Application of data mining to the spatial heterogeneity of foreclosed mortgages

The loss given a default (LGD) is a key component when calculating the credit risk associated with an asset portfolio. However, the issue of default probability has not often been addressed in past mortgage loan data mining studies. The LGD has rarely been used to assess the comprehensive credit risk for a portfolio of mortgage loans. The location of a mortgaged property is strongly correlated with the price of that property as well as providing social, demographic, and economic information which inherently characterizes the mortgage loan population. Thus, to make an accurate assessment of the credit risk associated with the loan portfolio, one requires a specific data mining technique capable of determining the heterogeneity of the portfolio across regions. The sample utilized in this study consists of data on two thousand foreclosed mortgages in Kaohsiung City. We first test the homogeneity between the different city districts; second, we estimate the magnitude of the heterogeneity, including the spatial heterogeneity; third, a prior distribution for the heterogeneity is formulated using data mining methods; finally, the overall LGD, showing the credit risk for a given default probability is calculated.

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