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Back-Testing: Using RS Edge to Validate a Prepayment Model

 

Most asset-liability management (ALM) models contain an embedded prepayment model for residential mortgage loans. To gauge their accuracy, prepayment modelers typically run a back-test comparing model projections to the actual prepayment rates observed. A standard test is to run a portfolio of loans as of a year ago using the actual interest rates experienced during this time as well as any additional economic factors used by the model such as home price appreciation or the unemployment rate. This methodology isolates the model’s ability to estimate voluntary payoffs from its ability to forecast the economic variables.

The graph below was produced from such a back-test. The residential mortgage loans in the bank’s portfolio as of 10/31/2016 were run through the ALM model (projections) and compared with the observed speeds (actuals). It is apparent that the model did not do a particularly good job forecasting the actual CPRs, as the mean absolute error is 5.0%. Prepayment model validators typically prefer to see mean absolute error rates no higher than 1 to 2%.

Does this mean there is something unique with the bank’s loan portfolio or servicing practices that would cause prepays to deviate from expectations, or does the prepayment model require calibration?

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 Dissecting the Problem

One strategy is to compare the bank’s prepayment experience to that of the market (see below). The “market” is the universe of comparable loans, in this case residential, conventional loans. This assessment should indicate whether the bank’s portfolio is unique or if it behaves similar to the market. Although this comparison looks better, there are still some material differences, especially at the beginning and end of the time series. 

Prepayments-bank-vs-market

Examining the portfolio composition reveals a number of differences which could be the source of the discrepancy. For example:

  • Larger-balance loans have a greater refinance incentive.
  • California loans historically prepay faster than the rest of the country, while New York loans are historically slower.
  • Broker and correspondent loans typically pay faster than retail originations.

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To compensate, the next step is to adjust the market portfolio to more closely mirror the attributes of the bank’s portfolio. Fine-tuning the “market” so that it better aligns with the bank’s channel and geographic breakout, as well as its larger average loan size, results in the following adjusted prepayment speeds.

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Conclusion

Prepayments for the bank’s mortgage portfolio track the market speeds reasonably well with no adjustments. Compensating for the differences in composition related to channel, geography, and loan size tracks even better and results in a mean absolute error of only 1.1%. This indicates that there is nothing unique or idiosyncratic with the bank’s portfolio that would cause projections from a market-based prepayment model to deviate significantly from the observed speeds. Consequently, the ALM prepayment model likely needs adjustments to its tuning parameters to better capture the current environment