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

Rotimi Boluwatife Abidoye, Ping Chuen Chan

Research output: Journal article publicationJournal articleAcademic researchpeer-review

52 Citations (Scopus)


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.
Original languageEnglish
Pages (from-to)71-83
Number of pages13
JournalPacific Rim Property Research Journal
Issue number1
Publication statusPublished - 1 Jan 2018


  • Artificial neural network
  • Hedonic pricing model
  • Predictive accuracy
  • Property valuation
  • Valuation accuracy

ASJC Scopus subject areas

  • Economics, Econometrics and Finance(all)


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