[Forecasting and Revenue Management in Hotel: A Case Study of a Hotel]

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Acknowledgement

I would take this opportunity to thank my research supervisor, family and friends for their support and guidance without which this research would not have been possible.

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I, [type your full first names and surname here], declare that the contents of this dissertation/thesis represent my own unaided work, and that the dissertation/thesis has not previously been submitted for academic examination towards any qualification. Furthermore, it represents my own opinions and not necessarily those of the University.

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Abstract

The arrivals forecast is one of the key inputs for a successful hotel revenue management system, but no research on the best forecasting method has been conducted. In this research, we used data as a case study from Choice Hotels and Marriott Hotels to test of forecasting method and to determine the most accurate method. Preliminary results using the Choice Hotel data show that pickup methods and regression produced the lowest error, while the booking curve and combination forecasts produced fairly inaccurate results. The more in-depth study using the Marriott Hotel data showed that exponential smoothing, pickup, and moving average models were the most robust.

Table of Content

Chapter 1: Introduction6

Chapter 2: Literature Review8

Revenue Management8

History of Revenue Management9

Importance of revenue management10

Conditions of Revenue Management10

Measurements11

Internal Measurements11

Revenue12

Occupancy Percentage12

Average Daily Rate13

RevPAR- Revenue per Available Room13

Contribution Margin (Net Revenue)13

Identical Net Revenue14

GOPPAR- Gross Operating Profit per Available room14

Overbooking and Cancellations15

External Measurements16

Forecasting16

Forecasting Methods16

Rate Management17

Dynamic Pricing17

Demand Generation in Revenue Management19

Chapter 3: Methodology20

Case Study20

Stage 1: Choice hotels20

Stage 2: Marriott hotels21

References24

Chapter 1: Introduction

Yield, or revenue, management, as commonly practiced in the hotel industry helps hotels decide on the most profitable mix of transient business. The transient forecast is the key driver of any revenue management system, yet no published research addresses the accuracy of hotel forecasting methods for transients. Kimes (1999, 1104) has previously studied the issue of hotel group forecasting accuracy. Accurate forecasts are crucial to good revenue management. Lee (1990, 46) found that a 10% increase in forecast accuracy in the airline industry increased revenue by 0.5-3.0% on high demand flights. A recent Wall Street Journal article said that Continental Airlines increased profits by $50 to $100 million per year from the use of their revenue management system (McCartney, 2000, 100). Detailed forecasts are the major input to most revenue management systems, and without accurate forecasts, the rate and availability recommendations produced by the revenue management system may be highly inaccurate.

The data that is used for hotel forecasting has two dimensions to it: when the reservation was booked and when the room was consumed. The booking information gives the manager additional detail which can be used to update the forecast. Without this information, the manager would have to rely solely on the historical information on the daily number of arrivals or rooms sold. In this research, we tested a variety of different forecasting methods on data from four hotels operated by Choice Hotels and two hotels operated by Marriott Hotels. The accuracy of the various methods was determined and methods providing ...