Use of Geographically Weighted Regression (GWR) Method to Estimate the Effects of Location Attributes on the Residential Property Values

https://doi.org/10.22146/ijg.27036

Mohd Faris Dziauddin(1*), Zulkefli Idris(2)

(1) Department of Geography & Environment Faculty of Human Sciences, Sultan Idris Education University, 35900 Tanjong Malim, Perak
(2) UMW Technology Sdn. Bhd.16-1, Level 16, Menara Prestige No 1, Jalan Pinang, 50450 Kuala Lumpur
(*) Corresponding Author

Abstract


This study estimates the effect of locational attributes on residential property values in Kuala Lumpur, Malaysia. Geographically weighted regression (GWR) enables the use of the local parameter rather than the global parameter to be estimated, with the results presented in map form. The results of this study reveal that residential property values are mainly determined by the property’s physical (structural) attributes, but proximity to locational attributes also contributes marginally. The use of GWR in this study is considered a better approach than other methods to examine the effect of locational attributes on residential property values. GWR has the capability to produce meaningful results in which different locational attributes have differential spatial effects across a geographical area on residential property values. This method has the ability to determine the factors on which premiums depend, and in turn it can assist the government in taxation matters.

Keywords


Geographically weighted regression; Kuala Lumpur ; Location attributes; Residential property values

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References

Ahlfeldt G. M. and Kavetsos G. (2014). Form or Function? The Impact of New Sports Stadia on Property Prices in London, Journal of the Royal Statistical Society: Series A (Statistics in Society), 177 (1), 169-190. Ahlfeldt, G. M. and Maennig, W. (2008). Impact of Sports Arenas on Land Values: Evidence from Berlin, The Annals of Regional Science, 44 (2), 205-227. Alonso, W. (1964). Location and Land Use: Towards a General Theory of Land Rent. Cambridge, MA: Harvard University Press. Anderson, H., Jonsson, L. and Ögren, M. (2010). Property Prices and Exposure to Multiple Noise Sources: Hedonic Regression with Road and Railway Noise, Environmental and Resource Economics, 45 (1), 73-89. Anselin, L. (1988). Spatial Econometrics: Methods and Models. Dordrecht: Kluwer Academic. Bitter, C., Mulligan, G., 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, 7-27. Chau, K.W., Yiu, C.Y., Wong, S.K. and Lai, L.W.C. (2003b). Hedonic Price Modelling of Environmental Attributes: A Review of the Literature and A Hong Kong Case Study, in L.W.C. Lai and F.T. Lorne (eds.) Understanding and Implementing Sustainable Development, New York: Nova Science, 87-110. Chin, T. L. and Chau, K. W. (2003). A Critical Review of Literature on the Hedonic Price Model, International Journal for Housing Science and Its Applications, 27 (2), 145-165. Conroy, S. J. and Milosch, J. L. (2011). An Estimation of the Coastal Premium for Residential Housing Prices in San Diego County, The Journal of Real Estate Finance and Economics.42, 211–228. Crespo, R. and Grêt-Regamey, A. (2013). Local Hedonic House-Price Modelling for Urban Planners: Advantages of Using Local Regression Techniques, Environment and Planning B: Planning and Design.40 (4), 664 – 682. Cropper, M.L., Deck, L.B. and McConnell, K.E. (1988). On the Choice of Functional Form for Hedonic Price Functions, The Review of Economics and Statistics, 70 (4), 668-675. Department of Statistics Malaysia (1980). The Malaysia Population and Housing Census. Kuala Lumpur: Department of Statistics Malaysia. Department of Statistics Malaysia (2010). The Malaysia Population and Housing Census. Kuala Lumpur: Department of Statistics Malaysia. Department of Valuation and Property Services (2013). Property Market Report. Putrajaya. Du, H. and Mulley, C. (2006). Relationship between Transport Accessibility and Land Value: Local Model Approach with Geographically Weighted Regression, Transportation Research Record, 1977 (1), 197-205. Dziauddin, M. F., Powe, N. and Alvanides, S. (2015). Estimating the Effects of Light Rail Transit (LRT) System on Residential Property Values Using Geographically Weighted Regression (GWR), Applied Spatial Analysis and Policy, 8, 1-25. Fotheringham, A. S., Brunsdon, C. and Charlton, M. (2002). Geographically Weighted Regression: The Analysis of Spatially Varying Relationships. England: John Wiley & Sons Ltd. Freeman, A.M.III (1979a). The Hedonic Price Approach to Measuring Demand for Neighbourhood Characteristics, In D. Segal (ed.), The Economics of Neighbourhood. New York: Academic Press, 193-217. Freeman, A.M.III (1979b). Hedonic Prices, Property Values and Measuring Environmental Benefits: A Survey of the Issues, Scandinavian Journal of Economics, 81, 154-173. Gibbons, S. (2014). Gone with the Wind: Valuing the Visual Impact of the Wind Turbines Through House Prices. Serc paper discussion: London School of Economics and Political Sciences & Spatial Economics Research Centre. Gibbons, S. and Machin, S. (2003). Valuing English Primary Schools, Journal of Urban Economics, 53, 197-219. Grömping, U. (2006). Relative Importance for Linear Regression in R: The Package Relaimpo, Journal of Statistical Software, 17, 1–27. Halvorsen, R. and Pollakowski, H. (1981). Choice of Functional Form for Hedonic Price Equations, Journal of Urban Economics, 10, 37-49. Hess, D.B. and Almeida, T.M. (2007). Impact of Proximity to Light Rail Rapid Transit on Station-Area Property Values in Buffalo, New York, Urban Studies, 44 (5-6), 1041-1068. Jim, C.Y. and Chen, W.Y. (2006b). Recreation-Amenity Use and Contingent Valuation of Urban Green Spaces in Guangzhou, China, Landscape and Urban Planning, 75 (25), 81-96. Jim, C.Y. and Chen, W.Y. (2009). Value of Scenic Views: Hedonic Assessment of Private Housing in Hong Kong, Journal of Landscape and Urban Planning, 91, 1-9. Jones, K. and Bullen, N. (1993). A Multilevel Analysis of the Variations in Domestic Property Prices: Southern England 1980-1987, Urban Studies, 30 (8), 1409- 1426. Lake, I. R., Bateman, I. J., Day, B. H. and Lovett, A. A. (2000). Assigning a Monetary Value to Noise Reduction Benefits: An Example From The UK, [online], Available at http://www.iccr-international.org/trans-talk/docs/ws2-lake.pdf Lansford, N. H, and Jones, L. L. (1995). Recreational and Aesthetic Value of Water Using Hedonic Price Analysis, Journal of Agricultural and Resource Economics, 20 (2), 341-355. Lazrak, F., Nijkamp, P., Rietveld, P. and Rouwendal, J. (2014). The Market Value of Cultural Heritage in Urban Areas: An Application of Spatial Hedonic Pricing, Journal of Geographical Systems, 16, 89–114. Lindeman, R. H., Merenda, P. F. and Gold, R. Z. (1980). Introduction to Bivariate and Multivariate Analysis. Scott Foresman, Glenview, IL. Malpezzi, S. (2002). Hedonic Pricing Models and House Price Indexes: A Select Review. In Kenneth Gibb and Anthony O’Sullivan (eds.), Housing Economics and Public Policy: Essays in Honour of Duncan Maclennan. Oxford: Blackwell Publishing, 67-89. Mills, E. S. (1972). Urban Economics. Illinois: Scott, Foresman and Company. Mitchell, D. (2000). Cultural Geography: A Critical Introduction. Oxford: Blackwell. Mulley, C. (2014). Accessibility and Residential Land Value Uplift: Identifying Spatial Variations in the Accessibility Impacts of A Bus Transitway, Urban Studies, 51 (8), 1707-24. Muth, R. F. (1969). Cities and housing: The Spatial Pattern of Urban Residential Land Use. Chicago: The University of Chicago Press. Orford, S. (1999).Valuing The Built Environment: GIS and House Price Analysis. England: Ashgate Publishing Ltd. Palmquist, R.B. (1984). Estimating the Demand for the Characteristics of Housing, Review of Economics and Statistics, 66, 394-404. Powe, N.A., Garrod, G.D., Brunsdon, C.F. and Willis, K.G. (1997). Using A Geographic Information System to Estimate an Hedonic Price Model of The Benefits of Woodland Access, Forestry,70 (2), 139-149. Ridker, R.G. and Henning, J.A. (1968). The Determination of Residential Property Values with Special Reference to Air Pollution, Review of Economics and Statistics, 49, 246-257. Rosen, S. (1974). Hedonic Prices and Implicit Markets: Product Differentiation in Pure Competitions, Journal of Political Economy, 72, 34-55. Simons, R.A., Seo, Y. and Rosenfeld, P. (2015). Modelling the Effects of Refinery Emissions on Residential Property Values, 37 (3), 321-342. Sirmans, G. S., and Macpherson, D. (2003). The State of Affordable Housing, Journal of Real Estate Literature, 11 (2), 131-156. Tse, R.Y.C. (2002). Estimating Neighbourhood Effects in House Prices: Towards A New Hedonic Model Approach, Urban Studies, 39 (7), 1165-1180. Tyrvainen, L. (1997). The Amenity Value of the Urban Forest: An Application of the Hedonic Pricing Method, Landscape and Urban Planning, 37 (3), 211-222. Vandegrift, D. and Lahr, M. (2011). Open Space, House Prices, and the Tax Base, The Annals of Regional Science, 46 (1), 83-100.



DOI: https://doi.org/10.22146/ijg.27036

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