CRE Loss Rate Model
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Moody’s Analytics CRE loss rate model is a simple yet appropriate method to estimate CECL allowance for smaller, less complex institutions with commercial real estate portfolios. It is a pool-level credit risk model that incorporates qualitative factors, such as mortgage characteristics and macroeconomic factors. Our CRE loss rate model is naturally scenario-conditioned, incorporating reasonable and supportable forecasts in its loss estimation.
Use trusted industry-based forecasts to assess your expected credit losses
- Lifetime expected credit losses based on client portfolio composition.
- Quarterly loss rate available until maturity.
- Output based on loan and property characteristics and macroeconomic forecasts.
- Extensive historical data covering multiple credit cycles.
- Results available under baseline, consensus, regulatory, or eight alternative scenarios.
- Detailed methodology and scenario documentation.
Understand portfolio credit risk
- Model produces expected credit losses (ECL) by vintage, property status, property type, and delinquency status.
- Construction loans are inherently riskier than permanent loans with everything else being equal.
- Origination loan-to-value (LTV) ratio is a key risk driver.
- Model accounts for remaining contractual lifetime.
- Portfolio credit risk is also affected by macroeconomic factors and CRE market cycle factors.
- Estimated lifetime loss rates for Moody’s CRE CRD portfolio under different scenarios are comparable to actual bank charge-off rates in various historical periods.
CRE Loss Rate Model is part of Moody’s Analytics Credit Loss and Impairment Analysis Suite, which improves credit loss estimation analysis and calculations. Its data integrity, analytics, and regulatory reporting solutions provide a modular, flexible, and comprehensive impairment solution that facilitates a firm’s efforts to calculate, manage, and report expected credit losses.
Related Solutions
Current Expected Credit Loss Model (CECL)
Moody’s Analytics provides tools for the most crucial aspects of the expected loss impairment model, with robust solutions to aggregate data, calculate expected credit losses, and derive and report provisions.
