Data Finds Minorities Still Face Higher Mortgage Denials

A new Financial Times analysis highlights stark disparities in mortgage approvals. Even when applicants report the same income, debt, and loan amounts, minority borrowers are more likely to be turned away.

Black applicants face more than double the rejection rate of white applicants for conventional mortgages. Hispanic and Asian applicants also see disproportionately high denials — a gap that raises serious questions for lenders and credit decision-makers.

Dennis Kelleher of Better Markets told Financial Times that “communities of color are still being discriminated against by the biggest financial institutions in this country.”

For subprime lenders, who already work with higher-risk or thin-file borrowers, the findings underline both a business challenge and a fairness issue.

Minority applicants continue to face higher rejection rates — a gap that suggests lenders may be overlooking qualified customers while exposing themselves to compliance and reputational risks.

Credit bureaus and lenders argue that disparities often stem from incomplete data. Public mortgage datasets, such as Home Mortgage Disclosure Act filings, don’t include actual credit scores, making it hard to parse borrower risk. But the problem runs deeper.

Traditional scoring models lean heavily on past borrowing and repayment history. For many minority consumers — especially those in underbanked neighborhoods — this leaves a thinner or even invisible credit file.

The National Fair Housing Alliance notes that non-white neighborhoods are more likely to have check cashers and payday lenders than mainstream banks. These lenders rarely report positive payment activity, meaning many responsible borrowers never see it reflected in their scores.

The result: Entire communities are undercounted in the very systems meant to measure financial trustworthiness.

That’s where alternative data comes in. Rent payments, utility bills, and cellphone payments are increasingly recognized as meaningful signals of creditworthiness.

Experian and other bureaus have long argued for the inclusion of this data, and Fannie Mae and Freddie Mac recently approved VantageScore models that account for rental payments. For subprime lenders, these inputs could mean the difference between a denial and an approval.

It’s about fairness and accuracy. A borrower who has consistently paid rent for years demonstrates the ability to manage housing obligations, even without a long credit history. Incorporating these signals can reduce false rejections while maintaining risk controls.

Pilot programs over the past few years suggest that adding rental and utility data can boost the scores of thin-file borrowers, potentially narrowing approval gaps between white and minority applicants.

For lenders, the upside is greater access without added default risk — but only if the data holds up. Back-testing and default-rate comparisons are critical; without them, lenders risk misjudging creditworthiness once the models go live.

Installing new data flows is complex. Banks must put controls in place to comply with the Fair Credit Reporting Act and fair-lending rules. That means setting clear cutoff thresholds, monitoring for disparate impact, and running fairness checks alongside risk analysis.

Adding more data doesn’t come cheap. Lenders will have to support technological improvements, third-party relationships, and validation. And smaller banks must overcome more hurdles. The payoff for attracting high-quality borrowers may exceed the investment, especially if competitors choose that path.

A number of lenders are experimenting with enriched scoring models that incorporate rent payment and utility data, presenting themselves as inclusive. This places the squeeze on subprime lenders: if they slip up, they risk losing market ground to competitors that rush to approve or grant better perks to the underserved.

Laws like the Equal Credit Opportunity Act and the Community Reinvestment Act were meant to prevent discrimination, but persistent gaps suggest compliance alone isn’t enough.

Regulators and activists are paying attention. Lenders that adopt fairer scoring models can reduce legal risk and protect their reputations.

Strong fairness practices don’t just limit penalties — they also open the door to new borrowers. In a competitive market, that consistency can become a differentiator.