APPARATUS AND METHOD FOR MODELING LOAN ATTRIBUTES
A method, system, and computer program product for generating a model for predicting loan behavior, including receiving loan data for a plurality of loans; preparing the loan data for analysis; grouping the loans into a plurality of hierarchical segments based on shared characteristics; generating a logistic regression model for each segment; and generating an overall prediction model for at least one of prepayment, delinquency, and default across the plurality of segments. Grouping the loans into a plurality of segments based on shared characteristics may include grouping the loans based on loan type, change in Housing Price Index (HPI) since origination, and loan age. Generating a logistic regression model for each segment may include generating a regression model for the probabilities of each of prepayment, default, and delinquency for each of the segments.
1 . A method of generating a model for predicting loan behavior, the method comprising:
receiving loan data for a plurality of loans;
preparing the loan data for analysis;
grouping the loans into a plurality of hierarchical segments based on shared characteristics;
generating a logistic regression model for each segment; and
generating an overall prediction model for at least one of prepayment, delinquency, and default across the plurality of segments.
2 . The method of claim 1 , wherein preparing the loan data for analysis includes at least one of formatting, imputing missing data, and applying an outlier treatment to the loan data.
3 . The method of claim 2 , wherein imputing missing data includes resetting interest rates applicable to ARM products.
4 . The method of claim 2 , wherein applying an outlier treatment includes limiting the values of a particular field to a certain range.
5 . The method of claim 1 , wherein grouping the loans into a plurality of segments based on shared characteristics includes grouping the loans based on loan type.
6 . The method of claim 5 , wherein grouping the loans into a plurality of segments based on shared characteristics further includes grouping the loans based on change in Housing Price Index (HPI) since origination.
7 . The method of claim 6 , wherein grouping the loans into a plurality of segments based on shared characteristics further includes grouping the loans based on loan age.
8 . The method of claim 7 , wherein generating a logistic regression model for each segment includes generating a regression model for the probabilities of at least one of prepayment, default, and delinquency for each of the segments.
9 . The method of claim 8 , wherein generating a logistic regression model for each segment includes generating a regression model for the probabilities of each of prepayment, default, and delinquency for each of the segments.
10 . The method of claim 9 , further comprising:
generating a calendar month wise model by applying the corresponding model to generate probabilities for each segment for the calendar month and combining the generated probabilities.
11 . The method of claim 9 , further comprising:
scoring each loan at each age for probability or prepayment, default, and delinquency based on the corresponding generated models and the relevant data for each loan.
12 . The method of claim 9 , further comprising:
calculating the current amount outstanding at the end of each month based on the generated probability models.
13 . The method of claim 12 , further comprising:
calculating a probability of prepayment from the prepayment model; and
calculating a projected unpaid principle balance at each age of the loan by multiplying the probability of prepayment by the current unpaid balance.
14 . A system for generating a model for predicting loan behavior, the system comprising:
means for receiving loan data for a plurality of loans;
means for preparing the loan data for analysis;
means for grouping the loans into a plurality of hierarchical segments based on shared characteristics;
means for generating a logistic regression model for each segment; and
means for generating an overall prediction model for at least one of prepayment, delinquency, and default across the plurality of segments.
15 . The system of claim 14 , wherein grouping the loans into a plurality of segments based on shared characteristics includes grouping the loans based on loan type, change in Housing Price Index (HPI) since origination, and loan age.
16 . The system of claim 15 , wherein generating a logistic regression model for each segment includes generating a regression model for the probabilities of each of prepayment, default, and delinquency for each of the segments.
17 . A system for generating a model for predicting loan behavior, the system comprising:
a processor;
a user interface functioning via the processor; and
a repository accessible by the processor; wherein
the repository is configured to receive and store loan data for a plurality of loans, and wherein the processor is configured to:
prepare the loan data for analysis;
group the loans into a plurality of hierarchical segments based on shared characteristics;
generate a logistic regression model for each segment; and
generate an overall prediction model for at least one of prepayment, delinquency, and default across the plurality of segments.
18 . The system of claim 17 , wherein grouping the loans into a plurality of segments based on shared characteristics includes grouping the loans based on loan type, change in Housing Price Index (HPI) since origination, and loan age.
19 . The system of claim 18 , wherein generating a logistic regression model for each segment includes generating a regression model for the probabilities of each of prepayment, default, and delinquency for each of the segments.
20 . A computer program product comprising a non-transitory computer usable medium having control logic stored therein for causing a computer to exchange user-generated community information, the control logic comprising:
first computer readable program code means for receiving loan data for a plurality of loans;
second computer readable program code means for preparing the loan data for analysis;
third computer readable program code means for grouping the loans into a plurality of hierarchical segments based on shared characteristics;
fourth computer readable program code means for generating a logistic regression model for each segment; and
fifth computer readable program code means for generating an overall prediction model for at least one of prepayment, delinquency, and default across the plurality of segments.
21 . The computer program product of claim 20 , wherein grouping the loans into a plurality of segments based on shared characteristics includes grouping the loans based on loan type, change in Housing Price Index (HPI) since origination, and loan age.
22 . The computer program product of claim 21 , wherein generating a logistic regression model for each segment includes generating a regression model for the probabilities of each of prepayment, default, and delinquency for each of the segments.