Skip to main content

Advertisement

SpringerLink
Log in

Urban Structure, Housing Prices and the Double Role of Amenity: A Study of Nanjing, China

  • Published:
Applied Spatial Analysis and Policy Aims and scope Submit manuscript

Abstract

The skyrocketing housing prices in Chinese metropolises have generated broad concerns. Recent studies have moved beyond hedonic approaches considering housing attributes, location, and neighborhood by introducing urban structure and amenities as factors in housing prices. However, the role of amenities is often simplified, and the influence of urban structure is explored mainly using distance to CBD or concentric rings. This study more carefully examines the role of amenities in determining housing prices through a case study of Nanjing, China, adopting the self-organizing map and spatial regime modeling using remote sensing and point-of-interest data. We find that the regime of urban structure sways the hedonic factors' significance and positivity. Amenities play a double role in housing markets, as they act both as a determinant of housing price and an indicator of urban structure. Our study provides an improved framework of housing prices, which is applicable to studies of other cities. It also suggests that public policies should consider amenities more carefully to make cities more polycentric and livable.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4

Similar content being viewed by others

Data Availability

Some or all data, models, or code generated or used during the study are proprietary or confidential in nature and may only be provided with restrictions.

References 

  • Agostini, C. A., & Palmucci, G. A. (2008). The anticipated capitalisation effect of a new metro line on housing prices. Fiscal Studies, 29(2), 233–256.

    Article  Google Scholar 

  • Alonso, W. (1964). Location and land use. Cambridge: Harvard University Press.

    Book  Google Scholar 

  • Anselin, L. (1995). Local indicators of spatial association—LISA. Geographical Analysis, 27(2), 93–115.

    Article  Google Scholar 

  • Anselin, L., & Lozano-Gracia, N. (2009). Spatial hedonic models. In T. C. Mills, & K. Patterson (Eds.), Palgrave handbook of econometrics: Volume 2: Applied econometrics (pp. 1213–1250). London: Palgrave Macmillan. https://doi.org/10.1057/9780230244405_26

  • Arribas-Bel, D., & Schmidt, C. R. (2013). Self-organizing maps and the US urban spatial structure. Environment and Planning b: Planning and Design, 40(2), 362–371.

    Article  Google Scholar 

  • Bação, F., Lobo, V., & Painho, M. (2004). Geo-self-organizing map (Geo-SOM) for building and exploring homogeneous regions. In International Conference on Geographic Information Science (pp. 22–37). Springer, Berlin, Heidelberg.

  • Ball, M. J., & Kirwan, R. M. (1977). Accessibility and supply constraints in the urban housing market. Urban Studies, 14(1), 11–32.

    Article  Google Scholar 

  • Black, S. E., & Machin, S. (2011). Housing valuations of school performance. In Handbook of the Economics of Education (Vol. 3, pp. 485–519). Elsevier.

  • Bohman, H. (2021). Same, same but different? Neighbourhood effects of accessibility on housing prices. Transport Policy, 107, 52–60.

    Article  Google Scholar 

  • Bourassa, S. C., Hamelink, F., Hoesli, M., & MacGregor, B. D. (1999). Defining housing submarkets. Journal of Housing Economics, 8(2), 160–183.

  • Brécard, D., Le Boennec, R., & Salladarré, F. (2018). Accessibility, local pollution and housing prices. Evidence from Nantes Métropole, France. Economie Et Statistique/economics and Statistics, 500, 97–115.

    Article  Google Scholar 

  • Cao, K., Diao, M., & Wu, B. (2019). A big data–based geographically weighted regression model for public housing prices: A case study in Singapore. Annals of the American Association of Geographers, 109(1), 173–186.

    Article  Google Scholar 

  • Cao, W., Dong, L., Wu, L., & Liu, Y. (2020). Quantifying urban areas with multi-source data based on percolation theory. Remote Sensing of Environment, 241, 111730.

    Article  Google Scholar 

  • Case, K. E., & Mayer, C. J. (1996). Housing price dynamics within a metropolitan area. Regional Science and Urban Economics, 26(3–4), 387–407.

    Article  Google Scholar 

  • Chen, G. (2012). Housing the urban poor in post-reform China: Some empirical evidence from the city of Nanjing. Cities, 29(4), 252–263.

    Article  CAS  Google Scholar 

  • Chen, J., & Hao, Q. (2008). The impacts of distance to CBD on housing prices in Shanghai: A hedonic analysis. Journal of Chinese Economic and Business Studies, 6(3), 291–302.

    Article  Google Scholar 

  • Chen, W. Y., & Li, X. (2017). Cumulative impacts of polluted urban streams on property values: A 3-D spatial hedonic model at the micro-neighborhood level. Landscape and Urban Planning, 162, 1–12.

    Article  CAS  Google Scholar 

  • Chen, J., Gao, J., & Chen, W. (2016). Urban land expansion and the transitional mechanisms in Nanjing, China. Habitat International, 53, 274–283.

    Article  Google Scholar 

  • Chen, Y., Liu, X., Li, X., Liu, X., Yao, Y., Hu, G., ... & Pei, F. (2017). Delineating urban functional areas with building-level social media data: A dynamic time warping (DTW) distance based k-medoids method. Landscape and Urban Planning, 160, 48–60.

  • Chi, J., Jiao, L., Dong, T., Gu, Y., & Ma, Y. (2016). Quantitative identification and visualization of urban functional area based on POI data. Journal of Geomatics, 41(2), 68–73.

    Google Scholar 

  • Chow, G. C., & Niu, L. (2015). Housing prices in urban china as determined by demand and supply. Pacific Economic Review, 20(1), 1–16.

    Article  Google Scholar 

  • Concas, S. (2013). Accessibility and housing price resilience: Evidence from limited-access roadways in Florida. Transportation Research Record, 2357(1), 66–76.

    Article  Google Scholar 

  • Cordera, R., Coppola, P., Dell'Olio, L., & Ibeas, Á. (2019). The impact of accessibility by public transport on real estate values: A comparison between the cities of Rome and Santander. Transportation Research Part A: Policy and Practice, 125, 308–319.

  • CREI. (2019). China real estate information. Retrieved from http://www.crei.cn/. Accessed 19 May 2023.

  • De Bellefon, M. P., Combes, P. P., Duranton, G., Gobillon, L., & Gorin, C. (2021). Delineating urban areas using building density. Journal of Urban Economics, 125, 103226.

    Article  Google Scholar 

  • Deng, Y., Liu, J., Liu, Y., & Luo, A. (2019). Detecting Urban Polycentric Structure from POI Data. ISPRS International Journal of Geo-Information, 8(6), 283.

    Article  CAS  ADS  Google Scholar 

  • Feng, L., & Li, H. (2012). Spatial pattern analysis of urban sprawl: Case study of Jiangning, Nanjing, China. Journal of Urban Planning and Development, 138(3), 263–269.

    Article  MathSciNet  Google Scholar 

  • Feng, H., & Lu, M. (2013). School quality and housing prices: Empirical evidence from a natural experiment in Shanghai, China. Journal of Housing Economics, 22(4), 291–307.

    Article  Google Scholar 

  • Gabrielli, L., Giuffrida, S., & Trovato, M. R. (2017). Gaps and overlaps of urban housing sub-market: hard clustering and fuzzy clustering approaches. In Appraisal: From Theory to Practice (pp. 203–219). Springer, Cham.

  • Gao, S., Janowicz, K., & Couclelis, H. (2017). Extracting urban functional regions from points of interest and human activities on location-based social networks. Transactions in GIS, 21(3), 446–467.

    Article  Google Scholar 

  • Getis, A., & Ord, J. K. (2010). The analysis of spatial association by use of distance statistics. In L. Anselin, & S. Rey (Eds.), Perspectives on spatial data analysis. Advances in spatial science. Berlin, Heidelberg: Springer. https://doi.org/10.1007/978-3-642-01976-0_10

  • Glaeser, E. L., Kominers, S. D., Luca, M., & Naik, N. (2018). Big data and big cities: The promises and limitations of improved measures of urban life. Economic Inquiry, 56(1), 114–137.

    Article  Google Scholar 

  • Han, S. S., & Qin, B. (2009). The spatial distribution of producer services in Shanghai. Urban Studies, 46(4), 877–896.

    Article  Google Scholar 

  • Harris, R., Dong, G., & Zhang, W. (2013). Using Contextualized Geographically Weighted Regression to Model the Spatial Heterogeneity of Land Prices in Beijing. China. Transactions in GIS, 17(6), 901–919.

    Article  Google Scholar 

  • Helbich, M., Brunauer, W., Vaz, E., & Nijkamp, P. (2014). Spatial heterogeneity in hedonic house price models: The case of Austria. Urban Studies, 51(2), 390–411.

    Article  Google Scholar 

  • Hu, Y., & Han, Y. (2019). Identification of Urban Functional Areas Based on POI Data: A Case Study of the Guangzhou Economic and Technological Development Zone. Sustainability, 11(5), 1385.

    Article  Google Scholar 

  • Jim, C. Y., & Chen, W. Y. (2007). Consumption preferences and environmental externalities: A hedonic analysis of the housing market in Guangzhou. Geoforum, 38(2), 414–431.

    Article  Google Scholar 

  • Jin, C., & Choi, M. J. (2019). The causal structure of land finance, commercial housing, and social housing in China. International Journal of Urban Sciences, 23(2), 286–299.

    Article  Google Scholar 

  • Kan, H. Y., Forsyth, A., & Rowe, P. (2017). Redesigning China’s superblock neighbourhoods: Policies, opportunities and challenges. Journal of Urban Design, 22(6), 757–777.

    Article  Google Scholar 

  • Kane, T. J., Riegg, S. K., & Staiger, D. O. (2006). School quality, neighborhoods, and housing prices. American Law and Economics Review, 8(2), 183–212.

    Article  Google Scholar 

  • Kim, S. G., Cho, S. H., Lambert, D. M., & Roberts, R. K. (2010). Measuring the value of air quality: Application of the spatial hedonic model. Air Quality, Atmosphere & Health, 3(1), 41–51.

    Article  Google Scholar 

  • Kohonen, T., Huang, T. S., & Schroeder, M. R. (2012). Self-Organizing Maps. Springer.

    Google Scholar 

  • Lee, J., Irwin, N., Irwin, E., & Miller, H. J. (2021). The role of distance-dependent versus localized amenities in polarizing urban spatial structure: A spatio-temporal analysis of residential location value in Columbus, Ohio, 2000–2015. Geographical Analysis, 53(2), 283–306.

    Article  CAS  Google Scholar 

  • Li, H., Wei, Y. D., Liao, F. H., & Huang, Z. (2015). Administrative hierarchy and urban land expansion in transitional China. Applied Geography, 56, 177–186.

  • Li, H., Wei, Y. D., Yu, Z., & Tian, G. (2016). Amenity, accessibility and housing values in metropolitan USA: A study of Salt Lake County, Utah. Cities, 59, 113–125.

    Article  Google Scholar 

  • Li, H., Wei, Y. D., Wu, Y., & Tian, G. (2019). Analyzing housing prices in Shanghai with open data: Amenity, accessibility and urban structure. Cities, 91, 165–179.

    Article  Google Scholar 

  • Li, Y., & Wei, Y. D. (2010). The spatial-temporal hierarchy of regional inequality of China. Applied Geography, 30(3), 303–316.

    Article  Google Scholar 

  • Liu, Y., He, S., & Wu, F. (2012). Housing differentiation under market transition in Nanjing. China. the Professional Geographer, 64(4), 554–571.

    Article  Google Scholar 

  • Liu, Y., Yue, W., Fan, P., & Song, Y. (2015). Suburban residential development in the era of market-oriented land reform: The case of Hangzhou, China. Land Use Policy, 42, 233–243.

    Article  Google Scholar 

  • Luo, J., & Wei, Y. D. (2006). Population distribution and spatial structure in transitional Chinese cities: A study of Nanjing. Eurasian Geography and Economics, 47(5), 585–603.

    Article  MathSciNet  Google Scholar 

  • Ma, Z., Li, C., & Zhang, J. (2018). Affordable housing brings about socio-spatial exclusion in Changchun, China: Explanation in various economic motivations of local governments. Habitat International, 76, 40–47.

    Article  Google Scholar 

  • Miao, R., Wang, Y., & Li, S. (2021). Analyzing Urban Spatial Patterns and Functional Zones Using Sina Weibo POI Data: A Case Study of Beijing. Sustainability, 13(2), 647.

    Article  Google Scholar 

  • Mills, E. S. (1972). Studies in the Structure of the Urban Economy. Baltimore: Johns Hopkins Press.

    Google Scholar 

  • Muth, R. F. (1975). Cities and housing: The spatial pattern of urban residential land use. Chicago: University of Chicago Press.

    Google Scholar 

  • NBS (National Bureau of Statistics). (2019). China urban statistical yearbook. Beijing: China Statistical Press [various years].

    Google Scholar 

  • NBS (National Bureau of Statistics) (2019a). Real estate indicators of Nanjing (latest 10 years). Available online, http://data.stats.gov.cn/english/easyquery.htm?cn=E0105. Accessed 19 May 2023.

  • Netusil, N. R. (2013). Urban environmental amenities and property values: Does ownership matter? Land Use Policy, 31, 371–377.

    Article  Google Scholar 

  • Paez, A., Uchida, T., & Miyamoto, K. (2001). Spatial association and heterogeneity issues in land price models. Urban Studies, 38(9), 1493–1508.

  • Páez, A., Long, F., & Farber, S. (2008). Moving window approaches for hedonic price estimation: An empirical comparison of modelling techniques. Urban Studies, 45(8), 1565–1581.

  • Pei, T., Sobolevsky, S., Ratti, C., Shaw, S. L., Li, T., & Zhou, C. (2014). A new insight into land use classification based on aggregated mobile phone data. International Journal of Geographical Information Science, 28(9), 1988–2007.

    Article  Google Scholar 

  • Qian, Z., Fu, H., Wang, Y., & Zhang, Y. (2019). Characteristics of urban expansion and morphological evolution in Nanjing from 2004 to 2016. Remote Sensing for Land & Resources [j], 31(2), 149–156.

    Google Scholar 

  • Qin, X., Wei, Y. D., Yu, Z., & Xiong, N. (2022). Urbanization, suburbanization, and population redistribution in urban China: A case study of Nanjing. Journal of Urban Planning and Development, 148(4), 05022034.

  • Rosen, S. (1974). Hedonic prices and implicit markets: Product differentiation in pure competition. Journal of Political Economy, 82(1), 34–55.

  • Sairi, N. A. M., Burhan, B., & Safian, E. E. M. (2022). Identifying housing submarkets in Johor Bahru and Kulai, Malaysia: A data-driven method. Real Estate Management and Valuation, 30(2), 1–11.

    Article  Google Scholar 

  • Schnare, A. B., & Struyk, R. J. (1976). Segmentation in urban housing markets. Journal of Urban Economics, 3(2), 146–166.

    Article  Google Scholar 

  • Sheppard, S. (1999). Hedonic analysis of housing markets. Handbook of Regional and Urban Economics, 3(1), 595–1635.

    Google Scholar 

  • Soltani, A., Pettit, C. J., Heydari, M., & Aghaei, F. (2021). Housing price variations using spatio-temporal data mining techniques. Journal of Housing and the Built Environment, 36(3), 1199–1227.

    Article  Google Scholar 

  • Song, W., & Liu, C. (2017). Spatial differentiation of gated communities in Nanjing. International Journal of Urban Sciences, 21(3), 312–325.

    Article  Google Scholar 

  • Song, W., & Zhu, X. (2010). Gentrification in urban China under market transformation. International Journal of Urban Sciences, 14(2), 152–163.

    Article  Google Scholar 

  • Tian, G., Wei, Y. D., & Li, H. (2017). Effects of accessibility and environmental health risk on housing prices: A case of Salt Lake County, Utah. Applied Geography, 89, 12–21.

    Article  Google Scholar 

  • Tse, R. Y. (2002). Estimating neighbourhood effects in house prices: Towards a new hedonic model approach. Urban Studies, 39(7), 1165–1180.

    Article  Google Scholar 

  • Vesanto, J., & Alhoniemi, E. (2000). Clustering of the self-organizing map. IEEE Transactions on Neural Networks, 11(3), 586–600.

    Article  CAS  PubMed  Google Scholar 

  • Walker, R. A. (2018). A theory of suburbanization: Capitalism and the construction of urban space in the United States. In M. Dear, & A. Scott (Eds.), Urbanization and urban planning in capitalist society (1st ed., pp. 383–429). Routledge. https://doi.org/10.4324/9781351068000

  • Wang, D., & Li, S. M. (2006). Socio-economic differentials and stated housing preferences in Guangzhou, China. Habitat International, 30(2), 305–326.

  • Wang, M. (2021). Polycentric urban development and urban amenities: Evidence from Chinese cities. Environment and Planning b: Urban Analytics and City Science, 48(3), 400–416.

    Google Scholar 

  • Wang, Y., Wu, K., Zhao, Y., Wang, C., & Zhang, H. O. (2022). Examining the Effects of the Built Environment on Housing Rents in the Pearl River Delta of China. Applied Spatial Analysis and Policy, 15(1), 289–313.

    Article  Google Scholar 

  • Watkins, C. A. (2001). The definition and identification of housing submarkets. Environment and Planning A, 33(12), 2235–2253.

    Article  Google Scholar 

  • Wei, Y. D. (Ed.). (2017). Urban Land and Sustainable Development. Basel, Switzerland: MDPI.

    Google Scholar 

  • Wei, Y. D., Wu, Y., Xiao, W., & Wu, X. (2023). Preferences and barriers of urban vulnerable population in affordable housing in urban China. Manuscript in preparation.

  • Wen, H., & Tao, Y. (2015). Polycentric urban structure and housing price in the transitional China: Evidence from Hangzhou. Habitat International, 46, 138–146.

    Article  Google Scholar 

  • Wen, H., Zhang, Y., & Zhang, L. (2014). Do educational facilities affect housing price? An empirical study in Hangzhou, China. Habitat International, 42, 155–163.

    Article  ADS  Google Scholar 

  • Wen, H., Zhang, Y., & Zhang, L. (2015). Assessing amenity effects of urban landscapes on housing price in Hangzhou. China. Urban Forestry & Urban Greening, 14(4), 1017–1026.

    Article  Google Scholar 

  • Wen, H., Jin, Y., & Zhang, L. (2017). Spatial heterogeneity in implicit housing prices: Evidence from Hangzhou, China. International Journal of Strategic Property Management, 21(1), 15–28.

    Article  Google Scholar 

  • Wu, C., & Sharma, R. (2012). Housing submarket classification: The role of spatial contiguity. Applied Geography, 32(2), 746–756.

    Article  Google Scholar 

  • Wu, Q., Cheng, J., Chen, G., Hammel, D. J., & Wu, X. (2014). Socio-spatial differentiation and residential segregation in the Chinese city based on the 2000 community-level census data: A case study of the inner city of Nanjing. Cities, 39, 109–119.

    Article  Google Scholar 

  • Wu, C., Ye, X., Du, Q., & Luo, P. (2017). Spatial effects of accessibility to parks on housing prices in Shenzhen, China. Habitat International, 63, 45–54.

    Article  Google Scholar 

  • Wu, J., Song, Y., Liang, J., Wang, Q., & Lin, J. (2018). Impact of mixed land use on housing values in high-density areas: Evidence from Beijing. Journal of Urban Planning and Development, 144(1), 05017019.

    Article  Google Scholar 

  • Wu, Y., Wei, Y. D., & Li, H. (2020). Analyzing spatial heterogeneity of housing prices using large datasets. Applied Spatial Analysis and Policy, 13(1), 223–256.

    Article  Google Scholar 

  • Xiao, Y., Chen, X., Li, Q., Yu, X., Chen, J., & Guo, J. (2017). Exploring determinants of housing prices in Beijing: An enhanced hedonic regression with open access POI data. ISPRS International Journal of Geo-Information, 6(11), 358.

    Article  ADS  Google Scholar 

  • Xu, H., Zhu, J., & Wang, Z. (2019). Exploring the spatial pattern of urban block development based on POI analysis: A case study in Wuhan, China. Sustainability, 11(24), 6961.

    Article  Google Scholar 

  • Yuan, F., Wei, Y. D., & Xiao, W. (2019). Land marketization, fiscal decentralization, and the dynamics of urban land prices in transitional China. Land Use Policy, 89, 104208.

  • Yuan, F., Wei, Y. D., & Wu, J. (2020). Amenity effects of urban facilities on housing prices in China: Accessibility, scarcity, and urban spaces. Cities, 96, 102433.

    Article  Google Scholar 

  • Zhai, W., Bai, X., Shi, Y., Han, Y., Peng, Z. R., & Gu, C. (2019). Beyond Word2vec: An approach for urban functional region extraction and identification by combining Place2vec and POIs. Computers, Environment and Urban Systems, 74, 1–12.

    Article  Google Scholar 

  • Zhang, X., Liu, X., Hang, J., Yao, D., & Shi, G. (2016). Do urban rail transit facilities affect housing prices? Evidence from China. Sustainability, 8(4), 380.

    Article  Google Scholar 

  • Zhang, X., Du, S., & Wang, Q. (2017). Hierarchical semantic cognition for urban functional zones with VHR satellite images and POI data. ISPRS Journal of Photogrammetry and Remote Sensing, 132, 170–184.

    Article  ADS  Google Scholar 

  • Zhu, J., Pawson, H., Han, H., & Li, B. (2022). How can spatial planning influence housing market dynamics in a pro-growth planning regime? A case study of Shanghai. Land Use Policy, 116, 106066.

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Geography, University of Utah, Salt Lake City, UT, 84108, USA

    Meitong Liu & Yehua Dennis Wei

  2. Key Research Institute of Yellow River Civilization and Sustainable Development, Henan University, Kaifeng, 475001, China

    Yehua Dennis Wei

  3. Department of Urban and Rural Planning, Wuhan University, Wuhan, 430072, Hubei, China

    Yangyi Wu

Authors
  1. Meitong Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Yehua Dennis Wei
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Yangyi Wu
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Yehua Dennis Wei.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Liu, M., Wei, Y.D. & Wu, Y. Urban Structure, Housing Prices and the Double Role of Amenity: A Study of Nanjing, China. Appl. Spatial Analysis 17, 27–53 (2024). https://doi.org/10.1007/s12061-023-09536-9

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12061-023-09536-9

Keywords

Search

Navigation