Key Findings of 2012 ATRS Global Airport Performance Benchmarking project

Transcription

1 Key Findings of 2012 ATRS Global Airport Performance Benchmarking project ww.atrsworld.org Prof. Tae Hoon Oum, Sauder School of Business, University of British Columbia, Canada Dr. Yapyin Choo, The Air Transport Research Society (ATRS), Prof. Chunyan Yu, Embry Riddle Aeronautical University, Daytona Beach, FL, USA) 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)

2 Outline Objective of the ATRS Benchmarking Study Airports Included and ATRS Database Some Characteristics of Sample Airports Methodology Key Results on Efficiency User Charge Comparisons Air Transport Research Society (ATRS) 2

3 Objective of the Benchmarking Study To provide a comprehensive, unbiased comparison of airport performance focusing on Productivity and Operating/Mgt Efficiency Unit Cost Competitiveness Comparison of User Charge Levels Our study does not treat service quality differentials across airports Air Transport Research Society (ATRS) 3

4 Airports Included in the 2012 Report Canada-US Europe 77 airports 55 airports 16 airport groups Asia Pacific 9 airport groups 35 Asian airports 16 Oceania airports Total 183 airports 25 airport groups Air Transport Research Society (ATRS) 4

5 ATRS Airport Database, The ATRS Database contains historic information (since FY 2001) in the following geographic regions: Asia Pacific including Oceania; Europe; North America The data in each regions is segregated into: Traffic statistics and composition Airport characteristics (capacity, type of ownership etc) Aeronautical Activities and Revenue Non-Aeronautical Activities and Revenue Labor and other Operating Expenses Financial info on Balance Sheets Visit for more details. 5

6 Outline Objective of the ATRS Benchmarking Study Airports Included and ATRS Database Some Characteristics of Sample Airports Methodology Key Results on Efficiency User Charge Comparisons Air Transport Research Society (ATRS) 6

7 PEK H ND H KG DXB CG K SIN BKK CAN PVG SYD KUL ICN NRT SH A DEL BOM SZX MEL TPE MNL BNE G MP KIX AKL XMN SUB MAA PER NG O H AK ADL H KT CH C OOL CMB WLG PEN MFM CNS CNX G UM DRW NAN PNH TSV REP H DY NTL ZQN DUD CEI LH R CDG FRA MAD AMS FCO MUC IST LG W BCN ORY ZRH CPH VIE OSL DUS MXP STN DUB MAN BRU ARN ATH TXL LIS H AM H EL TLV GVA ISG CG N NCE LTN WAW EDI BH X LED LIN BUD NAP H AJ RIX CIA BSL TRN SOF MLA BEG ZAG KEF BTS LUX SZG LJU TLL ATL ORD LAX DFW DEN JFK IAH LAS SFO PH X MIA CLT MCO EWR MSP DTW YYZ SEA PH L BOS LG A IAD BWI FLL SLC H NL DCA MDW SAN YVR TPA PDX YUL YYC STL MCI MEM MKE OAK CLE RDU BNA SMF SNA H OU AUS SJC MSY PIT SAT CVG DAL IND RSW CMH YEG PBI ABQ JAX BDL ANC ONT YOW BUR PVD RNO TUS YH Z OKC YWG SDF RIC ALB YYJ YQB YYT YQR 100,000 Passengers Traffic, FY2010 (in 000 passengers) 90,000 80,000 70,000 60,000 Asia Pacific Europe North America 50,000 40,000 30,000 20,000 10,000 0 Air Transport Research Society (ATRS) 7

8 PEK HND HKG DXB CGK SIN BKK CAN PVG SYD LHR CDG FRA MAD AMS FCO MUC IST LGW BCN ATL ORD LAX DFW DEN JFK IAH LAS SFO PHX Passenger Traffic (000)- Top 10 Airports: 2006, 2008, ,000 90,000 80,000 Asia Pacific Europe North America 70,000 60,000 50,000 40,000 30,000 20,000 10, Air Transport Research Society (ATRS) 8

9 PEK CGK SIN BOM MNL AKL MAA PER NAN GUM CMB REP HDY CDG AMS IST CPH DUS GVA TXL LIS EDI BHX CRL BSL BEG MLA LAX LAS CVG YYZ SLC SFO DCA FLL PDX SAN SMF BNA DAL ABQ BUR PVD RSW YOW Aircraft Movements, 2010 ( 000 ATM) 1, Asia Pacific Europe North America Air Transport Research Society (ATRS) 9

10 Passengers per Aircraft Movements, Asia Pacific Europe North America Air Transport Research Society (ATRS) 10

11 4,500 Air Cargo Traffic, 2010 ( 000 metric tons) 4,000 3,500 3,000 2,500 Asia Pacific Europe North America 2,000 1,500 1, Air Transport Research Society (ATRS) 11

12 % Non-Aero Revenue, 2010 Air Transport Research Society (ATRS) 12 0% 10% 20% 30% 40% 50% 60% 70% 80% SIN OOL TSV HKG AKL CAN DUD PER MEL NAN CMB BNE DEL GMP SYD ICN NGO WLG CHC HAK ZQN NTL GUM NRT PVG PEK ADL CEI BKK HDY HKT CNX MAA XMN SZX DRW KUL KIX PEN CGK FRA BSL CDG ORY OSL NCE HAJ IST DUB AMS TLL CPH CIA FCO BUD ATH TLV VIE TRN LTN ZRH MXP LIN BHX MAN GVA NAP HAM LJU SZG SOF LGW ARN MUC RIX LHR STN EDI ISG LIS BRU MLA CGN DUS TXL BTS BCN MAD WAW HEL BEG JAX OKC TPA RIC BUR PHX YYJ MCI BNA MCO ATL RNO YEG CMH RDU MSP IND YQR DAL SAT FLL PBI SLC TUS MKE YYC DFW YVR YWG RSW SNA CLT ALB SAN YQB SMF AUS ABQ SJC PVD YYT YUL LAS SDF YHZ DCA BOS OAK HNL DTW YOW CVG BDL PDX SFO IAD SEA HOU LAX ONT DEN PIT BWI CLE MSY MDW PHL MIA YYZ IAH ORD EWR LGA STL MEM JFK ANC % Non-Aero Rev Mean North America Europe Asia Pacific

13 Outline Objective of the ATRS Benchmarking Study Airports Included and ATRS Database Some Characteristics of Sample Airports Methodology Key Results on Efficiency User Charge Comparisons Air Transport Research Society (ATRS) 13

14 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] Air Transport Research Society (ATRS) 14

15 Methodology: EFFICIENCY MEASUREMENT Variable Factor Productivity (VFP) Index Impossible - Total Factor Productivity (TFP) because of capital input cost accounting problem Unit Operating Cost Competitiveness Index: Combines VFP and Input Price Index We explored Alternative approaches: Data Envelopment Analysis (DEA) Econometric Cost Function Approach including Stochastic Frontier methods Note: Industry acceptance of our report using more advanced/sophisticated methods is one of our major concern 15

16 Multilateral Aggregation Method This multilateral output (input) index procedure uses the following revenue (cost) shares to aggregate output (inputs). Air Transport Research Society (ATRS) 16

17 ATL RDU RNO CLT PBI BNA MSP JAX LGA SAT TPA SNA MCO MKE FLL Airport Ranking Residual Ranking Comparison of Top 15 Airports Residual VFP Ranking Residual DEA Ranking Residual SFA Ranking Air Transport Research Society (ATRS) 17

18 BOS CVG CLE SJC ALB PHL DFW STL ONT LAX ORD BWI PIT MSY MIA Airport Ranking Residual Ranking Comparison of Bottom 15 Airports Residual VFP Ranking Residual SFA Ranking Residual DEA Ranking Air Transport Research Society (ATRS) 18

19 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 Airport Ranking Residual Ranking Comparison of Mid-Ranked 15 Airports Residual VFP Ranking Residual DEA Ranking Residual SFA Ranking Air Transport Research Society (ATRS) 19

20 Outline Objective of the ATRS Benchmarking Study Airports Included and ATRS Database Some Characteristics of Sample Airports Methodology Key Results on Efficiency User Charge Comparisons Air Transport Research Society (ATRS) 20

21 GMP ICN HKG CAN SIN CGK HAK GUM XMN PEK CNX HKT HDY PVG BKK SZX NRT DEL KUL PEN KIX NGO CMB KAC APII AAI AOT MAHB Gross Variable Factor Productivity (VFP) Asia (HKG=1.0), Airports Airport Groups Mean Mean Air Transport Research Society (ATRS) 21

22 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 [2010 Iceland volcano ash cloud (For European airports)] We compute residual (Net) variable factor productivity (RVFP) measures after removing effects of these Factors Air Transport Research Society (ATRS) 22

23 Gross Variable Factor Productivity vs Residual VFP: Asia (HKG=1.0), Gross VFP Residual VFP Air Transport Research Society (ATRS) 23

24 GMP Residual (Net) Variable Factor Productivity: (after removing factors beyond managerial control ) : Asia (HKG=1.0) ICN HKG SIN CAN GUM CGK HAK HDY CNX HKT XMN PEK BKK PVG DEL SZX KUL NRT PEN KIX NGO CMB KAC AAI APII AOT MAHB Gimpo, Incheon, Hong Kong Airports Airport Groups Residual VFP Mean Air Transport Research Society (ATRS) 24

25 WLG NTL NAN ADL PER DRW AKL BNE CHC ADG ZQN DUD MEL APAC QAL SYD 1.2 Residual (Net) Variable Factor Productivity Oceania (SYD=1.0) Airport Groups 1 Airports Sydney, Queenstown SYD ZQN DUD MEL DRW AKL BNE CHC PER NTL NAN ADL WLG APAC QAL ADG Residual VFP Mean Air Transport Research Society (ATRS) 25

26 TAV FCO BCN ARN STN DUB MAN LGW ORY MXP VIE MUC LHR FRA DUS ANA ADR Avinor Swedavia BAA DAA ADP MAG AENA Finavia Fraport SEA Berlin AMS MAD IST BRU CDG Schiphol ZRH ATH CPH OSL Residual (Net) Variable Factor Productivity: Europe Passengers > 15 million ( CPH=1.0) Airports Copenhagen, Oslo, Zurich Airport Groups Residual VFP Mean Air Transport Research Society (ATRS) 26

27 BEG BTS CGN TXL WAW LIN SOF TLL LJU NAP TLV TRN MLA RIX SZG BUD HEL HAM ISG EDI LIS CIA BHX BSL HAJ LTN NCE GVA Net Variable Factor Productivity: Europe Passengers < 15 million ( CPH=1.0) Geneva, Nice, Edinburgh Residual VFP Mean Air Transport Research Society (ATRS) 27

28 ATL Net Variable Factor Productivity: N. America Passengers > 15 million (YVR=1.0) CLT MSP YVR MCO TPA PHX FLL SFO HNL SEA LGA SLC BOS JFK MDW EWR PHL DTW SAN LAS DCA IAD IAH DFW BWI ORD MIA LAX DEN Atlanta, Charlotte, Minneapolis/St Paul Residual VFP Mean Air Transport Research Society (ATRS) 28

29 YQB SNA PBI YEG YQR TUS MKE DAL PDX YYJ YOW IND YYT PVD RSW ABQ CMH MEM OAK SMF YWG MSY SJC YHZ SAT MCI AUS ALB BUR CVG YUL ANC SDF HOU ONT PIT CLE STL OKC RNO YYC BNA JAX RDU RIC Residual (Net) Variable Factor Productivity: N. America Passengers < 15 million (YVR=1.0) Raleigh-Durham, Richmond, Oklahoma City Residual VFP Mean Air Transport Research Society (ATRS) 29

30 Top Efficiency Performers (2012) (based on Net VFP index=operating/management efficiency) Asia Pacific: Asian Airports: Gimpo, Incheon, Hong Kong Oceania Airports: Sydney, Queenstown Europe: Large Airports (> 15 million pax): Copenhagen, Oslo, Zurich Small/Medium Airports (< 15 millions Pax): Geneva, Nice, Edinburgh North America (Canada/US): Large Airports (> 15 million pax): Atlanta, Charlotte, Minneapolis/St Paul Small/Medium Airports (< 15 millions Pax): Raleigh-Durham, Richmond, Oklahoma City Air Transport Research Society (ATRS) 30

31 Effects of Business Strategies Diversification of Revenue Source is Good Airports with larger share of non-aeronautical revenue achieve higher efficiency (Net VFP) Outsourcing improves efficiency Airports contracting out various services to outside operator achieve higher efficiency Outsourcing terminal operations improve efficiency 36

32 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 Air Transport Research Society (ATRS) 37

33 KUL GUM DXB CMB SUB BKK CEI CNX HDY HKT CGK MNL PNH REP BOM DEL MAA CAN HAK PEK PVG SHA SZX XMN TPE DUD MFM ICN GMP HKG ZQN PER SIN NAN WLG AKL NTL TSV OOL NTL-Night SYD NGO NRT DRW KIX HND DUS STN-off peak ARN LTN BRU STN-peak MLA LGW-off-peak FRA BSL-FR sector RIX TRN MUC FCO NAP MXP LUX LIN CIA LIS BSL-CH sector IST VIE HAM GVA KEF EDI MAN-peak MAN-offpeak TXL SOF-offpeak HEL CPH LED NCE CGN SOF-peak ZRH MAD HAJ BUD TLL CDG ORY BCN TLV ATH BEG OSL AMS LHR WAW BHX ZAG LGW-peak BTS LJU SZG DUB CLT ATL RDU SLC FLL RIC BNA MEM SAT PBI TUS JAX PHX MSY SAN TPA MCO PHL ANC IND MCI SDF MIA HOU SNA MSP ABQ ONT SJC LAS ALB IAH OKC RSW RNO SEA OAK YVR AUS PDX MDW IAD DCA BDL CLE YQR DEN CVG DFW YOW PVD DTW HNL YEG SMF PIT CMH BWI LAX YHZ YYC YWG BOS ORD YUL SFO JFK Off Peak JFK Peak YQB STL EWR Off Peak EWR Peak LGA Off Peak LGA Peak YYZ US$ Landing Charges for Boeing , 2011 (in US$) 7,000 6,000 5,000 4,000 3,000 2,000 Asia Pacific Europe North America 1,000 0 Landing Charges for Boeing 767 Mean Air Transport Research Society (ATRS) 38

34 Asia Pacific: Combined Landing and GMP CGK PER ZQN DXB ICN ADL WLG CEI CNX HDY HKT BKK MEL TSV PNH REP NTL OOL DRW US$ SYD BNE BNE-PEAK NRT-1N NRT-1S NRT-2 NGO HND AKL KIX NAN Passenger Charge for Boeing 767, 2011 (in US$) 18,000 16,000 14,000 12,000 Lowest charges: Kuala Lumpur Low Cost Carrier Terminal, Dunedin (New Zealand) Highest charges: Nadi (Fiji), Kansai 10,000 8,000 NTL-Night CNS 6,000 4,000 2,000 KUL-LCCT DUD TPE HKG BOM DEL MAA GUM SIN-LCCT KUL CMB MNL MFM CAN HAK PEK PVG SHA SZX XMN SIN SUB 0 Combined Landing and Passenger Charges for Boeing 767 Mean Air Transport Research Society (ATRS) 39

35 RIX NAP CIA KEF ARN FCO - T1, T2 TLL BSL-FR sector CGN GVA STN-off peak MAN-peak LTN HEL STN-peak CPH - LCCT FCO OSL BCN BUD - LCC T1 TRN LIN MXP NCE LGW-off-peak CPH IST BSL-CH sector EDI DUS SOF-offpeak MAD HAM HAJ SOF-peak MLA LIS TXL LJU VIE LED BUD BRU BEG - T1 MUC BHX AMS DUB TLV - T1 FRA ATH LGW-peak ZAG SZG BEG WAW ZRH US$ Europe: Combined Landing and Passenger Charge for Boeing 767, 2011 (in US$) Lowest charges: Riga, Luxembourg Highest charges: London Heathrow, Ben Gurion (Tal Aviv) BTS ORY CDG TLV LHR LUX MAN-offpeak Combined Landing and Passenger Charges for Boeing 767 Mean Air Transport Research Society (ATRS) 40

36 Cost per Enplanement for Airlines (CPE) CPE = sum of landing fees, terminal arrival fee, rents and utilities, terminal apron charges/tiedowns, and passengers other aeronautical payments to airports divided by enplaned passengers Air Transport Research Society (ATRS) 41

37 BUR DAL ATL SLC MEM MCI PHX TPA JAX SAT MSP BNA MKE FLL MCO RIC OKC RDU DFW SAN SDF RNO TUS CMH PBI LAS ALB RSW HOU CVG AUS BDL DTW ABQ PHL MDW ANC OAK BWI MSY SNA HNL LAX IAH PVD IND SJC SEA SMF PDX DEN CLE DCA ONT BOS ORD IAD SFO STL YYJ YQR YYT YHZ YEG YWG YYC YQB YOW YVR $US LGA MIA PIT EWR YUL JFK YYZ North America: Cost per Enplaned Passenger, 2010 (in US$) Canada: Lowest CPE: Victoria, Regina Highest CPE: Toronto, Montreal United States: Lowest CPE: Bob Hope, Dallas Love Field Highest CPE: New York JFK, Newark Liberty Air Transport Research Society (ATRS) 42

38 Empirical Studies on Factor Affecting Aeronautical User Charge Evidence of cross-subsidization from non-aeronautical revenue Cost strongly affects the airport charges Airport charges for hub airports are more expensive Airport charges for Southwest airports are cheaper, but not significant for airports dominated by low cost airlines overall Connecting traffic, dominant airline %, and no. of nearby airports negatively relate to airport charges but not significant All ownership variables are not significant. Higher landing charges lead to lower terminal charges and vice versa.

39 Empirical Results 1 Dependent Variable: ln Aero Rev per ATM Random Effect Fixed Effect Hausman Taylor Panel IV (EC2SLS) ln Aero Rev per ATM Coef. Std. Err. Coef. Std. Err. Coef. Std. Err. Coef. Std. Err. Non-Aero share ** ** ** ** ln Unit Cost ** ** ** ** Transit Dominant * International ** ** ** ** ln Pax ** ** ** ** ln ATM ** ** ** ** ln Cargo ** * ** ** No of nearby airport D hub ** ** ** D port D authority D lowcost * * ** ** ** ** ** ** ** ** ** ** ** ** ** ** ** ** ** ** ** ** ** ** ** ** ** ** Constant ** ** ** ** Observation R-squared

40 Empirical Results 2 Dependent Variable: ln Aero Rev per ATM Random Effect Hausman Taylor Panel IV (EC2SLS) ln Aero Rev per ATM Coef. Std. Err. Coef. Std. Err. Coef. Std. Err. Non-Aero share ** ** ** ln Unit Cost ** ** ** Transit Dominant International ** ** ** ln Pax ** ** ** ln ATM ** ** ** ln Cargo ** ** ** No of nearby airport D hub ** ** ** D port D authority D southwest ** * ** * ** ** ** ** ** ** ** ** ** ** ** ** ** ** ** ** ** ** ** Constant ** ** ** Observation R-squared

41 Empirical Result Dependent Variable: ln Landing Fee per ATM Random Effect Fixed Effect Hausman Taylor Panel IV (EC2SLS) ln Landing Fee per ATM Coef. Std. Err. Coef. Std. Err. Coef. Std. Err. Coef. Std. Err. Terminal Share ** ** ** ** Non-Aero share ** ** ** ** ln Unit Cost ** ** ** ** Transit Dominant International * * ln Pax ** ** ** ** ln ATM ** ** ** ** ln Cargo ** * ** ** No of nearby airport D hub ** ** ** D port D authority * D southwest ** * ** * * * ** ** ** ** ** ** ** ** ** ** ** ** ** ** ** ** ** ** * ** ** ** ** Constant ** ** ** ** Observation R-squared

42 Empirical Result Dependent Variable: ln Terminal Fee per Pax Random Effect Fixed Effect Hausman Taylor Panel IV (EC2SLS) ln Terminal Fee per Pax Coef. Std. Err. Coef. Std. Err. Coef. Std. Err. Coef. Std. Err. Land Share ** ** ** ** Non-Aero share ** ** ** ** ln Unit Cost ** * ** ** Transit Dominant International ** ** ln Pax ** ** ** ** ln ATM * ln Cargo ** * ** ** No of nearby airport D hub ** ** ** D port D authority D southwest ** (dropped) ** * ** * ** ** ** ** ** ** ** ** ** ** ** ** ** ** ** ** ** ** ** ** ** ** ** ** ** ** Constant ** ** ** ** Observation R-squared

43 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 Report sale finances our annual benchmarking research project Air Transport Research Society (ATRS) 48

44 Thank You 2013 ATRS World Conference (Bergamo, Italy June 26-29, 2013)