Modelling spatial dependence in mortgage credit models
The recent financial crisis in the US has generated concerns about credit contagion, that is, how the deterioration of a borrower’s future ability to honour his/her debt obligations can affect the ability of other borrowers to repay. After the housing credit boom in the mid-‐2000s, the housing downturn of the late 2000s saw dramatic increases in mortgage borrowers that defaulted on their debt obligations. The aim of this research is to explore how integrating the geographical locations of US mortgage borrowers into credit risk models can improve the predictive accuracy of credit risk assessments, thereby potentially decreasing the losses on mortgage loans that strongly contributed to the recent financial crisis. This research will propose a regression model for estimating the propensity of US mortgage holders to deault on their loans that includes the
effects of the neighbours’ characteristics in an accurate rare event method. This subject is in line with the second key theme of the Regional Studies Association on Spatial Theory and Methods. The main advantage of this research is to increase the number of accurately forecasted mortgages defaults. We also expect that the conventional model without neighbours’ characteristics will underestimate the risk created by relaxing lending standards.