Do Lenders Still Discriminate? A Robust Approach for Assessing Differences in Menus
Findings that minority consumers pay higher interest rates compared with white consumers who are observably similar with respect to the mortgage market can be interpreted as evidence that lenders systematically discriminate against minority borrowers. An alternative explanation for the interest rate disparity is that minority consumers are more constrained in their choice of how much to pay in upfront fees (also known as discount points) in return for lower interest rates. The availability of this choice between a higher upfront fee and a higher interest rate creates a “menu problem” that complicates the detection of lender discrimination and renders methods employed by some studies susceptible to false results. The authors develop a solution to this problem by defining a test statistic for equality in rate and discount point menus and a difference-in-menus metric for assessing whether one group of borrowers would prefer to switch to another group’s menus. They use data from 2018 and 2019 on borrowers’ chosen mortgage rates and discount points to examine whether lenders discriminated against minority borrowers by offering them a distribution of menus that was worse than the one offered to observationally similar non-Hispanic white borrowers.
- According The authors detect inequality between the menus for conforming mortgages offered to non-Hispanic white borrowers and those offered to Black borrowers and Hispanic borrowers.
- On average, Black borrowers obtaining conforming mortgages would be willing to increase their interest rate by at least 2.0 basis points in order to switch to the menus of non-Hispanic white borrowers.
- Hispanic borrowers, on average, would be willing to pay 1.5 basis points more in interest rate in order to switch menus with non-Hispanic white borrowers.
- The differences between the menus offered to non-Hispanic white borrowers and those offered to minority borrowers are concentrated among borrowers who have lower loan-to-value ratios and higher credit scores—the more creditworthy borrowers.
This study’s results are of interest from a regularity perspective because the Fair Lending Act prohibits pricing differentials by race. Furthermore, among the type of mortgages that the authors focus on, those issued to minority borrowers are likely more valuable than those issued to observably similar non-Hispanic white borrowers due to the former groups’ lower prepayment risk (and because the lenders are insured against default by either the GSEs or the Federal Housing Administration). Therefore, if lenders do use unobservable characteristics correlated with race when pricing mortgages, legal or not, the inequality in pricing that the authors observe would be difficult to justify.
We use a new methodology to assess mortgage pricing discrimination faced by minority borrowers. We identify a “menu problem” that comes from the multidimensional nature of mortgage pricing: When getting a mortgage, borrowers can choose to avoid closing costs and pay a high interest rate or contribute to closing costs to get a lower rate. While data on both dimensions of mortgage pricing are by now often available, intuitively attractive metrics of lender pricing discrimination used in the literature can lead to both false and contradictory results. For example, it is sometimes observed that conditional on rate, minority borrowers pay the same closing costs as white borrowers, but conditional on closing costs, minority borrowers pay a higher rate. Though generally underappreciated, the menu problem is broadly relevant in economic assessments of differences in opportunity given data on outcomes. We develop a solution to the menu problem by defining (1) a test statistic for equality in menus and (2) a difference in menus (DIM) metric for assessing whether one group of borrowers would prefer to switch to another group’s menus, both based on pairwise dominance relationships in the data. Our proposed solution is robust to arbitrary heterogeneity in borrower preferences across racial groups. We show how our metrics can be computed using methods from optimal transport and also devise a new procedure for hypothesis testing in this class of problems based on directional differentiation. Finally, we implement our methodology on a data set linking 2018–2019 Home Mortgage Disclosure Act (HMDA) data to Optimal Blue rate locks, and present novel results on mortgage pricing discrimination.