themortgagellm^™

What it does

Gradient-boosting model rating a Fannie or Freddie loan's probability of being repurchased for rep-and-warranty defect. Trained on the 2013-2023 GSE cohorts (28M+ loans), tested on 2024-2025: AUC 0.72 — the cross-cycle generalization number. (For comparison, a random-split test within 2013-2023 gives AUC 0.81.) Isotonic-calibrated; the 11-year training window spans distinct rate regimes (post-crisis QM, 2021 refi boom, 2022-23 rate spike) for cycle-robust generalization. Use it for post-funding QC, R&W reserve setting, MSR risk adjustment.

Why it matters. Repurchase events are rare (~0.13-0.24% by vintage from our Rule 33 work) but expensive — typically $30-50K per loan in scratch-and-dent loss for the originator. Price R&W exposure into your origination tiers: for high-repurchase-risk profiles, retain more loan-level scrutiny pre-funding, charge a higher upfront margin, or route to channels with lower R&W exposure.

› Try it on the home page (Loan-level model scoring → Repurchase risk)

API connector

Programmatic access. Calibrated probability + risk band + operating recommendation in the response.

POST /api/score_repurchase
Content-Type: application/json

{
  "borrower_fico": 720,
  "dti": 38,
  "original_ltv": 80,
  ...

  // Optional: "seller_name" (your originating lender) adds finer-grained
  //           signal; omit it for the population-baseline pattern.
}

Schema reference (request / response shape): GET /api/score_repurchase/schema

Model metadata (training cohort, AUC, calibration): GET /api/score_repurchase/info