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

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

Design a hierarchical MF delinquency forecasting system

Quick Overview

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.

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.

Quick Overview

Context

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

Submit Your Answer to Earn 20XP

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

Submit Your Answer to Earn 20XP