Accounting for Spatial Variation of Land Prices in Hedonic Imputation House Price Indices: a Semi-Parametric Approach

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Abstract

Location is capitalized into the price of the land the structure of a property is built on, and land prices can be expected to vary significantly across space. We account for spatial variation of land prices in hedonic house price models using geospatial data and a semi-parametric method known as mixed geographically weighted regression. To measure the impact on aggregate price change, quality-adjusted (hedonic imputation) house price indices are constructed for a small city in the Netherlands and compared to price indices based on more restrictive models, using postcode dummy variables, or no location information at all. We find that, while taking spatial variation of land prices into account improves the model performance, the Fisher house price indices based on the different hedonic models are almost identical. The land and structures price indices, on the other hand, are sensitive to the treatment of location.

Brunsdon, C., A.S. Fotheringham, and M.E. Charlton. 1996. “Geographically Weighted Regression: A Method for Exploring Spatial Nonstationarity.” Geographical Analysis 28: 281–298. DOI: http://dx.doi.org/10.1111/j.1538-4632.1996.tb00936.x.

Brunsdon, C., A.S. Fotheringham, and M.E. Charlton. 1999. “Some Notes on Parametric Significance Tests for Geographically Weighted Regression.” Journal of Regional Science 39: 497–524. DOI: http://dx.doi.org/10.1111/0022-4146.00146.

Casetti, E. 1972. “Generating Models by the Expansion Method: Applications to Geographical Research.” Geographical Analysis 4: 81–91. DOI: http://dx.doi.org/10.1111/j.1538-4632.1972.tb00458.x.

Clapp, J.M. 2004. “A Semiparametric Method for Estimating Local House Price Indices.” Real Estate Economics 32: 127–160. DOI: http://dx.doi.org/10.1111/j.1080-8620.2004.00086.x.

Davis, M.A. and J. Heathcote. 2007. “The Price and Quantity of Residential Land in the United States.” Journal of Monetary Economics 54: 2595–2620. DOI: https://doi.org/10.1016/j.jmoneco.2007.06.023.

Davis, M.A. and M.G. Palumbo. 2008. “The Price of Residential Land in Large US Cities.” Journal of Urban Economics 63: 352–384. DOI: https://doi.org/10.1016/j.jue.2007.02.003.

de Groot, H.L.F., G. Marlet, C. Teulings, and W. Vermeulen. 2015. Cities and the Urban Land Premium. Cheltenham: Eaward Elgar.

de Haan, J. 2010. “Hedonic Price Indexes: A Comparison of Imputation, Time Dummy and ‘Re-Pricing’ Methods.” Jahrbücher für Nationalökonomie und Statistik 230: 772–791.

Diewert, W.E., J. de Haan, and R. Hendriks. 2011. “The Decomposition of a House Price Index into Land and Structures Components: A Hedonic Regression Approach.” The Valuation Journal 6: 58–105.

Diewert, W.E., J. de Haan, and R. Hendriks. 2015. “Hedonic Regressions and the Decomposition of a House Price Index into Land and Structure Components.” Econometric Reviews 34: 106–126. DOI: http://dx.doi.org/10.1080/07474938.2014.944791.

Diewert, W.E., S. Heravi, and M. Silver. 2009. “Hedonic Imputation Versus Time Dummy Hedonic Indexes.” In Price Index Concepts and Measurement, edited by W.E. Diewert, J.S. Greenlees, and C.R. Hulten, 161–196. Chicago: University of Chicago Press.

Diewert,W.E.andC. Shimizu.2013. ResidentialProperty Price IndexesforTokyo.Vancouver: The University of British Columbia (UBC Discussion Paper Series No. 13-07).

Dorsey, R.E., H. Hu, W.J. Mayer, and H. Wang. 2010. “Hedonic Versus Repeat-Sales Housing Price Indexes for Measuring the Recent Boom-Bust Cycle.” Journal of Housing Economics 19: 75–93. DOI: https://doi.org/10.1016/j.jhe.2010.04.001.

Eurostat, ILO, IMF, OECD, UNECE, and World Bank. 2013. Handbook on Residential Property Price Indices. Luxemburg: Publications Office of the European Union.

Fotheringham, A.S., C. Brunsdon, and M.E. Charlton. 1998a. “Scale Issues and Geographically Weighted Regression.” In Modelling Scale in Geographical Information Science, edited by N.J. Tate and P.M. Atkinson, 123–140. Chichester: Wiley.

Fotheringham, A.S., C. Brunsdon, and M.E. Charlton. 2002. Geographically Weighted Regression: The Analysis of Spatially Varying Relationships. Chichester: John Wiley & Sons.

Fotheringham, A.S., M.E. Charlton, and C. Brunsdon. 1998b. “Geographically Weighted Regression: A Natural Evolution of the Expansion Method for Spatial Data Analysis.” Environment and Planning A 30: 1905–1927. DOI: https://doi.org/10.1068/a301905.

Francke, M.K. and A.M. van de Minne. 2017. “Land, Structure and Depreciation.” Real Estate Economics 45: 415–451. DOI: http://dx.doi.org/10.1111/1540-6229.12146.

Geniaux, G. and C. Napoléone. 2008. “Semi-Parametric Tools for Spatial Hedonic Models: An Introduction to Mixed Geographically Weighted Regression and Geoadditve Models.” In Hedonic Methods in Housing Markets: Pricing Environmental Amenities and Segregation, edited by A. Baranzini, J. Ramirez, C. Schaerer, and P. Thalmann, 101–127. New York: Springer.

Hill, R.J. and D. Melser. 2008. “Hedonic Imputation and the Price Index Problem: An Application to Housing.” Economic Inquiry 46: 593–609. DOI: http://dx.doi.org/10.1111/j.1465-7295.2007.00110.x.

Hill, R.J., D. Melser, and I. Syed. 2009. “Measuring a Boom and Bust: The Sydney Housing Market 2001 – 2006.” Journal of Housing Economics 18: 193–205. DOI: https://doi.org/10.1016/j.jhe.2009.07.010.

Hill, R.J. and M. Scholz. 2017. “Can Geospatial Data Improve House Price Indexes? A Hedonic Imputation Approach with Splines.” Review of Income and Wealth Forthcoming. DOI: http://dx.doi.org/10.1111/roiw.12303.

Hurvich, C.M. and C.L. Tsai. 1989. “Regression and Time Series Model Selection in Small Samples.” Biometrika 76: 297–307. DOI: https://doi.org/10.1093/biomet/76.2.297.

Jones, J.P. and E. Casetti. 1992. Applications of the Expansion Method. London: Routledge.

Kuminoff, N.V. and J.C. Pope. 2013. “The Value of Residential Land and Structures During the Great Housig Boom and Bust.” Land Economics 89: 1–29. DOI: https://doi.org/10.3368/le.89.1.1.

Mei, C., N. Wang, and W. Zhang. 2006. “Testing the Importance of the Explanatory Variables in a Mixed Geographically Weighted Regression Model.” Environment and Planning A 38: 587–598. DOI: https://doi.org/10.1068/a3768.

Pace, R.K., R. Barry, J.M. Clapp, and M. Rodriquez. 1998. “Spatiotemporal Autoregressive Models of Neighborhood Effects.” Journal of Real Estate Finance and Economics 17: 15–33. DOI: https://doi.org/10.1023/A:1007799028599.

Rambaldi, A.N., R.R.J. McAllister, and C.S. Fletcher. 2015. Decoupling Land Values in Residential Property Prices: Smoothing Methods for Hedonic Imputed Price Indices. Queensland: University of Queensland (School of Economics Discussion Paper Series No: 549).

Rambaldi, A.N. and D.S.P. Rao. 2011. Hedonic Predicted House Price Indices Using Time-Varying Hedonic Models with Spatial Autocorrelation. Queensland: University of Queensland (School of Economics Discussion Paper Series No: 432).

Rambaldi, A.N. and D.S.P. Rao. 2013. Econometric Modeling and Estimation of Theoretically Consistent Housing Price Indexes. Queensland: University of Queensland (CEPA Working Paper Series No: WP03/2013).

Sun, H., Y. Tu, and S.-M. Yu. 2005. “A Spatio-Temporal Autoregressive Model for Multi-Unit Residential Market Analysis.” The Journal of Real Estate Finance and Economics 31: 155–187. DOI: https://doi.org/10.1007/s11146-005-1370-0.

Tu, Y., S.-M. Yu, and H. Sun. 2004. “Transaction-Based Office Price Indexes: A Spatiotemporal Modeling Approach.” Real Estate Economics 32: 297–328. DOI: http://dx.doi.org/10.1111/j.1080-8620.2004.00093.x.

van de Minne, A.M. and M.K. Francke. 2012. “De waardebepaling van grond en opstal [The determination of the value of land and structures].” Real Estate Research Quarterly 11: 14–24.

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