AbstractThis thesis documents the research that has led to the development of a number of methodologies for combining existing artificial intelligence and statistical techniques into a form appropriate for the development of an intelligent appraisal system for use in the residential property appraisal profession. The methodologies illustrate how regression based appraisal models, previously restricted to homogeneous data, can be applied to heterogeneous data without significant loss in accuracy. The majority of research, previous to this, has addressed this problem by manually selecting homogeneous sub-regions from a heterogeneous parent region. However, the main drawback with this approach is that the segregation of parent regions into sub-regions relies upon a significant amount of a priori knowledge pertaining to the location of the property being valued. The requirement for a commercial residential property appraisal system is one that given sufficient training evidence can automatically learn how to value a property in any region and be able to modify this knowledge over time.
Two methodologies are proposed within the thesis to address this requirement. The first, using a technique known as the Kohonen Self Organising Map, makes an assumption that residential properties that share sufficient characteristics can be appraised using the same function The Kohonen Self Organising Map is used to cluster properties with respect to their property characteristics and locational characteristics represented using a mortgage transaction database and UK Census statistics. Aptness of each cluster to define a homogeneous subset suitable to train a regression model, such as multiple regression analysis or a neural network, is estimated using a form of 'nearest neighbour' analysis. The second methodology, improves on the previous by transforming the static 'cluster then observe' solution to a more dynamic one using a Genetic Algorithm to evolve good clusters from those that at first inspection were mediocre.
Another issue that has hindered the development of intelligent residential property appraisal systems has been the inability of such models to express their underlying functional form. This is addressed from two perspectives in this thesis: Rules are derived that describe the characteristic make-up of the formed clusters and, alternative modelling techniques are used to generate the final training models that are able to express their functional form as a set of induced rules.
The work contained within this thesis demonstrates the feasibility of such an automatic stratification approach. Also, the research illustrates that by observing the characteristics of the generated clusters formed, a useful insight into both the underlying reasoning of the generated models and also of the locational and financial makeup of the subject location can be gained.
|Date of Award||Jul 1999|
|Supervisor||Andrew Ware (Supervisor)|