IDEAS home Printed from https://ideas.repec.org/a/pal/jorapm/v23y2024i3d10.1057_s41272-023-00445-7.html
   My bibliography  Save this article

Predicting daily hotel occupancy: a practical application for independent hotels

Author

Listed:
  • Apostolos Ampountolas

    (Boston University)

  • Mark Legg

    (Penn State Berks)

Abstract

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.

Suggested Citation

  • Apostolos Ampountolas & Mark Legg, 2024. "Predicting daily hotel occupancy: a practical application for independent hotels," Journal of Revenue and Pricing Management, Palgrave Macmillan, vol. 23(3), pages 197-205, June.
    • Handle: RePEc:pal:jorapm:v:23:y:2024:i:3:d:10.1057_s41272-023-00445-7
    DOI: 10.1057/s41272-023-00445-7
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1057/s41272-023-00445-7
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1057/s41272-023-00445-7?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Smyl, Slawek, 2020. "A hybrid method of exponential smoothing and recurrent neural networks for time series forecasting," International Journal of Forecasting, Elsevier, vol. 36(1), pages 75-85.
    2. Kim, Sungil & Kim, Heeyoung, 2016. "A new metric of absolute percentage error for intermittent demand forecasts," International Journal of Forecasting, Elsevier, vol. 32(3), pages 669-679.
    3. 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.
    4. Haensel, Alwin & Koole, Ger, 2011. "Booking horizon forecasting with dynamic updating: A case study of hotel reservation data," International Journal of Forecasting, Elsevier, vol. 27(3), pages 942-960, July.
    5. Haensel, Alwin & Koole, Ger, 2011. "Booking horizon forecasting with dynamic updating: A case study of hotel reservation data," International Journal of Forecasting, Elsevier, vol. 27(3), pages 942-960.
    6. Spyros Makridakis & Evangelos Spiliotis & Vassilios Assimakopoulos, 2018. "Statistical and Machine Learning forecasting methods: Concerns and ways forward," PLOS ONE, Public Library of Science, vol. 13(3), pages 1-26, March.
    7. Luis Nobre Pereira & Vitor Cerqueira, 2022. "Forecasting hotel demand for revenue management using machine learning regression methods," Current Issues in Tourism, Taylor & Francis Journals, vol. 25(17), pages 2733-2750, September.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. 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.
    2. Ulrich Gunter, 2021. "Improving Hotel Room Demand Forecasts for Vienna across Hotel Classes and Forecast Horizons: Single Models and Combination Techniques Based on Encompassing Tests," Forecasting, MDPI, vol. 3(4), pages 1-36, November.
    3. Jennifer L. Castle & Jurgen A. Doornik & David F. Hendry, 2021. "Forecasting Principles from Experience with Forecasting Competitions," Forecasting, MDPI, vol. 3(1), pages 1-28, February.
    4. Makridakis, Spyros & Spiliotis, Evangelos & Assimakopoulos, Vassilios, 2020. "The M4 Competition: 100,000 time series and 61 forecasting methods," International Journal of Forecasting, Elsevier, vol. 36(1), pages 54-74.
    5. Apostolos Ampountolas, 2019. "Forecasting hotel demand uncertainty using time series Bayesian VAR models," Tourism Economics, , vol. 25(5), pages 734-756, August.
    6. Spiliotis, Evangelos & Makridakis, Spyros & Kaltsounis, Anastasios & Assimakopoulos, Vassilios, 2021. "Product sales probabilistic forecasting: An empirical evaluation using the M5 competition data," International Journal of Production Economics, Elsevier, vol. 240(C).
    7. Makridakis, Spyros & Spiliotis, Evangelos & Assimakopoulos, Vassilios & Chen, Zhi & Gaba, Anil & Tsetlin, Ilia & Winkler, Robert L., 2022. "The M5 uncertainty competition: Results, findings and conclusions," International Journal of Forecasting, Elsevier, vol. 38(4), pages 1365-1385.
    8. Wellens, Arnoud P. & Boute, Robert N. & Udenio, Maximiliano, 2024. "Simplifying tree-based methods for retail sales forecasting with explanatory variables," European Journal of Operational Research, Elsevier, vol. 314(2), pages 523-539.
    9. Semenoglou, Artemios-Anargyros & Spiliotis, Evangelos & Makridakis, Spyros & Assimakopoulos, Vassilios, 2021. "Investigating the accuracy of cross-learning time series forecasting methods," International Journal of Forecasting, Elsevier, vol. 37(3), pages 1072-1084.
    10. Naragain Phumchusri & Phoom Ungtrakul, 2020. "Hotel daily demand forecasting for high-frequency and complex seasonality data: a case study in Thailand," Journal of Revenue and Pricing Management, Palgrave Macmillan, vol. 19(1), pages 8-25, February.
    11. Zvi Schwartz & Timothy Webb & Jean-Pierre I van der Rest & Larissa Koupriouchina, 2021. "Enhancing the accuracy of revenue management system forecasts: The impact of machine and human learning on the effectiveness of hotel occupancy forecast combinations across multiple forecasting horizo," Tourism Economics, , vol. 27(2), pages 273-291, March.
    12. Wellens, Arnoud P. & Udenio, Maxi & Boute, Robert N., 2022. "Transfer learning for hierarchical forecasting: Reducing computational efforts of M5 winning methods," International Journal of Forecasting, Elsevier, vol. 38(4), pages 1482-1491.
    13. E. Martinez-De-Pison & J. Fernandez-Ceniceros & A. V. Pernia-Espinoza & F. J. Martinez-De-Pison & Andres Sanz-Garcia, 2016. "Hotel Reservation Forecasting Using Flexible Soft Computing Techniques: A Case of Study in a Spanish Hotel," International Journal of Information Technology & Decision Making (IJITDM), World Scientific Publishing Co. Pte. Ltd., vol. 15(05), pages 1211-1234, September.
    14. Makridakis, Spyros & Spiliotis, Evangelos & Assimakopoulos, Vassilios, 2022. "M5 accuracy competition: Results, findings, and conclusions," International Journal of Forecasting, Elsevier, vol. 38(4), pages 1346-1364.
    15. Evangelos Spiliotis & Spyros Makridakis & Artemios-Anargyros Semenoglou & Vassilios Assimakopoulos, 2022. "Comparison of statistical and machine learning methods for daily SKU demand forecasting," Operational Research, Springer, vol. 22(3), pages 3037-3061, July.
    16. Apostolos Ampountolas, 2021. "Modeling and Forecasting Daily Hotel Demand: A Comparison Based on SARIMAX, Neural Networks, and GARCH Models," Forecasting, MDPI, vol. 3(3), pages 1-16, August.
    17. Petropoulos, Fotios & Apiletti, Daniele & Assimakopoulos, Vassilios & Babai, Mohamed Zied & Barrow, Devon K. & Ben Taieb, Souhaib & Bergmeir, Christoph & Bessa, Ricardo J. & Bijak, Jakub & Boylan, Joh, 2022. "Forecasting: theory and practice," International Journal of Forecasting, Elsevier, vol. 38(3), pages 705-871.
      • Fotios Petropoulos & Daniele Apiletti & Vassilios Assimakopoulos & Mohamed Zied Babai & Devon K. Barrow & Souhaib Ben Taieb & Christoph Bergmeir & Ricardo J. Bessa & Jakub Bijak & John E. Boylan & Jet, 2020. "Forecasting: theory and practice," Papers 2012.03854, arXiv.org, revised Jan 2022.
    18. Hewamalage, Hansika & Bergmeir, Christoph & Bandara, Kasun, 2021. "Recurrent Neural Networks for Time Series Forecasting: Current status and future directions," International Journal of Forecasting, Elsevier, vol. 37(1), pages 388-427.
    19. Nghia Chu & Binh Dao & Nga Pham & Huy Nguyen & Hien Tran, 2022. "Predicting Mutual Funds' Performance using Deep Learning and Ensemble Techniques," Papers 2209.09649, arXiv.org, revised Jul 2023.
    20. González-Sopeña, J.M. & Pakrashi, V. & Ghosh, B., 2021. "An overview of performance evaluation metrics for short-term statistical wind power forecasting," Renewable and Sustainable Energy Reviews, Elsevier, vol. 138(C).

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:pal:jorapm:v:23:y:2024:i:3:d:10.1057_s41272-023-00445-7. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.palgrave.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.