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Optimal Dynamic Hotel Pricing

Author

Listed:
  • John Rust

    (Georgetown University)

  • Sungjin Cho

    (Seoul National University)

Abstract

We analyze a confidential reservation database provided by a luxury hotel, ”hotel 0”, based in a major US city that enables us to observe individual reservations and cancellations at a daily frequency over a 37 month period. We show how the hotel sets prices for various classes of customers and how its prices vary over time. Hotel pricing is a challenging high-dimensional problem since hotels must not only set prices for each current date, but they must also quote prices for a range of future dates, room types and customer types. Our data reveal the full path of room rates quoted for different types of rooms and customers in advance of the date of occupancy. We find large within and between week variability in room prices, as well as huge seasonal variations in average daily rates and occupancy rates, not only for the hotel we study but also for its direct competitors. We formulate and estimate a structural model of optimal dynamic hotel pricing using the Method of Simulated Moments (MSM). The estimated model provides accurate predictions of the actual prices set by this firm and resulting paths of bookings and cancellations. Prices quoted for bookings in advance of occupancy generally decline as the date of occupancy arrives for non-busy days, but can increase dramatically in the final days before occupancy on busy days when management forecasts a high probability of sell-out. Hotel 0’s prices co-move strongly with it competitors’ prices and we show that a simple price-following strategy where hotel 0 undercuts its competitors’ average price by a fixed percentage provides a good first approximation to its pricing behavior. However we show that simple price-following is suboptimal: when hotel 0 expects to sell out, it is optimal to depart from price-following and increase its price significantly above its competitors. On non-busy days, it is not optimal for hotel 0 to cut its prices in the final days before arrival to try to increase occupancy unless its competitors cut their prices. Though price- following has the superficial appearance of collusive behavior mediated by the use of a commercial revenue management system (RMS), our results suggest that hotel 0’s pricing is competitive and is best described as a rational best response to its beliefs about demand and the prices set by its competitors. In fact hotel 0 regularly disregards the recommended prices of its RMS, which it regards as too low compared to the prices it actually sets.

Suggested Citation

  • John Rust & Sungjin Cho, 2018. "Optimal Dynamic Hotel Pricing," 2018 Meeting Papers 179, Society for Economic Dynamics.
    • Handle: RePEc:red:sed018:179
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    References listed on IDEAS

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    Cited by:

    1. X. D'Haultfoeuille & P. Fevrier & L. Wilner, 2012. "Demand Estimation in the Presence of Revenue Management," Documents de Travail de l'Insee - INSEE Working Papers g2012-13, Institut National de la Statistique et des Etudes Economiques.
    2. Daniel Garcia & Juha Tolvanen & Alexander K. Wagner, 2022. "Demand Estimation Using Managerial Responses to Automated Price Recommendations," Management Science, INFORMS, vol. 68(11), pages 7918-7939, November.
    3. Kevin R. Williams, 2022. "The Welfare Effects of Dynamic Pricing: Evidence From Airline Markets," Econometrica, Econometric Society, vol. 90(2), pages 831-858, March.
    4. Nicola Lacetera & Claudio A. Piga & Lorenzo Zirulia, 2021. "Sticky Price for Declining Risk? Business Strategies with “Behavioral” Customers in the Hotel Industry," NBER Working Papers 28456, National Bureau of Economic Research, Inc.
    5. 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.
    6. Ali Hortacsu & Olivia R. Natan & Hayden Parsley & Timothy Schwieg & Kevin R. Williams, 2021. "Organizational Structure and Pricing: Evidence from a Large U.S. Airline," Cowles Foundation Discussion Papers 2312R4, Cowles Foundation for Research in Economics, Yale University, revised Jun 2023.
    7. Kevin R. Williams, 2017. "Dynamic Airline Pricing and Seat Availability," Cowles Foundation Discussion Papers 2103R, Cowles Foundation for Research in Economics, Yale University, revised May 2020.
    8. Marcella Nicolini & Claudio A. Piga & Andrea Pozzi, 2023. "From uniform to bespoke prices: Hotel pricing during EURO 2016," Quantitative Marketing and Economics (QME), Springer, vol. 21(3), pages 333-355, September.
    9. Aniko …ry & Ali Horta su & Kevin Williams, 2022. "Dynamic Price Competition: Theory and Evidence from Airline Markets," Cowles Foundation Discussion Papers 2341R1, Cowles Foundation for Research in Economics, Yale University, revised Apr 2023.
    10. Gaurab Aryal & Charles Murry & Jonathan W. Williams, 2018. "Price Discrimination in International Airline Markets," Boston College Working Papers in Economics 968, Boston College Department of Economics.
    11. Kevin R. Williams, 2017. "The Welfare Effects of Dynamic Pricing: Evidence from Airline Markets," Cowles Foundation Discussion Papers 2103R2, Cowles Foundation for Research in Economics, Yale University, revised Jun 2021.
    12. Ali Hortacsu & Olivia R. Natan & Hayden Parsley & Timothy Schwieg & Kevin R. Williams, 2021. "Organizational Structure and Pricing: Evidence from a Large U.S. Airline," Cowles Foundation Discussion Papers 2312R3, Cowles Foundation for Research in Economics, Yale University, revised Jan 2023.

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