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