Content area
Full Text
Abstract The intention of buying a home is revealed by many potential home buyers when they turn to the Internet to search for their future residence. This paper examines the extent to which future cross-sectional differences in home price changes are predicted by online search intensity in prior periods. Our findings are economically meaningful and suggest that abnormal search intensity for real estate in a particular city can help predict the city's future abnormal housing price change. On average, cities associated with abnormally high real estate search intensity consistently outperform cities with abnormally low real estate search volume by as much as 8.5% over a two-year period.
(ProQuest: ... denotes formulae omitted.)
As individuals commonly use the Internet to search for information, the value of their aggregated search data as a predictive tool is increasingly recognized by academics and practitioners in different fields. Rangaswamy, Giles, and Seres (2009) interpret the search trails left by individuals as "what we collectively think'' and "what might happen in the future.'' Similarly, Batelle (2005) refers to the aggregated search data as a database of intentions.
Google, the most popular search engine in the United States,1 publicly provides data on search intensity of many keywords. This relatively new source of information creates a window to witness the collective thoughts or intentions of individuals in ways we were unable to do just a few years ago. Tuna (2010) notes that according to Google's chief economist, search queries such as "unemployment office'' and "jobs'' help predict initial jobless claims. Choi and Varian (2009) provide evidence that search behavior forecasts automobile sales and tourism and Ginsberg et al. (2009) suggest that influenza-related search terms predict the proportion of patients visiting health professionals with related symptoms. More recently, Da, Engleberg, and Gao (2011) and Joseph, Wintoki, and Zhang (2011) find that the search intensity for stock tickers predicts future abnormal stock returns and trading volume.
This paper explores whether online search intensity for queries that include the words "real estate'' or "rent'' help predict home prices. Particularly, we examine the extent to which future cross-sectional differences in home price changes are predicted by cross-sectional differences in current online search intensity. We argue that search intensity for real estate terms for a particular city...