themortgagellm^™

What it does

Government-insured EPD model. Same 60+DQ-within-12mo target as the GSE EPD model, but trained on Ginnie Mae loan-level data (FHA / VA / USDA-RD / Native American PIH). Trained on 2018-2023 GNMA cohorts (6M loans), tested on 2024: AUC 0.76 (cross-cycle holdout; random-split within 2018-2023 gives AUC 0.81). Isotonic-calibrated. Per-agency AUC: VA 0.85, PIH 0.83, USDA 0.81, FHA 0.78.

Input schema differs from the GSE models — uses agency (F/V/R/N), credit_score (not borrower_fico), ltv (not original_ltv), upfront_mip_rate, annual_mip_rate, etc.

Why it matters. Government-insured loans run structurally higher early-default risk than GSE conforming (1.5-2x baseline), and the loss economics are different — FHA's MIP doesn't fully cover servicer P&I advances or HUD-claim haircuts, and VA no-down-payment loans concentrate first-time-buyer risk. Price elevated GNMA EPD profiles into a tighter origination overlay, escalate marginal FICO/DTI applications, and use the per-agency calibration (VA / FHA / USDA / PIH each get distinct band thresholds) for product-mix decisions.

› Try it on the home page (Loan-level model scoring → GNMA EPD)

API connector

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

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

{
  "agency": "F",
  "credit_score": 680,
  "ltv": 95,
  "dti": 42,
  "loan_interest_rate": 6.5,
  "upfront_mip_rate": 1.75,
  "annual_mip_rate": 0.55,
  "state": "TX",
  ...
}

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

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