Design a hierarchical MF delinquency forecasting system
Forecasting 90+ Day Delinquency Rates for Multifamily Loans: Hierarchical, Leakage-Safe System Design
Context
Design a system to forecast 90+ day delinquency rates for multifamily loans 1–12 months ahead. Forecasts are required at both the Metropolitan Statistical Area (MSA) level and the national aggregate. A critical constraint is that all forecasts must be coherent, meaning they sum or aggregate correctly across the hierarchy.
Monthly data available includes:
- Loan performance:
loan_id,msa,property_type,unpaid_principal (UPB),interest_rate,LTV,DSCR,origination_date,delinquency_status. - Property financials:
NOI,occupancy(note: consider reporting lags). - Macro series:
msa_unemployment,CPI,mortgage_rates(note: these may be revised ex post; consider using real-time vintages). - Dated policy shocks: Specific policy changes with known effective dates.
The target variable is defined as the UPB-weighted 90+ day delinquency rate at each forecast horizon.
Task
Design a comprehensive hierarchical forecasting system that explicitly addresses the following requirements:
- Coherent Forecasts Across Levels: Compare and contrast bottom-up reconciliation with MinT (Minimum Trace) reconciliation, explaining when each approach is preferred for ensuring hierarchical consistency.
- Leakage-Safe Feature Engineering: Detail strategies for creating features such as lags, rolling windows, calendar effects, and external regressors. Specifically address how to handle real-time versus revised macro data and reporting lags to rigorously avoid lookahead bias.
- Robust Cross-Validation: Propose an expanding-window cross-validation strategy. This must include an embargo period around splits and prevent cross-sectional leakage, particularly when loans might migrate across MSAs.
- Regime Shift Handling: Describe methods to detect and manage regime shifts (e.g., abrupt policy changes, COVID-like economic shocks). This should involve change-point tests or covariate-shift diagnostics and strategies for ensembling models across different regimes.
- Model Comparison: Compare an XGBoost model, specifically incorporating monotonic constraints, against a regularized dynamic panel model that includes fixed effects and AR(1) errors. Outline strategies for model tuning, selection, and ensuring interpretability for both.
- Evaluation and Monitoring: Define key metrics and processes for evaluating and continuously monitoring the system's performance. This includes WAPE (Weighted Absolute Percentage Error) by MSA, calibration of delinquency buckets, CRPS (Continuous Ranked Probability Score) for probabilistic outputs, SHAP (SHapley Additive exPlanations) stability, drift alerts, and the appropriate retraining cadence.
Throughout your design, be explicit about how lookahead bias is avoided at every step.
AI interview prep
Core dilemma
Balancing the need for highly accurate, robust, and interpretable hierarchical forecasts in a dynamic financial environment, while rigorously preventing data leakage and ensuring operational scalability.
3 tradeoffs and 3 stress tests locked
Interview prep
Unlock the interviewer X-Ray rubric, tradeoff matrix, stress tests, clarifying questions, and key concepts.
Design a hierarchical MF delinquency forecasting system
Forecasting 90+ Day Delinquency Rates for Multifamily Loans: Hierarchical, Leakage-Safe System Design
Context
Design a system to forecast 90+ day delinquency rates for multifamily loans 1–12 months ahead. Forecasts are required at both the Metropolitan Statistical Area (MSA) level and the national aggregate. A critical constraint is that all forecasts must be coherent, meaning they sum or aggregate correctly across the hierarchy.
Monthly data available includes:
- Loan performance:
loan_id,msa,property_type,unpaid_principal (UPB),interest_rate,LTV,DSCR,origination_date,delinquency_status. - Property financials:
NOI,occupancy(note: consider reporting lags). - Macro series:
msa_unemployment,CPI,mortgage_rates(note: these may be revised ex post; consider using real-time vintages). - Dated policy shocks: Specific policy changes with known effective dates.
The target variable is defined as the UPB-weighted 90+ day delinquency rate at each forecast horizon.
Task
Design a comprehensive hierarchical forecasting system that explicitly addresses the following requirements:
- Coherent Forecasts Across Levels: Compare and contrast bottom-up reconciliation with MinT (Minimum Trace) reconciliation, explaining when each approach is preferred for ensuring hierarchical consistency.
- Leakage-Safe Feature Engineering: Detail strategies for creating features such as lags, rolling windows, calendar effects, and external regressors. Specifically address how to handle real-time versus revised macro data and reporting lags to rigorously avoid lookahead bias.
- Robust Cross-Validation: Propose an expanding-window cross-validation strategy. This must include an embargo period around splits and prevent cross-sectional leakage, particularly when loans might migrate across MSAs.
- Regime Shift Handling: Describe methods to detect and manage regime shifts (e.g., abrupt policy changes, COVID-like economic shocks). This should involve change-point tests or covariate-shift diagnostics and strategies for ensembling models across different regimes.
- Model Comparison: Compare an XGBoost model, specifically incorporating monotonic constraints, against a regularized dynamic panel model that includes fixed effects and AR(1) errors. Outline strategies for model tuning, selection, and ensuring interpretability for both.
- Evaluation and Monitoring: Define key metrics and processes for evaluating and continuously monitoring the system's performance. This includes WAPE (Weighted Absolute Percentage Error) by MSA, calibration of delinquency buckets, CRPS (Continuous Ranked Probability Score) for probabilistic outputs, SHAP (SHapley Additive exPlanations) stability, drift alerts, and the appropriate retraining cadence.
Throughout your design, be explicit about how lookahead bias is avoided at every step.