Online lenders charge minority borrowers significantly more, as do human loan officers
Online and human lenders earn 11-17% more profit on minority borrowers by charging higher rates to African Americans and Latinos, the study found. Black and Latino consumers pay 5.6 to 8.6 basis points higher interest on home-purchase loans than their white or Asian counterparts with similar credit profiles, whether they got their loans through a face-to-face or online process. The effect is less on refinancing, with black and Latino borrowers paying 3 basis points more.
The disparity causes African Americans and Latinos together to pay up to half a billion dollars more in mortgage interest each year, the study found.
“Distancing from humans should eliminate malicious forms of discrimination,” said Adair Morse, professor of finance at the Haas School of Business at Berkeley, who co-authored the paper. “But we are entering an era where we use variables to statistically discriminate between people in loans.”
The results are significant as more and more consumers buy mortgages online. Almost half of the top 2,000 mortgage lenders offer complete online mortgage applications.
Morse and his colleagues — Nancy Wallace and Richard Stanton at Haas and Robert Bartlett at Berkeley Law — focused on single-family, 30-year fixed-rate home loans issued between 2008 and 2015. They were able to link rate data to interest, loan terms, property location, income and credit ratings with first-time borrowers race. All loans were guaranteed by government-sponsored companies Fannie Mae and Freddie Mac, allowing researchers to remove credit risk as a factor in price differences.
“Even controlling for creditworthiness, we see discriminatory effects in the rates at which borrowers obtain mortgages,” Bartlett said.
The researchers said the racial disparities could result from algorithms that use machine learning and big data to charge higher interest rates to borrowers who might be less likely to shop around. For example, the algorithms can take into account a borrower’s neighborhood – noting who lives in the banking deserts – or other characteristics such as their high school or college. The consumers least likely to shop around are also black or Latino.
It is legal to use statistical data to set prices that help maximize profits – in theory. The problem arises when the data correlates with race, independent of credit risk. Discriminating against minority borrowers — even unintentionally — is illegal unless it’s based on their creditworthiness, Bartlett said.
Bartlett said banks that increasingly use big data to determine approvals or loan rates should be audited to ensure their methods don’t discriminate against minority borrowers who have the same credit scores as whites.
The researchers described a few silver linings in their study. Increased competition among lenders has resulted in less discrimination overall. And when it comes to determining whether to accept or decline a loan, online lenders don’t discriminate against minorities, while their human counterparts are 4% more likely to reject Latino borrowers. and African Americans.
On the contrary, online lenders end up meeting the needs of people discriminated against by face-to-face lenders, according to the study.
“Rejecting loans would be money left on the table for lenders,” Morse said.