2013 V41 4: pp. 887–924 DOI: 10.1111/1540-6229.12020 REAL ESTATE ECONOMICS Is Selection Bias Inherent in Housing Transactions? An Equilibrium Approach Anna Chernobai* and Ekaterina Chernobai** We develop an equilibrium model for residential housing transactions in an economy with houses that differ in their quality and households that differ in their planned holding horizon. We show that, in equilibrium, a clientele ef- fect persists, with long-horizon buyers overwhelmingly choosing higher quality properties and short-horizon buyers settling for lower quality properties. This clientele effect creates a sample selection bias: the properties that are on the market are predominantly of lower quality. Since these are the preferred choice of short-horizon buyers, they demonstrate a faster turnover. Both the clientele effect and the selection bias are more pronounced with an increase in the variance of house quality and in the variance of the planned holding horizon. Our theoretical model supports empirical evidence on the existence of such bias in home price indices and explains it by the differences in ex ante holding horizons. The majority of the literature on residential housing pricing and liquidity fo- cuses on sell-side variables such as physical, location and neighborhood at- tributes of the property, as well as seller characteristics. However, pricing and liquidity are likely to also be influenced by buyer-side characteristics, regarding which the literature has been more silent. As a result of buyer-side differences, clienteles may form in that a certain class of houses would be preferred over other classes by a particular type of buyers. Clienteles may differ in their holding periods. In this case, the types of properties on and off the market would be systematically misrepresenting the entire housing stock. This phe- nomenon has been observed empirically and is commonly referred to as “sam- ple selection bias” or “transaction bias” present in house price indices. Repeat sales house price indices attempt to correct for this bias by controlling for the time between sales; see, for example, Bourassa, Hoesli and Sun (2006), Case, Pollakowski and Wachter (1991), Case and Shiller (1987), Costello and Watkins (2002), Dreiman and Pennington-Cross (2004), Englund, Quigley and Redfearn *M.J. Whitman School of Management, Department of Finance, Syracuse University, Syracuse, NY 13244 or annac@syr.edu. **California State Polytechnic University Pomona, Pomona, CA 91768 or echer- nobai@csupomona.edu. C  2013 American Real Estate and Urban Economics Association