Loan Prepayment Modeling Afshin Goodarzi Risk Monitors Inc. 50 Main Street White Plains, NY, 10606 afshin@riskmonitors.com Ron Kohavi Silicon Graphics, Inc. 2011 N. Shoreline Blvd Mountain View, CA 94043 ronnyk@engr.sgi.com Richard Harmon Risk Monitors Inc. 50 Main Street White Plains, NY, 10606 r harmon@riskmonitors.com Aydin Senkut Silicon Graphics, Inc. 2011 N. Shoreline Blvd Mountain View, CA 94043 asenkut@engr.sgi.com Abstract Loan level modeling of prepayment is an important as- pect of hedging, risk assessment, and retention efforts of the hundreds of companies in the US that trade and initiate Mortgage Backed Securities (MBS). In this pa- per we review and investigate different aspects of mod- eling customers who have taken jumbo loans in the US using MineSet TM . We show how refinancing costs differ across states and counties, and which attributes make good predictor variables for prepayment forecasts. Our data comes from the McDASH Analytics database con- taining real data, which tracks loans over the past nine years at monthly intervals. Introduction Loan level modeling of prepayment is an important as- pect of hedging, risk assessment, and retention efforts of the hundreds of companies in the US that trade and initiate Mortgage Backed Securities (MBS) (Richard & Roll 1989; Brown 1992; Harmon 1996). With at least 52 million mortgages (according to the Mortgage Bankers Association estimates of end of the year 1997) outstand- ing in the US and the securities being traded every day the stakes are very high and the potential gains/losses are substantial. Our studies indicate that different pre- payment estimation/forecast methodologies can easily introduce a 20% to 30% difference in the cash flow of a portfolio. For a typical portfolio, such differences could easily be measured in the hundreds of millions of dollars per year in cash flow alone. Despite the importance of having loan-level prepay models, models are unavailable except possibly to the large institutional investors that can put the research resources together to come up with these models. Such companies would maintain the secrecy of these models as a competitive advantage. In a collaboration between Risk Monitors, Inc. and Silicon Graphics Inc., we have embarked on building such models using MineSet TM (Brunk, Kelly, & Ko- havi 1997). This project involved the identification and verification of drivers for prepayment forecasts. Even Copyright c  1998, American Association for Artificial In- telligence (www.aaai.org). All rights reserved. though most of the drivers of prepayment are rooted in economic theories and analysis, we still need to verify these theoretical assumptions against the wealth of his- torical data that is available to us today. This paper lays out some of the results of this ongoing effort. Prepayment in Mortgages A typical mortgagee makes a commitment to pay the mortgagor in equal payments on a monthly basis for the term of a loan. Included in each contract is the right of the mortgagee to exercise his/her right to payoff (or prepay) the loan at any point in time. Furthermore, this option is typically exercisable with no financial penal- ties to be paid to the mortgagor. A mortgage loan is prepaid due to the sale of the underlying property or due to refinancing into another loan. The mortgagor may also terminate the mortgage loan when the mort- gagee defaults on the required payments. There are numerous reasons for the mortgagee to pre- pay the loan but the most significant factors are typi- cally driven by changes in interest rates, employment status, family status, income, relocation, retirement, health related impacts, etc. Among the financial in- centives that contribute to the prepayment of mortgage loans, the most significant is the incentive to refinance an existing loan into a loan with a lower interest rate and payment requirements. Mortgage investors, mortgage servicers, and other owners of mortgage related financial instruments, are exposed to significant interest rate risk when loans are prepaid and to credit risk when loans are terminated due to default. Prepayments will halt the stream of cash flows that owners of mortgage related financial instru- ments expect to receive. In may cases this will result in a lower than expected return on their investment. For example, if interest rates decline, there will typically be a subsequent increase in prepayment activity which forces investors to reinvest the unexpected additional cash flows at the new lower interest rate level. This will result in a lower expected return on their mortgage- related investment. On the other hand, if interest rates increase, there will typically be a subsequent decrease in prepayment activity which will force investors to wait for a longer period before they can reinvest the cash