Predicting Housing Value: Genetic Algorithm Attribute Selection and Dependence Modelling Utilising the Gamma Test

In this paper we show, by means of an example of its application to the problem of house price forecasting, an approach to attribute selection and dependence modelling utilising the Gamma Test (GT), a non-linear analysis algorithm that is described. The GT is employed in a two-stage process: first the GT drives a Genetic Algorithm (GA) to select a useful subset of features from a large dataset that we develop from eight economic statistical series of historical measures that may impact upon house price movement. Next we generate a predictive model utilising an Artificial Neural Network (ANN) trained to the Mean Squared Error (MSE) estimated by the GT, which accurately forecasts changes in the House Price Index (HPI). We present a background to the problem domain and demonstrate, based on results of this methodology, that the GT was of great utility in facilitating a GA based approach to extracting a sound predictive model from a large number of inputs in a data-point sparse real-world application.