Access to fair mortgage products is crucial to many families’ ability to become homeowners. Mortgage access depends on assessments of the risk that a borrower will default, which typically are made through traditional credit scores and other financial information from lending institutions. Legacy models, or traditional credit scoring models, exclude many families from homeownership because these models do not assess consistent gig work, self-employment, a history of timely rent payments, among many other variables that demonstrate a person’s credit worthiness. These models work to disproportionately exclude people of color from accessing credit and therefore homeownership. On October 13, 2021, the National Housing Conference hosted a virtual “Tech & Housing” symposium, with one panel examining how artificial intelligence (AI) and machine learning might help make access to mortgages more equitable without adding risk to the overall housing finance system. Panelists included Sheree Garner, regulatory counsel for government affairs at Rocket Mortgage; Teddy Flo, chief legal officer at Zest AI; Phil Bracken, managing director at VantageScore Solutions; and Ethan Dornhelm, vice president of the Fair Isaac Corporation’s scores and predictive analytics unit. Moderating the discussion was Michael Akinwumi, who leads the Tech Equity Initiative at the National Fair Housing Alliance.
The panelists began by laying out the inadequacies of the traditional measures of borrower risk used in legacy credit scoring models. Bracken pointed out that research by the Joint Center for Housing Studies of Harvard University projects that 80 percent of new household formation over the next 20 years will be among people of color, yet legacy credit assessment systems disproportionately exclude underserved markets and people of color from homeownership. An inherent flaw in those systems, says Flo, is the limited number of data points the models are capable of assessing — data points that systemically favor white borrowers. Borrowers of color, said Garner, are less likely to meet the traditional models’ standards for an acceptable level of lending risk. As the realities of the U.S. economy change, said Garner, leaders in applying AI to credit decisions must educate policymakers and the government-sponsored enterprises (Fannie Mae and Freddie Mac) on why the use of novel data types will accurately reflect borrower risk without increasing risk to the housing finance system.
The panelists agreed that alternative data sources are key to creating fairer risk assessment models that will increase racial and gender equity in access to homeownership. Bracken estimated that, with novel data sources, AI has the potential to provide credit scores for 37 million Americans who currently are not scorable; of those, up to 13 million could have a credit score of 620 or higher, meaning that, with some effort, they could soon be considered eligible for mortgages.