IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v14y2022i6p3523-d773180.html
   My bibliography  Save this article

The Dynamic Evolution of the Structure of an Urban Housing Investment Niche Network and Its Underlying Mechanisms: A Case Study of 35 Large and Medium-Sized Cities in China

Author

Listed:
  • Linyan Wang

    (School of Economics and Management, Xi’an University of Technology, Xi’an 710054, China)

  • Haiqing Hu

    (School of Economics and Management, Xi’an University of Technology, Xi’an 710054, China)

  • Xianzhu Wang

    (School of Business, Anhui University of Technology, Ma’anshan 243002, China)

Abstract

With the growth of urban agglomerations, the spatial diffusion of housing investment is clear; however, little research has been carried out to address its network characteristics and underlying mechanisms of influence. Using data on 35 large and medium-sized cities, this paper applies niche theory to housing investment, constructing a housing investment niche index that includes resources, the housing market, the social economy, and policy. The purpose is to study the characteristics of the network structure and its mechanisms of influence based on an improved gravity model and a temporal exponential random graph model (TERGM). Supported by the analysis of the network structure, we find that the node degree within the network is low, the network density exhibits an inverted “V” shape, and the network level suggests the existence of a “rich cities club”. According to the local network clustering analysis, the cities are divided into three clusters: the Yangtze River Delta region, the Beijing–Tianjin–Hebei region, and the central and eastern regions. Furthermore, analysis of the endogeneity in the structure reveals that there is a hierarchy of cities with high economic development levels, which makes it difficult to establish an investment network with strong relationships. The effects of the attributes are consistent with the predictions of location theory and new economic geography theory. The external network effects conform to the law of the general gravity model. Our research provides insights into the ways in which the misplaced competition in housing investment between cities in a region and the flow of production factors can be reasonably guided.

Suggested Citation

  • Linyan Wang & Haiqing Hu & Xianzhu Wang, 2022. "The Dynamic Evolution of the Structure of an Urban Housing Investment Niche Network and Its Underlying Mechanisms: A Case Study of 35 Large and Medium-Sized Cities in China," Sustainability, MDPI, vol. 14(6), pages 1-21, March.
    • Handle: RePEc:gam:jsusta:v:14:y:2022:i:6:p:3523-:d:773180
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/14/6/3523/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/14/6/3523/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Hillebrand, Marten & Kikuchi, Tomoo, 2015. "A mechanism for booms and busts in housing prices," Journal of Economic Dynamics and Control, Elsevier, vol. 51(C), pages 204-217.
    2. Joshua Aizenman & Yothin Jinjarak & Huanhuan Zheng, 2019. "Housing Bubbles, Economic Growth, and Institutions," Open Economies Review, Springer, vol. 30(4), pages 655-674, September.
    3. Evert Meijers & Martijn Burger & Edward L. Glaeser & Giacomo A. M. Ponzetto & Yimei Zou, 2016. "Urban networks: Connecting markets, people, and ideas," Papers in Regional Science, Wiley Blackwell, vol. 95(1), pages 17-59, March.
    4. Evert Meijers & Martijn Burger & Evert J. Meijers & Martijn J. Burger & Marloes M. Hoogerbrugge, 2016. "Borrowing size in networks of cities: City size, network connectivity and metropolitan functions in Europe," Papers in Regional Science, Wiley Blackwell, vol. 95(1), pages 181-198, March.
    5. Desmarais, B.A. & Cranmer, S.J., 2012. "Statistical mechanics of networks: Estimation and uncertainty," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 391(4), pages 1865-1876.
    6. Stefano Battiston & Joao F. Rodrigues & Hamza Zeytinoglu, 2007. "The Network Of Inter-Regional Direct Investment Stocks Across Europe," Advances in Complex Systems (ACS), World Scientific Publishing Co. Pte. Ltd., vol. 10(01), pages 29-51.
    7. Hongjie Bao & Ling Shan & Yufei Wang & Yuehua Jiang & Cheonjae Lee & Xufeng Cui, 2021. "How Does Local Real Estate Investment Influence Neighborhood PM 2.5 Concentrations? A Spatial Econometric Analysis," Land, MDPI, vol. 10(5), pages 1-21, May.
    8. Peter Byrne & Stephen Lee, 2010. "Spatial concentration in industrial real estate," Journal of Property Investment & Finance, Emerald Group Publishing Limited, vol. 28(1), pages 6-23, February.
    9. Evert Meijers & Martijn Burger & Roberto Camagni & Roberta Capello & Andrea Caragliu, 2016. "Static vs. dynamic agglomeration economies. Spatial context and structural evolution behind urban growth," Papers in Regional Science, Wiley Blackwell, vol. 95(1), pages 133-158, March.
    10. Leifeld, Philip & Cranmer, Skyler J., 2019. "A theoretical and empirical comparison of the temporal exponential random graph model and the stochastic actor-oriented model," Network Science, Cambridge University Press, vol. 7(1), pages 20-51, March.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Xuhong Zhang & Haiqing Hu & Cheng Zhou, 2023. "Spatiotemporal Evolution and Cause Analysis of Innovation Ecosystem Niche Fitness: A Case Study of the Yellow River Basin," Sustainability, MDPI, vol. 15(12), pages 1-16, June.

    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. Tian, Meng & Wang, Yiwei & Wang, Yiran, 2023. "High-speed rail network and urban agglomeration economies: Research from the perspective of urban network externalities," Socio-Economic Planning Sciences, Elsevier, vol. 85(C).
    2. Evert Meijers & Martijn Burger & Martijn J. Burger & Evert J. Meijers, 2016. "Agglomerations and the rise of urban network externalities," Papers in Regional Science, Wiley Blackwell, vol. 95(1), pages 5-15, March.
    3. Camilla Lenzi & Giovanni Perucca, 2021. "Not too close, not too far: Urbanisation and life satisfaction along the urban hierarchy," Urban Studies, Urban Studies Journal Limited, vol. 58(13), pages 2742-2757, October.
    4. Yang, Xiaolan & Wang, Rui & Guo, Dongmei & Sun, Weizeng, 2020. "The reconfiguration effect of China's high-speed railway on intercity connection ——A study based on media attention index," Transport Policy, Elsevier, vol. 95(C), pages 47-56.
    5. Wenfang Fu & Chuanjian Luo & Modan Yan, 2023. "Does Urban Agglomeration Promote the Development of Cities? Evidence from the Urban Network Externalities," Sustainability, MDPI, vol. 15(12), pages 1-20, June.
    6. Akihiro Otsuka, 2021. "A new approach to inter‐regional network externalities in Japan," Regional Science Policy & Practice, Wiley Blackwell, vol. 13(3), pages 1051-1067, June.
    7. Liang Wang & Fangfang Zhang & Yuzhu Zang & Jian Duan, 2022. "Understanding the Regional Integration Process from the Perspective of Agglomeration and Urban Networks: Case Study in Central China," IJERPH, MDPI, vol. 19(19), pages 1-15, October.
    8. Xiaoxia Gong & Fanglei Zhong, 2021. "The Impact of Borrowing Size on the Economic Development of Small and Medium-Sized Cities in China," Land, MDPI, vol. 10(2), pages 1-18, January.
    9. Wenfang Fu & Chuanjian Luo & Shan He, 2022. "Does Urban Agglomeration Promote the Development of Cities? An Empirical Analysis Based on Spatial Econometrics," Sustainability, MDPI, vol. 14(21), pages 1-23, November.
    10. Jinliang Jiang & Zhensheng Xu & Jiayi Lu & Dongqi Sun, 2022. "Does Network Externality of Urban Agglomeration Benefit Urban Economic Growth—A Case Study of the Yangtze River Delta," Land, MDPI, vol. 11(4), pages 1-19, April.
    11. Daidai Shen & Jean-Claude Thill & Jiuwen Sun, 2020. "Are Chinese Cities Oversized?," International Regional Science Review, , vol. 43(6), pages 632-654, November.
    12. Liu, Linqing & Shen, Mengyun & Sun, Da & Yan, Xiaofei & Hu, Shi, 2022. "Preferential attachment, R&D expenditure and the evolution of international trade networks from the perspective of complex networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 603(C).
    13. 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.
    14. Li Fang, 2020. "Agglomeration and innovation: Selection or true effect?," Environment and Planning A, , vol. 52(2), pages 423-448, March.
    15. Di Zhu & Yinghong Wang & Shangui Peng & Fenglin Zhang, 2022. "Influence Mechanism of Polycentric Spatial Structure on Urban Land Use Efficiency: A Moderated Mediation Model," IJERPH, MDPI, vol. 19(24), pages 1-18, December.
    16. Mitra, Arup & Nagar, Jay Prakash, 2018. "City size, deprivation and other indicators of development: Evidence from India," World Development, Elsevier, vol. 106(C), pages 273-283.
    17. Hongming Zhang & Tieshan Sun & Yuanxi Li, 2022. "Network capital and economic growth of major urban agglomeration regions in China," Regional Science Policy & Practice, Wiley Blackwell, vol. 14(S2), pages 60-75, November.
    18. Shuai Shi & Kathy Pain, 2020. "Investigating China’s Mid-Yangtze River economic growth region using a spatial network growth model," Urban Studies, Urban Studies Journal Limited, vol. 57(14), pages 2973-2993, November.
    19. Roberta Capello & Andrea Caragliu, 2021. "Regional growth and disparities in a post‐COVID Europe: A new normality scenario," Journal of Regional Science, Wiley Blackwell, vol. 61(4), pages 710-727, September.
    20. Schachter, Jonathan A. & Mancarella, Pierluigi & Moriarty, John & Shaw, Rita, 2016. "Flexible investment under uncertainty in smart distribution networks with demand side response: Assessment framework and practical implementation," Energy Policy, Elsevier, vol. 97(C), pages 439-449.

    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:14:y:2022:i:6:p:3523-:d:773180. 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.