Skip to main content
SpringerLink
Log in

Impact of Accessibility on Housing Prices in Dalian City of China Based on a Geographically Weighted Regression Model

  • Published:
Chinese Geographical Science Aims and scope Submit manuscript

Abstract

This paper studies the relationship between accessibility and housing prices in Dalian by using an improved geographically weighted regression model and house prices, traffic, remote sensing images, etc. Multi-source data improves the accuracy of the spatial differentiation that reflects the impact of traffic accessibility on house prices. The results are as follows: first, the average house price is 12 436 yuan (RMB)/m2, and reveals a declining trend from coastal areas to inland areas. The exception was Guilin Street, which demonstrates a local peak of house prices that decreases from the center of the street to its periphery. Second, the accessibility value is 33 minutes on average, excluding northern and eastern fringe areas, which was over 50 minutes. Third, the significant spatial correlation coefficient between accessibility and house prices is 0.423, and the coefficient increases in the southeastern direction. The strongest impact of accessibility on house prices is in the southeastern coast, and can be seen in the Lehua, Yingke, and Hushan communities, while the weakest impact is in the northwestern fringe, and can be seen in the Yingchengzi, Xixiaomo, and Daheishi community areas.

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

Similar content being viewed by others

References

  • Ajaz Ahmed M A, Abd-Elrahman A, Escobedo F J et al., 2017. Spatially-explicit modeling of multi-scale drivers of aboveground forest biomass and water yield in watersheds of the Southeastern United States. Journal of Environmental Management, 199: 158–171. doi: 10.1016/j.jenvman.2017.05.013

    Article  Google Scholar 

  • AlKahtani S J H, Xia J C, Veenendaaland B et al., 2015. Building a conceptual framework for determining individual differences of accessibility to tourist attractions. Tourism Management Perspectives, 16: 28–42. doi: 10.1016/j.tmp.2015.05.002

    Article  Google Scholar 

  • Avila-Flores D, Pompa-Garcia M, Antonio-Nemiga X et al., 2010. Driving factors for forest fire occurrence in Durango State of Mexico: a geospatial perspective. Chinese Geographical Science, 20(6): 491–497. doi: 10.1007/s11769-010-0437-x

    Article  Google Scholar 

  • Brunsdon C, Fotheringham A S, Charlton M, 1999. Some notes on parametric significance tests for geographically weighted regression. Journal of Regional Science, 39(3): 497–524. doi: 10.1111/0022-4146.00146

    Article  Google Scholar 

  • Cardozo O D, García-Palomares J C, Gutiérrez J, 2012. Application of geographically weighted regression to the direct forecasting of transit ridership at station-level. Applied Geography, 34: 548–558. doi: 10.1016/j.apgeog.2012.01.005

    Article  Google Scholar 

  • Chalkias C, Papadopoulos A G, Kalogeropoulos K et al., 2013. Geographical heterogeneity of the relationship between childhood obesity and socio-environmental status: empirical evidence from Athens, Greece. Applied Geography, 37: 34–43. doi: 10.1016/j.apgeog.2012.10.007

    Article  Google Scholar 

  • Chen Q, Mei K, Dahlgren R A et al., 2016. Impacts of land use and population density on seasonal surface water quality using a modified geographically weighted regression. Science of the Total Environment, 572: 450–466. doi: 10.1016/j.scitotenv. 2016.08.052

    Article  Google Scholar 

  • Chen Y M, Liu X P, Li X et al., 2016. Mapping the fine-scale spatial pattern of housing rent in the metropolitan area by using online rental listings and ensemble learning. Applied Geography, 75: 200–212. doi: 10.1016/j.apgeog.2016.08.011

    Article  Google Scholar 

  • Chiou Y C, Jou R C, Yang C H, 2015. Factors affecting public transportation usage rate: geographically weighted regression. Transportation Research Part A: Policy and Practice, 78: 161–177. doi: 10.1016/j.tra.2015.05.016

    Google Scholar 

  • Dai X Z, Bai X, Xu M, 2016. The influence of Beijing rail transfer stations on surrounding housing prices. Habitat International, 55: 79–88. doi: 10.1016/j.habitatint.2016.02.008

    Article  Google Scholar 

  • De La Luz Hernández-Flores M, Otazo-Sánchez E M, Galeana-Pizaña M et al., 2017. Urban driving forces and megacity expansion threats. Study case in the Mexico City periphery. Habitat International, 64: 109–122. doi: 10.1016/j. habitatint. 2017.04.004

    Google Scholar 

  • Du H B, Mulley C, 2006. Relationship between transport accessibility and land value: local model approach with geographically weighted regression. Transportation Research Record: Journal of the Transportation Research Board, 1977: 197–205. doi: 10.3141/1977-25

    Article  Google Scholar 

  • Dziauddin M F, 2009. Measuring the Effects of the Light Rail Transit (LRT) System on House Prices in the Klang Valley, Malaysia. Newcastle, UK: Newcastle University.

    Google Scholar 

  • Dziauddin M F, Ismail K, Othman Z, 2015. Analysing the local geography of the relationship between residential property prices and its determinants. Bulletin of Geography. Socioeconomic Series, 28(28): 21–35. doi: 10.1515/bog-2015-0013

    Google Scholar 

  • Emamgholizadeh S, Shahsavani S, Eslami M A, 2017. Comparison of artificial neural networks, geographically weighted regression and Cokriging methods for predicting the spatial distribution of soil macronutrients (N, P, and K). Chinese Geographical Science, 27(5): 747–759. doi: 10.1007/s11769-017-0906-6

    Article  Google Scholar 

  • Fotheringham A S, Brunsdon C, Charlton M, 2002. Geographically Weighted Regression: the Analysis of Spatially Varying Relationships. Chichester: Wiley, 283–285.

    Google Scholar 

  • Fotheringham A S, Charlton M E, Brunsdon C, 1998. Geographically weighted regression: a natural evolution of the expansion method for spatial data analysis. Environment and Planning A, 30(11): 1905–1927. doi: 10.1068/a301905

    Article  Google Scholar 

  • Fotheringham A S, Crespo R, Yao J, 2015. Exploring, modelling and predicting spatiotemporal variations in house prices. The Annals of Regional Science, 54(2): 417–436. doi: 10.1007/s 00168-015-0660-6

    Article  Google Scholar 

  • Geniaux G, Martinetti D, 2017. A new method for dealing simultaneously with spatial autocorrelation and spatial heterogeneity in regression models. Regional Science and Urban Economics. doi: 10.1016/j.regsciurbeco.2017.04.001

    Google Scholar 

  • Griffin G P, Jiao J, 2015. Where does bicycling for health happen? Analysing volunteered geographic information through place and plexus. Journal of Transport & Health, 2(2): 238–247. doi: 10.1016/j.jth.2014.12.001

    Article  Google Scholar 

  • Guo Y X, Tang Q H, Gong D Y et al., 2017. Estimating groundlevel PM2.5 concentrations in Beijing using a satellite-based geographically and temporally weighted regression model. Remote Sensing of Environment, 198: 140–149. doi: 10.1016/j. rse.2017.06.001

    Article  Google Scholar 

  • Harris P, Brunsdon C, Gollini I et al., 2015. Using bootstrap methods to investigate coefficient non-stationarity in regression models: an empirical case study. Procedia Environmental Sciences, 27: 112–115. doi: 10.1016/j.proenv.2015.07.106

    Article  Google Scholar 

  • Huang B, Wu B, Barry M, 2010. Geographically and temporally weighted regression for modeling spatio-temporal variation in house prices. International Journal of Geographical Information Science, 24(3): 383–401. doi: 10.1080/136588108026 72469

    Article  Google Scholar 

  • Ibeas Á, Cordera R, Dell’Olio L et al., 2012. Modelling transport and real-estate values interactions in urban systems. Journal of Transport Geography, 24: 370–382. doi: 10.1016/j.jtrangeo. 2012.04.012

    Article  Google Scholar 

  • Jang M, Kang C D, 2015. Retail accessibility and proximity effects on housing prices in Seoul, Korea: a retail type and housing submarket approach. Habitat International, 49: 516–528. doi: 10.1016/j.habitatint.2015.07.004

    Article  Google Scholar 

  • Jeon C H, Park J S, Lee J H et al., 2017. Comparison of brain computed tomography and diffusion-weighted magnetic resonance imaging to predict early neurologic outcome before target temperature management comatose cardiac arrest survivors. Resuscitation, 118: 21–26. doi: 10.1016/j.resuscitation. 2017.06.021

    Article  Google Scholar 

  • Jiang J F, Kell S, Fan X C et al., 2015. The wild relatives of grape in China: diversity, conservation gaps and impact of climate change. Agriculture, Ecosystems & Environment, 210: 50–58. doi: 10.1016/j.agee.2015.03.021

    Article  Google Scholar 

  • Kestens Y, Thériault M, Des Rosiers F, 2006. Heterogeneity in hedonic modelling of house prices: looking at buyers’ household profiles. Journal of Geographical Systems, 8(1): 61–96. doi: 10.1007/s10109-005-0011-8

    Article  Google Scholar 

  • Kontokosta C E, Jain R K, 2015. Modeling the determinants of large-scale building water use: implications for data-driven urban sustainability policy. Sustainable Cities and Society, 18: 44–55. doi: 10.1016/j.scs.2015.05.007

    Article  Google Scholar 

  • Li C, Zhao J, Xu Y, 2017. Examining spatiotemporally varying effects of urban expansion and the underlying driving factors. Sustainable Cities and Society, 28: 307–320. doi: 10.1016/j. scs.2016.10.005

    Article  Google Scholar 

  • Lin T, Xia J H, Robinson T P et al., 2014. Spatial analysis of access to and accessibility surrounding train stations: a case study of accessibility for the elderly in Perth, Western Australia. Journal of Transport Geography, 39: 111–120. doi: 10.1016/j.jtrangeo.2014.06.022

    Article  Google Scholar 

  • Lu B, Harris P, Charlton M et al., 2015. Calibrating a geographically weighted regression model with parameter-specific distance metrics. Procedia Environmental Sciences, 26: 109–114. doi: 10.1016/j.proenv.2015.05.011

    Article  Google Scholar 

  • Luo Ganghui 2007. Spatial Structure of Urban Housing Land Prices based on GWR Model. Hangzhou: Zhejiang University, 165. (in Chinese)

    Google Scholar 

  • Lv Z, 2016. Spatial Differentiation of Urban Residential Land Price and Its Influencing Factors based on GWR Model. Lanzhou: Gansu Agricultural University, 65. (in Chinese)

  • Ministry of Housing and Urban-Rural Development of the People’s Republic of China, 2016. CJJ 37-2012 Code for design of urban road engineering. Beijing: China Architecture & Building Press.

    Google Scholar 

  • Propastin P, 2012. Modifying geographically weighted regression for estimating aboveground biomass in tropical rainforests by multispectral remote sensing data. International Journal of Applied Earth Observation and Geoinformation, 18: 82–90. doi: 10.1016/j.jag.2011.12.013

    Article  Google Scholar 

  • Ramezankhani R, Hosseini A, Sajjadi N et al., 2017. Environmental risk factors for the incidence of cutaneous leishmaniasis in an endemic area of Iran: a GIS-based approach. Spatial and Spatio-temporal Epidemiology, 21: 57–66. doi: 10.1016/j.sste. 2017.03.003

    Article  Google Scholar 

  • Robinson D P, Lloyd C D, McKinley J M, 2013. Increasing the accuracy of nitrogen dioxide (NO2) pollution mapping using geographically weighted regression (GWR) and geostatistics. International Journal of Applied Earth Observation and Geoinformation, 21: 374–383. doi: 10.1016/j.jag.2011.11.001

    Article  Google Scholar 

  • Shen Y, Karimi K, 2017. The economic value of streets: mix-scale spatio-functional interaction and housing price patterns. Applied Geography, 79: 187–202. doi: 10.1016/j. apgeog. 2016.12.012

    Article  Google Scholar 

  • Sheng J C, Han X, Zhou H, 2017. Spatially varying patterns of afforestation/reforestation and socio-economic factors in China: a geographically weighted regression approach. Journal of Cleaner Production, 153: 362–371. doi: 10.1016/j.jclepro. 2016.06.055

    Article  Google Scholar 

  • Song X D, Brus D J, Liu F et al., 2016. Mapping soil organic carbon content by geographically weighted regression: a case study in the Heihe River Basin, China. Geoderma, 261: 11–22. doi: 10.1016/j.geoderma.2015.06.024

    Article  Google Scholar 

  • State Construction Commission 1980. Interim Provisions on quota targets of urban planning.

    Google Scholar 

  • Tu J, Tu W, Tedders S H, 2016. Spatial variations in the associations of term birth weight with ambient air pollution in Georgia, USA. Environment International, 92–93: 146–156. doi: 10.1016/j.envint.2016.04.005

    Article  Google Scholar 

  • Wen H Z, Xiao Y, Zhang L, 2017. Spatial effect of river landscape on housing price: an empirical study on the Grand Canal in Hangzhou, China. Habitat International, 63: 34–44. doi: 10.1016/j.habitatint.2017.03.007

    Article  Google Scholar 

  • Wu C, Ye X Y, Du Q Y et al., 2017. Spatial effects of accessibility to parks on housing prices in Shenzhen, China. Habitat International, 63: 45–54. doi: 10.1016/j.habitatint.2017.03.010

    Article  Google Scholar 

  • Wu S, Yang H, Guo F et al., 2017. Spatial patterns and origins of heavy metals in Sheyang River catchment in Jiangsu, China based on geographically weighted regression. Science of the Total Environment, 580: 1518–1529. doi: 10.1016/j.scitotenv. 2016.12.137

    Article  Google Scholar 

  • Yu D L, Wei Y D, Wu C S, 2007. Modeling spatial dimensions of housing prices in Milwaukee, WI. Environment and Planning B: Planning and Design, 34(6): 1085–1102. doi: 10.1068/b 32119

    Article  Google Scholar 

  • Zhang H, Guo L, Chen J et al., 2014. Modeling of spatial distributions of farmland density and its temporal change using geographically weighted regression model. Chinese Geographical Science, 24(2): 191–204. doi: 10.1007/s11769-013-0631-8

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Human Settlements Research Center, Liaoning Normal University, 116029, Dalian, China

    Jun Yang, Yajun Bao, Yuqing Zhang & Xueming Li

  2. Key Laboratory of Land Surface Pattern and Simulation, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, 100101, China

    Jun Yang & Quansheng Ge

Authors
  1. Jun Yang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Yajun Bao
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Yuqing Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Xueming Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Quansheng Ge
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Jun Yang.

Additional information

Foundation item: Under the auspices of National Natural Science Foundation of China (No. 41471140, 41771178), Liaoning Province Outstanding Youth Program (No. LJQ2015058)

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Yang, J., Bao, Y., Zhang, Y. et al. Impact of Accessibility on Housing Prices in Dalian City of China Based on a Geographically Weighted Regression Model. Chin. Geogr. Sci. 28, 505–515 (2018). https://doi.org/10.1007/s11769-018-0954-6

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11769-018-0954-6

Keywords

Search

Navigation