The aim of this paper is to investigate the comparative performance of an artificial neural network (ANN) and several multiple regression techniques in terms of their predictive accuracy and capability of being used within the mass appraisal industry.
The methodology first tested that the data set had neglected non‐linearity which suggested that a non‐linear modelling technique should be applied. Given the capability of ANNs to model non‐linear data, this technique was used along with an OLS regression model (baseline model) and two non‐linear multiple regression techniques. In addition, the models were evaluated in terms of predictive accuracy and their capability of use within the mass appraisal environment.
Previous studies which have compared the predictive performance of an ANN model against multiple regression techniques are inconclusive. Having superior predictive capability is important but equally important is whether the technique can be successfully employed for the mass appraisal of residential property. This research found that a non‐linear regression model had higher predictive accuracy than the ANN. Also the output of the ANN was not sufficiently transparent to provide an unambiguous appraisal model upon which predicted values could be defended against objections.
The research provides an informative view as to the efficacy of ANN methodology within the real estate field. A number of issues have been raised on the applicability of ANN models within the mass appraisal environment.
This work demonstrates that ANNs whilst useful as a predictive tool have a limited practical role for the assessment of residential property values for property tax purposes.
The work has taken forward the debate on the usefulness of ANN techniques within the mass appraisal environment.
McCluskey, W., Davis, P., Haran, M., McCord, M. and McIlhatton, D. (2012), "The potential of artificial neural networks in mass appraisal: the case revisited", Journal of Financial Management of Property and Construction, Vol. 17 No. 3, pp. 274-292. https://doi.org/10.1108/13664381211274371Download as .RIS
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