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The goal of Cityscape is to bring high-quality original research on housing and community development issues to scholars, government officials, and practitioners. Cityscape is open to all relevant disciplines, including architecture, consumer research, demography, economics, engineering, ethnography, finance, geography, law, planning, political science, public policy, regional science, sociology, statistics, and urban studies.

Cityscape is published three times a year by the Office of Policy Development and Research (PD&R) of the U.S. Department of Housing and Urban Development.

  • Borrower Beware
  • Volume 18, Number 2
  • Managing Editor: Mark D. Shroder
  • Associate Editor: Michelle P. Matuga

Trend-Spotting in the Housing Market

Nikos Askitas
Institute for the Study of Labor

Data Shop

Data Shop, a department of Cityscape, presents short articles or notes on the uses of data in housing and urban research. Through this department, the Office of Policy Development and Research introduces readers to new and overlooked data sources and to improved techniques in using well-known data. The emphasis is on sources and methods that analysts can use in their own work. Researchers often run into knotty data problems involving data interpretation or manipulation that must be solved before a project can proceed, but they seldom get to focus in detail on the solutions to such problems. If you have an idea for an applied, data-centric note of no more than 3,000 words, please send a one-paragraph abstract to david.a.vandenbroucke@hud.gov for consideration.

I create a time series of weekly ratios of Google searches in the United States on buying and selling in the real estate category of Google Trends, whereby I call this ratio the Google U.S. Housing Market BUSE index, or simply the BUSE index. It expresses the number of “buy” searches for each “sell” search, which I consider to be a good proxy of the number of prospective homebuyers for each prospective homeseller in the pool of prospective housing market participants by means of certain regularity assumptions on the distribution of Internet users. The BUSE index—which can be perceived as a behavioral macroeconomic indicator—has several unique, desirable properties, which make it useful for understanding and nowcasting the U.S. housing market. It has a significant correlation with the Standard & Poor’s/Case-Shiller® U.S. National Home Price Index. Because the latter is monthly and is published as a 3-month moving average with a 2-month lag and the Google Trends data are weekly, the result is a shortterm nowcast of housing prices in the United States. I show how these Google data can be used to create a consistent narrative of the post-bubble-burst dynamics in the U.S. housing market and propose the BUSE index as an instrument for monitoring housing market conditions in real time.

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