Pearson New International Edition International pcl tp indd 1



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Professional Front Office Management Pearson New International Edition by Robert Woods, Jack D. Ninemeier, David K. Hayes, Michele A. Austin (z-lib.org)

F
H
I
J
K
L
M
N
O
P
Q
B
C
D
E
Stay-
G
Due
Avail-
Trans
Trans
Group Group
Total
Occ
Group Potential
Date
Day
Rooms
DHS
overs
Arrivals
Out
able
Ngtd
Gtd
Ngtd
Gtd
Occupied
%
Block
%
7/1/200x
Thurs.
141
0
5
1
9
114
0
1
0
0
6
4.26%
21
19.15%
7/2/200x
Fri.
141
2
5
4
1
-1
0
3
0
1
9
6.38%
131
99.29%
7/3/200x
Sat.
141
0
9
3
0
119
0
3
0
0
12
8.51%
10
15.60%
7/4/200x
Sun.
141
0
4
0
8
127
0
0
0
0
4
2.84%
10
9.93%
7/5/200x
Mon.
141
0
3
0
1
130
0
0
0
0
3
2.13%
8
7.80%
7/6/200x
Tues.
141
0
3
0
0
53
0
0
0
0
3
2.13%
85
62.41%
7/7/200x
Wed.
141
0
3
2
0
51
0
2
0
0
5
3.55%
85
63.83%
7/8/200x
Thurs.
141
0
4
4
1
118
0
4
0
0
8
5.67%
15
16.31%
7/9/200x
Fri.
141
0
4
52
4
20
0
8
31
13
56
39.72%
65
85.82%
7/10/200x
Sat.
141
0
47
9
9
20
0
5
1
3
56
39.72%
65
85.82%
7/11/200x
Sun.
141
0
9
2
47
113
0
2
0
0
11
7.80%
17
19.86%
7/12/200x
Mon.
141
0
4
0
7
125
0
0
0
0
4
2.84%
12
11.35%
7/13/200x
Tues.
141
0
4
3
0
24
0
2
0
1
7
4.96%
110
82.98%
7/14/200x
Wed.
141
0
5
3
2
23
0
2
0
1
8
5.67%
110
83.69%
7/15/200x
Thurs.
141
0
7
7
1
10
0
6
0
1
14
9.93%
117
92.91%
7/16/200x
Fri.
141
0
12
14
2
103
0
3
1
10
26
18.44%
12
26.95%
7/17/200x
Sat.
141
0
21
31
5
50
1
3
1
26
52
36.88%
39
64.54%
7/18/200x
Sun.
141
0
21
6
31
73
0
2
0
4
27
19.15%
41
48.23%
7/19/200x
Mon.
141
0
9
28
18
70
2
10
0
16
37
26.24%
34
50.35%
7/20/200x
Tues.
141
0
35
7
2
26
0
6
0
1
42
29.79%
73
81.56%
7/21/200x
Wed.
141
0
37
2
5
20
0
1
0
1
39
27.66%
82
85.82%
7/22/200x
Thurs.
141
0
16
3
23
31
0
0
0
3
19
13.48%
91
78.01%
7/23/200x
Fri.
141
0
10
18
9
63
0
5
1
12
28
19.86%
50
55.32%
7/24/200x
Sat.
141
0
26
9
2
75
0
2
0
7
35
24.82%
31
46.81%
7/25/200x
Sun.
141
0
7
2
28
132
0
2
0
0
9
6.38%
0
6.38%
7/26/200x
Mon.
141
0
5
13
4
92
0
4
0
9
18
12.77%
31
34.75%
7/27/200x
Tues.
141
0
12
1
6
106
0
1
0
0
13
9.22%
22
24.82%
7/28/200x
Wed.
141
0
8
0
5
71
0
0
0
0
8
5.67%
62
49.65%
7/29/200x
Thurs.
141
0
4
0
4
92
0
0
0
0
4
2.84%
45
34.75%
7/30/200x
Fri.
141
0
0
16
4
83
0
4
2
10
16
11.35%
42
41.13%
7/31/200x
Sat.
141
0
12
8
4
57
0
3
0
5
20
14.18%
64
59.57%
Total
4371
2
351
248
242
2190
3
84
37
124
599
13.70%
1580
36.15%
A
246


F. Stayovers are the number of rooms occupied by guests on the previous day
who are not scheduled to check out. (Recall that this number will be decreased
by the actual number of early departures and increased by the number of ac-
tual overstays experienced by the hotel on that day.)
G. Arrivals include all guests with reservations recorded in the PMS. The num-
ber in this column represents rooms, not the number of guests staying in the
rooms, which is computed by adding the data in columns J, K, L, and M.
Some PMS reports provide both numbers—rooms and number of guests
staying in rooms.
H. Due Out (departures) estimates the number of rooms occupied by guests who
indicated at the time of check-in that this was their intended departure date.
I. Available is the total number of additional rooms in the hotel that may be
reserved by guests (column D, less columns E, F, G, and P), as rooms in col-
umn P are already blocked (held) for group guests.
J. Trans Ngtd is the PMS code for transient, Non guaranteed room reservations.
(Recall that individuals with these confirmed reservations tend to be no-
shows at a higher rate than are guests with reservations that are guaranteed.)
K. Trans Gtd is the PMS code for transient, guaranteed room reservations.
L. Group Ngtd is the PMS code for group, non guaranteed room reservations.
M. Group Gtd is the PMS code for group, guaranteed room reservations.
N. Total Occupied is the number of rooms calculated to be occupied on that
date (column F plus column G).
O. Occupancy % is column N divided by column D. In some PMS reports, OOO
rooms are subtracted from column D before the calculation is made. In that
case, the computation would be modified: column N

(column D

column E).
P. Group Block is the number of rooms currently held (usually determined by
contracts) for the hotel’s group guests. As these rooms are reserved by indi-
vidual guests or by rooming list, they will be subtracted from this column
and added to column K or L, depending on the type of reservation made.
Q. Potential % is the occupancy percentage the hotel would experience if all cur-
rently reserved rooms and currently blocked group rooms were sold on an
individual date (columns F and G, plus column P).
Even though each PMS may be different, the conceptual factors affecting occu-
pancy (availability) remain unchanged and are expressed as follows:
Total rooms
Less
Unavailable rooms
Less
Committed rooms
Equal Sellable (available) rooms
Perhaps the most important concept expressed in Figure 17 is, simply:
The data in a PMS summary represent the current status of hotel occupancy; the
data are not an estimate of how many rooms will actually be sold on any day.
In other words, the number of rooms that will actually be sold on any given night is
directly related to the actual pickup of any group blocks held and, equally as impor-
tant, the rooms pricing decisions made by the FOM or other person serving as the
MANAGING FORECAST DATA
247


revenue manager. These pricing decisions are always best made after reviewing the
current data summaries produced by the PMS.
The occupancy reported in Figure 17 is rather low. The hotel will probably
achieve a July occupancy percentage much in excess of that indicated. This is typi-
cally the case with current data 30 days before, or even closer to, the forecast date.
Many July guests will not make reservations until closer to the date on which they
will arrive. Also, many arriving guests will be walk-ins. The lead time between when
guests make reservations and the time of their actual arrival has been shortening in
recent years. In many hotels, 50 percent or more of reservations are made within
seven days of the reserving guest’s arrival date. This makes forecasting more complex.
First, the value of current data is reduced, and second, the FOM must rely more on
historical data and estimates of future data trends.

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