Technical Papers
Oct 17, 2024

Impact of Accessibility on Residential Location Choice: A Case Study of Hangzhou, China

Publication: Journal of Urban Planning and Development
Volume 151, Issue 1

Abstract

This study examines the factors that influence households’ location choice in the context of rapid urbanization in Hangzhou, China, where new housing and transportation infrastructure are expanding. A conditional logit model based on community scale is developed to consider the impact of location accessibility characteristics on households’ housing choices, as well as the differences in choices across households. Considering the background of high house prices, households will prefer house prices as the determining factor in the tradeoff between housing prices and accessibility. The multinomial logit model model of joint selection of housing prices and location is constructed to study the differences in the preferences of different types of households in housing choices. The study finds that (1) accessibility and housing prices are significant factors that affect residential location choice. Low-income families place more importance on public transportation conditions near the community. (2) Accessibility factors, including commuting time and the convenience of transportation facilities, such as the subway and expressway exit, have significant explanatory power on residential location choice. Holding other factors constant, for every minute increase in commuting time, the odds of residents choosing a residential location decrease by 0.96 times. (3) Residents with different socioeconomic characteristics exhibited certain differences in their housing price and accessibility preferences. Specifically, households with a college degree or higher are 8.59 times more likely to choose a residential area away from the city center and at a higher price than those with less education.

Practical Applications

Housing location choice is a widely discussed topic. There are diversified preferences based on the stages of urban development and social, economic, and cultural backgrounds. This study focuses on cities that are in the stage of rapid urbanization and are densely populated. Public facilities such as subways and commercial centers have been continuously improved, and cities continue to expand and have developed into multicenter spatial structures. Thus, the choice of housing location is accompanied by many other considerations, including the need for housing quality, public amenities, and housing prices. Taking Hangzhou, China, as a case study, the study finds that commuting time has a significant impact on location choice, and the proximity of transportation facilities, such as subways and expressways, has a significant positive impact on community choice. The tradeoff between housing prices and location varies from person to person. Highly educated people choose neighborhoods with high housing prices, even if their accessibility to traditional city centers is low, reflecting their relatively high housing affordability and pursuit of housing quality. Therefore, for urban governments, promoting the equalization of public services is conducive to the stable and balanced development of the housing market.

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Data Availability Statement

Some or all data, models, or codes that support the findings of this study are available from the corresponding author upon reasonable request.

Acknowledgments

Thanks to the editor and anonymous referees for excellent comments and suggestions. This research was supported by the National Natural Science Foundation of China (No. 72174178).

References

Alonso, W. 1964. Location and land use: Toward a general theory of land rent. Cambridge, UK: Harvard University Press.
Bayoh, I., E. G. Irwin, and T. Haab. 2006. “Determinants of residential location choice: How important are local public goods in attracting homeowners to central city locations?” J. Reg. Sci. 46 (1): 97–120. https://doi.org/10.1111/j.0022-4146.2006.00434.x.
Beckers, P., and S. Boschman. 2019. “Residential choices of foreign highly skilled workers in the Netherlands and the role of neighbourhood and urban regional characteristics.” Urban Stud. 56 (4): 760–777. https://doi.org/10.1177/0042098017741262.
Ben-Akiva, M., and J. L. Bowman. 1998. “Integration of an activity-based model system and a residential location model.” Urban Stud. 35 (7): 1131–1153. https://doi.org/10.1080/0042098984529.
Bina, M., K. M. Kockelman, and D. Suescun. 2009. “Location choice vis-à-vis transportation: The case of recent homebuyers.” In Proc., 11th Int. Conf. of Travel Behaviour Research, edited by R. Kitamura and T. Yoshii, 597–619. Bingley, UK: Emerald Group Publishing.
Boschman, S., and M. van Ham. 2015. “Neighbourhood selection of non-western ethnic minorities: Testing the own-group effects hypothesis using a conditional logit model.” Environ. Plann. A: Econ. Space 47 (5): 1155–1174. https://doi.org/10.1177/0308518X15592300.
Cao, X., P. L. Mokhtarian, and S. L. Handy. 2009. “Examining the impacts of residential self-selection on travel behaviour: A focus on empirical findings.” Transp. Rev. 29 (3): 359–395. https://doi.org/10.1080/01441640802539195.
Cao, X. J., and J. Schoner. 2014. “The influence of light rail transit on transit use: An exploration of station area residents along the Hiawatha line in Minneapolis.” Transp. Res. Part A Policy Pract. 59: 134–143. https://doi.org/10.1016/j.tra.2013.11.001.
Cervero, R., and J. Day. 2008. “Suburbanization and transit-oriented development in China.” Transp. Policy 15 (5): 315–323. https://doi.org/10.1016/j.tranpol.2008.12.011.
Choudhury, C. F., and S. Bint Ayaz. 2015. “Why live far? Insights from modeling residential location choice in Bangladesh.” J. Transp. Geogr. 48: 1–9. https://doi.org/10.1016/j.jtrangeo.2015.08.001.
Chu, Y.-L., Y. Deng, and R. R. Liu. 2017. “Impacts of new light rail transit service on riders’ residential relocation decisions.” J. Publ. Transp. 20 (2): 152–165. https://doi.org/10.5038/2375-0901.20.2.8.
Cockx, K., and F. Canters. 2020. “Determining heterogeneity of residential location preferences of households in Belgium.” Appl. Geogr. 124: 102271. https://doi.org/10.1016/j.apgeog.2020.102271.
Cohen, J. P., and C. C. Coughlin. 2008. “Spatial hedonic models of airport noise, proximity, and housing prices.” J. Reg. Sci. 48 (5): 859–878. https://doi.org/10.1111/j.1467-9787.2008.00569.x.
Combes, P. P., and G. Duranton. 2006. “Labour pooling, labour poaching, and spatial clustering.” Reg. Sci. Urban Econ. 36 (1): 1–28. https://doi.org/10.1016/j.regsciurbeco.2005.06.003.
Dahlberg, M., M. Eklöf, P. Fredriksson, and J. Jofre-Monseny. 2012. “Estimating preferences for local public services using migration data.” Urban Stud. 49 (2): 319–336. https://doi.org/10.1177/0042098011400769.
De Vos, J., and F. Witlox. 2013. “Transportation policy as spatial planning tool; reducing urban sprawl by increasing travel costs and clustering infrastructure and public transportation.” J. Transp. Geogr. 33: 117–125. https://doi.org/10.1016/j.jtrangeo.2013.09.014.
Deka, D. 2018. “Are millennials moving to more urbanized and transit-oriented counties?” J. Transp. Land Use 11 (1): 443–461. https://doi.org/10.5198/jtlu.2018.1345.
Diao, M., D. Leonard, and T. F. Sing. 2017. “Spatial-difference-in-differences models for impact of new mass rapid transit line on private housing values.” Reg. Sci. Urban Econ. 67: 64–77. https://doi.org/10.1016/j.regsciurbeco.2017.08.006.
Dreger, C., and Y. Zhang. 2013. “Is there a bubble in the Chinese housing market?” Urban Policy Res. 31 (1): 27–39. https://doi.org/10.1080/08111146.2012.711248.
Guevara, C. A., and M. Ben-Akiva. 2006. “Endogeneity in residential location choice models.” Transp. Res. Record. 1977 (1): 60–66. https://doi.org/10.1177/0361198106197700108.
Guo, Y., and S. Peeta. 2020. “Impacts of personalized accessibility information on residential location choice and travel behavior.” Travel Behav. Soc. 19: 99–111. https://doi.org/10.1016/j.tbs.2019.12.007.
Habib, M. A., and E. J. Miller. 2009. “Reference-dependent residential location choice model within a relocation context.” Transp. Res. Record. 2133 (1): 92–99. https://doi.org/10.3141/2133-10.
Hilber, C. A. L., and C. J. Mayer. 2009. “Why do households without children support local public schools? Linking house price capitalization to school spending.” J. Urban Econ. 65 (1): 74–90. https://doi.org/10.1016/j.jue.2008.09.001.
Hui, E. C. M., J. W. Zhong, and K.-H. Yu. 2012. “The impact of landscape views and storey levels on property prices.” Landscape Urban Plann. 105 (1-2): 86–93. https://doi.org/10.1016/j.landurbplan.2011.12.002.
Kim, H.-S., G.-E. Lee, J.-S. Lee, and Y. Choi. 2019. “Understanding the local impact of urban park plans and park typology on housing price: A case study of the busan metropolitan region, Korea.” Landscape Urban Plann. 184: 1–11. https://doi.org/10.1016/j.landurbplan.2018.12.007.
Kim, T.-K., M. W. Horner, and R. W. Marans. 2005. “Life cycle and environmental factors in selecting residential and job locations.” Hous. Stud. 20 (3): 457–473. https://doi.org/10.1080/02673030500062335.
Knox, P., and S. Pinch. 2014. Urban social geography: An introduction. New York: Routledge.
Kryvobokov, M., A. Mercier, A. Bonnafous, and D. Bouf. 2013. “Simulating housing prices with UrbanSim: Predictive capacity and sensitivity analysis.” Lett. Spatial Resour. Sci. 6 (1): 31–44. https://doi.org/10.1007/s12076-012-0084-1.
Lee, B. H., P. Waddell, L. Wang, and R. M. Pendyala. 2010. “Reexamining the influence of work and nonwork accessibility on residential location choices with a microanalytic framework.” Environ. Plann. A: Econ. Space 42 (4): 913–930. https://doi.org/10.1068/a4291.
Li, R. Y. M., and H. C. Y. Li. 2018. “Have housing prices gone with the smelly wind? Big data analysis on landfill in Hong Kong.” Sustainability 10 (2): 341. https://doi.org/10.3390/su10020341.
Li, Z., and F. Wu. 2008. “Tenure-based residential segregation in post-reform Chinese cities: A case study of Shanghai.” Trans. Inst. Br. Geogr. 33 (3): 404–419. https://doi.org/10.1111/j.1475-5661.2008.00304.x.
Magliocca, N. R., D. G. Brown, V. D. McConnell, J. I. Nassauer, and S. E. Westbrook. 2014. “Effects of alternative developer decision-making models on the production of ecological subdivision designs: Experimental results from an agent-based model.” Environ. Plann. B: Urban Anal. City Sci. 41 (5): 907–927. https://doi.org/10.1068/b130118p.
Mankiw, N. G. 2021. Principles of economics. Singapore: Cengage Learning Asia Pte Ltd.
Marshall, A. 2009. Principles of economics: Unabridged, 8th ed. New York: Cosimo Classics.
McFadden, D. 1973. “Conditional logit analysis of qualitative choice behavior.” In Frontiers in Economics, edited by P. Zarembka, 105–142. New York: Academic Press.
Melia, S., K. Chatterjee, and G. Stokes. 2018. “Is the urbanisation of young adults reducing their driving?” Transp. Res. Part A Policy Pract. 118: 444–456. https://doi.org/10.1016/j.tra.2018.09.021.
Olaru, D., B. Smith, and J. H. Taplin. 2011. “Residential location and transit-oriented development in a new rail corridor.” Transp. Res. Part A Policy Pract. 45 (3): 219–237. https://doi.org/10.1016/j.tra.2010.12.007.
Pinjari, A. R., C. R. Bhat, and D. A. Hensher. 2009. “Residential self-selection effects in an activity time-use behavior model.” Transp. Res. Part B Methodol. 43 (7): 729–748. https://doi.org/10.1016/j.trb.2009.02.002.
Saghapour, T., and S. Moridpour. 2019. “The role of neighborhoods accessibility in residential mobility.” Cities 87: 1–9. https://doi.org/10.1016/j.cities.2018.12.022.
Schirmer, P. M., M. A. Van Eggermond, and K. W. Axhausen. 2014. “The role of location in residential location choice models: A review of literature.” J. Transp. Land Use 7 (2): 3–21. https://doi.org/10.5198/jtlu.v7i2.740.
Schmidheiny, K. 2006. “Income segregation and local progressive taxation: Empirical evidence from Switzerland.” J. Public Econ. 90 (3): 429–458. https://doi.org/10.1016/j.jpubeco.2005.09.003.
Sun, W., S. Zheng, D. M. Geltner, and R. Wang. 2017. “The housing market effects of local home purchase restrictions: Evidence from Beijing.” J. Real Estate Financ. Econ. 55: 288–312. https://doi.org/10.1007/s11146-016-9586-8.
Sun, Y., and Y. Cui. 2018. “Evaluating the coordinated development of economic, social, and environmental benefits of urban public transportation infrastructure: Case study of four Chinese autonomous municipalities.” Transp. Policy 66: 116–126. https://doi.org/10.1016/j.tranpol.2018.02.006.
Tao, S., S. Y. He, and S. Luo. 2020. “The influence of Job accessibility on local residential segregation of ethnic minorities: A study of Hong Kong.” Popul. Space Place 26 (8): e2353. https://doi.org/10.1002/psp.2353.
Wang, M., Y. Yang, S. Jin, L. Gu, and H. Zhang. 2016. “Social and cultural factors that influence residential location choice of urban senior citizens in China—The case of Chengdu city.” Habitat Int. 53: 55–65. https://doi.org/10.1016/j.habitatint.2015.10.011.
Xu, W., A. Guthrie, Y. Fan, and Y. Li. 2017. “Transit-oriented development in China: Literature review and evaluation of TOD potential across 50 Chinese cities.” J. Transp. Land Use 10 (1): 743–762.
Yi, C., and S. Lee. 2014. “An empirical analysis of the characteristics of residential location choice in the rapidly changing Korean housing market.” Cities 39: 156–163. https://doi.org/10.1016/j.cities.2014.03.002.
Zhang, W., and K. Kockelman. 2014. “Urban sprawl, job decentralization, and congestion: The welfare effects of congestion tolls and urban growth boundaries.” In Proc., 93rd Annual Meeting of the Transportation Research Board, 12–16. Washington, DC: Transportation Research Board.
Zhuge, C., C. Shao, J. Gao, C. Dong, and H. Zhang. 2016. “Agent-based joint model of residential location choice and real estate price for land use and transport model.” Comput. Environ. Urban Syst. 57: 93–105. https://doi.org/10.1016/j.compenvurbsys.2016.02.001.
Zolfaghari, A., A. Sivakumar, and J. W. Polak. 2012. “Choice set pruning in residential location choice modelling: A comparison of sampling and choice set generation approaches in Greater London.” Transp. Plann. Technol. 35 (1): 87–106. https://doi.org/10.1080/03081060.2012.635420.

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Go to Journal of Urban Planning and Development
Journal of Urban Planning and Development
Volume 151Issue 1March 2025

History

Received: Oct 5, 2023
Accepted: Jun 24, 2024
Published online: Oct 17, 2024
Published in print: Mar 1, 2025
Discussion open until: Mar 17, 2025

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Center of Real Estate Study, Zhejiang Univ., Hangzhou 310058, China (corresponding author). ORCID: https://orcid.org/0000-0002-3864-2602. Email: [email protected]
Center of Real Estate Study, Zhejiang Univ., Hangzhou 310058, China. Email: [email protected]
Center of Real Estate Study, Zhejiang Univ., Hangzhou 310058, China. Email: [email protected]
Dept. of Public and International Affairs, City Univ. of Hong Kong, Hong Kong 999077. ORCID: https://orcid.org/0000-0002-6974-891X. Email: [email protected]

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