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Title: Improving property valuation accuracy : a comparison of hedonic pricing model and artificial neural network
Authors: Abidoye, RB 
Chan, APC 
Keywords: Artificial neural network
Hedonic pricing model
Property valuation
Valuation accuracy
Predictive accuracy
Issue Date: 2018
Publisher: Routledge, Taylor & Francis Group
Source: Pacific Rim property research journal, 2018, v. 24, no. 1, p. 71-83 How to cite?
Journal: Pacific Rim property research journal 
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.
ISSN: 1444-5921
EISSN: 2201-6716
DOI: 10.1080/14445921.2018.1436306
Rights: © 2018 Pacific Rim Real Estate Society
This is an Accepted Manuscript of an article published by Taylor & Francis in Pacific Rim Property Research Journal on 08 Feb 2018 (Published online), available online:
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