• Paul E. Carrillo

    1. George Washington University, U.S.A.
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    • This article was previously circulated under the title “An Empirical Two-sided Equilibrium Search Model of the Real Estate Market,” and it is based on my doctoral dissertation. I owe special thanks to my dissertation advisors Steven Stern, Maxim Engers, and Edgar Olsen. I am also grateful to three referees for their valuable comments. In addition, I would like to thank John Rust, David Albouy, Jim Albrecht, Naoko Akashi, Patrick Bajari, Christina Rennhoff, Andrey Pavlov, Katja Seim, Ken Wilbur, and Anthony Yezer, workshop participants at the Lincoln Institute of Land Policy Urban Economics and Public Finance 2009 Conference, the AREUEA 2007 Mid-year Conference, the Ifo Institute Workshop on the Economics of Information and Network Industries, and the Society for Computational Economics 11th International Conference, as well as seminar participants at George Washington University, Indiana University, the Inter-American Development Bank, the New York Fed, Queens University, University of Cincinnati and the University of Virginia, for useful discussions and comments. I also gratefully acknowledge financial support from the Banco Central del Ecuador and from the University of Virginia’s Bankard Fund for Political Economy. The usual disclaimer applies. Please address correspondence to: Paul E. Carrillo, Department of Economics and Elliot School of International Affairs, George Washington University, 2115 G Street NW, Suite 364, Washington, DC 20052. Phone: 1 202 994 7608. E-mail:

  • Manuscript received February 2008; revised November 2009.


This article specifies and estimates a computationally tractable stationary equilibrium model of the housing market. The model is rich and incorporates many of its unique features: buyers’ and sellers’ simultaneous search behavior, heterogeneity in their motivation to trade, transaction costs, a trading mechanism with posting prices and bargaining, and the availability of an exogenous advertising technology that induces endogenous matching. Estimation uses Maximum Likelihood methods and Multiple Listing Services data. The estimated model is used to simulate housing market outcomes when (a) the amount of information displayed on housing listings increases and (b) real estate agent’s commission rates change.