Modeling Mortgage Prepayments with Rate History and Refinancing Incentives
Summary
The document explains why mortgage prepayment forecasting can be difficult when a model uses borrower traits and current conditions alone. Borrowers’ refinancing choices depend on the path of interest rates: the same current mortgage rate can imply different prepayment incentives depending on whether rates have recently risen or fallen. A suggested extension to a probit model is to measure the current rate relative to a recent low, capturing some of that history.
The response also outlines alternatives of differing complexity: fitting a PSA model with fewer parameters, using option-adjusted spread analysis to represent refinancing decisions, or simulating interest-rate paths and prepayments with Monte Carlo methods. It names the two-factor Hull-White model as one possible rate-path framework. These are suggestions, not comparative evidence of forecast accuracy. The document gives no calibration details, performance metrics, or assurance that these methods transfer unchanged to an emerging market portfolio.
Key ideas
- Prepayment behavior can depend on the historical path of interest rates as well as current rates and borrower attributes.
- A rate’s distance from a recent low is suggested as a possible probit-model feature.
- A PSA model offers a simpler specification with fewer parameters.
- Option-adjusted spread analysis can represent borrowers’ refinancing and prepayment decisions.
- Monte Carlo rate-path simulation, including a two-factor Hull-White model, is presented as a more sophisticated approach.
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Full text
# What is the best way to forecast prepayment rate in an emerging market mortgage loan portfolio? # What is the best way to forecast prepayment rate in an emerging market mortgage loan portfolio? I constructed a model to forecast the prepayment rates for a mortgage loan portfolio (of mortgages in an emerging market) using probit regression on factors such as loan-to-value, PTI, time from settlement, and several characteristics of the borrower. However when testing with historical data the forecast is not so accurate. What is the best model to make accurate forecasts of prepayment rates for mortgage loans? ## Answer by Ram Ahluwalia (score 6) https://quant.stackexchange.com/a/2891 Pre-payment rates are difficult to forecast because of path dependency. The historical interest rate path - not just current market conditions and borrower characteristics - matters because borrowers may have exercised their right to call the mortgage bond and re-finance if rates had previously been at lower levels than the current rate. None on the variables you have identified account for these interest rate paths. For example, if rates are at 4% and have generally been rising there is considerably less prepayment risk then if rates are at 4% and have been declining. In a probit model, you could try adding a variable such as the distance of the 30-yr fixed mortgage rate from the lowest mortgage rate over the last X periods. A more typical and sophisticated approach is to model pre-payments and MBS valuations by monte carlo simulations of many interest rate paths. A simpler way to go about this would be to fit the terms to a PSA model. There are far fewer parameters here. You could also use option-adjusted spread analysis to determine borrower's optimal refinancing/pre-payment decision. Finally, along the lines of the monte carlo approach indicated above you could apply the two-factor Hull White model to simulate various interest rate paths.
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