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Spatial Econometric Approaches to Estimating Hedonic Property Value Models

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  • Wang, Li
  • Ready, Richard C.

Abstract

The inclusion of spatial correlation of house price in hedonic pricing model may produce better marginal implicit price estimate(s) of the environmental variable(s) of interest. Most applications where a spatial econometric model is applied to the estimation of a hedonic property value model have used either a spatial lag model or a spatial autoregressive (SAR) error model. Incorrect spatial specification may produce even worse estimate outcome than OLS. Three issues regarding the specification of a spatial hedonic pricing model are considered. First, we question the "convention" of row-standardizing the spatial weights matrix. Second, we argue that the spatial error component (SEC) model is more theoretically intuitive and appealing for modeling house price. Third, we explore whether the choice of spatial model is important, empirically, using a large house sale dataset that includes measures of proximity to landfills. With one exception, estimated marginal implicit prices are fairly robust across all models.

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Paper provided by American Agricultural Economics Association (New Name 2008: Agricultural and Applied Economics Association) in its series 2005 Annual meeting, July 24-27, Providence, RI with number 19174.

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Date of creation: 2005
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Handle: RePEc:ags:aaea05:19174

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Keywords: row-standardization; spatial econometrics; SEC model; SAR error model; spatial lag model; hedonic pricing; landfill; house price; Public Economics;

References

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  1. Pace, R Kelley & Gilley, Otis W, 1997. "Using the Spatial Configuration of the Data to Improve Estimation," The Journal of Real Estate Finance and Economics, Springer, vol. 14(3), pages 333-40, May.
  2. Kelejian, Harry H & Prucha, Ingmar R, 1999. "A Generalized Moments Estimator for the Autoregressive Parameter in a Spatial Model," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 40(2), pages 509-33, May.
  3. Anselin, Luc, 2002. "Under the hood : Issues in the specification and interpretation of spatial regression models," Agricultural Economics, Blackwell, vol. 27(3), pages 247-267, November.
  4. Kim, Chong Won & Phipps, Tim T. & Anselin, Luc, 1998. "Measuring The Benefits Of Air Quality Improvement: A Spatial Hedonic Approach," 1998 Annual meeting, August 2-5, Salt Lake City, UT 20959, American Agricultural Economics Association (New Name 2008: Agricultural and Applied Economics Association).
  5. Robert W. Paterson & Kevin J. Boyle, 2002. "Out of Sight, Out of Mind? Using GIS to Incorporate Visibility in Hedonic Property Value Models," Land Economics, University of Wisconsin Press, vol. 78(3), pages 417-425.
  6. Anselin, Luc & Bera, Anil K. & Florax, Raymond & Yoon, Mann J., 1996. "Simple diagnostic tests for spatial dependence," Regional Science and Urban Economics, Elsevier, vol. 26(1), pages 77-104, February.
  7. Anselin, Luc & Moreno, Rosina, 2003. "Properties of tests for spatial error components," Regional Science and Urban Economics, Elsevier, vol. 33(5), pages 595-618, September.
  8. Basu, Sabyasachi & Thibodeau, Thomas G, 1998. "Analysis of Spatial Autocorrelation in House Prices," The Journal of Real Estate Finance and Economics, Springer, vol. 17(1), pages 61-85, July.
  9. Kelejian, Harry H & Prucha, Ingmar R, 1998. "A Generalized Spatial Two-Stage Least Squares Procedure for Estimating a Spatial Autoregressive Model with Autoregressive Disturbances," The Journal of Real Estate Finance and Economics, Springer, vol. 17(1), pages 99-121, July.
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