1. U.S. Lodging Industry Briefing and Forecast
The IACBV Outlook Forum
October 5, 2004
Bjorn Hanson, Ph.D.
Global Hospitality Industry Leader
PricewaterhouseCoopers LLP

2. Topics LODGING INDUSTRY PERFORMANCE LODGING INDUSTRY FORECASTS
1.2 Why is this Cycle so Different?
1.3 Does the Term “Recovery” Apply?
LODGING INDUSTRY FORECASTS
2.1 U.S. Lodging Forecast
3. EFFECTS OF THE INTERNET ON PRICING

3. LODGING INDUSTRY PERFORMANCE
1.1

4. U.S. Occupancy Remains Below Long-Term Trend Line
Occupancy Percentage % % %
YTD ‘ % % % % % %
Sources: PricewaterhouseCoopers LLP (1927 to 1986 and 2003), Smith Travel Research (1987 to 2002).

5. 2003 Occupancy 2004 Occupancy 59.2% occupancy lower in:
7 of the past 75 years
2004 Occupancy
60.6%
occupancy lower in:
10 of the past 75 years
Source: PricewaterhouseCoopers LLP based on PricewaterhouseCoopers and Smith Travel Research data.

6. For the First Time Since 1930 to 1933, Nominal ADR Decreased or Remained Unchanged in Three Consecutive Years
Percentage Change From Prior Year % % %
Sources: PricewaterhouseCoopers LLP (1967 to 1986), Smith Travel Research (1987 to 2003).

7. YTD 2004 Nominal Average Daily Rate Above Previous Years
Nominal ADR
2004
2000
2003
Sources: PricewaterhouseCoopers LLP (1967 to 1986), Smith Travel Research (1987 to 2004).

8. YTD 2004 Real Average Daily Rate Similar to Previous Years
Real ADR
2000
2004
2003
Sources: PricewaterhouseCoopers LLP (1967 to 1986), Smith Travel Research (1987 to 2004).

9. Decompression of Chain Scale Demand Performance
Rooms Sold (Average Daily, Seas. Adj.) in 1997 Q1 = 100
Upscale
Midscale without F&B
Luxury
Economy
Upper Upscale
Midscale with F&B
Source: PricewaterhouseCoopers LLP based on Smith Travel Research data through June 2004.

10. Rate Remains a Challenge for the Higher-Rate Segments
Average Daily Rate (Seas. Adj.) in 1997 Q1 = 100
Luxury
Midscale without F&B
Upper Upscale
Midscale with F&B
Economy
Upscale
Sources: PricewaterhouseCoopers LLP based on Smith Travel Research data through June 2004.

11. WHY IS THIS CYCLE SO DIFFERENT?
1.2

12. Demand Elasticity and Correlation to Real GDP
Time Period
Elasticity
Correlation
.89
.93
.29
.83
1967 –
1991 –
2004 estimate 0.9
Source: PricewaterhouseCoopers LLP.

13. Air Travel Demand Has Declined Even More Significantly than Lodging Demand
1987 value = 100
US Real GDP
Lodging Demand (Room Nights Sold)
Air Travel Demand (Passenger Enplanements)
Sources: Lodging demand – PricewaterhouseCoopers LLP based on Smith Travel Research data; Real GDP- U.S. Bureau of Economic Analysis; Air travel demand - Air Transport Association.

14. Long-Term Average: 92,176 3.6% of existing supply
Room Starts Declined, But Not to Level of Previous Troughs, and Are Now Increasing
Room Starts
Long-Term Average: 92, % of existing supply
Sources: PricewaterhouseCoopers LLP (2004 to 2006), PricewaterhouseCoopers LLP based on F.W. Dodge data (1967 to 2003).

15. DOES THE TERM “RECOVERY” APPLY?
1.3

16. Lodging Demand Trend Line for 2001 to 2003 is Below Long-Term Trend Line…
Average Daily Room Nights Sold in Thousands (Seas. Adj.)
Long-Run Demand Trend
Actual Demand
Demand Trend
Source: PricewaterhouseCoopers LLP based on Smith Travel Research data through June 2004.

17. … as is the ADR Trend Line…
Average Daily Rate (Seas. Adj.)
Long-Run ADR Trend
ADR Trend
Actual ADR
Source: PricewaterhouseCoopers LLP based on Smith Travel Research data through June 2004.

18. …as is the RevPAR Trend Line
Average Revenue Per Available Room (Seas. Adj.)
Long-Run RevPAR Trend
Actual RevPAR
RevPAR Trend
Source: PricewaterhouseCoopers LLP based on Smith Travel Research data through June 2004.

19. U.S. LODGING FORECAST
2.1

20. PwC Forecasts for 2002 and 2003 Occupancy Occupancy
December 13, %
Actual 2002 (STR) %
Occupancy
December 12, 2002 (brief Iraq war) 59.6%
Actual 2003 (STR) %
Actual 2002 Occupancy according to STR was originally forecasted in 2002 to be 59.3%. However, latest May 2004 forecasts show occupancy to be 58.9%.
Source: PricewaterhouseCoopers LLP, Smith Travel Research (actuals).

21. U.S. Macroeconomic Assumptions
FORECAST
Real GDP Growth
Consumer Price Inflation
Source: Macroeconomic Advisers L.L.C. as of December 2003.

22. U.S. Lodging Industry Forecasts
Percentage Change from Prior Year
FORECAST
2000
2001
2002
2003
2004
2005
2006
Average Daily Rooms Sold
3.5
-3.4
0.3
1.6
3.7
2.8
2.7
End-of-Year Supply
2.6
1.9
1.6
1.1
1.3
1.5
1.7
Occupancy Level (%)
63.3
59.7
58.9
59.1
60.6
61.5
62.1
Average Daily Rate
5.3
-1.4
-1.4
0.2
3.7
3.5
3.4
Revenue per Available Room
6.1
-6.9
-2.6
0.5
6.3
5.0
4.5
Sources: PricewaterhouseCoopers LLP (2004 to 2006), Smith Travel Research (2000 to 2003).

23. Occupancies Will Increase in All Segments in 2004 through 2006
Percentage Change from Prior Year
FORECAST
Segment
2000
2001
2002
2003
2004
2005
2006
U.S.
0.3
-3.6
-0.7
0.2
1.6
0.9
0.6
Luxury
8.2
-8.7
-0.4
1.3
2.8
0.7
0.9
Upper Upscale
1.1
-6.7
0.6
0.1
1.9
1.2
1.2
Upscale
1.0
-5.3
-0.1
0.7
2.5
1.0
1.1
Midscale with F&B
0.3
-3.9
-1.3
-0.1
1.3
0.5
0.8
Midscale without F&B
-0.1
-2.5
-0.2
0.1
2.0
1.9
1.7
Economy
0.3
-2.0
-1.8
-0.6
1.3
0.3
1.2
Sources: PricewaterhouseCoopers LLP (2004 to 2006), Smith Travel Research (2000 to 2003).

24. …and Combine with ADR Increases for Strong RevPAR Growth in 2004 through 2006
Percentage Change in Nominal RevPAR from Prior Year
FORECAST
Segment
2000
2001
2002
2003
2004
2005
2006
U.S.
5.4
-6.9
-2.6
0.5
6.3
5.0
4.5
Luxury
8.1
-14.2
-5.1
1.2
8.7
5.7
6.2
Upper Upscale
5.5
-11.6
-3.4
-1.7
6.1
5.8
6.3
Upscale
4.8
-8.4
-4.9
-1.0
7.2
4.9
5.3
Midscale with F&B
3.8
-6.8
-4.1
-0.5
4.3
3.8
4.7
Midscale without F&B
4.8
-1.6
-0.7
0.5
5.7
6.2
5.6
Economy
2.7
-3.2
-3.5
-1.2
4.0
3.6
5.2
Sources: PricewaterhouseCoopers LLP (2004 to 2006), Smith Travel Research (2000 to 2003).

25. Nominal U.S. RevPAR Levels Will Achieve 2000 Q4 Levels by 2005 Q3
Percentage Change
Seas. Adj. Nominal RevPAR
FORECAST
RevPAR
RevPAR Percentage
Change
Sources: PricewaterhouseCoopers L.L.P. (2004 Q3 to 2005 Q4), Smith Travel Research (1998 Q1 to 2004 Q2).

26. Real U.S. RevPAR Levels Will Achieve 2001Q2 or 1996 Q2 Levels by 2006 Q4
Percentage Change
Seas. Adj. Real RevPAR
FORECAST
RevPAR Percentage
Change
RevPAR
Sources: PricewaterhouseCoopers L.L.P. (2004 Q3 to 2006 Q4 forecasts), Smith Travel Research (1995 Q1 to 2004 Q2), Bureau of Labor Statistics (CPI history), Macroeconomic Advisers L.L.C. (CPI forecasts).

27. PricewaterhouseCooper’s Forecast of 6
PricewaterhouseCooper’s Forecast of 6.3 percent RevPAR growth would be the highest since 1984

28. EXPLORATORY ANALYSIS OF THE EFFECT OF INTERNET ON PRICING3
EXPLORATORY ANALYSIS OF THE EFFECT OF INTERNET ON PRICING

29. Internet Effect Conclusions
Incremental bookings due to the Internet contributed 26,000 rooms per night, providing $715 million in additional revenue to U.S. hotels.
In 2003, approximately 7.5 percent of Internet hotel bookings were “incremental” bookings.
The increased transparency of the Internet has slowed ADR growth, transferring $1.987 billion in ADR from hotels to consumers.
Strategies for hotel companies tie into game-theoretic concepts (to be refined & simplified):
Selling to a buyer with an unknown valuation:
Set at least two prices, guaranteeing the purchase at the higher price. The buyer who receives utility from a particular hotel’s amenities and location will pay a higher price (through transparent distribution) than the buyer who only receives utility from a certain type of hotel (opaque distribution).
Maximize the expected difference in utility between the price categories. Reserving loyalty program awards, room upgrades, and other “perks” for the guaranteed-product buyer can protect the difference in consumer utility between the guaranteed and non-guaranteed prices.
Price competition with imperfect information:
Establish automatic responses to discounting. Matching (or beating) the lowest price removes incentive for distributors to “cheat” by lowering price, as 1) the potential for gaining incremental sales volume by lowering prices is reduced or removed, and 2) consumers act as an additional, rapid source for pricing information.
Provide a centralized source for pricing for all properties and distributors. By loading all rates into a CRS and providing access to the CRS to distributors, seller information is improved as all rates for a property may be monitored at once and individual properties automatically follow corporate pricing strategy.
The net effect of the Internet for U.S. hotels in was a net loss of $1.272 billion.
Brands lost $3.18 of their rate premium in 2003.
Source: PricewaterhouseCoopers LLP.

30. Increased Use of the Internet Has Contributed to a $3
Increased Use of the Internet Has Contributed to a $3.18 Decline of Rate Premium by Brands
Ratio of Chain to Independent
Internet bookings as share of all hotel bookings
Occupancy
ADR
Internet Bookings
Price Competition
Internet Effects
Source: PricewaterhouseCoopers LLP based on Smith Travel Research data through October 2003; PhoCusWright.

31. Internet Effect Conclusions
2003
2005
Incremental bookings due to the Internet (rooms per night)
Revenue gain from incremental Internet bookings ($ millions)
Revenue loss from slower ADR growth due to transparency and competition of Internet
Net Internet Effect
26,000
45,000
$715
$1,240
($1.987)
billion
($2.9)
billion
($1.272)
billion
($1.66)
billion

33. U.S. Lodging Industry Briefing and Forecast
The IACBV Outlook Forum
October 5, 2004
Bjorn Hanson, Ph.D.
Global Hospitality Industry Leader
PricewaterhouseCoopers LLP