We show that lenders face more uncertainty when assessing default risk of historically under-served groups in US credit markets and that this information disparity is a quantitatively important driver of inefficient and unequal credit market outcomes. We first document that widely used credit scores are statistically noisier indicators of default risk for historically under-served groups. This noise emerges primarily through the explanatory power of the underlying credit report data (e.g., thin credit files), not through issues with model fit (e.g., the inability to include protected class in the scoring model). Estimating a structural model of lending with heterogeneity in information, we quantify the gains from addressing these information disparities for the US mortgage market. We find that equalizing the precision of credit scores can reduce disparities in approval rates and in credit misallocation for disadvantaged groups by approximately half.
But in the biggest ever study of real-world mortgage data, economists Laura Blattner at Stanford University and Scott Nelson at the University of Chicago show that differences in mortgage approval between minority and majority groups is not just down to bias, but to the fact that minority and low-income groups have less data in their credit histories.
This means that when this data is used to calculate a credit score and this credit score used to make a prediction on loan default, then that prediction will be less precise. It is this lack of precision that leads to inequality, not just bias.
But Blattner and Nelson show that adjusting for bias had no effect. They found that a minority applicant’s score of 620 was indeed a poor proxy for her creditworthiness but that this was because the error could go both ways: a 620 might be 625, or it might be 615.
Credit analytics firm FICO posits that the reason for the correlation of credit history and claim probability is that “individuals who closely and cautiously monitor and manage their finances tend to also take better care of their cars and homes and are, generally, more diligent in their risk management habits.” Because such individuals are found across demographic classifications, the discrimination argument becomes hard to uphold.
If insurers find that credit scores have bearing on accident propensity, insurers should be allowed to use them. Preventing insurers from deploying basic tools required to generate appropriate risk-adjusted prices leads to mispricing of risk, harming insurance buyers as well as insurers. What is more, such deprivation leads to unintended negative consequences—an unfair socialization of risk, leaving customers either overcharged or undercharged. Executive fiat prohibiting insurers from accessing the tools of their trade is tantamount to Pharaoh ordering the Israelites of old to make bricks without straw. Bad business, bad policy.
In a new blog post for the International Monetary Fund, four researchers presented their findings from a working paper that examines the current relationship between finance and tech as well as its potential future. Gazing into their crystal ball, the researchers see the possibility of using the data from your browsing, search, and purchase history to create a more accurate mechanism for determining the credit rating of an individual or business. They believe that this approach could result in greater lending to borrowers who would potentially be denied by traditional financial institutions.
At its heart, the paper is trying to wrestle with the dawning notion that the institutional banking system is facing a serious threat from tech companies like Google, Facebook, and Apple. The researchers identify two key areas in which this is true: Tech companies have greater access to soft-information, and messaging platforms can take the place of the physical locations that banks rely on for meeting with customers.
The Federal Reserve Bank of New York on Wednesday released its quarterly report on household debt and credit for the final three months of 2020, with its strategists and statisticians deciding to dig deeper into mortgage originations, the types of homebuyers during the Covid-19 pandemic and to what extent Americans are taking out cash against their home equity. While much of what they found confirms many of the narratives about the housing market, it’s the sheer magnitude of the move that’s breathtaking and puts into context where the economy stands almost one year after the coronavirus crisis began in the U.S.
At the highest level, mortgage originations reached almost $1.2 trillion in the final three months of 2020, the highest quarterly volume in the history of the New York Fed’s data, which begins in 2000. Americans refinanced more mortgage debt last year than any time since 2003, while mortgages taken out to purchase a home surged to the highest since 2006. First-time buyers took on more debt than at any time in history, while mortgages for repeat buyers and those looking for a second home or an investment property reached the highest in more than a decade.
Meanwhile, home prices soared across the U.S., with the S&P CoreLogic Case-Shiller index jumping 9.5% in November, the most since 2014 (December’s figures will be released next week). This surge led to “a notable increase in cashout refinance volumes, which spiked in the fourth quarter of 2020 and show no sign of abating,” the New York Fed researchers said in a blog post. Collectively, homeowners withdrew $182 billion in home equity in 2020, or an average of about $27,000 for each household. Even those who chose not to take out extra cash saved an average of $200 a month on their mortgage payments.