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Modelling property values in Nigeria using artificial neural network

Author

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  • Rotimi Boluwatife Abidoye
  • Albert P. C. Chan

Abstract

Unreliable and inaccurate property valuation has been associated with techniques currently used in property valuation. A possible explanation for these findings may be due to the utilisation of traditional valuation methods. In the current study, an artificial neural network (ANN) is applied in property valuation using the Lagos metropolis property market as a representative case. Property sales transactions data (11 property attributes and property value) were collected from registered real estate firms operating in Lagos, Nigeria. The result shows that the ANN model possesses a good predictive ability, implying that it is suitable and reliable for property valuation. The relative importance analysis conducted on the property attributes revealed that the number of servants’ quarters is the most important attribute affecting property values. The findings suggest that the ANN model could be used as a tool by real estate stakeholders, especially valuers and researchers for property valuation.

Suggested Citation

  • Rotimi Boluwatife Abidoye & Albert P. C. Chan, 2017. "Modelling property values in Nigeria using artificial neural network," Journal of Property Research, Taylor & Francis Journals, vol. 34(1), pages 36-53, January.
    • Handle: RePEc:taf:jpropr:v:34:y:2017:i:1:p:36-53
    DOI: 10.1080/09599916.2017.1286366
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    References listed on IDEAS

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    1. Carlos A. Rodriguez, 2016. "Comparative Analysis of the Autoregressive Equation that Describe the Generating Information Process of Inflation in Regards of a Methodological Change of Puerto Rico's Consumer Price Index (CPI)," Athens Journal of Business & Economics, Athens Institute for Education and Research (ATINER), vol. 2(3), pages 251-276, July.
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    Cited by:

    1. Thanh Le Huu Ngoc & Ngu Nguyen Huu, 2024. "Application of Hedonic Model to Determine Residential Land Valuation in Suburban Areas: The Case of Hue City, Vietnam," Real Estate Management and Valuation, Sciendo, vol. 32(2), pages 70-79.
    2. Daikun Wang & Victor Jing Li, 2019. "Mass Appraisal Models of Real Estate in the 21st Century: A Systematic Literature Review," Sustainability, MDPI, vol. 11(24), pages 1-14, December.
    3. Sisman, S. & Aydinoglu, A.C., 2022. "Improving performance of mass real estate valuation through application of the dataset optimization and Spatially Constrained Multivariate Clustering Analysis," Land Use Policy, Elsevier, vol. 119(C).
    4. MEHMET Erkek & KAMİL Çayırlı & ALİ Hepşen, 2020. "Predicting House Prices in Turkey by Using Machine Learning Algorithms," Journal of Statistical and Econometric Methods, SCIENPRESS Ltd, vol. 9(4), pages 1-3.
    5. Tekin Mert & Sari Irem Ucal, 2022. "Real Estate Market Price Prediction Model of Istanbul," Real Estate Management and Valuation, Sciendo, vol. 30(4), pages 1-16, December.
    6. Doan, Quang Cuong, 2023. "Determining the optimal land valuation model: A case study of Hanoi, Vietnam," Land Use Policy, Elsevier, vol. 127(C).

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