Racial disparities in home selling

Leonel Diego Drukker and Lei Ma ∗

February 23, 2024

Abstract

In this study, we investigate the fnancial disparities faced by black sellers in the

US housing market. Using repeat-sale transactions from 2003 to 2020, we document

that black sellers earn, on average, 0.36% lower annualized unlevered returns on their

property sales compared to non-black sellers, when selling comparable properties at

the same time in the same neighborhood. We fnd that the racial disparities in housing

returns are not explained by seller characteristics, property renovations, the race of the

buyer, seller agent fxed efects, and appraisal measures. However, controlling for listing

prices and time on market reduces the racial gap to efectively zero. This suggests that

black sellers face higher search frictions, which leads to worse selling outcomes.

∗Leonel Diego Drukker: University of California Berkeley, Haas School of Buisness,

ldrukker@berkeley.edu. Lei Ma: Boston University, Department of Economics, leim@bu.edu. We

would like to thank Adam Guren, Sasha Indarte, Amir Kermani, Kevin Lang, Tim McQuade, Ulrike

Malmendier, Daniele Paserman, David Sraer, Nancy Wallace, and Yuriy Gorodnichenko for many helpful

discussions and comments. We thank the Fisher Center for Real Estate and Urban Economics for providing

the data. Special thanks to Paulo Issler for his support in understanding the data. We also thank seminar

participants at UC Berkeley’s Real Estate and Macroeconomics seminars and BU’s applied micro workshop

for their feedback.

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1 Introduction

Housing wealth serves as a fundamental means for households to accumulate wealth. Historically, various forms of structural discrimination have hindered Black households from

entering homeownership and benefting from its wealth appreciation benefts. It might be

expected that such discriminatory practices are no longer relevant today, but many papers

fnd that Black households continue to face a higher rate of mortgage application rejections,

pay more when purchasing homes, and experience lower housing returns during distressed

sales (Bhutta, Hizmo, and Ringo (2022), Bayer, Casey, et al. (2017), Kermani and Wong

(2022)).

In this paper, we focus on the home selling process and view the home as a fnancial asset

that provides capital gains returns. We investigate whether there exist any racial disparities

during the selling process since the early 2000s, and if present, what are the underlying

mechanisms that drive these racial disparities. One would expect identical houses in the

same neighborhood to have the same sale price and experience the same house price growth

on average. If all else equal, one would expect that the race of the owner should have no

impact on the selling outcomes. However, the data suggests otherwise.

We use repeat-sale transactions to estimate the racial gap in housing returns for regular

non-distressed sales. We compare housing returns of properties in the same Census tract,

sold in the same year and quarter, but where one is owned by a black household and the

other one owned by a non-black household. The repeat-sale sample allows us to control for

time-invariant property characteristics, thereby addressing omitted variable bias that may

be present in the hedonic approach. To identify the race of the sellers and buyers, we merge

the property transaction data with self-reported race from the Home Mortgage Disclosure

Act (HMDA).

Using over 1.1 million repeat-sale transactions in the US, we fnd that on average, black

households earn 1.74–1.88% lower total unlevered capital gains when selling their homes. To

adjust for varying duration of homeownership, we also calculate the annualized capital gains.

We fnd that the annualized unlevered return gap for black sellers ranges between -0.36%

to -0.38%. Due to our inclusion of Census tract and year-quarter date of sale fxed efects,

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we state that diferences across neighborhoods nor diferences in market timing explain the

estimated racial gap.

We then explore the sources of the racial gap. Our main contribution to the literature is

identifying which factors do and do not contribute to explaining the racial gap in unlevered

housing returns. The home-selling process involves multiple parties including the seller,

potential buyers, and intermediaries like realtors and appraisers. Discrimination can occur

during the interactions between these parties. Moreover, unobserved property characteristics,

potentially correlated with race, can contribute to diferential selling outcomes. We examine

each of these factors to identify which ones contribute most to the existence of the gap.

We frst examine the sellers’ reason to sell. Liquidity-constrained sellers may be willing

to sell at lower prices to ease their fnancial constraints. To investigate this mechanism, we

merge the property transaction data with mortgage performance data from Black Knight

McDash. Using the seller’s mortgage delinquency status as a proxy for liquidity constraints,

we fnd that the racial gap in annualized housing returns continues to exist.

We also explore whether unobserved housing quality can account for the diferential

returns. Although we observe a detailed set of structural property characteristics, there

exist other property features that impact property values but are not observed in the data.

For example, the age of appliances, the aesthetic appeal, and the number of parking spaces

can afect property values. To the best of our knowledge, a comprehensive dataset of all these

variables does not exist, so we choose to focus on substantial renovations and remodeling to

capture diferences in housing quality. If black homeowners systematically put less efort into

maintaining and renovating their homes, it is reasonable to expect that they will experience

lower housing returns. We use building permit data to obtain the renovation status of the

property two years before the home sale. We fnd that property renovations do not explain

the racial gap.

Next, we investigate whether potential racial prejudices by white buyers can explain the

gap. One may speculate that white buyers hold prejudiced beliefs against black sellers and

thus ofer lower prices. However, after controlling for the race of the buyer, we fnd that black

sellers experience lower housing returns regardless of the buyer’s race. This suggests that

any possible direct discrimination between the seller and the buyer is not a dominating force

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driving the racial gap. It is important to note that racial prejudices from potential buyers

may afect this gap if the home seeker is unwilling to purchase a home owned by a black

seller or is more willing to purchase a home owned by a white seller. This decision adds an

additional friction to black sellers seeking to match with a potential buyer and would show

up in our search friction analysis.

Given that none of the characteristics of the property, seller, and buyer explain much of

the gap, we shift our focus to intermediaries. We begin by examining the role of real estate

agents and brokers, who charge commission fees to sellers for their services in facilitating

housing transactions. If the realtor holds prejudiced biases against black sellers or believes

that black-owned homes are of inferior quality, they may recommend lower selling prices or

put in less efort in selling the property. On the other hand, if black sellers select agents

who are less experienced or of lower quality, this can also lead to worse selling outcomes.

To test the mechanism, we merge property listing data to control for real estate agents and

brokerage ofce fxed efects. We fnd that controlling for real estate agents and brokerage

ofces only marginally reduces the racial gap.

Additionally, we examine the role of another key intermediary in the selling process:

appraisers. Appraisers are expected to provide objective market valuations of properties,

which can help lenders, sellers, and buyers determine a fair price. A report from Freddie

Mac (Mac (2021)) fnds that homes in predominantly black neighborhoods often receive much

lower appraisal values compared to similar homes in predominantly white neighborhoods.

Motivated by this fact, we would want to control for the appraisal value at the time of sale.

Unfortunately, we do not observe the actual appraisal estimates at the time of sale, so we

calculate an implied appraisal value by multiplying the appraisal value at the time the seller

refnances their mortgage with the tract-level housing price growth rate. We fnd the implied

appraisal values suggest, on average, black homes are not valued less than non-black homes.

Moreover, controlling for this appraisal does not explain the racial gap.

Lastly, we examine the racial diferences during the listing process. We fnd that black

sellers often set lower listing prices, negatively adjust their listing prices while a home is

on the market, ofer greater price discounts, and take longer to sell their properties. These

fndings suggest that black sellers may experience higher search frictions, compelling them

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to reduce listing prices to increase the probability of sale. Within the context of a search

model, our fndings suggest that black sellers face a diferent level of market tightness than

non-black sellers, which induces the price disparity. To test the mechanism, we control for

listing price and days on market. Controlling for the listing price accounts for approximately

three-fourths of the racial gap. The racial gap reduces to almost zero after we control for

both factors.

However, it is important to note that the listing price may be a noisy measure of a

home’s true market value, as it can afected by the biases from various parties. Sellers may

tie the listing price to the appraised value that they observe – in which case, any bias from

the appraisers would be present in the listing price. Also, agents or the sellers themselves

may negatively infuence the listing price due to their knowledge that the market on average

discounts black home prices or that black homes stay on the market longer. The latter

suggests that listing prices are infuenced by diferences in time on market which is likely

caused by search frictions.

Time on market maps directly to probability a seller matches with a buyer. Given that

black sellers face longer time on market and time on market partially explains the racial gap

in housing returns, we conclude that black sellers face diferent levels of market tightness

than non-black sellers. Within the context of a search model, these diferences in levels of

market tightness directly afect prices. Overall, we fnd that search frictions faced by black

sellers explain a large share of the racial gap in housing returns. Additionally, we argue that

the estimated contribution of 25% in explaining the racial gap by search frictions is a lower

bound due to the likely impact of time on market on listing prices.

We also provide estimates of the racial gap in neighborhoods with diferent socioeconomic

characteristics. We defne a Census tract as a neighborhood and group neighborhoods into

quartiles based on their racial composition, education, income, and homeownership rate. We

fnd that the racial gap is economically signifcant in all types of neighborhoods. However, we

also fnd that the racial gap is larger in neighborhoods with higher population shares of black

residents and lower levels of household income. These results are consistent with fndings

in the literature that uses HUD survey data which provides evidence of racial steering and

black home seekers being shown fewer homes (such as Ondrich, Stricker, and Yinger (1998),

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Yinger (1998), Galster and Godfrey (2005), Zhao, Ondrich, and Yinger (2006), Christensen

and Timmins (2022), and Christensen and Timmins (2023)).

Section 2 describes the data used in the analysis. Section 3 describes our empirical

strategy to recover the racial gap and presents the baseline results. Section 4 explores the

sources of the racial gap. Section 5 provides neighborhood heterogeneity analysis. Section 6

concludes.

Related literature

Our paper contributes to a growing literature that documents racial disparities during home

transactions. Kermani and Wong (2022) fnd that minority home sellers are more likely to

enter into distressed sales and when they do, they lose more of their home values compared to

white homeowners. Furthermore, our baseline estimates of the annualized unlevered returns

gaps for non-distressed sales are of similar magnitudes to their estimates. Our paper’s

main contribution is to investigate and explain where this gap comes from. Bayer, Ferreira,

and Ross (2016) fnds similar results that minority homeowners are less likely to sustain

homeownership when facing adverse economic shocks. Bayer, Casey, et al. (2017) focuses on

the home buying process and fnds that black and Hispanic homebuyers pay a 1.6% premium

when purchasing similar housing in the same neighborhood. Courant (1978) demonstrates

how this premium can arise in a search model where white sellers are unwilling to sell to

black buyers. Our paper focuses on the home selling process for regular non-distressed home

sales. We merge several micro datasets, including building permits, mortgage performance,

and property listing data, to gain a comprehensive understanding of the scope and sources

of the racial gap in selling.

Our paper is also related to the literature that examines the role of real estate agents

in housing transactions. Several papers (such as Ondrich, Stricker, and Yinger (1998),

Yinger (1998), Galster and Godfrey (2005), Zhao, Ondrich, and Yinger (2006), Christensen

and Timmins (2022), and Christensen and Timmins (2023)) use the audit studies by the

Department of Housing and Urban Development (HUD) to investigate the prevalence of

discriminatory practices by real estate brokers. They fnd that real estate brokers show

fewer homes to black households, steer them toward less desirable neighborhoods, and cater

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to the prejudiced beliefs of white buyers. Gilbukh and Goldsmith-Pinkham (2023) shows

that inexperienced real estate agents often cause worse selling outcomes for their clients. We

use property listing data to control for seller real estate agent fxed efects and examine how

much seller real estate agents are responsible for the racial gap in housing returns. We fnd

little evidence that seller agents contribute to the racial gap, but acknowledge that our data

samples are smaller for this analysis. Ideally we would also investigate the efects of the

buyers’ agents, however we lack the data required to perform these analyses.

Our study contributes to the growing body of literature that uses high-quality microdata

to explore the persistent racial disparities in the housing and fnancial markets. AvenancioLe ́on and Howard (2022) show that black homeowners pay more property taxes due to

infated property assessment values for tax purposes. Bhutta, Hizmo, and Ringo (2022),

Frame et al. (2021), Gerardi, Willen, and D. H. Zhang (2023), and D. Zhang and Willen

(2020) fnd that minority borrowers are less likely to get mortgage approvals and face higher

interest rates. Butler, Mayer, and Weston (2022), Argyle et al. (2023), and Laou ́enan and

Rathelot (2022) fnd that black households face racial discrimination in the auto loan market,

short-term rental market, and personal bankruptcy courts.

Our paper is also closely related to papers that study diferential housing outcomes for

other demographic groups. For example, Goldsmith-Pinkham and Shue (2023) fnd that

single women experience 1.5% lower annualized housing returns compared to single men.

Similar to Goldsmith-Pinkham and Shue (2023), we explore various channels that can drive

the gap by utilizing detailed property transactions and listing data.

2 Data

We compile data from various sources to gain a comprehensive view of the sales process and

the key players involved. In addition to sales records, we also gather information on the

renovation status of the property, the sellers’ mortgage payment status history, and property

listing details.

Property transaction and characteristics. The property transactions and sales

records are from ATTOM, a proprietary data provider that compiles national data from

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county deed and assessor records. For each property transaction, we observe the transaction date, sale price, deed type, property type, transaction type, property address, and the

distressed status of the sale (i.e., foreclosure, short sale). We restrict our sample to repeatsale transactions, that is, properties that were transacted at least twice during our sample

period. We exclude non-arms length transactions and partial-consideration sales as well as

non-individual sellers. We also merge the property transaction data with ATTOM’s assessor

panel to get detailed property characteristics, such as the number of bedrooms, bathrooms,

and assessment values.

Race of sellers and buyers. While the property transaction records provide the names

of the sellers and buyers, their race is not recorded in the dataset. We merge the selfreported race of mortgage applicants from the Home Mortgage Disclosure Act (HMDA)

to the ATTOM dataset. We follow the same merging procedure used in previous studies,

such as Bayer, Casey, et al. (2017), Bartlett et al. (2022), Kermani and Wong (2022), and

Avenancio-Le ́on and Howard (2022). We link each property transaction to its respective

mortgage origination record using the time and amount of the property transaction, as well

as the Census tract where the property is located1. To identify the race of the seller, we use

the race of the buyer from the previous transaction, where the current seller was the prior

buyer.

Given that multiple individuals may be involved in selling and purchasing the home,

such as a married couple, we diferentiate between mixed-race households and households

consisting of members from a single race. We defne a household to be black if either the

main applicant or any of the co-applicants are black, so black households can include mixedrace households by defnition. We defne a household to be black-only if both the main

applicant and co-applicants are black. White and white-only households are defned in a

similar manner.

1Note that we are only able to match properties that were purchased with a mortgage. Moreover, the

merge is only up to 2016 because HMDA rounds property values to the nearest $10,000s which makes it

more challenging to fnd perfect matches. So our sample is unable to include selling transactions where the

seller purchased their home after 2016. Combined with the transaction data, we are able to observe the race

of buyers between 2003 and 2016 and consequently, the race of sellers conditional on purchasing their home

between 2003 and 2016.

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Borrower and appraisals. We get loan-level mortgage performance data from Black

Knight McDash. It provides monthly mortgage payment history for each mortgage, and

appraisal values if the mortgage was refnanced. We merge the loan-level information to

property transaction data from ATTOM following the same procedure used in Issler et al.

(2023) and Kermani and Wong (2022). The matching algorithm uses a set of property

and transaction attributes, such as loan amount, loan purpose, and interest rate, to fnd

the k-nearest neighbors. We use the mortgage delinquency status as a measure of liquidity

constraints for sellers. Specifcally, we create an indicator variable to determine whether the

seller was delinquent for 90 days within twelve months prior to selling their home. If the

seller’s mortgage was refnanced within two years of the sale, we use the appraisal value to

impute the appraisal value at the time of sale by multiplying it with tract-level housing price

growth rate between the appraisal date and the sale date.

Building permits. We use national-level building permits data from BuildFax. We

construct indicator variables for properties that have undergone signifcant improvements

two years before the sale date. The improvements we consider include roof replacement,

home remodeling, and other substantial renovations that require a permit. For the subsample

used in the empirical analysis, we drop property transactions in Census tracts that have zero

building permits.

Property listings. We use national-level property listing data between January 2011

and December 2020 from Altos’s Multiple Listing Service (MLS) data. The property listing

data provides weekly updates on the listing price, close price, days on market, and names

of sellers’ real estate agents and broker ofces. We merge the listing data to the property

transaction records using the property address, price, and sale date.

Neighborhood characteristics. We use the 2010 American Community Survey 5-

year estimates from IPUMS to obtain sociodemographic characteristics at the Census tract

level. The neighborhood characteristics we use include racial composition, median household

income, median education level, and homeownership share.

Final data sample. Our main sample consists of 1.1 million repeat-sale transactions

of single-family homes between 2003 and 2020. We restrict our sample to non-distressed,

arms-length, and full-consideration sales. We drop properties that have undergone structural

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modifcations, that is, properties with diferent numbers of bedrooms and bathrooms between

the two transactions. We also exclude transactions with price diferences exceeding 100%

or falling below -100% to remove outliers. Finally, we remove transactions where the time

between purchasing and selling the property is less than two years.

3 The racial gap in housing returns

This section describes our empirical strategy to identify the racial gap in housing returns

during property sales and presents our baseline results.

3.1 Empirical strategy

The ideal experiment is to compare the selling outcomes of two identical homes sold by sellers

of diferent races. To replicate the ideal experiment as much as possible, we use a repeatsales approach. The key advantage of the repeat-sale approach is that it controls for any

time-invariant property characteristics between the two transactions, including unobserved

property characteristics. We focus on comparing the growth in prices between homes within

the same neighborhood while controlling for the timing of the sale and the mean house

price growth between purchase and sale dates. This allows us to control for diferences

in neighborhood amenities and time-varying market conditions. By using a repeat-sales

approach, adding neighborhood and time fxed efects, and accounting for mean house price

growth between purchase and sale, we believe we are able to appropriately isolate the racial

gap in housing returns.

Our baseline specifcation estimates whether sellers of diferent races realize diferent

housing returns. For property i located in neighborhood c sold in time t1 and purchased in

time t0, we run the following regression

ri,c,t0,t1 = β1(SellerRacei,c,t1 = Black) + γXc,i,t0,t1 + αc,t1 + εi,c,t. (1)

The dependent variable is the housing return on property i. The parameter of interest

is β, which captures diferential housing returns attributed to the race of the seller in the

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transaction occurring at time t1. Xc,i,t0,t1 denotes the control variable we use, specifcally

the Census-tract level housing price growth rate, calculated between the year the home was

sold and the year the home was purchased. The control variable allows us to directly control for diferential house price growth trends across diferent neighborhoods. αc,t1 denotes

neighborhood-by-time fxed efects, capturing unobserved shocks at neighborhood-time levels. For example, a neighborhood may start with few nice amenities but experience positive

amenity shocks fve years later, possibly due to the opening of a new school or a beautiful

park. In our estimation, we use Census tracts to defne neighborhoods c and year-quarter of

sale date to defne time t1.

We use two measures of housing returns. The frst is the total unlevered capital gains

between the sale date t1 and purchase date t0, measured as the log price diference between

the two transactions,

tot ri,c,t0,t1 = ln Pi,c,t1 − ln Pi,c,t0 .

Our preferred measure is annualized returns to standardize housing returns across transactions with varying holding periods. Unlike total capital gains, which do not diferentiate

the lengths of ownership, the annualized measure explicitly accounts for the variation in

the lengths of asset holding. We defne the annualized unlevered capital returns similar to

Goldsmith-Pinkham and Shue (2023) and Kermani and Wong (2022). It is defned as

 365/(t1−t0)

ann Pi,c,t1 r = − 1 i,c,t0,t1 Pi,c,t0

where the diference between the two transaction dates t1 and t0 is measured in days.

3.2 Baseline results

Table 1 presents the average racial gap in total and annualized capital gains from housing

using the baseline regression from Equation 1. Intuitively, the regression compares homes

located in the same neighborhoods sold in the same year-quarter, difering only in being

owned by black versus non-black homeowners. In all specifcations, we cluster standard

errors at the Census tract level. As discussed in Section 2, we use two measures to categorize

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the race of the seller: whether at least one member of the sellers is black, and whether all

sellers involved in the transactions are black.

Columns (1) and (2) show that on average, black sellers realize 1.74% lower total capital

gains when selling their houses compared to non-black sellers. If the sellers are black only,

the total capital gains are 1.88% lower. Columns (3) and (4) present the average gap in

annualized capital gains for black and black-only sellers, respectively. We fnd that the

average gap in annualized capital gains of a black seller is 0.36% lower than that of nonblack sellers, and 0.38% lower for black-only sellers than that of non-black-only sellers.

Table 1: Gap in returns to housing

Capital gains returns Annualized returns

(1) (2) (3) (4)

Black seller -0.0174∗∗∗ -0.0036∗∗∗

Black-only seller

(0.0011)

-0.0188∗∗∗

(0.0013)

(0.0003)

-0.0038∗∗∗

(0.0003)

Observations

R2

1151718

0.701

1151718

0.701

1151718

0.680

1151718

0.680

Notes: This table presents the average racial gap in total and annualized capital gains using the baseline regression Equation

1. Columns (1) and (2) report the gap in total capital gains, while Columns (3) and (4) report the gap in annualized returns.

Black sellers are defned as sellers that include at least one black individual, and black-only sellers are defned as sellers that

are all black individuals. All regressions include the Census tract-level annualized mean house price growth rate between the

purchase year and sale year, as well as the Census tract by sale year-quarter fxed efects. Standard errors are clustered at the

Census tract levels. * denotes p < 0.05, ** denotes p < 0.01, *** denotes p < 0.001.

To assess the magnitude of the gap in dollar terms, imagine two homes, both in the same

neighborhood and each bought for $250,000 in February 2012, but one was purchased by

a black-only household, while the other one was purchased by a non-black-only household.

Furthermore, imagine these two homes were sold after eight years, approximately the average

duration of homeownership. In this time period, mean house price growth in the US was

approximately 60%. If the non-black household’s home was sold for $400,000, our baseline

results suggest that the black-only household would have received $11,323 less when selling

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the home.

One concern with our fndings may be that the gap solely arises due to Bayer, Casey,

et al. (2017)’s fnding that black home buyers pay more for their homes, which mechanically

reducing the returns to housing. If this were true then we would expect the returns gap from

a white seller who purchased from a black seller to be no diferent from the returns gap from

a white seller who purchases from a white seller. We fnd this not to be the case. We shed

light on this matter by regressing the annualized housing returns on three binary indicator

variables which take the value of one if a black seller purchases their home from a black

seller, if a black seller purchases their home from a white seller, or if a white seller purchases

their home from a black seller. The last combination of a white seller purchasing their home

from a white seller is omitted. The regression specifcation follows our baseline specifcation

except that we focus on estimating the coefcients of the three indicator variables. Lastly,

we restrict the data to only include transactions from the four race pairs mentioned above.

Appendix Table A1 displays the regression results. Each regression column corresponds

to a diferent set of defnitions for black and white sellers. Column (1) investigates the interactions between black and white households allowing for mixed race households in both sets,

Column (2) investigates the interactions between black and white-only households allowing

for mixed race households with black sellers, Column (3) investigates the interactions between black-only and white-only households excluding mixed race households, and Column

(4) investigates the interactions between black-only and white households allowing for mixed

race households with white sellers. Qualitatively, the results are consistent in each of the

four columns. We fnd that the racial gaps that black sellers face are essentially the same

in magnitude regardless if they are purchasing their home from a white seller or a black

seller relative to the returns a white seller receives when purchasing their home from a white

seller. On the other hand, white sellers on average earn a premium when purchasing their

homes from a black seller. If the racial gap in returns to housing were coming from a buyers’

premium that black sellers pay, then we would not see a diferential between the returns

gained by a white seller who purchased their home from a black seller and the returns gained

by a white seller who purchased their home from a white seller. Because white sellers gain,

on average, higher annual returns of 0.32–0.35% when they purchased their home from a

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black seller than from a white seller, we interpret these results as evidence that the racial

gaps in housing returns that black sellers experience are not solely explained by a racial gap

on the purchasing side.

4 Explanations of the racial gap

In the last section, we documented that on average black households receive lower returns

on their homes relative to non-black households. We explore the underlying mechanisms

driving the gap. We use the annualized housing return as the outcome variable to account

for diferences in holding periods. We fnd that this negative gap in returns is not explained

by liquidity constraints, nor by observable diferences in housing renovations. Using proxy

measures for appraisals, we also fnd no evidence that appraisal values explain away the gap.

However, controlling for listing price and time on market, which is likely infuenced by real

estate agents involved in a housing transaction, explains most of the gap. The results suggest

that black sellers face more search frictions and it is the main driver of the racial gap.

4.1 Seller and property characteristics channel

We begin by examining the role of the seller’s liquidity position on sale price diferences. If

the seller is liquidity-constrained, they may be willing to accept lower prices to sell the house

faster. To test the mechanism, we bring in additional data from Black Knight McDash’s

mortgage performance data. We use the seller’s mortgage delinquency status as a proxy for

the seller’s liquidity position. If a seller has been delinquent on their mortgage payments for

more than 90 days, they are nearing default and face the high risk of losing their home soon.

Consequently, a liquidity-constrained seller may forgo taking the time to search for a buyer

with a high ofer in return for selling their home quickly in order to alleviate their budget

constraint. Our liquidity constraint control is an indicator variable that identifes whether

the seller was 90 days or more delinquent on their mortgage within 12 months of selling their

home.

On the other hand, the disparities in housing return might be attributable to diferences in

unobserved property characteristics correlated with race. Any relation between diferential

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investment in a seller’s home and their race could potentially generate a racial gap. We

test the validity of this claim by merging building permit data from BuildFax. We create

three indicator variables to capture the renovation status of a property: the installation of a

new roof, the presence of home remodeling, and other types of replacements that require a

permit. We measure these variables within two years before the sale. We acknowledge that

there exist many property features that can impact property values but are not observed in

the data. Examples include the aesthetic appeal of the house, the availability of a central

air conditioning system, the age of appliances, and the status of the basement fnishing.

To the best of our knowledge, a complete dataset including all property features does not

exist. Therefore, we focus on signifcant renovations and remodeling to capture diferences

in housing quality, as these factors are likely to have a more pronounced impact on property

values compared to other minor property features.

Table 2 presents estimates of the annualized racial gap, after controlling for the seller’s

liquidity status and the renovation status of the properties. Since we merge the main transaction sample with data from the Black Knight McDash and BuildFax data, the size of the

merged dataset drops from 1.1 million to about 600,000 observations. In Columns (1) and

(4), we rerun the baseline regression Equation 1. The coefcients estimated from the newly

merged samples have similar magnitudes to those in the baseline sample, shown in Columns

(3) and (4) of Table 1. The similarity suggests that the characteristics of the newly merged

samples closely align with those in the baseline sample.

In Columns (2) and (5), we control for the mortgage delinquency status of the seller. The

estimated racial gap in annualized housing returns is -0.29%, which is very similar to the

baseline estimates of -0.32% in Columns (1) and (4). This suggests that the diferences in

need for liquidity between black and non-black sellers do not substantially account for the gap

in fnal housing returns. We further add controls of property improvements in Columns (3)

and (6). The estimated annualized racial gap remains at -0.29%, the same as the estimates

in Columns (2) and (5). The results indicate that neither the seller liquidity nor property

renovations explain an economically signifcant part of the observed racial gap.

15
Table 2: Seller and property characteristics channel

(1) (2) (3) (4) (5) (6)

Black seller -0.0032∗∗∗ -0.0029∗∗∗ -0.0029∗∗∗

Black-only seller

Delinquent 90d+

Roof

(0.0003) (0.0003)

-0.0098∗∗∗

(0.0003)

(0.0003)

-0.0098∗∗∗

(0.0003)

0.0030∗∗∗

-0.0032∗∗∗

(0.0003)

-0.0029∗∗∗

(0.0003)

-0.0098∗∗∗

(0.0003)

-0.0029∗∗∗

(0.0003)

-0.0098∗∗∗

(0.0003)

0.0030∗∗∗

Replacement

Remodel

(0.0005)

0.0028∗∗∗

(0.0004)

0.0114∗∗∗

(0.0005)

0.0028∗∗∗

(0.0004)

0.0114∗∗∗

(0.0008) (0.0008)

Observations 593895 593895 593895 593895 593895 593895

R2 0.723 0.723 0.724 0.723 0.723 0.724

Notes: This table presents the estimates of the racial gap in annualized returns when we control

for seller’s mortgage delinquency status and property renovations. All regressions follow the same

specifcation in Equation 1. All regressions include the Census tract-level annualized mean house price

growth rate between the purchase year and sale year, as well as the Census tract by sale year-quarter

fxed efects. The frst three columns show the estimates for black sellers, while the last three columns

show the estimates for black-only sellers. Standard errors are clustered at the Census tract levels. *

denotes p < 0.05, ** denotes p < 0.01, *** denotes p < 0.001.

16
4.2 Race of buyers

Next, we examine the role of the buyer, with a specifc focus on the buyer’s race. Given the

historical prevalence of prejudice against black households by white individuals, we assess

whether black sellers receive lower housing returns when selling their homes to white buyers.

If white buyers hold prejudiced beliefs against black sellers or their homes, they may ofer

lower prices. The race of the buyer is determined by the self-reported race from HMDA. We

classify a buyer as white if the household is exclusively white, meaning the household is not

mixed-race.

Table 3 presents the results while accounting for the race of the buyer. The specifcations

are similar to our baseline regressions, with an additional interaction term between the

seller’s race and the buyer’s race. Columns (1) and (3) follow the same specifcations as

the baseline regressions shown in Columns (3) and (4) of Table 1, respectively. The sample

including the buyer’s race is smaller because we are not always able to identify the race of

the buyer when we merge the baseline sample with HMDA. The magnitudes of the racial

gap are slightly smaller in this subset of the data, but they remain economically meaningful.

Columns (2) and (4) present results where we explore the role of white buyers in explaining the racial gap. We interact the race of the seller with an indicator for the buyer being

identifed as white-only, i.e., all mortgage applicants and co-applicants are reported as white.

We fnd that the interaction does not absorb the racial gap estimate for black sellers. If the

racial gap was primarily caused by potentially prejudiced white buyers, then the interaction

term between a black seller and a white buyer would yield a statistically signifcant negative

estimate that absorbs the coefcient of the black seller indicator. However, the estimate on

the interaction term is close to zero and is neither statistically nor economically signifcant.

Also, the estimated racial gaps in annualized housing returns are -0.23% for black sellers

and -0.30% for black-only sellers. The magnitudes are similar to the baseline regressions

without accounting for the race of the buyer. Overall, black sellers experience lower housing

returns regardless of the race of the buyer, suggesting that any potential direct discrimination

between the seller and the buyer is not a dominating force driving the racial gap.

17
Table 3: Race of buyers

(1) (2) (3) (4)

Black seller -0.0027∗∗∗ -0.0023∗∗

Black seller × White buyer

Black-only seller

Black-only seller × White buyer

White buyer

(0.0005) (0.0008)

-0.0007

(0.0011)

0.0005∗∗

(0.0002)

-0.0032∗∗∗

(0.0006)

-0.0030∗∗∗

(0.0009)

-0.0002

(0.0012)

0.0005∗∗

(0.0002)

Observations

R2

412203

0.737

412203

0.737

412203

0.737

412203

0.737

Notes: This table presents the estimates of the racial gap when we control for buyer’s race. We

use the subsample that we can identify the buyer’s race for this table. In Columns (1) and (3),

we rerun the baseline regression for black and black-only sellers to get the baseline racial gap. We

include an interaction term between the race of the seller and the race of the buyer in Columns

(2) and (4). All regressions include the Census tract-level annualized mean house price growth

rate between the purchase year and sale year, as well as the Census tract by sale year-quarter

fxed efects. Standard errors are clustered at the Census tract levels. * denotes p < 0.05, **

denotes p < 0.01, *** denotes p < 0.001.

18
4.3 Seller real estate agent and broker ofce

The majority of real estate transactions in the US are facilitated by real estate agents.

Specifcally, the listing agent, who represents the seller, plays a crucial role in shaping the

selling outcomes. The selling agent provides a range of services, including advising on the

list price, staging and advertising the property, showing homes to potential buyers, and

negotiating the close price on behalf of the seller. If black sellers work with realtors who

exert lower eforts due to racial prejudice, inexperience, or lower intrinsic quality, black sellers

may experience worse selling outcomes. In this section, we use the detailed property listing

data from Altos to investigate how much of the gap can be attributed to real estate agents.

We rerun our baseline regression using the repeat-sale sample of the matched transactionlisting data. To isolate the role of real estate agents, we control for listing agent fxed efects.

The names of real estate agents are often missing in the data, so we exclude listings and

associated transactions without agent names. The sampling criteria shrink our sample size

signifcantly, with the subsample with real estate agent information dropping to less than

50,000 observations. This reduction in sample size is due to the lack of data reporting issues

in Altos, rather than a lack of representations of real estate agents in these transactions. So

we create another subsample where we observe brokerage ofces and include them as fxed

efects. The results for annualized returns are presented in Table 4. In Columns (1) to (4), we

use the sample in which we can identify the real estate agents on the listings. Analogously,

Columns (5) to (8) use the sample where we can identify the brokerage ofce. To ensure the

subsamples are comparable to our primary sample, we rerun the baseline regressions without

agent and brokerage controls. The results are shown in Columns (1), (3), (5), and (7). The

estimated racial gaps in the subsamples are around -0.30% to -0.38%, which are very similar

to our baseline results shown in Table 1.

Columns (2) and (4) estimate the racial gap including listing agent fxed efects. We fnd

that adding listing agent fxed efects attenuates the racial gap to some extent, reducing the

annualized gap by 0.06% for black sellers and 0.03% for black-only sellers. However, they still

do not fully explain the racial gap. Columns (6) and (8) estimate the racial gap including

brokerage ofce fxed efects. We fnd that adding brokerage ofce fxed efects only reduces

19
Table 4: Role of listing agent and broker ofces

(1) (2) (3) (4) (5) (6) (7) (8)

Black seller -0.0030∗∗ -0.0024 -0.0038∗∗∗ -0.0035∗∗∗

Black-only seller

(0.0011) (0.0014)

-0.0036∗∗

(0.0013)

-0.0033∗

(0.0017)

(0.0005) (0.0005)

-0.0038∗∗∗

(0.0006)

-0.0036∗∗∗

(0.0006)

Observations

R2

48852

0.735

48852

0.846

48852

0.735

48852

0.846

231727

0.714

231727

0.743

231727

0.714

231727

0.743

FE agent agent ofce ofce

Notes: This table presents estimates of the racial gap in annualized returns when we control for real

estate agents and brokerage ofce fxed efects. Columns (1) to (4) use the matched transaction-listing

sample in which the real estate agent information is available. Columns (5) to (8) use the matched

transaction-listing sample in which the brokerage ofce is available. All regressions include the Census

tract-level annualized mean house price growth rate between the purchase year and sale year, as well

as the Census tract by sale year-quarter fxed efects. Standard errors are clustered at the Census tract

levels. * denotes p < 0.05, ** denotes p < 0.01, *** denotes p < 0.001.

the gap marginally, reducing the annualized gap by 0.03% for black-sellers and 0.02% for

black-only sellers. We conclude that on average, any direct discrimination from real estate

agents towards their black clients does not contribute much to the gap in housing returns.

4.4 Appraisal measures

Appraisals play a crucial role in real estate transactions by providing objective accurate

evaluations of a property’s market value. The appraisal estimates are widely used in housing

transactions. For example, sellers use appraisal values as benchmarks for setting listing

prices, lenders use them to assess the collateral value, and buyers use them as references

when negotiating the fnal sale prices with sellers.

When appraising a home, appraisers fll out a form detailing the characteristics of a home

and provide an estimation of the fair market value. The appraiser records structural features

such as the number of bedrooms and bathrooms. These objective measures leave little room

for any sort of bias. Subjective measures such as the drive through a home’s neighborhood

20
is more likely to afect neighborhood diferences in the racial gap which are shown in FHFA

blog posts (Broadnax and Wylie (2021) and Vajja (2023)), but less likely to afect the withinneighborhood gap that we estimate. However, other subjective measures such as the quality

of the home’s features may allow any sort of bias to pass in. This introduces the potential

for subjective racial biases to infuence the fair evaluations. Recent newspaper stories have

reported instances in which the appraisal value rose by over $30,000 after a black couple

removed family photos and other personal items (Romo (2023)).

Unfortunately, we do not have data on the actual appraisal estimates that home sellers

receive before listing their homes on the market, as well as the appraisal estimates requested

by lenders on the buyers’ side. Therefore, we impute a measure that is likely to be highly

correlated with the actual appraisal estimates.

We impute an implied appraisal value based on the appraisal values the seller receives

when they refnance their property. For households who refnance their mortgages, the

lender re-appraises the property value in order to determine the loan terms of the new

mortgage. Using loan characteristics data from Black Knight McDash, we are able to obtain

the appraised property value ordered by the lenders for refnancing purposes. We then

multiply the appraisal value by the FHFA’s house price growth rate at the Census tract level

to impute the appraisal value at the time of sale. The key assumption made here is that the

racial gap in appraisals does not vary between refnancing a mortgage and selling a home.

We frst re-estimate the racial gap in housing returns if sellers were to receive sale prices

equal to the imputed appraisal values. Table 5 presents the results. We recalculate annualized returns using the implied appraisal value as the numerator, and rerun the baseline

regressions. Columns (1) and (2) report a positive racial gap of 0.44% for black-sellers and

0.48% for black-only sellers if sale prices equal to the implied refnance appraisal values.

This result is the opposite of the racial gap in returns to housing which we have been fnding and strongly suggests that, on average, the value of homes black owners sell is not less

than the value of homes white owners sell. We view this as strong evidence that homes

sold by black sellers should, on average, grow in value by at least as much as homes sold

by non-black sellers – in other words, these results strongly suggest that racial diferences in

unobserved home investment or depreciation are likely not valid explanations for the racial

21
gap in housing returns. It is important to note that this data sample restricts sellers to those

who refnanced their mortgage. The next step is to frst see if a racial gap exists with this

subset of the data and then see if controlling for these implied appraisal values absorbs the

racial gap.

Table 5: Appraisal analysis

(1) (2)

Black seller 0.0044∗

(0.0019)

Black-only seller 0.0048∗

(0.0021)

Observations 117027 117027

R2 0.584 0.584

Notes: This table presents the estimates of the racial gap in annualized housing returns after controlling

for appraisal values. We use three imputed measures for appraisal values. Columns (1) and (2) use

the imputed appraisal value based on the appraised value the seller received when they refnance their

mortgages. It is adjusted by housing price growth between the appraisal date and sale data. Columns (3)

and (4) use the assessed market value from the County’s assessor records. Columns (5) and (6) use the

listing price from the property listing data. All regressions include Census tract-level annualized mean

house price growth rate between the purchase year and sale year, as well as the Census tract by sale

year-quarter fxed efects. Standard errors are clustered at the Census tract levels. * denotes p < 0.05,

** denotes p < 0.01, *** denotes p < 0.001.

Figure 1 presents the point estimates and 95% confdence intervals for the annualized

racial gap after controlling for the implied appraisal measure. We do not control for house

price growth in the plots.2 In each plot, the frst coefcient provides the annualized gap

without controls, and the second coefcient provides the annualized gap after controlling for

the appraisal measure.

The results in Figure 1 use our implied appraisal from refnance measure. It provides

2We exclude the FHFA house price growth control because the price growth from our appraisal measures

and the seller’s purchase price serve a similar role.

22
Figure 1: Gap controlling for implied appraisals

-.006

-.005

-.004

-.003

-.002

-.001

0

Gap Gap with control

-.006

-.005

-.004

-.003

-.002

-.001

0

Gap Gap with control

Black sellers Black-only sellers

Notes: This Figure plots the point estimates and 95% confdence intervals for the racial gap in annualized

housing returns after controlling for appraisal measures. Panel (a) shows the estimates using implied appraisal derived from the appraisal value at mortgage refnancing. Each plot shows the estimates without

and with appraisal controls for black and black-only sellers. All regressions include the Census tract by sale

year-quarter fxed efects. Standard errors are clustered at the Census tract levels. * denotes p < 0.05, **

denotes p < 0.01, *** denotes p < 0.001.

two plots: one for sellers identifed as black and another for sellers identifed as black-only.

The subsample is restricted to households with refnanced mortgages. For this subsample,

the annualized gaps without controlling for appraisal values are -0.22% and -0.27% for black

and black-only sellers, respectively, smaller than the estimates provided in Columns (2) and

(4) of Table 1, but they are still statistically signifcant at the 95% level. The estimated gaps

after including the implied appraisal values have similar magnitudes, with values -0.19% and

-0.22%, respectively. The results suggest that on average appraisers likely do not play a role

in explaining the racial gap.

4.5 Search frictions

This section investigates the role of listing prices and time on market in explaining the gap

black homeowners face when selling their homes. We fnd that accounting for both the time

on market and listing price can explain nearly all of the racial gap.

23
We frst document a few key facts about racial diferences in listing outcomes, shown in

Table 6. The listing price and time on market information are merged in from Altos MLS

data. We use the baseline regression specifcation shown in Equation 1, but replace outcome

variables with listing characteristics.

Listing price is an estimate of the market value of a home, and it is often closely tied

to an appraised value sellers receive. A seller’s agent may also provide input on the listing

price – for instance, if an agent believes that it would be difcult to fnd a buyer for a house,

and thus be on the market for an extended period of time, they may suggest lowering the

listing price. Additionally, sellers themselves may have their own views on what the listing

price should be – potentially lowering the listing price themselves if their home is listed for

a long period of time without receiving a sufcient ofer. As a result, a listing price may be

infuenced by the bias of several players. If these biases are correlated with race of the seller,

they are likely to infuence the relative diference in sale prices between black and non-black

sellers.

The regression specifcation in Columns (1) and (2) is similar to that in Table 5 where

we re-estimate the racial gap in housing returns if sellers were to receive sale prices equal to

the last listing price we observe prior to sale. The results indicate that black sellers would

face lower annualized returns implied by our listing price measure. Given our fndings from

Section 4.4, we suspect that racial diferences in listing price are likely due to the infuence

of biased parties and not due to unobserved quality of the home.

Columns (3) and (4) regress the log change between the fnal listing price and the frst

listing price on the race of the seller. We fnd that black and black-only sellers lower their

listing prices by 0.06% more than non-black and non-black-only sellers. Columns (5) and

(6) regress the log diference between the fnal sale price and the fnal listing price on the

race of the seller. The regression results indicate that black and black-only sellers sell their

homes at larger discounts of -0.24% and -0.31% relative to non-black sellers.

Columns (7) and (8) regress the number of days it takes to sell the house on the race of

the seller. We fnd that black homes are listed on the market for over a week longer than

non-black homes. Given that we include interacted neighborhood and time of sale fxed

efects, the diference in days on market between black and non-black sellers is particularly

24
Table 6: Listings characteristics gaps

Listing price gap Listing price change Sale-listing gap Days on market

(1) (2) (3) (4) (5) (6) (7) (8)

Black seller -0.0033∗∗∗ -0.0006∗∗ -0.0024∗∗ 8.4589∗∗∗

(0.0005) (0.0002) (0.0008) (0.8008)

Black-only seller -0.0032∗∗∗ -0.0006∗ -0.0031∗∗∗ 9.4490∗∗∗

(0.0005) (0.0003) (0.0009) (0.9259)

Observations 296052 296052 595242 595242 595245 595245 595249 595249

R2 0.705 0.705 0.348 0.348 0.411 0.411 0.458 0.458

Notes: This table presents the racial diferences in listing outcomes using the matched transaction-listing

sample. The frst two columns show the racial diference in listing price adjustment, measured by the log

diference between the frst and fnal listing prices. The third and fourth columns show the racial diference in

the sale-listing price gap, measured as the diference between the sale price and the fnal listing price. The last

two columns show the racial diference in the number of days on market. All regressions include the Census

tract by sale year-quarter fxed efects. Standard errors are clustered at the Census tract levels. * denotes

p < 0.05, ** denotes p < 0.01, *** denotes p < 0.001.

25
stark. The results show that black sellers face lower probabilities of matching with a potential

buyer, suggesting that black sellers may face higher search frictions during the selling process.

Additionally, this racial diference in time on market may likely explain at least a part of

the racial diference in listing prices we display in Columns (1) and (2). Within the context

of a search model, our fndings suggest that black sellers face a diferent level of market

tightness than non-black sellers. This result is consistent with the results from the literature

that uses the HUD Discrimination survey to show that real estate agents have steered white

buyers away from black neighborhoods, shown black home buyers fewer homes, and catered

to prejudiced preferences of home buyers (Galster and Godfrey (2005), Ondrich, Stricker,

and Yinger (1998), Yinger (1998), Zhao, Ondrich, and Yinger (2006), Turner et al. (2013)).

Several factors can explain the racial gap in listing outcomes. First, black sellers learn

about their true property values during the listing process. If the house receives few visits

and ofers from buyers, the black seller may learn that they need to adjust prices to attract

buyers. Second, the listing agent may put less efort in facilitating the sale of the blackowned properties, lowering the probability of sale. As a result, the black seller may respond

by lowering listing prices to increase the probability of sale. Third, black sellers may have

less negotiating power when closing the sale, and this can explain why there is a gap between

the fnal sale price and fnal listing prices. The diference in negotiation skills can be due to

bad recommendations by the listing agent, inexperience by sellers, or property defects found

during home inspections.

Our next set of results tests whether listing behaviors can explain the racial gap. Table

7 uses our baseline specifcation, and we incrementally add controls for listing price growth

and time on market.3 The sample is smaller than the baseline sample because we are not

able to fnd matches for all transactions when merging with the MLS data.

In Columns (1) and (4), we rerun the baseline regression using the new subsamples. The

estimated racial gaps are of similar magnitudes as the results in Table 1. We control for

the annualized price growth between the fnal listing price and purchase price in Columns

(2) and (5). Here, the racial gap in annualized returns decreases substantially, from -0.4%

3We exclude the FHFA house price growth control because the price growth from listing price and the

seller’s purchase price serves a similar role.

26
Table 7: Search frictions regressions

(1) (2) (3) (4) (5) (6)

Black seller -0.0041∗∗∗ -0.0010∗ -0.0004

(0.0004) (0.0004) (0.0004)

Black seller × 1/Time on mkt -0.0013∗∗

(0.0005)

Black-only seller -0.0043∗∗∗ -0.0010∗ -0.0004

(0.0004) (0.0005) (0.0004)

Black-only seller × 1/Time on mkt -0.0015∗∗

(0.0005)

Listing price growth 0.7793∗∗∗ 0.7773∗∗∗ 0.7793∗∗∗ 0.7773∗∗∗

(0.1031) (0.1030) (0.1031) (0.1030)

1/Time on mkt 0.0079∗∗∗ 0.0079∗∗∗

(0.0007) (0.0007)

Observations 440386 440386 440386 440386 440386 440386

R2 0.543 0.905 0.907 0.543 0.905 0.907

Notes: This table presents the estimates of the racial gap in annualized returns after controlling for

listing price diference and weeks on market. All regressions include the Census tract by sale yearquarter fxed efects. Standard errors are clustered at the Census tract levels. * denotes p < 0.05,

** denotes p < 0.01, *** denotes p < 0.001.

to -0.1%. Including the listing price reduces the gap for two main reasons. First, any bias

inherent in the listing price can transmit to the racial gap in the fnal returns. Second, the

listing price refects unobserved heterogeneity of the property itself. Although we cannot

disentangle these two forces from the listing price, our results from Section 4.4 suggest the

latter explanation is less likely to be legitimate. Additionally, listing price may be lower

precisely because black sellers’ homes spend more time on the market – suggesting that

search frictions may directly play a role in the lower listing prices black sellers set.

Columns (3) and (6) include the inverse of the total weeks the home is on the market as

well as its interaction with the race of the seller and the listing price control. We include

the inverse of time on market because it directly maps to the probability of matching with

a buyer. We fnd that accounting for both the listing price and time on market reduces

27
the racial gap by a factor of ten, and it removes the statistical signifcance of the estimate

as well. We interpret these results as evidence that accounting for the diferential search

frictions black sellers face explains the price disparity. When the time on market decreases,

that is, it takes a short time to fnd a buyer, non-black sellers see a higher increase in their

annualized returns than black sellers due to the negative coefcient in the interaction term.

For instance, the median number of weeks on market in our data is approximately 5 weeks,

and the results in Table 6 give the average within-neighborhood diference estimates in time

on market between black and non-black sellers and black-only and non-black-only sellers of

8.5 and 9.4 days or close to one and half weeks. If it took a black seller 5 weeks to match

with a buyer, a diference in time on market of one and a half weeks between black and

non-black sellers and black-only and non-black-only sellers yields a diference in annualized

returns of approximately 0.09% and 0.1%, respectively. This equates to roughly one-quarter

of the total racial gap we estimate. However, because diferences in time on market are likely

to impact listing prices, we believe our estimates of search frictions explaining the racial gap

to be a lower bound.

5 Neighborhood heterogeneity

This section provides heterogeneity analysis at the Census tract level. Our neighborhoodlevel analysis provides estimates of the racial gap in annualized unlevered returns to housing

across neighborhoods with diferent socioeconomic characteristics. These estimates give us

an understanding on which types of neighborhoods have higher or lower estimated racial

gaps.

We defne neighborhoods using Census tracts, a common defnition used in the literature.

We use neighborhood characteristics from the 2010 American Community Survey 5-year

estimates. We group Census tracts into quartiles based on the share of black residents,

the share of white residents, the homeownership rate, the share of residents with a college

degree, and the median household income. Figure 2 displays the racial gap estimates in

annualized unlevered housing returns across neighborhoods for each of our socioeconomic

characteristics. Overall, we fnd that the annualized gap persists in each quartile group of

28
Census tracts for every socioeconomic variable we examine.

Figure 2 Panel (a) displays the racial gap estimates across neighborhoods with diferent

black population shares. Neighborhoods with a higher share of black population experience

larger racial gaps in housing returns. In neighborhoods at the bottom quartile of black population, the racial gaps are approximately -0.29% for black sellers and -0.32% for black-only

sellers, and the estimates are statistically signifcant. However, although we estimate negative gaps in the second quartile, the estimates are statistically insignifcant. The statistical

insignifcance is probably due to the mechanical fact that these neighborhoods have very few

black sellers. In neighborhoods at the top quartile of black population, the racial gap increases to -0.39% for black sellers and -0.42% for black-only sellers. We believe this is due to

search frictions negatively impacting black sellers more in neighborhoods with higher black

populations. Additionally, we fnd that the racial gaps are generally higher for households

identifed as black-only.

Figure 2 Panel (b) displays the racial gap estimates across neighborhoods with diferent

shares of white residents. We fnd lower gaps in annualized housing returns in neighborhoods

with the lowest shares of minority residents, though the relationship does not appear to be

linear. The results show that the estimated gaps are consistently negative.

Figure 2 Panels (c) and (d) display the racial gap estimates by the quartile groups of

neighborhood homeownership rates and of median household income, respectively. We fnd

that the largest gaps are in neighborhoods in the bottom quartiles of median household

income with estimates as large as -0.39% and -0.42% for black and black-only sellers, respectively. This is likely because neighborhoods with low levels of income typically have higher

minority populations. Given this relationship, it is likely that search frictions negatively

impact black sellers more in neighborhoods with lowest income levels than in neighborhoods

with higher income levels. We fnd a fatter relationship between homeownership rates and

the racial gap in housing returns. Overall, the results show that the estimated gaps are

consistently negative across these types of neighborhoods.

Finally, Figure 2 Panel (e) displays the racial gap estimates by the quartile groups of

neighborhood shares with college education. In short, we fnd a fatter relationship between

college education levels of a neighborhood and the racial gap in housing returns. We fnd that

29
Figure 2: Annualized housing return gaps by socioeconomic variable

(a) Share of black residents (b) Share of white residents

-.006

-.004

-.002

0

Black sellers Black-only sellers

1 2 3 4 1 2 3 4

-.006

-.004

-.002

0

Black sellers Black-only sellers

1 2 3 4 1 2 3 4

(c) Homeownership rate (d) Median household income

-.006

-.004

-.002

0

Black sellers Black-only sellers

1 2 3 4 1 2 3 4

-.006

-.004

-.002

0

Black sellers Black-only sellers

1 2 3 4 1 2 3 4

(e) Share of college educated

-.006

-.004

-.002

0

Black sellers Black-only sellers

1 2 3 4 1 2 3 4

Notes: This Figure presents the annualized housing returns in Census tracts with diferent sociodemographic

characteristics. Census tracts are grouped into quartiles based on their characteristics indicated by the title

for each panel. All regressions include Census tract-level annualized mean house price index growth between

purchase year and sale year and Census tract × year-quarter fxed efects. Standard errors are clustered at

the tract level. * denotes p < 0.05, ** denotes p < 0.01, *** denotes p < 0.001.

30
black sellers realize lower housing returns if their properties are in neighborhoods with lower

education levels ranging from -0.43% to -0.26%. However, the gaps for black-only sellers do

not follow a similar pattern. One consistency is that the estimated gaps are slightly larger for

black-only sellers compared to black sellers realizing an average racial gap of up to -0.46%.

To summarize, we fnd that both black and black-only sellers have realized lower housing

returns in all quartiles of neighborhoods for each of the neighborhood characteristics we

examine. This indicates that the racial disparity in housing returns is not confned to certain

neighborhoods, but is broadly evident across various geographical areas. We also fnd that

the gap tends to be larger in neighborhoods with higher shares of black residents and lower

incomes.

6 Conclusion

This paper investigates whether there exists a racial gap in unlevered housing returns for

regular non-distressed sales. Although many papers have studied racial disparities in the

housing market, no one has focused on the within-neighborhood disparities during the homeselling process for non-distressed home sales nor provided a detailed empirical explanation of

the main causes for the disparities. We use over 1.1 million repeat-sale transactions between

2003 and 2020 in our analysis. When estimating the diferences in housing returns between

black and non-black sellers, we control for Census tract by sale year-quarter fxed efects and

tract-level house price growth between purchase and sale dates. Our main fnding is that

black and black-only sellers on average realize 1.7% and 1.9% lower total capital gains, and

0.36% and 0.38% lower annualized returns, respectively, when selling their homes. We also

fnd that the gap exists in all types of neighborhoods.

To explore the sources of the gap, we bring in additional data from various sources to

examine the roles of seller’s liquidity, property renovations, race of the buyer, real estate

agents, and appraisers on the racial gap. We fnd that none of these factors can explain

much of the gap. However, our appraisal analysis strongly suggests that, on average, black

homes are not valued less than non-black homes.

Motivated by the facts that black sellers tend to set lower listing prices and experience

31
longer time on market, we investigate the role of search frictions. We frst show that black

sellers list their homes at lower prices and for over a week longer than non-black sellers.

These results suggest that black sellers face diferent levels market tightness than their nonblack neighbors. Within the context of a simple search model, these diferent levels of market

tightness directly afect prices. Accounting for listed prices is key because it is a measure

of a home’s market value and because its value can be infuenced by the biases of several

players such as home appraisers, real estate agents, and sellers who likely internalize the

racial diferences in time on market. As a result, we control for listing prices and days

on market, and fnd that the racial gap drops to near zero, suggesting that both the biases

behind listing prices and the search frictions faced by black sellers are the dominating factors

contributing to the gap.

32
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35
A Additional tables and graphs

Table A1: Race pair transactions

black seller transactions black-only seller transactions

(1) (2) (3) (4)

Black 1st seller, Black 2nd seller -0.0031∗ -0.0039∗∗

(0.0014) (0.0015)

Black 1st seller, White-only 2nd seller 0.0024∗∗∗

(0.0005)

White-only 1st seller, Black 2nd seller -0.0032∗∗∗

(0.0005)

Black 1st seller, White 2nd seller 0.0025∗∗∗

(0.0005)

White 1st seller, Black 2nd seller -0.0032∗∗∗

(0.0005)

Black-only 1st seller, Black-only 2nd seller -0.0034∗ -0.0037∗

(0.0016) (0.0017)

Black-only 1st seller, White-only 2nd seller 0.0035∗∗∗

(0.0006)

White-only 1st seller, Black-only 2nd seller -0.0035∗∗∗

(0.0005)

Black-only 1st seller, White 2nd seller 0.0036∗∗∗

(0.0006)

White 1st seller, Black-only 2nd seller -0.0035∗∗∗

(0.0005)

Observations 222572 264049 219567 260891

R2 0.730 0.729 0.730 0.729

Regressions include Census tract-level annualized mean house price index growth between purchase year and sale year and Census tract ×

year-quarter fxed efects. Reported standard errors are clustered at the tract level. * denotes p < 0.05, ** denotes p < 0.01, *** denotes

p < 0.001.

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Table A2: Listings characteristics mean unconditional gaps

Black vs. non-black sellers

listing price chg ln sale listing gap days on mkt

0 .0022365 -.0242126 66.4779

1 -.0030063 -.0259817 69.65689

Black-only vs. non-black-only sellers

listing price chg ln sale listing gap days on mkt

0 .0021992 -.024188 66.45347

1 -.0028762 -.0272893 71.28378

37