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

• 成果类型: Article
• 署名作者: Bhutta, Neil; Hizmo, Aurel; Ringo, Daniel
• 署名单位: Federal Reserve System - USA; Federal Reserve Bank - Philadelphia; Federal Reserve System - USA; Federal Reserve System Board of Governors
• 刊物名称: JOURNAL OF FINANCE
• ISSN/ISSBN: 0022-1082
• DOI: 10.1111/jofi.13444
• 发表日期: 2025
• 页码: 1463-1496
• 关键词: field experiment; DISCRIMINATION; MARKET; FinTech

摘要

We assess racial discrimination in mortgage approvals using confidential data on mortgage applications. Minority applicants tend to have lower credit scores and higher leverage, and are less likely to receive algorithmic approval from race-blind automated underwriting systems (AUS). Observable applicant-risk factors explain most of the racial disparities in lender denials. Further, exploiting the AUS data, we show there are risk factors we do not observe, and these factors at least partially explain the residual 1 to 2 percentage point denial gaps. We conclude that differential treatment plays a more limited role in generating denial disparities than previous research suggests.