Estimating the residence price by linear regression model and Geographical Information Systems (GIS)
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Published:
Mar 22, 2017
Updated:
2017-03-22
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2017-03-22 (1)
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Abstract
Owning a residence is also considered as an investment tool, determining the price of a residence with the desired properties has become one of the most important questions to be answered in social life. In this study, price estimations of residences located in nine of the central districts of Ankara city (Turkey) were carried out via multi linear regression model and geographical distributions of these residences were revealed on GIS environment to perform various query, spatial analysis and documentation operations. In addition, thematic maps regarding residence prices in the study region were produced.Â
Keywords: estimating the residence price; Geographical Information Systems; online marketing; regression analysis
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Turk, T., Tuna, M. F., & Kitapci, O. (2017). Estimating the residence price by linear regression model and Geographical Information Systems (GIS). New Trends and Issues Proceedings on Humanities and Social Sciences, 3(4), 208–218. https://doi.org/10.18844/prosoc.v3i4.1567
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Selim, S. (2008). Determinants of house prices in Turkey: A hedonic regression model. Dogus University Journal, 9 (1), 65-76.
Yu, S. M., Han, S. S. & Chai, C. H. (2007). Modelling the value of view in high rise apartments: A 3-D GIS Approach. Environment and Planning B- Planning and Design, 34 (1), 139-153.
Bin, O. (2004). A prediction comparison of housing sales prices by parametric versus semi-parametric regressions. Journal of Housing Economics, 13, 68-84.
Dekkers, J. & Koomen, E. (2008). Valuation of open space: Hedonic house price analyses in the Dutch Ranstad region. EconPapers 24. JEL classification: C51, C53, R14, Q24.
Dumm, R. E., Sirmans, G. S. & Smersh G. (2011). The capitalization of building codes in house prices. Journal of Real Estate Finance and Economics, 42, 30-50.
Holly, S., Pesaran, M. H. & Yamagata T. (2011). The spatial and temporal diffusion of house prices in the UK. Journal of Urban Economics, 69, 2-23.
Kiel, K. A. & Zabel, J. E. (2008). Location, location, location: The 3L Approach to house price determination. Journal of Housing Economics, 17, 175-190.
Kusan, H., Aytekin, O. & Ozdemir, I. (2010). The use of fuzzy logic in predicting house selling prices. Expert Systems with Applications, 37, 1808-1813.
Lake, I. R., Lovett, A. A., Bateman, I. J. & Langford, I.H. (1998). Modelling environmental influences on property prices in an urban environment. Computers, Environment and Urban Systems, 22 (2), 121-136.
Lake, I. R., Lovett, A. A., Bateman, I. J. & Day, B. (2000). Using GIS and large-scale digital data to implement hedonic pricing studies. International Journal of Geographical Information Science, 14 (6), 21-541.
Miller, F. L. (2007). GIS Tutorial For Marketing (first edition). ESRI Press, California.
Ozsoy, O. & Sahin, H. (2009). Housing price determinants in Istanbul, Turkey. International Journal of Housing Markets and Analysis, 2 (2), 167-178.
Ozturk, N. & Fitoz, E. (2009). Turkiye’de konut piyasasının belirleyicileri: Ampirik bir uygulama. ZKU Journal of Social Sciences, 5 (10), 21-46.
Piazzesi, M., Schneider, M. & Tuzel, S. (2007). Housing, consumption and asset pricing. Journal of Financial Economics, 83, 531-569.
Roche, M.J. (2001). The rise in house prices in Dublin: bubble, fad or just fundamentals. Economic Modelling, 18, 281-295.
Scherthanner, H. & Asche, H. (2010). The Potsdam housing market: A GIS-based spatial analysis using FOS. REAL CORP 2010 Proceedings, Tagungsband Vienna, 18-20 May.
Selim, S. (2008). Determinants of house prices in Turkey: A hedonic regression model. Dogus University Journal, 9 (1), 65-76.
Yu, S. M., Han, S. S. & Chai, C. H. (2007). Modelling the value of view in high rise apartments: A 3-D GIS Approach. Environment and Planning B- Planning and Design, 34 (1), 139-153.