Hedonic pricing models in real property valuation have been frequently applied in many research studies and projects since it was introduced by Rosen in 1974. The development of Geographic Information Systems (GIS) in the recent decades has gradually supports the usage of hedonic model in the spatial data pricing model studies. Beside the basic advantages of GIS to position properties in terms of their geographic coordinates, it has the capabilities of dealing with reasonable amount of data, and wide choices of analysis that make it powerful tool to facilitate the building and implementation of the hedonic models within its framework. Many studies have employed GIS in real property valuation in their present work and for the future prediction. This paper reviews the works of literature on the GIS applications in the real property valuation employing the hedonic pricing models.
REFERENCES(24)
1.
Bujanda, A and Fullerton, TM 2017. Impacts of transportation infrastructure on single-family property values. Applied Economics. 49(1) 1-17.
Cebula, R 2009. The hedonic pricing model applied to the housing market of the city of savannah and its Savannah historic landmark district. The Review of Regional Studies. 39(1) 9-22.
Cellmer, R 2011. Spatial Analysis of The Effect of Noise on The Prices and Value of Residential Real property. Geomatics And Environmental Engineering. 5(4) 13-28.
Dziauddin, M 2009. Measuring The Effects of The Light Rail Transit (LRT) System on House Prices in the Klang Valley, Malaysia. Ph.D. Thesis, Newcastle University, 517.
Eboy, O and Samat, N 2015. Modeling property rating valuation using Geographical Weighted Regression (GWR) and Spatial Regression Model (SRM): The case of Kota Kinabalu, Sabah. Malaysian Journal of Society and Space.. 11(11) 98-109.
Giaccaria, S and Frontuto, V 2007. GIS and Geographically Weighted Regression in stated preferences analysis of the externalities produced by linear infrastructures. Working paper series. Working paper No. 10/2007. University of Torino. 32.
Helbich, M, Brunauer, W, Vaz, E and Nijkamp, P 2013. Spatial heterogeneity in hedonic house price models: the case of Austria. Tinbergen Institute Discussion Paper TI 2013-171/VIII. 24.
Kong, F, Yin, H, Nakagoshi, N 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, 240-252.
Lehner, M 2011. Modelling housing prices in Singapore applying spatial hedonic regression. M.S. Thesis, Dept. of Civil, Environmental and Geomatic Engineering. Swiss Federal Institute of Technology Zurich (ETH Zurich), 108.
Lozano-Gracia, N and Anselin, L 2011. Is the price right? Assessing estimates of cadastral values for Bogotá, Colombia. Regional Science Policy & Practice 4 (4), 495-508.
Lu, B, Charlton, M, Harris, P and Fotheringham, 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.
Noor, N, Asmawi, MZ and Abdullah, A 2015. Sustainable urban regeneration: GIS and hedonic pricing method in determining the value of green space in housing area. Procedia - Social and Behavioral Sciences. 170, 669-679.
Ottensmann, J, Payton, S and Man, 2008. J Urban location and housing prices within a hedonic model. The Journal of Regional Analysis and Policy, 38(1), 19-35.
Oud, DAJ 2017. GIS based property valuation. M.S. Thesis, Dept. of Geographical sciences-geo-information applications and management, Delft University of Technology (TUD), University of Twente (UT) - ITC Utrecht University (UU), and Wageningen University & Research (WUR), 70.
Randeniya, TD, Ranasinghe, G and Amarawickrama, S 2017. A model to estimate the implicit values of housing attributes by applying the hedonic pricing method. International Journal of Built Environment and Sustainability. 4(2), 113-120.
Yang, Y, Liu, J, Xu, S and Zhao, Y 2016. An Extended Semi-Supervised Regression Approach with Co-Training and Geographical Weighted Regression: A Case Study of Housing Prices in Beijing. International Journal of Geo-Information.5(1), 4-15.
Zhang, R, Du, Q, Geng, J, Liu, B and Huang, Y 2015. An improved spatial error model for the mass appraisal of commercial real property based on spatial analysis: Shenzhen as a case study. Habitat International. 46, 196-205.
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