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Original Research Article

Impact of neighborhood features on housing resale prices in Zhuhai (China) based on an (M)GWR model

ORCID Icon & ORCID Icon
Pages 146-169 | Received 01 Jul 2021, Accepted 13 Jan 2022, Published online: 14 Feb 2022

References

  • Akaike, H. (1974). A new look at the statistical model identification. IEEE Transactions on Automatic Control, 19(6), 716–723.
  • Anselin, L. (1995). Local Indicators of Spatial Association—LISA. Geographical Analysis. 27(2), 93–115.
  • Anselin, L. (1998). GIS Research Infrastructure for Spatial Analysis of Real Estate Markets. Journal of Housing Research, 9(1), 113–133.
  • Bae, H., & Chung, I. H. (2013). Impact of school quality on house prices and estimation of parental demand for good schools in Korea. KEDI Journal of Educational Policy, 10(1), 43–61.
  • Baranzini, A., & Schaerer, C. (2011). A sight for sore eyes: Assessing the value of view and land use in the housing market. Journal of Housing Economics, 20(3), 191–199.
  • Bartik, T. J., & Smith, V. K. (1987). Chapter 31 Urban amenities and public policy. Handbook of Regional and Urban Economics, 2, 1207–1254.
  • Brunsdon, C., Fotheringham, A. S., & Charlton, M. E. (1996). Geographically Weighted Regression: A Method for Exploring Spatial Nonstationarity. Geographical Analysis. 28(4), 281–298.
  • Bureau, Z. S. (2020). Zhuhai Statistical Yearbooks 2019. China Statistics Press, Zhuhai, Guangdong, China, (Ed.).
  • Burnham, K. P., & Anderson, D. R. (2004). Multimodel Inference:Understanding AIC and BIC in Model Selection. Sociological Methods & Research, 33(2), 261–304.
  • Cao, K., Diao, M., & Wu, B. (2018). A Big Data–Based Geographically Weighted Regression Model for Public Housing Prices: A Case Study in Singapore. Annals of the Association of American Geographers, 1–14. doi:10.1080/24694452.2018.1470925
  • Cebula, R. (2009). The Hedonic Pricing Model Applied to the Housing Market of the City of Savannah and Its Savannah Historic Landmark District. Review of Regional Studies, 39. doi:10.52324/001c.8197
  • Chen, L., & Grant, R. (2021). Built environment, special economic zone, and housing prices in Shenzhen, China. Applied Geography, 129, 102429.
  • Clark, D. E., & Herrin, W. E. (2000). The Impact of Public School Attributes on Home Sale Prices in California. Growth and Change, 31(3), 385–407.
  • Dai, X., Bai, X., & Xu, M. (2016). The influence of Beijing rail transfer stations on surrounding housing prices. Habitat International, 55, 79–88.
  • de Araujo, P., & Cheng, K. (2017). DO PREFERENCES FOR AMENITIES DIFFER AMONG HOME BUYERS? A HEDONIC PRICE APPROACH. Review Urban &Regional Devel, 29(3), 165–184.
  • Debrezion, G., Pels, E., & Rietveld, P. (2011). The Impact of Rail Transport on Real Estate Prices:An Empirical Analysis of the Dutch Housing Market. Urban Studies, 48(5), 997–1015.
  • Diao, M., & Ferreira, J. (2010). Residential Property Values and the Built Environment: Empirical Study in the Boston, Massachusetts, Metropolitan Area. Transportation Research Record, 2174(1), 138–147.
  • Dubé, J., Thériault, M., & Des Rosiers, F. (2013). Commuter rail accessibility and house values: The case of the Montreal South Shore, Canada, 1992–2009. Transportation Research Part A: Policy and Practice, 54, 49–66.
  • Dziauddin, M. F. (2019). Estimating land value uplift around light rail transit stations in Greater Kuala Lumpur: An empirical study based on geographically weighted regression (GWR). Research in Transportation Economics, 74, 10–20.
  • Fotheringham, C. M. E., & Brunsdon, C. (1998). Geographically Weighted Regression: A Natural Evolution of the Expansion Method for Spatial Data Analysis. Environment and Planning A: Economy and Space, 30(11), 1905–1927.
  • Fotheringham, Y. W., & Kang, W. (2017). Multiscale Geographically Weighted Regression (MGWR). Annals of the American Association of Geographers, 107(6), 1247–1265.
  • Gatto, M. (2000). Pricing Biodiversity and Ecosystem Services: The Never-Ending Story. BioScience, 50, 347–355.
  • Gelfand, A. E., Ghosh, S. K., Knight, J. R., & Sirmans, C. F. (1998). Spatio-Temporal Modeling of Residential Sales Data. Journal of Business and Economic Statistics, 16(3), 312–321.
  • Geng, J., Cao, K., Yu, L., & Tang, Y. (2011). Geographically Weighted Regression model (GWR) based spatial analysis of house price in Shenzhen. Proceedings - 2011 19th International Conference on Geoinformatics, Geoinformatics 2011, Shanghai, China, 1–5. doi:10.1109/GeoInformatics.2011.5981032
  • Gollini, I., Lu, B., Charlton, M., Brunsdon, C., & Harris, P. (2015). GWmodel: An R Package for Exploring Spatial Heterogeneity Using Geographically Weighted Models. Journal of Statistical Software, 63(17), 1–50.
  • Golub, A., Guhathakurta, S., & Sollapuram, B. (2012). Spatial and Temporal Capitalization Effects of Light Rail in Phoenix:From Conception, Planning, and Construction to Operation. Journal of Planning Education and Research, 32(4), 415–429.
  • Hanink, D. M., Cromley, R. G., & Ebenstein, A. Y. (2012). Spatial Variation in the Determinants of House Prices and Apartment Rents in China. The Journal of Real Estate Finance and Economics, 45(2), 347–363.
  • Harris, P., Fotheringham, A. S., & Juggins, S. (2010). Robust Geographically Weighted Regression: A Technique for Quantifying Spatial Relationships Between Freshwater Acidification Critical Loads and Catchment Attributes. Annals of the Association of American Geographers, 100(2), 286–306.
  • 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.
  • He, Y., Sheng, Y., Jing, Y., Yin, Y., & Hasnain, A. (2020). Uncorrelated Geo-Text Inhibition Method Based on Voronoi K-Order and Spatial Correlations in Web Maps. ISPRS International Journal of Geo-Information, 9(6), 381.
  • 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.
  • Horrace, W. C., & Oaxaca, R. L. (2006). Results on the bias and inconsistency of ordinary least squares for the linear probability model. Economics Letters, 90(3), 321–327.
  • Hui, E. C. M., Chau, C. K., Pun, L., & Law, M. Y. (2007). Measuring the neighboring and environmental effects on residential property value: Using spatial weighting matrix. Building and Environment, 42(6), 2333–2343.
  • 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.
  • Jim, C. Y., & Chen, W. Y. (2010). External effects of neighbourhood parks and landscape elements on high-rise residential value. Land Use Policy, 27(2), 662–670.
  • Lan, F., Wu, Q., Zhou, T., & Da, H. (2018). Spatial Effects of Public Service Facilities Accessibility on Housing Prices: A Case Study of Xi’an, China. Sustainability, 10(12), 4503.
  • Leong, -Y.-Y., & Yue, J. C. (2017). A modification to geographically weighted regression. International Journal of Health Geographics, 16(1), 11.
  • Li, Q. Z., & Shi, X. (2020). Decoding spatiotemporal patterns of urban land sprawl in Zhuhai, China. Applied Ecology and Environmental Research, 18, 913–927.
  • Liang, X., Liu, Y., Qiu, T., Jing, Y., & Fang, F. (2018). The effects of locational factors on the housing prices of residential communities: The case of Ningbo, China. Habitat International, 81, 1–11.
  • Limsombunchai, V., Gan, C., & Lee, M. (2004). House Price Prediction: Hedonic Price Model vs. Artificial Neural Network. American Journal of Applied Sciences, 1. doi:10.3844/ajassp.2004.193.201
  • Liu, N., & Zhi, H. (2019). Housing Impact of the Hong Kong-Zhuhai-Macao Bridge on Second-Hand Housing Prices in Zhuhai. Paper presented at the The 2nd International Conference on Urban Informatics, Hong Kong, China.
  • Lu, B., Brunsdon, C., Charlton, M., & Harris, P. (2017). Geographically weighted regression with parameter-specific distance metrics. International Journal of Geographical Information Science, 31(5), 982–998.
  • Lu, B., Charlton, M., Harris, P., & Fotheringham, A. S. (2014). Geographically weighted regression with a non-Euclidean distance metric: A case study using hedonic house price data. International Journal of Geographical Information Science, 28(4), 660–681.
  • Nilsson, P. (2014). Natural amenities in urban space – A geographically weighted regression approach. Landscape and Urban Planning, 121, 45–54.
  • O’Sullivan, D. (2003). Geographically Weighted Regression: The Analysis of Spatially Varying Relationships, by A. S. Fotheringham, C. Brunsdon, and M. Charlton. John Wiley & Sons, 35(3), 272–275.
  • Osland, L. (2010). An Application of Spatial Econometrics in Relation to Hedonic House Price Modeling. Journal of Real Estate Research, 32, 289–320.
  • Pace, R. K., Barry, R., Clapp, J. M., & Rodriquez, M. (1998). Spatiotemporal Autoregressive Models of Neighborhood Effects. The Journal of Real Estate Finance and Economics, 17(1), 15–33.
  • Rosen, S. (1974). Hedonic Prices and Implicit Markets: Product Differentiation in Pure Competition. Journal of Political Economy, 82(1), 34–55.
  • Shabana, Ali, G., Bashir, M. K., & Ali, H. (2015). Housing valuation of different towns using the hedonic model: A case of Faisalabad city, Pakistan. Habitat International, 50, 240–249.
  • Sheng, N., & Tang, U. W. (2013). Zhuhai. Cities, 32, 70–79.
  • So, H. M., Tse, R. Y. C., & Ganesan, S. (1997). Estimating the influence of transport on house prices: Evidence from Hong Kong. Journal of Property Valuation and Investment, 15(1), 40–47.
  • Tse, R. Y. C. (2002). Estimating Neighbourhood Effects in House Prices: Towards a New Hedonic Model Approach. Urban Studies, 39(7), 1165–1180.
  • Wang, J., Lee, C. L., & Shirowzhan, S. (2021). Macro-Impacts of Air Quality on Property Values in China—A Meta‐Regression Analysis of the Literature. Buildings, 11, 48.
  • Wei, Y., Lam, P. T. I., Chiang, Y. H., & Leung, B. Y. P. (2014). The Changing Real Estate Supply and Investment Patterns in China: An Institutional Perspective on Affordable Housing. Berlin, Heidelberg. Springer.
  • 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.
  • Wen, H., Xiao, Y., & Zhang, L. (2017a). School district, education quality, and housing price: Evidence from a natural experiment in Hangzhou, China. Cities, 66, 72–80.
  • Wen, H., Xiao, Y., & Zhang, L. (2017b). Spatial effect of river landscape on housing price: An empirical study on the Grand Canal in Hangzhou, China. Habitat International, 63, 34–44.
  • Wen, H., Zhang, Y., & Zhang, L. (2014). Do educational facilities affect housing price? An empirical study in Hangzhou, China. Habitat International, 42, 155–163.
  • 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.
  • Wheeler, D. C., & Páez, A. (2010). Geographically Weighted Regression. In M. M. Fischer & A. Getis (Eds.), Handbook of Applied Spatial Analysis: Software Tools, Methods and Applications (pp. 461–486). Berlin, Heidelberg: Springer Berlin Heidelberg.
  • 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.
  • Xian, S., Li, L., & Qi, Z. (2019). Toward a sustainable urban expansion: A case study of Zhuhai, China. Journal of Cleaner Production, 230, 276–285.
  • Yang, L., Zhou, J., Shyr, O. F., & Huo, D. (2019). Does bus accessibility affect property prices? Cities, 84, 56–65.
  • Yeung, Y.-M., Lee, J., & Kee, G. (2009). China’s Special Economic Zones at 30. Eurasian Geography and Economics, 50(2), 222–240.
  • Zhang, L., Zhou, J., & Hui, E. C.-M. (2020). Which types of shopping malls affect housing prices? From the perspective of spatial accessibility. Habitat International, 96, 102118.
  • Zheng, M. (2014). An Empirical Evaluation of OLS Hedonic Pricing Regression on Singapore Private Housing Market. Master of Science Thesis submitted to Royal Institute of Technology, Stockholm, Sweden. doi:10.13140/RG.2.2.24071.24484
  • Zhou, J.-Y., Pan, W.-T., & Zhuang, M.-E. (2019). Research on Housing Price Factors of Zhuhai City:Under the background of the Opening of Hong Kong - Zhuhai - Macao Bridge. Journal of Economics and Business, 2. doi:10.31014/aior.1992.02.02.100