Analysis opportunity for vintage analysis
Vintage analysis, specifically vintage pools, present numerous useful opportunities for any firm seeking to further understand the risks within specific portfolios. While most lenders have relatively strong reporting and metrics at hand for their own loan portfolio monitoring…these to understand the specific performance characteristics of their own portfolios — the ability to observe trends and benchmark against similar industry characteristics can enhance their insights significantly.
Assuming that a lender possesses the vintage data and vintage analysis capability necessary to perform benchmarking on its portfolio, the next step is defining the specific metrics upon which any comparisons will be made. As mentioned in a previous posting, three aspects of vintage performance are often used to define these points of comparison:
- Vintage delinquency including charge-off curves, which allows for an understanding of the repayment trends within each pool. Specifically, standard delinquency measures (such as 30+ Days Past Due (DPD), 60+ DPD, 90+ DPD, and charge-off rates) provide measures of early and late stage delinquencies in each pool.
- Payoff trends, which reflect the pace at which pools are being repaid. While planning for losses through delinquency benchmarking is a critical aspect of this process, so, too, is the ability to understand pre-repayment tendencies and trends. Pre-payment can significantly impact cash-flow modeling and can add insight to interest income estimates and loan duration calculations.
As part of the Experian-Oliver Wyman Market Intelligence Reports, these metrics are delivered each quarter, and provide a consistent, static pool base upon which vintage benchmarks can be conducted.
Clearly, this is a rather simplified perspective on what can be a very detailed analysis exercise. A properly conducted vintage analysis needs to consider aspects such as: lender portfolio mix at origination; lender portfolio footprint at origination; lender payoff trends and differences from benchmarked industry data in order to properly balance the benchmarked data against the lender portfolio.
