Analysing the local geography of the relationship between residential property prices and its determinants
DOI:
https://doi.org/10.1515/bog-2015-0013Abstract
This paper analyses the local geography of the relationship between residential property prices and its determinants. A semiparametric geographically weighted regression (S-GWR) technique is employed to explore this relationship. Selling prices, structural and locational attributes data were collected from the database of the Department of Valuation and Services of Malaysia, selected maps and reports. The outcome of this paper shows a strong geographically varying relationship between residential property prices and its determinants in which the residential property price determinants have a positive impact on prices in some areas but negative or no impact on the others. The magnitude of the effect is also found to be geographically varied; the capitalisation in residential property prices is found greater in some areas but less or with no effect in some other parts of the areas. The use of S-GWR technique makes it possible to reveal such geographically varying relationships, thus leading to a better understanding of the relationship between residential property prices and its determinants.References
Ahldelft, G.M. and Maening, M., 2010: Impact of sports arenas on land values: evidence from Berlin. In: The Annals of Regional Science, Vol. 44 (2), pp. 205-227. DOI: http://dx.doi.org/10.1007/s00168-008-0249-4
Anderson, H., Jonsson, L. and Ogren, M., 2009: Property prices and exposure to multiple noise sources: Hedonic regression with road and railway noise, available at: www.toulouse.inra.fr/ierna/seminaires/Anderson_etal_Noise_Hedonic_Lerun.pdf , DoA: 26 July 2009.
Anselin, L., 1988: Spatial Econometrics: Methods and Models, Dordrecht: Kluwer Academic.
Can, A. and Megbolugbe, I., 1997: Spatial dependence and house price index construction. In: Journal of Real Estate Finance and Economics, Vol. 14, pp. 203-222.
Cohen, J.P. and Coughlin, C.C., 2008: Spatial hedonic models of airport noise, proximity, and housing prices. In: Journal of Regional Science, Vol. 48, pp. 859–878. DOI: http://dx.doi.org/10.1111/j.1467-9787.2008.00569.x
Crespo, R. and Grêt-Regamey, A., 2013: Local hedonic house-price modelling for urbanplanners: advantages of using local regression techniques. In: Environment and Planning B: Planning and Design, Vol. 40, pp. 664–682. DOI: http://dx.doi.org/10.1068/b38093
Cropper, M.L., Deck, L.B. and McConnell, K.E., 1988: On the choice of functional form for hedonic price functions. In: The Review of Economics and Statistics, Vol.70 (4), pp. 668-675.
Do, A.Q. and Grunditski, G., 1995: Golf-course and residential house prices: An empirical examination’. In: Journal of Real Estate Finance and Economics, 10 (3), pp. 261-270.
Du, H. and Mulley, C., 2006: Relationship between transport accessibility and land value: Local model approach with geographically weighted regression. In: Transportation Research Record, Vol. 1977 (1), pp. 197-205.
Dziauddin, M.F., Alvanides, S., and Powe, N.A., 2013; Estimating the effects of light rail transit (LRT) system on the property values in the Klang Valley, Malaysia: A hedonic house price approach. In: Jurnal Teknologi (Sciences and Engineering), Vol. 61, pp. 37-49.
Dziauddin, M.F., 2009: Measuring the effects of the light rail transit (LRT) system on house prices in the Klang Valley, Malaysia, Ph.D. Thesis, Newcastle University.
Dziauddin, M.F., 2013: Estimating the effects of urban forests on house prices: A geographically weighted regression (GWR) approach. In: Geografia, 1 (2), pp. 80-100.
Fotheringham, A.S., Brunsdon, C. and Charlton, M.E., 1998: Geographically weighted regression: A natural evolution of the expansion method for spatial data analysis. In: Environment and Planning A, Vol. 30, pp. 1905-1927.
Fotheringham, A.S., Brundson, C. and Charlton, M.E., 2002:Geographically weighted regression: The analysis of spatially varying relationships, West Sussex: John Wiley and Sons Ltd.
Garrod, G. and Willis, K., 1992: Amenity value of forests in Great Britain and its impact on the internal rate of return from forestry. In: Forestry, Vol.65 (3), pp. 331-346.
Hashim, Z.A., 2010: House price and affordability in housing in Malaysia. In: Akademika, pp. 37-46.
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. In: Urban Studies, Vol. 44 (5-6), pp. 1041-1068. DOI: http://dx.doi.org/10.1080/00420980701256005
Irwin, E.G., 2002: The effects of open space on house prices. In: Land Economics, Vol. 78 (4), pp. 465-480.
Jim, C.Y. and Chen, W.Y., 2009:Value of scenic views: Hedonic assessment of private housing in Hong Kong. In: Journal of Landscape and Urban Planning, Vol. 91, Issue 4, pp. 226-234. DOI: http://dx.doi.org/10.1016/j.landurbplan.2009.01.009
Lansford, N.H. and Jones, L.L., 1995: Recreational and aesthetic value of water using hedonic price analysis. In: Journal of Agricultural and Resource Economics, Vol. 20 (2), pp. 341-355.
Linneman, P., 1981: The demand for residence site characteristics. In: Journal of Urban Economics,Vol.9, pp. 129-149.
Mennis, J., 2006: Mapping the results of geographically weighted regression. In: The Journal of Cartographic, Vol. 43 (2), pp. 171-179. DOI: http://dx.doi.org/10.1179/000870406X114658
Mills, E.S. and Simenauer, R., 1996: New hedonic estimates of regional constant quality house prices. In: Journal of Urban Economics, Vol. 39, pp. 209-215.
Mitchell, D.M., 2000: School quality and housing values. In: Journal of Economics, Vol. 26, pp. 53-68.
Nakaya, T., Fotheringham, A.S, Charlton, M.E. and Brunsdon, C., 2009: Semiparametric geographically weighted generalised linear modelling in GWR 4.0, available at: http://www.geocomputation.org/2009/PDF/Nakaya_et_al.pdf,DoA: 7 March 2014.
Ohsfeldt, R.L., 1988: Implicit markets and the demand for housing characteristics. In: Regional Science and Urban Economics, Vol. 18, pp. 321-343.
Orford, S., 1999: Valuing the Built Environment: GIS and House Price Analysis, Aldershot: Ashgate Publishing Ltd.
Palmquist, R.B., 1984: Estimating the demand for the characteristics of housing. In: Review of Economics and Statistics, Vol. 66, pp. 394-404.
Powe, N.A., Garrod, D. and Willis, K.G., 1995: Valuation of urban amenities using an hedonic price model. In: Journal of Property Research, Vol. 12, pp. 137-147.
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. In: Forestry, Vol. 70 (2), pp. 139-149.
So, H.M., Tse, R.Y.C. and Ganesan, S., 1997: Estimating the influence of transport on house prices: Evidence from Hong Kong. In: Journal of Property Valuation and Investment, Vol. 15 (1), pp. 40-47.
Tse, R.Y.C., 2002: Estimating neighbourhood effects in house prices: Towards a new hedonic model approach. In: Urban Studies, Vol. 39 (7), p. 1165-1180.
Tu, C.C., 2005:How does a new sports stadium affect housing values? The case of FedEx field. In: Land Economics, Vol. 81 (3), pp. 379-395.
Tyrväinen, L. and Miettinen, A., 2000: Property prices and urban forest amenities. In: Journal of Environmental Economics and Management, Vol. 39, pp. 205–23.
Tyrvainen, L., 1997: The amenity value of the urban forest: An application of the hedonic pricing method. In: Landscape Urban Planning, Vol. 37, pp. 211-222.
Downloads
Published
How to Cite
Issue
Section
License
Title, logo and layout of journal Bulletin of Geography. Socio-economic Series are reserved trademarks of Bulletin of Geography. Socio-economic Series.Stats
Number of views and downloads: 631
Number of citations: 8
