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Estimating Hedonic Models: Implications of the Theory

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  • Helen Tauchen
  • Ann Dryden Witte

Abstract

In this paper we consider the conditions under which instrumental variables methods are required in estimating a hedonic price function and its accompanying demand and supply relations. We assume simple functional forms that permit an explicit solution for the equilibrium hedonic price function. The principles are the same for models in which no analytic solution exists, but having the solutions makes the issues far more transparent. The need for instrumental variables estimation is directly analogous for the classical demand and supply model with undifferentiated products and for the hedonic model with differentiated products. In estimating individual demand and supply functions, instrumental variables estimation is required if the consumer and firm unobservables, which give rise to the error terms in the demand and supply functions, are correlated across consumers/firms within a community. In estimating inverse demand/supply functions, which are referred to as bid/offer functions in the hedonic model, instrumental variables estimation is required even if the unobservables are not correlated across agents within a community. If the unobservables are not correlated across agents within a community, then community binaries or the means of observable consumer and firm characteristics can be used as instruments. If the unobservables are correlated then only the latter can be used. The error term in the hedonic price function is often assumed to be uncorrelated with the chosen attributes. This assumption may be reasonable if consumers have quasilinear preferences. If not, then the error term in the price function may affect the utility-maximizing amounts of the attributes. The feasible instruments again depend upon whether the error term is correlated for agents within a community. If not, then community binaries or observed individual characteristics may be used as instruments. If so, then the community binaries are correlated with the error terms and cannot serve as instruments.

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  • Helen Tauchen & Ann Dryden Witte, 2001. "Estimating Hedonic Models: Implications of the Theory," NBER Technical Working Papers 0271, National Bureau of Economic Research, Inc.
    • Handle: RePEc:nbr:nberte:0271
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    Cited by:

    1. James J. Heckman, 2019. "The Race Between Demand and Supply: Tinbergen’s Pioneering Studies of Earnings Inequality," De Economist, Springer, vol. 167(3), pages 243-258, September.
    2. Fève, Frédérique & Fève, Patrick & Florens, Jean-Pierre, 2002. "Attribute Choices and Structural Econometrics of Price Elasticity of Demand," IDEI Working Papers 155, Institut d'Économie Industrielle (IDEI), Toulouse, revised 2003.
    3. William F. Vásquez & Laura Beaudin, 2020. "On the use of hypothetical price data to estimate hedonic models in a developing country context," Letters in Spatial and Resource Sciences, Springer, vol. 13(3), pages 219-231, December.
    4. Hussain, Iftikhar, 2023. "Housing market and school choice response to school quality information shocks✰," Journal of Urban Economics, Elsevier, vol. 138(C).
    5. Henrik Andersson, 2008. "Willingness to Pay for Car Safety: Evidence from Sweden," Environmental & Resource Economics, Springer;European Association of Environmental and Resource Economists, vol. 41(4), pages 579-594, December.
    6. Celia Bilbao & Amelia Bilbao & José Labeaga, 2010. "The welfare loss associated to characteristics of the goods: application to housing policy," Empirical Economics, Springer, vol. 38(2), pages 305-323, April.
    7. Ivar Ekeland & James J. Heckman & Lars Nesheim, 2004. "Identification and Estimation of Hedonic Models," Journal of Political Economy, University of Chicago Press, vol. 112(S1), pages 60-109, February.
    8. Palmquist, Raymond B., 2006. "Property Value Models," Handbook of Environmental Economics, in: K. G. Mäler & J. R. Vincent (ed.), Handbook of Environmental Economics, edition 1, volume 2, chapter 16, pages 763-819, Elsevier.
    9. Prodosh Simlai, 2018. "Spatial Dependence, Idiosyncratic Risk, and the Valuation of Disaggregated Housing Data," The Journal of Real Estate Finance and Economics, Springer, vol. 57(2), pages 192-230, August.
    10. Manuel Landajo & Celia Bilbao & Amelia Bilbao, 2012. "Nonparametric neural network modeling of hedonic prices in the housing market," Empirical Economics, Springer, vol. 42(3), pages 987-1009, June.
    11. Machin, Stephen, 2011. "Houses and schools: Valuation of school quality through the housing market," Labour Economics, Elsevier, vol. 18(6), pages 723-729.

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    JEL classification:

    • D4 - Microeconomics - - Market Structure, Pricing, and Design
    • H0 - Public Economics - - General

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