Divergence of Urban Function and Its Influences on Urban Land Prices: Evidence from Cities in the Yangtze River Economic Belt in China
Publication: Journal of Urban Planning and Development
Volume 147, Issue 3
Abstract
China's urban land price has experienced a long-term increase that has been accompanied by urbanization. This paper will analyze the divergence of urban function between cities in the Yangtze River Economic Belt from 2009 to 2016 and will explore the effect of this on the urban land price. The results show that there is a significant divergence in urban function between cities, Shanghai, China, leads all the cities, and the central cities have a significantly higher urban function than the other cities. From the demand perspective, residential and commercial land prices are significantly influenced by gross domestic product (GDP) per capita, and the latter is determined by the size of the population. From the supply perspective, the residential land price is determined by government and transport functions and other functions are open, with the sum of coefficients even larger than that of the demand side. The commercial land price is driven significantly by urban government and transport functions in fixed effect regression (FE) and two stage least square regression (2SLS). The estimation of generalized method of moments (GMM) shows that the producer servicing function has a significant influence on the commercial land price and the effect of government function is not significant. The public service function does not have any positive influence on both types of land prices. Land prices were not proved to be significantly influenced by land supply and the coefficients in the GMM were statistically significant. This could provide a basis for local governments to formulate city-specific land policies.
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Acknowledgments
This paper was financially supported by the General Planning Project of NSSFC “Functional Spatial Distribution Imbalance and Rebalance of Urban Agglomerations in Yangtze River Economic Belt from the Perspective of Industrial Upgrade” (17BJL053).
References
Bowes, D. R., and K. R. Ihlanfeldt. 2001. “Identifying the impacts of rail transit stations on residential property values.” J. Urban Econ. 50: 1–25. https://doi.org/10.1006/juec.2001.2214.
Chen, Y., and C. Lyu. 2014. “Land as power: Interaction mechanism and operation logic between house price and land price in the process of urbanization.” J. Jiangsu Admin. Inst. 3: 51–60.
China's Land Surveying and Planning Institute. 2010-2017. “Annual report of China urban land price.” Beijing: Geological Press.
Cui, X., J. Ge, Y. Liu, and Y. Cui. 2017. “Difference of influencing factors of land price in urban agglomerations.” Mod. Urban Res. 6: 106–112.
Dai, X., X. Bai, and M. Xu. 2016. “The influence of Beijing rail transfer stations on surrounding housing prices.” Habitat Int. 55: 79–88. https://doi.org/10.1016/j.habitatint.2016.02.008.
El Araby, M. M. 2003. “The role of the state in managing urban land supply and prices in Egypt.” Habitat Int. 27 (3): 429–458. https://doi.org/10.1016/S0197-3975(02)00068-1.
Gao, X., and Y. Asami. 2007. “Effect of urban landscapes on land prices in two Japanese cities.” Landscape Urban Plann. 81 (1–2): 155–166. https://doi.org/10.1016/j.landurbplan.2006.11.007.
Ghebreegziabiher D., P. Eric, and R. Piet. 2011. “The impact of rail transport on real estate prices: An empirical analysis of the Dutch housing market.” Urban Stud. 48: 997–1015. https://doi.org/10.1177%2F0042098010371395.
Hu, S., S. Yang, W. Li, C. Zhang, and F. Xu. 2016. “Spatially non-stationary relationships between urban residential land price and impact factors in Wuhan city, China.” Appl. Geogr. 68: 48–56. https://doi.org/10.1016/j.apgeog.2016.01.006.
Kheir, N., and B. A. Portnov. 2016. “Economic, demographic and environmental factors affecting urban land prices in the Arab sector in Israel.” Land Use Policy 50: 518–527. https://doi.org/10.1016/j.landusepol.2015.08.031.
Kuang, W., and T. Li. 2018. “Land transfer method, land price and house price.” J. Financ. Res. 3: 43–55.
Li, H., Y. D. Wei, and Y. Wu. 2019. “Analyzing housing prices in Shanghai with open data: Amenity, accessibility and urban structure.” Cities. 91: 165–179. https://doi.org/10.1016/j.cities.2018.11.016.
Mao, F., and W. Qiu. 2013. “Vertical decentralization, horizontal competition and land price distortion.” Res. Econ. Manage. 12: 35–47.
National Bureau of Statistics of China. 2010-2017. “China statistical yearbook.” Beijing: China Statistical Press.
Sun, B., and H. Chen. 2016. “Impact of urban spatial structure on land price.” Urban Probl. 4: 31–38.
Tang, Y., and Q. Wu. 2018. “Influencing factors of land finance system on real estate price.” Econ. Theory Manage. 3: 43–55.
Wang, Z., S. Zhang, Y. Zhang, and J. Zhang. 2016. “Analysis of spillover effect of urban green space on residential land price.” J. Arid Land Resourc. Environ. 4: 24–29.
Wen, H., and A. C. Goodman. 2013. “Relationship between urban land price and housing price: Evidence from 21 provincial capitals in China.” Habitat Int. 40: 9–17. https://doi.org/10.1016/j.habitatint.2013.01.004.
Ye, G., K. Zhu, and J. Zhang. 2016. “Causal relationship between house price and land price.” China Land Sci. 6: 62–70.
Yu, H., H. Pang, and M. Zhang. 2018. “Value-added effects of transit-oriented development: The impact of urban rail on commercial property values with consideration of spatial heterogeneity.” Pap. Reg. Sci. 97: 1375–1386. https://doi.org/10.1111/pirs.12304.
Yuan, F., Y. D. Wei, and W. Xiao. 2019. “Land marketization, fiscal decentralization, and the dynamics of urban land prices in transitional China.” Land Use Policy 89: 104208. https://doi.org/10.1016/j.landusepol.2019.104208.
Zhang, B., Y. Ma, and J. Liu. 2015. “The impact of public goods supply on urban internal land prices.” Contemp. Econ. Jpn. 1: 1–14.
Zheng, S., Y. Cao, and H. Liu. 2008. “The dominance of urban value in housing price and its policy implications.” Urban Dev. Stud. 1: 45–52.
Zheng, S., M. E. Kahn, and H. Liu. 2010. “Towards a system of open cities in China: Home prices, FDI flows and air quality in 35 major cities.” Reg. Sci. Urban Econ. 40 (1): 1–10. https://doi.org/10.1016/j.regsciurbeco.2009.10.003.
Zheng, S., Z. Shi, and J. Wu. 2014. “Land transfer, urban construction and real estate price.” J. Xuehai 5: 157–163.
Zhou, Y., J. Yang, Y. Huang, and G. J. D. Hewings. 2018. “The impact of high-speed railway on urban land price and its mechanism.” China Ind. Econ. 5: 118–136.
Zhu, D., and S. Xu. 2013. “Research on the relationship between housing demand and land and housing supply based on urban population change.” China Land Sci. 27 (11): 45–51.
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Published In
Journal of Urban Planning and Development
Volume 147 • Issue 3 • September 2021
Copyright
© 2021 American Society of Civil Engineers.
History
Received: May 6, 2020
Accepted: Dec 1, 2020
Published online: Jun 2, 2021
Published in print: Sep 1, 2021
Discussion open until: Nov 2, 2021
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