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A Big Data–Based Geographically Weighted Regression Model for Public Housing Prices: A Case Study in Singapore

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  • Kai Cao
  • Mi Diao
  • Bo Wu

Abstract

In this research, three hedonic pricing models, including an ordinary least squares (OLS) model, a Euclidean distance–based (ED-based) geographically weighted regression (GWR) model, and a travel time–based GWR model supported by a big data set of millions of smartcard transactions, have been developed to investigate the spatial variation of Housing Development Board (HDB) public housing resale prices in Singapore. The results help identify factors that could significantly affect public housing resale prices, including the age and the floor area of the housing units, the distance to the nearest park, the distance to the central business district (CBD), and the distance to the nearest Mass Rapid Transit (MRT) station. The comparison of the three models also explicitly shows that the two GWR models perform much better than the traditional linear hedonic regression model, given the identical variables and data used in the calibration. Furthermore, the travel time–based GWR model has better model fit compared to the ED-based GWR model in the case study. This study demonstrates the potential value of the big data–based GWR model in housing research. It could also be applied to other research fields such as public health and criminal justice.

Suggested Citation

  • Kai Cao & Mi Diao & Bo Wu, 2019. "A Big Data–Based Geographically Weighted Regression Model for Public Housing Prices: A Case Study in Singapore," Annals of the American Association of Geographers, Taylor & Francis Journals, vol. 109(1), pages 173-186, January.
    • Handle: RePEc:taf:raagxx:v:109:y:2019:i:1:p:173-186
    DOI: 10.1080/24694452.2018.1470925
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    Citations

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    Cited by:

    1. Kang, Yuhao & Zhang, Fan & Peng, Wenzhe & Gao, Song & Rao, Jinmeng & Duarte, Fabio & Ratti, Carlo, 2021. "Understanding house price appreciation using multi-source big geo-data and machine learning," Land Use Policy, Elsevier, vol. 111(C).
    2. Daikun Wang & Victor Jing Li & Huayi Yu, 2020. "Mass Appraisal Modeling of Real Estate in Urban Centers by Geographically and Temporally Weighted Regression: A Case Study of Beijing’s Core Area," Land, MDPI, vol. 9(5), pages 1-18, May.
    3. Yang Wang & Kangmin Wu & Jing Qin & Changjian Wang & Hong’ou Zhang, 2020. "Examining Spatial Heterogeneity Effects of Landscape and Environment on the Residential Location Choice of the Highly Educated Population in Guangzhou, China," Sustainability, MDPI, vol. 12(9), pages 1-20, May.
    4. Chen, Feng & Mei, Chang-Lin, 2021. "Scale-adaptive estimation of mixed geographically weighted regression models," Economic Modelling, Elsevier, vol. 94(C), pages 737-747.
    5. Cankun Wei & Meichen Fu & Li Wang & Hanbing Yang & Feng Tang & Yuqing Xiong, 2022. "The Research Development of Hedonic Price Model-Based Real Estate Appraisal in the Era of Big Data," Land, MDPI, vol. 11(3), pages 1-30, February.
    6. Jakob A. Dambon & Stefan S. Fahrländer & Saira Karlen & Manuel Lehner & Jaron Schlesinger & Fabio Sigrist & Anna Zimmermann, 2022. "Examining the vintage effect in hedonic pricing using spatially varying coefficients models: a case study of single-family houses in the Canton of Zurich," Swiss Journal of Economics and Statistics, Springer;Swiss Society of Economics and Statistics, vol. 158(1), pages 1-14, December.
    7. Sisman, S. & Aydinoglu, A.C., 2022. "A modelling approach with geographically weighted regression methods for determining geographic variation and influencing factors in housing price: A case in Istanbul," Land Use Policy, Elsevier, vol. 119(C).

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