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Estimation of Housing Price Variations Using Spatio-Temporal Data

Author

Listed:
  • Jorge Chica-Olmo

    (Department of Quantitative Methods for Economics and Business, University of Granada, 18011 Granada, Spain)

  • Rafael Cano-Guervos

    (Department of Quantitative Methods for Economics and Business, University of Granada, 18011 Granada, Spain)

  • Mario Chica-Rivas

    (Department of Mathematical Analysis, University of Granada, 18071 Granada, Spain)

Abstract

This paper proposes a hedonic regression model to estimate housing prices and the spatial variability of prices over multiple years. Using the model, maps are obtained that represent areas of the city where there have been positive or negative changes in housing prices. The regression-cokriging (RCK) method is used to predict housing prices. The results are compared to the cokriging with external drift (CKED) model, also known as universal cokriging (UCK). To apply the model, heterotopic data of homes for sale at different moments in time are used. The procedure is applied to predict the spatial variability of housing prices in multi-years and to obtain isovalue maps of these variations for the city of Granada, Spain. The research is useful for the fields of urban studies, economics, real estate, real estate valuations, urban planning, and for scholars.

Suggested Citation

  • Jorge Chica-Olmo & Rafael Cano-Guervos & Mario Chica-Rivas, 2019. "Estimation of Housing Price Variations Using Spatio-Temporal Data," Sustainability, MDPI, vol. 11(6), pages 1-21, March.
    • Handle: RePEc:gam:jsusta:v:11:y:2019:i:6:p:1551-:d:213821
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    References listed on IDEAS

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    2. Roberto Cervelló-Royo & Marina Segura & Regina García-Pérez & Baldomero Segura-García del Río, 2021. "An Analysis of Preferences in Housing Demand by Means of a Multicriteria Methodology (AHP). A More Sustainable Approach," Sustainability, MDPI, vol. 13(14), pages 1-16, July.
    3. Zhuoma Garang & Cifang Wu & Guan Li & Yuefei Zhuo & Zhongguo Xu, 2021. "Spatio-Temporal Non-Stationarity and Its Influencing Factors of Commercial Land Price: A Case Study of Hangzhou, China," Land, MDPI, vol. 10(3), pages 1-27, March.
    4. Robert J. Hill & Alicia N. Rambaldi, 2022. "Hedonic Models and House Price Index Numbers," Springer Books, in: Duangkamon Chotikapanich & Alicia N. Rambaldi & Nicholas Rohde (ed.), Advances in Economic Measurement, chapter 0, pages 413-444, Springer.
    5. Xiaotong Guo & Lingyan Li & Haiyan Xie & Wei Shi, 2020. "Improved Multi-Objective Optimization Model for Policy Design of Rental Housing Market," Sustainability, MDPI, vol. 12(14), pages 1-23, July.
    6. Shihong Zeng & Xinwei Zhang & Xiaowei Wang & Guowang Zeng, 2019. "Population Aging, Household Savings and Asset Prices: A Study Based on Urban Commercial Housing Prices," Sustainability, MDPI, vol. 11(11), pages 1-21, June.
    7. Fahim Ullah & Samad M. E. Sepasgozar, 2020. "Key Factors Influencing Purchase or Rent Decisions in Smart Real Estate Investments: A System Dynamics Approach Using Online Forum Thread Data," Sustainability, MDPI, vol. 12(11), pages 1-36, May.
    8. Gao, Qishuo & Shi, Vivien & Pettit, Christopher & Han, Hoon, 2022. "Property valuation using machine learning algorithms on statistical areas in Greater Sydney, Australia," Land Use Policy, Elsevier, vol. 123(C).
    9. Hyunsoo Kim & Youngwoo Kwon & Yeol Choi, 2020. "Assessing the Impact of Public Rental Housing on the Housing Prices in Proximity: Based on the Regional and Local Level of Price Prediction Models Using Long Short-Term Memory (LSTM)," Sustainability, MDPI, vol. 12(18), pages 1-25, September.
    10. 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).
    11. Polixeni Iliopoulou & Elissavet Feloni, 2022. "Spatial Modelling and Geovisualization of House Prices in the Greater Athens Region, Greece," Geographies, MDPI, vol. 2(1), pages 1-21, February.

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