Exploring Gender and Racial Disparities in Mortgage Pricing: A Comprehensive Analysis of Risk-Based Lending Practices

Hongyan Liang ()
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Hongyan Liang: Gies College of Business, University of Illinois Urbana-Champaign, United States Author-2-Name: Zilong Liu Author-2-Workplace-Name: Gies College of Business, University of Illinois Urbana-Champaign, United States Author-3-Name: Author-3-Workplace-Name: Author-4-Name: Author-4-Workplace-Name: Author-5-Name: Author-5-Workplace-Name: Author-6-Name: Author-6-Workplace-Name: Author-7-Name: Author-7-Workplace-Name: Author-8-Name: Author-8-Workplace-Name:

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Abstract: " Objective - This study examines whether risk-based mortgage pricing in the U.S. is neutral with respect to borrower gender and race/ethnicity, and whether any observed pricing gaps persist after controlling for borrower credit risk and loan characteristics using a nationally representative dataset. Methodology - Using the National Survey of Mortgage Originations (NSMO) Public Use File covering originations from 2013–2020, we evaluate demographic disparities in mortgage rate spreads and default outcomes through a unified empirical framework. Specifically, we employ exact matching and propensity score matching to compare observably similar borrowers, interaction regression models to test whether standard risk factors are priced differently across demographic groups, and residual-based diagnostics to assess unexplained pricing components after conditioning on observable risk factors. Findings - Across methods, female borrowers face higher mortgage rate spreads than comparable male borrowers, despite exhibiting similar or lower observed default risk. Racial disparities are more nuanced: Black and Hispanic borrowers exhibit higher raw loan costs and higher default rates, but conditional rate-spread differences largely attenuate after controlling for observable risk factors, whereas Asian borrowers consistently receive more favorable pricing. Interaction effects indicate that the marginal pricing of key risk variables differs across demographic groups, and residual analysis highlights a persistent unexplained gender pricing component. Novelty - The paper contributes by combining a uniquely rich dataset that links borrower demographics with detailed credit quality and performance measures and by integrating matching, interaction modeling, and residual diagnostics within one unified framework to evaluate both level differences and differential marginal pricing in mortgage rates. Type of Paper - Empirical"

Keywords: Mortgage Pricing; Demographic Disparities; Gender Bias; Racial Bias; Machine Learning; Fair Lending; Propensity Score Matching; Financial Regulation; Credit Risk; Algorithmic Fairness (search for similar items in EconPapers)
JEL-codes: C21 D63 G21 G28 J15 (search for similar items in EconPapers)
Pages: 23
Date: 2026-03-31
New Economics Papers: this item is included in nep-mid
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Published in Global Journal of Business and Social Science Review, Volume 14, Issue 1

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Persistent link: https://EconPapers.repec.org/RePEc:gtr:gatrjs:gjbssr673

DOI: 10.35609/gjbssr.2026.14.1(1)

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