YANG Jun, BAO Yajun, ZHANG Yuqing, LI Xueming, GE Quansheng. Impact of Accessibility on Housing Prices in Dalian City of China Based on a Geographically Weighted Regression Model[J]. Chinese Geographical Science, 2018, 28(3): 505-515. doi: 10.1007/s11769-018-0954-6
Citation: YANG Jun, BAO Yajun, ZHANG Yuqing, LI Xueming, GE Quansheng. Impact of Accessibility on Housing Prices in Dalian City of China Based on a Geographically Weighted Regression Model[J]. Chinese Geographical Science, 2018, 28(3): 505-515. doi: 10.1007/s11769-018-0954-6

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

doi: 10.1007/s11769-018-0954-6
Funds:  Under the auspices of National Natural Science Foundation of China (No. 41471140, 41771178), Liaoning Province Outstanding Youth Program (No. LJQ2015058)
More Information
  • Corresponding author: YANG Jun.E-mail:yangjun@lnnu.edu.cn
  • Received Date: 2017-09-08
  • Rev Recd Date: 2017-12-29
  • Publish Date: 2018-06-27
  • 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.
  • [1] 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
    [2] 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
    [3] 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
    [4] 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
    [5] 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
    [6] 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
    [7] 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
    [8] 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
    [9] 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
    [10] 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
    [11] 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
    [12] 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
    [13] 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.
    [14] 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
    [15] 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
    [16] Fotheringham A S, Brunsdon C, Charlton M, 2002. Geographically Weighted Regression:the Analysis of Spatially Varying Relationships. Chichester:Wiley, 283-285.
    [17] 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
    [18] 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
    [19] 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
    [20] 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
    [21] 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
    [22] 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
    [23] 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
    [24] 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
    [25] 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
    [26] 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
    [27] 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
    [28] 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
    [29] 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
    [30] 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
    [31] 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
    [32] 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
    [33] Luo Ganghui, 2007. Spatial Structure of Urban Housing Land Prices based on GWR Model. Hangzhou:Zhejiang University, 165. (in Chinese)
    [34] 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)
    [35] 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.
    [36] 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
    [37] 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
    [38] 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
    [39] 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
    [40] 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
    [41] 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
    [42] State Construction Commission, 1980. Interim Provisions on quota targets of urban planning. 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
    [43] 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
    [44] 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
    [45] 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
    [46] 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
    [47] 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
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Impact of Accessibility on Housing Prices in Dalian City of China Based on a Geographically Weighted Regression Model

doi: 10.1007/s11769-018-0954-6
Funds:  Under the auspices of National Natural Science Foundation of China (No. 41471140, 41771178), Liaoning Province Outstanding Youth Program (No. LJQ2015058)
    Corresponding author: YANG Jun.E-mail:yangjun@lnnu.edu.cn

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.

YANG Jun, BAO Yajun, ZHANG Yuqing, LI Xueming, GE Quansheng. Impact of Accessibility on Housing Prices in Dalian City of China Based on a Geographically Weighted Regression Model[J]. Chinese Geographical Science, 2018, 28(3): 505-515. doi: 10.1007/s11769-018-0954-6
Citation: YANG Jun, BAO Yajun, ZHANG Yuqing, LI Xueming, GE Quansheng. Impact of Accessibility on Housing Prices in Dalian City of China Based on a Geographically Weighted Regression Model[J]. Chinese Geographical Science, 2018, 28(3): 505-515. doi: 10.1007/s11769-018-0954-6
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