Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/72591
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, DOI: 10.1080/14445921.2018.1436306, Published online: 08 Feb 2018 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.
URI: http://hdl.handle.net/10397/72591
ISSN: 1444-5921
EISSN: 2201-6716
DOI: 10.1080/14445921.2018.1436306
Appears in Collections:Journal/Magazine Article

Access
View full-text via PolyU eLinks SFX Query
Show full item record

Page view(s)

48
Citations as of Feb 18, 2018

Google ScholarTM

Check

Altmetric


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.