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Spatio-Temporal Non-Stationarity and Its Influencing Factors of Commercial Land Price: A Case Study of Hangzhou, China

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  • Zhuoma Garang

    (Land Academy for National Development, Zhejiang University, Hangzhou 310029, China)

  • Cifang Wu

    (Land Academy for National Development, Zhejiang University, Hangzhou 310029, China)

  • Guan Li

    (Law School, Ningbo University, Ningbo 315211, China)

  • Yuefei Zhuo

    (Law School, Ningbo University, Ningbo 315211, China)

  • Zhongguo Xu

    (Law School, Ningbo University, Ningbo 315211, China)

Abstract

Investigating the characteristics and mechanisms of the spatial and temporal variations of commercial land prices and its major subdivisions has great theoretical and practical significance in the study of urban economy and its spatial refinement management. Unlike general commodity prices, land prices are influenced by geographical location and tend to fluctuate over time. However, most scholars have not explored the influence mechanism of commercial land prices in both time and space. To help bridge this gap, this study takes the sample commercial land prices in the main urban area of Hangzhou from 2006 to 2015 as the empirical research object and investigates the spatiotemporal evolution mechanism of urban commercial land prices through a comparative analysis of the multiple regression analysis (MRA) with ordinary least squares (OLS), the geographically weighted regression (GWR), the temporally weighted regression (TWR), and the geographically and temporally weighted regression (GTWR) models. Results indicate that the land prices of land for financial facilities (Commercial Land Category 1) and commercial-business land (Commercial Land Category 2) in Hangzhou show different spatial and temporal evolutions and are influenced by the common factors of residential land price level (PL), maturity of living services (EN), and plot ratio (FRO) in the district. Meanwhile the main difference between the two influencing factors is the significant difference in sensitivity to locational centrality and industrial structure. Furthermore, we find that the spatial and temporal evolution of commercial land prices has three main mechanism: location selection, point-axis evolution, and function-promoting. Our findings will provide guidelines for scientifically guiding the coordinated development of urban land price and industrial economy and realizing the fine management and allocation of urban spatial resources.

Suggested Citation

  • 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.
  • Handle: RePEc:gam:jlands:v:10:y:2021:i:3:p:317-:d:520536
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    References listed on IDEAS

    as
    1. Tian, Li & Liang, Yinlong & Zhang, Bo, 2017. "Measuring residential and industrial land use mix in the peri-urban areas of China," Land Use Policy, Elsevier, vol. 69(C), pages 427-438.
    2. Cheng, Jing, 2021. "Analysis of commercial land leasing of the district governments of Beijing in China," Land Use Policy, Elsevier, vol. 100(C).
    3. Mathur, Shishir, 2020. "Impact of transit stations on house prices across entire price spectrum: A quantile regression approach," Land Use Policy, Elsevier, vol. 99(C).
    4. Nichols, Joseph B. & Oliner, Stephen D. & Mulhall, Michael R., 2013. "Swings in commercial and residential land prices in the United States," Journal of Urban Economics, Elsevier, vol. 73(1), pages 57-76.
    5. Timothy J. Fik & David C. Ling & Gordon F. Mulligan, 2003. "Modeling Spatial Variation in Housing Prices: A Variable Interaction Approach," Real Estate Economics, American Real Estate and Urban Economics Association, vol. 31(4), pages 623-646, December.
    6. J F McDonald & D P McMillen, 1990. "Employment Subcenters and Land Values in a Polycentric Urban Area: The Case of Chicago," Environment and Planning A, , vol. 22(12), pages 1561-1574, December.
    7. Haizhen Wen & Zaiyuan Gui & Chuanhao Tian & Yue Xiao & Li Fang, 2018. "Subway Opening, Traffic Accessibility, and Housing Prices: A Quantile Hedonic Analysis in Hangzhou, China," Sustainability, MDPI, vol. 10(7), pages 1-23, June.
    8. Davis, Morris A., 2009. "The price and quantity of land by legal form of organization in the United States," Regional Science and Urban Economics, Elsevier, vol. 39(3), pages 350-359, May.
    9. Qin, Yu & Zhu, Hongjia & Zhu, Rong, 2016. "Changes in the distribution of land prices in urban China during 2007–2012," Regional Science and Urban Economics, Elsevier, vol. 57(C), pages 77-90.
    10. Atack, Jeremy & Margo, Robert A, 1998. ""Location, Location, Location!" The Price Gradient for Vacant Urban Land: New York, 1835 to 1900," The Journal of Real Estate Finance and Economics, Springer, vol. 16(2), pages 151-172, March.
    11. Chamblee, John F. & Dehring, Carolyn A. & Depken, Craig A., 2009. "Watershed development restrictions and land prices: Empirical evidence from southern Appalachia," Regional Science and Urban Economics, Elsevier, vol. 39(3), pages 287-296, May.
    12. Han, M.Y. & Chen, G.Q. & Dunford, M., 2019. "Land use balance for urban economy: A multi-scale and multi-type perspective," Land Use Policy, Elsevier, vol. 83(C), pages 323-333.
    13. 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.
    14. McDonald, John F. & Osuji, Clifford I., 1995. "The effect of anticipated transportation improvement on residential land values," Regional Science and Urban Economics, Elsevier, vol. 25(3), pages 261-278, June.
    15. Yizhou Wu & Peilei Fan & Heyuan You, 2018. "Spatial Evolution of Producer Service Sectors and Its Influencing Factors in Cities: A Case Study of Hangzhou, China," Sustainability, MDPI, vol. 10(4), pages 1-23, March.
    16. Yanjing Zhang & Zhengguo Su & Guan Li & Yuefei Zhuo & Zhongguo Xu, 2018. "Spatial-Temporal Evolution of Sustainable Urbanization Development: A Perspective of the Coupling Coordination Development Based on Population, Industry, and Built-Up Land Spatial Agglomeration," Sustainability, MDPI, vol. 10(6), pages 1-20, May.
    17. Robert W. Paterson & Kevin J. Boyle, 2002. "Out of Sight, Out of Mind? Using GIS to Incorporate Visibility in Hedonic Property Value Models," Land Economics, University of Wisconsin Press, vol. 78(3), pages 417-425.
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