African American and Latino borrowers continue to pay higher interest rates on their mortgages, even when the loan is completed online.
A recent study by the University of California Berkeley revealed that lenders make between 11% and 17% more profit on purchase loans to minority borrowers.
In all, minority homebuyers pay up to half a billion dollars more in interest every year than white borrowers with comparable credit scores, the study found.
The study merged data from the Home Mortgage Disclosure Act with datasets from ATTOM, McDash and Equifax to collect information that includes loan terms, interest rates, performance, property location, credit history and race and ethnicity. It focused on 30-year, fixed-rate, single-family residential loans originated from 2008-2015 and guaranteed by Fannie Mae and Freddie Mac.
The researchers found that while discriminatory lending practices have traditionally been a result of human prejudice, certain algorithms that drive the digital mortgage process perpetuate the bias. According to the report, these algorithms are designed to target applicants who might not shop their mortgage to find the cheapest deal.
Adair Morse, co-author of the study and finance professor at UC Berkeley’s Haas School of Business, said the mode of lending discrimination has shifted from human bias to algorithmic bias.
“Even if the people writing the algorithms intend to create a fair system, their programming is having a disparate impact on minority borrowers – in other words, discriminating under the law.”
Morse said this “algorithmic strategic pricing” uses big data and machine learning to price loans according to the extent of competition the lender might encounter with a specific customer, perhaps looking at geography or borrower characteristics.
“There are a number of reasons that ethnic minority groups may shop around less – it could be because they live in financial deserts with less access to a range of products and more monopoly pricing, or it could be that the financial system creates an unfriendly atmosphere for some borrowers,” Morse said. “The lenders may not be specifically targeting minorities in their pricing schemes, but by profiling non-shopping applicants they end up targeting them.”
Robert Bartlett, co-author of the study and professor at Berkeley Law, suggested there could be legal implications for lenders using these algorithms.
Bartlett said fair lending laws prohibit price discrimination and that courts have ruled that pricing variations can only be justified by credit risk.
“The novelty of our empirical design is that we can rule out the possibility that these pricing differences are due to differences in credit risk among borrowers,” Bartlett said.
While the study revealed a disturbing trend affecting minority borrowers, it did note that lending discrimination overall has been on a steady decline. According to the authors, their research found two silver linings:
“Algorithmic lending seems to have increased competition or encouraged more shopping with the ease of applications. Also, whereas face-to-face lenders act in negative welfare manner toward minorities in application accepts/rejects, fintechs do not discriminate in application rejections.”