228
Views
5
CrossRef citations to date
0
Altmetric
Research Articles

Estimation of Spatial Association Between Housing Price and Local Environmental Amenities in Kolkata, India Using Hedonic Local Regression

, ORCID Icon, &

References

  • Adair, A. S., J. N. Berry, and W. S. McGreal. 1996. Hedonic modelling, housing submarkets and residential valuation. Journal of Property Research 13 (1):67–83. https://doi.org/10.1080/095999196368899.
  • Anselin, L. 2010. Thirty years of spatial econometrics. Papers in Regional Science 89:3–25. doi:10.1111/j.1435-5957.2010.00279.x.
  • Artell, J. 2013. Lots of value? A spatial hedonic approach to water quality valuation. Journal of Environmental Planning and Management 57 (6):862–82. https://doi.org/10.1080/09640568.2013.772504.
  • Artell, J. 2014. Lots of value? A spatial hedonic approach to water quality valuation. Journal of Environmental Planning and Management, 57 (6):862–82. https://doi.org/10.1080/09640568.2013.772504.
  • Bagheri, N., A. Holt, and G. L. Benwell. 2009. Using geographically weighted regression to validate approaches for modeling accessibility to primary health care. Applied Spatial Analysis and Policy 2:177–94. https://doi.org/10.1007/s12061-009-9021-0.
  • Bhatta, B. 2009. Analysis of urban growth pattern using remote sensing and GIS: A case study of Kolkata, India. International Journal of Remote Sensing 30 (18): 4773–46. https://doi.org/10.1080/01431160802651967.
  • Bitter, C., G. F. Mulligan, and S. Dall'erba. 2006. 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.
  • Brown, S., V. L. Versace, L. Laurenson, D. Ierodiaconou, J. Fawcett, and S. Salzman. 2012. Assessment of spatiotemporal varying relationships between rainfall, land cover and surface water area using geographically weighted regression. Environmental Modeling & Assessment 17 (3):241–54. https://doi.org/10.1007/s10666-011-9289-8.
  • Brueckner, J. K. 2000. Urban sprawl: Diagnosis and remedies. International Regional Science Review 23 (2):160–71. https://doi.org/10.1177/016001700761012710.
  • Chen, J., and Q. Hao. 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. https://doi.org/10.1080/14765280802283584.
  • Clement, F., D. Orange, M. Williams, C. Mulley, and M. Epprecht. 2009. Drivers of afforestation in Northern Vietnam: Assessing local variations using geographically weighted regression. Applied Geography 29 (4):561–76. https://doi.org/10.1016/j.apgeog.2009.01.003.
  • Du, Q., C. Wu, X. Ye, F. Ren, and Y. Lin. 2018. Evaluating the effects of landscape on housing prices in urban China. Tijdschrift voor Economische en Sociale Geografie. doi:10.1111/tesg.12308.
  • Du, S., Q. Wang, and L. Guo. 2014. Spatially varying relationships between land-cover change and driving factors at multiple sampling scales. Journal of Environmental Management 137 (1):101–10.
  • Edadan, N. 2015. Structural determinants of unregulated urban growth and residential land pricing: Case of Bangalore. Journal of Urban Planning and Development 141 (4):5014022. https://doi.org/10.1061/(asce)up.1943-5444.0000236.
  • Eksioglu, G., Cetintahra, Cubukcu, E., and Cetintahra. 2015. The influence of environmental aesthetics on economic value of housing: an empirical research on virtual environments. Journal of Housing and the Built Environment, 30:331–40. https://doi.org/10.1007/s10901-014-9413-6.
  • Fotheringham, A. S., C. Brunsdon, and M. Charlton. 2003. Geographically Weighted Regression: The Analysis of Spatially Varying Relationships. John Wiley & Sons.
  • Fotheringham, A. S., R. Crespo, and J. Yao. 2015. Geographical and temporal weighted regression (GTWR). Geographical Analysis 47 (4):431–52. https://doi.org/10.1111/gean.12071.
  • Fotheringham, A. S., and T. M. Oshan. 2016. Geographically weighted regression and multicollinearity: Dispelling the myth. Journal of Geographical Systems 18 (4):303–29. https://doi.org/10.1007/s10109-016-0239-5.
  • Fujita, M. 1989. Urban economic theory: Land use and city size. London: Cambridge University Press.
  • Geng, B., H. Bao, and Y. Liang. 2015. A study of the effect of a high-speed rail station on spatial variations in housing price based on the hedonic model. Habitat International 49:333–39. https://doi.org/10.1016/j.habitatint.2015.06.005.
  • Glaesener, M. L., and G. Caruso. 2015. Neighborhood green and services diversity effects on land prices: Evidence from a multilevel hedonic analysis in Luxembourg. Landscape and Urban Planning 143:100–11. https://doi.org/https://doi.org/10.1016/j.landurbplan.2015.06.008.
  • Goffette-Nagot, F., I. Reginster, and I. Thomas. 2011. Spatial analysis of residential land prices in Belgium: Accessibility, linguistic border, and environmental amenities. Regional Studies 45 (9):1253–68. https://doi.org/10.1080/00343404.2010.484417.
  • Haitao, Z., G. U. O. Long, C. Jiaying, F. U. Peihong, G. U. Jianli, and L. Guangyu. 2014. Modeling of spatial distributions of farmland density and its temporal change using geographically weighted regression model. Chinese Geographical Science 24 (2):191–204. https://doi.org/10.1007/s11769-013-0631-8.
  • Helbich, M., W. Brunauer, E. Vaz, and P. Nijkamp. 2013. Spatial heterogeneity in hedonic house price models: The case of Austria. Urban Studies 51 (2):390–411.
  • Huang, H., and L. Yin. 2014. Creating sustainable urban built environments: An application of hedonic house price models in Wuhan, China. Journal of Housing and the Built Environment 30 (2):219–35. https://doi.org/10.1007/s10901-014-9403-8.
  • Ibarra, A. A., L. Zambrano, E. L. Valiente, and A. Ramos-Bueno. 2013. Enhancing the potential value of environmental services in urban wetlands: An agro-ecosystem approach. Cities 31:438–43. https://doi.org/10.1016/j.cities.2012.08.002.
  • Indian Property Service. 2015. Kolkata residential real estate overview. https://www.icicihomesearch.com/resourceContent/kolkataresidentialrealestateoverview-january2012_January_2012_3537.pdf (accessed on 10 December 2016).
  • Keskin, B. 2008. Hedonic analysis of price in the Istanbul housing market. International Journal of Strategic Property Management 12 (2):125–38. https://doi.org/10.3846/1648-715X.2008.12.125-138.
  • Kim, C. W., T. T. Phipps, and L. Anselin. 2003. Measuring the benefits of air quality improvement: A spatial hedonic approach. Journal of Environmental Economics and Management 45 (1):24–39. https://doi.org/https://doi.org/10.1016/S0095-0696(02)00013-X.
  • Kong, F., H. Yin, and N. Nakagoshi. 2007. Using GIS and landscape metrics in the hedonic price modeling of the amenity value of urban green space: A case study in Jinan City, China. Landscape and Urban Planning 79 (3):240–52. https://doi.org/10.1016/j.landurbplan.2006.02.013.
  • Kwong, L. M. K., T. Ogwang, and L. Sun. 2016. Semiparametric versus parametric hedonic wine price models: An empirical investigation. Applied Economics Letters 24 (13):897–901. https://doi.org/10.1080/13504851.2016.1240330.
  • Lai, S. K., and L. D. Hopkins. 1989. The meanings of trade-offs in multiattribute evaluation methods: A comparison. Environment and Planning B: Planning and Design 16 (2):155–70. https://doi.org/10.1068/b160155.
  • Lancaster, K. J. 1966. A new approach to consumer theory. Journal of Political Economy 74 (2):132–57.
  • Lee, K. H., and M. A. Schuett. 2014. Exploring spatial variations in the relationships between residents’ recreation demand and associated factors: A case study in Texas. Applied Geography 53:213–22.
  • Ligus, M., and P. Peternek. 2016. Measuring structural, location and environmental effects: A hedonic analysis of housing market in Wroclaw, Poland. Procedia: Social and Behavioral Sciences 220:251–60. https://doi.org/10.1016/j.sbspro.2016.05.497.
  • Mahalik, M. K., and H. Mallick. 2011. What causes asset price bubble in an emerging economy? Some empirical evidence in the housing sector of India. International Economic Journal 25 (2):215–37. https://doi.org/https://doi.org/10.1080/10168737.2011.586806.
  • Mondal, B., D. N. Das, and G. Dolui. 2015. Modeling spatial variation of explanatory factors of urban expansion of Kolkata: A geographically weighted regression approach. Modeling Earth Systems and Environment 1 (3):29. https://doi.org/10.1007/s40808-015-0026-1.
  • Nakaya, T. 2014. GWR4 user manual. Accessed November 4, 2013. http://www.st-andrews.ac.uk/geoinformatics/wp-content/uploads/GWR4manual_201311.pdf.
  • Nilsson, P. 2014. The influence of urban and natural amenities on second home prices. Journal of Housing and the Built Environment 30 (3):427–50. https://doi.org/10.1007/s10901-014-9421-6.
  • Paper, O. 2005. Multicollinearity and correlation among local regression coefficients in geographically weighted regression. Journal of Geographical Systems (7):161–87. https://doi.org/10.1007/s10109-005-0155-6.
  • Reginster, I., and F. Goffette-Nagot. 2005. Urban environmental quality in two Belgian cities, evaluated on the basis of residential choices and GIS data. Environment and Planning A, 37 (6):1067–90. https://doi.org/10.1068/a3735a.
  • Robinson, D. P., C. D. Lloyd, and J. M. McKinley. 2013. Increasing the accuracy of nitrogen dioxide ({NO}2) pollution mapping using geographically weighted regression ({GWR}) and geostatistics. International Journal of Applied Earth Observation and Geoinformation 21 (2):374–83. https://doi.org/10.1016/j.jag.2011.11.001.
  • Rosen, S. 1974. Hedonic prices and implicit markets: Product differentiation in pure competition. Journal of Political Economy 82 (1):34–55.
  • Roy, A. 2010. Re-forming the megacity: Calcutta and the rural–urban interface. In Megacities: Urban form, governance, and sustainability, ed. A. Sorensen and J. Okata, 93–109. New York: Springer.
  • Sander, H. A., and R. G. Haight. 2012. Estimating the economic value of cultural ecosystem services in an urbanizing area using hedonic pricing. Journal of Environmental Management 113:194–205. https://doi.org/10.1016/j.jenvman.2012.08.031.
  • Sander, H. A., and S. Polasky. 2009. The value of views and open space: Estimates from a hedonic pricing model for Ramsey County, Minnesota, USA. Land Use Policy 26 (3):837–45. https://doi.org/10.1016/j.landusepol.2008.10.009.
  • Sander, H. A., S. Polasky, and R. G. Haight. 2010. The value of urban tree cover: A hedonic property price model in Ramsey and Dakota Counties, Minnesota, USA. Ecological Economics 69 (8):1646–56. https://doi.org/10.1016/j.ecolecon.2010.03.011.
  • Schläpfer, F., F. Waltert, L. Segura, and F. Kienast. 2015. Valuation of landscape amenities: A hedonic pricing analysis of housing rents in urban, suburban and periurban Switzerland. Landscape and Urban Planning 141:24–40. https://doi.org/10.1016/j.landurbplan.2015.04.007.
  • Shafizadeh-Moghadam, H., and M. Helbich. 2015. Spatiotemporal variability of urban growth factors: A global and local perspective on the megacity of Mumbai. International Journal of Applied Earth Observation and Geoinformation 35:187–98. https://doi.org/10.1016/j.jag.2014.08.013.
  • Shi, P., and D. Yu. 2014. Assessing urban environmental resources and services of Shenzhen, China: A landscape-based approach for urban planning and sustainability. Landscape and Urban Planning 125:290–97. https://doi.org/10.1016/j.landurbplan.2014.01.025.
  • Sivaramakrishnan, K. C., A. Kundu, and B. N. Singh. 2005. Handbook of urbanization in India: an analysis of trends and processes. USA: Oxford University Press.
  • Tiwari, P., and J. Parikh. 1998. Affordability, housing demand and housing policy in urban India. Urban Studies 35 (11):2111–29. https://doi.org/10.1080/0042098984033.
  • Wei, C., and F. Qi. 2012. On the estimation and testing of mixed geographically weighted regression models. Economic Modeling 29 (6):2615–20. https://doi.org/10.1016/j.econmod.2012.08.015.
  • Wen, H., and A. C. Goodman. 2013. Relationship between urban land price and housing price: Evidence from 21 provincial capitals in China. Habitat International 40:9–17. https://doi.org/10.1016/j.habitatint.2013.01.004.
  • Wu, C., X. Ye, Q. Du, and P. Luo. 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.
  • Wu, C., X. Ye, F. Ren, Y. Wan, P. Ning, and Q. Du. 2016. Spatial and social media data analytics of housing prices in Shenzhen, China. PLoS ONE 11 (10):e0164553. doi:10.1371/journal.pone.0164553.
  • Xiao, Y., S. Orford, and C. J. Webster. 2015. Urban configuration, accessibility, and property prices: A case study of Cardiff, Wales. Environment and Planning B: Planning and Design 43 (1):108–29. https://doi.org/10.1177/.
  • Xu, L., H. You, D. Li, and K. Yu. 2016. Urban green spaces, their spatial pattern, and ecosystem service value: The case of Beijing. Habitat International 56:84–95. https://doi.org/10.1016/j.habitatint.2016.04.005.
  • Yao, J., and A. S. Fotheringham. 2015. Local spatiotemporal modeling of house prices: A mixed model approach. The Professional Geographer 68 (2):189–201. https://doi.org/10.1080/00330124.2015.1033671.
  • Zhang, A., Q. Qi, L. Jiang, F. Zhou, and J. Wang. 2013. Population exposure to PM 2.5 in the urban area of Beijing 8 (5). https://doi.org/10.1371/journal.pone.0063486.

Reprints and Corporate Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

To request a reprint or corporate permissions for this article, please click on the relevant link below:

Academic Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

Obtain permissions instantly via Rightslink by clicking on the button below:

If you are unable to obtain permissions via Rightslink, please complete and submit this Permissions form. For more information, please visit our Permissions help page.