1. Hotel Math 101 (the Metrics behind STAR Reports and Data)
The SHARE Center
Supporting Hotel-related Academic Research and Education
Steve Hood
Senior Vice President of Research
Smith Travel Research

2. Outline Property Data Comp Set Data Industry Data Corporate Data
International Issues
Additional Data

3. Property Data

4. Starts with Raw Data
____________for every hotel is obtained from clients via corporate feeds or web entry
Sample monthly file:
Daily file would look the same except for the date field, YYYYMMDD or

5. STR Data Guidelines
Supply (__________) – the number of rooms in a hotel multiplied by the days in the month
Demand (_________) – number of rooms sold by a hotel, does not include comp rooms or “no-shows”
Revenue – total room revenue generated from the _________, includes __________not resort fees, nothing else such as _______

6. Key Performance Indicators
From these raw data values, STR calculates the three key performance indicators (KPIs), which are used for reports:
Occupancy - %
Average Daily Rate (ADR)- $
Revenue per Available Room (RevPAR)- $ important metric, based upon all rooms, some feel like it is better measurement of profitability

7. Occupancy = Demand / Supply
Definition
The percentage of available rooms that were sold during a specific time period.
Calculation
Occupancy is calculated by dividing the demand (number of rooms sold) by the supply (number of rooms available), this is a percentage
Occupancy = Demand / Supply

8. Monthly Occupancy - FormulaB C D E F G 1
Supply
Demand
Revenue
(Formula)
Occupancy (%) 2
Jan-10
3100
2345
198765
2345/3100
75.65% 3
Feb-10
2800
2002
175432 4
Mar-10
1776
175012 5
Apr-10
3000
2468
234567 6
May-10
2987
312345
You could multiply times 100 or format as a percentage

9. ADR
Definition
A measure of the average rate paid for rooms sold during a specific time period.
Calculation
ADR is calculated by dividing the room revenue by the demand (rooms sold), this is a dollar amount
ADR = Revenue / Demand

10. Monthly ADR - Formula A B C D E F G 1 Supply Demand Revenue (Formula)2
Jan-10
3100
2345
198765
198765/2345
84.76 3
Feb-10
2800
2002
175432
175432/2002
87.63 4
Mar-10
1776
175012
98.54 5
Apr-10
3000
2468
234567
95.04 6
May-10
2987
312345
104.57
You could format as a “$” or as a number with 2 decimals

11. RevPAR = Revenue / Supply
Definition
A measure of the revenue that is generated by a property in terms of each room available. This differs from ADR because RevPAR is affected by the amount of unoccupied rooms, while ADR only shows the average rate of rooms actually sold.
Calculation
RevPAR is calculated by dividing the dividing the room by the average rate of rooms actually sold.
RevPAR = Revenue / Supply

12. Monthly RevPAR – FormulaB C D E F G 1
Supply
Demand
Revenue
(Formula)
RevPAR ($) 2
Jan-10
3100
2345
198765
D2/B2
64.12 3
Feb-10
2800
2002
175432
D3/B3
62.65 4
Mar-10
1776
175012
D4/B4
56.46 5
Apr-10
3000
2468
234567
D5/B5
78.19 6
May-10
2987
312345
D6/B6
100.76
You could format as a “$” or as a number with 2 decimals

13. Percent Change = ((This Year – Last Year) / Last Year) * 100
Percent Changes
Definition
The comparison of s(TY) numbers vs. Last year(LY) numbers. The percent change illustrates the amount of growth (up, flat, or down) from the same period last year.
Calculation
Percent Change = ((This Year – Last Year) / Last Year) * 100

14. Demand Percent Change A B C D E F G 1 This Year Last YearA B C D E F G 1
This Year
Last Year
Percent Change 2
Demand
(Formula) 3
Jan-10
2345
2456
-4.52 4
Feb-10
2002
2112
-5.21 5
Mar-10
1776
1750
1.49 6
Apr-10
2468
5.25 7
May-10
2987
2555
16.91
You could multiply times 100 or format as a percentage

15. ADR Percent Change A B C D E F G 1 This Year Last Year Percent ChangeA B C D E F G 1
This Year
Last Year
Percent Change 2
ADR
(Formula) 3
Jan-10
84.76
81.93 4
Feb-10
87.63
88.85 5
Mar-10
98.54
100.07 6
Apr-10
95.04
95.24 7
May-10
104.57
116.93
You could multiply times 100 or format as a percentage

16. Daily vs. Monthly Data
Formulas for KPIs and Percent Changes are the same
The date fields are different:
– monthly
– daily
Most daily percent changes are based upon ________, in other words _____________________________
Thu compared to Thu
Sat compared to Sat

17. Multiple Time Periods Multiple time periods for monthly data include:
Year-to-Date (YTD)
Running 12-Month (12 Month Moving Avg)
Running 3-Month
Multiple time periods for daily data include:
Current Week
Month-to-Date (YTD)
Running 28-Day (different than Running 4-wk)
The metrics for these time periods are based upon the aggregated raw data.

18. YTD Supply, Demand, & RevenueA B C D 1
Supply
Demand
Revenue 2
Jan-10
3100
2345
198765 3
Feb-10
2800
2002
175432 4
Mar-10
1776
175012 5
Apr-10
3000
2468
234567 6
May-10
2987
312345 7
(Formula)
sum(B2:B6)
sum(C2:C6)
sum(D2:D6) 8
May YTD
15100
11578
Use the SUM function to aggregate the raw values

19. YTD Occupancy, ADR, & RevPARA B C D E F G 1
Supply
Demand
Revenue
Occupancy
ADR
RevPAR 2
Jan-10
3100
2345
198765 3
Feb-10
2800
2002
175432 4
Mar-10
1776
175012 5
Apr-10
3000
2468
234567 6
May-10
2987
312345 7
YTD
15100
11578
76.7
94.67
72.59 8
(Formula)
C7/B7*100
D7/C7
D7/B7
Aggregate raw values, then apply same formulas as before

20. Other Multiple Time Periods
The Raw data for other monthly and daily time periods are calculated the same way by aggregating the raw data for every month or day in the entire time period
The calculated metrics (Occupancy, ADR, and RevPAR) for multiple time periods are always calculated from ___________________
Numbers for multiple time periods never use averages of monthly values

21. Percent Changes for Multiple Time Periods
The percent changes for multiple time periods are based on the aggregated values or the calculated metrics which are derived from the aggregated values for this year compared to the same values for last year
Percent changes for daily data are based upon groups of comparable days, with the exception of Month-to-Date numbers which are based on a date-to-date comparison

22. YTD Percent Changes A B C D E F G H I J K L M N O P
This Year
Last Year
Percent Changes 1
Date 
Sup-ply
Dem-and
Revenue
Occu-pancy
ADR
Rev-PAR
Occupancy
RevPAR 2
Jan-10
3100
2345
198765
2456
201234 3
Feb-10
2800
2002
175432
2112
187654 4
Mar-10
1776
175012
1750
175123 5
Apr-10
3000
2468
234567
223344 6
May-10
2987
312345
2555
298765 7
YTD
15100
11578
76.7
94.67
72.59
11218
74.3
96.82
71.93
3.2
-2.2
0.9 8
(Formula)
(E7-K7)/K7*100
(F7-L7)/F7*100
(G7-M7)/G7*100
Aggregate 1st, KPI formulas 2nd, % Change formulas 3rd

23. Full Availability – Subject Hotel
Occasionally a subject hotel may report a Supply number that is different than the number of rooms in the property times the days in the period
If this happens in the case of the subject hotel, their STAR report will always reflect the Supply and the corresponding Occupancy based upon the number _________________.
STR does not change the Supply number of the subject hotel on their own STAR report

24. Full Availability Example - SubjectA B C D E F G H 1
Date
# Rms
Actual Supply
Report-ed Supply
Demand
Revenue
Formula
Occu-pancy 2
Jan-10
100
3100
2345
198765
D2 / E2 * 100
75.6 3
Feb-10
2800
2744
2002
175432
D3 / E3 * 100
73.0 4
Mar-10
2945
1776
175012
D4 / E4 * 100
60.3 5
Apr-10
3000
2700
2468
234567
D5 / E5 * 100
91.4 6
May-10
2987
312345
D6 / E6 * 100
96.4
Occupancy for Subject based on reported Supply, not Actual

25. Weekday/Weekend and Day of Week Data vs. Monthly Data
Sometimes a hotel will submit daily data that does not add up exactly to the monthly number
There are good reasons for this; some systems do not accept adjustments to daily data, only to the month numbers
STR will slightly adjust the daily numbers based upon the monthly data when they are aggregated by day of week and weekday/weekend
Use percentages for each day, ensures WD/WE adds up

26. Percent Changes and WD/WE or Day of Week Data
____________ (WD/WE) Percent Changes compare all the aggregated weekday or weekend data (per month or other time period) this year to the same data last year
____________(DOW) Percent Changes compare all the aggregated daily data for a single day (per month or other time period) this year to the same data last year

27. Running 4 Week Data
The Weekly Reports compare individual daily data for the Current Week to the Running 4 Week numbers
The Running 4 Week numbers are the aggregated data __________________, i.e.: _____________
A hotel can compare their Monday performance metrics to the average of the last 4 Mondays

28. Competitive Set Data

29. Key Performance Indicators for the Competitive Set
Numbers for the comp set are derived based on aggregated raw data
Supply, Demand, and Revenue numbers are the combined values of each hotel in the comp set
Occupancy, ADR, and RevPAR numbers are bases on the aggregated Supply, Demand, and Revenue

30. Including or Excluding the Subject Hotel in the Competitive Set
STR allows companies to choose whether to include or exclude the data for the subject hotel in the numbers for the comp set
Historically companies usually included the data for the subject hotel, but more recently most companies have decide to exclude the subject
People feel that having the subject data included in the comp set numbers distorts the comp set

31. Comp Set Supply, Demand, & RevenueA B C D E 1
Property
Date
Supply
Demand
Revenue 2
11111
May-10
3100
2222
187654 3
22222
3255
2468
198765 4
33333
2945
2345
223344 5
44444
2790
1987
165432 6
5555
3410
3210
298765 7
Comp Set
15500
12232 8
(Formula)
sum(C2:C6)
sum(D2:D6)
sum(E2:E6)
Aggregate raw values for each member of the comp set

32. Comp Set Occupancy, ADR, & RevPARA B C D E F G H 1
Property
Date
Supply
Demand
Revenue
Occupancy
ADR
RevPAR 2
11111
May-10
3100
2222
187654
 71.7
 84.46
 60.53 3
22222
3255
2468
198765
 75.8
 80.54
 61.06 4
33333
2945
2345
223344
 79.6
 95.24
 75.84 5
44444
2790
1987
165432
 71.2
 83.26
 59.29 6
5555
3410
3210
298765
 94.1
 93.07
 87.61 7
Comp Set
15500
12232
78.9
87.80
69.29 8
(Formula)
D7/C7*100
E7/D7
E7/C7
Apply KPI formulas to aggregated comp set data

33. Percent Change Numbers for the Competitive Set
Percent Change numbers for the comp set are calculated similarly to the ones for the subject property
These numbers show increases or decreases in performance this year versus last year

34. Comp Set Occupancy, ADR, & RevPAR
Percent Changes A B C D E F G H I J K 1
This Year
Last Year
Percent Changes 2
Date
Occu-pancy
ADR
Rev-PAR
Occupancy
RevPAR 3
Comp Set
May-10
78.9
87.80
69.29
82.6
93.86
77.50
-4.4
-6.5
-10.6 4
(Formula)
(C7-F7)/F7*100
(D7-G7)/G7*100
(E7-H7)/H7*100
Calculate TY & LY KPIs, then apply % Change formulas

35. Index Numbers
The Index numbers compare the performance of the subject property to the comp set
Subject / Comp Set * 100
A number greater than 100 means the subject property _outperformed___________ the comp set and a number below 100 means the comp set ______________the subject property
Index numbers are available for Occupancy, ADR, RevPAR and the Percent Changes
Index numbers are percentages, multiple * 100 or format as %

36. Occupancy, ADR, & RevPAR IndexesA B C D E F G H I J
Subject Property
Comp Set
Index Numbers 1
Occu-pancy
ADR
Rev-PAR
Occupancy
RevPAR 2
May-10
96.4
104.57
100.76
78.9
87.80
69.29 3
(Formula)
Calc KPIs for Subject & Comp, then apply Index formula

37. Index Percent Change Numbers
First you calculate the Index numbers this year for Occupancy, ADR, and RevPAR
Next you calculate the Index numbers for last year using the same formulas
Then you can calculate the Percent Changes for the Index numbers, this shows whether the Subject is improving
Indexes could be below 100 TY, but if Percent Changes are positive, Subject is improving

38. Occupancy, ADR, & RevPAR Index
Percent Changes A B C D E F G H I J 1
Index Numbers 2
This Year
Last Year
Percent Change 3
Date
Occu-pancy
ADR
RevPAR
Occupancy 4
May-10
122.1
119.1
145.4
99.8
124.6
124.4
22.3
-4.4
16.9 5
(Formula)
(B2-E2)/E2 *100
(C2-F2)/F2 *100
(D2-G2)/G2 *100
Calc indexes TY & LY, then apply % Change formulas

39. Ranking Data – What is it?
STAR Property Reports include Ranking information for Occupancy, ADR, RevPAR and each Percent Change, comparing the subject hotel to the comp set
The Ranking data would be in the format of “X of Y”, where X is the subject hotel’s position and Y is the number of participating properties in the comp set, for example “2 of 7” would mean the subject hotel had 2nd best value in the comp set of 7
Ranking data gives you more than just the KPIs & Indexes

40. Occupancy Ranking Data – How?
The values for each hotel in the comp set including the subject hotel are sorted and then the position of the subject hotel is determined within the group
STR#
1234
2345
3456
4567 (Subject)
5678
6789
Value 87 85 83 82 78 75
Rank
1 of 6
2 of 6
3 of 6
4 of 6
5 of 6
6 of 6
Subject had the 4th highest occupancy in the comp set of 6

41. Subject had the 2nd highest ADR (with 2 others) in comp set
ADR Ranking Data – Ties
If two or more hotels are tied, i.e.: they have the same value, then each hotel would get the same number
STR#
1234
2345
3456
4567 (Subject)
5678
6789
Value
$97
$95
$92
$88
Rank
1 of 6
2 of 6
5 of 6
6 of 6
Subject had the 2nd highest ADR (with 2 others) in comp set

42. Multiple Time Periods and Comp Set Data
Multiple time periods are handled the same way for a comp set as they are handled for a subject property
The Raw data for monthly and daily time periods are always aggregated and then calculations are applied to the aggregated data

43. Sufficiency of Comp Set Data
If a Comp Set has 3 or more participating hotels (submitting actual data) then that comp set is defined as “Sufficient”
The numbers for that comp set can then appear on the STAR report
Multi-year numbers are considered to be sufficient if greater than 50% of the months or day included in the multi-year period are sufficient

44. Full Availability and Comp Sets
Occasionally a hotel in the comp set may report a Supply number that is different than the number of rooms in the property times the days in the period
In those cases, STR uses the Supply number based upon full availability, not the number that the hotel reports

45. Full Availability ExampleA B C D E F G H I 1
Property
Date
# Rms
Actual Supply
Reported Supply
Demand
Revenue
Occu-pancy (Full)
Occu- pancy (Report) 2
11111
May-10
100
3100
2222
187654 3
22222
105
3255
3340
2468
198765 4
33333 95
2945
2900
2345
223344 5
44444 90
2790
2199
1987
165432 6
5555
110
3410
3210
298765 7
Comp Set
15500
(14949)
12232
78.9
(81.8) 8
(Formula)
sum (D2:D6)
sum (F2:F6)
sum (G2:G6)
D7/F7 *100
Formulas are based upon Actual Supply, not Reported

46. Non-Reporting Hotels in the Comp Set
There may be situations where one or more hotels in a comp set does not report data for a month or more
First, the Supply, Demand, and Revenue for the participating properties is aggregated. This is the “Sample” Supply, Demand, and Revenue.
Next, an Occupancy and ADR is calculated based on the Sample data

47. Non-Reporting Hotels in the Comp Set - continued
Then the Supply is determined for all hotels in the comp set, simply the number of rooms times the days in the month. This is referred to as the “Census” Supply.
This Supply number is multiplied times the Sample Occupancy to derive the Census Demand
The Census Demand is multiplied times the Sample ADR to derive the Census Revenue

48. Non-Reporting Hotel ExampleA B C D E F G H 1
Property
Date
# Rms
Supply (Actual)
Demand
Revenue
Occu-pancy
ADR 2
11111
May-10
100
3100
2222
187654 3
22222
105
3255
2468
198765 4
33333 95
2945
2345
223344 5
44444 90 6
5555
110
3410
3210
298765 7
Comp Set Sample #s
410
12710
10245
908528
80.6
88.68 8
Comp Set Census #s
500
15500
12494 9
(Formula)
C7 * 31
D8 * G7 /
E8 * H7
Calc Occ & ADR based on Sample, multiply * Total Supply

49. Industry Data

50. Industry Data Basics
STR uses a variety of segments to analyze performance of the hotel industry
There are __________(market, tract) and ________ (scale, location) categorizations
STAR Reports and corporate data files will frequently compare a subject hotel to nearby industry segments
Publications and Destination Reports will also display the performance of industry segments

51. The Methodology for Industry Data versus Comp Set Data
The methodology used for arriving at industry numbers is different than the one for arriving at comp set numbers
Actual data is used for hotels that participate and “modeled data” is used for hotels that do not participate
The Actual and Modeled data is aggregated for all hotels in each industry segment

52. Modeling of Industry Data
STR estimates the data of non-participating hotels to help increase the accuracy of industry data
Data for a non-participant is estimated based on participating hotels that are closest to the non- participant based on geography and price level
No modeled data is ever used in the Comp Set numbers
Possible to explain technical procedure used for modeling

53. Key Performance Indicators
for Industry Segments
The Actual and Modeled data is aggregated for all hotels in each industry segment
Supply, Demand, and Revenue numbers are the combined values of each hotel in the comp set
Occupancy, ADR, and RevPAR numbers are based on the aggregated Supply, Demand, and Revenue

54. Industry Supply, Demand, & RevenueA B C D E F G 1
Property
Date
# Rms
Type of Data
Supply
Demand
Revenue 2
11110
May-10
100
Actual
3100
2222
187654 3
22220
105
3255
2468
198765 4
33330 95
Modeled
2945
2345
223344 5
44440 90
2790
2456
234567 6
5550
110
3410
3210
298765 7
6660 85
2635
2511
201234 8
7770
115
3565
3012
312345 9
Tract Scale
700
21700
18224 10
(Formula)
sum (E2:E8)
sum (F2:F8)
sum (G2:G8)
Accumulate Actual & Modeled Supply, Demand, & Revenue

55. Industry Occupancy, ADR, & RevPARA B C D E F G H I J 1
Property
Date
# Rms
Type of Data
Supply
Demand
Revenue
Occu-pancy
ADR
Rev-PAR 2
11110
May-10
100
Actual
3100
2222
187654 3
22220
105
3255
2468
198765 4
33330 95
Modeled
2945
2345
223344 5
44440 90
2790
2456
234567 6
5550
110
3410
3210
298765 7
6660 85
2635
2511
201234 8
7770
115
3565
3012
312345 9
Tract Scale
700
21700
18224
84.0
90.91
76.34 10
(Formula)
F9/E9 *100
G9/F9
G9/E9
Apply KPI formulas to accumulated raw data

56. Percent Change Numbers for the Industry Segment
Percent Change numbers for the industry segment are calculated exactly like the ones for the comp set or the subject property
These numbers show increases or decreases in performance this year versus last year

57. Multiple Time Periods and Industry Data
Multiple time periods are handled exactly the same for an industry as for a comp set or a subject property
The Raw data for monthly and daily time periods are always aggregated and then calculations are derived based upon the aggregated data

58. Sufficiency of Industry Data
If an Industry segment has 4 or more hotels that submit actual data, then that segment is defined as “Sufficient”
The numbers for that industry segment can then appear on STAR reports and elsewhere
Multi-year numbers are considered to be sufficient if greater than 50% of the months or day included in the multi-year period are sufficient

59. Full Availability
Occasionally a hotel in the industry segment may report a Supply number that is different than the number of rooms in the property times the days in the period
In those cases, STR uses the Supply number based upon full availability, not the number that the hotel reports

60. Corporate Data

61. What do Companies Receive?
Most corporate headquarters receive reports listing each of their hotels and the various performance metrics, referred to as “Index Reports”. These may be subtotaled.
Some companies receive “Summary Reports” aggregating data for their hotels based upon various subtotal groups.
Many companies receive data files containing this same type of data to use internally

62. Who do Companies Compare Their Hotels to?
Most commonly, companies compare their hotels to the corresponding comp sets
Sometimes they compare their hotels to the corresponding industry segment of the subject property, such as a Market or Tract Scale
They may compare total Brand numbers to the corresponding Scale total, or to a group of other brands, referred to as a “Corporate Comp Set”

63. Corporate Aggregations
Hotels can be grouped based upon common fields such as Brand, State, or Operation
Hotels can also be grouped based upon user- defined variables, such as Sales Regions or Hotel Types
Raw data can be aggregated using Standard Weighting or Portfolio Weighting

64. International Issues

65. Industry Segments
In the US and in North America, probably the most popular industry segment to compare hotels to are Market Scale or Tract Scale
The Scale category is totally related to chain hotels
Outside North America, since there are much less chain hotels, Class is used instead and the poplar segments are Market Class and Tract Class

66. Currencies and Exchange Rates
Outside the US, most hotels want to see their STAR reports in their local currency
STAR obtains daily and monthly exchange rates for all currencies in the world (at least the countries that have hotels) from Oanda
Daily data utilizes the daily exchange rate
Monthly data utilizes the daily exchange rate for the last day of the month
Multi-year data is aggregated in local currency

67. Additional Data

68. Additional Issues/Topics
Segmentation Data (Group, Transient, Contract)
Additional Revenue Data (F&B, Other, Total)
Data within a Trend Report
Data within a Hotel Review or Destination Report