Hotel Math 101, The Metrics used by the Hotel Industry The SHARE Center Supporting Hotel-related Academic Research and Education

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

Property Data

How Does STR Obtain Raw Data? Most raw property sales data is directly exported from the systems of the hotel companies. This help increase the reliability of the data. Companies send STR a raw data file each month, week, and/or day. Some hotels and smaller companies enter the data on the STR web site. The web site can be used to enter monthly or daily data.

Sample Raw Data Here is a sample monthly raw data file that STR would receive, containing data for multiple hotels: In most cases, companies provide their own unique hotel identification without a hotel name A daily file would look the same except for the date field, YYYYMMDD or 20100725 Fictitious data, of course

Data Error Checks STR performs a large volume of comprehensive error checks upon the raw data. New data is compared to prior data for consistency. There are Occupancy and ADR limits related to geography and type of hotel. STR also tracks special events that would cause unusual Occupancies and ADRs. Any exceptions are verified with the data provider before the data is accepted.

STR Data Guidelines STR uses a strict set of definitions based on the “Uniform System of Accounts for the Lodging Industry” Supply (Rooms Available) – the number of rooms in a hotel multiplied by the days in the month Demand (Rooms Sold) – number of rooms sold by a hotel, does not include comp rooms or “no-shows” Revenue – total room revenue generated from the sale of rooms, includes service charges not resort fees, nothing else such as F&B Uniform System of Accounts available from the AHLA or HFTP

Key Performance Indicators From these raw data values, STR calculates the three hotel industry key performance indicators (KPIs) : 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

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

Monthly Occupancy - Formula B C D E F G 1 Supply Demand Revenue (Formula) Occupancy (%) 2 Jan-10 3100 2345 198765 C2 / B2 * 100 75.6 3 Feb-10 2800 2002 175432 C3 / B3 * 100 71.5 4 Mar-10 1776 175012 C4 / B4 * 100 57.3 5 Apr-10 3000 2468 234567 C5 / B5 * 100 82.3 6 May-10 2987 312345 C6 / B6 * 100 96.4 You could multiply times 100 or format as a percentage

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

Monthly ADR - Formula A B C D E F G 1 Supply Demand Revenue (Formula) 2 Jan-10 3100 2345 198765 D2 / C2 84.76 3 Feb-10 2800 2002 175432 D3 / C3 87.63 4 Mar-10 1776 175012 D4 / C4 98.54 5 Apr-10 3000 2468 234567 D5 / C5 95.04 6 May-10 2987 312345 D6 / C6 104.57 You could format as a “$” or as a number with 2 decimals

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 room revenue by the total number of rooms available. This is a dollar amount. RevPAR = Revenue / Supply

Monthly RevPAR – Formula B 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

Percent Change = (This Year – Last Year) / Last Year * 100 Percent Changes Definition The comparison of This Year (TY) numbers vs. Last Year (LY) numbers, whether a raw value or a KPI. The percent change illustrates the amount of growth (up, flat, or down) from the same period last year. Calculation The This Year number minus the Last Year number divided by the Last Year number. This is a percentage. Percent Change = (This Year – Last Year) / Last Year * 100 Remember the parentheses!

Demand Percent Change A B C D E F G 1 This Year Last Year   A B C D E F G 1 This Year Last Year Percent Change 2 Demand (Formula) 3 Jan-10 2345 2456 (B3-D3)/D3*100 -4.5 4 Feb-10 2002 2112 (B4-D4)/D4*100 -5.2 5 Mar-10 1776 1750 (B5-D5)/D5*100 1.5 6 Apr-10 2468 (B6-D6)/D6*100 5.2 7 May-10 2987 2555 (B7-D7)/D7*100 16.9 You could multiply times 100 or format as a percentage

Hint - % Changes for Raw Values The Percent Changes for raw values such as Supply, Demand, and Revenue are valuable bits of information Supply Percent Change shows whether there are more or less rooms this year versus last year Demand Percent Change shows whether there are more or less rooms sold (guests spending the night) this year versus last year Revenue Percent Change shows whether there is more or less money being made by the hotel or hotels (and therefore being spent by those guests)

ADR Percent Change A B C D E F G 1 2010 2009 Percent Change 2 ADR   A B C D E F G 1 2010 2009 Percent Change 2 ADR (Formula) 3 Jan-10 84.76 81.93 (B3-D3)/D3*100 3.4 4 Feb-10 87.63 88.85 (B4-D4)/D4*100 -1.4 5 Mar-10 98.54 100.07 (B5-D5)/D5*100 -1.5 6 Apr-10 95.04 95.24 (B6-D6)/D6*100 -0.2 7 May-10 104.57 116.93 (B7-D7)/D7*100 -10.6 You could multiply times 100 or format as a percentage

Hint - % Changes for KPIs Occupancy Percent Change shows whether the Occupancy this year is greater or less rooms than the Occupancy last year. This could be related to Supply and Demand changes. ADR Percent Change shows whether the average rate this year is greater or less than the average rate last year. RevPAR Percent Change shows whether the RevPAR amount is greater or less than the amount last year. This could be related to Occupancy and ADR differences.

Daily vs. Monthly Data The formulas for daily KPIs and Percent Changes are the same as for monthly The date fields are different: 201007 – monthly 20100725 – daily Most daily percent changes are based upon comparable days, in other words the same day of week with the closest date Thu 20100715 is compared to Thu 20090716 Sat 20100731 is compared to Sat 20090801

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 all of these time periods are based upon the aggregated raw data

YTD Supply, Demand, & Revenue   A 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 1096121 You can use the SUM function to aggregate the raw values

YTD Occupancy, ADR, & RevPAR   A 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 1096121 76.7 94.67 72.59 8 (Formula) C7/B7*100 D7/C7 D7/B7 Aggregate raw values, then apply same formulas as before

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 KPIs (calculated metrics of Occupancy, ADR, and RevPAR) for multiple time periods are always calculated from the aggregated raw data Numbers for multiple time periods never use averages of monthly or daily values. (Some people mistakenly compute YTD occupancy by adding the occupancy of each month and dividing by the number of months.) Aggregating accounts for different days in different months

Hint – Multiple Time Periods Current Month numbers show the performance for a single month and YTD numbers show how performance is unfolding for the current year. Running 3-Month numbers show a little more of a performance trend instead of just the Current Month number. Running 12-Month numbers show a longer performance trend. These numbers can be helpful at the beginning of the year when the YTD number only includes a small number of months. Running 12- Month numbers also remove seasonality effects.

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

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 1096121 76.7 94.67 72.59 11218 1086120 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

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 the hotel actually reported. STR does not change the Supply number of the subject hotel on their own STAR report “Full Availability” is an advanced concept

Full Availability Example - Subject   A 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

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 to generate day of week and weekday/weekend numbers Use percentages for each day, ensures WD/WE adds up

Percent Changes and WD/WE or Day of Week Data Weekday/Weekend (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 Day of Week (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

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 for a single day for the last 4 weeks, i.e.: the last 4 Mondays A hotel can compare their Monday performance metrics to the average of the last 4 Mondays to see if they are doing better or worse on a single day of the week

Questions Briefly describe how STR obtains raw property data Identify the various metrics used by the hotel industry Explain how metrics are calculated when it comes to multiple time periods Compare the differences between how monthly and daily data is treated Use Excel and sample raw data to demonstrate the formulas used to calculate the various numbers

Competitive Set Data

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

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 Some people feel that having the subject data included in the comp set numbers distorts or dilutes the comp set

Comp Set Supply, Demand, & Revenue   A 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 1073960 8 (Formula) sum(C2:C6) sum(D2:D6) sum(E2:E6) Aggregate raw values for each member of the comp set

Comp Set Occupancy, ADR, & RevPAR   A B C D E F G H 1 Property Date Supply Demand Revenue Occupancy ADR RevPAR 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 1073960 78.9 87.80 69.29 8 (Formula) D7/C7*100 E7/D7 E7/C7 Apply KPI formulas to aggregated comp set data

Percent Change Numbers for the Competitive Set Percent Change numbers for the comp set are calculated similarly to the ones for the subject property (This Year – Last Year) / Last Year * 100 These numbers show increases or decreases in performance this year versus last year

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

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

Importance of Index Numbers Index numbers tend to be relied upon heavily by the industry to evaluate the performance of hotels Occupancy, ADR, and RevPAR Index numbers show the current performance of the subject hotel compared to the comp set The index percent change numbers for these same KPIs show if the subject hotel is improving compared to the comp set The headquarters of many companies receive corporate index reports listing each hotel with their index numbers Some companies relate managers’ bonuses to index numbers

Occupancy, ADR, & RevPAR Indexes   A 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 69.29 122.1 119.1 145.4 3 (Formula) B2/E2*100 C2/F2*100 D2/G2*100 Calc KPIs for Subject & Comp, then apply Index formula

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 Index of 90 TY and 80 LY yields an Index % Chg of 12.5%

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

Hint - Index Percent Changes Here is a hypothetical situation - a subject hotel has Occupancy, ADR, and RevPAR indexes that are all below 100. The General Manager gets a call from the Regional Manager who says, “great job”. Why? The Regional Manager may be looking at index percent change numbers that are all strongly positive. So the subject hotel is under performing the comp set, but the subject hotel is catching up (improving more than the comp set average). The opposite scenario could also be true.

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 the 2nd best value in the comp set of 7 Ranking data gives you more than just the KPIs & Indexes

Occupancy Ranking Data – How? The values for each hotel in the comp set including the subject hotel are sorted Then the position of the subject hotel is determined within the group Here are sample values of the subject and the comp set 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

Subject had the 2nd highest ADR (with 2 others) in comp set ADR Ranking Data – Ties Just in case two or more hotels are tied when it comes to a specific value, i.e.: they have the same exact number, then each hotel would get the same number All hotels below with a $95 ADR get a rank of “2 of 6”: 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

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 the calculations are applied to the aggregated data

Sufficiency of Comp Set Data If a Comp Set has 3 or more participating hotels (submitting actual data) other than the subject, then that comp set is defined as “Sufficient”. The numbers for the comp set can then appear on the STAR report Percent change numbers will appear if the comp set had sufficient data this year and last year 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

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

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

Non-Reporting Hotel Example   A 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 1107961 9 (Formula) C7 * 31 D8 * G7 / 100 E8 * H7 Calc Occ & ADR based on Sample, multiply * Total Supply

“Consistent Sample” related to Comp Set data If a subject hotel has a non-reporting property (or a property that reports intermittently) in its’ comp set, that can possibly distort the comp set numbers. Or hotels that participate this year but not last year, or visa versa. You never know if a change in performance is related to what is actually happening with the comp set hotels or the fact that a single hotel’s data is missing There are ways for a subject hotel to remove a non- reporting property from its’ comp set (Participation information for the comp set is displayed on the Response page of the STAR Report)

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 Advanced concept

Full Availability Example   A 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 1073960 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

Questions Demonstrate how KPIs and percent changes are calculated when it comes to comp set data Demonstrate how indexes and ranking data are calculated comparing the subject to the comp set Explain the significance of indexes to the hotel industry Explain the effect of non-reporting hotels in a comp set When will different types of comp set data not appear on a STAR report?

Industry Data

Industry Data Basics STR uses a variety of segments to analyze performance of the hotel industry There are geographic (market, tract) and non-geographic (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

The Methodology for US Industry Data versus Comp Set Data The methodology used for arriving at US 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. (This is why STR includes non-participants in their Census database of hotels.) The Actual and Modeled data are aggregated for all hotels in each industry segment in order to arrive at performance numbers

Modeling of US 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 STR uses a different method for non-US industry numbers Possible to explain technical procedure used for modeling

Key Performance Indicators for Industry Segments The Actual and Modeled data are aggregated for all hotels in each industry segment (market, tract, …) Supply, Demand, and Revenue numbers are the combined values of each hotel in the industry segment The Key Performance Indicators (Occupancy, ADR, and RevPAR) are based upon the aggregated Supply, Demand, and Revenue numbers

Industry Supply, Demand, & Revenue   A 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 1656674 10 (Formula) sum (E2:E8) sum (F2:F8) sum (G2:G8) Accumulate Actual & Modeled Supply, Demand, & Revenue

Industry Occupancy, ADR, & RevPAR   A 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 1656674 84.0 90.91 76.34 10 (Formula) F9/E9 *100 G9/F9 G9/E9 Apply KPI formulas to the accumulated raw data

Percent Change Numbers for Industry Segments Percent Change numbers for industry segments are calculated exactly like the ones for the comp set or the subject property (This Year – Last Year) / Last Year * 100 These numbers show increases or decreases in performance this year versus last year

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 the monthly and daily time periods are always aggregated and then calculations are derived based upon the aggregated data

Supply Numbers Over Time The number of rooms available for an industry segment or any group of hotels, including a comp set, can vary over time due to: New hotels opening - Hotels closing Room additions - Room drops New Supply=Orig Supply+(Opens+Adds)–(Closes+Drops) The number of rooms available for segments such as scale, class, or brand can vary over time due to: Conversions in - Conversions out New=Orig+(Opens+Adds+CvIns)–(Closes+Drops+CvOuts)

Seasonally Closed Hotels Some hotels close for one or more months out of a year In the US, there are 1,460 seasonally closed hotels Many are in resorts areas such as beach or ski/mountain locations Most are closed during some of the winter months, although a few are closed during the summer Supply numbers for industry segments will also be affected by seasonally closed hotels

Sufficiency of Industry Data If an Industry segment has 4 or more hotels that submit actual data, then that segment is defined as “Sufficient”, similar to the comp set rule (3 required) The numbers for that industry segment can then appear on STAR reports and elsewhere. Industry data will not appear when the segment is insufficient. 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

“Consistent Sample” related to Industry Segment data If an industry segment has a non-reporting property (or a property that reports intermittently), that can possibly distort the performance numbers. Or a hotel that participates this year and not last year, or visa versa. Or hotels that open or close during the date range you are looking at. You never know if a change in performance is related to what is actually happening among the hotels in the industry segment or the fact that a single property’s data is missing (You can run a Trend report on a specific group of reporting hotels to analyze performance on a consistent sample.)

Leap Year Methodology The STR methodology for Leap Year assumes that February 29th never exists. If this methodology was not used, there would be an increase in Supply, Demand, and Revenue in Februarys during leap years. All raw February monthly data (property, comp set, and industry) for leap years is multiplied times 28/29 as if this month only had 28 days.

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 When calculating industry data, STR always uses the Supply number based upon full availability, not the number that the hotel reports Advanced concept

Questions Define an industry segment Demonstrate how KPIs and percent changes are calculated when it comes to industry segments Briefly explain how US industry data is “modeled” When will different types of industry data not appear on a STAR report?

Corporate Data

What is meant by Corporate Data? Individual hotels receive STAR reports with data for their subject property compared to their comp set and relevant industry segments The regional managers and the staff at corporate headquarters of these hotels are also very interested in this data Most hotel companies receive volumes of corporate data. These could be chains, management companies, and ownership groups.

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 reports may be subtotaled by various fields (region, brand, operation) Some companies receive “Summary Reports” aggregating data for their hotels based upon various subtotal groups. In addition to reports, companies also receive data files, so they can analyze this data and merge it with internal information

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”

Corporate Aggregations Hotels can be grouped based upon common fields such as Brand, Region, or Operation (Corporate versus Franchise) Hotels can also be grouped based upon user-defined variables, such as Sales Territories, Regional Managers, or Hotel Types The raw hotel and comp set data can be aggregated using various methods, i.e.: Standard Weighting or Portfolio Weighting

International Issues

US versus WW 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 popular segments are Market Class and Tract Class

Non-Reporting Hotels and Industry Data The US is the only country where property data is modeled for non-reporting hotels. The numbers for Industry segments in the US are based on a combination of Actual and Modeled data. Outside the US, the numbers for Industry segments are solely based on Actual data of participating hotels. The methodology used to derive metrics for industry segments is exactly the same as for competitive sets. The Occupancy & ADR of participating hotels are used to estimate non-participating hotels.

WW Participation Issues In some areas of the world, STR participation is still growing and the number of hotels submitting data may be smaller When requesting data back in time, you need to check past participation There may be enough hotels to pass sufficiency tests for recent months, but not back in time Also keep participation in mind when you are looking at year-over-year change to be sure it is not affected by new hotels starting to submit data

Currencies and Exchange Rates Outside the US, most hotels want to see their STAR reports in their local currency STR obtains daily and monthly exchange rates for all currencies in the world (at least the countries that have hotels) from Oanda (www.oanda.com) Daily data is converted using the daily exchange rate Monthly data is converted using the daily exchange rate for the last day of the month

Exchange Rates and Multiple time periods It is important to understand how exchange rates are handled when it comes to multiple time periods for monthly data, i.e.: YTD and Running 3 or 12 month numbers Raw data is aggregated using the exchange rate for each individual month and then the KPIs are derived. This methodology accounts for changing exchange rates. Multiple time periods for daily data, i.e.: weekly or Running 28-day numbers are calculated the same way, using the exchange rate for each individual day

Currencies and Corporate Data When companies obtain data from STR, they may request the numbers in multiple currencies, i.e. US Dollars, Euros, and Local. Analyzing the performance of hotels in a company spread over multiple countries can sometimes be distorted by fluctuating exchange rates. STR produces some data and reports for companies in a “constant currency”. This methodology applies a single exchange rate i.e.: the rate from January of the current year to the numbers for every month.

Additional Data

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 HOST Data

Questions? Steve Hood steve@str.com 615-824-8664, extension 3315 www.strglobal.com