How Much Does Racial Bias Affect Mortgage Lending? Evidence from Human and Algorithmic Credit Decisions

43 Pages Posted: 19 Jul 2021 Last revised: 28 Aug 2022

See all articles by Neil Bhutta

Neil Bhutta

Federal Reserve Bank of Philadelphia

Aurel Hizmo

Board of Governors of the Federal Reserve System

Daniel Ringo

Board of Governors of the Federal Reserve System

Multiple version iconThere are 2 versions of this paper

Date Written: August 2, 2022

Abstract

We assess racial discrimination in mortgage approvals using new data on mortgage applications. Minority applicants tend to have significantly lower credit scores, higher leverage, and are less likely than white applicants to receive algorithmic approval from race-blind government automated underwriting systems (AUS). Observable applicant-risk factors explain most of the racial disparities in lender denials. Further, we exploit the AUS data to show there are risk factors we do not directly observe, and our analysis indicates that these factors explain at least some of the residual 1-2 percentage point denial gaps. Our results imply significant progress in fair lending for mortgages over the last 30 years.

Keywords: Discrimination, mortgage lending, automated underwriting, credit score, fair lending

JEL Classification: G21, G28, R30, R51

Suggested Citation

Bhutta, Neil and Hizmo, Aurel and Ringo, Daniel, How Much Does Racial Bias Affect Mortgage Lending? Evidence from Human and Algorithmic Credit Decisions (August 2, 2022). Available at SSRN: https://ssrn.com/abstract=3887663 or http://dx.doi.org/10.2139/ssrn.3887663

Neil Bhutta (Contact Author)

Federal Reserve Bank of Philadelphia ( email )

10 Independence Mall
Philadelphia, PA 19106
United States

Aurel Hizmo

Board of Governors of the Federal Reserve System ( email )

20th Street and Constitution Avenue NW
Washington, DC 20551
United States

Daniel Ringo

Board of Governors of the Federal Reserve System ( email )

20th Street and Constitution Avenue NW
Washington, DC 20551
United States

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