Identification of Residential Property Sub-Markets using Evolutionary and Neural Computing Techniques

This paper expands on previous work considering methods of stratifying property data in order to enhance its susceptibility to modelling for mortgage value estimation. Previous work [1] considered a clustering approach using a Kohonen Self-Organising Map (SOM) to stratify the training data prior to training a suite of MLPs. Although the results were encouraging, the approach suffers from its estimation of trainability post-clustering. The following method ameliorates the approach by replacing the static clustering step with a dynamic genetic algorithm implementation. The results show a healthy improvement in accuracy over the non-stratified approach, and a more consistent level of accuracy compared with the Kohonen SOM approach. The paper concludes by analysing the underlying content of the derived stratas, thus providing a ‘human readable’ element to the approach that enhances its potential for acceptance by valuation institutions for as a complementary technique to traditional valuation methods.