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Modelling and prediction of a destination’s monthly average daily rate and occupancy rate based on hotel room prices offered online

Author

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  • Noelia Oses
  • Jon Kepa Gerrikagoitia
  • Aurkene Alzua

    (Centro de Investigación Cooperativa en Turismo – CICtourGUNE, Spain)

Abstract

Tourism metrics are essential for managing a destination. Hotel performance metrics such as average daily rate and occupancy rate are two of the most prominent metrics for the industry. The authors’ research group works on developing methods for estimating tourism metrics based on digital footprint. Data available publicly on the Internet, including hotel room prices, are collected daily. This article shows that the prices offered online have a high positive correlation with those reported by official statistics at the Nomenclature of Units for Territorial Statistics 2 level after the online prices have been preprocessed and, thus, the relevance of this data source is established. This article then presents a model for explaining and predicting mean hotel occupancy rates by destination based on these prices. The results are very promising, the fit is excellent and the predictions are also good. In summary, prices have moved from reflecting the expected demand to reflecting the actual demand and occupancy rate.

Suggested Citation

  • Noelia Oses & Jon Kepa Gerrikagoitia & Aurkene Alzua, 2016. "Modelling and prediction of a destination’s monthly average daily rate and occupancy rate based on hotel room prices offered online," Tourism Economics, , vol. 22(6), pages 1380-1403, December.
    • Handle: RePEc:sae:toueco:v:22:y:2016:i:6:p:1380-1403
    DOI: 10.5367/te.2015.0491
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    References listed on IDEAS

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    Cited by:

    1. Guizzardi, Andrea & Ballestra, Luca Vincenzo & D'Innocenzo, Enzo, 2022. "Hotel dynamic pricing, stochastic demand and covid-19," Annals of Tourism Research, Elsevier, vol. 97(C).
    2. Damonte, L. Taylor & Woodside, Arch G., 2021. "Are lodging revenue cycles leading indicators for shifts in financial well-being?," Journal of Business Research, Elsevier, vol. 129(C), pages 465-473.
    3. Walheer, Barnabé & Zhang, Linjia, 2018. "Profit Luenberger and Malmquist-Luenberger indexes for multi-activity decision making units: the case of the star-rated hotel industry in China," RIEI Working Papers 2018-06, Xi'an Jiaotong-Liverpool University, Research Institute for Economic Integration.
    4. Tianxiang Zheng & Shaopeng Liu & Zini Chen & Yuhan Qiao & Rob Law, 2020. "Forecasting Daily Room Rates on the Basis of an LSTM Model in Difficult Times of Hong Kong: Evidence from Online Distribution Channels on the Hotel Industry," Sustainability, MDPI, vol. 12(18), pages 1-17, September.
    5. Sainaghi, Ruggero & Phillips, Paul & Zavarrone, Emma, 2017. "Performance measurement in tourism firms: A content analytical meta-approach," Tourism Management, Elsevier, vol. 59(C), pages 36-56.

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