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The Power of Travel Search Data in Forecasting the Tourism Demand in Dubai

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  • Ahmed Shoukry Rashad

    (Dubai Economy and Tourism Department, Government of Dubai, Business Village A, Dubai 594, United Arab Emirates
    Economic Research Forum, Dokki, Giza 12311, Egypt)

Abstract

Tourism plays an important economic role for many economies and after the COVID-19 pandemic, accurate tourism forecasting become critical for policymakers in tourism-dependent economies. This paper extends the growing literature on the use of internet search data in tourism forecasting through evaluating the predictive ability of Destination Insight with Google, a new Google product designed to monitor tourism recovery after the COVID-19 pandemic. This paper is the first attempt to explore the forecasting ability of the new Google data. The study focuses on the case of Dubai, given its status as a world-leading tourism destination. The study uses time series models that account for seasonality, trending variables, and structural breaks. The study uses monthly data for the period of January 2019 to April 2022. We explore whether the internet travel search queries can improve the forecasting of tourist arrivals to Dubai from the UK. We evaluate the accuracy of forecasts after incorporating the Google variable in our model. Our findings suggest that the new Google data can significantly improve tourism forecasting and serves as a leading indicator of tourism demand.

Suggested Citation

  • Ahmed Shoukry Rashad, 2022. "The Power of Travel Search Data in Forecasting the Tourism Demand in Dubai," Forecasting, MDPI, vol. 4(3), pages 1-11, July.
    • Handle: RePEc:gam:jforec:v:4:y:2022:i:3:p:36-684:d:868494
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    References listed on IDEAS

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    Cited by:

    1. Jesse T. Richman & Ryan J. Roberts, 2023. "Assessing Spurious Correlations in Big Search Data," Forecasting, MDPI, vol. 5(1), pages 1-12, February.
    2. João Paulo Teixeira & Ulrich Gunter, 2023. "Editorial for Special Issue: “Tourism Forecasting: Time-Series Analysis of World and Regional Data”," Forecasting, MDPI, vol. 5(1), pages 1-3, February.

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