2014 ATRS Global Airport Performance Benchmarking Project

Key Findings Prof. Tae Hoon Oum, Dr. Sam 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 

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

2014 ATRS Global Airport Performance Benchmarking Project

Airport Database

200 MAJOR AIRPORTS  AROUND THE WORLD Canada (12)

2 new  airports United  States (66)

N. America,  78

Oceania  Countries (16)

Asia  Pacific, 53

Europe, 69

2 new  airports

1 new  airport

Objective

Data

Airport  Characteristics

Methodology

Asia  (37)

Efficiency & Cost

User Charge

26 AIRPORT GROUPS Asia Pacific  ( 9) Europe (17)

Objective

Data

Airport  Characteristics

Methodology

Efficiency & Cost

User Charge

ATRS AIRPORT DATABASE,  FY 2002‐2012 (11 years)

 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

2014 ATRS Global Airport Performance Benchmarking Project

Airport Characteristics

PASSENGERS TRAFFIC, FY2012 (IN ’000 PASSENGERS) 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 Objective

Data

Airport  Characteristics

Methodology

Efficiency & Cost

User Charge

PASSENGER TRAFFIC (’000)TOP 10 AIRPORTS: FY 2008, 2010, 2012 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

2008

Objective

Data

Airport  Characteristics

2010

2012

Methodology

Efficiency & Cost

User Charge

0

Asia Pacific

Objective Data

Europe

Airport  Characteristics Methodology ATL ORD DFW DEN LAX CLT IAH LAS PHX PHL DTW YYZ EWR MSP JFK SFO MIA LGA BOS SEA IAD MCO YVR DCA SLC MEM BWI FLL YYC YUL HNL MDW PDX ANC STL SAN CLE TPA RDU BNA IND HOU CVG MCI SDF OAK YEG YOW DAL PIT SAT MKE AUS YWG CMH SJC MSY SMF ABQ SNA BDL YQB PBI YHZ ONT JAX YYJ RSW RIC BUR OKC TUL TUS PVD

900

CDG FRA LHR AMS MUC MAD IST FCO BCN ZRH VIE CPH LGW ORY OSL BRU DUS ARN HEL PMI TXL MXP MAN GVA NCE DUB ATH HAM LIS STN PRG LED CGN SAW WAW LYS EDI AGP STR LPA KBP TLV LTN LIN BHX BUD BSL HAJ VCE LUX BGY GLA RIX BLQ ALC NAP BRS OPO TRN CIA TLL BEG SOF ZAG MLA KEF LJU BTS SZG

PEK HND CAN CGK PVG HKG DXB BKK SIN SYD KUL DEL ICN BOM SHA SZX MNL NRT MEL TPE BNE AKL XMN SUB GMP KIX CJU MAA PER WLG HAK NGO ADL CHC PUS HKT CMB PEN GUM CNS MFM OOL CNX TSV DRW REP DUD PNH NTL NAN HDY ZQN CEI

AIRCRAFT MOVEMENTS, FY 2012  (’000 ATM)

1,000

North America

800

700

600

500

400

300

200

100

Efficiency & Cost User Charge

0

Objective Data

Europe

Airport  Characteristics Methodology JFK MCO LAX SEA SFO RSW FLL MIA TPA ATL SAN SNA PHX MDW DFW BWI SMF DEN MSY HNL EWR LAS BOS YYZ AUS SJC MSP IAH CLT PDX MCI ORD JAX DTW IAD PBI HOU OAK SLC LGA PVD SAT PHL STL DCA RNO BNA PIT DAL BDL MKE TUS BUR RDU YUL ONT YVR YYC CMH OKC ABQ YHZ IND CLE ALB TUL YEG RIC CVG YYT YOW YQR YWG MEM

Asia Pacific

LHR LGW ALC TLV PMI MLA IST CDG AGP DUB STN BCN MAD AMS BGY SAW ORY FCO MAN FRA TXL LIS OPO VCE MXP LTN MUC BHX GLA LPA BRS SZG BUD STR CPH OSL DUS NAP ARN ZRH VIE HAM LIN KEF CIA LED BLQ EDI HEL KBP BRU ATH GVA PRG WAW SOF BEG CGN LYS RIX NCE TRN HAJ BSL BTS ZAG LJU TLL LUX

180

HND DXB HKG CMB BKK SIN NRT CGK HKT TPE ICN CJU PEK OOL GMP KUL MEL CEI SHA HDY MNL PER CAN PUS PVG BOM SYD SZX HAK DEL CNX BNE XMN NGO MAA NAN SUB MFM KIX PEN ADL ZQN PNH AKL CNS REP DRW CHC NTL TSV WLG GUM DUD

PASSENGERS PER AIRCRAFT MOVEMENTS, FY 2012

200

North America

160

140

120

100

80

60

40

20

Efficiency & Cost User Charge

AIR CARGO TRAFFIC, FY 2012  (’000 METRIC TONS) 4,500

Asia Pacific

Europe

North America

4,000

3,500

3,000

2,500

2,000

1,500

1,000

500

0

Objective

Data

Airport  Characteristics

Methodology

Efficiency & Cost

User Charge

HKG

0.0%

Objective Data ZRH

FCO

Airport  Characteristics % Non‐Aero Rev

Methodology PBI

TUS

YYC

DAL

Efficiency & Cost

Mean 

User Charge

MEM

JFK

LGA

EWR

STL

CLE

PHL

IAD

ORD

YYZ

MIA

MDW

MSY

IAH

PIT

SEA

DEN

LAX

YWG

BWI

CVG

BDL

DCA

OAK

DTW

SFO

HNL

ONT

YUL

PDX

YOW

HOU

BOS

YHZ

PVD

SAN

SDF

SMF

TUL

YYT

ABQ

LAS

CLT

AUS

RSW

SAT

ALB

SNA

YQB

SJC

MSP

YEG

SLC

YVR

DFW

RNO

Europe

MKE

CMH

RDU

IND

PHX

MCO

ATL

YQR

FLL

YYJ

RIC

MCI

TPA

BUR

JAX

OKC

BNA

BEG

LED

MAD

PMI

HEL

BCN

MLA

WAW

LPA

TXL

BLQ

AGP

ALC

CGN

DUS

VCE

LHR

SZG

OPO

LIS

STN

SAW

NAP

EDI

LGW

SOF

HAM

RIX

BGY

MUC

GVA

MAN

ARN

MXP

LIN

LJU

BUD

ZAG

LTN

STR

VIE

BHX

Asia Pacific

CIA

IST

TLV

DUB

TLL

TRN

CPH

AMS

HAJ

ATH

ORY

CDG

OSL

BSL

FRA

KEF

CGK

PEN

KUL

NGO

XMN

KIX

DRW

HKT

HDY

CNX

CEI

BKK

WLG

SZX

PVG

ADL

NRT

PEK

ZQN

GUM

NTL

AKL

HAK

MEL

SYD

SIN

DUD

CJU

BNE

ICN

CMB

CHC

CAN

PUS

TSV

OOL

GMP

% NON‐AERO REVENUE, FY 2012

80.0%

North America

70.0%

60.0%

50.0%

40.0%

30.0%

20.0%

10.0%

2014 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) ASIAN AIRPORTS (HKG=1.0), FY 2012 1.60

1.40

Airport Groups

Airports 1.20

1.00

0.80

0.60

0.40

0.20

MAHB

AOT

APII

KAC

CMB

NGO

KIX

KUL

PEN

SZX

NRT

BKK

HKT

CEI

HDY

CNX

XMN

PVG

GUM

PEK

CAN

SIN

CGK

ICN

HAK

GMP

HKG

PUS

CJU

0.00

Gross VFP

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

20

GROSS VARIABLE FACTOR PRODUCTIVITY VS  RESIDUAL VFP: ASIA (HKG=1.0), FY 2012 1.60

1.40

Airport Groups

Airports 1.20

1.00

0.80

0.60

0.40

0.20

Gross VFP

Residual VFP

MAHB

AOT

APII

KAC

CMB

NGO

KIX

KUL

HKT

SZX

PEN

CEI

BKK

NRT

HDY

CNX

XMN

PVG

PEK

CAN

GUM

SIN

CGK

ICN

GMP

HAK

HKG

CJU

PUS

0.00

2014 ATRS Global Airport Performance Benchmarking Project

Key Results on Efficiency & Cost

RESIDUAL (NET) VARIABLE FACTOR PRODUCTIVITY  (VFP): ASIA (HKG=1.0), FY 2012 1.60

Busan Gimhae, Jeju, Hong Kong 1.40

Airports

Airport Groups

1.20

1.00

0.80

0.60

0.40

0.20

Objective

Data

Airport  Characteristics

Methodology

Efficiency & Cost

User Charge

MAHB

AOT

APII

KAC

CMB

NGO

KIX

KUL

HKT

SZX

PEN

CEI

BKK

NRT

HDY

CNX

XMN

PVG

PEK

CAN

GUM

SIN

CGK

ICN

GMP

HAK

HKG

CJU

PUS

0.00

GROSS VARIABLE FACTOR PRODUCTIVITY VS  RESIDUAL VFP: Europe Large Airports (CPH=1.0), FY 2012 1.2

1

Airports

Airport Groups

0.8

0.6

0.4

0.2

Gross VFP

Residual VFP

AENA

Berlin

PPL

TAV

Finavia

Avinor

Fraport

Heathrow

DAA

ANA

SEA

Swedavia

MAG

ADP

ADR

Schiphol

TXL

DUS

MUC

FRA

LHR

MAD

VIE

DUB

ORY

LIS

LGW

MAN

IST

PMI

ARN

CDG

MXP

STN

FCO

BCN

OSL

AMS

ZRH

CPH

0

RESIDUAL (NET) VARIABLE FACTOR PRODUCTIVITY (VFP):  EUROPE LARGE AIRPORTS (CPH=1.0), FY 2012 1.2

ZRH

1

Airport Groups

0.2

0

Objective

Data

Airport  Characteristics

Methodology

Efficiency & Cost

User Charge

Berlin

TAV

AENA

PPL

Finavia

Fraport

Avinor

Heathrow

DAA

SEA

ANA

Swedavia

MAG

ADR TXL

DUS

MUC

MAD

DUB

LHR

FRA

0.4

VIE

ORY

LIS

LGW

MAN

PMI

CDG

IST

0.6

ARN

MXP

FCO

BCN

STN

AMS

Schiphol

OSL

0.8

Airports

ADP

CPH

Copenhagen Kastrup, Zurich, Oslo

GROSS VARIABLE FACTOR PRODUCTIVITY VS  RESIDUAL VFP: Europe Small & Medium Airport (CPH=1.0), FY 2012 1.4

1.2

1

0.8

0.6

0.4

0.2

Gross VFP

Residual VFP

LED

SOF

WAW

CGN

BEG

ZAG

LIN

HEL

STR

BUD

AGP

SZG

HAM

BLQ

ALC

TRN

NAP

MLA

RIX

LPA

BGY

LTN

EDI

TLL

LJU

HAJ

KEF

VCE

TLV

SAW

BHX

CIA

BSL

GVA

ATH

0

RESIDUAL (NET) VARIABLE FACTOR PRODUCTIVITY (VFP):  EUROPE SMALL & MEDIUM AIRPORTS (CPH=1.0), FY 2012 1

SOF

WAW

CGN

BEG

0.4

ZAG

LIN

HEL

STR

BUD

AGP

SZG

HAM

0.5

BLQ

ALC

TRN

NAP

MLA

RIX

LPA

0.6

BGY

LTN

EDI

TLL

LJU

HAJ

VCE

KEF

0.7

TLV

SAW

0.8

BHX

CIA

BSL

0.9

GVA

ATH

Athens, Geneva, Basel

LED

0.3

0.2

0.1

0

Objective

Data

Airport  Characteristics

Methodology

Efficiency & Cost

User Charge

GROSS VARIABLE FACTOR PRODUCTIVITY VS  RESIDUAL VFP: N. American Large Airports (YVR=1.0), FY 2012 1.400

1.200

1.000

0.800

0.600

0.400

0.200

Gross VFP

Residual VFP

IAD

LAX

DCA

ORD

DEN

MIA

IAH

SAN

BWI

PHL

HNL

LAS

JFK

DFW

SEA

MDW

EWR

DTW

BOS

SLC

FLL

LGA

SFO

PHX

MCO

TPA

YVR

MSP

CLT

ATL

0.000

RESIDUAL (NET) VARIABLE FACTOR PRODUCTIVITY (VFP): 

IAD

LAX

DCA

DEN

ORD

0.600

MIA

IAH

SAN

BWI

PHL

HNL

LAS

JFK

DFW

SEA

MDW

EWR

DTW

BOS

SLC

0.800

FLL

LGA

SFO

PHX

MCO

1.000

TPA

Atlanta,  Charlotte, Minneapolis St. Paul YVR

MSP

CLT

1.200

ATL

NORTH AMERICA LARGE AIRPORTS (YVR=1.0), FY 2012

0.400

0.200

0.000

Objective

Data

Airport  Characteristics

Methodology

Efficiency & Cost

User Charge

GROSS VARIABLE FACTOR PRODUCTIVITY VS  RESIDUAL VFP: N. AmericanSmall & Medium  Airport (YVR=1.0), FY 2012 1.200

1.000

0.800

0.600

0.400

0.200

OKC YYC RDU RIC BNA YEG PVD SJC PDX YYJ IND BDL SAT JAX TUS PBI ABQ MKE YYT MCI SNA MSY RNO RSW AUS YOW HOU MEM TUL YUL YWG SMF SDF DAL OAK YHZ STL CMH ALB BUR CVG CLE PIT ONT ANC YQB

0.000

Gross VFP

Residual VFP

RESIDUAL (NET) VARIABLE FACTOR PRODUCTIVITY (VFP):  N. AMERICA SMALL & MEDIUM AIRPORTS (YVR=1.0), FY 2012 OKC

1.200

0.800

0.600

BNA YEG PVD SJC PDX YYJ IND BDL SAT JAX TUS PBI ABQ MKE YYT MCI SNA MSY RNO RSW AUS YOW HOU MEM TUL YUL YWG SMF SDF DAL OAK YHZ STL CMH ALB BUR CVG CLE PIT ONT ANC

RIC

YYC RDU

1.000

Oklahoma City, Calgary, Raleigh‐Durham

YQB

0.400

0.200

0.000

Objective

Data

Airport  Characteristics

Methodology

Efficiency & Cost

User Charge

GROSS VARIABLE FACTOR PRODUCTIVITY VS  RESIDUAL VFP: Oceanian Airports (SYD=1.0), FY 2012

RESIDUAL (NET) VARIABLE FACTOR PRODUCTIVITY  (VFP): OCEANIA (SYD=1.0), FY 2012 Sydney, Dunedin, Melbourne

Objective

Data

Airport  Characteristics

Methodology

Efficiency & Cost

User Charge

TOP EFFICIENCY PERFORMERS (2014) (based on Net VFP index=operating/management efficiency) Asia Pacific:  • Asian Airports: • Busan Gimhae, Jeju, Hong Kong • Oceania Airports: • Sydney, Dunedin, Melbourne Europe:  • Large Airports (> 15 million pax): • Copenhagen Kastrup, Zurich, Oslo • Small/Medium Airports ( 15 million pax): • Atlanta, Charlotte, Minneapolis St Paul • Small/Medium Airports (