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Does historical data still matter for demand forecasting in uncertain and turbulent times? An extension of the additive pickup time series method for SME hotels

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Listed:
  • Cindy Yoonjoung Heo

    (HES-SO/ University of Applied Sciences and Art Western Switzerland)

  • Luciano Viverit

    (Hotelnet)

  • Luís Nobre Pereira

    (Universidade do Algarve)

Abstract

Demand forecast accuracy is critical for hotels to operate their properties efficiently and profitably. The COVID-19 pandemic is a massive challenge for hotel demand forecasting due to the relevance of historical data. Therefore, the aims of this study are twofold: to present an extension of the additive pickup method using time series and moving averages; and to test the model using the real reservation data of a hotel in Italy during the COVID-19 pandemic. This study shows that historical data are still useful for a SME hotel amid substantial demand uncertainty caused by COVID-19. Empirical results suggest that the proposed method performs better than the classical one, particularly for longer forecasting horizons and for periods when the hotel is not fully occupied.

Suggested Citation

  • Cindy Yoonjoung Heo & Luciano Viverit & Luís Nobre Pereira, 2024. "Does historical data still matter for demand forecasting in uncertain and turbulent times? An extension of the additive pickup time series method for SME hotels," Journal of Revenue and Pricing Management, Palgrave Macmillan, vol. 23(1), pages 39-43, February.
    • Handle: RePEc:pal:jorapm:v:23:y:2024:i:1:d:10.1057_s41272-023-00421-1
    DOI: 10.1057/s41272-023-00421-1
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    References listed on IDEAS

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    1. Hanyuan Zhang & Jiangping Lu, 2022. "Forecasting hotel room demand amid COVID-19," Tourism Economics, , vol. 28(1), pages 200-221, February.
    2. Kourentzes, Nikolaos & Saayman, Andrea & Jean-Pierre, Philippe & Provenzano, Davide & Sahli, Mondher & Seetaram, Neelu & Volo, Serena, 2021. "Visitor arrivals forecasts amid COVID-19: A perspective from the Africa team," Annals of Tourism Research, Elsevier, vol. 88(C).
    3. Weatherford, Larry R. & Kimes, Sheryl E., 2003. "A comparison of forecasting methods for hotel revenue management," International Journal of Forecasting, Elsevier, vol. 19(3), pages 401-415.
    4. Anna Maria Fiori & Ilaria Foroni, 2019. "Reservation Forecasting Models for Hospitality SMEs with a View to Enhance Their Economic Sustainability," Sustainability, MDPI, vol. 11(5), pages 1-24, February.
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