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3 SUMMARY Impact of Jet Fuel Price Uncertainty on Airport Planning and Development Recent volatility in aviation fuel prices has placed stress on airline cost structures, reduced profitability of particular aircraft types, and along with a historic recession has dampened overall economic activity and air travel. This extreme volatility has contributed to large and unexpected changes in activity at airports throughout the United States. This project involved the development of models of airport activity which can be used to assess uncertainty in future projections of airport activity, particularly as they relate to large swings in fuel prices. The models have been embedded inside a user-friendly software pro- gram, the Airport Forecasting Risk Assessment Program, in order to allow airport planners and sponsors to more accurately assess how fuel, economic, and other uncertainties may affect their own airports. 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 air- ports, 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). Primary findings from this analysis include the following: Two of the three economic recessions since 1989 occurred contemporaneously with major fuel price spikes. Nevertheless, the continuous run-up in fuel prices between 2002 and 2008, during a period of relatively strong overall economic growth, suggests there is no simple correlation. Airlines can adjust their schedules fairly quickly in response to fuel spikes, but such adjustments are constrained by airlines' limited ability to change their aircraft fleets in the short run. In general, airlines appear to react to fuel spikes and recessions with a lag. Carrier reactions to fuel price spikes depend not only on whether they believe the increases to be temporary or more permanent, but also on the demand for aviation services by con- sumers in the context of the overall macroeconomy, and how sensitive that demand is to changes in air fares. While it is difficult to tie observed changes in activity at a specific airport to changes in fuel prices, a more generic analysis of domestic airports suggests that, at least since 1997 (when legacy carriers had largely completed the buildup of their large connecting hubs), smaller airports have experienced relatively larger variations in annual activity. These findings formed the basis for designing the overall structure of, and inputs to, the air service models that are embedded in the final software. These models are intended to pro- vide a plausible description of the major factors that may affect observed changes in domes- tic activity at U.S. airports. Using data on airport-level seat departures over the past 20 years, four separate statistical models were developed that could be applied to 271 specific airports

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4 across the continental United States. The air service models explain percentage changes in annual seat offers. For projection purposes and use in the software, seat offers estimates from the statistical models are translated into operations and enplanements, which in turn are used to help project annual airport revenues. For ease of use, the software is embedded inside a standard Microsoft Excel spreadsheet file. Because every airport is different, the software tool is meant to assess risk in existing fore- casts. Such a forecast might be an internal projection made by or for airport staff, or it could be from an external source such as the FAA's Terminal Area Forecast (TAF). The software allows the user to undertake sensitivity studies by varying assumptions about the key drivers of airport activity, with the software generating a range of likely outcomes based on these assumptions. An important feature of the software is the ability to easily create a risk analysis using con- fidence 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. This approach answers a fundamental question: How might an airport forecast be affected given the historic errors in expected future jet fuel prices and economic growth? The software generates a one-page report that summarizes key inputs and the results of the risk analysis. This approach is designed to produce useful information for airport users to enable them to assess uncertainty about future air service, which in turn may have important implications for airport operating budgets and development programs. As with any forecasting process, the user is ultimately responsible for the assumptions used in the analysis. The software provides a structured way to improve airport forecasts and create sensitivity cases, but it is not a substitute for a well-thought-out analysis.