Minority Representation at Mortgage Lenders: Frame, Huang, Jiang, Lee, Liu, Mayer & Sunderam (2025)

What this is. The paper’s core results, the datasets it links, and the estimating equations: enough to know what it found and how, without reading all 52 pages. To replicate or extend it, read the full source at the original.

TL;DR

Using a new panel that links 5.65 million U.S. home purchase mortgage applications (2018-2019 HMDA) to the individual loan officers who handled them (via NMLS), Frame, Huang, Jiang, Lee, Liu, Mayer, and Sunderam (2025) establish two facts: (1) minorities are significantly underrepresented among loan officers (15% minority share versus 29-39% in comparable white-collar professions), and (2) minority borrowers are about 2 percentage points less likely to have their applications completed, 1.2-3 percentage points less likely to be approved (for high-discretion applications), and 2.5 percentage points less likely to originate a loan when handled by White loan officers. These gaps shrink substantially when the loan officer is also a minority. Critically, default rates on minority loans originated by White officers are 1.7-2.2 percentage points higher than for White borrowers, while minority-officer-matched minority loans default at the same rate as comparable White borrower loans. The pattern is consistent with minority loan officers having an informational advantage in handling minority borrower applications, rather than simple favoritism.

The paper contributes to three strands of the literature. First, it adds to the long tradition beginning with Munnell et al. (1996) on racial disparities in mortgage approval. Bhutta, Hizmo, and Ringo (2024) show the approval gap is largely explained by observed risk factors; this paper shows the residual gap is explained by the absence of minority loan officers who can supply soft information. Second, it extends the cultural-proximity credit result of Fisman, Paravisini, and Vig (2017) from Indian banks to the U.S. mortgage market, where automated underwriting and hard information dominate. Third, it complements Ambrose, Conklin, and Lopez (2021) and Bartlett et al. (2022): the minority-officer effect is strongest at small banks and weakest at FinTech lenders, consistent with FinTech reducing scope for loan-officer soft information.

Core results

Magnitudes and significance are as reported; \*/\*\*/\*\*\* = 10%/5%/1%. Locators point into the source PDF.

#	Result	Locator	Magnitude
R1	Minority applicants are 1.9 pp less likely to complete applications under White loan officers (within-officer comparison); gap is 1.1 pp smaller under minority officers	Table III, cols. (1)-(2), p. 1224	Minority: beta = -0.019*** (0.001); Minority x Minority Officer: beta = +0.011*** (0.002)
R2	For high-discretion applications, minority applicants are 2.9 pp less likely to be approved under White loan officers; gap is 1.2 pp smaller under minority officers	Table III, col. (5)-(6), pp. 1224-1227	Minority: -0.029*** (0.002); Minority x Minority Officer: +0.012*** (0.003) in col. (5)
R3	In all-in origination rates, minority applications are 5 pp less likely to originate under White loan officers; gap is 2.5 pp smaller (about 50%) under minority officers	Table III, cols. (7)-(8), p. 1224	Minority: -0.050*** (0.002); Minority x Minority Officer: +0.025*** (0.003) in col. (7)
R4	IV estimates (day-of-week instrument): high-discretion minority approval gap is 3.6 pp under White officers, and minority-officer interaction fully offsets the gap	Table IV, Panel C, col. (3), p. 1233	Minority: -0.031*** (0.003); Minority x Minority Officer: +0.036*** (0.016)
R5	FHA default analysis (OLS): minority borrowers with White officers default 1.8 pp more; Minority x Minority Officer interaction is -2.2 pp, eliminating the excess default rate	Table V, col. (1), p. 1237	Minority: +0.018*** (0.001); Minority x Minority Officer: -0.022*** (0.002)
R6	FHA default IV: minority borrowers with White officers default 1.7 pp more; IV interaction is -5.2 pp, fully eliminating the excess	Table V, col. (4), p. 1237	Minority: +0.017*** (0.002); Minority x Minority Officer: -0.052*** (0.025)
R7	Cross section of mechanism: effect concentrated in same-race/ethnicity pairings and in counties with a high share of non-native English speakers and low college share	Table VII, Panel A cols. (1),(3), pp. 1241-1243; Panel B cols. (2),(3)	Same Race triple interaction: +0.008*** (0.003); High Non-English Share triple interaction: +0.005* (0.003)
R8	Minorities are underrepresented among loan officers: 15% of loan officers are minorities versus 60.7% White (2019), while minorities are 39.3% of the U.S. labor force; minority share systematically below local population share	Table I, Panel A, p. 1219; Figure 1, p. 1221	84.6% White, 8.9% Hispanic, 1.8% Black, 4.7% Asian among N=255,277 loan officers

Overall (paper’s conclusion). Minority loan officers have an informational advantage in processing applications from minority borrowers: they achieve higher approval rates and lower default rates simultaneously, a pattern inconsistent with taste-based discrimination. The underrepresentation of minorities among loan officers therefore reduces minority access to credit, and this effect persists even in the hard-information-intensive U.S. mortgage market where automated underwriting systems dominate.

Theory / model

The paper has no formal model. The paper’s identification strategy rests on two testable hypotheses:

Hypothesis 1 (supply-side discrimination / information advantage). If minority loan officers have better soft information about minority borrowers, they can help those borrowers complete stronger applications, achieve higher approval rates, and generate loans that perform better (lower defaults). This is the authors’ preferred interpretation.

Hypothesis 2 (taste-based discrimination). If White loan officers discriminate against minority borrowers based on taste, they would apply stricter standards: minority borrowers handled by White officers would have lower approval rates (as observed) but also lower default rates (the paper’s Table V rules this out; default rates are in fact higher for minority loans handled by White officers).

The paper tests the hypotheses by examining approval and default rates jointly. Taste-based discrimination predicts lower approvals AND lower defaults for White-officer-handled minority loans. Information advantage predicts lower approvals AND higher defaults (because excluded minority borrowers are creditworthy, so the approved pool selected by stricter White officers is adversely selected relative to the approved pool selected by informed minority officers). The data match the information-advantage prediction.

Identification. Endogenous matching of loan officers to borrowers is a central concern. Two approaches address it:

1. Tight fixed effects: branch-year and branch-year-officer fixed effects isolate within-officer, within-branch variation in how the same officer treats minority versus White applicants.
2. Day-of-the-week instrument (Section II.C, pp. 1228-1234): exogenous variation in whether a minority officer handles a specific application is generated by minority officer work schedules.

Method

BIFSG race imputation (pp. 1215-1216). Loan officer race/ethnicity is not observed in NMLS. The paper applies the Bayesian Improved First Name Surname Geocoding (BIFSG) method of Voicu (2018) to infer each officer’s race/ethnicity. For surname $s$, first name $f$, and ZIP code $z$, the posterior probability of belonging to race group $r$ is (equation 1, p. 1215):

$$p(r \mid s, f, z) = \frac{p(r \mid s) \times p(f \mid r) \times p(z \mid r)}{\sum_{r=1}^{6} p(r \mid s) \times p(f \mid r) \times p(z \mid r)} \tag{1}$$

where $p(r \mid s)$ is the probability of belonging to race group $r$ given surname (from the 2010 Census surname list), $p(f \mid r)$ is the probability of having first name $f$ given race $r$ (from the Tzioumis 2018 list), and $p(z \mid r)$ is the probability of being in ZIP code $z$ given race $r$ (from the 2010 Census). Officers are assigned to the race group with the highest posterior probability.

Day-of-the-week instrument (pp. 1228-1229). For application $i$ opened at branch office $b$ on day of the week $d$ in week $w$, the instrument is the share of applications at the same branch on the same day of the week during the prior 12 weeks ($w - 12$ to $w - 1$) handled by minority officers:

$$Z_{i,b,d,w} = \frac{\text{\#Minority Officer Applications}_{b,d,w-12 \to w-1}}{\text{\#Applications}_{b,d,w-12 \to w-1}}$$

The first stage regresses an indicator for the application being handled by a minority officer on the instrument, branch-week fixed effects, day-of-the-week fixed effects, and controls (p. 1228):

$$\mathbf{1}\{\text{Minority Officer}\}_{i,b,d,w} = \alpha_{b,w} + \beta Z_{i,b,d,w} + \gamma' \mathbf{X}_{i,b,d,w} + \varepsilon_{i,b,d,w} \tag{3}$$

First-stage F-statistics exceed 15 across all samples (Table IV, Panel A). Covariate balance tests (Table IV, Panel B) show the instrument is uncorrelated with borrower age, income, loan amount, FICO, LTV, DTI, and AUS recommendation code.

Empirical specifications

The main estimating equation (equation 2, p. 1223) is a linear probability model:

$$Y_i = \beta_1 \mathbf{1}\{\text{Minority}\}_i + \beta_2 \mathbf{1}\{\text{Minority Officer}\}_i + \beta_3 \mathbf{1}\{\text{Minority}\}_i \times \mathbf{1}\{\text{Minority Officer}\}_i + \gamma' X_i + \varepsilon_i \tag{2}$$

where $Y_i$ is in turn: application completion, approval (conditional on completion), all-in origination, or default (90+ days delinquent). The parameter of interest is $\beta_3$, the differential effect of having a minority loan officer on outcomes for minority versus White applicants. Standard errors are two-way clustered by lender and county.

Application-level specifications (Table III, pp. 1223-1228):

• Columns (1) and (3), (5), (7): branch-year fixed effects and property-county fixed effects, plus Basic App Controls (loan type indicators, 10-year age bins, income-to-MSA-median centile bins, log(loan amount), jumbo indicator, joint application indicator). This exploits cross-officer variation within the same branch-year.
• Columns (2) and (4), (6), (8): replace branch-year FE with branch-year-officer FE, so identification is within-officer (comparing how the same officer treats minority versus White applicants). The $\beta_2$ coefficient on Minority Officer is absorbed; only $\beta_3$ is identified.
• For approval regressions (cols. 3-6), the sample is restricted to completed applications and the Extended App Controls add FICO-bin, LTV-bin, and DTI-bin indicators (all interacted with loan type) plus AUS output code fixed effects. The sample is further split into “low-discretion” (AUS approval rate > 90%) and “high-discretion” (AUS approval rate <= 90%) applications.

FHA default specification (Table V, p. 1237): The dependent variable is an indicator for the FHA loan ever becoming 90+ days delinquent. Controls (FHA Controls) include log(loan amount), income-to-MSA centile bins, FICO-bin, LTV-bin, DTI-bin (all interacted with loan type), interest rate, first-time buyer indicator, branch-year FE, property-county FE, and origination-month FE. The IV variant uses branch-month FE and day-of-the-week FE with the same instrument $Z_{i,b,d,w}$ recalculated on FHA loans at the branch over prior 12 months.

Mechanism tests (Tables VII-IX, pp. 1240-1251): Same base specification augmented with triple interactions for: same-race/ethnicity pairing, low-income borrower, small bank, FinTech lender, rural county, high non-English share, low college share (Table VII); loan officer minority application share and experience (Table VIII); and linear hard information variables (credit score, DTI) to test differential reactions to hard information (Table IX).

Datasets used

Dataset	Role in paper	Wiki page
NMLS Consumer Access (2012-2019)	Nationwide loan officer panel: name, employer, work address, career history; source for BIFSG race imputation	NMLS
Confidential HMDA (2018-2019, Federal Reserve)	Mortgage applications matched to loan officers via NMLS ID; includes FICO, LTV, DTI, AUS code (added to confidential version from 2018)	[no page yet]
FHA insured mortgage data (2000-2018, FHA/Federal Reserve)	Population of FHA single-family originations; used for default analysis (90+ days delinquent through 2019-Q3)	FHA
Black Knight McDash (matched to HMDA 2018-2019)	Monthly performance data; 60-day default within 24 months of origination; 36% match of HMDA approved mortgages	[no page yet]
U.S. Census Bureau (ZIP code level)	Demographic and economic characteristics (minority population share, PIPC, population density, non-English share, college share)	[no page yet]

Sample: HMDA analysis covers 5.65 million first-lien 30-year fixed-rate home purchase mortgage applications (owner-occupied single-family properties) in 2018-2019, after filters. FHA default sample: ~3.37 million loans originated 2012-2018, tracked through 2019-Q3.

When to read the full paper

Read the original if you are: studying the supply-side determinants of racial disparities in mortgage lending; evaluating the role of soft information in a setting dominated by hard information and automated underwriting; assessing whether minority officer representation has welfare-improving effects via credit expansion with no increase in default; or working on fair lending policy or the economics of racial diversity in financial services. The Internet Appendix (18+ tables) contains the BIFSG validation, subsample analyses by race/ethnicity pairing, shopping behavior robustness, and additional mechanism tests.

Attribution and rights

Source: peer-reviewed, The Journal of Finance 80(2), April 2025. Copyright 2025 the American Finance Association; portions contributed by U.S. Government employees are in the public domain in the USA. This distillation was extracted by an LLM on 2026-06-06 and is not human-verified or independently reproduced. The underlying confidential HMDA and FHA microdata are proprietary and were not accessed here. Extract-only; no PDF hosted.

Frame, W. Scott, Ruidi Huang, Erica Xuewei Jiang, Yeonjoon Lee, Will Shuo Liu, Erik J. Mayer, and Adi Sunderam. “The Impact of Minority Representation at Mortgage Lenders.” The Journal of Finance 80, no. 2 (April 2025): 1209-1260. DOI: 10.1111/jofi.13428.