Nowcasting Building Permits with Google Trends
David Coble () and
Pablo Pincheira ()
MPRA Paper from University Library of Munich, Germany
We propose a useful way to predict building permits in the US, exploiting rich real-time data from web search queries. The time series on building permits is usually considered as a leading indicator of economic activity in the construction sector. Nevertheless, new data on building permits are released with a lag close to two months. Therefore, an accurate now-cast of this leading indicator is desirable. We show that models including Google search queries nowcast and forecast better than our good, not naïve, univariate benchmarks both in-sample and out-of-sample. We also show that our results are robust to different specifications, the use of rolling or recursive windows and, in some cases, to the forecasting horizon. Since Google queries information is free, our approach is a simple and inexpensive way to predict building permits in the United States.
Keywords: Online Search; Prediction; Forecasting; Time Series; Building Permits; Real Estate; Google Trends. (search for similar items in EconPapers)
JEL-codes: C10 C5 C53 F3 F37 (search for similar items in EconPapers)
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