IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v15y2023i9p7026-d1129909.html
   My bibliography  Save this article

Spatial–Temporal Variation and Influencing Factors on Housing Prices of Resource-Based City: A Case Study of Xuzhou, China

Author

Listed:
  • Qing Yao

    (School of Public Management, Huazhong Agriculture University, Wuhan 430070, China)

  • Yingen Hu

    (School of Public Management, Huazhong Agriculture University, Wuhan 430070, China)

Abstract

The steady development of the real estate market is an important link in the transformation of resource-based cities. Taking the main urban area of Xuzhou, a typical resource-based city in China, as an example, this study used a spatial–temporal geographically weighted regression (GTWR) method guided by characteristic price theory to analyze the evolution of the spatial–temporal pattern of house prices in resource-based cities and the influencing factors. The results of the study showed that, from the time trend, Xuzhou’s main city house price trend underwent the obvious stages; from June 2015 to September 2016, the trend was stable, and from March 2017 to June 2018, it was an “up–down–up” trend. With respect to the spatial distribution of house prices, from June 2015 to June 2017, the traditional business district and the major city in the Xuzhou metropolitan area were the areas of highest house prices, and after September 2017, the structural features of the traditional business district, along with the main city of the metropolitan area and the new city, formed the areas of highest importance. With regard to the factors influencing house prices, the type of dwelling, according to the building characteristics, had the largest impact on house prices, and the enrichment of housing types became an effective way to regulate housing prices. The impact of location characteristics on house prices varied depending on differences in the public infrastructure surrounding housing, with the contribution of the planned metro stations to house prices not effectively emerging. The impact of neighborhood characteristics on house prices varied, with tertiary care hospitals having a ‘neighborhood avoidance effect’ on house prices, given that hospital-generated waste was too densely distributed around houses, suppressing neighborhood house prices. The results of this study indicated that, in the process of real estate market development in resource-based cities, the planning department should consider the different functions and division of labor in each region and scientifically formulate urban development plans to provide a good external environment for the healthy development of the city’s property market.

Suggested Citation

  • Qing Yao & Yingen Hu, 2023. "Spatial–Temporal Variation and Influencing Factors on Housing Prices of Resource-Based City: A Case Study of Xuzhou, China," Sustainability, MDPI, vol. 15(9), pages 1-20, April.
    • Handle: RePEc:gam:jsusta:v:15:y:2023:i:9:p:7026-:d:1129909
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/15/9/7026/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/15/9/7026/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. S. Cook & C. Thomas, 2003. "An alternative approach to examining the ripple effect in UK house prices," Applied Economics Letters, Taylor & Francis Journals, vol. 10(13), pages 849-851.
    2. Mathur, Shishir, 2019. "Impact of an urban growth boundary across the entire house price spectrum: The two-stage quantile spatial regression approach," Land Use Policy, Elsevier, vol. 80(C), pages 88-94.
    3. Markus K. Brunnermeier & Christian Julliard, 2008. "Money Illusion and Housing Frenzies," The Review of Financial Studies, Society for Financial Studies, vol. 21(1), pages 135-180, January.
    4. Baltagi, Badi H. & Fingleton, Bernard & Pirotte, Alain, 2014. "Spatial lag models with nested random effects: An instrumental variable procedure with an application to English house prices," Journal of Urban Economics, Elsevier, vol. 80(C), pages 76-86.
    5. Cohen, Jeffrey P. & Ioannides, Yannis M. & (Wirathip) Thanapisitikul, Win, 2016. "Spatial effects and house price dynamics in the USA," Journal of Housing Economics, Elsevier, vol. 31(C), pages 1-13.
    6. Brady, Ryan R., 2014. "The spatial diffusion of regional housing prices across U.S. states," Regional Science and Urban Economics, Elsevier, vol. 46(C), pages 150-166.
    7. Beenstock, Michael & Felsenstein, Daniel, 2015. "Estimating spatial spillover in housing construction with nonstationary panel data," Journal of Housing Economics, Elsevier, vol. 28(C), pages 42-58.
    8. Joachim Zietz & Emily Zietz & G. Sirmans, 2008. "Determinants of House Prices: A Quantile Regression Approach," The Journal of Real Estate Finance and Economics, Springer, vol. 37(4), pages 317-333, November.
    9. Nakamura, Shohei, 2017. "Tenure Security Premium in Informal Housing Markets: A Spatial Hedonic Analysis," World Development, Elsevier, vol. 89(C), pages 184-198.
    10. Liao, Wen-Chi & Wang, Xizhu, 2012. "Hedonic house prices and spatial quantile regression," Journal of Housing Economics, Elsevier, vol. 21(1), pages 16-27.
    11. Fan Liu & Min Min & Ke Zhao & Weiyan Hu, 2020. "Spatial-Temporal Variation in the Impacts of Urban Infrastructure on Housing Prices in Wuhan, China," Sustainability, MDPI, vol. 12(3), pages 1-18, February.
    12. Hyun, Dongwoo & Milcheva, Stanimira, 2018. "Spatial dependence in apartment transaction prices during boom and bust," Regional Science and Urban Economics, Elsevier, vol. 68(C), pages 36-45.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Yunlong Gong & Jan de Haan & Peter Boelhouwer, 2020. "Cross‐city spillovers in Chinese housing markets: From a city network perspective," Papers in Regional Science, Wiley Blackwell, vol. 99(4), pages 1065-1085, August.
    2. Gabriel S. Lee & Stefanie Braun, 2021. "Agglomeration Spillover Effects in German Land and House Prices at the City and County Levels," Working Papers 207, Bavarian Graduate Program in Economics (BGPE).
    3. Mateusz Tomal & Marco Helbich, 2023. "A spatial autoregressive geographically weighted quantile regression to explore housing rent determinants in Amsterdam and Warsaw," Environment and Planning B, , vol. 50(3), pages 579-599, March.
    4. Trojanek, Radoslaw & Huderek-Glapska, Sonia, 2018. "Measuring the noise cost of aviation – The association between the Limited Use Area around Warsaw Chopin Airport and property values," Journal of Air Transport Management, Elsevier, vol. 67(C), pages 103-114.
    5. Zahirovich-Herbert, Velma & Gibler, Karen M., 2014. "The effect of new residential construction on housing prices," Journal of Housing Economics, Elsevier, vol. 26(C), pages 1-18.
    6. Raul-Tomas Mora-Garcia & Maria-Francisca Cespedes-Lopez & V. Raul Perez-Sanchez & Pablo Marti & Juan-Carlos Perez-Sanchez, 2019. "Determinants of the Price of Housing in the Province of Alicante (Spain): Analysis Using Quantile Regression," Sustainability, MDPI, vol. 11(2), pages 1-33, January.
    7. Marcelo Cajias & Philipp Freudenreich & Anna Heller & Wolfgang Schaefers, 2018. "Censored Quantile Regressions and the Determinants of Real Estate Liquidity," ERES eres2018_203, European Real Estate Society (ERES).
    8. Mats Wilhelmsson, 2019. "Energy Performance Certificates and Its Capitalization in Housing Values in Sweden," Sustainability, MDPI, vol. 11(21), pages 1-16, November.
    9. Villar, Jaume García & Raya, Josep Maria, 2015. "Use of a Gini index to examine housing price heterogeneity: A quantile approach," Journal of Housing Economics, Elsevier, vol. 29(C), pages 59-71.
    10. Cynthia Fan Yang, 2021. "Common factors and spatial dependence: an application to US house prices," Econometric Reviews, Taylor & Francis Journals, vol. 40(1), pages 14-50, January.
    11. Angelos Mimis & Antonis Rovolis & Marianthi Stamou, 2013. "Property valuation with artificial neural network: the case of Athens," Journal of Property Research, Taylor & Francis Journals, vol. 30(2), pages 128-143, June.
    12. Jose Torres-Pruñonosa & Pablo García-Estévez & Josep Maria Raya & Camilo Prado-Román, 2022. "How on Earth Did Spanish Banking Sell the Housing Stock?," SAGE Open, , vol. 12(1), pages 21582440221, March.
    13. Tan, Ronghui & He, Qingsong & Zhou, Kehao & Xie, Peng, 2019. "The effect of new metro stations on local land use and housing prices: The case of Wuhan, China," Journal of Transport Geography, Elsevier, vol. 79(C), pages 1-1.
    14. Pontines, Victor, 2020. "A provincial view of consumption risk sharing in Korea:Asset classes as shock absorbers," Journal of the Japanese and International Economies, Elsevier, vol. 55(C).
    15. Marcelo Cajias & Philipp Freudenreich & Anna Freudenreich, 2020. "Exploring the determinants of real estate liquidity from an alternative perspective: censored quantile regression in real estate research," Journal of Business Economics, Springer, vol. 90(7), pages 1057-1086, August.
    16. I-Chun Tsai, 2019. "Interregional correlations in the US housing market at three price tiers," The Annals of Regional Science, Springer;Western Regional Science Association, vol. 63(1), pages 1-24, August.
    17. Ismail, Muhammad & Warsame, Abukar & Wilhelmsson, Mats, 2020. "Measuring Gentrification with Getis-Ord Statistics and Its Effect on Housing Prices in Neighboring Areas: The Case of Stockholm," Working Paper Series 20/19, Royal Institute of Technology, Department of Real Estate and Construction Management & Banking and Finance.
    18. Thomschke, Lorenz, 2015. "Changes in the distribution of rental prices in Berlin," Regional Science and Urban Economics, Elsevier, vol. 51(C), pages 88-100.
    19. Zhang, Lei & Yi, Yimin, 2017. "Quantile house price indices in Beijing," Regional Science and Urban Economics, Elsevier, vol. 63(C), pages 85-96.
    20. Jeffrey P. Cohen & Cletus C. Coughlin & Daniel Soques, 2019. "House Price Growth Interdependencies and Comovement," Working Papers 2019-028, Federal Reserve Bank of St. Louis, revised 11 Jan 2021.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jsusta:v:15:y:2023:i:9:p:7026-:d:1129909. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.