Forecasting Availability and Overbooking
Chapter Four
PowerPoints developed by
Bharath M. Josiam, Ph. D.
Professor, Hospitality Management
University of North Texas, Denton, TX, USA
And
Edited by Gary K. Vallen, Ed. D.
Professor, School of Hotel and Restaurant Management
Northern Arizona University, Flagstaff, AZ, USA
Check-In Check-Out: Managing Hotel Operations, 9e
Gary Vallen, Jerome Vallen
© 2013 by Pearson Higher Education, Inc
Upper Saddle River, New Jersey 07458 • All Rights Reserved
Objectives of Chapter 4
• Utilize and define basic terms and jargon
• An ability to perform a room count
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Unadjusted and adjusted room count
Differences in “rooms available” calculations
Forecasting rooms available for sale
Impact of overstays ,early arrivals, no-shows, and
cancellations on rooms available for sale
• A working knowledge of overbooking issues
– Legal ramifications of overbooking
– Anti-service issues at stake
– Possible solutions
Check-In Check-Out: Managing Hotel Operations, 9e
Gary Vallen, Jerome Vallen
© 2013 by Pearson Higher Education, Inc
Upper Saddle River, New Jersey 07458 • All Rights Reserved
Forecasting Available Rooms
• Automated Inventory Tracking Systems (Exhibit 4-1)
• Computer updates reservations in real time
• Shows projections a week at a time
• Shows today's arrivals by name, room type,
group affiliation, other codes
• Shows reservations by quality
• Shows room availability by room type and
status
Check-In Check-Out: Managing Hotel Operations, 9e
Gary Vallen, Jerome Vallen
© 2013 by Pearson Higher Education, Inc
Upper Saddle River, New Jersey 07458 • All Rights Reserved
Forecasting Available Rooms
•
Room counts done for each day in advance
– Less accurate as we look further ahead
• Exhibit 4-6 & 4-7
– Done many times a day for today
• 6 AM, 11AM, before and after 4/6 PM
• Terms to know:
• Committed Rooms
– (Yesterdays stayovers + today's reserved arrivals)
• Out of Order (OOO) Rooms = Rooms temporarily
unavailable due to fixable problems
– Can be fixed quickly if absolutely essential
– Can be sold at a discount un-fixed, with disclosure
• Out of Inventory (OOI) Rooms = Rooms unavailable longterm due to non-fixable problems
– Cannot be sold today due to unacceptable condition
– Exhibit 4-5 for inventory calculation issues
Check-In Check-Out: Managing Hotel Operations, 9e
Gary Vallen, Jerome Vallen
© 2013 by Pearson Higher Education, Inc
Upper Saddle River, New Jersey 07458 • All Rights Reserved
Forecasting Available Rooms
• Terms to know:
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Understay – Guest who leave earlier than expected
Overstay - Guest who stays longer than booked
Stayover - Continuing guest, as per booking
Overbooking - More rooms sold than available
Expected Arrivals - Guests booked to arrive today
Expected Departures - Guests booked to depart today
No show - Guest with confirmed/guaranteed booking who does
not arrive, but has not cancelled
Early Arrivals - Guest who arrive day/s before booking
Walk-ins - Guests without reservations needing rooms
Room Count - Status of rooms sold and available
House Count - Number of guests in hotel
Walking the guest - Sending a guest with a confirmed or
guaranteed booking to another hotel as we are full
Check-In Check-Out: Managing Hotel Operations, 9e
Gary Vallen, Jerome Vallen
© 2013 by Pearson Higher Education, Inc
Upper Saddle River, New Jersey 07458 • All Rights Reserved
Forecasting Available Rooms
• Components of the Simple Room Count (Exhibit 4-2)
– Only accounts for basic issues
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•
•
•
•
•
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•
Rooms available (A) = 1000
Occupied last night (B) = 950
Expected Check-outs (C) = 300
Stayovers (D = (B-C)) = 650
Today's reservations (E) = 325
Rooms committed today (F = (D+E)) = 975
Rooms available for sale (A - F) = 25
Occupancy percentage (F/A) = 97.5%
Check-In Check-Out: Managing Hotel Operations, 9e
Gary Vallen, Jerome Vallen
© 2013 by Pearson Higher Education, Inc
Upper Saddle River, New Jersey 07458 • All Rights Reserved
Forecasting Available Rooms
• Adjusted Room Count (Exhibit 4-3)
– More sophisticated, accounting for many issues
– Computing Rooms Available
• Rooms available (A) =
1000
• Occupied last night (B) =
950
• Expected Check-outs (C) =
300
– Add Understays (6%) =
+ 18
– Subtract Overstays (2%) =
-6
• Adjusted Departures (C1) =
312
• Adjusted Stayovers (D1 = (B - C1)) =
638
• Today's reservations (E) =
325
– Less Cancellations (2%) =
-7
– Less no-shows (5%) =
- 16
– Add early arrivals (1%) =
+3
• Today's Adjusted Reservations (E1) =
305
• Rooms committed today (F = (D1 + E1)) = 943
• Adjusted rooms available for sale (A - F) =
57
• Anticipated occupancy percentage (F/A)=
94.3%
Check-In Check-Out: Managing Hotel Operations, 9e
Gary Vallen, Jerome Vallen
© 2013 by Pearson Higher Education, Inc
Upper Saddle River, New Jersey 07458 • All Rights Reserved
Overbooking
• Overbooking - More bookings than rooms!
– Done deliberately for number of reasons
• Some guests will be no-shows
– Last minute change of plans
– Some guests deliberately make multiple bookings
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Some guests will be early departures
Some guests will be last minute cancellations
Too late to fill these last-minute empty rooms
So hotels overbook to protect itself from revenue loss
Done with historical statistics as guide
– Can go wrong for many reasons
– Problems if done too aggressively
Check-In Check-Out: Managing Hotel Operations, 9e
Gary Vallen, Jerome Vallen
© 2013 by Pearson Higher Education, Inc
Upper Saddle River, New Jersey 07458 • All Rights Reserved
Overbooking
• Reservations as legal contracts
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Courts say that reservations are legal contracts
However, not worthwhile for individuals to sue
Meeting planners have sued and won!
Looming Legislation
• Many states have passed laws to limit overbooking
• Hotels say others at fault too!
– Guests may overstay without notice (Most common excuse!)
» Some states allow guests to be physically ejected!
– Tour operators book multiple hotels in a city, but clients may
predominantly prefer one hotel
– Conventions are notorious for overbooking
Check-In Check-Out: Managing Hotel Operations, 9e
Gary Vallen, Jerome Vallen
© 2013 by Pearson Higher Education, Inc
Upper Saddle River, New Jersey 07458 • All Rights Reserved
Overbooking
• Common Overbooking Policies
– Hotel overbooking solutions (Exhibit 4-9)
– Check nearby hotels for room availability
– “Walk” overbooked guest to another property
• Chains do it within chain
• Watch for unethical FO Clerks who do it for money!
– Pay for taxi, phone call, comparable room
• Air-taxi in the Bahamas!
– Apologize with gift etc
– Overbooking and antiservice syndrome
• Industry should police itself, or congress will pass laws!
– Airlines are regulated by law - ask for volunteers and give them money and
free tickets
• Problem is due to few hotels with poor service
– Do not train employees to handle overbooking
– Pretend the guest never made a reservation!
Check-In Check-Out: Managing Hotel Operations, 9e
Gary Vallen, Jerome Vallen
© 2013 by Pearson Higher Education, Inc
Upper Saddle River, New Jersey 07458 • All Rights Reserved
Overbooking
• Minimizing the Overbooking Problem
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Accurate and updated room counts
Well trained employees
Hotel not being too greedy!
No-Show/Change Policies like airlines
Harsh cancellation penalties
Early departure fees
Third Party Guarantees
– Trip Insurance, Credit-Card Guarantees, Travel Agents Guarantees,
Corporate
• Advance-Deposit Reservations
– Quite a hassle in general, but may improve with technology
– Ultimately, balance risk of antagonizing guest with protecting
revenue!
Check-In Check-Out: Managing Hotel Operations, 9e
Gary Vallen, Jerome Vallen
© 2013 by Pearson Higher Education, Inc
Upper Saddle River, New Jersey 07458 • All Rights Reserved
Objectives of Chapter 4
• Utilize and define basic terms and jargon
• An ability to perform a room count
–
–
–
–
Unadjusted and adjusted room count
Differences in “rooms available” calculations
Forecasting rooms available for sale
Impact of overstays ,early arrivals, no-shows, and
cancellations on rooms available for sale
• A working knowledge of overbooking issues
– Legal ramifications of overbooking
– Anti-service issues at stake
– Possible solutions
Check-In Check-Out: Managing Hotel Operations, 9e
Gary Vallen, Jerome Vallen
© 2013 by Pearson Higher Education, Inc
Upper Saddle River, New Jersey 07458 • All Rights Reserved
Forecasting Available Rooms – In-class
Assignment
• Simple Room Count (Exhibit 4-2)
– Only accounts for basic issues
•
•
•
•
•
•
•
•
Rooms available (A) = 2500
Occupied last night (B) = 2275
Expected Check-outs (C) = 625
Stayovers (D = (B-C)) =
Today's reservations (E) = 900
Rooms committed today (F = (D+E)) =
Rooms available for sale (A - F) =
Occupancy percentage (F/A) =
Check-In Check-Out: Managing Hotel Operations, 9e
Gary Vallen, Jerome Vallen
© 2013 by Pearson Higher Education, Inc
Upper Saddle River, New Jersey 07458 • All Rights Reserved
Forecasting Available Rooms - In-class Assignment
• Adjusted Room Count (Exhibit 4-3)
– More sophisticated, accounting for many issues
– Computing Rooms Available
• Rooms available (A) =
2500
• Occupied last night (B) =
2275
• Expected Check-outs (C) =
625
– Add Understays (6%) =
+
– Subtract Overstays (2%) =
• Adjusted Departures (C1) =
• Adjusted Stayovers (D1 = (B - C1)) =
• Today's reservations (E) =
900
– Less Cancellations (2%) =
– Less no-shows (5%) =
– Add early arrivals (1%) =
+
• Today's Adjusted Reservations (E1) =
• Rooms committed today (F = (D1 + E1)) =
• Adjusted rooms available for sale (A - F) =
• Anticipated occupancy percentage (F/A) =
Check-In Check-Out: Managing Hotel Operations, 9e
Gary Vallen, Jerome Vallen
© 2013 by Pearson Higher Education, Inc
Upper Saddle River, New Jersey 07458 • All Rights Reserved