A paper by Collateral Analytics authors Dr. Anthony Pennington-Cross, Dr. Michael Sklarz and Dr. Norman Miller highlights the importance of providing a flexible modeling strategy when estimating risk for adjustable rate loans.

The new Collateral Analytics Credit Risk Model (CRM) provides expected performance measures for individual mortgages over their lifetime, while providing the same types of risk measures for a pool of mortgages over their lifetime.

It takes into consideration the nature of the location of the property and the characteristics of the loan, thus making it an ideal tool for Current Expected Credit Loss (CECL) compliance. The risk measures are available for new originations, seasoned loans and pools of loans. Embedded in the CRM is Collateral Analytics’ leading AVM technology and detailed models of default, prepayment and losses.

CECL requires that expected losses need to be projected for all loans, not just impaired loans, the report said.

“Therefore, banks and other financial institutions need robust models and credible economic and financial forecasts. To aid in this process, the new CRM includes a wide variety of fixed rate and adjustable mortgages,” Pennington-Cross, Sklarz and Miller added. “The backbone of the model is the estimated default and prepayment models. To help reflect structural and behavioral differences in real estate and mortgage markets, the model’s key parameters (relationships) are implemented at the regional level (census divisions). In addition, to help reflect the uniqueness of a fixed rate borrower and an adjustable rate borrower, adjustable and fixed rate mortgage parameters are estimated separately.”

The model, according to the paper, generates a variety of measures – such as mortgage value, ratio of mortgage value to unpaid balance, Credit Risk Spread (CRS), etc. – based in part on estimates of expected future defaults and the timing of future prepayments (primarily from refinances and moves).

The main drivers of defaults and losses are the updated loan-to-value ratio of the mortgage, borrower credit scores at origination, and labor market conditions. Other factors, such as location and more detailed characteristics of the loan also matter, the report stated.

“For adjustable rate loans, the characteristics of the loan become more important in at least two regards,” the authors said. “First, borrowers select into specific adjustable rate loans for unique reasons that are not observable, but the selection is typically an attempt to keep initial monthly payments lower than the payments on a comparable fixed rate loan. Second, the structure of an adjustable rate loan creates natural points in time where default and prepayment tend to increase substantially. For example, it is typical for an adjustable rate loan to have an initial time period where interest rates are fixed. After this time period, the interest rate on the loan and hence the monthly payment varies on a predetermined frequency. Consider a loan that is at its first reset date (the first time the interest rate can change). If interest rates have gone up since origination, then the borrower will experience an increase in the monthly payment.

“Even if interest rates have been flat, the borrower may face the same outcome if the initial rate on the loan is a ‘teaser’ that is lower than the fully adjusted rate (index plus margin). At this point in time (when payments increase) the borrower may decide to replace the existing loan through a refinance,” Pennington-Cross, Sklarz and Miller went on to say. “The new loan likely has lower monthly payments, at least until the new teaser expires. Alternatively, especially if the borrower cannot refinance into another loan, the borrow may not be able to make the larger payments and enter default. As a result, when interest rates increase or when ARM borrowers have low initial interest rates, prepayments and defaults will spike and increase expected losses and credit risk.”

The paper also gave examples of CRS.

“The CRS number is expressed in basis points, so a 1 percent CRS is expressed as 100 basis points. To calculate the credit risk charge it is necessary to simulate expected losses,” the authors said. “This is done using the parameters from the default, prepayment and losses models. To project the future, we use seven scenarios for house prices, which include expected, pessimistic (or stressed), and optimistic, as well as projections of interest rates and labor market conditions.”

Another feature of the Collateral Analytics CRM is the ability to highlight variations in credit risk among different markets, the paper stated.

“It takes into consideration the nature of the location of the property and the characteristics of the loan, thus making it an ideal tool for CECL compliance,” the authors said.