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Spatiotemporal Analysis of Housing Prices in China: A Big Data Perspective

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Abstract

Due to the rapid economic growth and urbanization, China’s real estate industry has been undergoing a fast-paced development in recent decades. However, the spatial imbalance between the economic growth in urban and that in rural areas and the excessive growth and fluctuations of house prices in both areas had quickly caught public’s attention. Not surprisingly, these issues had become a focus of urban and regional economic research. Efficient and accurate prediction of housing prices remains a much needed but disputable topic. Currently, based on the trends and changes in the financial market, population migration and urbanization processes, numerous case studies have been developed to evaluate the mechanism of real estate’s price fluctuations. However, few studies were conducted to examine the space-time dynamics of how housing prices fluctuated from a big data perspective. Using data from China’s leading online real estate platform {sofang.com}, we investigated the spatiotemporal trends of the fluctuations of housing prices in the context of big data. This paper uses spatial data analytics and modeling techniques to: first, identify the spatial distribution of housing prices at micro level; second, explore the space-time dynamics of residential properties in the market; and third, detect if there exist geographic disparity in terms of housing prices. Results from our analysis revealed the space-time patterns of the housing prices in a large metropolitan area, demonstrating the utility of big data and means of analyzing big data.

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Acknowledgments

This work has been supported by the National Science Foundation (1416509, 1535031, 1535081) and the National Natural Science Foundation of China (41301426).

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Authors and Affiliations

  1. School of Information Engineering, China University of Geosciences, Wuhan, China

    Shengwen Li & Junfang Gong

  2. Department of Geography, Kent State University, Kent, OH, USA

    Xinyue Ye & Jay Lee

  3. College of Environment and Planning, Henan University, Kaifeng, Henan, China and Department of Geography, Kent State University, Kent, OH, USA

    Jay Lee

  4. College of Economics, Jinan University, Guangzhou, China

    Chenglin Qin

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  1. Shengwen Li
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  2. Xinyue Ye
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  3. Jay Lee
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  4. Junfang Gong
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Correspondence to Xinyue Ye.

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Li, S., Ye, X., Lee, J. et al. Spatiotemporal Analysis of Housing Prices in China: A Big Data Perspective. Appl. Spatial Analysis 10, 421–433 (2017). https://doi.org/10.1007/s12061-016-9185-3

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  • DOI: https://doi.org/10.1007/s12061-016-9185-3

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