Can Everyone Tap into the Housing Piggy Bank?

Racial Disparities in Access to Home Equity ∗†

James N. Conklin Kristopher Gerardi Lauren Lambie-Hanson

February 22, 2024

Abstract

We document large racial disparities in the ability of homeowners to access their housing

wealth. Minority homeowners are significantly more likely to have their mortgage equity

withdrawal product applications rejected than White homeowners, and the disparities are

much larger than those found in prior studies that have focused on purchase and rate/term

refinance loans. Controlling for variables commonly used in the underwriting process significantly narrows the gaps, with the Black-White denial rate gap falling by more than 80%, and

the Hispanic-White gap falling by more than 70%. Credit scores and debt-to-income ratios are

the most important factors explaining the racial gaps, while differences in loan-to-value ratios

contribute only modestly. “Residual” disparities after conditioning on observable underwriting

factors are large and vary significantly across lenders. While these may result from differences

in unobserved underwriting factors, a battery of tests suggests that they are unlikely to fully

explain the conditional racial gaps in denial rates. Residual disparities tend to be larger in geographic areas characterized by more racial animus, which suggests that discriminatory forces

may play a role.

Keywords: housing wealth, mortgage, home equity, racial disparities

JEL classification: G21, G51, J15

∗James Conklin, jnc152@uga.edu, is at the Terry College of Business at the University of Georgia. Kristopher Gerardi, kristopher.gerardi@atl.frb.org, is at the Federal Reserve Bank of Atlanta. Lauren Lambie-Hanson,

Lauren.Lambie-Hanson@phil.frb.org, is at the Federal Reserve Bank of Philadelphia.

†We thank Stephanie Sezen for excellent research assistance and Bob Avery for sharing his HMDA Lender File.

We also are grateful to Neil Bhutta, Julia Cheney, Hajime Hadeishi, Bob Hunt, Tesia Lemelle, Dan Ringo, Dubravka

Ritter, Amanda Roberts, Anna Tranfaglia, Athena Tsouderou, and audience participants at the American Real Estate

and Urban Economics Association National Conference and the Federal Reserve Bank of New York for providing

helpful comments and suggestions. Barbara Brynko provided outstanding editorial assistance. An earlier version of

this paper was posted as Atlanta Fed Working Paper 2022-17. The views expressed in this paper are those of the

authors and not necessarily those of the Federal Reserve Bank of Atlanta, Federal Reserve Bank of Philadelphia, or

the Federal Reserve System. No statements here should be treated as legal advice. Philadelphia Fed working papers

are free to download at https://philadelphiafed.org/research-and-data/publications/working-papers.
1 Introduction

A large literature in economics has documented significant racial disparities in the ability to access mortgage credit to purchase a home.1 This literature has garnered considerable attention due,

in part, to the widespread belief that homeownership plays a key role in household wealth accumulation, especially for low-income and minority households.2 The concern is that if minority

households do not have the same opportunities to build wealth through homeownership, then it

will be very difficult to close the large existing gaps in racial wealth inequality.

In this paper, we shift the focus toward a related question that has received much less attention:

Are there disparities in the ability to access accumulated housing wealth across racial and ethnic

lines? The ability to access housing wealth is vital for many households, especially since housing

is the largest asset in most financial portfolios. Housing equity is used for numerous purposes,

such as smoothing consumption in the face of adverse income/employment shocks and financing

home improvement projects, businesses,3 large durable goods purchases, and even educational

costs.4 Housing equity is also an important tool for building intergenerational wealth, as parents

often bequeath their homes to their children (Begley, 2017). Thus, determining if there are large

disparities along racial linesin the ease of accessing housing equity and addressing those disparities

if they exist are critically important from a policy perspective.

Compared to many other assets, housing wealth is relatively illiquid. There are essentially

three ways to access accumulated housing wealth. First, homeowners can sell their homes and

1See Black, Schweitzer, and Mandell (1978), Munnell et al. (1996), and Charles and Hurst (2002) for classic

treatments. Ladd (1998) provides a survey of the literature.

2See Goodman and Mayer (2018), Charles and Hurst (2002), Shapiro (2006), Boehm and Schlottmann (2008).

Wainer and Zabel (2020), and Killewald, Pfeffer, and Schachner (2017) for examples.

3Adelino, Schoar, and Severino (2015), Kerr, Kerr, and Nanda (2022), and Corradin and Popov (2015) document

the importance of home equity for small business financing.

4Along with retirement savings, home equity associated with a primary residence is excluded from asset calculations in the federal student aid formula in Levine and Ritter (2022), so for this reason and others, it is an advantageous

form of wealth to use for funding post-secondary education, if it can be extracted. Benetton, Kudlyak, and Mondragon (2022) document in credit bureau data that parents also use their home equity to assist their children with down

payments when purchasing their first homes.

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transition into the rental market. Second, homeowners can downsize by selling their current homes

and purchasing less expensive houses. Third, homeowners can remain in their homes and extract

housing equity using a special type of mortgage, such as a home equity loan (HELoan), a cash-out

refinance, a home equity line of credit (HELOC), or a reverse mortgage. The first two methods

of accessing home equity require changing residences. Because this entails paying significant

transaction costs, most households that desire to tap into their housing wealth would likely prefer

to stay in their homes. In this paper, we focus on the third method of home equity extraction

(via mortgage products) since it is the only way for a homeowner to access housing wealth without

moving. Specifically, we analyze racial disparities in access to three mortgage products: HELoans,

HELOCs, and cash-out refinances (hereafter referred to as mortgage equity withdrawal (MEW)

products). We exclude reverse mortgages because they are available only to borrowers who are

62 or older and have very different underwriting rules compared to “forward” equity withdrawal

products (Mayer and Moulton, 2022).

Lender discrimination might play a larger role in home equity lending than in more automated

purchase and refinance lending because HELOC and HELoan lending, in particular, are less automated and rely more on face-to-face interaction between borrowers and lenders. Because most

purchase, rate/term, and cash-out refinances are sold to the government-sponsored enterprises or

are insured by the Federal Housing Administration or Veterans Affairs, these loans undergo rigid

review using automated underwriting systems and scorecards prepared by the agencies (see Bhutta,

Hizmo, and Ringo, 2021). HELOCs and HELoans are often held by lenders in portfolio, who

typically use internally developed underwriting models rather than the standardized automated underwriting models (like Desktop Underwriter⃝c ) that were established for secondary market sales.

This may have the effect of making loan decisions somewhat more subjective. Likewise, HELOC and HELoan applicants are more likely to interact in person with loan officers, whereas

lender/borrower interactions for purchase and refinance applications are more likely to be online

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or over the phone.5

Our paper is one of the first to measure racial disparities in access to MEW products, likely

due to a dearth of quality data on these types of loans.6 To conduct such an exercise, we use

confidential Home Mortgage Disclosure Act (HMDA) data from 2018 through 2021, which contain extensive information on applications for MEW products not present in other commonly used

mortgage datasets. In particular, the dataset has broad geographic coverage from many financial

institutions and includes key underwriting factors used by lenders in making credit decisions, enabling us to systematically look at racial disparities in housing wealth extraction in a way that

was not previously possible. Although the empirical evidence on race and housing wealth extraction is scant, there is anecdotal evidence suggesting that minority homeowners have a harder time

accessing MEW products.7

Denial rates are significantly higher for MEW products (cash-out refinances, HELoans, HELOCs) than for purchase-money mortgages and rate/term refinances. In our sample, 26% of MEW

withdrawal applications are denied, compared to 10% for non-MEW products. Unconditional

minority-White denial rate gaps (that is, the simple differences in denial rates between racial

groups, not controlling for any risk factors) are larger for MEW products relative to non-MEW

products. Figure 1 shows that for non-MEW applications, Black homeowners are 9 percentage

points more likely than White borrowers to be denied, while Hispanic and Asian borrowers are 4

percentage points and 1 percentage point more likely to be denied than White borrowers, respectively. For MEW products, the Black-White, Hispanic-White, and Asian-White denial rate gaps

widen to 21, 9, and 7 percentage points, respectively (Table 1). These differences are striking

— the unconditional minority-White denial disparities are 2 to 7 times larger for MEW products

than non-MEW mortgages. These large unconditional differences show that, on average, minor5Please see Section A.1 of the Online Appendix for more details on how we develop a proxy for in-person lending

using a feature of Home Mortgage Disclosure Act data reporting instructions.

6Two other studies deal with MEW and race (Carlin and Divringi, 2018; Do, 2012). We discuss both in some detail

below.

7For example, see Chiwaya and Ross (2020).

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ity homeowners do not have the same ability as White homeowners to access their accumulated

housing wealth to improve their overall economic and financial well-being. In other words, the

results suggest that even if policymakers are able to close the large gap in the minority-White

homeownership rate, there would still be an important source of racial inequality stemming from

the differential ability to access the financial benefits of owning a home.

But in order to understand what drives these large unconditional differences and determine the

appropriate policy response, it is important to introduce controls for loan and borrower risk factors

that are commonly used by lenders in the underwriting process. We show that nearly two-thirds

of the Black-White and Hispanic-White disparities in MEW product denial rates can be explained

by differences in homeowners’ credit scores and debt-to-income (DTI) ratios alone. Including

additional controls, such as the requested loan amount and the level of the applicant’s income,

further reduces MEW rejection rate disparities.

Surprisingly, controlling for the applicant’s combined loan-to-value (CLTV) ratio does not

have a material effect on the estimated denial rate gaps, which suggests that minority borrowers

are not being rejected for having insufficient levels of housing equity. We show that this is also

supported by HMDA data on the (self-reported) reasons for why lenders rejected MEW product

applications. While credit history and DTI ratios are the two most-cited reasons for why minority

applications are denied in our sample, insufficient collateral is the least-cited reason.8 Thus, the

requirement that applicants have high credit scores and low DTI ratios to successfully navigate the

underwriting process has a particularly large impact on minority households’ ability to access their

accumulated housing wealth.

For all three MEW products (cash-out refinances, HELoans, and HELOCs), accounting for

8There are a few caveats that are worth noting about the small effect of CLTV on the size of the minority-White

denial disparities. First, the CLTV measure for denied applications in HMDA data may reflect the applied-for CLTV

rather than the “actual” CLTV, since a loan might reach a rejection decision before it receives an appraisal (which

comes later in the underwriting process). Second, the sample period (2018–2021) was characterized by strong house

price growth across the entire country, which somewhat mitigates cross-sectional variation in how much equity would

be a binding constraint between racial groups. Thus, it isn’t clear that this result is generalizable to periods with less

robust house price growth.

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standard borrower and loan characteristics used in mortgage underwriting significantly reduces–

but does not fully eliminate–the gap in denial rates across borrowers. Black and Asian borrowers

remain about 4 percentage points more likely to be denied for MEW products than White borrowers, and Hispanic borrowers are about 3 percentage points more likely to be denied (relative to

White borrowers) after accounting for these observable factors. These residual racial disparities

are non-trivial and are consistent with, but certainly not proof of, racial discrimination. There are

additional factors, such as liquid assets and information about an applicant’s employment history,

that lenders take into consideration in the underwriting process but are not included in the HMDA

data.

In our remaining analysis, we focus on these residual denial rate disparities. We begin by examining patterns across types of financial institutions, which offer different mixes of loan products.

For example, HELOCs generally are provided by banks and credit unions, while a large share of

cash-out refinances are originated by nonbank lenders. The underwriting criteria likely vary across

different types of financial institutions, and thus, disparities in access to MEW may also. When we

estimate the models for each product type separately for each type of financial institution (banks,

nonbanks, and credit unions), minority-White denial rate gaps exist in all models. Interestingly,

although credit unions are the least likely to deny applications overall, the residual Black-White

gaps are largest for credit unions across all MEW products. This finding is closely related to empirical evidence suggesting that credit unions are less likely than banks to serve individuals of low

to moderate income.9 Our results speak to the inclusiveness of credit provision by credit unions

along another dimension – race.

We also test for differences in denial rates among different types of banks. According to the

Independent Community Bankers of America (ICBA), an industry trade group, “[M]inority banks

were formed to empower minorities and low-to-moderate income communities by providing them

9See Marshall and Pellerin (2017) for a discussion of this research.

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with access to credit, capital, and financial services.”10 Indeed, relative to majority-owned banks, a

larger share of originations by minority-owned banks are to minority borrowers (Breitenstein et al.,

2014). Since minority-owned banks are formed, at least in part, to serve the credit needs of minority

borrowers, a natural question is whether racial denial rate gaps are smaller at minority-owned

banks. We provide evidence consistent with this hypothesis. Relative to majority-owned banks,

minority-White denial gaps are smaller at minority-owned banks, and in some cases, reversed

(minorities are less likely to be denied). An important caveat however, is that only a small share

(0.65%) of the mortgage applications handled by banks are from minority banks, and effects may

be driven by a combination of treatment and borrowers’ self-selection into applying with these

lenders, so these results should be interpreted with caution.

We then turn our attention to the underlying mechanisms and try to determine the extent to

which the residual MEW denial rate disparities between White and minority applicants is due to

racial discrimination versus other explanations such as omitted variables that lenders use in their

approval process but that are unavailable in our data. While we do not have a precise way to

discern between different mechanisms, we attempt to shed light on this issue by implementing

four additional pieces of analysis. First, we estimate Kitagawa-Blinder-Oaxaca decompositions by

lender to determine the fraction of residual denial rate disparities that is due to explained (that is,

observable) factors versus unexplained factors. The reasoning goes that a lender that has applicants

with substantially greater differences in observable risk factors is also likely to have substantially

greater differences in underwriting variables not observable to the econometrician. We show that

the size of the unexplained denial disparities is actually not strongly correlated with the size of the

explained disparities across lenders, which suggests that residual minority-White denial rate gaps

are likely not driven solely by underwriting variables that we do not observe in our HMDA data.

Second, following Bhutta, Hizmo, and Ringo (2021), we make use of automated underwriting

system (AUS) decisions. AUSs use statistical models to score loan applications for credit risk and

10See https://www.icba.org/our-positions-a-z/minority-banking/minority-banks.

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ultimately provide a non-binding recommendation for whether an application should be approved.

AUS decisions, which are inherently race-blind, factor in an extensive set of credit risk factors

that are not observed in the HMDA data. We find racial gaps in AUS decisions, which suggests

that unobservable risk factors are correlated with race. However, even after we condition on AUS

decisions in our denial regressions, which should largely account for unobserved underwriting

factors, significant residual racial disparities remain.

Third, we examine the correlation between lender “strictness” on White applicants and lenderlevel residual racial denial disparities. As in Bhutta, Hizmo, and Ringo (2021), “strict” lenders are

those with a high propensity to decline White applicants after conditioning on credit risk factors

observed in HMDA. Unobserved credit risk factors likely play a large role in denial decisions for

strict lenders. If residual racial disparities are also large for strict lenders, then this would point

to unobserved underwriting factors driving racial denial rate gaps. We find no evidence to support

this hypothesis.

Fourth, we exploit geographic variation in racial animus using the Racial Animus Index constructed by Stephens-Davidowitz (2014). We show that there is a positive correlation between the

level of racial animus and the residual Black-White denial rate gaps across metropolitan statistical areas. This finding suggests that discrimination may be partially driving conditional racial

disparities in MEW rejection rates.

These racial disparities in MEW denial rates have large aggregate credit implications. In a

simple back-of-the-envelope calculation, we find that between 2018 and 2021, Black homeowners

applied for more than $46 billion in credit to extract their home equity, but were denied $23 billion.

We show that if Black applicants experienced the unconditional denial rate of White applicants,

they would have only been denied $11.8 billion. Conditioning on borrower and mortgage characteristics yields “excess” denials that are significantly smaller, but still non-trivial (∼ $2 billion for

Black borrowers).

While the bulk of our analysis focuses on denial rate disparities, we also explore the rela7
tionship between MEW product pricing and borrower race. We use the interest rate spread on

originated loans as our measure of price.11 Large unconditional minority-White pricing gaps exist

across all MEW products, but similar to our results on application denials, the gaps are significantly reduced, and in some cases, eliminated, once we include a host of control variables. In our

conditional specifications, Asian homeowners pay less than comparable Whites across all MEW

products. In contrast, Black borrowers face higher spreads than comparable White borrowers on

HELOCs. To our knowledge, ours is the first paper to examine the relationship between race and

mortgage pricing on MEW products.

The balance of the paper is organized as follows. We conduct a brief review of the relevant

literature in Section 2. Section 3 describes the HMDA data that are used in the analysis and how we

construct our sample. In Section 4, we present our main results on the extent of racial disparities in

MEW product denial rates and the most important underwriting factors that drive those disparities.

Section 5 discusses potential underlying causal mechanisms including discrimination and omitted

underwriting factors and conducts a simple exercise to determine the quantitative importance of

racial disparities in MEW rejection rates. Finally, Section 6 provides concluding remarks.

2 Literature Review

Our findings contribute to the broad literature on racial disparitiesin homeownership experiences.12

A number of studies examine the relationship between race and transition into homeownership

(Boehm and Schlottmann, 2004; Dawkins, 2005; Hall and Crowder, 2011), while others focus on

the forces that drive large unconditional minority-White gaps in homeownership rates, such as

income, wealth, age, family structure, and location (Coulson and Dalton, 2010; Deng, Ross, and

11The interest rate spread is defined asthe difference between a loan’s APR and the average prime offer rate (APOR).

APR incorporatesfees, points, and the contract interest rate. Thus, the interest rate spread captures multiple dimensions

of mortgage pricing.

12Our discussion in this section of the voluminous literature will necessarily be brief. We apologize to authors of

relevant papers that are not listed here.

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Wachter, 2003; Gabriel and Painter, 2003; Gabriel and Rosenthal, 2005; Gyourko, Linneman, and

Wachter, 1999; Hilber and Liu, 2008). Another stream of research investigates racial differences

in home equity and financial returns to homeownership (Flippen, 2004; Kahn, 2021; Kermani

and Wong, 2021; Krivo and Kaufman, 2004). We study a related, but distinct question: After

transitioning into homeownership, are there racial differences in the ability to access (and the cost

of accessing) housing wealth via MEW products?

Our research is also closely related to the literature examining racial disparities in access to

mortgage credit and mortgage pricing.13 For example, Black, Schweitzer, and Mandell (1978)

and Munnell et al. (1996) find that minority applicants are more likely to be denied a first-lien

mortgage, even after conditioning on a large set of controls. More recently, Park (2021) finds

racial disparities in denial rates in HMDA data even after conditioning on a model-based estimate

of expected loss.14 Frame et al. (2022) provide evidence that minority borrowers are less likely to

have their mortgage applications denied when working with minority loan officers. Bhutta, Hizmo,

and Ringo (2021) use the confidential HMDA data to show that conditioning on a “race-blind”

automated underwriting decision, which accounts for underwriting factors that are unobservable

in the HMDA data, reduces estimated disparities in mortgage denial rates.15 This field is not

populated for the HELOCs and HELoans we study.16

With respect to mortgage pricing, Bartlett et al. (2022) find that minorities pay slightly higher

interest rates (conditional on underwriting variables), on average, than comparable White borrowers in a sample of GSE-securitized and FHA-insured mortgages, while Kau, Keenan, and Munneke

(2012) find that borrowers in minority neighborhoods pay higher interest rates after controlling for

13See Ross and Yinger (2002) for a review of the early (pre-2000) literature and methodologies.

14Examining mortgage performance can provide insight into discrimination (see, for example, Becker (1993)).

Intuitively, if minorities are more likely to be rejected, but have better subsequent loan performance, this points to

discrimination. See Ross and Yinger (1999) and Park (2021) for a discussion of some of the limitations to this

approach. Note that our data do not include post-origination mortgage performance information.

15Similarly, even after controlling for information not contained in HMDA data (e.g., assets, self-employment) and

FHA’s underwriting algorithm, Park (2022) still finds racial disparities in the likelihood of FHA loan endorsement.

16This is likely because standard AUS systems used for originating a large share of purchase and refinance loans

(e.g., Desktop Originator, Loan Prospector) are not designed for underwriting HELOCs and HELoans.

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differences in the likelihood of default. But, using FHA-insured purchase loans, Bhutta and Hizmo

(2021) argue that higher interest rates paid by minorities are offset by lower fees (points) to obtain

the loan.17 In the subprime mortgage market, Ghent, Hernandez-Murillo, and Owyang (2014) find

that Black and Hispanic borrowers pay higher rates than comparable White borrowers on their

mortgages. Using a sample of loans from a large subprime lender, Ambrose, Conklin, and Lopez

(2021) show that minority-White fee gaps depend on the race of the mortgage broker. In contrast

with these previous studies that focus on first-lien mortgage products, we examine racial disparities

in denial rates and mortgage pricing for MEW products (which often are not first liens), including

two product types that are excluded from earlier studies: HELOCs and HELoans.

Finally, we contribute to the mortgage equity withdrawal literature. Canner, Durkin, and Luckett (1998) provide a detailed description of the institutional features of the HELOC and HELoan

market as of the late-1990s, as well as borrower characteristics related to their use. In a theoretical

contribution, Hurst and Stafford (2004) study home equity withdrawal as a mechanism to smooth

consumption in the face of negative income shocks. Agarwal, Ambrose, and Liu (2006), Benito

(2009), and Hurst and Stafford (2004) present empirical evidence consistent with home equity use

as a financial buffer for consumption smoothing purposes. Several papers examine the correlates

of the likelihood of withdrawing equity and the method (e.g., HELOC and HELoan) used to do

so (Benito, 2009; Canner, Durkin, and Luckett, 1998; Chen and Jensen, 1985; Duca and Kumar,

2014).18 Whereas most of the empirical studies in this literature rely on survey data, Agarwal et al.

(2011) use information on HELOC and HELoan applications to study dynamic contracting.

To our knowledge, there are only two studies that focus on the relationship between MEW and

race. Do (2012) uses American Community Survey (ACS) data to show that Black homeowners are

17Willen and Zhang (2021) reconcile the contradictory findings of Bartlett et al. (2022) and Bhutta and Hizmo

(2021), and they offer an econometric solution to what they call the “menu problem,” which refers to the trade-off

between the amount of upfront fees/points and the level of the interest rate that most borrowers face.

18For reasons already discussed, we exclude reverse mortgages from our analysis. For studies on the use and

performance of HECMs see Case and Schnare (1994), Davidoff (2014), Davidoff, Gerhard, and Post (2017), Haurin

et al. (2016), Moulton, Haurin, and Shi (2015), and Moulton, Loibl, and Haurin (2017).

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less likely than White homeowners to extract equity using MEW products. However, it isn’t clear

whether the difference in equity extraction is due to differences in underlying demand for MEW

products or due to differences in the rate at which lenders accept or reject MEW applications. By

focusing on MEW applications, we study racial disparities in MEW denials and pricing among

individuals who actually wanted a MEW product.19 In other words, we focus more on the supplyside of MEW products.

The second study examining MEW and race, Carlin and Divringi (2018), focuses on mortgage

equity withdrawals for a very specific purpose – home improvements. The authors use HMDA application data from 2015 through 2017 located in the Third Federal Reserve District, which covers

Delaware, southern New Jersey, and eastern and central Pennsylvania. Among applications where

the stated purpose of the loan is for home improvements, minority homeowners are approximately

twice as likely to be denied credit after controlling for a number of factors. Whereas Carlin’s

analysis focuses on Pennsylvania, Delaware, and Southern New Jersey, our sample includes applications from all 50 states. We also cover a broader range of loan purposes, as only about 20%

of our sample reported home improvement as the reason for attempting to extract housing equity.

Finally, it is important to note that Carlin and Divringi (2018) are unable to control for the credit

risk factors used in our study because those fields were unavailable in the HMDA data before 2018.

As we show below, these underwriting factors have a large impact on the size of estimated racial

disparities in MEW denials and pricing.

19This assumes that applying for a MEW product indicates demand for a MEW product, which seems like a reasonable assumption.

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3 Data Description and Sample Construction

3.1 Confidential HMDA Data

We use Home Mortgage Disclosure Act (HMDA) loan/application register data, which have been

used extensively in previous studies. HMDA data are the most comprehensive publicly available

source of mortgage lending application activity in the U.S. (Bhutta, Laufer, and Ringo, 2017), with

over 90% coverage of the U.S. mortgage market (Consumer Financial Protection Bureau, 2019).20

For each application contained in the public version of HMDA data, the lender reports race

and ethnicity, gender, and age of applicant and co-applicant (if applicable); combined income for

the applicant and co-applicants; property location (census tract) and property type (single-family

site-built and manufactured homes, as well as multi-family); occupancy type (primary residence,

second home, investment property); and loan features (interest only, prepayment penalty, other

non-amortizing features), as well as the amount of the loan. The data also record loan purpose

(purchase, home improvement, rate/term refinance, cash-out refinance, other purpose) and lien

priority (first or second lien). The lending institution that made the credit decision reports the

record and is identified in the data.

An important feature of HMDA data is that firms report the outcome of each loan application

(loan originated, application denied, application approved but not accepted, application withdrawn

by applicant, or file closed for incompleteness), known as the “action” on the application. Thus,

one can examine the correlates of loan outcomes, with application denials (rejections) being the

primary outcome of interest. However, historically HMDA data do not include key underwriting

variables that lenders use to make credit approval and pricing decisions. But, starting in 2018, the

20Coverage is not 100% because there are some reporting exemptions. Financial institutions that are exclusively

rural or that originated fewer than 100 closed-end mortgages in either of the last two years are not required to report

closed-end mortgage applications. See Federal Financial Institutions Examination Council (2019), Bhutta, Laufer, and

Ringo (2017), and Frame et al. (2022) for a more detailed discussion on HMDA reporting requirements. Open-end line

of credit (HELOC) reporting exemptions are discussed in further detail below. Note that both the publicly available

data and the confidential data available to financial regulators are anonymized.

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public HMDA data fields were expanded to include several new variables related to underwriting

risk, including but not limited to, the CLTV and DTI buckets. A subset of the data also includes the

contract interest rate and the interest rate spread, which captures the difference between the annual

percentage rate on the loan and a benchmark rate at the time the rate is set. Lenders were also

required to report borrower credit scores beginning in 2018, but this information is not available in

the public HMDA data.

Traditionally, financial institutions did not report open-end lines of credit (HELOCs) in their

HMDA data. However, beginning in 2018, HELOC reporting became mandatory, although there

is an exemption for financial institutions that originated fewer than 500 HELOCs in either of the

two previous years (Federal Financial Institutions Examination Council, 2019). Effectively, this

means that HELOC coverage from small financial institutions is limited.

In this study, we use the confidential version of HMDA data available to regulatory agencies.

There are two key features of the confidential version that distinguishes it from the public version.

First, whereas the public version includes only the calendar year in which the loan action wastaken,

the confidential version includes the exact action and application dates. Second, the confidential

version includes the applicant’s credit score, which is one of the most important variables used

in mortgage underwriting. The availability of credit score, CLTV, and DTI has sparked renewed

interest in using HMDA data to analyze the relationship between race and mortgage application

outcomes (Bhutta, Hizmo, and Ringo, 2021; Frame et al., 2022; Jiang, Lee, and Liu, 2021).

Not all inquiries consumers make to lenders become HMDA-reportable applications, so one

important limitation of any study using HMDA data is that interested applicants who become

discouraged from applying may be missing from the data. This can be problematic for studies

of racial differences in lending patterns if minority applicants are more likely to be discouraged

and thus excluded from the analysis. Although there is no way to directly count these missing

applications, we test for differences between racial groups in the “missing mass” of applications

along the dimension of credit scores. We describe this exercise (applied comparing Black and

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White applicants) and the results in Section A.5 of the Online Appendix. We find no evidence

of systematic differences in missing applications from Black as compared to White applicants,

which suggests that discouragement based on credit scores is similar between minority and White

borrowers. However, this should not be interpreted as proof that minority applicants experience the

same amount of overall discouragement as White borrowers. Therefore, as with all studies that use

HMDA data, our results should be interpreted as the outcomes consumers experience conditional

on applying.

3.2 Sample Construction

We use the confidential version of HMDA data from 2018 to 2021. Our primary interest lies

in products used for extracting mortgage equity, so our main sample excludes applications for

rate/term refinances and applications for home purchase mortgages.21 Thus, our main MEW sample includes cash-out refinances, HELoans, and HELOCs. Since we focus on credit approval and

pricing outcomes, we omit observations where the financial institution purchased the loan from

another lender or the application was a preapproval request. We also exclude reverse mortgages

and applications with loan amounts less than $5,000 or greater than $1,000,000. We further restrict

our sample to non-Hispanic White (hereafter “White”), Black, Hispanic White (“Hispanic”), and

Asian applicants.22 Our final MEW sample includes over 16 million observations across the 50

states and Washington, DC.

21At times we use rate/term refinances and purchase mortgage applications for comparison purposes. However, our

main analysis excludes these loan types.

22For each applicant and co-applicant, we apply a waterfall to code the loans into mutually exclusive and exhaustive

categories. Loans are classified as having an applicant who is Black if either the applicant or co-applicant is listed as

Black in the first or second reported race field for that applicant. If not, then the observation moves on to the next

phase of the waterfall, which identifies loans by Asian applicants. The third step of the waterfall looks for Hispanic

White applicants. In the fourth step, remaining observations (for White applicants) are coded as non-Hispanic White

if neither the applicant nor the co-applicant is coded as having a first or second race as Black or Asian and neither is

coded as having ethnicity of Hispanic. All other observations (such as those with no race reported) are excluded from

our analysis.

14
4 Results

We first look at mortgage denial rates by race and product type. We begin by exploring unconditional denial rate gaps and then explore how much of the gaps remain after taking into account

typical underwriting criteria used in making these loans. Throughout the paper, unless otherwise

noted, we focus on applications where a credit decision was made, which includes originated loans,

applications approved but for which the borrower did not accept the lender’s offer, and applications

denied by the lender. We exclude applications where the file is closed for incompleteness or the

application is withdrawn prior to a credit decision.23

4.1 Unconditional Denial Rates

Figure 1 reports unconditional denial rates (among those with a credit decision) by race for each

MEW product. For the purposes of comparison, we also report denial rates for non-MEW mortgages in the bottom right panel of the figure. A number of interesting facts are documented in

Figure 1. First, relative to non-MEW products (purchases and rate-term refinances), denial rates

for MEW products are significantly higher for all races. For example, White applicants are denied

credit on only 8% of non-MEW applications; however, this number doubles to 16% on cash-out

refinances. White denial rates climb even further to 29% on HELoans and 32% on HELOCs. This

pattern holds within each of the other races as well; denial rates are highest on HELOCs, followed

by HELoans, cash-out refinances, and non-MEW products, respectively.

Second, minority-White denial rate gaps are larger for MEW products relative to non-MEW

products. As an example, Black applicants are 9 percentage points (that is, 17% minus 8%) more

likely than White applicants to be denied non-MEW products. On cash-out refinances, the BlackWhite gap increases dramatically to 16 percentage points. The gap is even larger for HELoans

23Appendix Table A.1 reports application outcome shares by race across all of these categories. The sample of

applications where a credit decision is made includes almost 13 million observations.

15
(26 percentage points) and HELOCs (30 percentage points). This is notable as HELoans and

HELOCs have relatively low transaction costs, and thus are generally regarded as low-cost methods

of extracting home equity (Greenspan and Kennedy, 2008). The large racial disparities in denial

rates in cash-out refinances, HELoans, and HELOCs highlights the importance of analyzing MEW

products as we do in this study.

Third, although they are smaller than the Black-White gaps, the Hispanic- and Asian-White

denial gaps are also large for HELoans and HELOCs. Hispanic applicants are 11 percentage points

more likely to be denied than White applicants for HELoans and 22 percentage points more likely

for HELOCs. The corresponding Asian-White gaps for HELoans and HELOCs are 8 and 14

percentage points, respectively.

Overall, these comparisons suggest very different levels of access to MEW products for different racial groups and that minority homeowners, in particular, may have an especially difficult time tapping into accumulated housing wealth. We can borrow a framework from the 1978

federal Uniform Guidelines for Employee Selection Procedures to assess the magnitude of these

differences—the “four-fifths rule,” used for measuring disparities in hiring rates between groups

with the highest acceptance rates (usually White men) and a comparison group (usually a protected

class). In this framework, one divides the acceptance rate for a minority group’s job applications

by the White male acceptance rate to generate an “adverse impact ratio” (AIR). If the ratio is less

than 0.8, then there is evidence that the hiring practices have an adverse impact on the minority

group (Newman and Lyon, 2009). Using this as a heuristic for acceptance rates in mortgage applications, non-MEW products meet the four-fifths rule (with ratios ranging from 0.83 to 0.91),

whereas for HELOCs Black, Hispanic, and Asian applicants all have acceptance rates less than

80% of the White acceptance rate, indicating adverse impact.24

Although the unconditional denial rates exhibit large differences across race in Figure 1, sig24Among HELoan applications, the AIR is 0.63 for Black applicants but 0.85 and 0.89 for Hispanic and Asian

applicants, respectively. For cash-out refinances, the AIR is just above the borderline, at 0.81 for Black applicants.

16
nificant differences in observable borrower and loan characteristics exist across racial groups as

well. Table 1 reports average borrower and loan characteristics for MEW applications separately

by race. Loan amounts and credit scores, in particular, vary considerably across racial groups. In

the next section, we examine conditional correlations between race and MEW application denial

rates.

4.2 Relationship Between Race and Application Denial Rates for MEW

Applications, Conditional on Underwriting Factors

In the previous section, we documented large, unconditional differences in MEW product denial

rates across races. But, as Table 1 shows, there are borrower and loan characteristics that likely

covary with both race and the likelihood of an application being denied. To examine this possibility, we next estimate a series of loan application-level linear probability models (LPMs) of the

following form:25

Yi = β1Blacki + β2Hispanici + β3Asiani + Xiγ + ηt + λl + ωs + εi, (4.1)

where Yi is an indicator for whether borrower i’s mortgage application is denied. Blacki, Hispanici,

and Asiani are indicator variables set to one if the applicant is Black, Hispanic, or Asian, respectively. White is the excluded category in the econometric model, so the β coefficients should be

interpreted as relative to a White applicant. Xi is a vector of covariates that varies across models. ηt

and λl are application year and lender fixed effects, respectively. State fixed effects are represented

by ωs. Finally, εi is an error term.

25We also consider logit models and find that the average marginal effects of race are similar to the LPM coefficient

estimates. The logit results are reported in the Online Appendix.

17
4.2.1 Decomposition of Denial Rate Differences

Our empirical approach is to sequentially expand the covariates included in our regression models

to determine whether the inclusion of additional controls reduces (in absolute magnitude) the β

coefficients in equation (4.1). In other words, are the racial gaps smaller once we control for

factors used by lenders in mortgage underwriting (such as credit score)?

Table 2 reports coefficient estimates from equation (4.1) using the sample of MEW applications where a credit decision was made. We pool all MEW products together in this table, but

we estimate our models separately for each loan product type later in the analysis. Unless otherwise noted, standard errors are double-clustered at the lender and state levels. In the interest of

concision, we only report the race coefficients;26 however, the full set of coefficient estimates are

available in Table A.3 in the Online Appendix. As a baseline, column (1) only controls for applicant race so the coefficients can be interpreted as unconditional differences in denial rates relative

to White applicants. Black applicants are 21.2 percentage points more likely than White applicants

to be denied credit, while Hispanic applicants and Asian applicants are 9.5 and 7.6 percentage

points more likely to be denied, respectively.27 These racial gaps are large relative to the mean

denial rate of 26%, reported at the bottom of the table.

In column (2), we include application year fixed effects to account for temporal changes in

economic conditions and denial rates at the national level. Because mortgage regulations vary

across states, particularly with respect to foreclosure, we also include state fixed effects. Adding

these controls has a marginal impact on the racial gaps. Likewise, estimating the models separately

by year shows that the racial gaps persist throughout time in our sample, as discussed in the Section

A.2.7 of the Online Appendix.

26When an underwriting characteristic is not used to make a credit decision on a specific application, it is reported

as “not applicable” in HMDA data. This applies to a small share of our sample (see Online Appendix Table A.2). We

include “not applicable” indicators in our regression models.

27Notice that these unconditional differences are slightly larger than what can be inferred from Table 1 because

denial rates are rounded to the nearest percentage in that table.

18
In column (3), we add controls for DTI and credit score, two key factors used by lenders in

mortgage underwriting. More specifically, we flexibly control for these variables by creating DTI

and credit score bin dummies.28 Adding DTI and credit score bins reduces the racial gap for Black

and Hispanic applicants to 6.8 and 3.4 percentage points, respectively, reductions on the order of

63%-65%. The Asian gap declines somewhat less to 7.3 percentage points, but this still represents

a 16% reduction.

In column (4), we add dummies for the following CLTV bins: (0,60], (60,70], (70,75], (75,80],

(80,85] (85,90], (90,95], (95,98], (98,100], and CLTV missing. Including these dummies has

a minimal effect on the estimated racial gaps.29 Thus, underwriting factors associated with the

borrower’s ability and willingness to pay (DTI, credit score) explain more of the racial gaps than

the collateral-based factor of CLTV.

In column (5), we add a host of other application-level controls: loan amount bins, applicant income buckets, loan term bins, and product type dummies (HELoan, HELOC). We also

include dummies indicating whether the proceeds are used for home improvements, the existence

of a prepayment penalty, second-lien loan, FHA and VA loans, interest-only payments, other nonamortizing features, a second home purchase, an investment property purchase, the absence of

a co-applicant, and number of units (1-4) dummies. The Black, Hispanic, and Asian gaps are

reduced to 5.6, 2.7, and 5.8 percentage points, respectively.

Two recent studies document that minorities tend to sort into high-cost lenders and brokers

(Ambrose, Conklin, and Lopez, 2021; Bayer, Ferreira, and Ross, 2018). A similar type of sorting

could occur with respect to denials. For example, if minorities tend to apply to lenders with

conservative underwriting guidelines, this could explain the observed racial gaps in denials. After

adding lender fixed effects in column (6) to account for this possibility, we indeed see sizable

28The DTI bins are (0,25], (25,35], (35,45], (45,101], and DTI missing because it was reported as “not applicable.”

Credit score is binned as [300,620], [620,639), [640,660), [660,680), [680,700), [700,720), [720,740), [740,851), and

credit score not applicable.

29This finding holds even using an alternative ordering of the model controls, in which CLTV is added before credit

score and DTI. Results available upon request.

19
reductions in the Black-White and Asian-White denial gaps. Throughout the remainder of the

paper, we refer to the specification in column (6) as the saturated model.30

Overall, Table 2 shows that the large, unconditional minority-White denial rate gaps are reduced considerably after including standard control variables. Yet, even after including these

controls, the residual disparities are non-trivial (3–4 percentage points). We caution against interpreting the relationship between race and application denial as causal. There are underwriting

factors that likely covary with both race and denial that are not included in HMDA data, such as

an applicant’s liquid assets and employment type (salaried or self-employed). One might also be

tempted to interpret the reduction of racial disparities as we add controls to be evidence of only

minor racial differences in access to MEW products. We are careful not to make this claim, as differences in control variables that affect access to MEW products may themselves be the result of

systemic racial inequities. With these limitations in mind, the fact that controlling for basic underwriting variables reduces most of the minority-White gaps in denial rates suggests that taste-based

discrimination is unlikely to explain most of those unconditional disparities.31

4.2.2 Product Type

In Table 3, we estimate our denial rate models separately for each MEW product type. The sample

in Panels A, B, and C include cash-out refinances, HELoans, and HELOCs, respectively. Most of

the MEW applications are for cash-out refinances (7 million), with the next largest group being

HELOCs (4 million applications). Column (1) includes only applicant race controls, while column

30Table A.4 in the Online Appendix presents marginal effects estimates from logit models. The models do not converge when we include lender fixed effects, but the marginal effects from the logits are similar to the LPM coefficients

for the models we can estimate. Online Appendix Table A.5 explores the robustness of our results to alternative fixed

effects specifications. The race coefficient estimates are similar when we include lender by location (census tract)

fixed effects and location by time (county-year) fixed effects.

31We are also mindful that interactions between risk factors, such as credit score and DTI or credit score and CLTV,

can be important in underwriting decisions. However, including these interactions has almost no effect on the results

in column (6) of Table 2.

20
(2) reports estimates from the saturated specification (column (6) in Table 2).32 Column (1) shows

that unconditional minority-White denial rate disparities exist for all three product types, but the

gaps are much larger in our sample of HELoans and HELOCs. For example, Black applicants are

16.8 percentage points more likely than White applicants to be denied on a cash-out refinance, but

this Black-White gap increases to 25.9 percentage points for HELoans and 30.1 percentage points

for HELOCs. Similar patterns hold for the Hispanic-White and Asian-White gaps. Note, though,

that the mean denial rates for HELoans (33%) and HELOCs (37%) are also significantly higher

than the mean for cash-out refinances (18%).

For cash-out refinances in Panel A, moving to the saturated model in column (2) reduces the

Black coefficient from 16.8 to 4.0 percentage points, a reduction of 76%. The Hispanic coefficient

declines by approximately 64% in the saturated model, relative to its value in column (1). In

contrast, the Asian coefficient increases from 1.7 percentage points in column (1) to 2.6 percentage

points in column (2). Panel B focuses on HELoans and shows that there are dramatic reductions in

racial denial gaps once we include the full set of controls in column (2). The Black coefficient is

reduced by 80%, the Hispanic coefficient by 71%, and the Asian coefficient by 47%. We see even

larger coefficient reductions moving from column (1) to (2) for HELOCs, reported in Panel C.

Next we use an alternative methodology – Kitagawa-Blinder-Oaxaca decompositions – to

study differences in rejection rates (Kitagawa (1955), Blinder (1973), and Oaxaca (1973)). This

approach decomposes minority-White denial rate gaps into a part that is explained by group differences in characteristics, and a residual, or unexplained, component.33 The unexplained component

is often taken to be an estimate of the extent of discrimination, but we caution against such an

interpretation in this context due to omitted factors like liquid wealth and employment history that

lenders may take into account.

32Columns (1) and (2) in Table 3 are comparable to columns (1) and (6) in Table 2. Appendix Table A.6 shows the

full build-out table by product type where we include additional controls sequentially.

33See Jones and Kelley (1984), Jann (2008), and Blau and Kahn (2017) for more detailed discussions of the methodology. Blascak and Tranfaglia (2021) use this approach to study gender differences in bankcard credit limits. We use

the “twofold” decomposition, but results are similar using the “threefold” approach.

21
The following equations illustrate the decomposition. We want to determine how much of the

mean difference in rejection rates between two groups, A and B, can be attributed to explained and

unexplained components. We begin with separate application-level OLS (LPM) denial models for

each group:

YA = XA

′ βA + εA, (4.2)

and

YB = XB

′ βB + εB, (4.3)

where Y is an indicator for whether an individual’s application is denied (we suppress i subscripts

to simplify notation). X is a vector of of explanatory variables that affect denial.

Let βˆ

A and βˆB be the OLS estimates of βA and βB, respectively. Mean values are denoted with

a bar over the variable. Assuming that there is no discrimination against group A (only against

group B), we can decompose the group difference in denial rates as follows:

Y A − Y B = (XA − XB)

′

βˆ

A + XB

′

(βˆ

A − βˆB). (4.4)

The first term on the right-hand side is the explained component; the amount of the mean difference

in rejection rates that is due to the groups having different characteristics (Xs), on average. The

second term, the unexplained component, captures that the two groups are treated differently in

underwriting, as indicated by the difference in βsˆ .

In our context, we will focus on denial rate gaps between two groups: Black and White applicants. The decomposition approach has a few useful properties in our setting. First, it tells us

what share of the rejection gaps are explained by differences in observed characteristics (e.g., credit

score, DTI, LTV, location) between Black and White applicants. Second, the difference can further

22
be decomposed to measure the detailed contributions of single predictors (or groups of predictors)

on minority-White denial gaps. For example, it can tell us how much of the Black-White rejection

gap is due to differences in credit scores across groups, and how much is due to DTI differences.34

In this analysis, we exclude Hispanic and Asian applicants and focus solely on the BlackWhite denial rate differences. We perform the decompositions separately by product type and

display the results in Figure 2. The included controls are the same as in column (5) of Table 2, i.e.,

the main model but excluding lender fixed effects.35 The total height of the bars in each product

type is the mean difference in denial rates between Black and White applicants. The shading

indicates how much of the overall denial rate difference is attributed to different factors. Across

each of the three product types, approximately 5 percentage points of the Black-White denial gap

remains unexplained.36 However, large portions of the overall gap are explained by differences in

characteristics between Black and White applicants. For example, the largest contributor to denial

gaps across all three loan products is differences in credit scores between the two groups. DTI and

other control differences also explain a sizable portion of the denial gaps in both HELoans and

HELOCs. In contrast, there appears to be little difference in LTV ratios between Black and White

applicants in all products.

In summary, Figure 2 shows that a large portion of Black-White denial gaps is explained

by differences in observable characteristics across the two groups of applicants, and that credit

scores are the most important underwriting factor. However, a sizable portion of the denial gaps

remain unexplained, even after determinants of credit decisions are taken into account. Finally,

while the unexplained gaps in the Kitagawa-Blinder-Oaxaca decompositions are consistent with

discrimination, it is important to note that they may also be due to omitted factors in the underlying

34This is similar in spirit to our approach in Section 4.2.1, where we sequentially expanded the covariates in our

regression model and observed changes in the race coefficients after adding controls, although the order in which

controls are added can impact the magnitude of the subsequent changes in coefficient estimates. In contrast, the

Kitagawa-Blinder-Oaxaca decompositions do not have this “ordering” issue.

35This is except the race coefficients, which are excluded, since we estimate models separately for Black and White

applicants.

36This unexplained portion is similar to the Black coefficient in column (5) of Table 2.

23
group-specific denial regressions used to create the decompositions.

4.2.3 Lender Type

The different MEW products we examine tend to be handled by different types of financial institutions. For example, HELOCs generally are offered by banks and credit unions, while a large

share of cash-out refinances is originated by nonbank lenders. The underwriting criteria may vary

across different types of financial institutions, and thus, disparities in access to MEW may also

vary across lender types. Thus, in this section, we estimate our models separately for each of

the product-by-institution type groupings. The first three columns of Table 4 include cash-out refinance applications from banks, nonbanks, and credit unions. Figure 3 plots model coefficients

from this table. Nonbanks handle most of the cash-out refinances (61%), followed by banks (31%),

with the remaining market share belonging to credit unions. The coefficient estimates in the first

three columns show that minority applicants are more likely than White applicants to be denied a

cash-out refinance, regardless of the type of financial institution. Interestingly, for all three minority groups, racial denial gaps are smallest at nonbanks.

Turning to the observation counts in columns (4)-(6), we see that credit unions have a much

larger market share in HELoans compared to cash-out refinances (35% versus 9%). Similar to

the cash-out refinance patterns, all of the minority coefficient estimates for HELoans are positive

across the different types of financial institutions. The Black-White denial gap is largest at credit

unions.

Columns (7)-(9) focus on HELOCs. Banks handle most HELOC applications (77%), followed

by credit unions (22%). Nonbanks receive a trivial share of the HELOC applications (1%). Similar

to cash-out refinances and HELoans, the minority coefficients for HELOCs are all positive across

lender types. However, the Hispanic gap is small and indistinguishable from zero for nonbanks.

Again, we see that the residual Black-White denial rate gap is largest at credit unions.

To summarize, there are two key findings in Table 4 and Figure 3. First, across all product

24
types and lender types, minorities are more likely to be denied credit relative to comparable White

applicants. Second, the residual Black-White denial rate gap is largest at credit unions, especially

for HELOCs and HELoans. This is particularly interesting because the inclusiveness of lending by

credit unions, especially with respect to low-to-moderate income borrowers, has been questioned

in previous empirical studies.37

Table 5 tests whether minority-White applicant denial rate gaps vary between majority- and

minority-owned banks. We merge the HMDA data with the “Avery HMDA Lender File,” that

distinguishes banks’ ownership based on National Information Center data.38 Because minority

ownership does not vary within lender, we exclude lender fixed effects from our models in Table

5. Minority-owned banks tend to be small lenders, so we control for bank size in all regressions

in Table 5.39 We exclude HELOCs from our analysis here because very few minority lenders offer

them. Specifically, there are only 2-3 minority-owned banks that receive HELOC applications in

any year in our sample. In contrast, on average, there are 16 minority-owned banks with cash-out

applications each year and 14 with HELoan applications.

Column (1) of Table 5 provides a baseline regression for the sample of cash-out refinancing and HELoan applications by majority- and minority-owned banks, with coefficient estimates

similar to our full MEW sample in Table 2 (with no lender fixed effects). In column (2), we

include a minority-owned bank dummy and its interaction with applicant race. Minority banks

are 6.9 percentage points less likely to deny a White applicant, on average, compared to nonminority owned banks. Hispanic and Asian applicants are 3.9 percentage points (0.036 + -0.075

37See Marshall and Pellerin (2017) and citations therein.

38The Avery file is provided by Bob Avery of the Federal Housing Finance Agency for 2018–2021 and is available

at https://sites.google.com/site/neilbhutta/data.

39Banks are classified into one of three groups: small, intermediate small, and large using Community Reinvestment

Act (CRA) asset size thresholds available at https://www.ffiec.gov/cra/pdf/2021_Asset_Size_Threshold.pdf. Asset

size for HMDA-reporting banks is available in the Avery file and is sourced from the Call Report. If asset information

is missing, but the bank is affiliated with another banking institution, we use the affiliate’s asset information to classify

bank size. Visual inspection of bank names and (low) mortgage origination volumes for independent banks that have

missing asset information suggests that these are small banks, and we classify them as such. Appendix Table A.8

shows denial regressions separated out by bank size categories.

25
= -0.039) and 6.7 percentage points (0.061 + -0.128 = -0.067), respectively, less likely to be denied than comparable White applicants at minority-owned banks. In column (3), we separate out

the minority-owned banks by the race of minority ownership. We group others, including Black-,

Native American-, and multiracial minority-owned banks, into one category (Other Minority Bank)

because the number of institutions in each of these groups is small. Hispanic-White applicant denial gaps are reduced at all minority-owned bank types (Hispanic, Asian, and other minority-owned

banks). In contrast, the Asian-White gap is only statistically significantly reduced at Asian-owned

banks. No clear pattern emerges for Black applicants at minority-owned banks. Although there is

some evidence consistent with minority applicants receiving more favorable treatment at minorityowned banks, we caution against interpreting the majority- versus minority-owned bank results

too strongly because applications to minority-owned banks represent a small share (0.65%) of the

sample in Table 5, and a borrower’s choice of which type of lender to apply to may be endogenous.

4.3 Denial Reasons

For denied mortgage applications, lenders report the reason(s) for denial: credit history, collateral,

incomplete application, employment history, insufficient cash for down payment/closing, unverifiable info, and insurance denied. The last four categories constitute a small share of the denials,

so we group these together in a category called “Other.” Lenders can report multiple reasons for

denial, however, the overwhelming majority (79%) of denied applications in our sample include

only one denial reason. Our analysis in this section is based on the first denial reason listed, but

results are similar when we allow for multiple denial reasons (see Online Appendix Figure A.1).

Figure 4 shows the rate of each denial reason (conditional on denial) for each race category

split out by MEW product type. Notice that within any race-product type combination, the shares

sum to 100%. The top panel includes application denials for cash-out refinances. For White

applicants, the share is fairly similar across all denial reasons. However, for Black applicants

26
credit history stands out as the most likely reason for denial. Hispanic and Asian applicants, on the

other hand, tend to be denied due to high DTI ratios.

Credit history appears to be a larger driver of denials in HELoans. Relative to cash-out refinances, the share of HELoan denials due to credit history is higher for all racial groups. The

increase is particularly high for Black and Hispanic homeowners. For example, whereas 23%

of Hispanic cash-out refinance denials are due to credit history, this number climbs to 35% for

HELoans (the middle panel of Figure 4). Turning to HELOCs, the share of denials due to credit

history climbs even further for White, Black, and Asian applicants. As with HELoans, the share is

particularly high for Black and Hispanic applicants, at 57% and 46%, respectively.

A key takeaway from Figure 4 is that credit history is a major reason for MEW denial, particularly for Black homeowners on HELoans and HELOC applications. Surprisingly, collateral (e.g.,

lack of equity) plays a fairly limited role in denials for all races in HELoans and HELOCs. Thus, it

appears that denied applicants tend to have adequate equity in their homes, but their credit history

precludes them from accessing this equity.

4.4 Relationship Between Race and Price on MEW Products

In this section, we examine whether mortgage pricing for MEW products varies by race. In a

recent study, Bartlett et al. (2022) show that Black and Hispanic borrowers pay higher interest

rates on first-lien GSE-securitized mortgages and FHA-insured loans. In related work, Bhutta and

Hizmo (2021) recognize that in addition to interest rates, fees and points are important dimensions

of mortgage pricing. In a sample of FHA-insured first-lien mortgages, Bhutta and Hizmo (2021)

show that minorities pay higher interest rates, but this is offset by lower fees. In contrast with

these previous studies, we focus on MEW products, many of which are not first-lien mortgages.

In addition, whereas the studies mentioned previously focus on FHA-insured and GSE-securitized

loans, a large share of the MEW products in our study, in particular HELoans and HELOCs, are

27
not backed by the U.S. government. There is limited research on MEW product pricing, and to our

knowledge, this paper is the first to examine racial pricing disparities in this market.

Our measure of mortgage pricing is the interest rate spread, defined as the difference between

the loan’s APR and the average prime offer rate (APOR). Rate spread is meant to capture the

premium (or discount) that a borrower pays, relative to a benchmark rate on a prime mortgage

with similar terms (e.g., fixed-rate or adjustable-rate, lien status, and loan maturity). Note that the

APR is calculated based on the interest rate, points, and fees associated with a loan, and thus, it

accounts for the different dimensions of mortgage pricing discussed in Bhutta and Hizmo (2021).40

In earlier waves of HMDA data, lenders were only required to report whether the rate spread on a

loan was greater than 150 bps (1.5 percentage points). However, during the period covered in our

sample, the rate spread is reported for nearly all applications that result in originated loans.

We estimate pricing regressions using equation (4.1) where the dependent variable Yi now

becomes rate spread. Our sample includes only originated mortgages, since we do not have pricing

information for applications that are denied. Columns (1) and (2) of Table 6 report results for cashout refinances. For these loans, the average rate spread is 43 bps. Controlling just for origination

month and state, Black and Hispanic borrowers pay an additional 13.6 and 11.1 bps, respectively,

relative to White borrowers for cash-out refinances. Asian homeowners, on the other hand, pay

6.3 bps less than White homeowners. Once we account for underwriting factors and other controls

in column (2), pricing differences for Black and Hispanic borrowers disappear. The Asian pricing

discount also declines after including controls.

Columns (3) and (4) focus on HELoan pricing differences. Note that the mean rate spread

for HELoans (139 bps or 1.39 percentage points) is much higher than for cash-out refinances (43

bps or 0.43 percentage points). Conditioning on just origination month and state, Black borrowers

pay an additional 47.6 bps on average, while the corresponding figure for Hispanic borrowers

is 20.2 bps. Similar to column (1), Asian borrowers pay lower prices for HELoans in column

40Bhutta and Hizmo (2021) also discuss limitations of the APR measure.

28
(3). In the saturated regression model reported in column (4), there is no statistically significant

difference between what Black and Hispanic borrowers pay for HELoans relative to comparable

White borrowers, while Asian borrowers pay 7.3 bps less.

In columns (5) and (6), the mean rate spread for HELOCs (85 bps or 0.85 percentage points)

falls between cash-out refinances and HELoans. Consistent with columns (1) and (3), Black and

Hispanic borrowers pay higher prices on average for HELOCs, while Asian borrowers pay less.

Once we add controls, the gaps significantly narrow; Black borrowers pay 10.0 bps more for

HELOCs than comparable White borrowers, while Asian borrowers pay 5.6 bps less. Like with

cash-out refinances and HELOans, the HELOC pricing gap for Hispanic and White borrowers is

not statistically different from zero.41

Similar to our results on mortgage denials, we find that unconditional minority pricing gaps on

all MEW products are large. However, once we account for a number of control variables, these

gaps are significantly reduced. Black borrowers pay more than comparable White borrowers for

HELOCs (about 10 bps), while Asian borrowers face slightly lower mortgage prices than comparable White borrowers. Hispanic and White borrowers’ pricing look statistically indistinguishable,

conditional on controls.

5 Discussion

We have documented large, unconditional differences in average denial rates for mortgage equity

withdrawal products across racial groups. Similarly, we also find large unconditional differences

in MEW pricing across racial groups in a sample of originated loans. In both cases, the racial

disparities significantly decline when we control for differences in observable loan and borrower

41We caution that HELOCs often come with introductory pricing that is different from what the borrower will pay

later in the life of the loan. We are not able to distinguish introductory from subsequent pricing and assume the rate

spreads we observe correspond to what the borrower is charged at the beginning of the life of the loan. This could

affect the interpretation of racial differences in pricing over the life of the loan, if some racial groups are more likely

than others to take out HELOCs with low introductory rate periods.

29
characteristics as well as lender fixed effects. In this section, we begin by discussing a few ways

one might interpret these patterns. We implement four additional pieces of analysis to shed light

on the extent to which discrimination could explain our results. We then conduct a back-of-theenvelope calculation to try to determine how much racial differences in denial rates translate into

differences in the amount of housing wealth accessed by minority vs. White homeowners.

5.1 Interpretation and Mechanisms

One may be tempted to interpret the small, conditional denial rate disparities as evidence that

racial discrimination in the MEW product market is quantitatively unimportant. However, we believe that this would be a naïve interpretation, as it is unclear exactly what factors some of our

controls are picking up. For example, while controlling for credit scores, DTI ratios, and CLTV

ratios significantly lowers denial rate disparities between Black and White applicants by approximately two-thirds (columns (1) vs. (3) in Table 2), the differences in the levels of these controls

across racial groups may themselves be reflective of structural discrimination against minority applicants. For example, the large differences in average credit scores and DTI ratios between White

and minority individuals documented in Table 1 may be due to historical lending practices that

were discriminatory. Alternatively, discrimination in other markets (e.g., labor markets) may cause

racial disparities in factors used in underwriting. Although incorporating these factors into credit

decisions may be justified in terms of credit risk, by doing so, credit markets may “import” structural discrimination from other markets. Regardless of the source of minority-White disparities in

underwriting factors, credit markets have the potential to serve as a mechanism for perpetuating

disparities through differential access to MEW products.

Similarly, it may be tempting to interpret the remaining, small, but statistically significant conditional disparities in MEW product denial rates and prices as arising from racially discriminatory

practices on the part of lenders. However, while the existence of such disparities is consistent with

30
the presence of racial discrimination, it is also consistent with more benign explanations. One such

explanation is the absence of information in the HMDA data about variables that may play a significant role in the underwriting process for certain lenders. For example, the HMDA data do not

include any information on an applicant’s liquid assets. Many mortgage lenders require that a potential borrower have sufficient liquid assets (i.e., funds in a checking/savings account) to be able

to cover a certain number of mortgage payments in the event of an adverse financial shock. Additionally, the HMDA data do not include any information about an applicant’s employment history.

Many lenders require proof that a potential borrower has held a stable job for a certain amount

of time prior to approval. If minority applicants have lower amounts of liquid assets and higher

employment volatility compared to White applicants, then we would expect to see disparities in

denial rates that would reflect those differences.

Although we cannot definitively say whether unobserved underwriting factors or racial discrimination explain the residual denial rate gaps, we perform four types of additional analysis

that provide suggestive evidence on this front. First, we exploit lender-level heterogeneity in

our Kitagawa-Blinder-Oaxaca decomposition. Second, we examine the relationship between race,

AUS decisions, and lender denials. Third, we implement an exercise that correlates residual denial

rate gaps with a lender-specific measure of “strictness” on unobservable underwriting factors. All

three of these exercises speak to the unobservable underwriting factors hypothesis.42 Finally, we

examine whether racial disparities are more pronounced in geographic areas that are characterized

by high levels of racial animus, which would be consistent with discrimination.

5.1.1 Lender-Level Decompositions

We begin by separately estimating the Kitagawa-Blinder-Oaxaca decompositions of Black-White

denial rate gaps for each of the 50 largest lenders (in terms of volume) in each product type. The

results are displayed in Figure 5. In a given panel (product type), each vertical bar represents

42The AUS and lender strictness analsyis were first developed in Bhutta, Hizmo, and Ringo (2021).

31
a different lender.43 The height of the bar, marked with a black dot, is the total (unconditional)

Black-White denial rate gap for that lender, with the gray and red portions indicating the explained

and unexplained components, respectively. For each product type, lenders are ordered by the size

of the explained component. This means that Black and White applicants are observably similar at

a lender that falls on the left-hand side of the figure. On the other hand, Black and White applicants

are very different on observable dimensions at lenders that lie on the right side of the panels.

The first notable observation from Figure 5 is that all of the black dots have positive values,

meaning that Black applicants have higher unconditional rejection rates at every single lender in all

product types. Within each product type, however, significant heterogeneity exists across lenders

in the overall Black-White denial gap, the explained component, and the unexplained portion of

the gap. For example, there is almost no difference in denial rates for the leftmost lender in the

bottom panel, but other HELOC lenders have Black-White gaps of nearly 40 percentage points.

For several of those high-gap HELOC lenders, though, virtually all of the gap is due to differences

in observable characteristics between Black and White applicants. In a few cases, the unexplained

component is actually negative, suggesting that Black applicants may actually get more favorable

treatment at those lenders.

Although the heterogeneity across lenders and product types in Figure 5 is interesting in its

own right, the results may also speak to whether unobservable underwriting factors drive residual

racial disparities in rejection rates. For example, it may be reasonable to assume that if a specific

lender receives applications from Black and White applicants that are very different on observable

underwriting variables, those same borrowers likely also differ significantly along underwriting

factors that are observable to the lender, but not included in the HMDA data. For example, if a

lender’s Black and White applicants have very different credit scores, DTIs, and CLTVs, we might

expect the applicants to also have very different levels of liquid assets and employment histories.

Conversely, if a lender’s Black and White applicants are similar in terms of HMDA variables, they

43The same lender can appear in more than one of the product types.

32
may also be similar in terms of underwriting factors that are unobservable to the econometrician. If

this assumption is reasonable, then we would expect to see a strong correlation between the size of

explained (gray) and unexplained (red) components of the denial gap across lenders. However, this

is not borne out in Figure 5, which shows that the size of the unexplained denial disparities between

Black and White applicants is not highly correlated with the size of the explained disparities.44 This

provides suggestive evidence that residual Black-White denial rate gaps are not purely driven by

underwriting factors that are not observed in HMDA.

5.1.2 AUS Decisions and Lender Denials

As an additional test of whether unobserved credit risk factors drive minority-White denial gaps,

we follow the approach of Bhutta and Hizmo (2021) by focusing on a subset of mortgage applications that received an Automated Underwriting System (AUS) recommendation. Applicant

information for most of the cash-out refinances in our sample was entered into an AUS, which

scores the loans for credit risk using statistical models and ultimately provides a recommendation

for whether the application may be approved. Importantly, the AUS recommendation is based not

only on the credit risk factors observed in HMDA, but also on an extensive set of other creditrelevant information. For example, Fannie Mae’s Desktop Underwriter (DU), one of the most

commonly used AUSs, considers the risk factors observed in HMDA as well as characteristics

including, but not limited to, the borrower’s liquid reserves, public record information, length of

credit history, and income variability. Note that AUSs are explicitly forbidden from considering

race, other prohibited factors, and neighborhood location, and regulators review these systems to

ensure that they are race-blind (Bhutta, Hizmo, and Ringo, 2021).

If inherently race-blind AUS denial recommendations are conditionally correlated with applicant race, this would suggest that unobserved risk factors vary across racial groups. To test this, we

44The correlation coefficient for the explained and unexplained components is actually negative for HELoans (r =

-0.10, p = .492) and HELOCs (r = -0.38, p = .007). The correlation is positive but weak and marginally significant for

cash-out refinances (r = 0.26, p = .073).

33
estimate our fully saturated model (column 2, Panel A of Table 3) using the AUS denial recommendation indicator as the dependent variable. This indicator takes a value of one for applications that

did not receive an “accept/eligible” or equivalent AUS decision. Our sample here includes cashout refinance applications where an AUS decision is observed.45 Column (1) of Table 7 shows that

minority applicants are more likely to receive an AUS denial recommendation. Because AUSs are

race-blind, this implies that race is correlated with unobserved credit risk factors.

Note, though, that an AUS decision is only a recommendation. A lender can still deny an

application that receives an AUS approval recommendation, and it can originate a loan that had

a negative AUS recommendation. This enables estimation of our application denial regression

after conditioning on the AUS decision, which will largely account for quantifiable credit risk

factors that are correlated with race, but unobserved in HMDA. Column (3) of Table 7 shows that

compared to column (2) (which does not include the AUS denial indicator), racial denial rate gaps

decline after controlling for the AUS recommendation. However, significant residual racial denial

rate gaps persist in column (3). Black, Hispanic, and Asian applicants are still 2.7, 1.0, and 2.0

percentage points more likely to be denied after conditioning on AUS recommendation. Our cashout results echo the first-lien mortgage application findings of Bhutta, Hizmo, and Ringo (2021).

We cannot rule out the possibility that unobserved credit risk factors fully explain racial denial rate

gaps, but since AUS recommendations capture many of these factors, the results in Table 7 cast

doubt on this interpretation.

5.1.3 Lender “Strictness”

In this section we implement an additional exercise, developed in Bhutta, Hizmo, and Ringo

(2021), to test whether unobservable underwriting factors explain variation in the residual denial

rate gaps. The exercise involves two steps. First, we estimate our fully saturated denial rate model

45AUS recommendations are not applicable in HMDA data for the vast majority of HELOC and HELoan applications, because standard AUS are not designed for use with these product types.

34
by product type (column 2, Table 3) on our sample of the top 50 lenders (within each product

type). The models are estimated using only applications from White borrowers. We extract the

estimated lender fixed effects from the regression output and interpret them as a measure of lender

“strictness” in underwriting with respect to variables that are omitted from the HMDA dataset. The

measure is constructed using only White applicants in order to isolate differences across lenders in

strictness that are not contaminated by differential treatment of minority applicants. Positive values indicate lenders that impose tougher underwriting standards than average on White applicants,

while a value of zero means a lender’s rejection rate of White borrowers is average, conditional on

observable factors.

The second step of the exercise involves estimating the correlation across lenders between the

strictness measure and residual Black-White denial gap disparities. If those disparities are due to

differences in how lenders account for unobservable factors that differ between Black and White

applicants in their underwriting processes, then we should expect a positive correlation between

the strictness measures and the residual disparities. Bhutta, Hizmo, and Ringo (2021) find a strong,

positive correlation for both purchase mortgages and refinance loans and concludes that residual

denial gaps for those mortgage products are “at least partly a result of tight lender standards on

unobservable factors.” (p. 16)

Figure 6 displays scatter plots of residual Black-White denial gaps (y-axis) against the lender

strictness measures (x-axis) for each of the three MEW product types in our sample. We show two

plots for cash-out refinances in the upper panel of the figure. The upper left plot includes in the

lender fixed effect regressions a control for the AUS decision, while the upper right plot does not.

Comparing the two plots suggests that the AUS control, which is not available for HELOCs and

HELoans, does not have a significant impact on this analysis.

In contrast to the results in Bhutta, Hizmo, and Ringo (2021) corresponding to purchase and refinance mortgages, the correlations between the lender strictness measures and the residual BlackWhite denial gaps for MEW products are weak. The correlation is essentially zero for cash-out

35
refinances and HELoans and is slightly negative for HELOCs. These patterns are consistent with

the results from the lender-level Kitagawa-Blinder-Oaxaca decompositions in the previous section

and suggest that underwriting factors that are not observed in HMDA are unlikely to explain all of

the residual denial rate gaps.

5.1.4 Media Market Racial Animus

To shed light on whether the racial disparities in denial rates might be the result of discrimination,

we exploit geographic variation in racial animus and test whether the size of racial disparities in rejection rates positively covaries with the level of racial animus across metropolitan statistical areas

(MSAs). Intuitively, are the minority-White denial gaps more pronounced in areas with more prejudiced populations? To proxy for racial animus, we employ the Racial Animus Index developed by

Stephens-Davidowitz (2014), which relies on the percentage of Google search queries containing

racially charged language.46 Higher levels of the index represent greater racial bias against people

who are Black. Stephens-Davidowitz’s racial animus measure has been used to study income mobility (Chetty et al., 2019) and discrimination in labor markets (Kline, Rose, and Walters, 2022)

and higher education bond markets ((Dougal et al., 2019). For the markets covered in our data, the

mean (median) of the index is 63 (60), while the standard deviation is 19.

We estimate our main regression model separately for each of the 184 media markets and

collect the Asian, Black, and Hispanic coefficients from each regression.47 Figure A.4 displays

binned scatter plots of the race coefficients by the media market’s level of racial animus. All

three panels show that minority-White gaps are elevated in areas characterized by greater racial

46Stephens-Davidowitz’s animus measure is available for 196 media markets in the United States. The measure was

calculated using data from the 2004–2007 period and may not accurately reflect geographic differences in the extent

of racial animus during our sample period. Since HMDA data do not include a media market identifier, we manually

match loans’ counties to their MSAs and then to media markets. There are 184 separate media markets represented in

our mortgage data. Also, the measure is based on racially charged terms related to the Black population. This may not

be a good indicator of the level of racial animus toward other minority groups.

47Independent variables exclude state fixed effects, but otherwise are the same as in Table 2, column (5). The results

are very similar if state fixed effects are included.

36
animus.48 The correlation between racial animus and the Black, Hispanic, and Asian coefficients

is 0.30, 0.20, and 0.30, respectively, all statistically significantly at the 1% confidence level.

Further, by incorporating it into our main model as a control, we find that a one-standarddeviation increase in media market racial animus leads to a statistically and economically significant 0.7-1.0 percentage point increase in the Black-White denial gap and a 0.9 percentage point

increase in the Asian-White gap (see Table A.9). The increase in the Hispanic-White gap (0.5

percentage point) is smaller and not statistically significant when state fixed effects are included.

Taken together, these results are consistent with discrimination partially driving the conditional

racial disparities in MEW denial rates. A stronger measure of animosity, especially one more targeted to picking up bias against Hispanic and Asian people, might have greater explanatory power.

5.2 Aggregate Differences in MEW Amounts by Race

The high denial rates for MEW products apparent in Figure 1 suggest significant unmet demand for

these products. Moreover, the significantly higher minority denial ratesthat we have documented in

this paper imply that the unmet demand is much higher for minority homeowners. In this section,

we perform a simple back-of-the-envelope calculation to estimate the differences in the amount

of housing equity accessed by minority and White homeowners. We perform the exercise using

both conditional and unconditional denial rate differences, since as we discussed previously, both

measures are relevant from a policy perspective. Although this exercise is informative and relevant

to policy discussions, it should not be interpreted as a welfare analysis because we are unable

to measure the full benefits and costs (e.g., higher default rates) of increased access to mortgage

equity withdrawals.

Table A.10 in the Online Appendix presents the results of these calculations for all four racial

groups we study. We estimate that, in total between 2018 and 2021, Black homeowners in our

48These results use the animus index where the property is located. An alternative approach is to use the animus

measure where the mortgage loan originator is located. Results are similar when using this alternative approach.

37
sample applied to extract $46.4 billion in home equity, but were denied $23 billion.49 Had they

experienced the denial rate of White applicants (ignoring underwriting factors), they would have

only been denied $11.8 billion, or about half as much demand would have gone unmet.50

“Excess” denials conditional on borrower and mortgage characteristics are much smaller but

still reflect a large amount of locked-up home equity that borrowers were unable to access. A

back-of-the-envelope calculation using our regression model results in Table 3 suggests that the

“excess” denials for Black borrowers, controlling for observable loan and borrower characteristics,

was $2.0 billion over this 4-year period.51 Hispanic and Asian applicants were denied the ability to

cash out about $2.2 billion and $3.9 billion in equity more than White borrowers, respectively, after

controlling for these factors. Although excess denials are smaller after accounting for underwriting

factors, they are still large, at 18%, 25%, and 46% of the unconditional excess denials for Black,

Hispanic, and Asian applicants, respectively. Or in other words, observable underwriting factors

can explain only about four-fifths of the Black-White gap, three-quarters of the Hispanic-White

gap, and a bit over half of the Asian-White gap.

6 Conclusion

Scrutiny of the racial homeownership gap has led to initiatives to help address purchase lending

disparities, such as Fannie Mae and Freddie Mac’s Equitable Housing Finance Plans announced in

49We estimate the amount of loan proceeds that each product application, if successful, would yield using a simple

formula: Loan proceeds for HELOCs and HELoans are equal to 97.5% of the applied-for loan amount. (We assume

2.5% closing costs, which represent equity withdrawn but not received as cash by the borrower.) Because cash-out

refinances include paying off the existing mortgage lien(s), a smaller portion of the loan amount becomes cash to

the borrower. Gross loan proceeds for cash-out refinances are estimated as 16% of the loan amount for conventional

loans and 11% for FHA/VA loans in the data used by Gerardi, Lambie-Hanson, and Willen (2022), with net proceeds

subtracting $2,000 and 1% of the total loan amount.

50For example, Black HELOC applicants had an unconditional denial rate of 62.4%, vs. 32.3% for White applicants.

They were denied $11.5 billion in equity withdrawal via these HELOCs, an estimated $5.5 billion “excess” relative to

the rate at which White applicants were denied ($18.4 billion * (0.624-0.323)).

51We can calculate this by multiplying the estimated total loan proceeds applied for by Black homeowners ($18.4

billion in the case of HELOCs) by the corresponding Black coefficient (0.044) in Table 3’s Model 2. Calculations are

very similar if we instead use loan proceeds-weighted denial rates and weighted regression coefficients.

38
June 2022. Likewise, recent studies have documented lower rates of rate/term refinancing among

Black and Hispanic consumers in periods of falling interest rates, calling for the consideration of

ratchet mortgages and other interventions that would better ensure that lower mortgage interest

rates get passed on to borrowers equitably (Gerardi, Lambie-Hanson, and Willen, 2022; Gerardi,

Willen, and Zhang, 2020). But far less attention has been paid to date about the barriersto accessing

mortgage financing to help a homeowner withdraw the housing wealth he or she has accumulated.

Lenders offering home equity products often advertise to homeowners using messages such as

“Don’t borrow from a bank! Borrow from yourself.” Indeed, home equity products generally offer

lower interest rates than credit cards or other products, and they can be a useful tool for borrowers

who need cash to complete home repairs or improvements, pay medical debt, or send a child to

college. But as we show in this paper, MEW products have very high denial rates, especially

for minority homeowners. Much of the minority-White gap in denial rates can be explained by

borrower characteristics such as credit score, signaling that the underwriting system for these loans

has a particularly large impact on these consumers. Americans hold record levels of home equity,

following the historic house price increases of 2020–2021. Policymakers and researchers should

not assume this newfound housing wealth will be equally liquid among all homeowners.

39
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47
7 Figures

16

32

20 17

29

55

40 37

32

62

54

46

8

17

12 9

0 20 40 60 0 20 40 60

White Black Hispanic Asian White Black Hispanic Asian

White Black Hispanic Asian White Black Hispanic Asian

Cash-Out Refis HELoans

HELOCs Non-MEW

% of Applications

Figure 1. Application Denial Rates

Note: Denial rates conditional on credit decision, 2018–2021. Non-MEW includes first-lien purchase loans and rate/term refinances. Source: Home Mortgage Disclosure Act data.

48
0

5%

10%

15%

20%

25%

30%

35%

Cash-Out Refis HELoans HELOCs

Credit Score DTI

CLTV Explained by Other Controls

Unexplained

Figure 2. Decomposition of Denial Rate Differences by Product Type

Note: Kitagawa-Blinder-Oaxaca decomposition of Black-White denial rate differences by product

type, 2018–2021. Independent variables are the same as in Table 2 column (5). Source: Home

Mortgage Disclosure Act data.

49
-.02

0 .02 .04 .06 .08

Conditional Minority-White Denial Gap

Black Hispanic Asian

Banks Nonbanks

Credit Unions

Cash-Out Refis

-.02

0 .02 .04 .06 .08

Conditional Minority-White Denial Gap

Black Hispanic Asian

Banks Nonbanks

Credit Unions

HELoans

-.02

0 .02 .04 .06 .08

Conditional Minority-White Denial Gap

Black Hispanic Asian

Banks Nonbanks

Credit Unions

HELOCs

Figure 3. Conditional Minority-White Differences in Denial Rates by Institution Type and Product

Type

Note: Coefficients and 95% confidence intervals from denial rate models with full set of borrower

and loan controls. Model specifications are the same as in Table 4. Source: Home Mortgage

Disclosure Act data.
24%

23%

16%

19%18%

23%

32%

12%

15%

18%

28%

23%

11%

16%

21%

31%

16%

12%

20%

21%

0 10 20 30

% of Applications

White Black Hispanic Asian

Cash-Out Refis

DTI Credit History

Collateral Incomplete Application

Other

27%

32%

13%

8%

20%

24%

45%

8%

5%

18%

30%

35%

10%

6%

19%

34%

20%

11%

9%

25%

0 10 20 30 40 50

% of Applications

White Black Hispanic Asian

HELoans

DTI Credit History

Collateral Incomplete Application

Other

28%

39%

16%

4%

12%

22%

57%

9%

2%

10%

30%

46%

11%

2%

10%

38%

31%

15%

4%

13%

0 20 40 60

% of Applications

White Black Hispanic Asian

HELOCs

DTI Credit History

Collateral Incomplete Application

Other

Figure 4. Application Denial Reason

Note: First reported denial reason for denied applications, 2018–2021. Source: Home Mortgage

Disclosure Act data.
0

10%

20%

30%

40%

Percentage Points of Denial Rate Gap

5 10 15 20 25 30 35 40 45 50

Endowment Rank

Unexplained Explained

Total Black-White Gap

Cash-Out Refis

0

10%

20%

30%

40%

Percentage Points of Denial Rate Gap

5 10 15 20 25 30 35 40 45 50

Endowment Rank

Unexplained Explained

Total Black-White Gap

HELoans

0

10%

20%

30%

40%

Percentage Points of Denial Rate Gap

5 10 15 20 25 30 35 40 45 50

Endowment Rank

Unexplained Explained

Total Black-White Gap

HELOCs

Figure 5. Decomposition of Denial Rate Differences by Lender

Note: Kitagawa-Blinder-Oaxaca decomposition of Black-White denial rate differences by product

type, 2018–2021. Each panel includes separate decompositions for the top 50 largest lenders in

that product type. Independent variables are the same as in Table 2 column (5). Source: Home

Mortgage Disclosure Act data.
.15

Cash−Out: Without AUS Decision Control

.1

.05

0

−.5 0 .5 1

Unexplained

Lender Strictness

Black−White Denial Gap

Unexplained Black−White Denial Gap

.15

Cash−Out: With AUS Decision Control

.1

.05

0

−.5 0 .5 1

Lender Strictness

.15

HELoan

.1

.05

0

−.5 0 .5 1

Unexplained

Lender Strictness

Black−White Denial Gap

Unexplained Black−White Denial Gap

.15

HELOC

.1

.05

0

−.5 0 .5 1

Lender Strictness

Figure 6. Lender Strictness and Unexplained Racial Gaps

Note: Includes cash-out refinance, home equity loan, and home equity line of credit applications 

from Black and White consumers reported by financial institutions in 2018–2021. Sample is restricted to the top 50 lenders by application within each product type. “Strictness” (x-axis) reflects 

lender fixed effects from the fully saturated denial rate model, estimated on White applicants only. 

“Unexplained racial gap” (y-axis) is the unexplained Black-White denial rate gap from the decompositions reported in Figure 5. Source: Home Mortgage Disclosure Act data.
0

.01

.02

.03

.04

.05

.06

.07

.08

Coefficient in Denial Model (Black-White Gap)

20 40 60 80 100

Area Racial Animus

Black Applicants

0

.01

.02

.03

.04

.05

.06

.07

.08

Coefficient in Denial Model (Hispanic-White Gap)

20 40 60 80 100

Area Racial Animus

Hispanic Applicants

0

.01

.02

.03

.04

.05

.06

.07

.08

Coefficient in Denial Model (Asian-White Gap)

20 40 60 80 100

Area Racial Animus

Asian Applicants

Figure 7. Denial Rate Differences and Racial Animus

Note: Minority-White difference, 2018–2021. Each panel represents a binned scatterplot of minority coefficients obtained from separate regressions for each of the 184 media markets in our

sample. Media market level racial animus is from Stephens-Davidowitz (2014). Independent variables exclude state fixed effects, but otherwise are the same as in Table 2 column (5). Source:

Home Mortgage Disclosure Act data. 54
8 Tables

Table 1. Descriptive Statistics for MEW Products

White Black Hispanic Asian

Income (thousands $) 118.1 93.1 102.3 147.8

DTI (%) 36.5 41.2 40.7 40.3

Credit Score 735 688 715 745

CLTV (%) 65.2 68.4 64.9 63.4

Loan Amount 185,529 166,378 195,211 264,885

Units 1.0 1.1 1.1 1.0

Loan Term (months) 301 311 310 315

Second Home (d) 0.02 0.01 0.01 0.02

Investment Property (d) 0.04 0.06 0.06 0.10

No Co-applicant (d) 0.53 0.72 0.54 0.53

Home Improvement (d) 0.22 0.23 0.21 0.23

Second Lien (d) 0.30 0.27 0.28 0.35

Prepayment Penalty (d) 0.08 0.08 0.08 0.13

Interest Only (d) 0.16 0.09 0.11 0.15

Other Nonamortizing Features (d) 0.02 0.02 0.03 0.03

Application to Minority-Owned Bank (d) 0.001 0.001 0.002 0.009

Application Denied (d) 0.23 0.44 0.32 0.30

Cash-Out Refinance (d) 0.55 0.59 0.59 0.51

Home Equity Loan (HEloan) (d) 0.12 0.12 0.12 0.10

Home Equity Line of Credit (HELOC) (d) 0.33 0.29 0.29 0.39

# Observations 9,572,670 1,011,791 1,184,479 870,398

Note: This table reports mean values for observations populated on the variable in question. The sample includes

MEW products (cash-out refinances, HELoans, and HELOCs) for which a credit decision was made. See the Table

A.2 of the Online Appendix for data on the rate at which these fields are missing by loan type and racial/ethnic group.

Dichotomous variables are signified by (d). Source: Home Mortgage Disclosure Act data.

55
Table 2. Applicant Race and Likelihood of Denial on MEW Products

Dependent Var: Loan Denied (d)

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

Black (d) 0.212*** 0.192*** 0.068*** 0.065*** 0.056*** 0.042***

(0.022) (0.019) (0.008) (0.008) (0.005) (0.004)

Hispanic (d) 0.095*** 0.092*** 0.034** 0.034** 0.027** 0.028***

(0.025) (0.015) (0.011) (0.011) (0.008) (0.005)

Asian (d) 0.076*** 0.087*** 0.073*** 0.075*** 0.058*** 0.043***

(0.018) (0.013) (0.012) (0.012) (0.008) (0.005)

Year FE N Y Y Y Y Y

State FE N Y Y Y Y Y

DTI Buckets N N Y Y Y Y

Credit Score Buckets N N Y Y Y Y

CLTV Buckets N N N Y Y Y

Other Controls N N N N Y Y

Lender FE N N N N N Y

# Observations 12,638,969 12,638,969 12,638,969 12,638,969 12,638,969 12,638,969

Adjusted R2 0.020 0.047 0.240 0.259 0.316 0.380

Mean Denial Rate 0.26 0.26 0.26 0.26 0.26 0.26

Note: This table reports coefficient estimates from linear probability models where the dependent variable is an indicator for whether the application was denied. The sample includes MEW products (cash-out refinances, HELoans,

HELOCs) for which a credit decision was made. Dichotomous variables are signified by (d). Standard errors, clustered at the state and lender levels, are in parentheses. *** p<0.001, ** p<0.01, * p<0.05. Source: Home Mortgage

Disclosure Act data.

56
Table 3. Applicant Race and Likelihood of Denial by Product Type

Panel A: Cash-Out Refinances

(1) (2)

Black 0.168*** 0.040***

Hispanic

Asian

(0.023)

0.045**

(0.014)

0.017*

(0.004)

0.016***

(0.004)

0.026***

(0.007) (0.003)

# Observations

Adjusted R2

Mean Denial Rate

7,030,943

0.015

0.18

7,030,943

0.289

0.18

Panel B: Home Equity Loans (HELoans)

Black 0.259*** 0.050***

(0.026) (0.005)

Hispanic 0.112*** 0.032***

(0.017) (0.004)

Asian 0.085*** 0.045***

(0.024) (0.006)

# Observations 1,479,900 1,479,900

Adjusted R2 0.027 0.445

Mean Denial Rate 0.33 0.33

Panel C: Home Equity Lines of Credit (HELOCs)

Black 0.301*** 0.044***

(0.012) (0.004)

Hispanic 0.213*** 0.046***

(0.021) (0.006)

Asian 0.133*** 0.051***

(0.018) (0.008)

# Observations 4,128,076 4,128,076

Adjusted R2 0.039 0.469

Mean Denial Rate 0.37 0.37

Year FE N Y

State FE N Y

DTI Buckets N Y

Credit Score Buckets N Y

CLTV Buckets N Y

Other Controls N Y

Lender FE N Y

Note: This table reports coefficient estimates from linear probability models where the dependent variable is an indicator for whether the application was denied. The sample in Panels A, B, and C are cash-out refinances, HELoans, and

HELOCs, respectively, for which a credit decision was made. Standard errors, clustered at the state and lender levels,

are in parentheses. *** p<0.001, ** p<0.01, * p<0.05. Source: Home Mortgage Disclosure Act data.
58

Table 4. Likelihood of Denial by Type of Financial Institution

Cash-Out Refinances HELoans HELOCs

Banks Nonbanks Credit Unions Banks Nonbanks Credit Unions Banks Nonbanks Credit Unions

(1) (2) (3) (4) (5) (6) (7) (8) (9)

Black 0.045*** 0.038*** 0.047*** 0.044*** 0.047*** 0.061*** 0.038*** 0.017** 0.062***

(0.004) (0.004) (0.005) (0.006) (0.010) (0.007) (0.004) (0.006) (0.005)

Hispanic 0.025*** 0.012** 0.028*** 0.037*** 0.023*** 0.030*** 0.049*** 0.005 0.032***

(0.004) (0.004) (0.003) (0.005) (0.003) (0.005) (0.007) (0.011) (0.004)

Asian 0.034*** 0.022*** 0.029*** 0.056*** 0.022** 0.040*** 0.053*** 0.016* 0.039***

(0.005) (0.002) (0.004) (0.006) (0.008) (0.003) (0.008) (0.006) (0.005)

Year FE Y Y Y Y Y Y Y Y Y

State FE Y Y Y Y Y Y Y Y Y

DTI Buckets Y Y Y Y Y Y Y Y Y

Credit Score Buckets Y Y Y Y Y Y Y Y Y

CLTV Buckets Y Y Y Y Y Y Y Y Y

Other Controls Y Y Y Y Y Y Y Y YLender FE Y Y Y Y Y Y Y Y Y

# Observations 2,152,332 4,258,328 620,279 723,378 237,960 518,558 3,169,720 56,414 901,942Adjusted R-2 0.329 0.281 0.346 0.469 0.388 0.403 0.487 0.147 0.363

Mean Denial Rate 0.17 0.18 0.13 0.41 0.27 0.23 0.42 0.30 0.21

Note: This table reports coefficient estimates from linear probability models where the dependent variable is an indicator for whether the application was

denied. The saturated model is estimated separately for each lender type / MEW product combination. Standard errors, clustered at the state and lender

levels, are in parentheses. *** p<0.001, ** p<0.01, * p<0.05. Source: Home Mortgage Disclosure Act data.
Table 5. Likelihood of Denial by Lender Minority Ownership Status

(1) (2) (3)

Black Applicant 0.060*** 0.060*** 0.060***

(0.006) (0.006) (0.006)

x Minority Bank -0.021

(0.029)

x Hispanic Bank 0.023

(0.083)

x Asian Bank -0.013

(0.036)

x Other Minority Bank -0.042

(0.036)

Hispanic Applicant 0.035*** 0.036*** 0.036***

(0.008) (0.008) (0.008)

x Minority Bank -0.075***

(0.019)

x Hispanic Bank -0.079**

(0.025)

x Asian Bank -0.088**

(0.028)

x Other Minority Bank -0.054*

(0.026)

Asian Applicant 0.056*** 0.061*** 0.061***

(0.010) (0.010) (0.010)

x Minority Bank -0.128**

(0.046)

x Hispanic Bank 0.089

(0.053)

x Asian Bank -0.140**

(0.041)

x Other Minority Bank -0.023

(0.024)

Minority Bank -0.069***

(0.015)

Hispanic Bank -0.072*

(0.031)

Asian Bank -0.065***

(0.012)

Other Minority Bank -0.072**

(0.024)

Year FE Y Y Y

State FE Y Y Y

DTI Buckets Y Y Y

Credit Score Buckets Y Y Y

CLTV Buckets Y Y Y

Other Controls Y Y Y

Lender FE N N N

Lender Size Y Y Y

# Observations 2,875,830 2,875,830 2,875,830

Adjusted R2 0.35 0.35 0.35

Mean Denial Rate 0.23 0.23 0.23

Note: This table reports coefficient estimates from linear probability models where the dependent variable is an indicator for whether the application was denied. The estimation sample is restricted to applications for cash-out refinances

and HELoans reported by bank lenders. Minority ownership status is provided in Bob Avery’s HMDA Lender File,

using National Information Center data. Standard errors, clustered at the state and lender levels, are in parentheses.

*** p<0.001, ** p<0.01, * p<0.05. Source: Home Mortgage Disclosure Act data.
Table 6. Applicant Race and Mortgage Pricing by Product Type

Cash-Out Refis HELoans HELOCs

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

Black

Hispanic

Asian

0.136***

(0.017)

0.111***

(0.014)

-0.063***

0.009**

(0.003)

0.004

(0.004)

-0.048***

0.476***

(0.098)

0.202***

(0.030)

-0.097

0.005

(0.018)

-0.004

(0.007)

-0.073***

0.529***

(0.078)

0.200***

(0.029)

-0.087*

0.100***

(0.028)

0.012

(0.008)

-0.056**

(0.013) (0.009) (0.060) (0.017) (0.033) (0.016)

Year–Month FE Y Y Y Y Y Y

State FE

DTI Buckets

Credit Score Buckets

Y

N

N

Y

Y

Y

Y

N

N

Y

Y

Y

Y

N

N

Y

Y

Y

CLTV Buckets

Other Controls

N

N

Y

Y

N

N

Y

Y

N

N

Y

Y

Lender FE N Y N Y N Y

# Observations

Adjusted R2

Mean Rate Spread

5,324,103

0.086

0.43

5,324,103

0.505

0.43

862,169

0.047

1.39

862,169

0.685

1.39

2,333,437

0.069

0.85

2,333,437

0.610

0.85

Note: This table reports coefficient estimates from regressions where the dependent variable is interest rate spread.

The sample includes originated loans. Standard errors, clustered at the state and lender levels, are in parentheses. ***

p<0.001, ** p<0.01, * p<0.05. Source: Home Mortgage Disclosure Act data.

60
Table 7. AUS Decisions in Cash-Out Refinances

(1)

“AUS Denial”

(2)

Application Denial

without AUS Outcome Control

(3)

Application Denial

with AUS Outcome Control

Black 0.020***

(0.003)

0.037***

(0.004)

0.027***

(0.003)

Hispanic 0.006***

(0.002)

0.013***

(0.003)

0.010***

(0.003)

Asian 0.007***

(0.001)

0.023***

(0.002)

0.020***

(0.002)

AUS “Denial” 0.464***

(0.025)

Year FE

State FE

DTI Buckets

Credit Score Buckets

CLTV Buckets

Other Controls

Lender FE

AUS “Denial”

Y

Y

Y

Y

Y

Y

Y

N

Y

Y

Y

Y

Y

Y

Y

N

Y

Y

Y

Y

Y

Y

Y

Y

# Observations

Adjusted R2

Mean Outcome

6,138,590

0.203

0.08

6,138,590

0.208

0.13

6,138,590

0.311

0.13

Note: Includes cash-out refinance applications. Standard errors, clustered at the state and lender levels, are in

parentheses. *** p<0.001, ** p<0.01, * p<0.05. Source: Home Mortgage Disclosure Act data.

61
Can Everyone Tap into the Housing Piggy Bank?

Racial Disparities in Access to Home Equity

Online Appendix

This Appendix supplementsthe empirical analysisin Conklin, Gerardi, and Lambie-Hanson (2023).

Table of Contents

A.1 In-Person Lending Proxy 2

A.2 Additional Summary Statistics, Full Model Results, and Robustness Checks 3

A.2.1 Application Outcomes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3

A.2.2 Observations Without Underwriting Characteristics . . . . . . . . . . . . . . 4

A.2.3 Primary Regressions with All Coefficients Reported . . . . . . . . . . . . . . 5

A.2.4 Logit Models . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8

A.2.5 Denial Rate Regressions with Alternative Fixed Effects . . . . . . . . . . . . 9

A.2.6 Decomposing Denial Rate Gaps by Product Type . . . . . . . . . . . . . . . 10

A.2.7 Application Year . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11

A.2.8 Bank Size Regressions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13

A.2.9 Investigating Additional Denial Reasons . . . . . . . . . . . . . . . . . . . . 14

A.3 Media Market Racial Animus 16

A.4 Back-of-the-Envelope “Excess” Dollars Denied Calculations 17

A.5 Discouragement of Potential Applicants 18

1
A.1 In-Person Lending Proxy

We can use a feature of HMDA data reporting instructions to calculate a proxy for in-person lending: Lenders that meet with applicants during the application process(prior to closing) must impute

each applicant’s race, ethnicity, and gender “based on visual observation or surname” of the applicant if the applicant declines to supply that information in the application process. Although most

applicants (81%) supply the information themselves, the rate of imputation among the remainder

is an imperfect but useful indicator of the frequency with which applications are taken in person.

Among applications without race supplied by the applicant, lenders imputed race on 46% of HELOC and 31% of HELoans applications, vs. 7% for cash-out and rate/term refinances and 13%

of purchases, suggesting that lenders are much more likely to work with HELOC and HELoan

applicants in person, as compared to applicants seeking other types of mortgages.

2
A.2 Additional Summary Statistics, Full Model Results, and

Robustness Checks

A.2.1 Application Outcomes

Table A.1. Application Outcomes by Race

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

White Black Hispanic Asian

Purchase

Loan originated 77.6 66.5 72.6 72.1

Application approved but not accepted 2.0 2.3 2.2 2.3

Application denied 5.0 10.8 8.0 6.3

Application withdrawn by applicant 13.7 17.7 15.1 16.6

File closed for incompleteness 1.6 2.6 2.1 2.7

Denied (among loans with decisions) 5.9 13.6 9.6 7.8

Rate/Term

Loan originated 66.9 52.1 58.8 65.4

Application approved but not accepted 2.7 4.0 3.5 2.6

Application denied 8.7 15.7 12.2 8.5

Application withdrawn by applicant 15.2 18.1 17.2 16.1

File closed for incompleteness 6.5 10.2 8.3 7.4

Denied (among loans with decisions) 11.2 21.9 16.4 11.1

Cash-out Refi (MEW)

Loan originated 63.8 46.5 58.0 59.5

Application approved but not accepted 2.1 2.3 2.6 2.6

Application denied 12.2 23.4 15.3 13.0

Application withdrawn by applicant 16.0 19.1 17.2 17.5

File closed for incompleteness 5.9 8.7 6.9 7.5

Denied (among loans with decisions) 15.6 32.4 20.1 17.3

HELoan (MEW)

Loan originated 54.5 33.7 44.4 43.0

Application approved but not accepted 3.0 2.5 3.4 3.2

Application denied 23.3 44.0 32.0 27.6

Application withdrawn by applicant 13.6 13.8 13.7 17.1

File closed for incompleteness 5.7 6.0 6.5 9.1

Denied (among loans with decisions) 28.9 54.8 40.1 37.4

HELOC (MEW)

Loan originated 57.5 31.2 38.9 44.3

Application approved but not accepted 3.2 2.8 2.7 3.0

Application denied 29.0 56.5 48.0 39.7

Application withdrawn by applicant 6.7 6.0 6.2 7.5

File closed for incompleteness 3.6 3.4 4.2 5.5

Denied (among loans with decisions) 32.3 62.4 53.5 45.6

Observations 35,618,574 4,531,154 5,364,350 4,653,681

Note: This table reports mortgage application outcomes by applicant race. The first two

panels include product types (purchase and rate/term refinances) that are not mortgage

equity withdrawals. The third, fourth, and fifth panels include mortgage equity withdraw

(MEW) products. Source: Home Mortgage Disclosure Act data.

3
A.2.2 Observations Without Underwriting Characteristics

Table A.2. Proportion of Observations Without Underwriting Characteristics

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

White Black Hispanic Asian

Cash-out Refi (MEW)

Income 0.01 0.01 0.01 0.02

DTI 0.02 0.03 0.03 0.03

Credit Score 0.02 0.02 0.03 0.03

CLTV 0.02 0.03 0.03 0.02

HELoan (MEW)

Income 0.02 0.03 0.02 0.03

DTI 0.03 0.06 0.04 0.03

Credit Score 0.04 0.06 0.04 0.04

CLTV 0.05 0.09 0.06 0.04

HELOC (MEW)

Income 0.02 0.03 0.03 0.03

DTI 0.02 0.05 0.03 0.03

Credit Score 0.02 0.03 0.02 0.02

CLTV 0.03 0.07 0.04 0.03

Note: This table reports the proportion of observations in the estimation sample that are reported as “not applicable” on control variables.

Source: Home Mortgage Disclosure Act data.

4
A.2.3 Primary Regressions with All Coefficients Reported

Table A.3. Likelihood of Denial for MEW Products

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

Race and Ethnicity (d) (omitted: Non-Hispanic White)

Black 0.212*** 0.192*** 0.068*** 0.065*** 0.056*** 0.042***

(0.022) (0.019) (0.008) (0.008) (0.005) (0.004)

Hispanic 0.095*** 0.092*** 0.034** 0.034** 0.027** 0.028***

(0.025) (0.015) (0.011) (0.011) (0.008) (0.005)

Asian 0.076*** 0.087*** 0.073*** 0.075*** 0.058*** 0.043***

(0.018) (0.013) (0.012) (0.012) (0.008) (0.005)

Year (d) (omitted: 2018)

2019 -0.036*** -0.015* -0.014* 0.002 0.007

(0.007) (0.006) (0.006) (0.007) (0.005)

2020 -0.111*** -0.039*** -0.030** 0.014 0.036***

(0.011) (0.010) (0.010) (0.008) (0.008)

2021 -0.141*** -0.071*** -0.060*** -0.007 0.013

(0.015) (0.014) (0.014) (0.008) (0.009)

DTI (d) (omitted: < 25)

[25, 35) -0.045*** -0.045*** -0.044*** -0.047***

(0.007) (0.007) (0.005) (0.005)

[35, 45) -0.055*** -0.054*** -0.062*** -0.069***

(0.011) (0.011) (0.008) (0.008)

[45, 101) 0.224*** 0.215*** 0.184*** 0.164***

(0.015) (0.015) (0.016) (0.014)

NA 0.208*** 0.063 0.055 0.100*

(0.059) (0.053) (0.046) (0.038)

Credit Score (d) (omitted: ≥ 740)

[300, 620) 0.563*** 0.538*** 0.560*** 0.532***

(0.021) (0.020) (0.019) (0.016)

[620, 640) 0.286*** 0.272*** 0.313*** 0.306***

(0.027) (0.025) (0.024) (0.023)

[640, 660) 0.242*** 0.228*** 0.262*** 0.256***

(0.024) (0.022) (0.022) (0.021)

[660, 680) 0.178*** 0.168*** 0.192*** 0.187***

(0.017) (0.016) (0.016) (0.016)

[680, 700) 0.118*** 0.110*** 0.128*** 0.126***

(0.011) (0.010) (0.011) (0.011)

[700, 720) 0.077*** 0.071*** 0.084*** 0.083***

(0.008) (0.008) (0.009) (0.009)

[720, 740) 0.044*** 0.040*** 0.050*** 0.050***

(0.005) (0.005) (0.006) (0.006)

NA 0.299*** 0.255*** 0.259*** 0.342***

(0.053) (0.051) (0.052) (0.045)

5
Table A.3. (cont.) Likelihood of Denial for MEW Products

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

CLTV (d) (omitted: ≤ 60)

(60, 70] -0.033*** -0.011** -0.006*

(0.005) (0.003) (0.002)

(70, 75] -0.028*** -0.004 0.006

(0.007) (0.005) (0.003)

(75, 80] -0.025** 0.006 0.018**

(0.008) (0.008) (0.006)

(80, 85] 0.048** 0.082*** 0.085***

(0.014) (0.013) (0.012)

(85, 90] 0.053** 0.072*** 0.094***

(0.016) (0.018) (0.017)

(90, 95] 0.260*** 0.269*** 0.287***

(0.029) (0.027) (0.027)

(95, 98] 0.240*** 0.272*** 0.300***

(0.030) (0.028) (0.026)

(98, 100] 0.031 0.128*** 0.141***

(0.025) (0.023) (0.020)

NA 0.246*** 0.252*** 0.301***

(0.054) (0.040) (0.047)

Income (d) (omitted: ≥ $500, 000)

[0, $30k) 0.113*** 0.148***

(0.020) (0.014)

[$30k, $60k) 0.023 0.054***

(0.013) (0.010)

[$60k, $90k) -0.013 0.017

(0.011) (0.009)

[$90k, $150k) -0.027** -0.002

(0.010) (0.008)

[$150k, $500k) -0.026*** -0.012

(0.007) (0.006)

NA -0.069** -0.075***

(0.021) (0.020)

Loan Amount (d) (omitted: ≥ $750, 000)

[$5,000, $50,000) -0.035* -0.040**

(0.015) (0.012)

[$50,000, $100,000) -0.043** -0.061***

(0.014) (0.010)

[$100,000, $250,000) -0.053*** -0.069***

(0.013) (0.010)

[$250,000, $500,000) -0.057*** -0.055***

(0.010) (0.009)

[$500,000, $750,000) -0.034*** -0.027***

(0.007) (0.006)

6
Table A.3. (cont.) Likelihood of Denial for MEW Products

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

Loan Term (d) (omitted: (20-30] years)

≤ 5 years -0.060 -0.020

(0.041) (0.015)

(5, 15] years -0.046*** -0.013*

(0.010) (0.005)

(15, 20] years -0.017 -0.004

(0.012) (0.007)

(30, 40] years (HELOCs only) -0.033 -0.123

(0.117) (0.152)

FHA (d) (cash-out only) -0.131*** -0.108***

(0.024) (0.017)

VA (d) (cash-out only) -0.098** -0.137***

(0.028) (0.015)

HELoan (d) 0.075** 0.045***

(0.024) (0.012)

HELOC (d) 0.147*** 0.140***

(0.027) (0.032)

Second Lien (d) 0.013 0.008

(0.009) (0.006)

Home Improvement (d) 0.005 0.007

(0.006) (0.005)

Prepayment Penalty (d) 0.097** 0.033

(0.030) (0.028)

Interest Only (d) -0.097** -0.110***

(0.030) (0.028)

Other Nonamortizing Features (d) 0.159** 0.218**

(0.057) (0.070)

Second Residence (d) 0.105*** 0.107***

(0.010) (0.008)

Investment Property (d) 0.049*** 0.081***

(0.012) (0.010)

No Co-applicant (d) 0.043*** 0.034***

(0.003) (0.002)

Total Units (d) (omitted: 1)

2 0.043*** 0.045***

(0.006) (0.006)

3 0.047*** 0.056***

(0.011) (0.009)

4 0.048*** 0.051***

(0.012) (0.011)

# Observations 12,638,969 12,638,969 12,638,969 12,638,969 12,638,969 12,638,969

Adjusted R2 0.020 0.047 0.240 0.259 0.316 0.380

Mean Denial Rate 0.26 0.26 0.26 0.26 0.26 0.26

Year FE N Y Y Y Y Y

State FE N Y Y Y Y Y

DTI Buckets N N Y Y Y Y

Credit Score Buckets N N Y Y Y Y

CLTV Buckets N N N Y Y Y

Other Controls N N N N Y Y

Lender FE N N N N N Y

Note: This table reports expanded coefficient estimates from linear probability models where the dependent variable is

an indicator for whether the application was denied. The sample includes MEW products (cash-out refinances, HELoans,

HELOCs) for which a credit decision was made. Standard errors, clustered at the state and lender levels, are in parentheses.

*** p<0.001, ** p<0.01, * p<0.05. Source: Home Mortgage Disclosure Act data. 7
A.2.4 Logit Models

Table A.4. Logit Models of Denial for MEW Products

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

Black 0.212*** 0.186*** 0.059*** 0.056*** 0.046***

(0.007) (0.010) (0.005) (0.005) (0.003)

Hispanic 0.095*** 0.091*** 0.031*** 0.032*** 0.024***

(0.010) (0.010) (0.009) (0.008) (0.006)

Asian 0.076*** 0.088*** 0.073*** 0.075*** 0.054***

(0.006) (0.004) (0.005) (0.005) (0.006)

# Observations 12,639,272 12,639,272 12,639,272 12,639,272 12,639,272

Mean Denial Rate 0.26 0.26 0.26 0.26 0.26

Year FE N Y Y Y Y

State FE N Y Y Y Y

DTI Buckets N N Y Y Y

Credit Score Buckets N N Y Y Y

CLTV Buckets N N N Y Y

Other Controls N N N N Y

Lender FE N N N N N

Note: This table reports average marginal effect estimates from logit models where the

dependent variable is an indicator for whether the application was denied. The sample

includes MEW products (cash-out refinances, HELoans, HELOCs) for which a credit

decision was made. Standard errors, clustered at the state level, are in parentheses. ***

p<0.001, ** p<0.01, * p<0.05. Source: Home Mortgage Disclosure Act data.

8
A.2.5 Denial Rate Regressions with Alternative Fixed Effects

Table A.5. Likelihood of Denial–Robustness to Alternative Geographic Controls

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

Black

Hispanic

Asian

0.042***

(0.004)

0.028***

(0.005)

0.043***

(0.005)

0.035***

(0.003)

0.025***

(0.004)

0.045***

(0.005)

0.035***

(0.003)

0.027***

(0.004)

0.047***

(0.005)

0.041***

(0.003)

0.026***

(0.004)

0.045***

(0.005)

Year FE

State FE

DTI Buckets

Credit Score Buckets

CLTV Buckets

Other Controls

Lender FE

Census Tract FE

Lender x Tract FE

County x Year FE

Y

Y

Y

Y

Y

Y

Y

N

N

N

Y

Y

Y

Y

Y

Y

Y

Y

N

N

Y

Y

Y

Y

Y

Y

N

N

Y

N

Y

Y

Y

Y

Y

Y

Y

N

N

Y

# Observations

Adjusted R2

Mean Denial Rate

12,638,969

0.380

0.26

12,638,969

0.383

0.26

10,864,102

0.399

0.26

12,505,642

0.387

0.26

Note: This table reports coefficient estimates from linear probability models where the

dependent variable is an indicator for whether the application was denied. Model 1 is

the main model (Model 6 from Table 2). Model 2 adds tract fixed effects. Model 3

substitutes lender-by-tract fixed effects for the separate lender and tract fixed effects in

Model 2. Model 4 substitutes county-by-year fixed effects for the tract fixed effects in

Model 2. Standard errors, clustered at the state and lender levels, are in parentheses. ***

p<0.001, ** p<0.01, * p<0.05. Source: Home Mortgage Disclosure Act data.

9
A.2.6 Decomposing Denial Rate Gaps by Product Type

Table A.6. Applicant Race and Likelihood of Denial on MEW Products

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

Panel A: Cash-Out Refinances

Black 0.168*** 0.153*** 0.064*** 0.060*** 0.054*** 0.040***

(0.023) (0.019) (0.007) (0.007) (0.006) (0.004)

Hispanic 0.045** 0.049*** 0.011 0.012 0.010 0.016***

(0.014) (0.009) (0.007) (0.007) (0.006) (0.004)

Asian 0.017* 0.033*** 0.030*** 0.031*** 0.030*** 0.026***

(0.007) (0.005) (0.006) (0.006) (0.006) (0.003)

Observations 7,030,943 7,030,943 7,030,943 7,030,943 7,030,943 7,030,943

Adjusted R-squared 0.015 0.037 0.198 0.203 0.220 0.289

Mean Denial Rate 0.18 0.18 0.18 0.18 0.18 0.18

Panel B: Home Equity Loans (HELoans)

Black 0.259*** 0.237*** 0.079*** 0.076*** 0.063*** 0.050***

(0.026) (0.022) (0.009) (0.010) (0.009) (0.005)

Hispanic 0.112*** 0.111*** 0.034*** 0.034*** 0.031** 0.032***

(0.017) (0.012) (0.009) (0.009) (0.009) (0.004)

Asian 0.085*** 0.094*** 0.075*** 0.079*** 0.084*** 0.045***

(0.024) (0.021) (0.016) (0.016) (0.016) (0.006)

Observations 1,479,900 1,479,900 1,479,900 1,479,900 1,479,900 1,479,900

Adjusted R-squared 0.027 0.041 0.289 0.309 0.330 0.445

Mean Denial Rate 0.33 0.33 0.33 0.33 0.33 0.33

Panel C: Home Equity Lines of Credit (HELOCs)

Black 0.301*** 0.272*** 0.088*** 0.085*** 0.055*** 0.044***

(0.012) (0.010) (0.008) (0.008) (0.008) (0.004)

Hispanic 0.213*** 0.170*** 0.072*** 0.070*** 0.057*** 0.046***

(0.021) (0.012) (0.008) (0.009) (0.007) (0.006)

Asian 0.133*** 0.115*** 0.082*** 0.084*** 0.066*** 0.051***

(0.018) (0.013) (0.014) (0.013) (0.009) (0.008)

# Observations 4,128,076 4,128,076 4,128,076 4,128,076 4,128,076 4,128,076

Adjusted R2 0.039 0.066 0.329 0.358 0.402 0.469

Mean Denial Rate 0.37 0.37 0.37 0.37 0.37 0.37

Year FE N Y Y Y Y Y

State FE N Y Y Y Y Y

DTI Buckets N Y Y Y Y Y

Credit Score Buckets N Y Y Y Y Y

CLTV Buckets N N Y Y Y Y

Other Controls N N N Y Y Y

Lender FE N N N N Y Y

Note: Thistable reports coefficient estimatesfrom linear probability models where the dependent variable is an indicator for whether the application

was denied. The sample in Panel A, B, and C are cash-out refinances, HELoans, and HELOCs, respectively, for which a credit decision was made.

Standard errors, clustered at the state and lender levels, are in parentheses. *** p<0.001, ** p<0.01, * p<0.05. Source: Home Mortgage

Disclosure Act data.
A.2.7 Application Year

Since MEW products can be used to smooth consumption, and many households experienced negative income and wealth shocks during the COVID-19 pandemic, MEW demand may have shifted

as a result. At the same time, mortgage lenders may have updated underwriting policies in response

to evolving market conditions. These mortgage supply and demand shifts may have impacted racial

disparities in access to mortgage equity starting in 2020. We estimate our saturated denial model

separately by application year on the pooled sample of MEW products and report the results in

Table A.7. Notice first that the number of applications increases monotonically over time. In contrast, the mean denial rate declines every year in our sample and is almost halved moving from

2018 to 2021. The explanatory power of the fully saturated model, as indicated by the adjusted

R-squared, is relatively stable across all years, which might suggest that the implementation of

underwriting guidelines was also stable. Both the Hispanic and Asian coefficients decline over

time, but relative to the mean denial rate for the corresponding year (reported at the bottom of the

table), the magnitude of the gap is similar across years. In contrast, the Black coefficient decreases

modestly through 2020 but then reverts to 2018 levels in 2021. Relative to the mean denial rate,

this increase represents a near doubling in the Black-White gap from 2018 to 2021.

11
Table A.7. Likelihood of Denial by Year

2018 2019 2020 2021

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

Black 0.044*** 0.042*** 0.039*** 0.043***

(0.005) (0.004) (0.004) (0.004)

Hispanic 0.035*** 0.032*** 0.023*** 0.021***

(0.006) (0.005) (0.005) (0.005)

Asian 0.055*** 0.047*** 0.040*** 0.032***

(0.007) (0.005) (0.005) (0.005)

Year FE Y Y Y Y

State FE Y Y Y Y

DTI Buckets Y Y Y Y

Credit Score Buckets Y Y Y Y

CLTV Buckets Y Y Y Y

Other Controls Y Y Y Y

Lender FE Y Y Y Y

# Observations 2,764,965 2,915,279 3,065,300 3,893,569

Adjusted R2 0.379 0.403 0.381 0.361

Mean Denial Rate 0.34 0.30 0.22 0.20

Note: This table reports coefficient estimates from linear probability models where the dependent variable is an indicator for whether the application was denied. The saturated model is estimated separately for each application year.

Standard errors, clustered at the state and lender levels, are in parentheses. *** p<0.001, ** p<0.01, * p<0.05.

Source: Home Mortgage Disclosure Act data.

12
A.2.8 Bank Size Regressions

Table A.8. Likelihood of Denial by Bank Size

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

All Banks Large Banks Intermediate Banks Small Banks

Black 0.040*** 0.039*** 0.031*** 0.045***

(0.005) (0.005) (0.004) (0.004)

Hispanic 0.040*** 0.040*** 0.015*** 0.026***

(0.006) (0.006) (0.003) (0.006)

Asian 0.053*** 0.054*** 0.027*** 0.022**

(0.006) (0.006) (0.004) (0.007)

Year FE Y Y Y Y

State FE Y Y Y Y

DTI Buckets Y Y Y Y

Credit Score Buckets Y Y Y Y

CLTV Buckets Y Y Y Y

Other Controls Y Y Y Y

Lender FE Y Y Y Y

Lender Size N N N N

# Observations 6,045,550 5,551,570 296,170 197,810

Adjusted R2 0.446 0.448 0.335 0.390

Mean Denial Rate 0.33 0.35 0.12 0.22

Note: This table reports coefficient estimates from linear probability models where the dependent

variable is an indicator for whether the application was denied. Models are restricted to applications made to bank lenders. Lenders are classified by asset size as of the end of the year prior to

the HMDA reporting year, according to the Call Report, with data accessed through Bob Avery’s

HMDA Lender File. Standard errors, clustered at the state and lender levels, are in parentheses.

*** p<0.001, ** p<0.01, * p<0.05. Source: Home Mortgage Disclosure Act data.

13
A.2.9 Investigating Additional Denial Reasons
26%

27%

17%

20%

24%

25%

38%

14%

16%

24%

31%

29%

13%

17%

27%

33%

20%

13%

21%

28%

0 10 20 30 40

% of Applications

White Black Hispanic Asian

Cash-Out Refis

DTI Credit History

Collateral Incomplete Application

Other

32%

38%

16%

9%

26%

32%

55%

11%

6%

25%

36%

43%

13%

7%

25%

39%

26%

13%

10%

32%

0 20 40 60

% of Applications

White Black Hispanic Asian

HELoans

DTI Credit History

Collateral Incomplete Application

Other

36%

45%

24%

5%

20%

31%

65%

16%

3%

17%

40%

53%

20%

3%

19%

47%

37%

24%

4%

23%

0 20 40 60

% of Applications

White Black Hispanic Asian

HELOCs

DTI Credit History

Collateral Incomplete Application

Other

Figure A.1. All Application Denial Reasons Reported

Note: Reported denial reasons, 2018–2021. Lenders report up to 4 principal denial reasons for

each denied application. Source: Home Mortgage Disclosure Act data.

15
A.3 Media Market Racial Animus

Table A.9. Likelihood of Denial by Media Market Racial Animus

Dependent Var: Loan Denied (d)

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

Black 0.043*** 0.045*** 0.047*** 0.046*** 0.043***

(0.003) (0.003) (0.003) (0.003) (0.003)

Hispanic 0.027*** 0.030*** 0.030*** 0.032*** 0.028***

(0.005) (0.007) (0.007) (0.007) (0.005)

Asian 0.043*** 0.043*** 0.044*** 0.046*** 0.046***

(0.005) (0.005) (0.006) (0.005) (0.005)

Racial Animus, Standardized 0.002 -0.001

(0.002) (0.002)

x Black 0.007* 0.010***

(0.003) (0.003)

x Hispanic 0.005 0.005*

(0.005) (0.002)

x Asian 0.009*** 0.009***

(0.002) (0.002)

Year FE Y Y Y Y Y

State FE Y N N N Y

DTI Buckets Y Y Y Y Y

Credit Score Buckets Y Y Y Y Y

CLTV Buckets Y Y Y Y Y

Other Controls Y Y Y Y Y

Lender FE Y Y Y Y Y

# Observations 12,379,914 12,379,914 9,711,394 9,711,394 9,711,394

Adjusted R2 0.363 0.362 0.363 0.363 0.364

Mean Denial Rate 0.25 0.25 0.25 0.25 0.25

Note: This table reports coefficient estimates from linear probability models where the dependent variable

is an indicator for whether the application was denied. Media market level racial animus is from StephensDavidowitz (2014), standardized to mean = 0 and standard deviation = 1. Model 1 is the main model (Model

6 from Table 2) but restricted to applications with NMLS ID populated for the loan officer taking the loan

application (to facilitate robustness checks available upon request). Model 2 removes state fixed effects.

Model 3 restricts to loans for collateral properties located with core-based statistical areas. Model 4 adds

standardized racial animus and its interaction with applicant race and ethnicity dummy variables. Model 5

adds state fixed effects to demonstrate robustness. Standard errors, clustered at the state and lender levels,

are in parentheses. *** p<0.001, ** p<0.01, * p<0.05. Source: Home Mortgage Disclosure Act data.

16
A.4 Back-of-the-Envelope “Excess” Dollars Denied Calculations

Table A.10. Estimated Aggregate Dollars of Loan Proceeds to Borrowers Denied

White Black Hispanic Asian

Cash-out

[1] Average estimated cash to borrower per application $34,197 $28,807 $35,649 $50,755

Estimated aggregate loan proceeds (in billions)

[2] All applications with credit decision $180.9 $17.1 $24.9 $22.7

[3] Denial Rate 15.6% 32.4% 20.1% 17.3%

[4] Denied $, [2] * [3] $28.2 $5.5 $5.0 $3.9

[5] Denied $, given White denial rate [2] * [3, White] $28.2 $2.7 $3.9 $3.5

[6] Unconditional "excess" denied $, [4] - [5] $2.9 $1.1 $0.4

[7] Table 3, model 2 coefficient 0.040 0.016 0.026

[8] Conditional "excess" denied $, [2] * [7] $0.7 $0.4 $0.6

HELoan

[9] Average estimated cash to borrower per application $111,257 $86,718 $106,033 $181,452

Estimated aggregate loan proceeds (in billions)

[10] All applications with credit decision $124.9 $10.9 $15.3 $15.9

[11] Denial Rate 28.9% 54.8% 40.1% 37.4%

[12] Denied $, [10] * [11] $36.1 $6.0 $6.1 $5.9

[13] Denied $, given White denial rate [10] * [11, White] $36.1 $3.2 $4.4 $4.6

[14] Unconditional “excess” denied $, [12] - [13] $2.8 $1.7 $1.3

[15] Table 3, model 2 coefficient 0.050 0.032 0.045

[16] Conditional “excess” denied $, [10] * [15] $0.5 $0.5 $0.7

HELOC

[17] Average estimated cash to borrower per application $92,870 $62,865 $82,600 $150,726

Estimated aggregate loan proceeds (in billions)

[18] All applications with credit decision $293.3 $18.4 $28.2 $50.6

[19] Denial Rate 32.3% 62.4% 53.5% 45.6%

[20] Denied $, [18] * [19] 94.7 $11.5 $15.1 $23.1

[21] Denied $, given White denial rate [18] * [19, White] $94.7 $5.9 $9.1 $16.4

[22] Unconditional “excess” denied $, [20] - [21] $5.5 $6.0 $6.7

[23] Table 3, model 2 coefficient 0.044 0.046 0.051

[24] Conditional “excess” denied $, [18] * [23] $0.8 $1.3 $2.6

Total MEW

Estimated aggregate loan proceeds (in billions)

[25] All applications with credit decision $599.1 $46.4 $68.4 $89.2

[26] Denied $, [4] + [12] + [20] $159.0 $23.0 $26.2 $32.9

[27] Unconditional “excess” denied $, [6] + [14] + [22] $11.2 $8.8 $8.5

[28] Conditional “excess” denied $, [8] + [16] + [24] $2.0 $2.2 $3.9

Source: Home Mortgage Disclosure Act data.

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A.5 Discouragement of Potential Applicants

A caveat that applies universally to research using HMDA data to study denials is that applicants’

outcomes are only observed if they formally apply for a loan (or in the case of a purchase mortgage,

apply for a loan or for preapproval). Loan officers often have conversations with prospective

applicants before formal applications are submitted in order to discuss potential loan products and

terms, as well as provide guidance on what a borrower might qualify for, given a soft credit check

or circumstances a borrower shares with the lender (e.g., self-reported income and approximate

home value). If at this stage loan officers are more likely to discourage minority homeowners

from moving forward with an application relative to White homeowners, then the applications

observed in HMDA data will provide an incomplete picture of all interested applicants, and results

of differential denial rates conditional on applying could be biased.

One way in which this scenario may play out is if an applicant’s credit score is slightly below

the threshold required to qualify for a certain loan product or particular pricing for that product.

The loan officer may discourage an applicant from proceeding with the application, and if no application is made, the applicant will not appear in HMDA data. As shown in Figure A.2, there does

appear to be a “missing mass” of applications just below salient credit score thresholds (indicated

in black) for Black and White borrowers.1 In particular, there are notably fewer loans just below

620 and 700 than at or just above those scores.

If loan officers are biased against working with Black applicants, one might expect the missing

mass just below these credit score thresholds to be greater for Black than White borrowers, which

would bias the results presented in papers such as this one. For the mortgage equity withdrawal

applications we study, there is no evidence of a differential in missing mass. As shown in Figure

A.3, the ratio of applications by homeowners with scores of 615–619 to those with scores of 620–

624 (and for 695–699 compared to 700–704) is actually higher for Black than white borrowers.

1Our analysis in this section focuses exclusively on Black and White borrowers, but in unreported results the

patterns are very similar for Asian and Hispanic borrowers.

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Thus, we find no evidence of racial differences in discouragement with respect to credit score.

Note, though, that differential rates of discouragement may apply along other dimensions, such as

income, wealth, employment status, or credit history characteristics not captured within the credit

score itself.

Figure A.2. Credit Score Distribution of Mortgage Equity Withdrawal Applications Submitted by

Black and White Borrowers

Note: Includes cash-out refinance, home equity loan, and home equity line of credit applications

from Black and White consumers reported by financial institutions in 2018–2021, with action type

codes 1–5. Source: Home Mortgage Disclosure Act data.

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Figure A.3. Ratio of Applications Just below to Just above 620 and 700 Credit Score Thresholds

Figure A.4. Distribution of Credit Scores and Ratio of Scores Just below to Just above Common

Thresholds

Note: In the top panel, the ratio is the number of applications from borrowers with scores 615–619

to those with scores 620–624. In the lower panel, the ratio is for those with scores 695–699 to

700–704. Source: Home Mortgage Disclosure Act data.

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