Some of the most important problems in our society are not secret. But that does not mean they don’t need investigating.
One of those hidden-in-plain-sight problems is the massive wealth disparity between White and Black families in the U.S. In 2019, White families had an average net worth of $983,400 while the average Black family’s net worth was about a tenth of that, $142,500, according to the Federal Reserve
One well-documented reason for the gap has been the disparity in homeownership, which has been a key method of building wealth and passing it on to the next generation. The Federal Reserve study found a particularly stark gap in homeownership between young (under 35) White and Black people. In that group, nearly half of the White families owned a home, while just 17 percent of Black families did.
The wealth and homeownership gaps are similar between White and Latino families, according to the Fed.
Some of the homeownership gap is due to disparities in mortgage lending: applications from Blacks and Latinos are denied more often than those from White applicants. And that disparity is powered in part by several algorithms—mainly those created or required by Fannie Mae and Freddie Mac—that are a key part of the process of approving and denying loan applications.
The Markup investigative data reporter Emmanuel Martinez has been trying to get to the root of the mortgage lending gap for years. In 2018, he published a landmark investigation at the nonprofit newsroom Reveal showing that redlining in mortgage lending persisted
in 61 metro areas even when he controlled for nine different variables including applicants’ income, loan amount, and neighborhood. That investigation landed him and his reporting partner, Aaron Glantz, two of the most coveted prizes in investigative journalism: the Selden Ring
and a finalist for the Pulitzer Prize
The lending industry responded that the gaps between people of color and White applicants could be explained by financial characteristics that were not available in the public data: debt-to-income ratio, combined loan-to-value ratio, and credit score. So, last year, when Emmanuel learned that the first two of those three factors were now part of the public data, he decided to see whether the industry was right. Would these factors erase the gaps?
He spent more than a year analyzing more than 17 million records, running more than 150 statistical regression equations. He found that the industry’s defense didn’t hold water.
Holding 17 factors steady in a complex statistical analysis
of 2019 conventional mortgage applications, Emmanuel found that lenders were 40 to 80 percent more likely to reject an applicant of color than a White applicant.