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down forecasts for those same loans/pools. This is a useful parameter to use if the model is underpredicting or overpredicting prepayments for premiums, as it can affect those loan/pools without changing the values for discounts. Values for refi can be any number greater than 0, and the default value is 1.0.
3. Turnover The turnover tuning parameter is a multiplier on the Turnover_SMM component of the model. Changing this value will raise or lower the level of basic housing turnover forecasted by the model. This will affect all loans/pools, regardless of refinance incentive. Using the turnover tuning parameter can be helpful whenever there are temporary housing spikes or slowdowns, allowing the model to reflect short-term changes. Values for turnover can be any number greater than 0, and the default value is 1.0.
4. Cashout Tuning the cashout tuning parameter allows the user to strengthen or weaken the amount of Cashout_SMM in the model forecast. This piece of the model is designed to capture the additional amount of prepayments seen from home price appreciation, where borrowers cash out the equity in their homes by refinancing into a new loan. The model will forecast a given amount of cashout_SMM based on the two-year change of the national house price index. For example, the model might forecast an additional 3 CPR for a two-year home price change of 12%, but tuning the cashout parameter will allow the user to increase or decrease that CPR for the given level of 12% appreciation. This parameter also works as a multiplier, and the values can be any number greater than 0, and the default value is 1.0.
5. Credit Cure The credit cure tuning parameter functions in the same way that cashout does. It is a multiplier directly on the Credit_Cure_SMM forecasted by the model. The credit cure function attempts to forecast prepayments by a borrower who has seen a rise in their credit-worthiness, and can refinance a loan to a better interest rate. This function is heavily dependent on the age of the pool/loan, so the credit cure tuning parameter allows the user to increase or decrease the amount of SMM the model forecasts at any given age of the pool/loan. Values for credit cure can be any number greater than 0, and the default value is 1.0.
