Real estate markets are characterized by heterogeneous goods, infrequent trading, geographic segmentation and information asymmetries. One of the most important differences between a perfectly competitive market and the housing market is the heterogeneity in the cost of acquiring information due to information asymmetry for different buyer types.

Historically, research shows that out-of-town buyers are informationally disadvantaged and therefore pay higher prices compared to in-town buyers (Lambson et al. (2004), Clauretie and Thistle (2007), and Zhou et al. (2014)). However, with the recent advent and usage of online platforms like Zillow, Trulia and Redfin a plethora of information about the housing market is provided free. If information asymmetry or search costs decrease systematically with increased availability of information, then the price paid for any property should be more equal, exhibiting less price dispersion, for all buyer types.

This research aims to investigate the price differentials between in-town and out-of-town buyers over a ten year window, testing whether information efficiency has improved over time. This time period overlaps the general dissemination and utilization of improved home search web sites.

As a theoretical framework, a sequential search model for heterogeneous buyers of real estate is applied following Wheaton (1990), Turnbull and Sirmans (1993), and Lambson et al. (2004) to examine how prices will be affected with our assumption of decreasing search costs. Hence, in a second step, a hedonic regression model is used for six US cities in two different time frames in order to show whether the premium paid by out-of-town buyers has decreased with increasing information efficiency according to Ihlanfeld and Mayock (2010), and Ling et al. (2016).

To the best of our knowledge, this is the first paper which compares the out-of-town buyer premium of different cities over time with respect to increased information efficiency.