themortgagellm^™

What it does

Gradient-boosting classifier rating the probability that a HMDA-style application closes as an originated loan (action_taken = 1). Trained on the 2018-2023 HMDA Snapshot LAR (49M applications, 50% deterministic sample of the ~99M-app universe), tested on 2024-2025 (22.5M apps): AUC 0.92 (cross-cycle holdout; random-split within 2018-2023 gives AUC 0.93). Isotonic-calibrated; calibration is near-perfect (ECE = 0.0004 on test). Industry baseline pull-through across the training window is 61.7%. Same HMDA-style input schema as the HPML and denial models.

Why it matters. Pull-through is the single biggest driver of pipeline-hedge sizing accuracy: a 5-point miss on close rate compounds to a multi-million-dollar mark-to-market gap for a $1B / month originator (over-hedged or under-hedged into a rate move). Use the per-application prediction for secondary-marketing lock coverage, lead-quality pricing, and capacity planning — staff UW to expected close volume, not expected application volume. Also a sharp originator-quality benchmark: a broker panel running 5 pp below market on similar applications is signaling process friction.

› Try it on the home page (Loan-level model scoring → Pull-through)

API connector

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

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

{
  "loan_type": "1",                 // 1=Conv, 2=FHA, 3=VA, 4=USDA-RD
  "lien_status": "1",
  "loan_purpose": "1",
  "occupancy_type": "1",
  "cltv": 80,
  "loan_amount": 350000,
  "property_value": 437500,
  "income": 120,
  "loan_to_income_ratio": 2.92,
  "state_code": "CA",
  "lei": "549300...",
  ...
}

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

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