Design a hierarchical MF delinquency forecasting system

Q: Design a hierarchical MF delinquency forecasting system

This question evaluates competency in hierarchical time-series forecasting, leakage-safe feature engineering and validation, regime-shift detection, model comparison and reconciliation, and production-ready evaluation and monitoring for multifamily loan delinquency prediction while explicitly addressing lookahead bias.

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Q: What difficulty level is this interview question?

This is a hard difficulty Machine Learning question, commonly asked during Onsite rounds at Freddie Mac.

Q: What role is this question designed for?

This question is commonly asked for Data Scientist candidates at Freddie Mac during technical interviews.

Question

Forecasting 90+ Day Delinquency Rates for Multifamily Loans: Hierarchical, Leakage-Safe System Design

Context

You need to forecast 90+ day delinquency rates for multifamily loans 1–12 months ahead at both the MSA level and the national aggregate. Coherent forecasts must sum/aggregate correctly across the hierarchy. Data are monthly and include:

Loan performance: loan_id, msa, property_type, unpaid_principal (UPB), interest_rate, LTV, DSCR, origination_date, delinquency_status.
Property financials: NOI, occupancy (note reporting lags).
Macro series: msa_unemployment, CPI, mortgage_rates (potentially revised ex post; consider real-time vintages).
Dated policy shocks with known effective dates.

Define the target as the UPB-weighted 90+ day delinquency rate at each horizon.

Task

Design a hierarchical forecasting system that:

Coherent forecasts across levels
- Compare bottom-up vs. MinT reconciliation and explain when each is preferred.
Leakage-safe feature engineering
- Lags, rolling windows, calendar effects, and external regressors.
- Address real-time vs revised macro data and reporting lags to avoid lookahead bias.
Expanding-window cross-validation
- Use an embargo around splits; prevent cross-sectional leakage when loans migrate across MSAs.
Regime shift handling
- Detect and handle regime shifts (e.g., abrupt policy changes, COVID-like shocks) via change-point tests or covariate-shift diagnostics and ensembling across regimes.
Model comparison
- Compare an XGBoost model with monotonic constraints to a regularized dynamic panel model with fixed effects and AR(1) errors. Describe tuning, selection, and interpretability strategies.
Evaluation and monitoring
- Define WAPE by MSA, calibration of delinquency buckets, CRPS for probabilistic outputs, SHAP stability, drift alerts, and retrain cadence.

Be explicit about avoiding lookahead bias at every step.

Design a hierarchical MF delinquency forecasting system

Quick Overview

Forecasting 90+ Day Delinquency Rates for Multifamily Loans: Hierarchical, Leakage-Safe System Design

Context

Task

Solution

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