Mortgage Credit-Risk Forecasting
This research project builds a reproducible baseline for temporal mortgage credit-risk forecasting using Fannie Mae and Freddie Mac loan-performance data.
Research question
Can temporal loan-performance, borrower-origination, and macroeconomic features predict future mortgage delinquency or default risk more robustly than static credit-risk baselines?
Modeling approach
The project starts conservatively with auditable baselines: data ingestion, schema standardization, target construction, time-aware validation, logistic regression, tree-based models, and calibration metrics. More advanced temporal models belong later, after the baseline is credible.
Why it matters
Mortgage credit-risk models are central to portfolio monitoring, housing finance, stress testing, and public-sector economic analysis. The key data-science challenge is not just prediction; it is preventing leakage, defining targets correctly, and validating models in time-aware ways.
Status
This is a private research-in-progress repository and paper path. It is framed as a baseline research project that can later support simulation-augmented forecasting and agent-based stress features.