Transcript Prices, Fares, and Yields - Sauder School of Business

Biography for William Swan
Chief Economist, Seabury-Airline
Planning Group. AGIFORS Senior
Fellow. ATRG Senior Fellow.
Retired Chief Economist for Boeing
Commercial Aircraft 1996-2005
Previous to Boeing, worked at
American Airlines in Operations
Research and Strategic Planning
and United Airlines in Research and
Development. Areas of work
included Yield Management, Fleet
Planning, Aircraft Routing, and
Crew Scheduling. Also worked for
Hull Trading, a major market maker
in stock index options, and on the
staff at MIT’s Flight Transportation
Lab. Education: Master’s,
Engineer’s Degree, and Ph. D. at
MIT. Bachelor of Science in
Aeronautical Engineering at
Princeton. Likes dogs and dark
beer. ([email protected])
© Scott Adams
International Yield, 2003$/Km
Prices, Fares, and Yields
0.12
0.11
Estimated Fares
0.10
0.09
reported yields
0.08
trend at -2.4%/year
0.07
0.06
1975
1980
1985
1990
1995
2000
2005
William M Swan
Chief Economist
Boeing Commercial Marketing
July 2003
What is to Come
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Yield Changes overstate Fare Changes
Example Dramatizing Effects
Hard Work on Vocabulary and Distinctions
Hard Work on Real Data
Real Data shows Declining Discount Fares
What is Yield?
• THE common measure of airline fares
• Defined as (Revenues/RPK)
• Data often is international yields
– US carriers
– Long, consistent source of data
• US dollar base avoids exchange rate issues
• Published government reports date to 1960s
“Econometrics”?
RPK = GDP * Yield-
RPK is air travel in Revenue Passenger Kilometers
GDP is Gross Domestic Product, size of economy
Yield is measure of prices
 is income elasticity
 is price elasticity
Regress in log-space and publish
Yield Overstates Fare Declines
Yield is an Imperfect Statistic
Yield is an average:
 Average yield declines with more long trips
 Average yield declines with more discount (pleasure) trips
International Yield, 2003$/Km
Under half of yield decline is decline in fares:
 Business fares have gone up?
 Pleasure fares have gone down, and quality to match
0.12
0.11
Estimated Fares
0.10
0.09
reported yields
0.08
0.07
0.06
1975
trend at -2.4%/year
1980
1985
1990
1995
2000
2005
Concepts by Example
• An example will clarify concepts
• Shows “elasticity” calculated from yield
• Values differ with type of market change
Example Market: Tech Airways
• Long-haul 10,000km
– 20 business @ $800 fare ($750 net of tax)
– 80 leisure @ $350 fare ($300 net of tax)
• Regional 1600km
– 60 business @ $350 ($316 net of tax)
– 240 leisure @ $150 ($135 net of tax)
• Yield is $0.061/km (net of tax)
Less Work, More Play
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Leisure demands each up 20%
No change in fares
Rise in load factor
Yield now $0.058, down 5%
“Elasticity” (%RPK / %yield) = -4.1
Mix of leisure vs. business trips changed
Globalization Triumphs
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Business and Leisure Long-Haul up 20%
Growth from base case, not leisure case
Fares not changed
Load factor long-haul rises
Yield now $0.058km (again), down 5%
“Elasticity” = -2.9
Mix of Long vs. Short trips has changed
Junk Fares Triumph
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New leisure fares of $300 & $113
Leisure fare elasticity of -2.0
1/3 more long-haul leisure passengers
75% more regional leisure passengers
Yield now $0.050/km (down 18%)
“Elasticity” = -2.2
Increased total revenues & load factor
Gouge the Business Market
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Business Fares raised to $850 and $400
Business fare elasticity = -0.5
Business passengers down 3% and 7%
Yield now $0.0623/km (up 2%)
“Elasticity” = -0.3
Increased total revenues
General Fare Decrease
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Decrease all fares 5% (this time before-tax)
Fare elasticities of -0.5 and -2.0, as before
Yield now $0.058/km (yet again)
RPKs up 8%
“Elasticity” = -1.3
This is a believable value
Review the Bidding
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Base Case Yield = $0.061
Increase Leisure Mix: $0.058, E=-4.1
Increase Long-Haul Mix: $0.058, E=-2.9
Decrease Leisure Fares: $0.050, E=-2.2
Increase Business Fares: $0.062, E=-0.3
Across-the-board Decrease: $0.058, E=-1.3
ALL THESE THINGS HAPPENED
Conclusions
• Yield can decline without fare changes
– More long-haul trips
– More leisure-fare trips
• “Elasticity” depends on which fares change
– Decreasing leisure fares increases revenues
– Increasing business fares increases revenues
– Across-the-board decrease is what we imagine?
Agonizing Detail is Available
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US ticket sample data backs up yield totals
Represents trip-by-trip ticket lift
Allows fares by airport pair
Allows distribution of fares within pair
Economists “dream data”
– Unique transaction level detail
• Domestic data is completely public
Fare (US$ without tax, one-way)
A Real Distribution of Fares
“Typical” Atlantic Airport-Pair
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60 50 40
Percentile
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Defining Fares
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“Market” is airport-pair: example: SEA-TLS
“Fare” is one-way, net of taxes (unfortunately)
“Discount Fare” is 25%ile fare from distribution
“Business-A Fare” is 90%ile fare
“Business-B Fare” is derived using assumptions
– Discount fare is 67% of tickets
– Average fare is correct
• “Bus-B” approach worked better than “Bus-A”
Fare (US$ without tax, one-way)
Fare Distribution: Atlantic
2000
1500
1000
90%ile,
Business
Fare
25%ile,
Discount
Fare
500
0
100
90
80
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60 50 40
Percentile
30
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Quibble #1: Zero Fares
• “Average Fare” data includes frequent flyer
trips at zero fare
• Our treatment eliminated these records
• All treatments eliminate unreasonably high
fare records—they distort the averages
• Better a small, cleaner, sample
• Used only one-way or clear round-trip tix
Fare (US$ without tax, one-way)
US
Domestic
Fare
Distribution
1000
90%ile,
Business
Fare
800
600
400
25%ile,
Discount
Fare
200
0
100
90
80
70
60 50 40
Percentile
30
20
10
0
Quibble # 2: Back-to-back
• Synthetic business round trips made
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Purchase 2 round-trips with Saturday stays
Use only one leg of each for 3-day trip
Ticket sample fare is half of round trip
Actual revenue is 2x (a “hidden” business fare)
High no show on non refundables from this
• Distorts fare data, but not total yields
Fare Trend Calculations
• One observation=one airport pair > 24 tix.
– Data is average, discount, or business fare
• Cluster all pairs by 250 mile block
• Get median fare and distance
– Median avoids outliers
• Regress linear fare formula with distance
– Clustering avoids overweighting short-haul
Headline Fare Values
• Use fare trend formula
– E.g.: Fare = $110 + $0.05/km
• Exercise at fixed distance
– 1600km for US domestic
– 7100km for Atlantic
• Data for 10 years: 1991-2001
– Third Quarter data
– Huge processing, simple “fare” value result
Yield Declines Faster Than Prices
$240
$220
US Domestic at 1600 km
Average
$200
$180
$160
Yield
$140
Discount
$120
$100
3q91 3q92 3q93 3q94 3q95 3q96 3q97 3q98 3q99 3q00 3q01
Yield overstates Fare Declines
• Average fares down 0.4%/year
– US domestic case example
• Reported yield decline is 1.4% per year
– Many expected 2.1% per year
– Missing 0.7% is CPI deflator overstating
• “Elasticities” based on yield understate
– By factor of 3 or 4
Yield Decline in Discount Fares
• Discount Fare declined 1.2% per year
– Regression slope of 11 years’ data
– Fairly robust measurement
• Business-B Fare declined 0.1% per year
– Dubious measurement and significance
• Yield decline driven by discount fares only
International Yield, Also
US International Trend Fare at 7100 km
$650
$600
$550
Average Trend Price
Trend Price based on
Reported Yield
$500
$450
$400
Leisure Trend Price
$350
$300
3q91 3q92 3q93 3q94 3q95 3q96 3q97 3q98 3q99 3q00 3q01
Quarter and Year
International Case is Worse
• Mix of Atlantic, Pacific, Latin America
– 42%, 43%, 15% in 1991 RPKs (US carriers)
– 47%, 32%, 21% in 2001 RPKs (US carriers)
– Major changes in distance and fare mix
• Yield down 3.5% per year
– Average fares down 1.6% per year
– Discount fares down 3% per year
– Business-B fares down 0.5% per year
Costs Down 0.4% per Year
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Longer hauls mean lower $/ASK
Smaller airplanes mean higher $/ASK
Net is 0.4% per year
Based on constant factor productivities
Airline costs declined more
– Increased productivity
– Better airplanes, higher utilization, etc.
Flow-by-Flow Cost Savings
WORLD
Atlantic
Pacific
Asia-Europe
Other Long
N.America
Europe
Asia
Other Short
Saving
$/yr
-0.4%
0.3%
-0.0%
0.1%
0.2%
-0.4%
-0.9%
-0.3%
-0.3%
1985
Km
1048
6667
7772
8928
5104
921
643
843
1103
2000 1985 2000
Km Seats Seats
1321 192 187
6932 320 281
9150 347 346
9395 362 336
5805 302 274
1148 155 140
821
145 138
939
241 220
1285 190 171
Conclusions
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Yield Declines overstate Fare Declines
Fare declines driven by discount fares
Business fares nearly constant
Intrinsic cost declines less than fare declines
– Based on changes in distance and airplane size
– Unexplained share implies productivity gains
• Measurement requires excruciating effort