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Prediction accuracy in mass appraisal: a comparison of modern approaches

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  • W.J. McCluskey
  • M. McCord
  • P.T. Davis
  • M. Haran
  • D. McIlhatton
Abstract
The advancement of computational software within the last decade has facilitated enhanced uptake of mass appraisal methodologies by the valuation and prediction accuracy in computer-assisted mass appraisal community for price modelling, estimation and tribunal defence. Applying a sample of 2694 residential properties, this paper assesses and analyses a number of geostatistical approaches relative to an artificial neural network (ANN) model and the traditional linear hedonic pricing model for mass appraisal valuation accuracy and price estimation purposes. The findings demonstrate that the geostatistical localised regression approach is superior in terms of model explanation, reliability and accuracy. ANNs can be shown to perform very well in terms of predictive power, and therefore valuation accuracy, outperforming the traditional multiple regression analysis (MRA) and approaching the performance of spatially weighted regression approaches. However, ANNs retain a 'black box' architecture that limits their usefulness to practitioners in the field. In relation to cost-effectiveness and user-friendly applicability for the valuation community, the MRA approach outperforms the 'black box' nature of the ANN technique, with the geographically weighted regression approach providing the best balance of outright performance and transparency of methodology. It is this spatially weighted approach utilising absolute location which appears to represent the way forward in developing the practice of mass appraisal.

Suggested Citation

  • W.J. McCluskey & M. McCord & P.T. Davis & M. Haran & D. McIlhatton, 2013. "Prediction accuracy in mass appraisal: a comparison of modern approaches," Journal of Property Research, Taylor & Francis Journals, vol. 30(4), pages 239-265, December.
    • Handle: RePEc:taf:jpropr:v:30:y:2013:i:4:p:239-265
    DOI: 10.1080/09599916.2013.781204
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    1. repec:arz:wpaper:eres1996-157 is not listed on IDEAS
    2. Howard James, 1996. "The Reliability of Artificial Neural Networks for Property Data Analysis," ERES eres1996_157, European Real Estate Society (ERES).
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