 years was

estimated to have 30 percent higher mobility rate if housing prices had not declined (Chan 2001, 584).

Cunningham and Engelhardt (2008) study the mobility effects of the Tax Payer Relief Act of 1997 and follow an earlier literature that views capital gains

taxation as a transaction cost (Englund 1986). The Taxpayer Relief Act eliminated the “age of 55 rule,” which had allowed certain older homeowners to exclude up to $125,000 from taxable gains resulting from the sale of a previous home

(Cunningham and Engelhart 2008, 805). The legislative change was exploited using a difference-in-difference framework that compared mobility rates for homeowners just above and below the age threshold before and after the tax law change. Results suggested that the elimination of the age-55 rule increased the mobility rate of

homeowners in their early 50s by 20 to 30 percent (Cunningham and Engelhart 2008, 814).

Wasi and White (2005) considered state-level property tax changes and attempted to identify any lock-in effects introduced by California’s Proposition 13.

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limited by a maximum inflation adjustment of two percent per year. Third, homes

were effectively reassessed when they were sold, and the adjusted value for tax purposes was based on the purchase price. Given the rapid growth in Bay Area home prices (Figure 2.6), the author’s speculated that the property tax provisions may have created a strong disincentive to move.5

0 50 100 150 200 250 Ja n-87 Ja n-88 Ja n-89 Ja n-90 Ja n-91 Ja n-92 Ja n-93 Ja n-94 Ja n-95 Ja n-96 Ja n-97 Ja n-98 Ja n-99 Ja n-00 Ja n-01 Ja n-02 Ja n-03 Ja n-04 Ja n-05 Ja n-06 Ja n-07 Ja n-08 Time In d e x

Figure 2.6. Monthly Housing Price Trends in San Francisco: 1987 to 2008

Source: Data from S&P/Case-Shiller Home Price Indices, 2009.

To measure the property tax lock-in effect, the authors collected public use

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mobile populations and traditional property tax policies (e.g., Texas, Florida). Their

findings suggest California residents stayed in their homes longer than similar households in Texas and Florida. In addition, California residents that lived in rapidly appreciating homes along the coastal areas stayed in homes longer when compared to similar households with lower appreciation rates.

Ferreira (2008) examined the mobility effects of two subsequent amendments to California’s Proposition 13 (i.e., Proposition 60 and Proposition 90). As described in Ferreira, the amendments allowed homeowners over the age of 55 to “fix” their

property taxes when moving because they could transfer the value of their original home to their new home for property tax purposes. Proposition 60 was adopted in 1986 and only applied to within county moves, whereas Proposition 90 was adopted in 1988 and allowed the provisions to be applied to between county moves. To

measure the mobility effects, IPUMS data was chosen, and a regression

discontinuity design was applied to California households at the age threshold specified by Proposition 60. After the subsequent amendments were adopted, Ferreira found that mobility rates of 55-year old homeowners are approximately

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for homeowners in other control groups (households in different states and California households in 1980) (Ferreira 2008, 3).

Another recent paper by Ferreira, Gyourko, and Tracy (2008) appears to be the first to simultaneously consider all three indirect barriers to moving discussed in

this chapter: fluctuations in mortgage rates and housing prices and property tax law changes. In addition, the authors make a contribution to one other area of the mobility literature because they provide new evidence on the relationship between

mobility and self-reported neighborhood quality.

The empirical analysis used a house-based panel from the American Household Survey and specified a binary probit model where the dependent variable identifies whether the house changed owners between the two survey

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Conclusions

In their noteworthy review of urban mobility studies, Quigley and Weinberg predicted that mobility models using the benefit-cost principle would be important tools for learning about household moving decisions (1977, 60). They were right; three decades later, the idea that people choose to move when benefits outweigh

costs continues to motivate social science research. This review included examples of recent papers and focused on a variety of indirect barriers that can influence residential mobility rates.

Despite these advances, the relationship between mobility and one aspect of neighborhood quality, changes in air pollution, is not well understood. Although there is evidence that communities experience population gains when the air

becomes cleaner (e.g., Kahn 2000), the literature has not considered household-level

mobility responses to air quality changes (e.g., length of stay). In addition, existing papers have typically adopted a modeling framework that assumes costless mobility (e.g., Banzhaf and Walsh 2008). As a result, the existing empirical literature has not fully considered how indirect moving barriers differ by demographic groups and

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Boehm, Thomas P. and Keith R. Ihlanfeldt. 1986. Residential mobility and neighborhood quality. Journal of Regional Science 26, no. 2: 411–424 Chan, Sewin. 2001. Spatial lock-in: do falling house prices constrain

residential mobility? Journal of Urban Economics 49: 567–586.

Cunningham, Christopher R. and Gary V. Engelhardt. 2008. Housing capital-gains taxation and homeowner mobility: evidence from the Taxpayer Relief Act of 1997. Journal of Urban Economics 63: 803–815.

Englund, Peter. 1986. Transaction costs, capital-gains taxes, and housing demand.

Journal of Urban Economics 20: 274–290.

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CHAPTER THREE

SAN FRANCISCO BAY AREA HOUSING SALES DATA

This study uses a new and unique data set that combines an extensive set of real estate transactions with homebuyer characteristics and ozone pollution data.

The data set can be distinguished in three ways from another widely used government survey used to study housing questions— the American Housing Survey. First, actual versus self-reported measures of important variables are

included (e.g., home prices). Second, a sample of individual home buyers can be tracked as they moved within the Bay Area. As a result, a house-based panel anda home buyer–based panel can be created, while the American Housing Survey can only provide a house-based panel.6 Third, house-specific measures of outdoor air quality have been developed and added using monitor data for the entire San Francisco Bay Air Basin. In contrast, the American Housing Survey does not contain air quality information and only has self-reported assessments of overall

neighborhood quality.

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Overview

My analysis uses a sample of 794,162 housing sales obtained from the previous data work related to Bay Area real estate transactions (Bayer et al. 2008; Bishop and Timmins 2009). The commercial and public data sources are:

DataQuick real estate transactions: Purchased from a national real estate

company, these data provide actual transaction (instead of self-reported) prices and include information about housing characteristics (structural characteristics and geographic coordinates).

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

demographic information about the home buyers.

California Air Resources Board (CARB) air quality data: CARB provides the latest

27 years of monitor-level air quality data (1980 to 2006).

I organize and use the initial housing sample I created in three different ways. First, the hedonic analyses in Chapters 4 uses only repeat sales methods (N=471,651, or 60 percent of my housing sample). Second, the survival analysis in Chapter 5 also begins with the housing sample, but restricts it to housing sale observations that can

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matched over time. Specifically, I collect data for 11, 578 people who bought two

homes within the sample period (N=23,156, or 3 percent of my housing sample).

Housing Sales Sample

DataQuick includes a rich set of real estate transactions for 1990–2006

covering six key counties of the San Francisco Bay Area (i.e., Alameda, Contra Costa,

Marin, San Francisco, San Mateo, and Santa Clara). Transaction variables for the analysis include a unique parcel identifier, transfer value (e.g., sale price), sales date, and geographic information (e.g., census tract, latitude, longitude). DataQuick also provides several useful housing characteristics observed at the last transaction: lot

size, square footage, number of baths, and number of bedrooms (Table 3.1). To ensure consistency of zip codes across time, I used geographic information systems (GIS) software and ESRI Data: U.S. Zip Code Areas: 2000 to add of 5-digit U.S. zip

code to each house.

The complete DataQuick database was reviewed, and observations were selected for the study using the following criteria. First, I restricted the analysis to houses that sold one to three times during the sample period. These houses are

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Table 3.1. San Francisco Bay housing sales variables and descriptions

DataQuick variable Data set name Description

SA_PROPERTY_ID Idp Unique parcel identifier

SR_DATE_TRANSFER Saleyear

Document date for the transaction

SR_YR_BUILT Age

Age computed using sales year and the year in which property was constructed

SR_VAL_TRANSFER Price

Transfer value of the property, also referred to as sale amount or sale value

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 conditioned area square feet

SA_NBR_BATH Baths Number of bathrooms

SA_NBR_BEDRMS Bedrooms Number of bedrooms

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houses that may be bought and “flipped” for investment purposes or other unusual

reasons. For similar reasons, I dropped properties within this group that sold multiple times on the same day or the same year. Next, I screened properties for land-only sales or rebuilds and dropped all transactions for which the year built is missing or the transaction date is prior to the year built. To compute distances

between houses and air quality monitors, I needed the property’s geographic coordinates. Therefore, I dropped properties for which latitude and longitude were missing or miscodes (i.e., outside of the six counties). I 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, I restricted the sample to include only properties with the following ranges of attributes: only one housing unit, lot size (i.e., 1,000 to 70,000 square feet), square feet (i.e., 500 to 5,000 square

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Table 3.2. San Francisco Bay housing sales sample statistics, 1990 to 2006

Homes with:

Variable One sale Two sales Three Sales

Number of Houses 322,511 153,585 54,827

Total Sales 322,511 307,170 164,481

Price ($1,000) $411 ($261) $400 ($259) $377 ($250) Age 34.4 (23.4) 33.6 (23.0) 33.6 (22.9) Lotsize 7,568 (6,440) 6,901 (5,666) 6,300 (4,946) Sqft 1,756 (671) 1,676 (620) 1,592 (584) Baths 2.1 (0.7) 2.1 (0.7) 2.0 (0.67) Bedrooms 3.3 (0.8) 3.2 (0.8) 3.1 (0.8)

Annual Max 1 Hr Ozone Concentration (ppm) (3 yr simple moving average)

0.103 (0.008) 0.104 (0.008) 0.104 (0.008)

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0

.05

.1

.15

.2

Fraction

0 20000 40000 60000 80000

Lot Size (sqft)

Figure 3.1. Distribution of housing observations by lot size

0

.02

.04

.06

Fraction

0 1000 2000 3000 4000 5000

Finished Living Area (sqft)

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0

.1

.2

.3

.4

Fraction

1 2 3 4 5

Number of Baths

Figure 3.3. Distribution of housing observations by number of bathrooms

0

.1

.2

.3

.4

.5

Fraction

1 2 3 4 5

Number of Bedrooms

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0

.02

.04

.06

Fraction

0 500000 1000000 1500000

Sales Price (2000$)

Figure 3.5. Distribution of housing observations by sales price (2000$)

0

.02

.04

.06

.08

Fraction

.06 .08 .1 .12 .14

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Linking Buyer Information to Housing Sales

DataQuick variables such as census tract, mortgage loan amount, and lender’s name also allow some of the housing sales sample to be linked with additional information about the people who bought the house (Bishop and Timmins 2009). This feature of the data is unique and is useful for the statistical

models where buyer race and income information are used as explanatory variables. As shown in Table 3.3, buyer race and income information was available for 296,704 housing sales, approximately 37 percent of the total housing sales (794,162)

identified in Table 3.2. The sample statistics (mean and standard deviation) for the subset of housing sales with race and income information also are reported in Table 3.3.

Table 3.3. San Francisco Bay housing sales with buyer race and income, 1990 to 2006

Variable Values

Number of housing sales 296,704

Price ($1,000)

$454 ($255)

Age

36.5 (22.7)

Lotsize

7,209 (5,943)

Sqft

1,704 (631)

Baths

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Table 3.3. (continued)

Variable Values

Bedrooms

3.2 (0.8)

Real income expressed in 2000$ ($1,000)

$117 ($59)

White 58%

Asian 25%

Black 3%

Hispanic 13%

Note: Standard deviation reported in parenthesis.

Buyer-based panel

Another feature of the DataQuick/HMDA match process is that the same buyer was linked to other housing purchases that occurred during the sample period (Bishop and Timmins 2009). As a result, a buyer’s purchase decision can be

observed on more than one occasion, and this is useful for the correlation analysis performed in Chapter 4. To construct the buyer-based panel, the initial set of housing sales described in Table 3.2 were restricted to observations where the same

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representative of a “typical” buyer. In contrast, buyers who made three or more

purchases in the sample period may have faced unusual and unobserved circumstances that lead to more frequent moves.

For the buyer-based panel, a variable was calculated and added that

compares the price the buyer paid at the first observed purchase in the sample with

the house’s subsequent selling price when the buyer moved (e.g., the home appreciation rate experienced for the first home [sale price2/sale price1 −1]). The variable allows me to examine whether residential mobility behavior might be

influenced by the size of the previously owned home’s appreciation rate.

In the last step, observations with no race information for either purchase and observations that are missing real income (2000$) for the second purchase were excluded. One percent of observations from each tail of the real income (2000$)

distribution and the home appreciation rate distributions were dropped to minimize the effect of outliers for these two variables. In cases where conflicting race

information was provided for the first and second purchases, the reported race in the second purchase was used. If race information for the second purchase was not

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As noted above, the buyer-based panel uses only a very small share of the

initial housing sample (N=23,156, or 3 percent of my housing sample). In order to assess whether the sample restrictions raise any selection issues with respect to the demographic variables (i.e., race and income), the buyer-based panel sample statistics (Table 3.4) were compared with earlier versions of the matched

DataQuick/HMDA transactions reported by Bayer and colleagues (2008) and

directly with metro data for San Francisco-Oakland-Vallejo, California (id = 736) and San Jose, California (id = 740) included in the 2000 Integrated Public Use Microdata

Series (IPUMS) 5 percent sample. Using the comparisons, the restricted sample can be considered representative of the complete sample and the metro area IPUMS sample.

Table 3.4. Buyer-panel comparison with IPUMS 2000 5% sample

Variable SF Bay two buyer-panel IPUMS 5% sample

Years 1990 to 2006 2000

Households with:

Observations 11,578

Income: 186,874 Race/ethnicity: 3,702,460

Real income expressed in 2000$ ($1,000)

Mean = $138 Standard Deviation = $63

Mean = $114 Standard Deviation = $63

White 63% 57%

Asian 23% 23%

Black 2% 6%

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In Table 3.5, additional buyer sample statistics are provided by race/ethnicity. As shown, white and Asian buyers have similar income and home appreciation rates

from the previously owned home. In contrast, black and Hispanic homeowners had lower average incomes and their home appreciation rates were higher (84 and 75 percent) than white and Asian households (69 and 67 percent).

Table 3.5. Buyer-panel sample statistics by race/ethnicity

Variable All Buyers White Black Hispanic Asian

Number of

buyers 11,578 7,336 286 1,349 2,607

Real income expressed in 2000$ ($1,000)

Mean = $138

Standard Deviation =

$63

Mean = $144

Standard Deviation =

$65

Mean = $112

Standard Deviation =

$46

Mean = $112

Standard Deviation =

$49

Mean = $140

Standard Deviation = $60 Previous Home’s Appreciation Rate

Mean = 0.69

Standard Deviation =

0.59

Mean = 0.69

Standard Deviation =

0.57

Mean = 0.84

Standard Deviation =

0.70

Mean = 0.75

Standard Deviation =

0.72

Mean = 0.67

Standard Deviation =

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Air Quality

The Clean Air Act identifies six common air pollutants that are regulated by the U.S. Environmental Protection Agency (EPA). They include ground-level ozone, particle pollution, carbon monoxide, sulfur oxides, nitrogen oxides, and lead. To improve air quality, state and federal governments have developed air quality

standards for each pollutant that are designed to provide a definition of clean air. The standards are typically expressed in terms of pollution concentration; for example, ozone standards are expressed in parts per million (ppm). In most cases, California standards are more stringent than federal standards (Table 3.6).

Of the six common air pollutants, ground-level ozone and particle pollution are associated with the most widespread health risks, especially for certain groups of people (e.g., asthmatics, children, the elderly) (U.S. EPA 2007a). Ground-level ozone

is the primary component of smog; its main ingredients include volatile organic compounds (VOC) and nitrogen oxides (NOx) emitted from sources such as motor vehicles, industrial facilities, and electric utilities. When these ingredients react in sunlight, ground-level ozone is formed. Since weather conditions like high

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Table 3.6. Federal and State Air Quality Standards for Common Pollutants

Pollutant Averaging Time California National (Primary)

1 Hour 0.09 ppm −

Ozone (O3)

8 Hour 0.070 ppm

0.080 ppm (2007) 0.075 ppm (2008)

24 Hour 50 μg/m3 150 μg/m3

Particulate Matter

(PM10) Annual Arithmetic

Mean 20 μg/m3

24 Hour 35 μg/m3

Fine Particulate

Matter (PM2.5) Annual Arithmetic

Mean 12 μg/m3 15 μg/m3

8 Hour 9.0 ppm 9 ppm

1 Hour 20 ppm 35 ppm Carbon Monoxide

(CO)

8 Hour (Lake Tahoe) 6 ppm − Annual Arithmetic

Mean 0.030 ppm 0.053 ppm Nitrogen

Dioxide(NO2)

1 Hour 0.18 ppm Annual Arithmetic

Mean 0.030 ppm

24 Hour 0.04 ppm 0.14 ppm Sulfur Dioxide(SO2)

1 Hour 0.25 ppm −

30 Day Average 1.5 μg/m3

Lead

Calendar Quarter − 1.5 μg/m3

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California has divided the state into 15 air basins that have similar climate

and physical features (Figure 3.7). The San Francisco Bay air basin has cleaner air relative to the other basins because of its coastal climate (CARB 2007). However, the basin continues to deal with air quality issues; the federal government and

California designate the Bay Area as a non-attainment area for ground-level ozone

(Figure 3.8 and Figure 3.9).

Figure 3.7. California air basins

Source: Reprinted and adapted from CARB 2007, Figure 4-1.

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Figure 3.8. California air basin Ozone attainment status

Source: Reprinted from CARB 2007, Figures 1-5 and 1-6.

Ozone Trends

The early 1990s saw the implementation of several programs that would influence Bay Area air quality trends over time:7 the Clean Air Act Amendments, which included a pollution permit program for over 100 major polluting facilities; adoption of the first district Clean Air Plan; and public information programs designed to help reduce emissions from motor vehicles. The mid-1990s provided mixed results for these programs. In the same year (1995), the Bay Area reached

attainment under the federal ozone standard based on improvements in the

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proceeding years, and the area experienced its worst air quality in 10 years (Figure

3.9). Two years later, the Bay Area rebounded and saw the best air quality on record. However, the improvement was not enough to overcome the poor air quality

measures in 1995 and 1996. EPA reclassified the Bay Area as being in nonattainment under federal ozone standards.

0.000 0.020 0.040 0.060 0.080 0.100 0.120 0.140 0.160 0.180

1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 Year C o n ce n tr at io n ( p p m ) 0 5 10 15 20 25 30 35 40 D ay s

Maximum 1-Hr. Concentration Days Above State 1-Hr. Std.

Figure 3.9. San Francisco Air Basin ground-level ozone pollution, 1990–2006

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To meet these new challenges, several clean air initiatives (e.g., clean-burning

gasoline, vehicle and lawn mower buyback programs, new vehicle smog testing requirements, and bans on the use of garden and utility equipment during high pollution days) were adopted with some success. Since 2000, monitors measured ozone concentrations that exceeded the federal or state air quality standards on

fewer than 20 days (Figure 3.9).

Spatial Distribution of Ozone Pollution

The spatial distribution of ozone within the air basin is influenced by west to

east wind patterns and the mountains surrounding the Bay Area. Winds tend to push pollution away from the coast, and the mountains trap pollution within the region. Air pollution also escapes the Bay Area through certain mountain gaps and reaches other California air basins (CARB 2001, 38). CARB (2001) has identified the

two routes in the West—the Carquinez Strait, which carries air pollution to the Sacramento Valley, and the Altamont Pass, which carries pollution into the San Joaquin Valley (Figure 3.10). The only outside air basin that CARB has classified as a contributor to San Francisco Bay ozone pollution is the broader Sacramento area

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to eastern parts of the San Francisco Bay (CARB 2001, 26). Figure 3.11 provides

visual patterns of the spatial distribution of pollution for the first and last years of the data set (1990 and 2006). As shown, the patterns are consistent with descriptions of ozone transport described by CARB (2001).

Figure 3.10. San Francisco Bay ozone transport

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Figure 3.11. Ozone spatial distribution, 1990 and 2006

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Ozone Awareness

There are two public information programs in the Bay Area that communicate daily air quality information. The first is EPA’s Air Quality Index (AQI) (U.S. EPA 2003). The index ranges from 0 to 500 and includes 6 color-coded categories; the national

air quality standard for the index is represented by a value of 100. Local agencies compute the AQI using EPA-provided formulas and monitor information; the pollutant with the highest value is considered the AQI value for the day. In the Bay

Area, ozone and particle pollution often are the pollutants triggering the daily AQI value (U.S. EPA 2007b).

EPA regulations require local agencies to report AQI information for metropolitan statistical areas with 350,000 or more people (U.S. EPA 2006).

Next-day forecasts of the AQI are provided to the public through local media (e.g., TV, radio, and newspapers), telephone messages, or the internet. With the exception of Marin County, most counties experience 10 or more moderate (color code = yellow) AQI days (Figure 3.12). AQI values exceeding 100 (unhealthy for sensitive groups

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0 5 10 15 20 25 30 35 40 45 50 55 60 65 70 75 80 85 90 95 100 19 98 20 00 20 02 20 04 19 98 20 00 20 02 20 04 19 98 20 00 20 02 20 04 19 98 20 00 20 02 20 04 19 98 20 00 20 02 20 04 19 98 20 00 20 02 20 04

Alameda Co Contra Costa Co

Marin Co San

Francisco Co San Mateo Co Santa Clara Co County/Year N u m b er o f D ay s

Number of days AQI was Moderate

Number of days AQI was Unhealthy for Sensitive Groups Number of days AQI was Unhealthy

Figure 3.12. Bay Area air quality index (AQI) measures by county, 1998–2004

Source: Data from U.S. EPA 2007b

In addition to federal information programs, local agencies alert residents about air quality problems and encourage people to voluntarily take steps to reduce emissions (Bay Area Air Quality Management District 2009). The Bay Area Air Quality

Management District issues “Spare the Air” alerts when ozone is forecasted to be a

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activities. During 1990 to 2004, ozone Spare the Air advisories ranged 3 to 25 days

(Bay Area Air Quality Management District 2009).

Monitor Data

As shown in Table 3.7, thirty-eight monitors in the San Francisco Bay Air Basin provide annual maximum 1-hour ozone concentration statistics. The monitors

are part of a statewide system of over 250 monitors that collect pollution

measurements (CARB 2007). After recording the measurements, CARB checks data quality, reports, and stores the results.

Table 3.7. San Francisco Air Basin monitors

County ID Site Name Latitude Longitude Monitor

Santa Clara 2070 Mountain View-Cuesta 37.37 -122.08 1 Contra Costa 2102 Pittsburg-10th Street 38.02 -121.88 2

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Table 3.7. (continued)

County ID Site Name Latitude Longitude Monitor

Contra Costa 2804 Bethel Island Road 38.01 -121.64 20 Alameda 2806 Hayward-La Mesa 37.65 -122.03 21 Contra Costa 2831 Concord-2975 Treat Blvd 37.94 -122.02 22 Santa Clara 2865 San Jose-Moorpark Avenue 37.32 -121.93 23 Santa Clara 2936 San Jose-Tully Road 37.31 -121.85 24 Santa Clara 2969 San Jose-935 Piedmont Road 37.39 -121.84 25 Alameda 2973 San Leandro-County Hospital 37.71 -122.12 26 Marin 2986 Point Reyes National

Seashore

38.04 -122.80 27 Santa Clara 3000 San Jose-W San Carlos Street 37.32 -121.92 28 Santa Clara 3140 San Martin-Murphy Avenue 37.08 -121.60 29 Contra Costa 3207 San Pablo-El Portal 37.96 -122.34 30 Solano 3415 Fairfield-Chadbourne Road 38.23 -122.08 31 Alameda 3490 Livermore-793 Rincon

Avenue

37.68 -121.78 32 Santa Clara 3507 Sunnyvale-910 Ticonderoga 37.36 -122.05 33 Alameda 3659 Oakland-6701 International

Boulevard

37.79 -122.28 34 Contra Costa 3660 Crockett-1098 Pomona Street 38.05 -122.22 35 Santa Clara 3661 San Jose-Jackson Street 37.35 -121.89 36 Contra Costa 3668 San Pablo-Rumrill Blvd 37.96 -122.36 37 San Francisco 3682 San Francisco-Hunters Point 37.73 -122.38 38

Source: Data from CARB 2008

In February 2008, CARB provided the latest DVD-ROM with 27 years of air quality monitor data (1980 to 2006) (CARB 2008). In addition to pollution measures, the data set includes information on each monitor’s coverage with a variable that

ranges from 0 to 100; the variable indicates whether the monitor was active during months where high pollution concentrations are expected. For example, a monitor with a coverage number of 50 indicates that monitoring occurred 50 percent of the

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With the house and monitor geographic information (latitude and longitude

coordinates), house-specific maximum ozone concentrations (1990 to 2006) were calculated using an inverse-distance weighted average of all 38 San Francisco Air Basin monitors with at least 60 percent coverage for a given year (Table 3.8 and Table 3.9). For example, consider a hypothetical set of 5 monitors at distances of 5,

10, 15, 20, and 25 kilometers from a house. In 1995, assume the annual one hour max concentrations recorded by the monitors are 95, 110, 115, 96, and 102 ppb. The 1995 house-specific ozone measure would be calculated distance as weighted average of

all the monitor values is

Average Ozone = =

+ + + + × + × + × + × + × 25 1 20 1 15 1 10 1 5 1 102 25 1 96 20 1 115 15 1 110 10 1 95 5 1

101.90 ppb. Eq. 3.1

Since pollution levels tend to fluctuate from year to year and buyers may take into account recent pollution trends, a simple 3-year lagged moving average of each

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Table 3.8. One hour maximum ozone concentrations (ppm) by monitor, 1990–1996

CARB Monitor 1988 1989 1990 1991 1992 1993 1994 1995 1996

2070 1 0.120 0.110 0.100 0.120 0.110 0.110 0.084 0.116 0.106 2102 2 0.120 0.110 0.110 0.080 0.110 0.130 0.111 0.124 0.121 2105 3 0.090 0.090 0.070 0.090 0.080 0.080 0.082 0.097 0.083 2125 4 0.100 0.100 0.080 0.080 0.090 0.100 0.084 0.140 0.097 2225 5 0.100 0.080 0.060 0.060 0.080 0.110 0.064 0.114 0.088 2236 6 0.100 0.100 0.060 0.050 0.080 0.120 0.087 0.092 0.082 2293 7 0.130 0.120 0.130 0.120 0.120 0.130 0.120 0.153 0.100 2320 8 0.140 0.130 0.120 0.130 0.120 0.110 0.101 0.130 0.121 2372 9 0.150 0.140 0.130 0.140 0.110 0.130 0.129 0.155 0.138 2373 10 0.090 0.080 0.060 0.050 0.080 0.080 0.055 0.088 0.071 2397 11 0.130 0.110 0.100 0.100 0.100 0.130 0.107 0.129 0.113 2410 12 0.120 0.120 0.110 0.110 0.100 0.110 0.100 0.133 0.112 2413 13 0.120 0.130 0.120 0.100 0.120 0.110 0.112 0.134 0.110 2613 14 0.140 0.110 0.120 0.110 0.130 0.130 0.118 0.141 0.129 2622 15 0.100 0.080 0.060 0.080 0.070 0.080 0.089 0.088 0.105 2646 16 0.080 0.080 − − − − − − − 2655 17 0.100 0.100 0.090 0.110 0.090 0.120 0.092 0.130 0.090 2726 18 0.110 0.100 0.090 0.100 0.090 0.080 0.086 0.067 0.089 2739 19 0.120 0.120 0.110 − − − − − − 2804 20 0.110 0.110 0.120 0.110 0.110 0.110 0.113 0.128 0.137 2806 21 0.120 0.110 0.080 0.100 0.130 0.090 0.099 0.145 0.106 2831 22 0.140 0.110 0.110 0.110 0.110 0.130 0.121 0.152 0.127

2865 23 − − − − − − − − −

2936 24 − − − − − − − − −

2969 25 − − − − − 0.110 0.116 0.145 0.118 2973 26 − − 0.070 0.120 0.110 0.120 0.089 0.150 0.107 2986 27 − 0.080 0.080 0.070 0.066 − − − − 3000 28 − 0.110 0.130 0.080 0.110 0.130 0.098 − − 3140 29 − − − − − − 0.130 0.128 0.115

3207 30 − − − − − − − − −

3415 31 − − − − − − − − −

3490 32 − − − − − − − − −

3507 33 − − − − − − − − −

3659 34 − − − − − − − − −

3660 35 − − − − − − − − −

3661 36 − − − − − − − − −

3668 37 − − − − − − − − −

3682 38 − − − − − − − − −

Average 0.115 0.105 0.096 0.097 0.101 0.111 0.099 0.125 0.107 Minimum 0.080 0.080 0.060 0.050 0.066 0.080 0.055 0.067 0.071 Maximum 0.150 0.140 0.130 0.140 0.130 0.130 0.130 0.155 0.138

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Table 3.9. One hour maximum ozone concentrations (ppm) by monitor, 1997–2006

CARB Monitor 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006

2070 1 0.114 0.097 0.114 − − − − − − − 2102 2 0.087 0.097 0.098 0.107 0.118 0.111 0.094 0.090 0.094 0.105 2105 3 0.093 0.068 0.095 0.078 0.086 0.077 0.096 0.076 0.072 0.077 2125 4 0.090 0.066 0.082 0.083 0.105 0.090 0.113 0.097 0.084 0.085 2225 5 0.079 0.056 0.081 0.072 0.069 0.053 0.081 0.080 0.068 − 2236 6 − − − − − − − − − − 2293 7 0.109 0.115 0.133 0.102 0.109 0.110 0.123 0.090 0.105 0.102 2320 8 0.095 0.135 0.105 − 0.123 0.121 0.107 0.092 0.087 0.120 2372 9 0.114 0.146 0.146 0.137 − − − − − − 2373 10 0.068 0.053 0.079 0.058 0.082 0.054 0.085 0.093 0.058 0.053 2397 11 0.089 0.121 0.129 0.096 0.102 − − − − − 2410 12 0.103 0.119 0.113 0.079 0.091 0.109 0.101 0.104 0.087 0.080 2413 13 0.094 0.147 0.109 0.073 0.105 − − − − − 2613 14 0.097 0.133 0.117 0.080 0.118 0.113 0.124 0.093 0.110 0.116 2622 15 0.106 0.074 0.102 0.071 0.087 0.077 0.087 0.091 0.081 − 2646 16 − − − − − − − − − − 2655 17 0.084 0.125 0.115 0.077 0.099 0.116 0.105 0.092 0.091 0.096 2726 18 − − − − − − − − − − 2739 19 − − − − − − − − − − 2804 20 0.098 0.123 0.128 0.115 0.130 0.111 0.092 0.103 0.089 0.116 2806 21 0.112 0.116 0.123 0.111 0.103 0.093 0.116 0.088 0.093 0.101 2831 22 0.099 0.147 0.156 0.138 0.134 0.103 0.101 0.097 0.098 0.117 2865 23 − − − − − − − − − − 2936 24 − − − − − − − − − − 2969 25 0.095 0.129 0.116 0.096 0.091 0.090 0.104 0.093 0.110 − 2973 26 0.109 0.111 0.113 0.098 0.093 0.101 0.097 0.104 0.099 0.088 2986 27 − − − − − − − − − − 3000 28 − − − − − − − − − − 3140 29 0.091 0.144 0.125 0.113 0.117 0.119 0.112 0.094 0.108 0.123 3207 30 0.108 0.074 0.100 0.076 0.086 0.071 − − − − 3415 31 − − − − − 0.103 0.090 0.096 0.090 0.106 3490 32 − − − 0.152 0.113 0.160 0.128 0.113 0.120 0.127 3507 33 − − − − 0.082 0.091 0.113 0.102 0.097 0.106 3659 34 − − − − − 0.084 − − − − 3660 35 − − − − − 0.089 − − − − 3661 36 − − − − − − 0.119 0.090 0.113 0.118 3668 37 − − − − − − 0.091 0.105 0.066 0.061 3682 38 − − − − − − − 0.096 − −

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Conclusions

With the growth in the availability of new panel data sets, researchers

continue to develop and assess evidence about relationships between pollution and residential mobility. The data set used in this study makes a unique contribution because it includes information about repeat housing sales, repeat buyer choices,

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http://www.sparetheair.org/about/index.htm

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http://www.olin.wustl.edu/faculty/murphya/demand.pdf (accessed October

7, 2008).

Bishop, Kelly and Christopher Timmins. 2009. Simple, Consistent Estimation of the Marginal Willingness to Pay Function: Recovering Rosen's Second Stage without Instrumental Variables. Paper presented at the annual meeting of the Association of Environmental and Resource Economists/Allied Social Science Association, San Francisco, CA. January 4.

http://www.olin.wustl.edu/faculty/bishop/ozone.pdf (accessed January 20,

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January 2, 2009).

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no.14310 (September). http://www.nber.org/papers/w14310 (accessed November 15, 2008).

U.S. EPA (Environmental Protection Agency). 2003. Air Quality Index: a guide to air quality and your health. EPA-454/K-03-002.

http://airnow.gov/index.cfm?action=static.aqi (accessed January 2, 2009).

U.S. EPA (Environmental Protection Agency). 2006. Guidelines for the reporting of daily air quality –the Air Quality Index (AQI). EPA-454/B-06-001.

http://www.epa.gov/ttncaaa1/t1/memoranda/rg701.pdf (accessed January 2,

2009).

U.S. EPA (Environmental Protection Agency). 2007a. The plain english guide to the Clean Air Act. http://www.epa.gov/air/caa/peg/ (accessed January 2, 2009). U.S. EPA (Environmental Protection Agency). 2007b. Air Data web site.

Figure

Figure 2.1. Annual mobility rates, 1947 to 2007
Figure 2 1 Annual mobility rates 1947 to 2007 . View in document p.18
Figure 2.2. Types of moves: 1990 to 2000
Figure 2 2 Types of moves 1990 to 2000 . View in document p.18
Figure 2.5. Conventional 30-year fixed home mortgage rates:  1987 to 2007
Figure 2 5 Conventional 30 year fixed home mortgage rates 1987 to 2007 . View in document p.29
Figure 3.2. Distribution of housing observations by square feet
Figure 3 2 Distribution of housing observations by square feet . View in document p.45
Figure 3.3. Distribution of housing observations by number of bathrooms
Figure 3 3 Distribution of housing observations by number of bathrooms . View in document p.46
Figure 3.6. Distribution of housing observations by annual maximum 1 hr ozone  concentration (ppm)
Figure 3 6 Distribution of housing observations by annual maximum 1 hr ozone concentration ppm . View in document p.47
Figure 3.10. San Francisco Bay ozone transport
Figure 3 10 San Francisco Bay ozone transport . View in document p.59
Figure 3.11. Ozone spatial distribution, 1990 and 2006 Source: Author’s calculations using data from CARB 2008
Figure 3 11 Ozone spatial distribution 1990 and 2006 Source Author s calculations using data from CARB 2008. View in document p.60
Figure 3.12. Bay Area air quality index (AQI) measures by county, 1998–2004
Figure 3 12 Bay Area air quality index AQI measures by county 1998 2004 . View in document p.62
Table 3.7. (continued)
Table 3 7 continued . View in document p.64
Table 3.8.   One hour maximum ozone concentrations (ppm) by monitor, 1990–1996
Table 3 8 One hour maximum ozone concentrations ppm by monitor 1990 1996 . View in document p.66
Table 3.9. One hour maximum ozone concentrations (ppm) by monitor, 1997–2006
Table 3 9 One hour maximum ozone concentrations ppm by monitor 1997 2006 . View in document p.67
Figure 4.1. Locations of TRI facilities relative to neighborhood demographics (people of color)
Figure 4 1 Locations of TRI facilities relative to neighborhood demographics people of color . View in document p.78
Figure 4.2. Bay Area Hispanic or Latino population share by 2000 census tract
Figure 4 2 Bay Area Hispanic or Latino population share by 2000 census tract . View in document p.79
Figure 4.4. Bay Area non-Hispanic black population share by 2000 census tract
Figure 4 4 Bay Area non Hispanic black population share by 2000 census tract . View in document p.80
Figure 4.3. Bay Area Asian population share by 2000 census tract
Figure 4 3 Bay Area Asian population share by 2000 census tract . View in document p.80
Figure 4.5. Ozone spatial distribution, 2006 Source: Author’s calculations using data from CARB (2008)
Figure 4 5 Ozone spatial distribution 2006 Source Author s calculations using data from CARB 2008 . View in document p.81
Figure 4.6. Bay Area median household income by 2000 census tract:  1999
Figure 4 6 Bay Area median household income by 2000 census tract 1999 . View in document p.82
Table A.1.  Hedonic regressions: No House Fixed Effects
Table A 1 Hedonic regressions No House Fixed Effects . View in document p.110
Table 5.2.   Example house-period level data set
Table 5 2 Example house period level data set . View in document p.121
Table 5.3.   Life table describing all housing spells
Table 5 3 Life table describing all housing spells . View in document p.124
Figure 5.2. Estimated hazard probability, white and black homeowner
Figure 5 2 Estimated hazard probability white and black homeowner . View in document p.126
Figure 5.1. Estimated hazard probability for housing spells
Figure 5 1 Estimated hazard probability for housing spells . View in document p.126
Figure 5.3. Estimated hazard probability, white and Hispanic homeowner
Figure 5 3 Estimated hazard probability white and Hispanic homeowner . View in document p.127
Figure 5.4. Estimated hazard probability, white and Asian homeowner
Figure 5 4 Estimated hazard probability white and Asian homeowner . View in document p.127
Figure 5.5. Estimated survival probability for housing spells
Figure 5 5 Estimated survival probability for housing spells . View in document p.129
Figure 5.6. U.S. conventional 30-year fixed home mortgage rates:  1990 to 2006 Source:  Data from U.S
Figure 5 6 U S conventional 30 year fixed home mortgage rates 1990 to 2006 Source Data from U S. View in document p.135
Table 5.4. Real estate price index for San Francisco Bay using a repeat sale regression
Table 5 4 Real estate price index for San Francisco Bay using a repeat sale regression . View in document p.137
Figure 5.7. Annual Housing Price Trends in San Francisco:  1990 to 2006
Figure 5 7 Annual Housing Price Trends in San Francisco 1990 to 2006 . View in document p.138
Table 5.7.  Fitted baseline hazard estimates (logit)
Table 5 7 Fitted baseline hazard estimates logit . View in document p.143

References

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