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Predicting property price index using artificial intelligence techniques

Author

Listed:
  • Rotimi Boluwatife Abidoye
  • Albert P.C. Chan
  • Funmilayo Adenike Abidoye
  • Olalekan Shamsideen Oshodi

Abstract

Purpose - Booms and bubbles are inevitable in the real estate industry. Loss of profits, bankruptcy and economic slowdown are indicators of the adverse effects of fluctuations in property prices. Models providing a reliable forecast of property prices are vital for mitigating the effects of these variations. Hence, this study aims to investigate the use of artificial intelligence (AI) for the prediction of property price index (PPI). Design/methodology/approach - Information on the variables that influence property prices was collected from reliable sources in Hong Kong. The data were fitted to an autoregressive integrated moving average (ARIMA), artificial neural network (ANN) and support vector machine (SVM) models. Subsequently, the developed models were used to generate out-of-sample predictions of property prices. Findings - Based on the prediction evaluation metrics, it was revealed that the ANN model outperformed the SVM and ARIMA models. It was also found that interest rate, unemployment rate and household size are the three most significant variables that could influence the prices of properties in the study area. Practical implications - The findings of this study provide useful information to stakeholders for policy formation and strategies for real estate investments and sustained growth of the property market. Originality/value - The application of the SVM model in the prediction of PPI in the study area is lacking. This study evaluates its performance in relation to ANN and ARIMA.

Suggested Citation

  • Rotimi Boluwatife Abidoye & Albert P.C. Chan & Funmilayo Adenike Abidoye & Olalekan Shamsideen Oshodi, 2019. "Predicting property price index using artificial intelligence techniques," International Journal of Housing Markets and Analysis, Emerald Group Publishing Limited, vol. 12(6), pages 1072-1092, June.
    • Handle: RePEc:eme:ijhmap:ijhma-11-2018-0095
    DOI: 10.1108/IJHMA-11-2018-0095
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    Citations

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    Cited by:

    1. Mohamad ABU GHAZALEH, 2023. "Smartening up Ports Digitalization with Artificial Intelligence (AI): A Study of Artificial Intelligence Business Drivers of Smart Port Digitalization," Management and Economics Review, Faculty of Management, Academy of Economic Studies, Bucharest, Romania, vol. 8(1), pages 78-97, February.
    2. Dieudonné Tchuente & Serge Nyawa, 2022. "Real estate price estimation in French cities using geocoding and machine learning," Annals of Operations Research, Springer, vol. 308(1), pages 571-608, January.
    3. Mustafa Özer & Serap Kamişli & Muhammed Aslam Chelery Komath & Özlem Sayilir, 2022. "Asymmetric Causal Relations Between COVID-19 Economic Supports and Real Estate Price Shocks," International Real Estate Review, Global Social Science Institute, vol. 25(4), pages 479-498.

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