Forecasting building permits with Google Trends

David Coble and Pablo Pincheira

Empirical Economics, 2021, vol. 61, issue 6, No 12, 3315-3345

Abstract: Abstract We propose a useful way to predict building permits in the USA, exploiting rich data from web search queries. The relevance of our work relies on the fact that the time series on building permits is used as a leading indicator of economic activity in the construction sector. Nevertheless, new data on building permits are released with a lag of a few weeks. Therefore, an accurate nowcast of this leading indicator is desirable. In this paper, we show that models including Google search queries nowcast and forecast better than many of our good, not naïve benchmarks. We show this with both in-sample and out-of-sample exercises. In addition, we show that the results of these predictions are robust to different specifications, the use of rolling or expanding 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 USA.

Keywords: Online search; Prediction; Forecasting; Time series; Building permits; Google trends; C220; C530; E170; E270; E370; F370; L740 (search for similar items in EconPapers)
Date: 2021
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Citations: View citations in EconPapers (2)

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DOI: 10.1007/s00181-020-02011-1

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