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Predicting hotel occupancies with public data

Author

Listed:
  • Candy Mei Fung Tang

    (University of Macau, China)

  • Brian King

    (Hong Kong Polytechnic University, Hong Kong)

  • Stephen Pratt

    (Hong Kong Polytechnic University, Hong Kong)

Abstract

This study demonstrates the value of using readily available Organisation for Economic Co-Operation and Development (OECD) indicators to estimate hotel occupancy trends in a particular territory (Hong Kong) drawing upon almost four decades of data (1972–2010). The indicators predict the economic climate at global, regional and national levels by providing comprehensive statistically based economic information. They should be of particular benefit for resource-constrained small and/or independent hotel operations for whom accurate occupancy predictions facilitate a more targeted approach to strategic investments. Using the OECD indicators offers hoteliers an alternative to acquiring expensive data and/or consultant inputs. The article shows the merit of OECD composite indicators generally and of the consumer confidence index in particular as publicly available and reliable data which can provide hoteliers with early signals about shifting demand. It contributes to tourism economics by demonstrating the merit of indices as a supplement to both established demand forecasts and as an input to hotel revenue management systems.

Suggested Citation

  • Candy Mei Fung Tang & Brian King & Stephen Pratt, 2017. "Predicting hotel occupancies with public data," Tourism Economics, , vol. 23(5), pages 1096-1113, August.
  • Handle: RePEc:sae:toueco:v:23:y:2017:i:5:p:1096-1113
    DOI: 10.1177/1354816616666670
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    References listed on IDEAS

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    Cited by:

    1. Katarína ?ulková & Mária Jano?ková & Adriana Csikósová, 2019. "Prediction of tourism in the sustainable development of Carpatian region," Proceedings of Business and Management Conferences 8612326, International Institute of Social and Economic Sciences.
    2. Guizzardi, Andrea & Pons, Flavio Maria Emanuele & Angelini, Giovanni & Ranieri, Ercolino, 2021. "Big data from dynamic pricing: A smart approach to tourism demand forecasting," International Journal of Forecasting, Elsevier, vol. 37(3), pages 1049-1060.

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