Artificial Intelligence and its inherent bias seems to be an ongoing contributing factor in slowing minorities home loan approvals. An investigation by The Markup found lenders were more likely to deny home loans to people of color than to white people with similar financial characteristics. Specifically, 80% of Black applicants are more likely to be rejected, along with 40% of Latino applicants, and 70% of Native American applicants are likely to be denied. How detrimental is the secret bias hidden in mortgage algorithms?
The Breakdown You Need to Know:
It’s important to note that 45% of the country’s largest mortgage lenders now offer online or app-based loan origination, as FinTech looks to play a major role in reducing bias in the home lending market, CultureBanx reported. Not to mention that with AI involved minority borrowers who get approved online, they’re typically paying more under algorithmic lending. In 2017, $2.25 trillion of the $13 trillion of outstanding household debt in the U.S. was associated with minority households.
Through an analysis of 17 different constant factors of more than two million conventional national mortgage applications, the Associated Press looked deeper into this matter by city. It found that Chicago lenders were 150% more likely to reject Black applicants than similar white applicants. In Waco, TX , the situation is even worse because lenders were more than 200% more likely to reject Latino applicants than white applicants.
Homeownership’s High Stakes:
Disparities in homeownership rates are cited as the leading cause in the racial wealth gap. There are several studies which indicate the median white family holds more than ten times the wealth of the median African American family. McKinsey projected that closing the racial wealth gap could net the U.S. economy between $1.1 trillion and $1.5 trillion by 2028, and homeownership is a major component of that.
AI based lending should be much more altruistic when it comes to home loans because of the simple fact they don’t want to leave any money behind. A study from the National Bureau of Economic Research noted that “if lenders were to discriminate in the accept/reject decision, it would imply that money is left on the table. …(s)uch unprofitable discrimination must reflect a human bias by loan officers.”
The U.S. Census bureau reported that Black homeownership dropped to its lowest level at 40% and has been steadily declining since its 2004 peak. It is possible that AI could help reverse this trend as researchers calculate that, from 2009 to 2015, 0.74 to 1.3 million minority applicants were rejected, who would have been accepted were it not for discrimination by loan officers.
While home lending decisions are officially made by loan officers at each institution, they are largely driven by software, most of it mandated by a pair of quasi-governmental agencies. The American Bankers Association, The Mortgage Bankers Association, The Community Home Lenders Association, and The Credit Union National Association, all criticized The Markup’s analysis. The real devil is in the algorithmic detail and actual homeownership rates, which we know has continued to decline for African Americans over the past few decades.