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Exploring, modelling and predicting spatiotemporal variations in house prices

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  • A. Fotheringham
  • Ricardo Crespo
  • Jing Yao

Abstract

Hedonic price modelling has long been a powerful tool to estimate house prices in the real estate market. Increasingly, traditional global hedonic price models that largely ignore spatial effects are being superseded by models that deal with spatial dependency and spatial heterogeneity. In addition, many novel methods integrating spatial economics, statistics and geographical information science (GIScience) have been developed recently to incorporate temporal effects into hedonic house price modelling. Here, a local spatial modelling technique, geographically weighted regression (GWR), which accounts for spatial heterogeneity in housing utility functions is applied to a 19-year set of house price data in London (1980–1998) in order to explore spatiotemporal variations in the determinants of house prices. Further, based on the local parameter estimates derived from GWR, a new method integrating GWR and time series (TS) forecasting techniques, GWR–TS, is proposed to predict future local parameters and thus future house prices. The results obtained from GWR demonstrate variations in local parameter estimates over both space and time. The forecasted future values of local estimates as well as house prices indicate that the proposed GWR–TS method is a useful addition to hedonic price modelling. Copyright Springer-Verlag Berlin Heidelberg 2015

Suggested Citation

  • A. Fotheringham & Ricardo Crespo & Jing Yao, 2015. "Exploring, modelling and predicting spatiotemporal variations in house prices," The Annals of Regional Science, Springer;Western Regional Science Association, vol. 54(2), pages 417-436, March.
    • Handle: RePEc:spr:anresc:v:54:y:2015:i:2:p:417-436
    DOI: 10.1007/s00168-015-0660-6
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    References listed on IDEAS

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    Cited by:

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    3. Hao Wu & Hongzan Jiao & Yang Yu & Zhigang Li & Zhenghong Peng & Lingbo Liu & Zheng Zeng, 2018. "Influence Factors and Regression Model of Urban Housing Prices Based on Internet Open Access Data," Sustainability, MDPI, vol. 10(5), pages 1-17, May.
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    5. Zhang, Xiaoxiang & Yao, Jing & Wang, Jing & Sila-Nowicka, Katarzyna, 2020. "Changes of forestland in China's coastal areas (1996-2015): Regional variations and driving forces," Land Use Policy, Elsevier, vol. 99(C).
    6. Yigong Hu & Binbin Lu & Yong Ge & Guanpeng Dong, 2022. "Uncovering spatial heterogeneity in real estate prices via combined hierarchical linear model and geographically weighted regression," Environment and Planning B, , vol. 49(6), pages 1715-1740, July.
    7. Nuri Hacıevliyagil & Krzysztof Drachal & Ibrahim Halil Eksi, 2022. "Predicting House Prices Using DMA Method: Evidence from Turkey," Economies, MDPI, vol. 10(3), pages 1-27, March.
    8. Shengfu Yang & Shougeng Hu & Weidong Li & Chuanrong Zhang & Dongdong Song, 2020. "Spatio-Temporal Nonstationary Effects of Impact Factors on Industrial Land Price in Industrializing Cities of China," Sustainability, MDPI, vol. 12(7), pages 1-17, April.
    9. 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).
    10. Olaru, Doina & Mulley, Corinne & Smith, Brett & Ma, Liang, 2017. "Policy-led selection of the most appropriate empirical model to estimate hedonic prices in the residential market," Journal of Transport Geography, Elsevier, vol. 62(C), pages 213-228.
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    More about this item

    Keywords

    Hedonic price models; GWR; Spatiotemporal modelling; GIS; C3; R3;
    All these keywords.

    JEL classification:

    • C3 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables
    • R3 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - Real Estate Markets, Spatial Production Analysis, and Firm Location

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