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Predicting daily hotel occupancy: a practical application for independent hotels

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Journal of Revenue and Pricing Management Aims and scope


Accurately forecasting daily hotel occupancy is critical for revenue managers. Limited research focuses on predicting daily hotel occupancy by implementing traditional forecasting techniques, which only require a little statistical knowledge or expensive software for small independent properties. This study employs longitudinal daily occupancy data from multiple properties in urban settings within the United States to test four forecasting models for short-term (1–90 day) predictions. The results showed that Simple Exponential Smoothing (SES) was most accurate for four horizons, while Extreme Gradient Boosting (XGBoost) was better for shorter-term predictions in the other seven. In conclusion, these results demonstrate that small independent properties may successfully implement traditional forecasting methods for accurate daily occupancy forecasting.

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The data are available from the authors upon reasonable request, were used under license for the current study, and so are not publicly available.


  • Ampountolas, A. 2018. Forecasting hotel demand uncertainty using time series Bayesian VAR models. Tourism Economics 25 (5): 734–756.

    Article  Google Scholar 

  • Ampountolas, A. 2021. Modeling and forecasting daily hotel demand: A comparison based on Sarimax, Neural Networks, and GARCH models. Forecasting 3 (3): 580–595.

    Article  Google Scholar 

  • Ampountolas, A., and M.P. Legg. 2021. A segmented machine learning modeling approach of social media for predicting occupancy. International Journal of Contemporary Hospitality Management.

    Article  Google Scholar 

  • Chen, T., and C. Guestrin. 2016. Xgboost: A scalable tree-boosting system. In Proceedings of the 22nd ACM Sigkdd international conference on knowledge discovery and data mining, San Francisco, CA, August 13–17, 785–794.

  • De Livera, A.M., R.J. Hyndman, and R.D. Snyder. 2011. Forecasting time series with complex seasonal patterns using exponential smoothing. Journal of the American Statistical Association 106 (496): 1513–1527.

    Article  Google Scholar 

  • Fiori, A.M., and I. Foroni. 2019. Reservation forecasting models for hospitality SMEs with a view to enhance their economic sustainability. Sustainability 11 (5): 1274.

    Article  Google Scholar 

  • Haensel, A., and G. Koole. 2011. Booking horizon forecasting with dynamic updating: A case study of hotel reservation data. International Journal of Forecasting 27 (3): 942–960.

    Article  Google Scholar 

  • Hastie, T., R. Tibshirani, J.H. Friedman, and J.H. Friedman. 2009. The elements of statistical learning: Data mining, inference, and prediction, vol. 2, 1–758. New York: Springer.

    Google Scholar 

  • Hyndman, R.J., and G. Athanasopoulos. 2018. Forecasting: Principles and practice. Melbourne: OTexts.

    Google Scholar 

  • Hyndman, R., A.B. Koehler, J.K. Ord, and R.D. Snyder. 2008. Forecasting with exponential smoothing: The state space approach. Berlin, Heidelberg: Springer Science & Business Media.

    Book  Google Scholar 

  • Kim, S., and H. Kim. 2016. A new metric of absolute percentage error for intermittent demand forecasts. International Journal of Forecasting 32 (3): 669–679.

    Article  Google Scholar 

  • Makridakis, S., E. Spiliotis, and V. Assimakopoulos. 2018. Statistical and Machine Learning forecasting methods: Concerns and ways forward. PLoS ONE 13 (3): e0194889.

    Article  Google Scholar 

  • Pereira, L.N. 2016. An introduction to helpful forecasting methods for hotel revenue management. International Journal of Hospitality Management 58: 13–23.

    Article  Google Scholar 

  • Pereira, L.N., and V. Cerqueira. 2022. Forecasting hotel demand for revenue management using machine learning regression methods. Current Issues in Tourism 25 (17): 2733–2750.

    Article  Google Scholar 

  • Polt, S. 1998. Forecasting is difficult–especially if it refers to the future. In AGIFORS-reservations and yield management study group meeting proceedings, 61–91.

  • Schwartz, Z., M. Uysal, T. Webb, and M. Altin. 2016. Hotel daily occupancy forecasting with competitive sets: A recursive algorithm. International Journal of Contemporary Hospitality Management 28 (2): 267–285.

    Article  Google Scholar 

  • Shen, S., G. Li, and H. Song. 2011. Combination forecasts of international tourism demand. Annals of Tourism Research 38 (1): 72–89.

    Article  Google Scholar 

  • Smyl, S. 2020. A hybrid method of exponential smoothing and recurrent neural networks for time series forecasting. International Journal of Forecasting 36 (1): 75–85.

    Article  Google Scholar 

  • Song, H., E. Smeral, G. Li, and J. Chen. 2013. Tourism forecasting using econometric models. In Trends in European tourism planning and organisation, ed. C. Costa, E. Panyik, and D. Buhalis, 289–309. Bristol: Channel View.

    Chapter  Google Scholar 

  • Taylor, S.J. 2007. Introduction to asset price dynamics, volatility, and prediction. Introductory chapters. In Asset Price Dynamics, Volatility, and Prediction. Princeton: Princeton University Press, pp. 397–421.

  • Van Ryzin, G.J. 2005. Future of revenue management: Models of demand. Journal of Revenue and Pricing Management 4 (2): 204–210.

    Article  Google Scholar 

  • Webb, T., Z. Schwartz, Z. Xiang, and M. Altin. 2022. Hotel revenue management forecasting accuracy: The hidden impact of booking windows. Journal of Hospitality and Tourism Insights 5 (5): 950–965.

    Article  Google Scholar 

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Correspondence to Apostolos Ampountolas.

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Ampountolas, A., Legg, M. Predicting daily hotel occupancy: a practical application for independent hotels. J Revenue Pricing Manag 23, 197–205 (2024).

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