, Volume 34, Issue 1, pp 5-34
Date: 27 Jul 2007

Errors in variables and spatial effects in hedonic house price models of ambient air quality

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In the valuation of the effect of improved air quality through the estimation of hedonic models of house prices, the potential “errors in variables” aspect of the interpolated air pollution measures is often ignored. In this paper, we assess the extent to which this may affect the resulting empirical estimates for marginal willingness to pay (MWTP), using an extensive sample of over 100,000 individual house sales for 1999 in the South Coast Air Quality Management District of Southern California. We take an explicit spatial econometric perspective and account for spatial dependence and endogeneity using recently developed Spatial 2SLS estimation methods. We also account for both spatial autocorrelation and heteroskedasticity in the error terms, using the Kelejian–Prucha HAC estimator. Our results are consistent across different spatial weights matrices and different kernel functions and suggest that the bias from ignoring the endogeneity in interpolated values may be substantial.

This paper is part of a joint research effort with James Murdoch (University of Texas, Dallas) and Mark Thayer (San Diego State University). Their valuable input is gratefully acknowledged. The research was supported in part by NSF Grant BCS-9978058 to the Center for Spatially Integrated Social Science (CSISS), and by NSF/EPA Grant SES-0084213. Earlier versions were presented at the 5th International Workshop on Spatial Econometrics and Statistics, Rome, Italy, May 2006, the 53th North American Meetings of the Regional Science Association International, Toronto, ON, Nov. 2006, the 2007 Meetings of the Allied Social Science Assocations, Chicago, IL, Jan 2007, and at departmental seminars at the University of Illinois. Comments by discussants and participants are greatly appreciated. A special thanks to Harry Kelejian for his detailed and patient clarification of the HAC estimator. The usual disclaimer holds.