Social Science

Persistent Racial Disparities in U.S. Home Mortgage Lending: Evidence of Algorithmic and Systemic Bias from HMDA Data

Authors: Rickesh Thandalai Natarajan, Surender Thandalai Natarajan
Comments: 6 Pages.

This study analyzes racial disparities in U.S. home mortgage lending using comprehensive Home Mortgage Disclosure Act (HMDA) data spanning 2007-2016, encompassing nearly one million loan applications. Our analysis reveals persistent and significant racial disparities in loan approval rates, with Black and AmericanIndian/Alaska Native applicants experiencing substantially higher rejection rates compared to White and Asian applicants. Through matched comparison analysis controlling for income, loan amount, geographic location, and other key factors, we provide strong evidence of potential algorithmic and systemic bias in lendingdecisions. Black applicants face approval rates 21.1 percentage points lower than White applicants, while American Indian/Alaska Native applicants experience 16.6 percentage point gaps. Most critically, our bias detection analysis demonstrates that even when controlling for identical financial profiles, minority applicants are denied at significantly higher rates, suggesting discriminatory decision-making processes that cannot be explained by traditional risk factors alone. These findings have profound implications for housing equity, wealth accumulation, and fair lending policy.
Category: Social Science