Submitted · J. Real Estate Finance & Economics · 2026

Who Bears the Burden?

Heterogeneous Racial Approval Differentials in U.S. Mortgage Lending — causal forest double ML on 42.3 million applications.

ProblemBlack mortgage applicants are approved far less often than White applicants — the open question is how much of that survives once creditworthiness is accounted for.

ApproachCausal machine learning estimates an individual-level penalty for every applicant, then checks who specifically carries it.

ResultA 9.39-point penalty survives controls for 90.7% of Black applicants — and it's far larger under manual review than automated scoring.

SignificancePoints the cause at human discretion in underwriting, not the algorithms — the opposite of where bias concerns usually focus.

42.3MHMDA applications

−9.39 ppConditional penalty

90.7%Applicants penalised

17.9×Placebo signal-to-noise

The Question

Across 42.3 million HMDA mortgage applications (2020–2024), the raw Black–White approval gap is 14.95 percentage points. The interesting question is causal, not descriptive: how much of that gap survives once genuine creditworthiness is accounted for — and who, specifically, bears it?

Approach

I estimate individual-level conditional average treatment effects with double machine learning (LightGBM nuisance models, cross-fitting) and causal forests, adjusting for 33 creditworthiness controls — income, LTV, DTI, loan purpose and type, automated-vs-manual underwriting, and geography. Cross-fitting removes regularization bias so the effect estimate is not contaminated by the prediction models.

Triangulation

The headline estimate is triangulated across five independent identification strategies — DFL decomposition, within-lender fixed effects, RDD at the 80% LTV PMI threshold, difference-in-differences across the 2022–24 tightening, and Manski partial-identification bounds. A race-shuffle placebo yields a 17.9× signal-to-noise ratio; the DR-Learner replicates the estimate within 0.15 pp.

Outcome

A −9.39 pp mean conditional penalty remains after controls — 62.8% of the raw gap is left unexplained by observable creditworthiness. 90.7% of Black applicants face a negative conditional effect, and the penalty is largest under manual underwriting (−14.8 pp) versus automated systems (−6.2 pp), implicating human discretion over algorithmic scoring as the dominant channel.

Methodology Pipeline

Key Result

Manual underwriting−14.8 pp

Automated underwriting−6.2 pp

The gap is driven by human discretion, not automated scoring

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