InterVISTAS Airport Traffic Forecasting Workshop - Presentations, 10April2013

Canadian Forecasting Workshop Session 1: Introductory Remarks on the Science and Art Forecasting

Dr. Mike Tretheway InterVISTAS Consulting Chief Economist

Today’s Workshop 1. Introductory Remarks on Forecasting 2. Transport Canada PODM/PTAM models 3. Alternative ApproachesSingle Airport Forecasts 4. Incorporating Uncertainty into Air Traffic Forecasts 5. Current Outlook

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Today’s Workshop • Dr. Mike Tretheway •

Chief Economist, InterVISTAS Consulting Group

•

Technical Director, Business Line Aviation

• Ian Kincaid •

Vice President, Economic Analysis •

Head of Forecasting Practice

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Background Report ACRP 76 Addressing Uncertainty about Future Airport Activity Levels in Airport decision making Undertaken by •InterVISTAS Consulting Inc. •

Mike Tretheway Ian Kincaid

•HDR Inc. •

David Lewis Stéphane Gros 4

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Airport Forecasting Record .

• Forecasting is an essential tool for airports • Medium to long term master planning • Financial forecasts • Operational Forecasts

5

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Airport Forecasting Record .

• But the track record has not always been good

6

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Actual and Forecast Total Passenger Enplanements at Hartsfield-Jackson Atlanta International Airport

Atlanta 55

53

51

Actual Traffic TAF 2001 TAF 2003 TAF 2005 TAF 2007 TAF 2009

49

47

45

) io t(M lm p rE g n e s a P

43

41

39

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35 2000

2002

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7

2008

2010

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Washington Dulles 25

Actual and Forecast Total Passenger Enplanements at Washington Dulles International

Actual Traffic TAF 2000 TAF 2001 TAF 2003

20

TAF 2004 TAF 2005 TAF 2009

15

) io t(M lm p rE g n e s a P

10

5

0 1990

1992

1994

1996

1998

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2002

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Airport Forecasting Record .

• But the track record has not always been good • Albeit with some learning

9

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Actual and Forecast Total Passenger Enplanements at Lambert-St. Louis International Airport

St. Louis 30

Actual Traffic TAF 1998 TAF 2001

25

TAF 2002 TAF 2003 TAF 2009

20 TWA declares bankruptcy for the second time

15

) io t(M lm p rE g n e s a P

10 TWA declares bankruptcy

TWA declares bankruptcy for the third time and AA buys TWA. Construction of new runway begins

5

AA reduces services at STL AA terminates its focus city at STL

0 1985

1990

1995

10

2000

2005

2010

2015

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Actual and Forecast Total Passenger Enplanements at Lambert-St. Louis International Airport

St. Louis 30

Actual Traffic TAF 1998 TAF 2001

25

TAF 2002 TAF 2003 TAF 2009

20 TWA declares bankruptcy for the second time

15

) io t(M lm p rE g n e s a P

10 TWA declares bankruptcy

TWA declares bankruptcy for the third time and AA buys TWA. Construction of new runway begins

5

AA reduces services at STL AA terminates its focus city at STL

0 1985

1990

1995

11

2000

2005

2010

2015

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Airport Forecasting Record .

• Sometimes the long run forecast has been good • but with short term variance • And different traffic mix than original forecast

12

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12

U.S. Airways "de-hubs"

Actual Traffic 1987 Master Plan Forecast (Baseline) 10

9/11 and recession Recession

8

6

Piedmont announces hub

Southwest Airlines launches services

4 First Gulf War and recession

) io t(M lm p rE g n e s a P 2

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BWI

Actual and Forecast Total Passenger Enplanements at Baltimore/Washington International Thurgood Marshall Airport

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Airport Forecasting Record .

• Sometimes unanticipated events dramatically change a market

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New Orleans 8

Actual and Forecast Total Passenger Enplanements at Louis Armstrong New Orleans International Airport Actual Traffic TAF 2001

7

TAF 2004 TAF 2005 TAF 2009

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4 Hurricane Katrina

) io t(M lm p rE g n e s a P

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0 1980 1982 1984 1986 1988 1990 1992 1994 1996 1998 2000 2002 2004 2006 2008 2010 2012 2014

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Airport Forecasting Record .

• The forecasting record can also be one of underforecasting

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Bellingham WA 450

Actual and Forecast Total Passenger Enplanements at Bellingham International Airport

Actual Traffic TAF 2000

400

TAF 2003 Master Plan Forecast

350

300

250 Allegiant opens base at BLI in January 2008

200

) d u o h t(T lm p rE g n e s a P

150

100

50

0 1985

Allegiant enters in August 2004 and rapidly develops service

United Express / SkyWest exits in 2001

1990

1995

2000

17

2005

2010

2015

2020

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Guessing vs. Analysis Midway (1976)

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• Setting: Station Hypo •

Code breaking centre, Pearl Harbor Naval Base •

•

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Early April 1942 (US fleet crippled after 7Dec1941)

Issue: forecasting Japanese naval fleet intentions for coming 2 months •

MG is fictional character (Matt Garth)

•

JR is historical character (Joe Roquefort, head of Honolulu code breaking unit)

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o MG: Now the boss is afraid Yamamoto's going to jump back at us. But where?

JR: We got the latest intercepts here.

Here's a list of Japanese ships we suspect will be assigned to amphib operations south of Rabaul. The Coral Sea! That's where we think they'll strike next. But something else is stirring, something out our way. o MG: We need facts, not guesswork. o JR: Matt, we cracked Yamamoto's code, but we can't just reel it off. We get a flicker here and a glimmer there. o MG: How much can you decipher? o JR: Hell, maybe... o MG: Really decipher. o JR: Ten percent. o MG: That's one word in . For Christ's sake, you're guessing! o JR: We like to call it analysis

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Types of Airport Forecasts .

• Passenger traffic • Total pax • Enplaned/deplaned • O/D vs Connecting • Cargo tons • E/D vs on-board

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Types of Airport Forecasts .

• Aircraft Movements • Commercial • Heavy jet, turboprops, piston • Typically driven by • base pax/cargo-freighter forecast • Pax/aircraft (cargo/aircraft) • General Aviation • See recent trend • Other • Military (Busan, new Beijing examples), • rescue, government, tech stop 22

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Total GA Movements for Canada

Source: Statistics Canada, Table 401-007, 401-0015 and 401-0021.

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Types of Airport Forecasts .

• Operational Forecasts • Day by day, Hour by hour forecasts of a specific traffic type • E.g., volumes through a security point border process • Ex- Blackcomb Ski Corp.

• Recognize effects of • Annual volume drivers • weather, • Interaction between the sessions • Increased local skiing today means less in 2 weeks 25

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Forecast Probability and Risk .

• Low-Central-High • What are the probabilities of each scenario?

• Threshold • What is probability that traffic in each year will fall below 19mn pax?

• Risk • What is the 20/80% range of the forecast 10 years from now?

26

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Thank You

Subscribe to Monthly Aviation Intelligence Report www.InterVISTAS.com

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Transport Canada Forecasts InterVISTAS Consulting Group 10 April 2013

Outline • Overview of the Transport Canada Forecasts: •

Methodology

•

Data

•

Pros and cons

• Alternative Approaches: •

Single airport methodologies

•

Methodology

•

Addressing uncertainty

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Transport Canada Forecasts

Transport Canada Forecasts • Each year, Transport Canada generated medium and long term forecasts of Canadian air traffic •

Up to 14 years in the future

•

Forecasts of •

E/D passengers and passenger-kms

•

Air cargo tonnage

•

Aircraft movements (commercial and GA)

•

Breakdowns into domestic, transborder and international

•

Published forecasts provided national and regional forecasts (Atlantic, Ontario, Quebec, Prairies/Northern, Pacific) •

•

Most recent document was 2007

Forecasts for individual airports available for purchase

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Transport Canada Forecasts • Transport Canada forecasts based on two inter-connected models • Originally developed in 1976 • Generation of traffic and allocation of traffic: •

•

Generation: PODM-V2 (Passenger Origin-Destination Model) •

Forecasted Origin-Destination traffic

•

E.g., Vancouver-Montreal; Toronto – Los Angeles, etc.

Allocation: PTAM (Passenger Traffic Allocation Model) •

Allocates forecasted passenger traffic to air carrier operations

•

E.g., Forecast Vancouver-Montreal traffic allocated to direct service and to connections via Calgary, Toronto, etc.

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Transport Canada Forecasts • The Generation-Allocation approach is similar approach to that used in urban transport modeling •

Road systems

•

Toll roads

•

Public transport

• It allows changes in the network to affect traffic flows • However, the Transport Canada model does not address congestion or other constraints •

This is often a major factor in urban models

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PODM (Passenger Origin-Destination Model) • PODM:

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PODM (Passenger Origin-Destination Model) • PODM (Translated): •

Based on a zonal system

•

PODM forecasts traffic between zones

•

Domestic zones:

•

•

•

Approximately 36 zones based around major airports

•

E.g., Toronto - Pearson, City Centre, Hamilton, Oshawa, Buttonville, Kitchener) Vancouver – Vancouver, Abbotsford, Vancouver Harbour

Transborder zones: •

Approximately 20 zones

•

E.g., Los Angeles, New York, etc.

International zones: •

Country (e.g., UK) or continental (e.g., Africa)

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PODM (Passenger Origin-Destination Model) • PODM (Translated): •

Gravity Model:

Zone B

Zone A •

•

•

Traffic between two zones is a function of: Attractors:

Impeders:

Population GDP Linguistic similarities Other factors

Air fare Level of direct service Travel time by car

Model was directional 36

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PODM (Passenger Origin-Destination Model) • Calibration/estimation required a lot of data: •

O/D passenger Data • Directional Origin-Destination Database • Based on 10% sample of all air tickets • Especially developed for the model by Statistics Canada • Also used U.S. data for transborder

•

Air Fare Data • Airfare Basis Survey • Quarterly survey of domestic, transborder and international air passengers

•

OAG schedule data • For determining direct services

•

Socio-Economic Data • Population, GDP, etc.

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PODM (Passenger Origin-Destination Model)

• Separate models for full economy and discount economy •

Proxies for trip purpose but also included cross-elasticities (switching between full and discount)

• Based on data from several years – 1995 to 2001 •

Panel data: based on variation over time and between routes

• Calibrated to ensure that the model reasonably matches historical data – backcasting

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PODM (Passenger Origin-Destination Model) • Forecasting Traffic •

Requires forecasts of input variables

•

GDP, population: •

•

Based on Conference Board of Canada and other sources

Air Fare: •

Required separate model (Cost and Fare Model)

•

Air fare based on input costs:

fuel, labour, aircraft equipment, other plus productivity improvements

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PTAM (Passenger Traffic Allocation Model) • Having established the O/D flows, these need to be allocated to airlines •

Moving from O/D to E/D

• Also attempts to address the impact of O/D traffic on airline services •

Development of direct services

•

Incremental frequencies

• Incorporates assumptions about future airline fleets, aircraft technology and load factors • Could require iteration of the PDOM model: •

Introduction of direct service could stimulate O/D traffic 40

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PTAM (Passenger Traffic Allocation Model) • Overall assumption of greater allocation of traffic to direct services:

Source: Transport Canada Assumptions Report (2006-20)

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PTAM (Passenger Traffic Allocation Model) • Contained specific assumptions about the development of new direct services:

Source: Transport Canada Assumptions Report (2006-20) 42

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Pros and Cons • Positives •

Arguably the most sophisticated air traffic forecasting system in the world •

The FAA approach is much more basic: National traffic model based on econometric analysis Plus separate forecasts for some individual airports (Terminal Area Forecasts)

•

Could model in a complex way the implication of changes to the airline network

•

E.g., O/D – Edmonton to Europe • Starts connecting through Toronto (and other hubs) • Direct start-ups which impacts on traffic flows through Toronto

•

Similarly, the gateway impacts on Vancouver: E.g., YOW-YVR-HK now moves YOW-YYZ-HKG 43

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Pros and Cons • Negatives •

Data!

•

O/D Passenger and Air Fare Basis data generally not available due to confidentiality concerns • Used for national accounts requirements and other purposes • Raw data needs processing – Transport Canada had a bespoke data pulls to suit forecasting requirements

•

More restrictive than U.S. equivalents (available to U.S. citizens)

•

Alternative commercial sources are available – MIDT, DIIO, etc.

•

But costs are high: • Tens of thousands of dollars per airport

•

Prohibitive for individual airports but might still be useful for systemwide purposes (e.g., Nav Canada, CATSA)

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Pros and Cons • Negatives •

Complexity

•

Resources •

•

Required a small full-time team to maintain

Costs of model maintenance, calibration and result production development beyond the capabilities of individual airports and most other organisations

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Alternative Approaches

Single Airport Forecasts • Range of methodologies available: •

Time series / trend analysis

•

Bottom-up / schedule based

•

Econometric models

•

Market share models

• A combination of these approaches can be used

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Single Airport Forecasts • Time series / trend •

•

Based on historical traffic growth rates •

Statistical techniques (ARIMA)

•

“Historically has grown at 3.5% per annum so will grow at similar rates in the future”

Can also reference global forecasts by Boeing, Airbus, FAA, IATA, etc.

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Single Airport Forecasts • Bottom-up •

•

Tend to be supply-side: •

Development of new routes

•

Increase in frequencies and changes in gauge

Route-by-route forecasts of air service and passenger volumes •

Can be based on announced schedules in the short term

•

Guided by fleet acquisitions in the medium term

•

Harder for long term forecasts

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Single Airport Forecasts • Econometric models •

Traffic as a function of: •

GDP (or GDP per capita)

•

Personal income

•

Population

•

Air fare

•

One-off factors: SARS, air failure, 9/11 (historical events)

•

Separate models can sometime be developed for individual markets (Domestic, transborder, international)

•

Can be seen as simplified version of the Transport Canada model: •

One zone (airport) to small number of destination zones

•

Dependent on the data available for airport

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Single Airport Forecasts • Econometric models •

Requires forecasts of explanatory variables

•

Generally good sources available for GDP, population, etc.

•

Inclusion of air fare can be problematic: •

Hard to obtain historical data especially for a long time series (need 10 years at least)

•

Technical issues – fares are an endogenous variable, affected by demand and supply conditions • Requires use of advanced statistical techniques (Two stage least squared regressions)

•

Air fares also need to be forecast, e.g., using a airline cost model

•

As a result, air fares are often not included in the analysis

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Single Airport Forecasts • Yield Trend (U.S.) 0.45

1958 Boeing 707 enters service

0.40

1970 Boeing 747 enters service

1978 U.S. Airline Deregulation Act

2001 September 11th terrorist attacks

0.30

0.25

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0.05 Inflation Adjusted 2008 Dollars Nominal Dollars

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0.35

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Single Airport Forecasts • Market Share Model •

Generally involves forecasting the airport’s share of some aggregate measure of air traffic (national traffic, regional traffic)

•

Generally used where there are a number of airports realistically competing for the same traffic

•

•

E.g., UK – five airports compete for the same traffic in London alone (Heathrow, Gatwick, Luton, Stansted, London City)

•

New York market

•

San Francisco / Oakland

Few examples in Canada

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Concluding Comments Uncertainty

Addressing Uncertainty • Standard approach to uncertainty in both the Transport Canada forecasts and single airport forecasts •

Base case with low and high forecasts

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Addressing Uncertainty

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Addressing Uncertainty • High-Base-Low: •

Everything is bad or everything is good all at once

•

Variation tends to be arbitrary – why are these the outer bounds?

•

No information or assessment of likelihood

•

Often the range is not that large (+/- 25%) – history has shown us that bigger deviations are possible

•

Has little input into the planning process •

Low can be of interest for financing

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Addressing Uncertainty • Other approaches: •

“What-ifs”

•

Sensitivity tests

• These provide some information on the impact of specific factors or the outcome of certain events • Again, can be arbitrary without reference to the likelihood of such an outcome • More on advanced approaches to uncertainty this afternoon…

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Thank You! www.intervistas.com

Addressing Uncertainty in Air Traffic forecasting InterVISTAS Consulting Group 10 April 2013

Outline • Consequences of uncertainty • Causes of uncertainty • Identifying and evaluating risk • Incorporating risk into forecasting

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Consequences of Uncertainty

Washington Dulles International Airport 25

Actual Traffic TAF 2000 TAF 2001 TAF 2003

20

TAF 2004 TAF 2005 TAF 2009

15

) io t(M lm p rE g n e s a P

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0 1990

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2004

2006

2008

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Lambert-St. Louis International Airport 30

Actual Traffic TAF 1998 TAF 2001

25

TAF 2002 TAF 2003 TAF 2009

20 TWA declares bankruptcy for the second time

15

) io t(M lm p rE g n e s a P

10 TWA declares bankruptcy

TWA declares bankruptcy for the third time and AA buys TWA. Construction of new runway begins

5

AA reduces services at STL AA terminates its focus city at STL

0 1985

1990

1995

2000

64

2005

2010

2015

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Louis Armstrong New Orleans International Airport 8

Actual Traffic TAF 2001

7

TAF 2004 TAF 2005 TAF 2009

6

5

4 Hurricane Katrina

) io t(M lm p rE g n e s a P

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2

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0 1980 1982 1984 1986 1988 1990 1992 1994 1996 1998 2000 2002 2004 2006 2008 2010 2012 2014

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Bellingham International Airport 450

Actual Traffic TAF 2000

400

TAF 2003 Master Plan Forecast

350

300

250 Allegiant opens base at BLI in January 2008

200

) d u o h t(T lm p rE g n e s a P

150

100

50

0 1985

Allegiant enters in August 2004 and rapidly develops service

United Express / SkyWest exits in 2001

1990

1995

2000

66

2005

2010

2015

2020

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Baltimore/Washington International Thurgood Marshall Airport 12

U.S. Airways "de-hubs"

Actual Traffic 1987 Master Plan Forecast (Baseline) 10

9/11 and recession Recession

8

6

Piedmont announces hub

Southwest Airlines launches services

4

) io t(M lm p rE g n e s a P

First Gulf War and recession

2

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Causes of Uncertainty

Causes of Uncertainty • Economics •

Recessions and booms

•

Regional conditions (closure of a local business)

•

Fuel prices

• Airline Strategy •

Start, expand, contract or shut service

•

Transit traffic is fungible

• Airline failure/collapse •

Canadian, TWA, Swiss, Sabena

• Regulatory / policy •

Deregulation contributed to hubbing, changes in aircraft size, LCCs

•

Also taxes, security, bilaterals 69

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Causes of Uncertainty • Technology •

More economical aircraft make new routes possible

•

Implications for cargo – smaller aircraft reduce bellyhold; A380 has relatively small cargo space

• Airport Competition •

New airports emerge as competitors, e.g., on the U.S. border

• Social / cultural •

Concerns about environmental impacts

•

Use of new communications media (+ve or –ve impact?)

• Shock events •

9/11

•

SARS 70

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Identifying and Evaluating Risk

Identifying and Evaluating Risk • Develop a risk register •

Identify and evaluate various risks affecting the airport

•

What is the particular risk?

•

What is its likelihood?

•

What is the impact if it occurs (short and long term)

• Information can be elicited from the airport team •

Strategy

•

Marketing

•

Facilities

•

Finance

• Can also examine historical examples •

What was the impact of SAR on Toronto; how long was the recovery?

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Identifying and Evaluating Risk • Presenting the results – Heat diagram Very High

Likelihood

High Moderate



Low Very Low Very Low

Low

Moderate

High

Very High

Impact on Activity

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Identifying and Evaluating Risk 35%

Key: Macroeconomic Market

Use of internet for meetings

Regulatory/Policy

30%

Technology Social/Cultural High Speed Rail Competition

25%

Fuel price spikes

Loss/failure of Carrier X

New FAA taxes

Terrorist attack

-5

-4

20%

Increased security requirements

Economic recession

-3

Pandemic

-2

New aircraft technology

15%

ilty a b ro P

Shock Event

Economic boom

Open Skies Liberalization

Major tourism event

10%

Entry of new carrier (e.g., LCC)

5%

-1

0 Impact

< Threat 74

1

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Opportunity > Realizing the vision together

Incorporating Risk into Forecasting

Incorporating Risk into Forecasting â&#x20AC;˘ Risk analysis augments not replaces traditional forecasting Original Forecast

r g s P d e la p n E

Traffic impact of carrier exit and partial recovery

Time 76

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Incorporating Risk into Forecasting • A simple approach: •

Scenario analysis based on the risk register

•

Development scenarios based on the high probability and high impact events (both positive and negative)

•

Similar to high/low approach, but:

•

•

Based on comprehensive assessment of risk

•

Scenario can be produced to examine extreme events – stress testing

•

Should be a focus of planning decisions

However, the approach still lacks information on likelihood or probability

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Incorporating Risk into Forecasting 450 Actual Traffic 1980-2000 Post-Masterplan Traffic (2000-2010)

400

401

Masterplan Forecast Extreme Upside Scenario

354

Extreme Upside with New Carrier Exit

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Extreme Downside Scenario

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200 162

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Incorporating Risk into Forecasting • A more advanced approach: •

Monte Carlo simulations

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Uses randomisation / probabilities to explore uncertainty

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Model inputs are probability distributions rather than fixed numbers

•

Using computers, the models can be run multiple times, each time with the inputs randomly generated

•

With enough iterations, the range of outcomes can be determined and probabilities applied to them

•

Historical note: first used at Los Alamos in design of shiled for nuclear reactors

•

Has also been used in finance, project planning, telecoms design, medicine,…

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Incorporating Risk into Forecasting

Probability

â&#x20AC;˘ Normal distribution

10% to 90% Range

-8%

-7%

-6%

-5%

-4%

-3%

-2%

-1%

0%

1%

2%

3%

4%

5%

6%

7%

8%

Deviation from Long-Term Economic Growth Rate

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Incorporating Risk into Forecasting

Probability

â&#x20AC;˘ Pert Distribution

10% to 90% Range

0

500

1,000

1,500

2,000

2,500

Loss of Enplaned Passengers (Thousands)

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Incorporating Risk into Forecasting Density Function

Description UNIFORM – a distribution where all values within a range of potential outcomes have the same probability. For example, a uniform distribution should be used to characterize the impacts of a threat that may lead to a 10 percent reduction in airport activity, a 20 percent reduction, or any value in between, with the same probability. The uniform distribution is fully specified with a minimum value and a maximum value.

DISCRETE – a distribution where each potential outcome is represented by a single value and a corresponding probability. A discrete distribution is defined by a list of possible, discrete values and corresponding probabilities, where the sum of all probabilities is equal to 1.

GENERALIZED TRIANGULAR – a distribution that uses the median, lower percentile (such as 10%), and upper percentile (such as 90%) as input parameters. Based on these parameters, a triangular distribution is fitted to the data, and the absolute minimum and maximum are calculated as a function of the distribution. This distribution is often used for event risks, where there is equal probability of an input parameter being lower or higher than the median.

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Incorporating Risk into Forecasting â&#x20AC;˘ Determining the distribution (data based): 70 Histogram of GDP Growth Data Fitted Distribution 60

50

y c n u q re F

40

30

20

10

0 -1.5%

-0.5%

0.5%

1.5%

2.5%

3.5%

4.5%

5.5%

6.5%

7.0%

GDP Growth Rate (Mid-Point)

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Incorporating Risk into Forecasting • Other events based on judgement (or combination of judgement and data) • Can involve a complex set of connected inputs • E.g., exit of a carrier: •

Probability of exit

•

Impact of exit – loss of traffic (can be randomised)

•

Extent of recovery (also can be randomised)

•

Time to recover (also can be randomised)

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Incorporating Risk into Forecasting â&#x20AC;˘ Running the Monte Carlo (Sample)

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Incorporating Risk into Forecasting 35 Most Likely Forecast 25th / 75th Percentile Range 30

10th / 90th Percentile Range 5th / 95th Percentile Range

25

20

) io r(M g s P d e p lE a u n A

15

10

5

0 Year 0

Year 5

Year 10 86

Year 15

Year 20

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Incorporating Risk into Forecasting 14%

100% Probability Density (Left Hand Scale) Cumulative Probability (Right Hand Scale)

12% 80% 10%

60%

6%

y b ro P e tiv la m u C

40%

4% 20% 2%

0 ,5 7

0 ,5 7

0 5 ,2 7

,0 7

0 5 ,7 6

0 ,5 6

0 5 ,2 6

,0 6

0 ,7 5

,0 5

0 ,2 5

,0 5

0 5 ,7 4

0 ,5 4

0 5 ,2 4

,0 4

0 5 ,7 3

0 ,5 3

0 5 ,2 3

,0 3

0%

0 5 ,7 2

0%

0 ,5 2

ilty a b ro P

8%

Passenger Enplanements (Thousands) 87

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Incorporating Risk into Forecasting • Can answer questions like: •

What is the probability that passenger traffic growth will exceed 4% per annum over the next 20 years?

•

What is the probability that passenger traffic will be greater than 20 million in five years time?

•

What is the probability that passenger traffic in 2020 will be less than 25 million?

• Obvious applications for financial analysis • But can also be incorporated into planning…

88

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Incorporating Risk into Forecasting

89

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Thank You! www.intervistas.com

Canadian Forecasting Workshop Session 5: Current Outlook

Dr. Mike Tretheway InterVISTAS Consulting Chief Economist

Economic Update

Econo-geek Vocabulary If there is a recovery, will it be?

• V-shaped •

A rapid recovery back to previous level •

Most recessions are V-shaped

• U-shaped •

A period of stagnation, with a slow recovery

• L-shaped •

An extended period of stagnation •

Japan 1990s. Great Depression

• W-shaped •

A V-shaped recovery, followed by another recession •

US, 1970s 93

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US Real GDP Growth (Historical)

Sources: Historical â&#x20AC;&#x201C; Bureau of Economic Analysis; Recessions as defined by the National Bureau of Economic Research 94

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US Real GDP Growth (Historical)

W WW Sources: Historical â&#x20AC;&#x201C; Bureau of Economic Analysis; Recessions as defined by the National Bureau of Economic Research 95

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US Real GDP Growth (Historical)

V V V W WW Sources: Historical â&#x20AC;&#x201C; Bureau of Economic Analysis; Recessions as defined by the National Bureau of Economic Research 96

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US Real GDP- recent Recession begins

Recession has ended

Source: NBER 27May2010, BEA 23Mar2013 97

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Annualized Y-O-Y Growth Rate

US Real GDP Growth - Forecast

Forecast Data

Source: 2007-2012 U.S. Department of Commerce, Bureau of Economic Analysis 2013-2017 International Monetary Fund, World Economic Outlook Database, April 2011 98

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US Real GDP - Risk

• The recent recession had V-shaped recovery

• But there is still risk in the recovery •

Managing contraction of Fed Assets •

Without 2nd recession

•

Without inflation

•

will be a challenge

•

Risk: less than 50%, more than 25% 99

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US Real GDP (Historical)

Recessions are wiggles in a steadily growing economy

Sources: Historical (1946 to 2012) â&#x20AC;&#x201C; Bureau of 100 Economic Analysis;

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Canada Real GDP (Historical)

Recessions are wiggles in a steadily growing economy

Sources: Historical Canada GDP (1961 to 2012) 101â&#x20AC;&#x201C; Statistics Canada.

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Canada Real GDP Growth

Annualized Y-O-Y Growth Rate

Historical Data

Forecast Data

Sources: Historical and Forecast Data from International Monetary Fund, World Economic Database, October 2012.

Realizing the vision together 102

Real GDP Growth - Mexico

2 Recessions Sources: Historical Data: Mexico: International Monetary Fund Forecast Data: Mexico: International Monetary Fund 103

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Fuel Prices

Fuel Cost per Litre for Canadian Air Carriers

Source: 1980-1990- Statistics Canada, Aviation in Canada 2006-2011- Statistics Canada, 51-004-X 105

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Fuel Cost Percentage of Operating Expense Canadian Air Carriers

Source: 1980-1990- Statistics Canada, Aviation in Canada 2006-2011- Statistics Canada, 51-004-X 106

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Fuel Litres per RPK

Source: 1970-1990- Statistics Canada, Aviation in Canada 2006-2011- Statistics Canada, 51-004-X. Transport Canada 107

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Fuel Prices - historical

108

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Fuel Prices 2 year dramatic swing Prices rose by 250% Then crashed to 66% of original price

109

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Crude Oil Price Futures

110

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Oil Price Forecast

111

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Oil Price Consensus Forecast

â&#x20AC;˘Its not down â&#x20AC;˘Its not back to $145

112

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2 Sigma Range of Forecasts Forecast 95% ranges $120 $100 $80 upper 2 sigma $60

average low er 2 sigma

$40 $20 $2010

2011

2012

2013

2014

113

2015

2016

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2 Sigma Range of Forecasts Forecast 95% ranges $120 $100 $80 upper 2 sigma $60

average low er 2 sigma

$40

â&#x20AC;˘Everyone seems to agree

$20 $2010

2011

2012

2013

2014

114

2015

2016

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2 Sigma Range of Forecasts Note the scale

Forecast 95% ranges

$120 $100 $80 upper 2 sigma $60

average low er 2 sigma

$40

â&#x20AC;˘Everyone seems to agree

$20 $2010

2011

2012

2013

2014

115

2015

2016

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If History Repeats the Swingâ&#x20AC;Ś.

Oil Price with Full Historical Range

Note the scale

$300 $250 $200

Upper range $150

Base price forecast Lower range

$100 $50 $2010

2011

2012

2013

2014

116

2015

2016

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Forecast Components

Average Fare: Canada Nominal: Not Adjusted for Inflation

Source: Statistics Canada Average Fare data, Cat. 51 -004-X p = preliminary Major Air Carriers include Air Canada (mainline & AC Jazz), WestJet, Air Transat and Canada 3000

118

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Real Average Fare: Canada

Sources: Statistics Canada Average Fare data, Cat. 51 -004-X Statistics Canada Consumer Price Index p = preliminary air fare data Major Air Carriers include Air Canada (mainline & AC Jazz), WestJet, Air Transat and Canada 3000 119

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Commercial Aircraft: Canada

Source: Transport Canada Registered Commercial Aircraft database

120

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Passengers per Aircraft- Canada 1980-2011

Source: InterVISTAS Calculations with data from: Aviation in Canada (1980-1990) and Table 401-0009, Statistics Canada and Air Carrier Traffic at Canadian Airports. Statistics Canada 121

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Seats per Aircraft- Canada 1980-2011

Source: InterVISTAS Calculations with data from: Aviation in Canada (1980-1990) and Table 401-0009, Statistics Canada and Air Carrier Traffic at Canadian Airports. Statistics Canada and Transport Canada.

122

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Total GA Movements for Canada

Source: Statistics Canada, Table 401-007, 401-0015 and 401-0021.

123

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Cargo Tonnes Reporting: Statistics Canada vs. Actual Site Statistics Actual Statistics Airport Canada Statistics Site to Stats (Tonnes) (Tonnes) Can Ratio

Airport

Calgary Intl, Alta.

83,524

116,000

1.39

Edmonton Intl, Alta.

22,955

36,112

1.57

MontrĂŠal/Mirabel Intl, Que.

66,899

95,518

1.43

MontrĂŠal/Pierre Elliott Trudeau Intl, Que.

76,623

105,113

1.37

Toronto/Lester B Pearson Intl, Ont.

339,065

492,171

1.45

186,385

223,878

1.20

Source: Statistics Canada, 51-203-X. Individual Airport reports.

Vancouver Intl, B.C.

124

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Cargo Tons Gap: Site Stats to Stats Can

Source: InterVISTAS calculations with data from: Statistics Canada, 51-203-X. Individual Airport reports.

125

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US vs Canada Pax Traffic 1990-2012

Source: InterVISTAS Calculations with data from: Canada-Air Carrier Traffic at Canadian Airports. Statistics Canada US- 1960-2006 ATA , 2007-2012 BTS .

126

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Load Factor- Canada

Source: Aviation in Canada, Statistics Canada. Transport Canada 127

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Commercial Aircraft Movements

Source: Aviation in Canada (1980-1990) and Table 401-0009, Statistics Canada.

128

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Scheduled Flight Frequency: Domestic Canada

Annual Turboprop + Regional Jet Percentages 79%

72%

74%

75%

Source: Official Airline Guide Schedule Data, full year data for 1998, 2002, 2007, and 2012.

129

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Scheduled Seat Capacity: Domestic Canada

Annual Turboprop + Regional Jet Percentages 49%

37%

42%

50%

Source: Official Airline Guide Schedule Data, full year data for 1998, 2002, 2007, and 2012.

130

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Domestic Canada Scheduled Flight Frequency by Aircraft Body Type

Legend:

Widebody

Narrowbody

Regional Jets

Turboprops

Domestic Canada Scheduled Seat Capacity by Aircraft Body Type

Source: Official Airline Guide Schedule Data, full year data for 1998, 2002, 2007, and 2012.

131

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Canadian Air Carrier Total Revenue and Expenses

Source: Statistics Canada, 51-004-X 132

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Return on Assets for Canadian Air Carriers

Source: Statistics Canada, 1980-1985- Aviation in Canada. 2005-2011, 51-004-X. 133

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Accidents for Canadian Commercial Aircraft

Source: 2001-2011 Statistics Canada, 51-004-X. Transportation Safety Board of Canada 1970-1990 Statistics Canada, Aviation in Canada 134

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Thank You

Subscribe to Monthly Aviation Intelligence Report www.InterVISTAS.com

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