Modelling and Predicting of Australian Mortgage Delinquency Risk: A Preliminary Data Analysis

Abstract

This paper employs the parametric probit regression model, estimates the probability of default (PD) of Australian mortgages, and examines the nature of the relationships between the PD and some loan level variables such as loan-to-value ratio (LVR), loan documentation, loan type, loan purpose, and state. The data covers a cross-section of 25,537 mortgage loans, which were originated in the years 2004 to 2010. The data set has 694 default events defined by the delinquency of the mortgage borrower. In this preliminary analysis, we find that the parametric model specification does not capture the underlying relationships between the dependent variable PD and the other variables included in the model. In addition, we find that the PD and the LVR, which is known to be a key determinant of mortgage default, have a nonlinear relationship that is not fully captured by the probit model. Despite many forms of parametric nonlinear models being available in the literature, the process of finding a suitable parametric nonlinear model may not lead to a model that would capture the true nonlinear relationship between the PD and LVR. To overcome this problem, in our future research, we will assume an unknown functional form for this relationship, and then propose an estimation method for this semi parametric probit model. Based on the overall findings of our preliminary analysis, we provide a roadmap for the future research directions on robust modelling and predicting the PD of Australian mortgages, and for the need to expand the size of the data and the variables sets.