Abstract
Rent levels are a core indicator of the local housing market. Although existing studies have examined various factors influencing local rent levels, there is no systematic research on the impacts of the built environment on housing rents. This paper examines the mechanisms by which the built environment affects rents from five theoretical perspectives. Taking the Pearl River Delta (PRD) of China as a case study, four indicators—mixed land use (MLU), public service level (PSL), high-rise buildings (HRB), and air pollution (AP)—are used to represent the four aspects of the local built environment, namely, land use, public facilities & services, architectural scale, and health & comfort, respectively. The spatial regression and geodetector techniques are combined to illustrate the significant impacts of the built environment on housing rents. The results of Moran's I test reveal the existence of significant spatial autocorrelation of rents in the PRD. From the spatial regression analysis, this paper concludes that the degree of mixing of land use, the proportion of high-rise residential buildings, and the air pollution negatively impact housing rents. In contrast, the density of public service facilities positively affects housing rents. These findings are in line with the theoretical projections. The geodetector technique indicates that the effects of the four aspects of the built environment on housing rents are significantly different. AP has the strongest influence on housing rents. Our findings show that the built environment is an important factor affecting the local housing market. Improving the built environment should be carefully considered by decision-makers when they formulate cities' development strategies and draw up local housing development plans.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
References
Anselin, L., Syabri, I., & Kho, Y. (2006). GeoDa: An introduction to spatial data analysis. Geographical Analysis, 38(1), 5–22. https://doi.org/10.1111/j.0016-7363.2005.00671.x
Arbia, G. (2006). Spatial Econometrics: Statistical Foundations and Applications to Regional Convergence.
Bhat, C. R., & Guo, J. Y. (2007). A comprehensive analysis of built environment characteristics on household residential choice and auto ownership levels. Transportation Research Part B-Methodological, 41(5), 506–526. https://doi.org/10.1016/j.trb.2005.12.005
Bitter, C., Mulligan, G. F., & Dall’erba, S. (2007). Incorporating spatial variation in housing attribute prices: A comparison of geographically weighted regression and the spatial expansion method. Journal of Geographical Systems, 9(1), 7–27. https://doi.org/10.1007/s10109-006-0028-7
Black, A., Fraser, P., & Hoesli, M. (2006). House Prices, Fundamentals and Bubbles. Journal of Business Finance & Accounting, 33(9–10), 1535–1555. https://doi.org/10.1111/j.1468-5957.2006.00638.x
Can, A. (1992). Specification and Estimation of Hedonic Housing Price Models. Regional Science and Urban Economics, 22, 453–474. https://doi.org/10.1016/0166-0462(92)90039-4
Carlino, G. A., Chatterjee, S., & Hunt, R. M. (2007). Urban density and the rate of invention. Journal of Urban Economics, 61(3), 389–419. https://doi.org/10.1016/j.jue.2006.08.003
Chen, S., & Jin, H. (2019). Pricing for the clean air: Evidence from Chinese housing market. Journal of Cleaner Production, 206, 297–306. https://doi.org/10.1016/j.jclepro.2018.08.220
Cheng, Z., Li, L., & Liu, J. (2017). Identifying the spatial effects and driving factors of urban PM2.5 pollution in China. Ecological Indicators, 82, 61–75. https://doi.org/10.1016/j.ecolind.2017.06.043
Clark, E. (1988). The Rent Gap and Transformation of the Built Environment: Case Studies in Malmö 1860–1985. Geografiska Annaler: Series B, Human Geography, 70(2), 241–254. https://doi.org/10.1080/04353684.1988.11879569
Cobb, S. (1977). Site rent, air quality, and the demand for amenities. Journal of Environmental Economics and Management, 4, 214–218. https://doi.org/10.1016/0095-0696(77)90004-3
Colburn, G., & Allen, R. (2018). Rent burden and the Great Recession in the USA. Urban Studies, 55(1), 226–243. https://doi.org/10.1177/0042098016665953
Cui, N., Gu, H., Shen, T., & Feng, C. (2018). The impact of micro-level influencing factors on home value: A housing price-rent comparison. Sustainability, 10(12). https://doi.org/10.3390/su10124343.
Diamond, R. (2016). The determinants and welfare implications of US workers’ diverging location choices by skill: 1980–2000. American Economic Review, 106(3), 479–524. https://doi.org/10.1257/aer.20131706
Diao, M., & Ferreira, J., Jr. (2010). Residential property values and the built environment empirical study in the Boston, Massachusetts, Metropolitan area. Transportation Research Record (2174), 138–147. https://doi.org/10.3141/2174-18.
Eaton, J., & Eckstein, Z. (1997). Cities and growth: Theory and evidence from France and Japan. Regional Science and Urban Economics, 27(4–5), 443–474. https://doi.org/10.1016/s0166-0462(97)80005-1
Efthymiou, D., & Antoniou, C. (2013). How do transport infrastructure and policies affect house prices and rents? Evidence from Athens. Transportation Research Part A Policy and Practice, 52, 1–22. https://doi.org/10.1016/j.tra.2013.04.002
Esmaeilpoorarabi, N., Yigitcanlar, T., Guaralda, M., & Kamruzzaman, M. (2018). Evaluating place quality in innovation districts: A Delphic hierarchy process approach. Land Use Policy, 76, 471–486. https://doi.org/10.1016/j.landusepol.2018.02.027
Ettema, D., & Nieuwenhuis, R. (2017). Residential self-selection and travel behaviour: What are the effects of attitudes, reasons for location choice and the built environment? Journal of Transport Geography, 59, 146–155. https://doi.org/10.1016/j.jtrangeo.2017.01.009
Färe, R., & Knox Lovell, C. A. (1978). Measuring the technical efficiency of production. Journal of Economic Theory, 19(1), 150–162. https://doi.org/10.1016/0022-0531(78)90060-1
Filippini, M., Banfi, S., & Horehájová, A. (2008). Valuation of environmental goods in profit and non-profit housing sectors: Evidence from the rental market in the City of Zurich. Swiss Journal of Economics and Statistics (SJES), 144, 631–654. https://doi.org/10.1007/BF03399269
Gilbert, A. (2016). Rental housing: The international experience. Habitat International, 54, 173–181. https://doi.org/10.1016/j.habitatint.2015.11.025
Glaeser, E., Kolko, J., & Saiz, A. (2000). Consumer City. National Bureau of Economic Research, Inc, NBER Working Papers, 1.
Goodman, A. (1988). An econometric model of house price, permanent income, tenure choice, and housing demand. Journal of Urban Economics, 23, 327–353. https://doi.org/10.1016/0094-1190(88)90022-8
Greenwald, M. J., Boarnet, M. G., & Trb, T. R. B. (2001). Built environment as determinant of walking behavior - Analyzing nonwork pedestrian travel in Portland, Oregon. Land Development and Public Involvement in Transportation: Planning and Administration (pp. 33–42).
Grimes, A., & Aitken, A. (2010). Housing supply, land costs and price adjustment. Real Estate Economics, 38(2), 325–353. https://doi.org/10.1111/j.1540-6229.2010.00269.x
Groff, E. R. (2017). Measuring the influence of the built environment on crime at street segments. Jerusalem Review of Legal Studies, 15(1), 44–54. https://doi.org/10.1093/jrls/jlx005
Gu, H., Meng, X., Shen, T., & Wen, L. (2020). China’s highly educated talents in 2015: Patterns, determinants and spatial spillover effects. Applied Spatial Analysis and Policy, 13(3), 631–648. https://doi.org/10.1007/s12061-019-09322-6
Guan, Y., Kang, L., Wang, Y., Zhang, N.-N., & Ju, M.-T. (2019). Health loss attributed to PM2.5 pollution in China’s cities: Economic impact, annual change and reduction potential. Journal of Cleaner Production, 217, 284–294. https://doi.org/10.1016/j.jclepro.2019.01.284
Guite, H., Clark, C., & Ackrill, G. (2007). The impact of physical and urban environment on mental well-being. Public Health, 120, 1117–1126. https://doi.org/10.1016/j.puhe.2006.10.005
Gurran, N., & Phibbs, P. (2017). When tourists move in: How should urban planners respond to airbnb? Journal of the American Planning Association, 83(1), 80–92. https://doi.org/10.1080/01944363.2016.1249011
Hamidi, S., Zandiatashbar, A., & Bonakdar, A. (2019). The relationship between regional compactness and regional innovation capacity (RIC): Empirical evidence from a national study. Technological Forecasting and Social Change, 142, 394–402. https://doi.org/10.1016/j.techfore.2018.07.026
Haron, N., & Liew, C. (2013). Factors influencing the rise of house price in Klang Valley. International Journal of Research in Engineering and Technology, 2, 261–272. https://doi.org/10.15623/ijret.2013.0210039
Harvey, D. (1973). Social Justice and The City (Vol. 69).
Herbert, J., & Stevens, B. (2006). A model of the distribution of residential activity in urban areas. Journal of Regional Science, 2, 21–36. https://doi.org/10.1111/j.1467-9787.1960.tb00838.x
Holly, S., Pesaran, M. H., & Yamagata, T. (2010). A spatio-temporal model of house prices in the USA. Journal of Econometrics, 158(1), 160–173. https://doi.org/10.1016/j.jeconom.2010.03.040
Hossain, B., & Latif, E. (2009). Determinants of housing price volatility in Canada: A dynamic analysis. Applied Economics, 41(27), 3521–3531. https://doi.org/10.1080/00036840701522861
Huang, H., & Yin, L. (2015). Creating sustainable urban built environments: An application of hedonic house price models in Wuhan, China. Journal of Housing and the Built Environment, 30(2), 219–235. https://doi.org/10.1007/s10901-014-9403-8
Hwang, M., & Quigley, J. M. (2006). Economic fundamentals in local housing markets: Evidence from US metropolitan regions. Journal of Regional Science, 46(3), 425–453. https://doi.org/10.1111/j.1467-9787.2006.00480.x
Kang, C.-D. (2019). Effects of spatial access to neighborhood land-use density on housing prices: Evidence from a multilevel hedonic analysis in Seoul, South Korea. Environment and Planning B-Urban Analytics and City Science, 46(4), 603–625. https://doi.org/10.1177/2399808317721184
Kearns, A., Whitley, E., Mason, P., & Bond, L. (2012). “Living the High Life”? Residential, social and psychosocial outcomes for high-rise occupants in a deprived context. Housing Studies, 27(1), 97–126. https://doi.org/10.1080/02673037.2012.632080
King, K. E. (2015). Chicago residents’ perceptions of air quality: Objective pollution, the built environment, and neighborhood stigma theory. Population and Environment, 37(1), 1–21. https://doi.org/10.1007/s11111-014-0228-x
Kresl, P. K., Ietri D. (2017). Creating cities/building cities: Architecture and urban competitiveness[M]. Edward Elgar, 209 pp
Kroesen, M. (2019). Residential self-selection and the reverse causation hypothesis: Assessing the endogeneity of stated reasons for residential choice. Travel Behaviour and Society, 16, 108–117. https://doi.org/10.1016/j.tbs.2019.05.002
Kylili, A., Fokaides, P. A., & Lopez Jimenez, P. A. (2016). Key Performance Indicators (KPIs) approach in buildings renovation for the sustainability of the built environment: A review. Renewable & Sustainable Energy Reviews, 56, 906–915. https://doi.org/10.1016/j.rser.2015.11.096
Leung, K. M., & Yiu, C. Y. (2019). Rent determinants of sub-divided units in Hong Kong. Journal of Housing and the Built Environment, 34(1), 133–151. https://doi.org/10.1007/s10901-018-9607-4
Li, S., & Zhao, P. (2017). Exploring car ownership and car use in neighborhoods near metro stations in Beijing: Does the neighborhood built environment matter? Transportation Research Part D-Transport and Environment, 56, 1–17. https://doi.org/10.1016/j.trd.2017.07.016
Li, H., Wei, Y. D., & Wu, Y. (2019). Analyzing the private rental housing market in Shanghai with open data. Land Use Policy, 85, 271–284. https://doi.org/10.1016/j.landusepol.2019.04.004
Liang, X., Li, S., Zhang, S., Huang, H., & Chen, S. X. (2016). PM2.5 data reliability, consistency, and air quality assessment in five Chinese cities. Journal of Geophysical Research-Atmospheres, 121(17), 10220–10236. https://doi.org/10.1002/2016jd024877
Liao, F. H., Farber, S., & Ewing, R. (2015). Compact development and preference heterogeneity in residential location choice behaviour: A latent class analysis. Urban Studies, 52(2), 314–337. https://doi.org/10.1177/0042098014527138
Liebelt, V., Bartke, S., & Schwarz, N. (2019). Urban green spaces and housing prices: An alternative perspective. Sustainability, 11(13). https://doi.org/10.3390/su11133707.
Liebersohn, C. (2018). Housing demand, regional house prices and consumption.
Lu, S., Shi, C., & Yang, X. (2019). Impacts of built environment on urban vitality: Regression analyses of Beijing and Chengdu, China[J]. International Journal of Environmental Research and Public Health, 16(23), 4592. https://doi.org/10.3390/ijerph16234592
Luo, W., Jasiewicz, J., Stepinski, T., Wang, J., Xu, C., & Cang, X. (2016). Spatial association between dissection density and environmental factors over the entire conterminous United States. Geophysical Research Letters, 43(2), 692–700. https://doi.org/10.1002/2015gl066941
Mabon, L., Kondo, K., Kanekiyo, H., Hayabuchi, Y., & Yamaguchi, A. (2019). Fukuoka: Adapting to climate change through urban green space and the built environment? Cities, 93, 273–285. https://doi.org/10.1016/j.cities.2019.05.007
Maennig, W., & Dust, L. (2008). Shrinking and growing metropolitan areas asymmetric real estate price reactions? The case of German single-family houses. Regional Science and Urban Economics, 38(1), 63–69. https://doi.org/10.1016/j.regsciurbeco.2007.08.009
Matlack, J., & Vigdor, J. (2006). Do rising tides lift all prices? Income inequality and housing affordability. Journal of Housing Economics, 17, 212–224. https://doi.org/10.1016/j.jhe.2008.06.004
Mussa, A., Nwaogu, U. G., & Pozo, S. (2017). Immigration and housing: A spatial econometric analysis. Journal of Housing Economics, 35, 13–25. https://doi.org/10.1016/j.jhe.2017.01.002
Nakagawa, M., Saito, M., & Yamaga, H. (2007). Earthquake risk and housing rents: Evidence from the Tokyo Metropolitan Area. Regional Science and Urban Economics, 37(1), 87–99. https://doi.org/10.1016/j.regsciurbeco.2006.06.009
Pan, Y., Chen, S., Niu, S., Ma, Y., & Tang, K. (2020). Investigating the impacts of built environment on traffic states incorporating spatial heterogeneity. Journal of Transport Geography, 83,. https://doi.org/10.1016/j.jtrangeo.2020.102663
Potepan, M. J. (1996). Explaining intermetropolitan variation in housing prices, rents and land prices. Real Estate Economics, 24(2), 219–245. https://doi.org/10.1111/1540-6229.00688
Quigley, J., & Raphael, S. (2004). Is housing unaffordable? Why isn’t it more affordable? Journal of Economic Perspectives, 18, 191–214. https://doi.org/10.1257/089533004773563494
Rammer, C., Kinne, J., & Blind, K. (2020). Knowledge proximity and firm innovation: A microgeographic analysis for Berlin. Urban Studies, 57(5), 996–1014. https://doi.org/10.1177/0042098018820241
Reeder, A., Lambert, L., & Pasha-Zaidi, N. (2019). Happiness and the built environment. In L. Lambert & N. Pasha-Zaidi (Eds.), Positive psychology in the Middle East/North Africa: Research, policy, and practise (pp. 71–90). Springer International Publishing.
Rosen, S. (1974). Hedonic prices and implicit markets: Product differentiation in pure competition. Journal of Political Economy, 82, 34–55. https://doi.org/10.1086/260169
Rundle, A., Roux, A. V. D., Freeman, L. M., Miller, D., Neckerman, K. M., & Weiss, C. C. (2007). The urban built environment and obesity in New York City: A multilevel analysis. American Journal of Health Promotion, 21(4), 326–334. https://doi.org/10.4278/0890-1171-21.4s.326
Saiz, A. (2007). Immigration and housing rents in American cities. Journal of Urban Economics, 61(2), 345–371. https://doi.org/10.1016/j.jue.2006.07.004
Scott, O. (1997). Valuing the built environment: A GIS approach to the Hedonic Modelling of housing markets. University of Bristol.
Shen, Y., & Liu, H. Y. (2004). Housing prices and economic fundamentals: A cross city analysis of china for 1995–2002. Journal of Economic Research, 6, 75–85.
Smit, W., & Tucker, A. (2019). Mapping the Body: The Use of the Body Mapping Method to Explore Health and the Built Environment in Cape Town South Africa. 3, 45–47. https://doi.org/10.1080/24751448.2019.1571800
Smith, N. (1987). Gentrification and the rent gap. Annals of the Association of American Geographers, 77(3), 462–465. https://doi.org/10.1111/j.1467-8306.1987.tb00171.x
Stehlin, J. (2016). The post-industrial “shop floor”: Emerging forms of gentrification in San Francisco’s innovation economy. Antipode, 48(2), 474–493. https://doi.org/10.1111/anti.12199
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. https://doi.org/10.1016/j.apgeog.2017.09.010
Tiebout, C. (1956). A pure theory of local expenditure. Journal of Political Economy, 64, 416–424. https://doi.org/10.1086/257839
Vogiazas, S., & Alexiou, C. (2017). Determinants of housing prices and bubble detection: Evidence from seven advanced economies. Atlantic Economic Journal, 45(1), 119–131. https://doi.org/10.1007/s11293-017-9531-0
Wang, Z., & Zhang, Q. (2014). Fundamental factors in the housing markets of China. Journal of Housing Economics, 25,. https://doi.org/10.1016/j.jhe.2014.04.001
Wang, X., Hui, E.C.-M., & Sun, J.-X. (2017a). Population migration, urbanization and housing prices: Evidence from the cities in China. Habitat International, 66, 49–56. https://doi.org/10.1016/j.habitatint.2017.05.010
Wang, Y., Wang, S., Li, G., Zhang, H., Jin, L., Su, Y., & Wu, K. (2017b). Identifying the determinants of housing prices in China using spatial regression and the geographical detector technique. Applied Geography, 79, 26–36. https://doi.org/10.1016/j.apgeog.2016.12.003
Wang, J. F., Li, X. H., Christakos, G., Liao, Y. L., Zhang, T., Gu, X., & Zheng, X. Y. (2010). Geographical detectors-based health risk assessment and its application in the neural tube defects study of the Heshun Region, China. International Journal of Geographical Information Science, 24(1), 107–127. https://doi.org/10.1080/13658810802443457
Wen, H., Xiao, Y., Hui, E. C. M., & Zhang, L. (2018). Education quality, accessibility, and housing price: Does spatial heterogeneity exist in education capitalization? Habitat International, 78, 68–82. https://doi.org/10.1016/j.habitatint.2018.05.012
Wolpert, J. (2005). Behavioral aspects of the decision to migrate. Papers in Regional Science, 15, 159–169. https://doi.org/10.1111/j.1435-5597.1965.tb01320.x
Wu, W., & Niu, X. (2019). Influence of built environment on urban vitality: Case study of shanghai using mobile phone location data. Journal of Urban Planning and Development, 145(3). https://doi.org/10.1061/(asce)up.1943-5444.0000513.
Wu, K., Wang, Y., Ye, Y., Zhang, H., & Huang, G. (2019). Relationship between the built environment and the location choice of high-tech firms: Evidence from the Pearl River Delta. Sustainability, 11(13). https://doi.org/10.3390/su11133689.
Wu, K., Wang, Y., Zhang, H. O., Liu, Y., & Ye, Y. (2021a). Impact of the built environment on the spatial heterogeneity of regional innovation productivity: Evidence from the Pearl River Delta China. Chinese Geographical Science, 31(3), 413–428. https://doi.org/10.1007/s11769-021-1198-4.
Wu, K., Wang, Y., Zhang, H. O., Liu, Y., & Zhang, Y. (2021b). On innovation capitalization: Empirical evidence from Guangzhou China. Habitat International, 109,. https://doi.org/10.1016/j.habitatint.2021.102323
Xue, W., Zhang, J., Zhong, C., Li, X., & Wei, J. (2021). Spatiotemporal PM2.5 variations and its response to the industrial structure from 2000 to 2018 in the Beijing-Tianjin-Hebei region. Journal of Cleaner Production, 279. https://doi.org/10.1016/j.jclepro.2020.123742.
Yang, L., Wang, B., Zhou, J., & Wang, X. (2018). Walking accessibility and property prices. Transportation Research Part D-Transport and Environment, 62, 551–562. https://doi.org/10.1016/j.trd.2018.04.001
Zambrano-Monserrate, M. A., & Alejandra Ruano, M. (2019). Does environmental noise affect housing rental prices in developing countries? Evidence from Ecuador. Land Use Policy, 87,. https://doi.org/10.1016/j.landusepol.2019.104059
Zhang, C. (2015). Income inequality and access to housing: Evidence from China. China Economic Review, 36, 261–271. https://doi.org/10.1016/j.chieco.2015.10.003
Zhang, H., & Yin, L. (2019). A meta-analysis of the literature on the association of the social and built environment with obesity: Identifying factors in need of more in-depth research. American Journal of Health Promotion, 33(5), 792–805. https://doi.org/10.1177/0890117118817713
Zhang, L., Hong, J., Nasri, A., & Shen, Q. (2012). How built environment affects travel behavior: A comparative analysis of the connections between land use and vehicle miles traveled in U.S. cities. Journal of Transport and Land Use, 5, 40–52. https://doi.org/10.5198/jtlu.v5i3.266
Zhou, S., & Lin, R. (2019). Spatial-temporal heterogeneity of air pollution: The relationship between built environment and on-road PM2.5 at micro scale. Transportation Research Part D-Transport and Environment, 76, 305–322. https://doi.org/10.1016/j.trd.2019.09.004
Acknowledgements
This research was funded by the National Natural Science Foundation of China (No. 41871150), GDAS Project of Science and Technology Development (No. 2020GDASYL-20200104001), National Key Research and Development Program (No. 2019YFB2103101), and Special Project of the Institute of Strategy Research for Guangdong, Hong Kong, and Macao Greater Bay Area Construction (No. 2021GDASYL-20210401001).
The authors declare that they have no conflict of interest.
Funding
This research was funded by the National Natural Science Foundation of China (No. 41871150), GDAS Project of Science and Technology Development (No. 2020GDASYL-20200104001), National Key Research and Development Program (No. 2019YFB2103101), and Special Project of the Institute of Strategy Research for Guangdong, Hong Kong, and Macao Greater Bay Area Construction (No. 2021GDASYL-20210401001).
Author information
Authors and Affiliations
Contributions
Conceptualization, Yang Wang; methodology, Kangmin Wu and Yabo Zhao; formal analysis, Yang Wang, Kangmin Wu, and Yabo Zhao; writing—original draft preparation, Yang Wang, Changjian Wang, and Hong’ou Zhang; writing—review and editing, Yang Wang, Kangmin Wu, and Changjian Wang; visualization, Yang Wang and Kangmin Wu.
Corresponding author
Ethics declarations
Conflicts of Interest
The authors have no conflicts of interest to declare.
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
About this article
Cite this article
Wang, Y., Wu, K., Zhao, Y. et al. Examining the Effects of the Built Environment on Housing Rents in the Pearl River Delta of China. Appl. Spatial Analysis 15, 289–313 (2022). https://doi.org/10.1007/s12061-021-09412-4
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s12061-021-09412-4