A static pool analysis is a method used by debt investors to evaluate the historical performance of loans or other assets originated by a lending institution over time. By analyzing the performance of previous “vintages” of originated assets, investors can better understand the risk profile and expected performance of newly issued securities.
Most commonly, vintage curves are used by warehouse facility providers to determine performance, concentration limits, advance rates, and pricing for a portfolio.
Why Conduct a Static Pool Analysis?
There are several key reasons why an investor would conduct a static pool analysis when evaluating a securitization of loans or receivables:
Understand Historical Performance Trends
By breaking out historical loan performance by year or quarter of origination (known as vintages), debt investors can identify any improving or worsening trends in underwriting quality, asset performance characteristics, or economic conditions facing borrowers. This allows for a more accurate modeling of expected future performance.
Benchmark New Pools
By establishing historical performance baselines for loans based on origination period, an investor can determine if a new collateral pool deviates positively or negatively from those benchmarks. Investors can pull back on funding if the new cohorts are comparably worse.
Estimate Future Defaults and Losses
Based on historical default and loss data across loan vintages throughout economic cycles, investors can estimate expected lifetime cumulative losses for new collateral pools by modeling out potential default curves. Investors use default curves to price portfolios.
Assess Credit Policy Changes
Trends in default curves across different cohorts can also reveal if the originator is loosening up credit criteria to scale volumes or a scale where credit performance starts to worsen (with the same credit policy), etc. Credit quality sometimes worsens when scaling and improves when the lender reaches scale.
Estimating Future Defaults and Losses
Based on cumulative loss rates by vintage in a sector, investors can forecast potential losses on new originations both in the base case and stress scenarios using monte carlo methods. These feeds into pricing models and haircut guidelines.
Determining Maximum Advance Rates
Advance rates when funding collateral are set to a level where potential losses would not eliminate all credit enhancement and overcollateralization buffers as they build over time. Vintage curves help define appropriate ceilings per asset class.
Setting Concentration Limits
If some vintages show greater volatility or outsized losses, vintage concentration limits can be instituted. For example, balances with 5+ years seasoning may be capped at 25% of the warehouse portfolio.
In some, charting default curves of various loan grades in the portfolio can help identify the riskiest grades for lenders to put concentration limits on those.
Forecasting Future Cashflows
Analysts can use roll-rate models based on transitions between delinquency statuses by vintage to estimate the timing of borrower payments (or defaults) on warehouse collateral as a basis for pricing models.
Conclusion
Static pool analyses reveal valuable insights into the performance trends of loan collateral over time. Assessing vintages helps warehouse investors better model risks, expected returns, and structural protections needed to mitigate potential losses.