Many of the risks inherent in originating mortgages have been studied. The risk of default (the risk that the homeowner does not make their payments), for example is the basis of most underwriting guidelines and a major component of mortgage pricing. Secondary market risk, the risk that mortgage rates change between when the rate is locked and when the mortgage is sold on the bond markets, is also widely studied. There are many tools available to help mortgage lenders deal with default and secondary risks.
This analysis aims to measure a different, less studied risk in mortgage origination, the risk that a given mortgage application will fund and become a mortgage, also known as pull-through risk. Knowing which loans are likely to fund may help lenders mitigate the costs associated with processing mortgage applications that do not fund, either by adapting their guidelines to filter out mortgage applications that are not likely to fund early on, or by offering less attractive pricing for mortgages not likely to fund to dissuade mortgage brokers from bringing these applications to them in the first place.
Logistic Regression was used to study the causes that most effect whether a loan funds. Binary Decision Trees and Logistic Regression were used to classify/predict loan applications as will fund or will not fund. Further, a time-to-event analysis was conducted to model the cumulative incidence functions of the various loan application outcomes, and the cause-specific hazards associated with each of them.
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|Commitee:||Safer, Alan, Suaray, Kagba|
|School:||California State University, Long Beach|
|Department:||Mathematics and Statistics|
|School Location:||United States -- California|
|Source:||MAI 58/02M(E), Masters Abstracts International|
|Keywords:||Affordability, Analysis, Cost, Fund, Mortgage, Pull-through|
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