Improving property valuation accuracy: a comparison of hedonic pricing model and artificial neural network

Abstract

Inaccuracies in property valuation is a global problem. This could be attributed to the adoption of valuation approaches, with the hedonic pricing model (HPM) being an example, that are inaccurate and unreliable. As evidenced in the literature, the HPM approach has gained wide acceptance among real estate researchers, despite its shortcomings. Therefore, the present study set out to evaluate the predictive accuracy of HPM in comparison with the artificial neural network (ANN) technique in property valuation. Residential property transaction data were collected from registered real estate firms domiciled in the Lagos metropolis, Nigeria, and were fitted into the ANN model and HPM. The results showed that the ANN technique outperformed the HPM approach, in terms of accuracy in predicting property values with mean absolute percentage error (MAPE) values of 15.94 and 38.23%, respectively. The findings demonstrate the efficacy of the ANN technique in property valuation, and if all the preconditions of property value modeling are met, the ANN technique is a reliable valuation approach that could be used by both real estate researchers and professionals.

Keywords:

• Artificial neural network
• hedonic pricing model
• property valuation
• valuation accuracy
• predictive accuracy

Acknowledgements

We sincerely acknowledge the Research Grants Council of Hong Kong (SAR) and the Department of Building and Real Estate, The Hong Kong Polytechnic University, Hong Kong for providing financial and material support towards this research. We appreciate the real estate firms that provided the data used for this study. The constructive input of Mr. Olalekan Oshodi is well appreciated. The effort of Gbemi Ojewunmi and Esther Okorie that were engaged during the data collection exercise is appreciated.