Despite the latest technology disrupting real estate, people of color and minorities still face discrimination when it comes to home buying.
According to a new study by University of California, Berkeley “both online and face-to-face lenders charge higher interest rates to African American and Latino borrowers, earning 11 to 17 percent higher profits on such loans.” Even when those property buyers do qualify for a mortgage, the study finds that they end up paying up to half a billion dollars more in interest every year than white borrowers.
The study’s researchers, led by professors Nancy Wallace and Richard Stanton of the Haas School of Business and Prof. Robert Bartlett of Berkeley Law, collected data based on 30-year, fixed-rate loans that were issued from 2008 to 2015.
“The mode of lending discrimination has shifted from human bias to algorithmic bias,” said study co-author Adair Morse, a finance professor at UC Berkeley’s Haas School of Business. “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.”
The findings are especially surprising considering there are laws in place to prevent loan-lending discrimination based upon race or ethnicity. As it turns out, the discrimination culprit is technology itself. While it used to be humans with preferential tendencies who caused this in the past, it’s now the AI itself that lenders rely on that’s causing the loan disparity.
“The mode of lending discrimination has shifted from human bias to algorithmic bias,” wrote the study co-author Adair Morse, a finance professor at UC Berkeley’s Haas School of Business. “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.”
One of the main reasons for the discriminatory loans — which were based on fairly average data from recent years — is what the authors call “Algorithmic strategic pricing.” This refers to lenders using platform technology to cherry pick potential borrowers based on geography and resources to shop around for a mortgage.
“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 of the algorithmic-based pricing. “The lenders may not be specifically targeting minorities in their pricing schemes, but by profiling non-shopping applicants they end up targeting them.”
The study also comes on the heels of housing loan regulations recently being pared down. Back in July, the Department of Justice rescinded two dozen law enforcement guidance documents at the hands of former Attorney General Jeff Sessions. These included all-important directives and brochures that addressed housing discrimination and mortgage and home equity loan advice. During his short tenure at the DOJ, Sessions managed to ban the practice of loan issuing guidance documents and rescinded documents he felt were “unnecessary, outdated, inconsistent with existing law, or otherwise improper.”
Despite the study’s findings, the data does show an general trend of lending discrimination being on a steady decline, suggesting that AI technology may have increased equality in the mortgage lending area in general.
