Transcript Document

Biography for William Swan
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.
([email protected])
© Scott Adams
Airline Route Developments
The Unexpected
6
ASK index
5
4
World ASK Growth
3
Growth Fit with 1%
Annual Reduction
2
1
0
1970
1975
1980
1985
1990
1995
Bill Swan, Chief Economist, Boeing Marketing
2000
Airline Route Networks Change Over Time
Outline of Discussion
I.
The History of Route Developments
Similar patterns from all regions of the world
II.
Why Do These Patterns Dominate?
Several reasons, which is most important?
III. Implications for Airline Strategies
Historical trends could change
The burden of proof lies on explaining why
I.
Growth is Served by More Airplanes, Not Bigger
Jet Schedules Show Decreasing Seat Counts
Year
1985
1990
1995
2000
Seat Count
(Average)
192
195
194
187
Data from August schedules
ASKs
(100%=1985)
100%
138%
174%
225%
Average Capacities Are Static Or Down
Growth is similar for all regions
Airline
1990
2000
Domicile
Seat Count Seat Count
WORLD
195
187
China, Hong Kong
212
205
293
Southeast Asia
281
Europe
201
191
Oceania
215
212
Central America
150
135
Japan & Korea
290
271
Middle East
235
228
215
Africa
200
Southwest Asia
218
191
South America
155
136
North America
159
145
Russia Region
153
150
1900-2000
ASK growth
163%
353%
226%
200%
185%
185%
182%
167%
163%
162%
158%
132%
113%
Forecasters in 1983 Had a Really Hard Time
Forecasters in 1983 Had a Hard Time
200
Seats Per Airplane
190
180
170
160
150
140
130
120
1970
1975
1980
1985
1990
1995
2000
Forecasters in 1990 Were Still Confused
230
1990
FORECAST
Seats Per Airplane
220
210
200
190
180
170
2004 data
1990 data
160
150
140
130
1970
1975
1980
1985
1990
1995
2000
2005
2010
What We Missed: New Routes
Daily Departures per Nonstop Pair, average
3.5
3.0
Nonstop Pairs (index)
Departures/Pair
2.5
2.0
1.5
1.0
1970
1975
1980
1985
1990
1995
2000
2005
Air Travel Growth Has Been Met By
Increased Frequencies and Non-Stops
Air Travel Growth Has Been Met By
Increased Frequencies and Non-Stops
250
225
200
Index
1985=100
Air Travel
Frequencies
175
150
Non-Stop Markets
125
Average Stage Length
100
75
1985
Average Airplane Size
1990
1995
2000
Seat Count is -4% of World ASK Growth
Smaller Airplanes - 4%
Longer Ranges 13%
New
Markets
41%
Added
Frequency
50%
Growth Patterns the Same at Closer Detail
Similar patterns all over the world
NE Asia regional
Europe regional
SE Asia regional
Oceania regional
SW Asia regional
S America regional
C America regional
Mid East regional
N America regional
NE Asia-SE Asia
SE Asia-SW Asia
SE Asia-Oceania
Europe-SW Asia
Europe-S America
C America-N America
Europe-C America
Europe-Africa
Europe-N America
S America-N America
NE Asia-N America
Big Routes Do Not Mean Big Airplanes
450
400
Seats Per Departure
350
300
250
200
150
Average
100
50
0
0
2000
4000
6000
8000
10000
12000
Seats Per Day
All Airport Pairs under 5000km and over 1000 seats/day
All Airport Pairs under 5000km and over 1000 seats/day
14000
16000
18000
Size in 1990 Not a Forecast for Size in 2000
Size in 1990 Not a Forecast for Size in 2000
Seats/Dep in 2000 (same pair)
450
400
350
300
250
200
150
150
200
250
300
350
Seats/Departure in 1990, Atlantic pairs
400
450
Small Airplanes Not on New Routes
450
Seats
400
350
New
Old
300
250
200
Distance (km)
150
5000
7000
9000
Atlantic Airport Pairs with Service Aug 2000 but not Aug 1995
11000
Big Airports Do Not Mean Big Airplanes
Seats per Departure
350
300
250
200
150
100
50
0
0
200
400
600
800
Jet Departures Per Day
Top 12 Markets in 12 World Regions
1000
1200
1400
Fast Growth Does not Mean Big Airplanes
60
1985-2000
Change
Seats/
Departure
HAV
40
Dacca
NGO
BKK
20
Lagos
0
-50%
0%
50%
100%
-20
-40
Kirachi
ATH
Paris
-60
CCS
SGN
FUK
DEL
BOM
Change in City Population 1985-2000
Data
150%
trend line
II.
Why Does Growth Add Frequency?
Many expect more demand to lead to bigger airplanes
a. Deregulation causes one-time move to smaller airplanes.
Competition drives airlines to more routes and frequencies.
b. Economic savings of larger airplanes diminish with size
For new airplanes of similar missions.
c. Cost savings come from avoiding intermediate stops.
Connecting passengers pay a time and cost penalty.
d. Natural network development.
Route networks move from skeletal to highly-connected.
e. Travelers’ priorities change as economies get richer.
Higher value for timely services, less emphasis on lowest cost.
d. Networks Develop from Skeletal to Connected
High growth does not persist at initial gateway hubs
 Early developments build loads to use larger airplanes:
Larger airplanes at this state means middle-sized
Result is a thin network – few links
A focus on a few major hubs or gateways
In Operations Research terms, a “minimum spanning tree”
 Later developments bypass initial hubs:
Bypass saves the costs of connections
Bypass establishes secondary hubs
New competing carriers bypass hubs dominated by incumbents
Large markets peak early, then fade in importance
 Third stage may be non-hubbed low-cost carriers:
The largest flows can sustain service without connecting feed
High frequencies create good connections without hub plan
Skeletal Networks Develop Links to
Secondary Hubs
Early Skeletal Network
Later Development bypasses Early Hubs
Consolidation Theory:
A Story that Sounds Good
•
•
•
•
Large markets will need larger airplanes
Industry consolidation increases this trend
Alliances increase this trend
This trend is happening
Fragmentation Theory
• Large markets peak early
• Bypass flying bleeds traffic off early markets
– Some connecting travelers get nonstops
– Others get competitive connections
– Secondary airports divert local traffic
• New airlines attack large traffic flows
• Frequency competition continues
Route Development Data:
Measures What Really Happens
• Compare top 100 markets from Aug 1993
– Top 100 by seat departures
– Growth to Aug 2003
• Data from published jet schedules
Largest Routes are Not Growing
as bypass flying diverts traffic
60%
50%
World, 1993-2003
Top 100 Routes
40%
30%
20%
10%
0%
-10%
-20%
ASK growth
Frequency
growth
Airplane size
growth
Large Long Routes are Not Growing
as bypass flying diverts traffic
100%
World, 1993-2003
80%
Top 100 Routes >
5000 Km
60%
40%
20%
0%
-20%
ASK growth
Frequency
growth
Airplane
size growth
747
Departures
Very Largest Long Routes are Not Growing
as bypass flying diverts traffic
60%
50%
World, 1993-2003
40%
30%
Top 10 Routes >
5000 Km
20%
10%
0%
-10%
-20%
-30%
-40%
ASK growth
Frequency
growth
Airplane
size growth
747
Departures
JFK Gateway Hub Stagnant for 30 Years
1400
5% of US 48
1200
Departures/Day
JFK
1000
800
600
400
200
03
20
01
20
99
19
97
19
95
19
93
19
91
19
89
19
87
19
85
19
83
19
81
79
19
August Jet Schedules
19
77
19
75
19
73
19
19
71
0
August Jet Schedules
19
71
19
73
19
75
19
77
19
79
19
81
19
83
19
85
19
87
19
89
19
91
19
93
19
95
19
97
19
99
20
01
20
03
Seats/Departure
JFK Gateway Hub Airplane Size Is Declining
300
250
200
150
100
50
0
Herfinadahl Number of Competitors
Competition Rising in Long-Haul Flows
30
25
20
15
10
Atlantic
Pacific
Asia-Europe
Other Long
5
0
1970
1975
1980
1985
1990
1995
2000
2005
Networks Develop Beyond Early Airports
 Decline of Long-Haul Gateway Hubs 1990-2000:
Top 10 Airports’ Share of Departing ASKs
Market Flow
Share 1990
Share 2000
Asia-Europe
88%
70%
Trans Pacific
80%
69%
Atlantic
54%
49%
Congestion Has Not Slowed Route Developments
Congestion is not driving seats per departure up
Seat Counts at Top 5 Airports Show Little Congestion
World Region
Japan & Korea
Middle East
Southeast Asia
Southwest Asia
China, Hong Kong
Oceania
Central America
Africa
Europe
North America
South America
Russia Region
All 60 Airports
1990
Seats/Dep
281
226
227
219
209
173
173
169
168
157
154
147
180
2000
Seats/Dep
265
213
241
184
217
176
144
169
168
145
145
139
176











Congestion: Solutions From History
Congestion has been a cost, not a constraint

Solutions favored by airports:
1.
2.
3.
4.

Solutions provided by the airline market:
5.
6.
7.
8.

Redefining measurement of capacity movements
Technical improvements to raise capacity
Added runways
Building replacement airport
Using un-congested times of day
By-passing congested gateways with new nonstop markets
Building frequencies and connections at secondary hubs
Using secondary airports at congested cities
Solutions beginning to be used:
9. Reducing smaller, propeller aircraft movements
10. Moving small, short-haul jet movements to larger aircraft
Congestion Affects Short & Small Flights
60%
Share of Departures
50%
1990 Departures
2000 Departures
40%
30%
20%
10%
0%
717
737
757
767/777
747
Airplane Size Category (world fleet, all manufacturers)
Chicago Airplane Sizes Do Not Show Congestion
170
Seats/Departure
160
150
140
130
120
110
19
71
19
73
19
75
19
77
19
79
19
81
19
83
19
85
19
87
19
89
19
91
19
93
19
95
19
97
19
99
20
01
20
03
100
August Jet Schedules
Congestion is Not Driving 747 Shares UP
80%
60%
50%
HKG
HND
JFK
PEK
LHR
40%
30%
20%
AMS
CDG
FRA
LGW
LAX
10%
SFO
ORD
0%
19
85
19
87
19
89
19
90
19
93
19
95
19
97
19
99
20
01
20
03
747 Share of Departures
70%
NRT
Implications of History for Airlines
Route strategy should respect history
 Plan for growth:
70%-100% of it in added frequencies
 Plan for flexibility:
Long-term commitments should not hang on one specific future
 Plan to have more routes:
Growth will include new nonstop markets
 Plan to have more frequencies:
Growth will include more flights at more times of day
 Plan to face competition:
Competitors will by-pass your hub
 Plan to discuss history:
Leaders may imagine growth patterns different from history
Hubs: The Whys and Wherefores
•
•
•
•
Just over half of trips are connecting
Thousands of small connecting markets
Early hubs are Gateways
Later hubs bypass Gateways
– One third of bypass loads are local—saving the
connection
– One third of bypass loads have saved one connect of
two
– One third of bypass loads are merely connecting over
a new, competitive hub
• Growth is stimulated by service improvements
– Bypass markets grow faster than average
Most Markets are Small
14%
12%
Too Small For
Nonstop
10%
8%
6%
4%
00
<1
60
0
16
00
+
<8
00
<4
00
<2
00
<1
0
<5
5
<2
.1
25
<6
.2
5
<1
2.
5
2%
0%
<3
Share of RPKs
16%
Passengers per Day One Way
Half of Travel is in Connecting Markets
14%
12%
10%
Connecting Markets
8%
6%
4%
Nonstop Markets
2%
O&D Passengers per Day
+
16
00
00
<1
6
0
<8
0
0
<4
0
0
<2
0
0
<1
0
<5
0
<2
5
.5
<1
2
25
<6
.
12
5
0%
<3
.
Share of World RPKs
16%
Lots of O&D Connections
Share of O&D Passengers
100%
90%
4-leg connect
Double Connect
1-connect
thru
nonstop
80%
70%
60%
50%
40%
30%
20%
10%
0%
0
0
0
0
0
0
0
0
0
0
0
0
0
30 100 200 300 400 500 600 700 800 900 000 100 200
1
1
1
St. Mi. Range Block (excludes US domestic O&Ds)
400000
350000
300000
250000
200000
150000
100000
50000
0
3+legs
2-legs
Nonstops
30
0
10
00
20
00
30
00
40
00
50
00
60
00
70
00
80
00
90
00
10
00
11 0
00
12 0
00
0
ASMs (000/day)
Half the Trips are Connecting
St. Mi. Range Block (excluding US domestic)
Local Traffic Share of
Onboard
Connecting Share of Loads
Averages about 50%
80%
70%
60%
50%
40%
30%
20%
10%
0%
0
2000
4000
6000
Flight Distance (Km)
8000
10000
Local % of Onboard Load
Long-Haul Flights are from Hubs,
and carry mostly connecting traffic
100%
90%
80%
70%
60%
50%
40%
30%
20%
10%
0%
100
Point-to-Point Markets
Trend
150
Markets over 5000km
200
250
300
350
400
450
500
Seats per Departure
Hub Concepts
• Hub city should be a major regional center
– Connect-only hubs have not succeeded
– Early hubs are centers of regional commerce
• Early Gateway Hubs get Bypassed
– Early International hubs form at coastlines
– Interior hubs have regional cities on 2 sides
• Later hubs duplicate and compete with early hubs
–
–
–
–
Many of the same cities served
Which medium cities become hubs is arbitrary
Often better-run airport or airline determines success
Also the hub that starts first stays ahead
Three Kinds of Hubs
• International hubs driven by long-haul
–
–
–
–
Gateway cities
Many European hubs: CDG, LHR, AMS, FRA
Some evolving interior hubs, such as Chicago
Typically one bank of connections per day
• Regional hubs connecting smaller cities
– Most US hubs, with at least 3 banks per day
– Some European hubs, with 1 or 2 banks per day
• High-Density hubs without banking
– Continuous connections from continuous arrivals and departures
– American Airlines at Chicago and Dallas
– Southwest at many of its focus cities
Regional and Gateway Hubs in US
SFO
JFK
ORD
DEN
LAX
ATL
DFW
MIA
Secondary Hubs in US
SEA
MSP
ORD DTW PIT JFK
EWR
CVG
STL
SLC
SFO
LAX
DEN
ATL
PHX
DFW
IAH
MIA
Why Secondary Hubs?
Airlines Hate Competition
• Avoid “head-to-head” whenever possible
– Preferred carrier wins big
• Gets first choice of premium fare demand
• Gets full loads during off peaks
• Leaves 2nd choice carrier low yield, high peaking
– Result: Lots of new routes
Minot, N. D., USA, is served over one Hub
airport
MSP
PHX
LAS
DFW
SEA
DCA
DEN
MCO
LAX
SAT
ANC
ORD
ATL
SFO
EKO
IAH
LGA
Destination
Dist (km)
MINNEAPOLIS/ST. PAUL-INTL
724
PHOENIX, ARIZONA, USA-INTL
1879
LAS VEGAS, NEVADA, USA-MCCARRA
1771
DALLAS/FT. WORTH, TEXA-INTL
1747
SEATTLE/TACOMA, WASHIN-SEA/TAC
1574
WASHINGTON, DC, USA-NATIONAL
2211
DENVER, COLORADO, USA-INTL
974
ORLANDO, FLORIDA, USA-INTL
2797
LOS ANGELES, CALIFORNI-INTL
2139
SAN ANTONIO, TEXAS, USA
2097
ANCHORAGE, ALASKA, USA
3360
CHICAGO, ILLINOIS, USA-O'HARE
1263
ATLANTA, GEORGIA, USA
2152
SAN FRANCISCO, CALIFORNIA, USA
2082
ELKO, NEVADA, USA
1418
HOUSTON, TEXAS, USA-INTERCONT
2097
NEW YORK LA GUARDIA
2323
Rest of World (117 more Cities)
TOTAL/avg
2022
1818
Passengers
23.9
8.7
5.6
5.0
4.9
4.8
4.7
4.5
3.7
3.6
3.0
2.9
2.7
2.7
2.6
2.6
2.6
fare
$ 191
$ 215
$ 213
$ 241
$ 209
$ 309
$ 197
$ 226
$ 220
$ 331
$ 245
$ 225
$ 255
$ 219
$ 48
$ 290
$ 228
91
177
$ 240
$ 230
Minot Feeds to Minneapolis Hub
MOT
MSP
18:00 Bank Gives Minot 38 Destinations
Inbound Bank
Origin Depart Hub
city
time time
ONT
1200 1727
BOS
1505 1728
SNA
1200 1728
PSP
1210 1729
PDX
1210 1729
MSO
1355 1730
CWA
1630 1731
GFK
1620 1731
RST
1650 1732
SMF
1205 1732
ORD
1600 1734
DFW
1510 1735
YEG
1355 1735
YYC
1357 1735
ABQ
1405 1739
LNK
1615 1740
DCA
1559 1741
STL
1600 1742
LAX
1215 1744
YWG
1618 1744
BIS
1630 1747
Origin Depart Hub
city
time time
DLH
1655 1748
SAN
1210 1748
IND
1604 1749
TUL
1550 1750
DTW
1700 1753
GRB
1641 1755
MKE
1635 1756
SJC
1215 1756
RAP
1530 1757
DTW
1705 1759
DSM
1650 1759
MSN
1645 1800
MOT
1635 1800
SFO
1220 1800
BOI
1415 1804
GEG
1312 1804
ATL
1620 1805
MDW
1635 1809
CVG
1655 1809
CWA
1715 1815
Outbound Bank
==>
==>
==>
==>
==>
==>
==>
==>
==>
==>
==>
==>
==>
==>
==>
==>
==>
Hub Arrive Destin'
time time
city
1835
1836
1837
1838
1839
1839
1840
1841
1842
1843
1844
1845
1845
1845
1846
1847
1847
2030
1932
2159
2159
2209
2214
2207
2108
2139
2104
2210
2159
2022
2134
2208
2208
2253
MEM
FAR
IAD
RDU
PVD
GSO
BDL
GRR
BUF
OKC
ATL
ROC
SBN
DAY
CLT
DCA
TPA
Hub Arrive Destin'
time time
city
1848 2116 MBS
1849 2136 CMH
1850 2227 HPN
1850 2130 AZO
1850 2130 AZO
1850 2215 TYS
1850
900 LGW
1851 2142 DTW
1852 2128 FNT
1853 2217 BWI
1854 2246 BOS
1855 2255 ORF
1855 2008 MLI
1855 2124 LAN
1856 2126 DFW
1857 2158 YYZ
1858 2007 GRB
1859 2002 OMA
1900 2200
PIT
1900 2027 ORD
1901 2030 MCI
Minot Connects to the World
Value Created by Hubs
The idea in business is to Create Value
Do things people want at a cost they will pay
Hubs make valuable travel options
Feeder city gets “anywhere” with one connection
Feeder city can participate in trade and commerce
Hubs are cost-effective
Most destinations attract less than 10 pax/day
Connecting loads use cost-effective airplanes
Hubs Build Loads First, then Frequency
$600
Too Expensive
Trip Cost Per Seat
$500
$400
$300
$200
Good Balance
$100
Add Frequency
$0
50
100
150
Seats
200
250
Hubs Give Competitive Advantages
• Less peaking of demands, as variations in
different markets average out
• Dominate feeder legs
– Connect loads allow dominant frequency
– Connect loads avoid small, expensive airplanes
– Feeder cities can be “owned”
• Dominant airline will get 15% market share advantage
• Dominant airline can control sales channels
• Control of feeder cities makes airline attractive to alliances
Hubs Compete with Other Hubs
• Compete on quality of connection
– Does the airport “work?”
•
•
•
•
•
•
Short connecting times
Reasonable walking distances
Reliable baggage handling
Few delayed flights
Recovery from weather disruptions
Later flights for when something goes wrong
Hubs Develop Pricing Mixes
• Higher fares in captive feeder markets
• Low discount fares in selected connecting
markets to fill up empty seats
– Low connecting fares compete against
nonstops
– Select low fare markets against competition
– It pays to discount and fill
• Unless you discount your own high-fare markets
Hubs Win
• The dominant form of airline networks is hubs
and connections
• This is because networks are “thin”
– Meaning only a few, larger city pairs are nonstop
• As networks grow, secondary hubs develop
– Competing with early hubs
• Hubs dominate because they create good travel
– Save time over un-coordinated connections
– Avoid the use of small, expensive airplane sizes
Industry Growth is Small Markets
• Virtuous Circle:
– Better services: More Value
• Faster connections (add 15% demand for online)
• Fewer Stops (add 15% for each lost stop)
• Higher frequencies (add 15% for full-day schedule)
– Lower Costs: Lower Prices
• Higher traffic volumes mean lower costs
• Competitive choices eliminate monopoly pricing
• New “small” markets get new services
– Smaller towns, secondary city airports
– Grow network from “below”
Why Hubs Work
Revenue Benefits for Hubbing
Spring 2005 Research
Working Paper
Hubs Work
•
•
•
•
•
Fare Rise Linearly with Distance
Fares decline Linearly with Market Size
Hubs serve Smaller Connecting Markets
Hubs get premium revenues for connects
Low Cost Carriers price Connections High
– Tend to charge sum of local fares
– Prices match Hub Carriers’ prices
Hub Cost Carriers’ (HCCs) Fare
Trend is Linear with Distance
O-D Markets Without Low-Cost (LCC) Competition
$260
$240
Average Fare
$220
$200
$180
$160
HCCs Alone
$140
Linear (HCCs Alone)
$120
$100
0
US Domestic 2q04 Data
500
1000
1500
2000
City-Pair Distance (Mi.)
2500
Low-Cost Carriers’ (LCCs) Fares
are Linear With Distance
Average Fare
$200
$190
LCC Fares
$180
Linear (LCC Fares)
$170
$160
$150
$140
$130
$120
$110
$100
0
US Domestic 2q04 Data
500
1000
1500
2000
City-Pair Distance (Mi.)
2500
Hub Cost Carriers’ (HCCs) Fares
Match Low-Cost (LCC) Competition
HCCs Alone
HCCs with LCCs
LCC Fares
Linear (HCCs Alone)
Linear (HCCs with LCCs)
Linear (LCC Fares)
$260
$240
Average Fare
$220
$200
$180
$160
$140
$120
$100
0
US Domestic 2q04 Data
500
1000
1500
2000
City-Pair Distance (Mi.)
2500
HCC Fares Decline with Market Size
Average Fare 2q04 (adjusted to 1100mi)
$220
Connecting
$200
$180
HCCs without LCCs
$160
$140
$120
HCC fare Trend= $196 - 2.26 * Ln(Pax)
$100
1
3
5
7
9
13
20
30
41
61
Market Size, Log Scale
86
122 172 281 479
LCC Fares Decline with Market Size
Average Fare 2q04 (adjusted to 1100mi)
$180
Connecting
$170
$160
LCC fares
$150
$140
$130
$120
$110
LCC Trend = $178 + 11 * Ln(Pax)
$100
1
3
5
7
9
13
20
30
41
61
Market Size, Log Scale
86
122 172 281 479
Fares Decline with Market Size
Average Fare 2q04 (adjusted to 1100mi)
$220
Connecting
$200
$180
$160
$140
HCCs without LCCs
HCCs with LCCs
LCC fares
$120
$100
1
3
5
7
9
13
20
30
41
61
Market Size, Log Scale
86
HCC Fares are Slightly Higher Than
LCC Fares, adjusted for Market Size
Average Fare 2q04 (adjusted to 1100mi)
$220
Connecting
$200
$180
$160
$140
HCCs without LCCs
HCCs with LCCs
LCC fares
$120
$100
1
3
5
7
9
13
20
30
41
61
Market Size, Log Scale
86
122 172 281 479
The Real Difference is
Hubs Serve Many more Small Markets
• US HCCs have “given up” local markets
–
–
–
–
–
Nonstop markets to hub city
Used to gain premium revenues
Now required to match LCCs
Revenues no longer cover union labor costs
HCCs have given up most traffic to LCCs
• Hubs serve connecting markets
–
–
–
–
Share of HCC revenues in small markets high
Share of LCC revenues in small markets low
Fares in small markets higher
More small market revenues mean higher HCC fares
18%
Connections
16%
14%
12%
HCCs
LCCs
10%
8%
6%
4%
2%
12
2
17
2
28
1
47
9
86
61
41
30
20
13
9
7
5
3
0%
1
Share of Carrier Total Revenues
Hubs Emphasize Smaller Markets
O-D City Pair Market Size (log scale)
LCCs Share of Small Markets is 5%
Share of Larger Nonstop Markets is 25%
30%
Connections
25%
20%
15%
10%
5%
86
12
2
17
2
28
1
47
9
14
54
61
41
30
20
13
9
7
5
3
0%
1
of All Markets in Size Group
Discount Carriers Revenue Share
35%
O-D City Pair Market Size (log scale)
HCCs Raise Average Fare
By Emphasizing Connecting Markets
• Average Fare for All Passengers: $146
• Average Fare for HCC Passengers: $166
• Average Fare for LCC Passengers: $102
HCC Revenues are 1/3 Small Markets
LCC Revenues are 10% Small Markets
70%
Connections
50%
HCCs
LCCs
40%
30%
20%
10%
O-D City Pair Market Size (log scale)
9
47
1
28
2
17
2
12
86
61
41
30
20
13
9
7
5
3
0%
1
Cumulative Revenues
60%
Hubs Make Travel Possible
• Hubs exist to serve small markets
• For US domestic network
– 25% of revenues are from small markets
– Over 30% of HCC revenues
– Under 10% of LCC revenues
• International “small markets” add to this
• US has higher share nonstop than world
Economics of “Small Markets”
•
•
•
•
Half of world-wide loads are connecting
Small cities have small markets
Small Markets pay more
Value is there
– Small cities have lower living costs
• Lower housing costs
• Higher air travel costs
– Air Travel connects small cities to trade
Fares are Linear With Distance
• Average Fare = $153 + $0.043 * Dist
– R-square = 0.13
– All US domestic markets with valid data
– Excluding Hawaii
– Mix of HCC and LCC markets
– 18,000 data points (Airport Pair O-Ds)
Fares are Higher for Small Markets
(Includes both Small and LCC Presence Effects)
For Pax < 10/day
Fare = $117 + 0.046 * Distance
257 data points;
R-square = 0.42
For 10/day < Pax < 100/day
Fare = $106 + 0.037 * Distance
758 data points;
R-square = 0.37
For Pax > 100/day
Fare = $98 + 0.035 * Distance
671 data points;
R-square = 0.34
HCC Fares are Higher for Small Markets
For Pax < 10/day
Fare = $127 + 0.042 * Distance
R-square = 0.24
For 10/day < Pax < 100/day
Fare = $110 + 0.036 * Distance
R-square = 0.30
For Pax > 100/day
Fare = $115 + 0.031 * Distance
R-square = 0.22
LCC Fares are Higher for Small Markets
For Pax < 10/day
Fare = $111 + 0.0442 * Distance
R-square = 0.33
For 10/day < Pax < 100/day
Fare = $100 + 0.034 * Distance
R-square = 0.31
For Pax > 100/day
Fare = $83+ 0.032 * Distance
R-square = 0.38
LCCs Price Close to HCCs
in Very Small Markets
Pax < 10/day
HCC fare = $127 + 0.042 * Distance
LCC fare = $111 + 0.044 * Distance
LCCs Price Connections Close to HCCs
10/day < Pax < 100/day
HCC fare = $100 + 0.036 * Distance
LCC fare = $100 + 0.034 * Distance
LCCs Fares In Nonstop Markets are Low
HCC fares are a mix of all-HCC and with-LCC Markets
Pax > 100/day
HCC fare = $115 + 0.031 * Distance
LCC fare = $83 + 0.032 * Distance
Full Model Includes 3-4 Variables
Fare = $102 + 0.040* Distance
(R2 = 0.36)
Fare = $131 + 0.038* Distance – 6.4 * Ln(Pax)
(R2 = 0.36)
Fare = $153 + 0.037* Distance – 6.9 * Ln(Pax)
- $23 if LCC presence
(R2 = 0.48)
Fare = $151 + 0.037* Distance – 7.0 * Ln(Pax)
- $20 if LCC presence + $26 if HCC only
(R2 = 0.48)
William Swan:
Data Troll
Story Teller
Economist