Key Findings of 2013 ATRS Global Airport
Performance Benchmarking project
ww.atrsworld.org
Prof. Tae Hoon Oum
Dr. Yapyin Choo
Prof. Chunyan Yu
ATRS Global Airport Benchmarking Task Force Asia Pacific: P. Forsyth, Xiaowen Fu, Yeong‐Heok Lee, Yuichiro Yoshida, Japhet Law, Shinya Hanaoka
Europe: Nicole Adler, Jaap de Wit, Hans‐Martin Niemeier, Eric Pels
North America: Tae Oum, Bijan Vasigh, Jia Yan, Chunyan Yu
Middle East: Paul Hooper © Air Transport Research Society (ATRS)
OUTLINE
Objective of the ATRS Benchmarking Study
Airports Included and ATRS Database
Some Characteristics of Sample Airports
Methodology
Key Results on Efficiency and Costs
User Charge Comparisons
Objective
Data
Airport Characteristics
Methodology
Efficiency & Cost
User Charge
OBJECTIVE OF THE BENCHMARKING STUDY
 To provide a comprehensive, unbiased comparison of airport performance focusing on
 Productivity and Operating/Mgt Efficiency
 Unit Cost Competitiveness
 Airport User Charges
 Our study does not treat service quality differentials across airports because of our
research resource constraints
Objective
Data
Airport Characteristics
Methodology
Efficiency & Cost
User Charge
2013 ATRS Global Airport Performance Benchmarking Project
Airport Database
Airports Included in the 2012 Report
Canada (12)+US(65)
Europe
77 airports 77 airports
17 airport groups
Asia Pacific 35 Asian airports
16 Oceania airports
9 airport groups ‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐
Total
195 airports
26 airport groups
© Air Transport Research Society (ATRS)
5
ATRS AIRPORT DATABASE, FY 2002‐2011
 The ATRS Database contains historic information (since FY 2002) including financial data, traffic and capacity data for the major airports and airport groups in the following geographic regions:
 Asia Pacific including Oceania; Europe; North America
 Limited data on S. America and Africa
 The data in each continent is segregated into:
 Traffic statistics and composition
 Airport characteristics (runways, terminals, ownership form, etc)
 Aeronautical Activities and Revenue
 Non‐Aeronautical Activities and Revenue
 Labor input and other Operating Expenses
 Financial info obtained from Balance Sheets
 Visit http://www.atrsworld.org/Database.html for more details and to purchase.
Objective
Data
Airport Characteristics
Methodology
Efficiency & Cost
User Charge
Data Sources: FY 2002-2011
 Airport’s Financial Statements, Annual Reports and
direct data requests;
 US FAA, DOT statistics;
 Association of European Airlines (AEA) Statistics
 ICAO Digest of Statistics:
 annual and monthly traffic data
 annual financial data - not for all airports
 ACI; IATA
 annual traffic statistics; capacity information; airport
charges
 general information surveys (Asia Pacific and Europe)
occasional and not complete
 IMF and World Bank – various price indices
including GDP deflators for service sectors and PPP
 US Census Bureau, Statistics Canada – regionally
based Cost of Living Index
© Air Transport Research Society (ATRS)
7
2013 ATRS Global Airport Performance Benchmarking Project
Airport Characteristics
PEK
0
Objective
Data
Airport Characteristics
Methodology
IAD
FLL
SLC
BW I
Efficiency & Cost
User Charge
YQ R
YYT
YQ B
YYJ
ALB
RIC
SDF
YW G
YH Z
TU S
O KC
RN O
PVD
BU R
YOW
AN C
ONT
JAX
BDL
ABQ
PBI
YEG
CMH
CVG
IN D
RSW
DAL
SAT
SJC
PIT
MSY
SN A
SMF
AU S
CLE
RDU
H OU
O AK
BN A
MKE
MEM
MCI
YYC
STL
YU L
PDX
TPA
YVR
SAN
H NL
DCA
MDW
Europe
LG A
BOS
PH L
DTW
YYZ
SEA
MSP
EW R
MCO
MIA
LAS
CLT
PH X
IAH
SFO
JFK
DEN
DFW
LAX
O RD
ATL
LJU
BTS
SZG
LU X
TLL
KEF
ZAG
BEG
SOF
MLA
TRN
CIA
BSL
RIX
H AJ
BRS
N AP
BLQ
CRL
OPO
G LA
BG Y
LYS
BH X
BU D
LIN
W AW
EDI
LTN
LED
CG N
ALC
N CE
LPA
PRG
AG P
TLV
G VA
H AM
SAW
ATH
LIS
H EL
TXL
STN
DU B
BRU
ARN
MXP
MAN
DU S
Asia Pacific
OSL
VIE
CPH
PMI
ZRH
ORY
LG W
BCN
IST
MU C
FCO
MAD
AMS
FRA
CDG
LH R
DU D
CEI
ZQ N
N TL
TSV
REP
H DY
PN H
N AN
DRW
GUM
CN X
CN S
MFM
PEN
W LG
O OL
CH C
CMB
ADL
H KT
NGO
H AK
PER
MAA
SU B
AKL
KIX
XMN
G MP
BN E
TPE
MEL
SZX
MN L
BOM
SH A
N RT
ICN
SYD
DEL
KU L
PVG
CAN
BKK
SIN
CG K
DXB
H KG
HND
PASSENGERS TRAFFIC, FY2011 (IN ’000 PASSENGERS)
100,000
90,000
North America
80,000
70,000
60,000
50,000
40,000
30,000
20,000
10,000
PASSENGER TRAFFIC (’000)TOP 10 AIRPORTS:
FY 2007, 2009, 2011
100,000
90,000
Asia Pacific
Europe
North America
80,000
70,000
60,000
50,000
40,000
30,000
20,000
10,000
0
2007
Objective
Data
Airport Characteristics
2009
2011
Methodology
Efficiency & Cost
User Charge
0
Objective
Data
Europe
Airport Characteristics
Methodology
ATL
OR D
DFW
DEN
LAX
C LT
IAH
LAS
DTW
P HL
P HX
YYZ
JFK
EW R
M SP
SFO
M IA
LGA
B OS
SEA
M EM
MCO
SLC
YV R
IAD
DC A
HNL
BWI
FLL
YYC
YUL
P DX
M DW
C LE
STL
SAN
TP A
OAK
CVG
MKE
HOU
R DU
IND
SDF
MCI
P IT
YOW
YEG
AUS
SAT
DAL
YW G
CMH
SM F
AB Q
M SY
ANC
SJC
B DL
B NA
B UR
SNA
YHZ
JAX
ONT
R SW
YQB
YYJ
R IC
OK C
P VD
ALB
TUS
P BI
R NO
YYT
YQR
Asia Pacific
C DG
FR A
LHR
M AD
AM S
M UC
FC O
IST
BCN
ZR H
CPH
LGW
V IE
BRU
OSL
OR Y
DUS
AR N
MXP
HEL
PMI
ATH
TX L
M AN
DUB
GV A
HAM
NC E
PRG
LIS
STN
C GN
W AW
LYS
SAW
LED
LP A
B UD
AGP
EDI
TLV
LTN
LIN
B HX
B SL
C RL
LUX
HAJ
ALC
R IX
GLA
B GY
B LQ
BRS
NAP
OP O
C IA
SOF
B EG
TR N
ZAG
TLL
LJU
M LA
B TS
K EF
SZG
P EK
HND
C AN
C GK
P VG
HK G
SIN
DEL
BKK
SYD
K UL
B OM
SHA
IC N
SZX
M NL
M EL
NR T
B NE
TP E
AK L
XMN
GM P
M AA
K IX
SUB
P ER
W LG
HAK
NGO
ADL
C HC
NAN
HK T
P EN
GUM
CMB
C NS
M FM
OOL
C NX
TSV
DR W
R EP
P NH
NTL
HDY
DUD
ZQN
C EI
AIRCRAFT MOVEMENTS, FY 2010 (’000 ATM)
1000
North America
900
800
700
600
500
400
300
200
100
Efficiency & Cost
User Charge
0
Objective
Data
Europe
Airport Characteristics
Methodology
JFK
MCO
SEA
PBI
LAX
B NA
SFO
SNA
R SW
M DW
TP A
SAN
FLL
M IA
ATL
M SY
SJC
P HX
SM F
BWI
DFW
EW R
DEN
M SP
R NO
IAD
B OS
LAS
YYZ
OR D
IAH
C LT
AUS
MCI
DTW
JAX
P HL
P DX
SLC
SAT
DC A
LGA
DAL
STL
P VD
P IT
HNL
R DU
B DL
YUL
TUS
ONT
HOU
MKE
YVR
OK C
CMH
ABQ
YYC
OAK
IND
B UR
YEG
ANC
R IC
C LE
YHZ
C VG
ALB
YYT
YOW
YQR
M EM
YW G
SDF
YYJ
YQB
Asia Pacific
LHR
LGW
ALC
TLV
STN
PMI
IST
C DG
AGP
OR Y
AM S
B GY
FR A
M AD
DUB
SAW
FC O
M AN
BCN
M LA
LIS
MXP
OP O
TX L
LTN
LIN
GLA
LP A
AR N
B HX
M UC
DUS
NAP
OSL
CPH
EDI
C IA
ZR H
K EF
BRS
SZG
HAM
VIE
B LQ
TR N
LED
ATH
B UD
GVA
BRU
PRG
W AW
HEL
SOF
C GN
LYS
R IX
B EG
CRL
NC E
HAJ
B TS
B SL
ZAG
TLL
LJU
LUX
NR T
HND
BKK
HK G
SIN
TP E
IC N
HK T
P EK
GM P
OOL
SHA
CMB
C GK
K UL
C EI
M NL
M EL
HDY
SUB
C AN
K IX
SZX
P ER
SYD
HAK
B OM
DEL
P VG
B NE
C NX
XMN
NGO
M AA
M FM
ADL
ZQN
P EN
C NS
AK L
P NH
DR W
NTL
R EP
C HC
DUD
W LG
TSV
GUM
NAN
PASSENGERS PER AIRCRAFT MOVEMENTS,
FY 2011
200
North America
180
160
140
120
100
80
60
40
20
Efficiency & Cost
User Charge
Objective
TSV
OOL
HKG
0%
Data
Airport Characteristics
Methodology
Efficiency & Cost
User Charge
ANC
MEM
JFK
LGA
EWR
STL
CLE
YYZ
PHL
ORD
MDW
IAH
MIA
IAD
PIT
BWI
YWG
DEN
SEA
LAX
MSY
BDL
ONT
PDX
SFO
DCA
OAK
HNL
CVG
HOU
DTW
LAS
YOW
YUL
BOS
YHZ PVD
SDF
SAT
SAN
YYT
ABQ
ALB
AUS
YVR
CLT SJC
RSW
SNA
DFW
SMF
PBI
YYC SLC
YQB
TUS
YEG
MSP
DAL
MKE
IND
RDU
BNA
RNO
Europe
CMH
ATL
YQR
MCO
YYJ
FLL
PHX
JAX
RIC
MCI
BUR
TPA
OKC
BEG
LED
MAD
PMI
HEL
BTS
BCN
WAW
TXL
BLQ
AGP
ALC
LPA
DUS
CGN
MLA
SZG
LHR
SAW
OPO
LIS
STN
BGY
EDI
LGW
SOF
MUC
NAP
ARN
GLA
GVA
LJU
HAM
MAN
ZAG
LYS
BHX
LTN
MXP
LIN
TLL
ZRH
RIX
TLV
FCO
CIA
DUB
Asia Pacific
BUD
VIE
CPH
ATH
AMS
HAJ
TRN
NCE
ORY
CDG
BSL
OSL
FRA
PEN
CGK
KIX
KUL
NGO
XMN
DRW
SZX
CNX
HKT
HDY
CEI
BKK
ADL
WLG
PVG
NTL
PEK
NRT
ZQN
GUM
HAK
MEL
CHC
SYD
SIN
BNE
NAN
ICN
CMB
CAN
PER
GMP
AKL
DUD
% NON‐AERO REVENUE, FY 2011
80%
North America
70%
60%
50%
40%
30%
20%
10%
2013 ATRS Global Airport Performance Benchmarking Project
Methodology
AIRPORT PRODUCTIVITY INDEX
•
•
•
•
Outputs
Inputs
Aircraft movement
Passenger
{Cargo tonnes}
Non‐aeronautical revenue output
• Labour
• Other non‐capital (soft‐cost) input
• [Runways, terminal size, # of gates]
Objective
Data
Airport Methodology
Efficiency & Cost
© Air Transport Research Society (ATRS)
Characteristics
User Charge
METHODOLOGY: EFFICIENCY MEASUREMENT Variable Factor Productivity (VFP) Index
 Impossible ‐ Total Factor Productivity (TFP) because of capital input cost accounting problem (comparable across different countries)
 Unit Operating Cost Competitiveness Index: Combines VFP and Input Price Index
Objective
Data
Airport Characteristics
Methodology
Efficiency & Cost
User Charge
MULTILATERAL AGGREGATION METHOD
• This multilateral output (input) index procedure uses the following revenue (cost) shares to aggregate output (inputs)
Objective
Data
Airport Characteristics
Methodology
Efficiency & Cost
User Charge
GROSS VARIABLE FACTOR PRODUCTIVITY (VFP)
NORTH AMERICA LARGE AIRPORTS
(YVR=1.0), FY 2011
2
1.8
1.6
1.4
1.2
1
0.8
0.6
0.4
0.2
ATL
CLT
MSP
YVR
LGA
TPA
MCO
SLC
PHX
FLL
SFO
LAS
EWR
SEA
MDW
DTW
PHL
DCA
HNL
BOS
DFW
ORD
SAN
DEN
JFK
IAD
IAH
BWI
LAX
MIA
0
Objective
Data
Airport Characteristics
Methodology
Efficiency & Cost
User Charge
POTENTIAL REASONS FOR THE MEASURED PRODUCTIVITY (GROSS VFP) DIFFERENTIALS
Factors Beyond Managerial Control:
•
•
•
•
•
•
Airport size (Scale of aggregate output)
Average aircraft size using the airport
Share of international traffic
Share of air cargo traffic
Extent of capacity shortage ‐ congestion delay
Connecting/transfer ratio
We compute residual (Net) Variable Factor Productivity (RVFP) after removing effects of these Factors Objective
Data
Airport Methodology
Efficiency & Cost
Characteristics
© Air Transport Research Society (ATRS)
User Charge
19
GROSS VARIABLE FACTOR PRODUCTIVITY VS RESIDUAL VFP: NORTH AMERICA
(YVR=1.0), FY 2011
2
1.8
1.6
1.4
1.2
1
0.8
0.6
0.4
0.2
ATL
MSP
CLT
TPA
MCO
YVR
SFO
FLL
LGA
MDW
SLC
SEA
HNL
PHX
EWR
BOS
JFK
DTW
PHL
LAS
SAN
DCA
BWI
IAD
DFW
ORD
IAH
DEN
MIA
0
Gross VFP
Objective
Data
Airport Characteristics
Residual VFP
Methodology
Efficiency & Cost
User Charge
ALTERNATIVE APPROACHES
We explored Alternative approaches:
 Data Envelopment Analysis (DEA)
 Econometric Cost Function Approach including Stochastic Frontier methods (SFA)
 The rankings for top and bottom ranked airports
are consistent despite using VFP, DEA or SFA.
Note: Industry acceptance of our report using more advanced/sophisticated methods is one of our major concern
Residual VFP Ranking
Objective
Data
Residual DEA Ranking
FLL
MKE
MCO
SNA
TPA
SAT
LGA
JAX
MSP
BNA
PBI
CLT
RNO
RDU
55
50
45
40
35
30
25
20
15
10
5
0
ATL
Rank
RESIDUAL RANKING COMPARISON OF TOP 15 AIRPORTS IN US
Residual SFA Ranking
Airport Methodology
Efficiency & Cost
Characteristics
© Air Transport Research Society (ATRS)
User Charge
22
Residual VFP Ranking
Objective
Data
Residual DEA Ranking
MIA
MSY
PIT
BWI
ORD
LAX
ONT
STL
DFW
PHL
ALB
SJC
CLE
CVG
55
50
45
40
35
30
25
20
15
10
5
0
BOS
Rank
RESIDUAL RANKING COMPARISON OF BOTTOM 15 AIRPORTS IN US
Residual SFA Ranking
Airport Methodology
Efficiency & Cost
Characteristics
© Air Transport Research Society (ATRS)
User Charge
23
55
50
45
40
35
30
25
20
15
10
5
0
PDX
SAN
SLC
OAK
RIC
ABQ
SEA
HNL
EWR
IAD
LAS
MEM
MDW
DCA
IAH
JFK
IND
PHX
SMF
AUS
SDF
DEN
DTW
SFO
MCI
Rank
RESIDUAL RANKING COMPARISON OF MID‐RANKED 15 AIRPORTS IN US
Residual VFP Ranking
Objective
Data
Residual DEA Ranking
Residual SFA Ranking
Airport Methodology
Efficiency & Cost
Characteristics
© Air Transport Research Society (ATRS)
User Charge
24
2013 ATRS Airport Benchmarking Key Results on Efficiency & Cost
RESIDUAL (NET) VARIABLE FACTOR PRODUCTIVITY (VFP): EUROPE LARGE AIRPORTS (CPH=1.0), FY 2011
Copenhagen Kastrup, Athens, Zurich
ZRH
1
CPH
ATH
1.2
0.4
ADR
ADP
Swedavia
Avinor
SEA
DAA
MAG
Berlin
AENA
Fraport
Finavia
BAA
TAV
PPL
FCO
ARN
MXP
PMI
VIE
MAN
IST
LGW
STN
HEL
ORY
DUB
BCN
TXL
MAD
DUS
FRA
LHR
MUC
0.6
AMS
CDG
0.8
ANA
Schiphol
OSL
LIS
Airport Groups
0.2
0
Objective
Data
Airport Characteristics
Methodology
Efficiency & Cost
User Charge
RESIDUAL (NET) VARIABLE FACTOR PRODUCTIVITY (VFP): EUROPE SMALL & MEDIUM AIRPORTS (CPH=1.0), FY 2011
1
CGN
BTS
LIN
WAW
BEG
0.4
SOF
ZAG
BUD
SZG
RIX
AGP
0.5
HAM
GLA
MLA
NAP
ALC
TLL
BGY
0.6
TRN
LTN
LPA
EDI
KEF
HAJ
TLV
0.7
SAW
BLQ
LJU
BHX
0.8
CIA
NCE
BSL
0.9
GVA
Geneva, Basel, Nice
LED
0.3
0.2
0.1
0
Objective
Data
Airport Characteristics
Methodology
Efficiency & Cost
User Charge
Airports
HKG
HAK
GUM
1.2
Gimpo, Incheon, Guam
ICN
1.4
GMP
RESIDUAL (NET) VARIABLE FACTOR PRODUCTIVITY (VFP): ASIA (HKG=1.0), FY 2011
Airport Groups
MAHB
AOT
APII
AAI
KAC
KIX
NGO
CMB
0.4
KUL
NRT
SZX
BKK
HKT
XMN
PEN
0.6
PVG
CNX
CAN
HDY
PEK
0.8
CGK
SIN
1
0.2
0
Residual VFP
Objective
Data
Airport Characteristics
Mean
Methodology
Efficiency & Cost
User Charge
RESIDUAL (NET) VARIABLE FACTOR PRODUCTIVITY (VFP): OCEANIA (SYD=1.0), FY 2011
1.2
0.4
0.2
0
Residual VFP
Objective
Data
Airport Characteristics
Mean
Methodology
Efficiency & Cost
User Charge
ADG
APAC
NTL
NAN
0.6
WLG
ADL
ZQN
PER
BNE
DUD
CHC
OOL
MEL
DRW
0.8
QAL
Airport Groups
Airports
TSV
1
AKL
SYD
Sydney, Auckland, Townsville
RESIDUAL (NET) VARIABLE FACTOR PRODUCTIVITY (VFP): NORTH AMERICA LARGE AIRPORTS (YVR=1.0), FY 2011
1.6
ATL
1.8
Atlanta, Minneapolis St. Paul, Charlotte
LAX
MIA
DEN
IAH
0.6
ORD
DFW
IAD
BWI
DCA
LAS
SAN
PHL
DTW
JFK
BOS
PHX
EWR
0.8
HNL
SLC
SEA
MDW
LGA
SFO
FLL
1
YVR
MCO
TPA
1.2
CLT
MSP
1.4
0.4
0.2
0
Objective
Data
Airport Characteristics
Methodology
Efficiency & Cost
User Charge
RESIDUAL (NET) VARIABLE FACTOR PRODUCTIVITY (VFP): N. AMERICA SMALL & MEDIUM AIRPORTS (YVR=1.0), FY 2011
OKC
1.6
Oklahoma City, Richmond, Raleigh‐Durham
1.2
1
0.8
0.6
YYJ
YYC
BNA
YQR
PVD
BDL
PBI
TUS
RNO
YYT
JAX
MKE
YEG
SNA
ABQ
PDX
SJC
SAT
IND
MSY
RSW
DAL
YOW
SMF
YHZ
AUS
MCI
MEM
YWG
OAK
SDF
CMH
BUR
ANC
ALB
CVG
YUL
PIT
ONT
HOU
STL
CLE
YQB
RIC
RDU
1.4
0.4
0.2
0
Objective
Data
Airport Characteristics
Methodology
Efficiency & Cost
User Charge
TOP EFFICIENCY PERFORMERS (2013)
(based on Net VFP index=operating/management efficiency)
Asia Pacific: • Asian Airports:
• Gimpo, Incheon, Guam
• Oceania Airports:
• Sydney, Auckland, Townsville
Europe: • Large Airports (> 15 million pax):
• Copenhagen Kastrup, Athens, Zurich
• Small/Medium Airports (< 15 millions Pax):
• Geneva, Basel, Nice
Objective
Data
Airport Methodology
Efficiency & Cost
Characteristics
© Air Transport Research Society (ATRS)
User Charge
32
TOP EFFICIENCY PERFORMERS (2013)
(based on Net VFP index=operating/management efficiency)
North America: • Large Airports (> 15 million pax):
• {Atlanta (Globally Most Efficient Airport)}
• Minneapolis St Paul, Charlotte, Tampa
• Small/Medium Airports (< 15 millions Pax):
• Oklahoma City, Richmond, Raleigh‐Durham
Global (10th Global Excellence Award)
• Hartsfield‐Jackson Atlanta International Airport
Objective
Data
Airport Methodology
Efficiency & Cost
Characteristics
© Air Transport Research Society (ATRS)
User Charge
33
PAST AIRPORT EFFICIENCY EXCELLENCE TOP PERFORMERS, 2008 ‐ 2012
2008 2009 2010 2011 2012
North America
Europe
Asia‐Pacific
Objective
Hartsfield‐Jackson Atlanta International Airport
Copenhagen Kastrup
International Airport
Hong Kong International Airport
Data
Hartsfield‐Jackson Atlanta International Airport
Hartsfield‐Jackson Atlanta International Airport
Hartsfield‐Jackson Atlanta International Airport
Hartsfield‐Jackson Atlanta International Airport
Copenhagen Kastrup
International Airport
Large Airport Category: Oslo International Airport
Small/Medium Airport Category:
Geneva Cointrin
International Airport
Large Airport Category: Oslo International Airport
Copenhagen Kastrup
International Airport
Small/Medium Airport Category:
Genève Aéroport
Large Airport Category: Copenhagen Kastrup
International Airport
Small/Medium Airport Category:
Genève Aéroport
Hong Kong International Airport
Large Airport Category:
Hong Kong International Airport
Small/Medium Airport Category:
Seoul Gimpo
International Airport
Asian Airport Excellence Award:
Hong Kong International Airport
Oceania Excellence Award:
Sydney Airport
Asian Airport Excellence Award:
Seoul Gimpo
International Airport
Oceania Excellence Award:
Sydney Airport
Airport Characteristics
Methodology
Efficiency & Cost
User Charge
COST COMPETITIVENESS = NET VFP AND INPUT PRICE EFFECT EUROPE ‐ LARGE AIRPORTS (CPH=0.0)
0.4
CPH
ANA
LIS
ATH
0.2
Airport Groups
Airports
‐0.6
Finavia
‐0.4
PPL
TAV
MAG
ADR
Schiphol
Fraport
SEA
DAA
Swedavia
AENA
BAA
ADP
Berlin
‐0.2
MAN
PMI
FCO
STN
MXP
BCN
AMS
MAD
VIE
LGW
ARN
DUB
FRA
LHR
DUS
MUC
CDG
TXL
ZRH
ORY
HEL
OSL
0
Athens, Lisbon, ANA (Aeroportos de Portugal)
Avinor
‐0.8
‐1
Objective
Data
Airport Characteristics
Methodology
Efficiency & Cost
User Charge
COST COMPETITIVENESS = NET VFP AND INPUT PRICE EFFECT EUROPE ‐ SMALL & MEDIUM AIRPORTS (CPH=0.0)
ZAG
BUD
BEG
BHX
SAW
0.1
CIA
BTS
MLA
SOF
RIX
0.2
TLL
0.3
BSL
LJU
0.4
CGN
BLQ
‐0.5
SZG
TLV
LIN
‐0.4
HAM
NAP
GLA
LED
Ljubljana(Slovenia), Basel, Tallinn (Estonia)
AGP
‐0.3
TRN
BGY
KEF
NCE
EDI
LTN
HAJ
‐0.2
ALC
‐0.1
WAW
LPA
0
GVA
‐0.6
‐0.7
Objective
Data
Airport Characteristics
Methodology
Efficiency & Cost
User Charge
AAI
Airport Groups
HKG
XMN
Airports
CMB
CGK
GMP
0.5
HAK
COST COMPETITIVENESS = NET VFP AND INPUT PRICE EFFECT ASIA (HKG=0.0) – THE HIGHER THE BETTER
KAC
MAHB
AOT
APII
SIN
GUM
‐0.5
CAN
KUL
BKK
HKT
SZX
PEN
PEK
PVG
CNX
ICN
HDY
0
Haikou, Seoul Gimpo, Airport Authority of India
NRT
NGO
‐1.5
KIX
‐1
‐2
Cost Competitiveness
Objective
Data
Airport Characteristics
Methodology
Mean
Efficiency & Cost
User Charge
COST COMPETITIVENESS = NET VFP AND INPUT PRICE EFFECT OCEANIA (SYD=0.0)
QAL
Airport Groups
CHC
Airport s
DUD
0.2
AKL
0.3
SYD
0.1
ZQN
0
‐0.4
ADG
APAC
Queensland Airport Limited (QAL),
Auckland, Dunedin (NZ)
ADL
‐0.3
PER
BNE
DRW
‐0.2
WLG
MEL
‐0.1
NTL
‐0.5
‐0.6
Objective
Data
Airport Characteristics
Methodology
Efficiency & Cost
User Charge
COST COMPETITIVENESS = NET VFP AND INPUT PRICE EFFECT N. AMERICA ‐ LARGE AIRPORTS (YVR=0.0)
YVR
PHX
SLC
FLL
MSP
MCO
0.5
CLT
ATL
1
LAX
SFO
IAD
DCA
MIA
BOS
PHL
DEN
ORD
SAN
HNL
MDW
‐0.5
BWI
DFW
IAH
SEA
DTW
LAS
0
EWR
‐1
TPA
Atlanta, Charlotte, Orlando
LGA
JFK
‐1.5
‐2
Objective
Data
Airport Characteristics
Methodology
Efficiency & Cost
User Charge
COST COMPETITIVENESS: = NET VFP AND INPUT PRICE EFFECT N. AMERICA ‐ SMALL & MEDIUM AIRPORTS (YVR=0.0)
RDU
0.6
Oklahoma City, Richmond (Virginia), Raleigh‐Durham
RIC
0.8
OKC
1
YEG
AUS
RSW
MCI
PVD
MEM
SDF
YYJ
MKE
DAL
MSY
JAX
YQR
PBI
SAT
0.2
YYC
RNO
ABQ
IND
BNA
TUS
0.4
Objective
Data
Airport Characteristics
Methodology
Efficiency & Cost
ONT
User Charge
ANC
YQB
SJC
YOW
YHZ
YUL
CLE
STL
‐0.4
SMF
PIT
YWG
HOU
ALB
PDX
BUR
SNA
CVG
‐0.2
BDL
CMH
YYT
0
2013 ATRS Airport Benchmarking User Charge Comparison
0
Objective
Europe
Landing Charges for Boeing 767
Data
Airport Characteristics
Methodology
CLT
ATL
BUR
RIC
TUS
MEM
SAT
PBI
PHX
SLC
SAN
RNO
MCO
PHL
MSY
RDU
IND
SDF
TPA
BNA
HOU
JAX
ABQ
MCI
FLL
ONT
SNA
*ANC
MSP
SJC
MIA
RSW
ALB
IAH
LAS
OKC
*YVR
SEA
AUS
CMH
DCA
OAK
DTW
PDX
MDW
*YYJ
*YQR
DFW
BDL
IAD
*YOW
DEN
*YEG
PIT
*YHZ
BWI
HNL
SMF
LAX
*YYC
MKE
*YWG
*YUL
BOS
SFO
*YYT
ORD
*JFK Off Peak
STL
*JFK Peak
*YQB
*EWR Off Peak
*EWR Peak
*LGA Off Peak
*LGA Peak
*YYZ
Asia Pacific
NAP
BGY
DUS
ARN
STN‐off peak
BRU
LTN
MLA
STN‐peak
LGW‐off‐peak
RIX
FRA
BLQ
LUX
MUC
TRN
FCO
MXP
LIN
CIA
BSL (France Sector) PRG
LIS
OPO
VIE
ZAG
GLA
HEL
HAM
GVA
EDI
KEF
TXL
SOF‐offpeak
ALC
CPH
NCE
CGN
BSL (Swiss Sector)
SAW
MAN‐peak
IST
MAN‐offpeak
SOF‐peak
OSL
LED
AGP
LPA
PMI
LYS
BCN
ZRH
BUD
BEG
HAJ
TLL
MAD
CRL
ATH
TLV
CDG
ORY
AMS
WAW
LHR
BHX
BTS
LJU
LGW‐peak
SZG
DUB
BRS
GUM
KUL
DXB
CMB
TPE
SUB
BKK
CEI
CNX
HDY
HKT
CGK
MNL
BOM
DEL
MAA
PEN
PNH
REP
CAN
HAK
PEK
PVG
SHA
SZX
XMN
MFM
DUD
ICN
GMP
SIN
NAN
ZQN
PER
HKG
WLG
AKL
NTL
TSV
NTL‐Night
OOL
SYD
NGO
DRW
KIX
NRT
HND
US$
LANDING CHARGES FOR BOEING 767‐400, 2012 (IN US$)
8,000
7,000
6,000
5,000
4,000
3,000
North America
2,000
1,000
Mean
Efficiency & Cost
User Charge
ASIA PACIFIC: COMBINED LANDING AND PASSENGER CHARGES FOR BOEING 737‐800, 2012 (IN US$) 9000
Lowest charges: Taipei Taoyuan, Dunedin (New Zealand)
Highest charges: Osaka Kansai, Tokyo Narita
8000
7000
6000
5000
4000
3000
2000
1000
Airport Characteristics
Methodology
Efficiency & Cost
KIX
NRT‐2
NRT‐1S
AKL
User Charge
NRT‐1N
BNE
NGO
BNE‐Peak
SYD
HND
CNS
ADL
NTL‐Night
PER
MEL
OOL
DRW
REP
PNH
NTL
CHC
TSV
WLG
BKK
HKT
HDY
CEI
CNX
ICN
DXB
PEN
KUL
ZQN
SUB
CGK
GMP
SZX
XMN
SHA
PEK
PVG
HAK
CAN
SIN
Data
MFM
MNL
CMB
GUM
SIN (LCCs)
HKG
Objective
KUL (LCCs)
MAA
DEL
BOM
TPE
DUD
0
RIX
CRL
LUX
NAP
BGY
MAN‐offpeak
HEL
CIA
BLQ
ARN
KEF
TLL
CGN
GVA
ZAG
FCO (LCCs)
OSL
CPH(LCCs)
ALC
BSL (France Sector) MAN‐peak
AGP
LPA
PMI
STN‐off peak
FCO
BCN
MXP
TRN
NCE
STN‐peak
GLA
LTN
LIN
HAM
CPH
EDI
BUD (LCCs)
MAD
HAJ
MLA
BSL (Swiss Sector)
LYS
DUS
TXL
BEG (LCCs)
SOF‐offpeak
SAW
SOF‐peak
OPO
TLV(LCCs)
LGW‐off‐peak
BHX
IST
LIS
AMS
VIE
LED
MUC
WAW
DUB
BUD
BRU
SZG
BEG
BTS
LJU
ATH
FRA
ZRH
PRG
BRS
ORY
CDG
TLV
LGW‐peak
LHR
12000
EUROPE: COMBINED LANDING AND PASSENGER CHARGES FOR BOEING 737‐800, 2012 (IN US$) 10000
Lowest charges: Riga (Latvia), Brussel South Charleroi
Highest charges: London Heathrow, London Gatwick‐ Peak
8000
6000
4000
2000
0
Objective
Data
Airport Characteristics
Methodology
Efficiency & Cost
User Charge
NORTH AMERICA: COST PER ENPLANED PASSENGER, 2011 (IN US$) 40
Canada:
Lowest CPE: Victoria, Regina
Highest CPE: Toronto, Montreal
35
30
US$
25
20
Canadian Airports
US Airports
United States:
Lowest CPE: Charlotte, California Bob Hope (Burbank,CA)
Highest CPE: New York JFK, Newark Liberty
15
10
CPE
Objective
Data
Airport Characteristics
US Mean
YYJ
YQR
YEG
YYC
YYT
YQB
YVR
YHZ
YOW
YWG
YUL
YYZ
0
CLT
BUR
ATL
SLC
FLL
MCI
TPA
MEM
PHX
MKE
MSP
OKC
MCO
RIC
RDU
BNA
DFW
JAX
RNO
SDF
SAN
TUS
MSY
RSW
CMH
HOU
ALB
PBI
MDW
ABQ
AUS
LAS
DTW
BDL
SAT
IAH
PHL
SNA
BWI
SMF
ANC
IND
OAK
PVD
HNL
SEA
LAX
DEN
SJC
PDX
ONT
ORD
STL
DCA
BOS
SFO
LGA
PIT
MIA
IAD
EWR
JFK
5
Canada Mean
Methodology
Efficiency & Cost
User Charge
ASIA PACIFIC: COMBINED LANDING AND PASSENGER CHARGES FOR BOEING 767, 2012 (IN US$) 16,000
Lowest charges: Taipei Taoyuan, Dunedin (New Zealand)
Highest charges: Osaka Kansai, Tokyo Narita
14,000
12,000
US$
10,000
8,000
6,000
4,000
2,000
Combined Landing and Passenger Charges for Boeing 767
Objective
Data
Airport Characteristics
Methodology
KIX
NRT‐2
NRT‐1S
NRT‐1N
AKL
HND
NGO
BNE‐Peak
SYD
BNE
ADL
CNS
DRW
NTL‐Night
NTL
OOL
PER
MEL
REP
TSV
PNH
BKK
HKT
HDY
CEI
CNX
PEN
WLG
ZQN
ICN
DXB
SUB
CGK
KUL
GMP
SZX
XMN
SHA
PEK
PVG
HAK
SIN
CAN
MNL
MFM
CMB
SIN (LCCs)
KUL (LCCs)
HKG
GUM
MAA
DEL
BOM
TPE
DUD
0
Mean
Efficiency & Cost
User Charge
EUROPE: COMBINED LANDING AND PASSENGER CHARGES FOR BOEING 767, 2012 (IN US$) 18,000
Lowest charges: Riga (Latvia), Luxembourg
Highest charges: London Heathrow, Ben Gurion (Tal Aviv)
16,000
14,000
12,000
US$
10,000
8,000
6,000
4,000
0
RIX
LUX
CRL
NAP
BGY
MAN‐offpeak
CIA
ARN
HEL
BLQ
KEF
CGN
OSL
TLL
FCO (LCCs)
STN‐off peak
ZAG
GVA
ALC
MAN‐peak
STN‐peak
CPH(LCCs)
AGP
LPA
PMI
BSL (France Sector) LTN
FCO
BCN
TRN
MXP
NCE
BUD (LCCs)
LIN
LGW‐off‐peak
GLA
DUS
EDI
SOF‐offpeak
CPH
HAM
MAD
SOF‐peak
MLA
IST
HAJ
OPO
BSL (Swiss Sector)
LIS
TXL
BEG (LCCs)
LYS
SAW
TLV(LCCs)
VIE
BUD
LED
MUC
BHX
BRU
AMS
ATH
WAW
DUB
BEG
FRA
LGW‐peak
SZG
PRG
BTS
LJU
ZRH
BRS
ORY
CDG
TLV
LHR
2,000
Combined Landing and Passenger Charges for Boeing 767
Objective
Data
Airport Characteristics
Methodology
Mean
Efficiency & Cost
User Charge
NORTH AMERICA: COST PER ENPLANED PASSENGER, 2011 (IN US$) 40
Canada:
Lowest CPE: Victoria, Regina
Highest CPE: Toronto, Montreal
35
30
US$
25
20
Canadian Airports
US Airports
United States:
Lowest CPE: Charlotte, California Bob Hope (Burbank,CA)
Highest CPE: New York JFK, Newark Liberty
15
10
CPE
Objective
Data
Airport Characteristics
US Mean
YYJ
YQR
YEG
YYC
YYT
YQB
YVR
YHZ
YOW
YWG
YUL
YYZ
0
CLT
BUR
ATL
SLC
FLL
MCI
TPA
MEM
PHX
MKE
MSP
OKC
MCO
RIC
RDU
BNA
DFW
JAX
RNO
SDF
SAN
TUS
MSY
RSW
CMH
HOU
ALB
PBI
MDW
ABQ
AUS
LAS
DTW
BDL
SAT
IAH
PHL
SNA
BWI
SMF
ANC
IND
OAK
PVD
HNL
SEA
LAX
DEN
SJC
PDX
ONT
ORD
STL
DCA
BOS
SFO
LGA
PIT
MIA
IAD
EWR
JFK
5
Canada Mean
Methodology
Efficiency & Cost
User Charge
ATRS AIRPORT BENCHMARKING REPORT
 The ATRS Global Airport Performance Benchmarking Report : 3 volumes, over 600 pages of valuable data and analysis.
 Can be purchased by visiting www.atrsworld.org
 Report sale finances our annual benchmarking research project
© Air Transport Research Society (ATRS)
49
Thank You
2014 ATRS World Conference (Bordeaux, France, end of June, 2014)