Impact of Jet Fuel Price Uncertainty on Airport Planning and Development (2011) / Chapter Skim
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Background Research P A R T I
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Initial tasks in this project involved analysis of historical changes in fuel prices, a detailed literature review, collection of industry-level data, analysis of activity at different-sized airports, and an assessment of how airlines respond to fuel price changes. These efforts formed the basis for determining how airport activity may be affected by such changes (via air travel supply and demand impacts)
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An important feature of the software is the ability to easily create a risk analysis using confidence bands for whatever forecast is being examined; these bands are generated using an analysis based on the historic range of errors in expectations of jet fuel prices and gross domestic product (GDP) growth.
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The data and information obtained from these first four tasks form the basis for the presentation in Chapter 2, which discusses historical changes in airport activity and air services across the country, and how these observations can be correlated to overall economic activity in general and fuel prices in particular. Building upon that foundation, Tasks 5 and 6 focused on building sound statistical models to identify the primary determinants of airport activity.
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6Task 8 • Finalize Design • Acquire Data • Software • Beta Test Task 9 • Test Model at Various Airports • Refinements Task 10A • Draft Final Report • Working Model • Draft Manual Task 10B Final Report • Model • Users Manual Task 3 • Assess Demand • Specific Airports • Business Models Panel Review (30 Days) Task 4 • Aircraft Choice • Cost Changes • Fleets (TP, RJ, NB, WB)
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2.1 Fuel Price Uncertainty and the Economy The most recent fuel spike and recession are part of a larger, longer-term story about how the economy and fuel prices can affect airport activity. Exhibit I-3 shows the history of real jet fuel prices per gallon from 1989 through mid-2009.
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Recession periods and real jet fuel price per gallon. 13.5 14.0 14.5 15.0 15.5 16.0 16.5 G al lo ns (b illio ns )
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In fact, the fuel spike and the economic circumstances may very well have been linked. Higher fuel prices were suppressing aggregate demand even while there was turmoil in the credit markets.
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Jet Fuel as a Percentage of Revenue Domestic Seat Offers Recession Exhibit I-8. Jet fuel prices and recession drive unprecedented withdrawal of domestic air service.
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11 Note: WB = Widebody, NB = Narrowbody, RJ = Regional Jet Exhibit I-9. Percentage reduction in fleet vs.
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From that period forward there was a rapid increase, with the moving average peaking at 34 percent. An important question for this work effort was the extent to which the instability in fuel prices and the secular rise in real fuel prices over time have affected air services in the United States.
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fuel spike. 2.3 Changes in Air Services by Airport Type This section describes changes in air service (as measured by domestic seat offers)
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Distribution of changes in seat offers at non-hub airports, 2007–2009. -80 -60 -40 -20 0 20 40 60 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 max PHL LAS PIT MDW MDW LAS CVG PHL IAD IAD MDW FLL MDW JFK IAD IAD MDW JFK SFO DEN min IAD PHL MDW PIT DEN TPA DFW MDW JFK SLC EWR IAD BOS PIT STL PIT CVG PIT PIT CVG avg 4 -1 0 4 6 1 0 -3 -5 19 4 3 -9 -4 4 4 -4 2 -1 -7 airports that have been max and min 19 90 19 91 19 92 19 93 19 94 19 95 19 96 19 97 19 98 19 99 20 00 20 01 20 02 20 03 20 04 20 05 20 06 20 07 20 08 20 09 Year Pe rc en ta ge C ha ng e in S ea t O ffe rs max min avg Exhibit I-16.
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-200 -100 0 100 200 300 400 500 600 700 Pe rc en ta ge C ha ng e in S ea t O ffe rs max ACY ACY MSN GCN GSO BIL COS GCN MHT GPT SFB SFB SFB ACY SFB SFB BTR HPN SFB MLI min DSM CAE DAY SFB EUG ACY BIL GSO SFB GCN GCN SRQ EUG SFB LIT SBN GSO GCN ISP SFB avg 4 0 -2 -2 -1 -3 -1 -2 -7 15 14 2 -11 -5 8 5 -7 4 1 -14 airports that have been max and min Year max min avg 19 90 19 91 19 92 19 93 19 94 19 95 19 96 19 97 19 98 19 99 20 00 20 01 20 02 20 03 20 04 20 05 20 06 20 07 20 08 20 09 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 Exhibit I-18. Percentage change in seat offers at small hub airports.
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Again, the same airports are repeated as both showing maximum and minimum growth as carriers experiment with new air services at non-hub airports. Exhibit I-20 makes clear that there have been really two epochs in the last 20 years.
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Delayed Toledo N Overall capital projects Budget cut -12.5% -11% -54% S Overall capital projects Budget cut -0.4% 6% -23% Gate expansion Canceled Sioux City Tucson Action Sources: Trade and General Press Reports ProjectAirport Change in Seat Offers Butte Green Bay Dulles Int'l McCarran Int'l Oakland Exhibit I-21. Airport capital development projects and operating budgets 2008–2009.
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• While changes in air service are likely to be affected by fuel spikes and recessions, there are many local factors that also affect changes in air services. • Airport capital development programs were adversely affected by the severe recession and fuel spike.
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In addition, large hub airports that serve as primary connecting hubs for major airlines were broken out and treated separately from other large hub airports because their observed activity levels will depend not only on fuel prices, income changes, and other determinants of air C H A P T E R 3 Statistical Model Development 1 The analysis accounted for the possibility that an airport could change hub classification over the 20-year period.
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Minimum activity requirements were also imposed for the non-hub airport category,2 resulting in a total of 271 airports that were included in the final analysis, broken out as follows (as of 2009) : • Large connecting hub airports: 17 • Other large/medium hub airports: 43 • Small hub airports: 63 • Non-hub airports: 148 Some consideration was given to how best to measure and define air service levels at these airports.
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Among the potential macro variables, jet fuel cost (lagged by one year) and the 9-11 dummy variables for 2002 and 2003 have statistically significant negative impacts on observed seat offers.
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Not surprisingly, the trend component measured by the lagged value of daily seat-departures is much smaller for the connecting hubs' connecting traffic relative to their local traffic; this is consistent with the notion that there is significant random year-to-year variation in how traffic flows over carrier hubs.8 The impact of jet fuel costs and the 9-11 dummies are fairly consistent across airports, while local income effects are smaller at the small hub and non-hub airports. In addition, the effect of airline concentration (mea22 Model: Explanatory Variable 0.75240 (123.76*
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3.3.2 Financial Impacts The estimates of airport operations and enplanements provide a basis for estimating airport revenues. Unlike the air service models that were distinguished by airport hub size, there is a single model employed to estimate operating revenue encompassing all 271 airports in the analysis.
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This approach is designed to produce useful information for airport users. If there have been significant changes in expectations about the economy or jet fuel prices in the recent past, some airport sponsors may be asked questions or have concerns about future air service, which in turn would have important implications for their operating budgets and for their development programs.
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However, given the volatility in world oil prices, relying only on current or recent historic fuel prices as guides to what MODEL RISK ANALYSIS Macro Air Service Drivers • Jet Fuel Prices • GDP Growth USER CUSTOMIZATION* Local Air Service Drivers • Local Income • Competition at Airport • Competition from Nearby Airports • Average Aircraft Size at Airport Inflation *
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Heating oil prices are closely correlated with jet fuel prices, and the futures market for heating oil is large and very liquid.12 The described annual models would indicate that one should use today's jet fuel price to help project next year's seat departures at a given airport, but for practical purposes it is suggested that users consider looking at current prices for heating oil futures contracts at least several months out in order to get a better understanding of where jet fuel prices may be headed. An assessment of average national income growth suggests similar findings; as shown in Exhibit I-29, the historic data series is quite volatile.
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The analysis described here is based on 12-month-ahead contracts, which have been actively traded for many years. 14 Projections of local per capita income (the metric used in the air service models)
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– In practice, airport decision making is often reactive, not proactive or forward-looking. – Effect of fuel prices on airports depends primarily on airline reactions, which in turn are very dependent on many factors, including carrier financial strength, market competition, fleet composition, network effects, fuel hedging strategies, etc.
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Great care was taken to develop a statistically sound and defensible model of how airport activity may be affected by fuel price changes and other factors. By design, the model was then embedded in a software program to assist airport planners with anticipating changes to existing forecasts of air services.
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For example, those studies that focus on carrier entry patterns typically model discrete outcomes (i.e., an airline either does or does not offer service at a particular airport) .16 While carrier presence and traffic volumes are related, it is not always possible to distinguish one from the other because of variations in aircraft size, load factors, and flight frequency.
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Pai (2007) models traffic volume measured as flight frequency and finds that frequency increases with market population, income levels, and maximum airport runway length.
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Several authors recognize and attempt to control for product differentiation in their studies. The following are commonly used controls: • Nonstop verses connecting flights • Hub presence, captured as dummy variables or measures of hub size • Trip length • Flight frequency between airport pairs As noted earlier, these factors are also likely to affect carrier costs in addition to affecting service quality, and hence demand.
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Average population, average per capita income, average rates of income growth at market endpoints, distance to closest competing airport, trip distance, and distance form market endpoints to the geographic center of the United States.
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Costs not modeled explicitly. Distance between city pairs, airport presence (number of top 50 cities served by airline from airport)
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Many authors employ logit and probit models that are suitable for use when the dependent variable of interest is discrete. Many of the studies reviewed have used these estimators to model market entry decisions including, for example, Berry (1992)
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. On the Factors That Affect Airline Flight Frequency and Aircraft Size.
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