Can Everyone Tap into the Housing Piggy Bank? Racial Disparities in

Access to Home Equity

Jim Conklin

(with Kris Gerardi and Lauren Lambie-Hanson)

University of Georgia

FDIC Consumer Research Symposium

March 15, 2024

The views expressed are those of the authors and do not necessarily reflect those of any entities within

the Federal Reserve System. . . . . . . . . . . . . . . . . . . . .

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Conklin (UGA) Disparities in Access to Home Equity March 15, 2024 1 / 25
Fed disclaimer (co-authors) → They do not speak for:

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Conklin (UGA) Disparities in Access to Home Equity March 15, 2024 2 / 25
Fed disclaimer (co-authors) → They do not speak for:

Patrick T. Harker, Raphael Bostic, President Philly Fed President Atlanta Fed

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Conklin (UGA) Disparities in Access to Home Equity March 15, 2024 2 / 25
Fed disclaimer (co-authors) → They do not speak for:

Jerome Powell, Patrick T. Harker, Raphael Bostic, Chairman of Federal President Philly Fed President Atlanta Fed Reserve

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Conklin (UGA) Disparities in Access to Home Equity March 15, 2024 2 / 25
My disclaimer → I also don’t speak for my boss:

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Conklin (UGA) Disparities in Access to Home Equity March 15, 2024 3 / 25
My disclaimer → I also don’t speak for my boss:

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Conklin (UGA) Disparities in Access to Home Equity March 15, 2024 3 / 25
Motivation

Large minority-White gaps in homeownership rates

A lot of attention (policy/research) on closing ownership gaps,

because...

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Conklin (UGA) Disparities in Access to Home Equity March 15, 2024 4 / 25
Motivation

Large minority-White gaps in homeownership rates

A lot of attention (policy/research) on closing ownership gaps,

because...

Home equity is an important source of wealth for U.S. . . . . . . . . households . . . . . . . . . . . .

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Conklin (UGA) Disparities in Access to Home Equity March 15, 2024 4 / 25
Motivation

Home equity used for (to list a few):

Consumption smoothing

Finance home improvements

Finance education

Finance small businesses

Downpayment for home purchase by children

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Conklin (UGA) Disparities in Access to Home Equity March 15, 2024 5 / 25
Motivation

Home equity used for (to list a few):

Consumption smoothing

Finance home improvements

Finance education

Finance small businesses

Downpayment for home purchase by children

But, not trivial to access home equity!

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Conklin (UGA) Disparities in Access to Home Equity March 15, 2024 5 / 25
Motivation

Home equity used for (to list a few):

Consumption smoothing

Finance home improvements

Finance education

Finance small businesses

Downpayment for home purchase by children

But, not trivial to access home equity!

Three main ways:

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Conklin (UGA) Disparities in Access to Home Equity March 15, 2024 5 / 25
Motivation

Home equity used for (to list a few):

Consumption smoothing

Finance home improvements

Finance education

Finance small businesses

Downpayment for home purchase by children

But, not trivial to access home equity!

Three main ways: 1 Sell house (high transaction costs)

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Conklin (UGA) Disparities in Access to Home Equity March 15, 2024 5 / 25
Motivation

Home equity used for (to list a few):

Consumption smoothing

Finance home improvements

Finance education

Finance small businesses

Downpayment for home purchase by children

But, not trivial to access home equity!

Three main ways: 1 Sell house (high transaction costs)

2 Reverse mortgage (only for people over 62)

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Conklin (UGA) Disparities in Access to Home Equity March 15, 2024 5 / 25
Motivation

Home equity used for (to list a few):

Consumption smoothing

Finance home improvements

Finance education

Finance small businesses

Downpayment for home purchase by children

But, not trivial to access home equity!

Three main ways: 1 Sell house (high transaction costs)

2 Reverse mortgage (only for people over 62)

3 Mortgage equity withdrawal (MEW) products (e.g., HELOC)

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Conklin (UGA) Disparities in Access to Home Equity March 15, 2024 5 / 25
1

This paper → use 2018+ cHMDA data to answer the following questions

How difficult is it for homeowners to access their home equity via

MEW products?

Cash-out refinances

Home equity lines of credit (HELOCs)

Closed-end home equity loans (HELoans)

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Conklin (UGA) Disparities in Access to Home Equity March 15, 2024 6 / 25
This paper → use 2018+ cHMDA data to answer the following questions

1 How difficult is it for homeowners to access their home equity via

MEW products?

Cash-out refinances

Home equity lines of credit (HELOCs)

Closed-end home equity loans (HELoans)

2 Racial disparities in access to MEW products?

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Conklin (UGA) Disparities in Access to Home Equity March 15, 2024 6 / 25
This paper → use 2018+ cHMDA data to answer the following questions

1 How difficult is it for homeowners to access their home equity via

MEW products?

Cash-out refinances

Home equity lines of credit (HELOCs)

Closed-end home equity loans (HELoans)

2 Racial disparities in access to MEW products?

3 What explains racial disparities?

Underwriting factors?

Discrimination?

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Conklin (UGA) Disparities in Access to Home Equity March 15, 2024 6 / 25
This paper → use 2018+ cHMDA data to answer the following questions

1 How difficult is it for homeowners to access their home equity via

MEW products?

Cash-out refinances

Home equity lines of credit (HELOCs)

Closed-end home equity loans (HELoans)

2 Racial disparities in access to MEW products?

3 What explains racial disparities?

Underwriting factors?

Discrimination?

Under-explored area → dearth of data before cHMDA 2018+

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Conklin (UGA) Disparities in Access to Home Equity March 15, 2024 6 / 25
Main Findings

1 MEW rejection rates are substantially higher than purchase and rate-term refinance

rates.

26% vs. 10%

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Conklin (UGA) Disparities in Access to Home Equity March 15, 2024 7 / 25
Main Findings

1 MEW rejection rates are substantially higher than purchase and rate-term refinance

rates.

26% vs. 10%

2 Unconditional racial disparities in MEW denial rates are 2 to 7 times larger than in

purchase and rate-term applications.

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Conklin (UGA) Disparities in Access to Home Equity March 15, 2024 7 / 25
Main Findings

1 MEW rejection rates are substantially higher than purchase and rate-term refinance

rates.

26% vs. 10%

2 Unconditional racial disparities in MEW denial rates are 2 to 7 times larger than in

purchase and rate-term applications.

3 Standard underwriting factors explain a large portion of the Black-White and

Hispanic-White gaps.

Credit scores and DTI ratios are the most important factors.

Lender FEs also important.

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Conklin (UGA) Disparities in Access to Home Equity March 15, 2024 7 / 25
Main Findings

1 MEW rejection rates are substantially higher than purchase and rate-term refinance

rates.

26% vs. 10%

2 Unconditional racial disparities in MEW denial rates are 2 to 7 times larger than in

purchase and rate-term applications.

3 Standard underwriting factors explain a large portion of the Black-White and

Hispanic-White gaps.

Credit scores and DTI ratios are the most important factors.

Lender FEs also important.

4 “Residual” disparities non-trivial ⇒ 3–4 ppts.

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Conklin (UGA) Disparities in Access to Home Equity March 15, 2024 7 / 25
Main Findings

1 MEW rejection rates are substantially higher than purchase and rate-term refinance

rates.

26% vs. 10%

2 Unconditional racial disparities in MEW denial rates are 2 to 7 times larger than in

purchase and rate-term applications.

3 Standard underwriting factors explain a large portion of the Black-White and

Hispanic-White gaps.

Credit scores and DTI ratios are the most important factors.

Lender FEs also important.

4 “Residual” disparities non-trivial ⇒ 3–4 ppts.

5 Unobserved underwriting factors unlikely to be sole driver of “residual” disparities

(discrimination?) . . . . . . . . . . . . . . . . . . . .

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Conklin (UGA) Disparities in Access to Home Equity March 15, 2024 7 / 25
Data and Sample Construction

Confidential HMDA data in 2018–2021 period.

Additional loan and borrower characteristics added to HMDA reporting requirements in 2018.

DTI and LTV ratios.

Credit score (confidential files only)

HMDA coverage expanded to include HELOCs in 2018.

Exemption for institutions with fewer than 500 originations.

MEW products (HELOCs, HELoans, cash-out refis)

Asian, Black, Hispanic, and White applicants.

∼ 16 million applications across all 50 states.

Summary Statistics

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Conklin (UGA) Disparities in Access to Home Equity March 15, 2024 8 / 25
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

Application denial rates (and racial gaps) much higher on MEW

Mean Denial Rates

Non-MEW → 10%

Cash-out refi → 18%

HELoans → 33%

HELOCs → 37%

Minority-White denial gaps much

larger for MEW products!

B/W gap non-MEW → 9 ppts.

B/W gap HELOCs → 30 ppts.

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Conklin (UGA) Disparities in Access to Home Equity March 15, 2024 9 / 25
Back-of-the-Envelope Calculations – over our sample period

(under some assumptions)

(in billions) White Black Hispanic Asian

Applied to extract $599.1 $46.4 $68.4 $89.2

Denied $159.0 $23.0 $26.2 $32.9

Unconditional “excess” denied $11.2 $8.8 $8.5

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Conklin (UGA) Disparities in Access to Home Equity March 15, 2024 10 / 25
If you take nothing else away from the presentation...

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Conklin (UGA) Disparities in Access to Home Equity March 15, 2024 11 / 25
1

If you take nothing else away from the presentation...

Relatively hard to access equity via MEW (esp. HELOCs

and HELoans)

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Conklin (UGA) Disparities in Access to Home Equity March 15, 2024 11 / 25
If you take nothing else away from the presentation...

1 Relatively hard to access equity via MEW (esp. HELOCs

and HELoans)

2 Really hard for minorities . . . . . . . . . . . . . . . . . . . .

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Conklin (UGA) Disparities in Access to Home Equity March 15, 2024 11 / 25
Can Observable Risk Factors Explain Denial Gaps?

Regress application denial indicator on race/ethnicity dummies and loan, property, and

applicant risk factors, X.

Yit = β1Blacki + β2Hispanici + β3Asiani + Xitγ + ηt + λl + ωs + εit

Positive race/ethnicity coefficients ⇒ discrimination assuming X includes all relevant risk

factors. List of covariates

But, unobserved factors include: assets/liquid wealth, employment status and history, etc.

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Conklin (UGA) Disparities in Access to Home Equity March 15, 2024 12 / 25
. . .

Alternative Geographic Controls

Can Observable Risk Factors Explain Denial Gaps?

Dependent Var: Loan Denied (d)

(1)

Black (d) 0.212***

(0.022)

(2)

0.192***

(0.019)

(3)

0.068***

(0.008)

(4)

0.065***

(0.008)

(5)

0.056***

(0.005)

(6)

0.042***

(0.004)

Unconditional B/W denial gap =

21 ppts.

Hispanic (d)

Asian (d)

0.095***

(0.025)

0.076***

(0.018)

0.092***

(0.015)

0.087***

(0.013)

0.034**

(0.011)

0.073***

(0.012)

0.034**

(0.011)

0.075***

(0.012)

0.027**

(0.008)

0.058***

(0.008)

0.028***

(0.005)

0.043***

(0.005)

“Excess denials” of ∼ 4 ppts

remain.

Year FE

State FE

DTI Buckets

Credit Score Buckets

CLTV Buckets

N

N

N

N

N

Y

Y

N

N

N

Y

Y

Y

Y

N

Y

Y

Y

Y

Y

Y

Y

Y

Y

Y

Y

Y

Y

Y

Y

Estimated $2 billion in loan

proceeds denied “in excess” over

4 years. Other Controls N N N N Y Y

Lender FE

# Observations

Adjusted R2

Mean Denial Rate

N

12,638,969

0.020

0.26

N

12,638,969

0.047

0.26

N

12,638,969

0.240

0.26

N

12,638,969

0.259

0.26

N

12,638,969

0.316

0.26

Y

12,638,969

0.380

0.26

Which controls matter most?

Depends on order.

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

Standard errors double-clustered: state and lender. . . . . . . . . . . . . . . . . .

Logit Models

Results by Product Type

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Conklin (UGA) Disparities in Access to Home Equity March 15, 2024 13 / 25
Kitagawa/Oaxaca/Blinder (KOB) Decomposition (B/W Gaps)

0

5%

10%

15%

20%

25%

30%

35%

Cash-Out Refis HELoans HELOCs

Credit Score DTI

CLTV Explained by Other Controls

Unexplained

Credit score (40-45% of total B/W

gap)

Debt-to-income

Combined loan-to-value ratio

List of all covariates

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Results vary across different types of financial institutions

Banks, credit unions, and nonbanks

Minorities more likely to be denied across all product types and financial institutions.

Denial rate gaps generally smallest at nonbanks

B/W denial gap largest at credit unions (esp. HELoans and HELOCs)

Some evidence that Hispanics and Asians fare better at

minority-owned banks

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Conklin (UGA) Disparities in Access to Home Equity March 15, 2024 15 / 25
Residual racial disparities (after controlling for observables in

HMDA)

(AI image generator) supposed to say “acknowledged” ⇒ my job is safe...for now

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Conklin (UGA) Disparities in Access to Home Equity March 15, 2024 16 / 25
Conklin (UGA)

(assets, employment history, etc.)

Residual racial disparities (after controlling for observables in

HMDA)

(AI image generator) supposed to say “acknowledged” ⇒ my job is safe...for now

Discriminatory behavior or unobserved underwriting factors? . . . . . . . . . . . . . . . . . . . .

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Disparities in Access to Home Equity March 15, 2024 16 / 25
Scope for lender discrimination may be larger in MEW.

Standardized AUSs play a larger role in the purchase and rate-term refinance market

segments.

Secondary market / securitization ⇒ formal/rigid reviews using common AUSs (e.g.,

Desktop Underwriter©).

HELOCs and HELoans are often held in bank portfolios.

Internal underwriting models leave more room for subjective considerations.

MEW borrowers more likely to apply in-person.

We provide some evidence in paper

Exercise

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Conklin (UGA) Disparities in Access to Home Equity March 15, 2024 17 / 25
Are residual racial disparities driven solely by unobserved (to us)

underwriting factors? ⇒ NOT LIKELY !

1 Kitagawa-Blinder-Oaxaca decompositions by lender.

Are residual disparities greater at lenders with highest levels of explained disparities?

If Black applicants at a specific lender are very different on observables, they may also be

very different on unobservables. KOB

2 Control for AUS decision (only possible for cash-out refis)

Accounts for many underwriting factors not available in HMDA AUS

3 Lender strictness correlated with residual disparities?

Lenders that rely more on factors not in HMDA data have larger racial disparities? Strictness

4 Are magnitudes of residual disparities correlated with geographic

variation Animus . . . . . . . . . . . . . . . . . . . . in racial animus? . . . . . . . . . . . . . . . . . . . .

Conklin (UGA) Disparities in Access to Home Equity March 15, 2024 18 / 25
Conclusion

MEW products → high denial rates (26% vs. 10% for non-MEW)

44% among Black applicants, 32% among Hispanic

Underwriting factors (esp. credit scores and DTI) explain a large

portion of racial denial rate gaps

Racial disparities in MEW are quantitatively important

Black applicants: $11.2 ($2) billion in unconditional (conditional) excess equity denied.

Hispanic borrowers: $8.8 ($2.2) billion.

Asian borrowers: $8.5 ($3.9) billion.

“Residual disparities” → probably not driven solely by unobserved

underwriting factors

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Conklin (UGA) Disparities in Access to Home Equity March 15, 2024 19 / 25
SUPPLEMENTAL SLIDES

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Conklin (UGA) Disparities in Access to Home Equity March 15, 2024 19 / 25
Sample Summary Statistics

Table: 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

# Observations 9,572,670 1,011,791 1,184,479 870,398

Back . . . . . . . . . . . . . . . . . . . .

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Conklin (UGA) Disparities in Access to Home Equity March 15, 2024 19 / 25
Mean Values of Select Characteristics by Race Back

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

HELoan (d) 0.12 0.12 0.12 0.10

HELOC (d) 0.33 0.29 0.29 0.39

# Observations 9,572,670 1,011,791 1,184,479 870,398

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Conklin (UGA) Disparities in Access to Home Equity March 15, 2024 20 / 25
List of Covariates

Categorical variables:

Credit score, CLTV, DTI, Household income, Loan amount, Loan term, # units

Indicator variables:

Loan type (FHA, VA, GSE), Product type (HELOC, HELoan, Cash-out refi), Second lien,

Home improvement loan, Prepayment penalty, Interest-only loan, Non-amortizing features,

Second residence, Investment property, Co-applicant

Back (eqn.) Back (decompositions)

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Conklin (UGA) Disparities in Access to Home Equity March 15, 2024 20 / 25
Logit Models

Table: 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

Back . . . . . . . . . . . . . . . . . . . .

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Conklin (UGA) Disparities in Access to Home Equity March 15, 2024 20 / 25
Product Type Regressions

Table: Applicant Race and Likelihood of Denial: 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

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

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Conklin (UGA) Disparities in Access to Home Equity March 15, 2024 20 / 25
Product Type Regressions

Table: Applicant Race and Likelihood of Denial: HELoans

(1) (2)

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

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

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Conklin (UGA) Disparities in Access to Home Equity March 15, 2024 20 / 25
Product Type Regressions

Table: Applicant Race and Likelihood of Denial: HELOCs

(1) (2)

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

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Conklin (UGA) Disparities in Access to Home Equity March 15, 2024 20 / 25
Alternative Geographic Controls

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

Black 0.042*** 0.035*** 0.035*** 0.041***

(0.004) (0.003) (0.003) (0.003)

Hispanic 0.028*** 0.025*** 0.027*** 0.026***

(0.005) (0.004) (0.004) (0.004)

Asian 0.043*** 0.045*** 0.047*** 0.045***

(0.005) (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 N Y

Census Tract FE N Y N N

Lender x Tract FE N N Y N

County x Year FE N N N Y

Observations 12,638,969 12,638,969 10,864,102 12,505,642

Adjusted R-squared 0.380 0.383 0.399 0.387

Mean Denial Rate 0.26 0.26 0.26 0.26

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Conklin (UGA) Disparities in Access to Home Equity March 15, 2024 20 / 25
Minority-Owned Bank Shares

A lot of geographic heterogeneity in market shares of minority-owned banks.

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Conklin (UGA) Disparities in Access to Home Equity March 15, 2024 20 / 25
Face-to-face Interactions More Likely in MEW Space

Use feature of HMDA data reporting instructions to calculate a proxy for in-person

lending.

During application process lenders must impute each applicant’s race, ethnicity, and

gender “based on visual observation or surname” if the applicant declines to supply

that information on the application AND the lender meets with the applicant in

person prior to closing.

Rate of imputation among applicants who don’t supply information (∼ 20%) is good

indicator of frequency with which applications are taken in-person.

Lenders imputed race on 46% of HELOC and 31% of HELoans applications where borrowers

refused to provide information.

Only 7% for cash-out and rate/term refis and 13% for purchase loans.

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Conklin (UGA) Disparities in Access to Home Equity March 15, 2024 20 / 25
Unobservables? → Lender Decompositions

Top 50 Back largest lenders for each MEW product type.

Lender-level B/W KOB decomposition.

Sort lenders by size of “explained” component.

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

Size of “explained” component not highly correlated w/ size of “unexplained” component. . . . . . . . . . . . . . . . . . . . .

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Conklin (UGA) Disparities in Access to Home Equity March 15, 2024 21 / 25
Unobservables? → AUS Recommendation (Bhutta et al. (2022))

(cash-out refis) Back

(1) (2) (3)

“AUS Denial” Application Denial Application Denial

without AUS Outcome Control with AUS Outcome Control

Black 0.020*** 0.037*** 0.027***

(0.003) (0.004) (0.003)

Hispanic 0.006*** 0.013*** 0.010***

(0.002) (0.003) (0.003)

Asian 0.007*** 0.023*** 0.020***

(0.001) (0.002) (0.002)

AUS “Denial” 0.464***

(0.025)

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 Y Y Y

AUS “Denial” N N Y

# Observations 6,138,590 6,138,590 6,138,590

Adjusted R2 0.203 0.208 0.311 . . . . . . . . . . . . . . . . . . . . Mean Outcome 0.08 0.13 0.13. . . . . . . . . . . . . . . . . . . .

Conklin (UGA) Disparities in Access to Home Equity March 15, 2024 22 / 25
Unobservables? → Lender Strictness (Bhutta et al. (2022)) Back

1 Denial regression for White borrowers only → lender FE capture “lender

strictness” (lenders focus on factors not observed in HMDA)

2 Relationship between lender-level residual racial disparities and lender strictness

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Conklin (UGA) Disparities in Access to Home Equity March 15, 2024 23 / 25
Unobservables? → Lender Strictness in MEW (top 50 lenders) →

No relationship between lender strictness for Whites and B/W Denial Gap

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Conklin (UGA) Disparities in Access to Home Equity March 15, 2024 24 / 25
Minority denial gaps are positively correlated with location’s level of racial

animus Back

Media markets’ level of racial animus (Stephens-Davidowitz, 2014).

Google search queries only focused on racial animus toward Black individuals.

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

Positive correlation between “unexplained” denial rate gaps and racial animus at CBSA

level ⇒ consistent with discrimination being a factor.

1-std-dev ↑ in media market racial animus ⇒ 1.0 ppt ↑ in Black-White denial gap. . . . . . . . . . . . . . . . . . . . .

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Conklin (UGA) Disparities in Access to Home Equity March 15, 2024 25 / 25