1. Chapter 3 Hotel Structures

2. Learning Objectives
Understand the various decisions facing a reservations manager with regard to forecasting the number of rooms available for sale.
Understand and discuss the overbooking dilemma.
Define and utilize basic vocabulary terms and industry jargon specific to forecasting availability and overbooking reservations.
The ability to develop a practical working model of both a simple, unadjusted room count and an adjusted room count.
Understand deterrents for cancellations and early departures.

3. Forecasting Available Rooms (1 of 8)
Automated Inventory Tracking Systems
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 3

4. Automated Inventory Tracking Systems - Example of A One-Day Rooms Inventory
An example of a one-day rooms inventory screen. Most property management systems would have similar screens though the order and labeling will be somewhat different across all vendors. This is a 370-room corporate downtown property. More than 20% of all rooms are suites (or parlors); including 42 king suites (KGSU), 25 double queen suites (QQSU), and 15 parlors (PAR). Parlor rooms are attached to suites, allowing for a large living room feature. Parlors can usually be locked off and sold as stand-alone hotel rooms whereby the sofa(s) make into beds, or Murphy beds are provided in wall units, or rollaways are brought in.
The remainder of room types are executive rooms (EX), standard rooms (ST), and handicap-accessible rooms (HC).

5. Forecasting Available Rooms (2 of 8)
Room counts are done for each day in advance.
Less accurate as we look further ahead
Tables next slides
Done many times a day for today.
6 AM, 11AM, before and after 4/6 PM 5

6. Forecasting Available Rooms (3 of 8)
This room-availability forecast demonstrates why statistics that depend on the cumulative results of previous days’ forecasts grow less reliable the further the projected horizon. Each day’s values build on estimates from previous day’s (see arrows). If the actual number of rooms occupied in any preceding day is different than the mathematical base—and it always is—later forecasts become less and less accurate, since they begin with invalid figures.
For example, if the rooms occupied on February 3 are actually less than the 1,190 projected (less because of fewer walk-ins, EmoreXundeHrstay IdeBpartuIreTs, etc.),4the-n t6he rooms occupied on February 4 will also be lower than projected. The count for February 4 is based upon the number of rooms occu-). If t February 5, 6, 7, and so on may also be lower than projected

7. Forecasting Available Rooms (4 of 8)
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 long-term due to non-fixable problems
Cannot be sold today due to unacceptable condition
Exhibit 4-5 for inventory calculation issues 7

8. Forecasting Available Rooms (5a of 8)
This property management system reservation forecast report displays a 10-day view of rooms activity. It details arrival and departure projections for individual as well as group rooms. It also projects daily revenues from both group and individual rooms

9. Forecasting Available Rooms (5b of 8)

10. Forecasting Available Rooms (6 of 8)
Stayover - Continuing guest, as per booking
Understay – Guest who leave earlier than expected
Overstay - Guest who stays longer than booked
No show - Guest with confirmed/guaranteed booking who does not arrive, but has not cancelled
Cancellations - provides the opportunity to resell
Early Arrivals - Guest who arrive day/s before booking
Adjusting Toda’s Reservations
The Adjusted Result
Periodic Recounts
Adjusting by Reservation Quality 10

11. Forecasting Available Rooms (7 of 8) – Simple & Unadjusted Room Count

12. Forecasting Available Rooms (8 of 8) – Adjusted Room Count

13. Overbooking (1 of 8) 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
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
The Perfect Fill? 13

14. Overbooking (2 of 8)

15. Overbooking (3 of 8) Reservations as legal contracts.
Courts say that reservations are legal contracts.
However, not worthwhile for individuals to sue.
Meeting planners have sued and won!
Threat of Legislation.
State Legislation
Whose Fault? 15

16. Overbooking (4 of 8) Overbooking Policies Hotel overbooking solutions.
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. 16

17. Overbooking (5 of 8)

18. Overbooking (6 of 8) 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
No-Show Policies
Cancellation Policies 18

19. Overbooking (7 of 8) – Sample Cancellation Policy of OTAs

20. Overbooking (8 of 8) Minimizing the Overbooking Problem.
Increasingly Restrictive Policies.
Early Departure Fees
Third Party Guarantees.
Trip Insurance
Credit Card Disputes
Travel Agent Guarantees
Advance Deposit Reservations.