CECL-ALM Additional Services

We first perform data mining to identify concentrations in investments, loans, and deposits, recognizing that the risk can arise from different areas and can be interrelated. For example, if a financial institution had an indirect auto loan portfolio sourced from a limited number of dealers, understanding the percentage of the portfolio arising from each dealer and their relative credit performance (FICO and delinquency) would be important in understanding and managing credit risk. Or, if a financial institution had a concentration of residential real estate loans, it would be vulnerable to a downturn in real estate prices in a specific geographic area. Interrelated risks are exemplified in an institution with a concentration of long-term residential real estate loans and a relatively large portfolio of agency mortgage-backed securities. It would incur credit risk from the loans and heightened interest rate risk from the combination of loans and securities.

We collaborate with our clients to identify concentrations, then develop critical input assumptions. We believe the best analysis technique to be used depends on the type of loan. For example, a financial institution could analyze its commercial real estate loans by re-underwriting its largest loans based on its knowledge of the borrower and current and forecasted economic conditions. It could combine this with a historical migration analysis – how many loans with a risk rating of one migrated to lower ratings over time. Wilary Winn believes the sheer volume of residential real estate and consumer loans in a portfolio preclude loan-by-loan analyses and are best analyzed using statistical techniques. We begin with the contractual cash flows based on the attributes of the loan and adjust them for:

• Voluntary prepayments – called the conditional repayment rate (CRR)
• Involuntary prepayments or defaults – called the conditional default rate (CDR)
• Loss severity or loss given default – the loss that will be incurred (loss severity) 

A major advantage of this technique is its reliance on the use of the same credit indicators that financial institutions now use to underwrite loans and manage their loan portfolios, including FICO, loan term, and loan-to-value percentage.

The primary disadvantage of this technique is that most institutions have insufficient data to produce statistically valid loss estimates. We overcome this hurdle by combining industry-wide data with a financial institution’s specific performance. We do this by using a statistical theory called “creditability.” See our Implementing CECL white paper for more details.

Like many, we believe the most challenging part of capital stress testing is incorporating forecasted changes in macroeconomic conditions in its estimate. We believe the use of discounted cash flow analyses and updated credit indicators results in the most reliable approach to meeting this challenge, because we can apply assumptions from the bottom up rather than being forced to use a top-down assumption.

For example, when modeling the performance of residential real estate loans, we begin with an updated combined loan-to-value (LTV) based on a recent automated valuation model (AVM). We do not need to infer about the loan’s credit indicators as of the valuation date, as would be required by other techniques such as vintage analysis – we know what they are. To include short-term changes in housing prices, we utilize forecasts by metropolitan statistical area (MSA).

Longer term, we incorporate the forecasted change in national housing prices. In this way, we incorporate short-term changes which are more certain with a national forecast driven by forecasted economic conditions and historic performance. We use these estimates to change our loss severity estimates.

Our models also include a dynamic default vector that is tied to forecasted changes in housing prices. We change our rate of default based on changes to the estimated LTV given normal amortization, curtailments, and changes in housing prices. In this way, we are adjusting our loss estimates based on macroeconomic forecasts.