Pearson New International Edition International pcl tp indd 1

MANAGING FORECAST DATA
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Report run date: 
June 1, 200X
Altoona Hotel
Six-Month Pace Report
June 200X
1
2
3
4
5
6
7
8
9
PACE
MONTHLY
On-the-books
On-the-books
PACE
Variance
ADR
ADR
ADR
PLAN
Percent
Next 6 months
Prior 6 months
Variance
Percentage
Next 6 months
Prior 6 Months
Variance
(Budget)
of Budget
June 200X
$ 79,250
$ 75,466
$ 3,784
5.01%
$77.42
$70.40
$7.02
$150,450.00
53%
July 200X
$ 44,521
$ 43,703
$
818
1.87%
$79.25
$75.74
$3.51
$142,325.00
31%
August 200X
$ 17,080
$ 18,100
($ 1,020)
⫺
5.63%
$82.12
$80.82
$1.30
$163,650.00
10%
September 200X
$ 14,314
$ 8,752
$ 5,562
63.55%
$81.04
$78.77
$2.27
$155,475.00
9%
October 200X
$ 13,631
$ 7,741
$ 5,890
76.09%
$79.56
$77.43
$2.13
$189,315.00
7%
November 200X
$ 6,534
$ 2,369
$ 4,165
175.80%
$81.45
$82.34
($0.89)
$112,000.00
6%
Totals
$175,330
$156,131
$19,199
12.30%
$913,215.00
19%
FIGURE 18
Altoona Hotel six-month pace report.
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six months (see column 1). Note that as the monthly reservation data are
totaled further into the future, the current data related to that month are
reduced. In most hotels, this occurs because of the short-term booking nature
of transient guests.
Column 2 details the same information from the identical report created one
year ago. Column 3 and 4 report, respectively, the dollar and percentage variances
(differences) between the booking paces of the two time periods. Note that for
the six-month period examined, bookings are running at a pace approximately
12.3 percent above that of last year (column 4). This information would be help-
ful to the FOM forecasting future room demand for the hotel.
Figure 18 also shows the on-the-books ADR during the two comparative
periods (columns 5 and 6) and the dollars of ADR variance (column 7). ADR
is important because increases in this RevPar component affect the revenue
totals summed in columns 1 and 2. The report also details the hotel’s monthly
plan, or budget (column 8), and the percentage of the hotel’s planned rooms
sales that has been achieved (booked) as of the report date (column 9).
•
Future data. As FOMs forecast demand far into the future (12 or more
months), they face increasing challenges because the quality of information
available is diminished. New hotels open and existing hotels close. Economic
activity, legislation, and weather are hard-to-predict factors that affect demand.
Consider also terrorism: No one predicted the events of September 11, 2001,
and their impact on hotel occupancy in New York City and across the country.
Despite the difficulty in preparing them, accurate extended forecasts are impor-
tant for several reasons: determining long-range revenue budgets, planning for
human resources needs, and scheduling hotel renovations and repair. The most
important reason, however, for developing extended forecasts relates to their impact
on room pricing.