A new analysis reveals that Black homebuyers in the U.S. are 1.7 times more likely to be denied a mortgage compared to other applicants. The LendingTree study highlights persistent disparities driven mainly by debt-to-income ratios and credit history issues. Despite a gradual rise in Black homeownership rates, the gap between Black and other racial groups continues to widen nationwide.
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Stark Mortgage Disparities
According to LendingTree’s 2025 report, nearly 20% of mortgage applications from Black homebuyers are denied, compared to 11.27% for all applicants, a gap of 7.73 percentage points. In the nation’s 50 largest metro areas, the denial rate for all borrowers was 9.47% in 2024, while Black applicants faced a rate of 14.27%. Two Michigan cities, Grand Rapids and Detroit, topped the list, with denial rates for Black borrowers exceeding 20%.
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Lowest Gaps by City
On the other end, Salt Lake City, Utah, recorded the smallest gap in denial rates. There, the Black denial rate was just 0.24 percentage points higher than the overall rate. LendingTree’s data also placed three Texas and three California cities among the ten metros with the smallest disparities, indicating regional variations in lending patterns.
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Key Reasons for Denials
The primary reason for mortgage denials remains the applicant’s debt-to-income (DTI) ratio. In 2024, this factor accounted for 34.02% of all denials and 34.08% among Black applicants. However, credit history emerged as a more significant hurdle for Black homebuyers, responsible for 33.16% of their denials compared to 24.85% for the general applicant pool, marking an 8.31-point difference.
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AI’s Rising Role in Lending
As AI transforms the financial sector with an estimated $97 billion investment projected by 2027, concerns grow about whether these technologies will help reduce or deepen racial inequities. A 2024 Urban Institute analysis of federal mortgage data found that Black and Brown borrowers remain more than twice as likely to be denied loans as white applicants, despite longstanding protections under the Fair Housing Act of 1968.
Bias Risks in AI Systems
In an unequal society, AI can mirror and even amplify existing biases if not carefully monitored. “Bias and discrimination found in AI are often not the result of explicit design but can stem from factors like a lack of diverse design team members, unrepresentative or biased data, or just plain human oversight,” experts caution. Cases include facial recognition failing to identify darker skin tones and tenant screening algorithms disproportionately rejecting formerly incarcerated individuals.
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Costly Lending Discrimination
Discriminatory practices in both traditional and AI-powered lending carry significant financial consequences. A 2022 UC Berkeley study on fintech loans found that, on average, African American and Latinx borrowers pay nearly 5 basis points more in interest than their credit-equivalent white counterparts, translating to $450 million in extra annual interest payments nationwide.
