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Group forecasting accuracy in hotels

Author

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  • S E Kimes

    (School of Hotel Administration, Cornell University)

Abstract

Yield management helps hotels more profitably manage the capacity of their rooms. Hotels tend to have two types of business: transient and group. Yield management research and systems have been designed for transient business in which the group forecast is taken as a given. In this research, forecast data from approximately 90 hotels of a large North American hotel chain were used to determine the accuracy of group forecasts and to identify factors associated with accurate forecasts. Forecasts showed a positive bias and had a mean absolute percentage error (MAPE) of 40% at two months before arrival; 30% at one month before arrival; and 10–15% on the day of arrival. Larger hotels, hotels with a higher dependence on group business, and hotels that updated their forecasts frequently during the month before arrival had more accurate forecasts.

Suggested Citation

  • S E Kimes, 1999. "Group forecasting accuracy in hotels," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 50(11), pages 1104-1110, November.
    • Handle: RePEc:pal:jorsoc:v:50:y:1999:i:11:d:10.1057_palgrave.jors.2600770
    DOI: 10.1057/palgrave.jors.2600770
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    Citations

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    Cited by:

    1. Adam G. Walke & Thomas M. Fullerton Jr., 2019. "Metropolitan Hotel Sector Forecast Accuracy in El Paso," Atlantic Economic Journal, Springer;International Atlantic Economic Society, vol. 47(2), pages 179-191, June.
    2. 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.
    3. Yunmei Bai & Chun-Hung Tang, 2021. "Determining the optimal group rate in the hotel industry," Journal of Revenue and Pricing Management, Palgrave Macmillan, vol. 20(4), pages 446-456, August.
    4. Rimo Das & Harshinder Chadha & Somnath Banerjee, 2021. "Multi-layered market forecast framework for hotel revenue management by continuously learning market dynamics," Journal of Revenue and Pricing Management, Palgrave Macmillan, vol. 20(3), pages 351-367, June.
    5. Benoît Rottembourg & Jacques Masson, 2017. "When bid price is not enough: Taking better allotment decisions for Camping Revenue Management," Journal of Revenue and Pricing Management, Palgrave Macmillan, vol. 16(2), pages 115-124, April.
    6. Apostolos Ampountolas, 2019. "Forecasting hotel demand uncertainty using time series Bayesian VAR models," Tourism Economics, , vol. 25(5), pages 734-756, August.
    7. E. Andrew Boyd & Ioana C. Bilegan, 2003. "Revenue Management and E-Commerce," Management Science, INFORMS, vol. 49(10), pages 1363-1386, October.
    8. Larissa Koupriouchina & Jean-Pierre van der Rest & Zvi Schwartz, 2023. "Judgmental Adjustments of Algorithmic Hotel Occupancy Forecasts: Does User Override Frequency Impact Accuracy at Different Time Horizons?," Tourism Economics, , vol. 29(8), pages 2143-2164, December.

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