These are full involuntary prepayments which arise when the borrower is unable to make loan payments. The loan is foreclosed and liquidated to pay off the outstanding loan balance. The probability of default is impacted by the borrower’s level of equity in the house and his ability to meet debt obligations.
One way of modeling default probabilities is to determine a baseline hazard function by looking at the life lengths or survival times of loan level data. After the base line function is determined the influence of various factors are modeled on this default function. These factors include prepayment penalty tenor, borrower credit grade, LTV ratio, loan purpose, occupancy, documentation and property type. Increased/ Decreased risks of default arising due to variations in these factors will be assigned risk multipliers that will be applied to the base line function. A typical default model would base projected defaults on a historical behavioral database that segments and classifies borrowers based on a selection of the above factors and then groups their payment and default behavior across rating grades indicating likelihood or probability of default.
This refers to partial prepayments of the loan. Usually the borrower makes these unscheduled payments in order to reduce their debt more quickly and to build up the equity in their homes.
Usually when the loans are very seasoned and when there is only a small outstanding balance left the borrowers will fully payoff their loans.