Loss Aversion and Housing Studies

(ProQuest: . denotes formulae omitted.)Housing is characterized by durability, heterogeneity, spatial fixity, and extensive involvement of governments (Smith, Rosen, and Fallis, 1988). These characteristics pose unique challenges to housing market studies. Researchers have been constantly modifying standard economic models to account for these special traits. The efforts are most noticeable in the expansion of attributes in hedonic price models. Local public expenditure, for example, is included as an independent variable to capture the effect of government investment in local communities (Garcia, Montolio, and Raya, 2010). Distance to bus stops and city centers are used to capture the heterogeneity and local value of housing units (Soderberg and Janssen, 2001; Wang, Potogloub, Orford, and Gong, 2015). Lastly, intertemporal specifications are adopted to model house prices in various cycles (Edmonds, 1985; Knight, Dombrow, and Sirmans, 1995; Gatzlaff and Haurin, 1998; Hill, Melser, and Syed, 2009). Facing a constantly expanding list of attributes to be considered, some procedures and techniques have been developed to aid the selection of variables in hedonic price modeling (Huh and Kwak, 1997; Stadelmann, 2010). Recently, a notable change occurred in this research stream. Having considered a wide range of physical and institutional characteristics of housing markets, researchers and practitioners have become increasingly aware of the effect of psychological elements in this unique sector. Behavioral scientists have already identified applications in studies of pricing behaviors (Genesove and Mayer, 2001; Paraschiv and Chenavaz, 2011a; Sun and Ong, 2014), housing preferences (Arbel, Ben-Shahar, and Gabriel, 2014; Hui, Wong, Chung, and Lau, 2014), mortgage default and delinquency (Ong, Sing, and Teo, 2007; Ong, Neo, and Tu, 2008), and housing cycles (Arbel, Ben-Shahar, and Sulganik, 2009). Although the number of publications remains small, the evidence on the effect of behavioral biases is strong and consistent. Researchers are particularly concerned about the role of loss aversion in housing decisions.According to prospect theory (Kahneman and Tversky, 1979; Tversky and Kahneman, 1992), people decide by choosing the prospect with the highest utility. The utility is determined by two elements: a value function that assigns each event in the prospect a value and a weighting function that transforms the probability of the event. Loss aversion is a core feature that characterizes value function. it refers to people's asymmetric attitudes with respect to gains and losses, or more specifically, losses loom larger than gains. With loss aversion being able to explain economic phenomena, some of which are puzzling circumstances under expected utility theory, this concept has received wide attention. The literature on loss aversion has been expanding rapidly, with reported loss aversion effects varying considerably across research contexts. In a recent meta-analysis of loss aversion in production choices, Neumann and Bockenholt (2014) identified multiple moderators of loss aversion effects. They found that the loss aversion effect is significantly stronger for durable goods. Therefore, housing studies should pay attention to loss aversion, given the durability feature of housing products. Moreover, advances in the studies of loss aversion effects will significantly enhance our understanding on some much-debated topics in housing research. These topics include, but are not limited to, the dual role of housing products (i.e., being both consumption and investment goods), housing wealth effect, and housing cycles. The answers to these questions have significant implications on the decisions of homeowners and renters, developers, and government institutions. The investigation of loss aversion effects is both relevant and important in housing studies.As illustrated in Exhibit 1, homeownership is approximately 65% in the United States, with greater variation in the last two decades. …