Air Cargo Facility Planning and Development—Final Report (2015)

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Page 7-1 CHAPTER 7: TASK 5—AIR CARGO FORECAST TECHNIQUES CHAPTER OVERVIEW The air cargo industry is faced with some of the most challenging forecast challenges of any industry. Forecasts based on historic trends analysis are increasingly less reliable as future trends cannot be solely based on activities and practices that have evolved into combined modes of both air and truck transport. Over the last two decades, the magnitude and complexity of air cargo forecasting has grown enormously and airport planners are faced with the daunting task of accurately forecasting air cargo tonnage and operations for extensive periods of time. In this paper we survey different approaches and methodologies to forecasting air cargo tonnage and operations. We conclude with a discussion of forecasts utilized in recent air cargo elements of master plans. INTRODUCTION The principal purpose of Task 5 is to explore methodologies used to forecast air cargo demand at U.S. airports, as well as how forecasts are applied on a practical basis to airport planning. Task 5 then surveys forecasting methods used in recent airport planning efforts with ample context pursuant to the cargo function at these airports. Task 5 concludes with a consideration of risk factors that can produce dramatic divergences from forecasts. Generally, cargo forecasts are undertaken as part of an airports’ Master Planning activity, an environmental assessment, to accommodate facility improvements or in response to unforeseen demand or expectations of the local business community. They are then utilized to assist planners in the identification of appropriate areas for future cargo facility and ramp improvements. SOURCES OF INPUTS Virtually all U.S. airports at least track total cargo volume, as well as subsets such as freight (including express) and mail on a directional (inbound and outbound) basis. Commonly, these data sets are managed by airport accounting departments compiled from monthly operations reports used to settle landing fees and satisfy other carrier reporting requirements. Whether disseminated publicly, this data is kept by the airport on a carrier-level basis which can be organized into market share by individual carrier and/or type (all-cargo versus belly). For those airports to which it is applicable, cargo will also be organized into domestic and international increments. In addition to tonnage data, monthly airline reports provide critical inputs pursuant to monthly frequencies and aircraft types. There is no single standard for how or if airports generate public reports from this and other data. While the web page of the Port Authority of New York & New Jersey contains extensive monthly data sets pertaining not only to airport operations but also to Customs entries by country and commodity, for example, other airports may include nothing more than a single entry for total annual cargo in their public reports. Almost all member airports report annual tonnages to Airports Council International – North America, which publishes a Top 50 data set by year on its web site and a more extensive set for members only. However, U.S. airports are not compelled to join ACI-NA and such major cargo airports as UPS's regional hub in Rockford, IL will not be found in ACI-NA's statistics. Air cargo tonnage is typically reported by airports airport commissions and to the public on an annual basis but monthly reports are useful to isolate seasonal trends. While it is uncommon for carriers to

Page 7-2 report weekly or daily tonnage numbers, planners can use secondary references (such as OAG's Cargo Flight Guide) or request carrier schedules to record flight operations in peak period analysis – critical where aircraft parking ramp is at a premium. Aware of the limitations of individual references, it is advisable to use multiple sources of primary and secondary inputs. OAG's Cargo Flight Guide, for example, does not include schedules for integrated carriers and may also identify flights by ACMI carrier, rather than client. A variety of institutional sources are commonly used to calibrate individual airport forecasts, including forecasts by Boeing, Airbus, IATA and the FAA. These are detailed later in this section, including an assessment of the strengths and weaknesses of each source. For specialized facilities – such as cold storage – airport planners may seek trade data originating with U.S. Customs & Border Protection (CBP) that can quantify monthly and annual tons by commodity type for both import and export shipments cleared at the local Customs port. Much trade data can be accessed at no cost from the U.S. Census Bureau and through subscriptions to governmental sources such as the Bureau of Transportation Statistics (BTS) TranStats service. Secondary commercial providers also sell packaged reports often blending public and proprietary sources. There is no substitute for the unique perspectives obtained through original interviews and surveys with on-airport cargo-related tenants, as well as key off-airport constituencies. The former may include local station managers as well as corporate property managers and route planners who commonly have distinctive insights into carriers’ intentions for the local market. Off-airport constituencies may include freight forwarders, trucking companies and major shippers (manufacturers and distributors) of time-sensitive commodities. Area economic developers may also provide insights and data characterizing the local origin-and-destination market. METHODOLOGIES Time Series Trend Analysis One of the most common forms of statistical analysis is the discrete time series, which observes phenomenon through regularly spaced intervals. This is contrasted with the continuous time series, which records an observation at every instant of time. This analysis can be organized to measure trends which may be extended to forecast future values. To be used as a predictor, time series analysis requires confidence that the period to be forecasted will be much like the period from which the trend multiplier (usually a Compound Annual Growth Rate – CAGR) was derived. CAGR provides a “smoothed” rate of return describing yield on an annually compounded basis. One of its weaknesses is that it does not reflect volatility which can be substantial from one year to another but rather creates the illusion that there is a steady growth rate. It is noteworthy to point out that time series analyses are commonly used to “inform” airport planners of future growth rates based on historical activity but does not completely dictate growth rates since the market is susceptible to volatility. For many years, a twenty year horizon was the accepted time frame for forecasting. Clearly, the early years had the greatest credibility with the most distant years the weakest. Airport activity has been volatile as the airline industry has been impacted by uncontrollable factors such as escalating fuel prices, economic swings and labor issues.

Page 7-3 Longer historical periods are still often preferred but the beginning and ending years of the time series should be closely scrutinized for the effect that anomalous years can have on trend analysis. While it is customary to use increments such as decades in time series, a ten-year time series initiated with the extraordinary losses in 2002 would likely miss common peak years (useful in gauging historical capacity) from the late 1990s through 2000. On the other hand, a longer time series must be qualified in terms of applicability because the industry itself has changed so greatly since the 1990s. As documented in Task One's "State of the Air Cargo Industry," the demise of former all-cargo tenants such as Airborne Express, BAX Global, Emery Worldwide and Kitty Hawk have left sprawling vacancies at on-airport cargo facilities. In many predominantly domestic air cargo markets, market shares of FedEx and UPS have risen from around 50% twenty years ago to over 90% in 2012. Such market consolidations may have the twin effect of emptying multi-tenant buildings of failed former legacy carriers while leaving the surviving dominant carriers more likely to have required single-tenant (stand-alone) facilities dedicated to their individual operations. The ultimate outcome is a dearth of prospects to backfill vacancies. In international gateways, gains in international cargo tonnage have at least partially masked losses in domestic cargo. Total cargo tonnage may have changed very little in the course of twenty years, but the carrier composition may have changed dramatically. Similarly stark changes may have transpired in the mix of belly cargo market share versus freighter share. Table 7-1 (with content already presented in Task One) reveals that a Time Series of Calendar Year 2000 through 2010 would produce a negative multiplier in almost every U.S. airport market. Removing FedEx hub Memphis and UPS hub Louisville from the integrator hub and the South Central airports group would produce a -23% and -22% group composite for the period, as opposed to net growth. Similarly, removing Miami’s contribution would make a -13% performance an even worse -31% for the Southeastern airports group. Table 7-1 Air Cargo Growth/Decrease: CY 2000 – 2010 (inclusive). Top 101 U.S. Airports -12% Integrator Hubs (13 airports) 6% International Gateways (13 airports) -7% Northeastern (15 airports) -28% Southeastern (19 airports) -13% North-Central (25 airports) -34% South-Central ( 17 airports) 25% Northwestern (13 airports) -41% Southwestern (12 airports) -20% SOURCE: Airports Council International, Webber Air Cargo Analysis. Using growth rate multipliers derived from the period represented in the data of Table 7-1 for use in 20- to 30-year forecasts would have all but a rare few airports dwindling toward zero cargo by the end of the forecast periods. Fortunately, that level of pessimism is uncommon. Forecasts created in the last ten years have typically assumed that the bottom of the cycle had already been found and recovery would begin immediately. Unfortunately, forecasts based on that assumption have proven overly optimistic to date.

Page 7-4 Even if only to provide a contrast, time series analysis remains a useful planning tool. If concerns exist pursuant to anomalous years of data, multiple analyses can use a variety of beginning and ending years. Regardless of the interval, charting market deterioration since past peaks illuminates how much facilities capacity may exist from an airport's past peak demand. The ideal use for trend analysis has been described as a mature industry experiencing relatively consistent, gradual growth – a description that contrasts greatly with the recent experience of the U.S. air cargo industry. Regression Analysis (Econometric Modeling) Regression analysis is a statistical technique for estimating relationships between a dependent variable and one or more independent variables. Regression analysis helps explain how the typical value of the dependent variable changes when any one of the independent variables is varied, while the other independent variables are held fixed. Regression analysis is widely used for prediction and forecasting. Regression analysis is also used to understand which among the independent variables are related to the dependent variable, and to explore the forms of these relationships. In restricted circumstances, regression analysis can be used to infer causal relationships between the independent and dependent variables with the critical caveat that correlation does not always prove causation. The dependent variable of air cargo growth may be associated with such independent variables as jet fuel prices, gross domestic product (GDP), composite leading indicators (CLI) and population – customarily using a combination of time series and growth curves. Most U.S. airports only serve local or regional origin & destination markets. Therefore, cargo growth may track closely with local and/or regional economic attributes, so reliable functional relationships may exist between an airport’s cargo growth and the area GDP, income and population growth. However at international gateways air cargo growth may be at least as influenced by economic conditions in origin and destination countries rather than by local economic conditions since air cargo is often trucked great distances across several states, or across the country in many instances, to these international gateway airports. Econometric modeling (such as multiple regression analysis) is often perceived as more effective with broadly defined markets (countries and entire continents) in which multiple factors influence aggregate growth and other variables may be held constant. In this method there is an assumption that supply is unconstrained, which contrasts starkly with individual airports where cargo capacity is constrained by the hub-and-spoke systems of carriers and limited aircraft fleets. Any forecast strategy may (or may not) assume that capacity is constrained and planners/consultants need to decide when they forecast whether or not the forecasts should be constrained or unconstrained. There is value to both approaches, but master plans commonly assume an unconstrained local demand, so that the rest of the master plan can be focused on determining what facility plans are needed to accommodate that demand. The ability of carriers to shift capacity between airports (market share shift), as well as between air transport and other modes (particularly trucking) pose substantial risks to the assumption of unlimited capacity supply that meets graduated demand. U.S. airports have experienced extraordinary growth (Greensboro, NC with FedEx) and losses (Des Moines with UPS) attributable to network adjustments by integrated carriers that seemingly had nothing to do with local cargo demand generation.

Page 7-5 Like time series analysis, regression analysis is a useful tool to evaluate historical relationships between cargo growth and other econometric elements. However, it is an imperfect (wildly so in some circumstances) predictor of future trends – not least because of its assumption of unlimited capacity supply – and therefore should be considered only one of several potential analytical tools. Market Share Market share analysis compares local activity levels with a larger entity, most commonly in comparisons between a particular airport and its regional or total national traffic. Historical data is used to establish the ratio of local airport traffic to total national traffic – customarily using source data from the FAA Aerospace Forecasts document for national data. Much like the preceding methodologies, Market Share has limitations as a predictor. Most obviously, this methodology assumes that the proportion of activity that can be assigned to the local level is a regular and predictable quantity. As has already been established, the U.S. air cargo industry remains in the midst of a prolonged period of contraction that has touched most airports but not equally. For example as in Figure 7-1 depicting Hartsfield-Jackson Atlanta International Airport, some gateways were able to offset some domestic losses with international gains. Indications in late 2012 from the two dominant integrators suggested that near to medium-term domestic fleet utilization strategies may favor up-gauging aircraft size but serving fewer U.S. markets by air, while expanding the utilization of trucks for domestic feeder service. The impact may negate organic air cargo growth at many small and medium-sized markets or conversely may support growth at strategically located airports that can potentially serve as access points to multiple possibly larger markets. All of the preceding suggests that imperfections exist in market share modeling, as it pertains to projecting local airport trends relative to regional and national growth. Figure 7-1 ATL Annual Cargo (Metric Tons): CY 2000 – 2011. (SOURCE: Airports Council International, Webber Air Cargo Analysis.) Market share analysis at the individual airport-level is integral to understand how the market has evolved and therefore may indicate potential direction going forward. At the individual airport level, market shares of international and domestic, as well as belly cargo versus freighter cargo is essential for - 100,000 200,000 300,000 400,000 500,000 600,000 700,000 800,000 900,000 1,000,000 Total Domestic International

Page 7-6 facilities planning as this analysis informs judgments about future demand for freighter positions and other related considerations. Carrier market share – possibly through the prism of ground handlers possibly serving multiple carriers in a common warehouse and ramp space – is necessary for calculating the individual utilization rates of cargo facilities. In summary, market share analysis is an essential piece of air cargo analysis at the individual airport level but as a predictor of future relationships between local and national trends, it must be qualified. Institutional Forecasts The introductory section cited several sources of institutional forecasts commonly used by airport planners and others to calibrate local cargo forecasts. For the vast majority of U.S. airports, only domestic cargo is materially significant, as international shipments of local origin/destination will either be trucked or flown on a domestic segment to a gateway with trucking increasingly likely and therefore negligible impact on the feeder airport’s cargo totals. Forecasting inbound and outbound domestic cargo (and related translation into freighter operations) will suffice. However, at international gateways, directional (import and export) forecasts will often be segregated by region (for gateways with multiple transcontinental routes), although often a composite international multiplier entails the international market share and growth rates of each individual segment. Using institutional forecasts is not a substitute for other methodologies but more accurately a surrogate for the labor involved. Entities such as Boeing and Airbus perform intensive econometric modeling (GDP and fuel prices, to name but two independent variables) to inform their biennial forecasts – typically with budgets and other resources well beyond the means of airport planners and even most consulting firms. In fact, the FAA often cites the Boeing forecast, in particular for use in its own efforts. However, just as the U.S. economy is comprised of regional economies that may little resemble one another – the Rust Belt versus the Farm Belt, for example – local airports may not conform precisely to national economic expectations. If such institutional forecasts are used as the basis for individual airport forecasts, adjustments should be made to recognize local conditions. The latest Airbus effort is their “Global Market Forecast: 2012 – 2031.” It is an integrated document entailing both passenger and cargo forecasts – contrasted with Boeing which releases separate reports. Passenger forecasts are available from both sources and may be of particular use in incremental considerations of belly cargo capacity versus freighter demand. Both Airbus and Boeing forecasts are available as free downloads from the corporate websites. Significantly, both companies’ cargo forecasts project growth rates in terms of revenue-ton-kilometers, which clearly puts a premium on longer-haul segments (such as those over the Pacific), while airport cargo forecasts are typically expressed in cargo tons and flight operations as a derivative forecast. It should be noted that revenue-ton-kilometers is expressed as one ton of revenue-producing cargo flown one mile (Boeing) or kilometer (Airbus). One disadvantage of the Airbus forecasts has been that detailed cargo forecasts have been produced in less reliable intervals – not surprisingly for a manufacturer that has struggled competitively in the freighter market. A significant advantage is that Airbus’ market forecasts tend to be segmented into much smaller sub-continental groupings allowing more precisely delineated pairing of routes and markets. For international gateways with diverse networks of direct destinations, this advantage is invaluable. For airports where only domestic or perhaps only modest international service is offered,

Page 7-7 planners may use either (or both) the Airbus or Boeing forecasts for guidance. Whether one source is more conservative than the other is only evident on a segment-by-segment basis but not as a whole. Boeing’s “World Air Cargo Forecast 2012 – 2013” is the latest biennial installment of the cargo- specific document. Like the Airbus version, the Boeing twenty-year (through 2031 in the latest installment) forecast is compiled from econometric models and airline interviews undoubtedly enriched by Boeing’s dominance in the freighter market and resultant access to the insights of the world’s dominant freighter operators. While the Boeing forecasts are not as narrowly stratified as those of Airbus in terms of market segmentation, it has the significant advantage of a timely production schedule and relatively uniform structure over time – facilitating the reuse of forecast templates by airport planners. Perhaps its greatest virtue is that Boeing’s cargo forecast is unlikely to meet any critical opposition, as they are so ubiquitous in the efforts of the FAA and others. Clearly, the popularity of Boeing’s forecasts derives in large part to the acceptance of its methodology and its history of reliability. The FAA Terminal Area Forecast (TAF) summary historical and forecast statistics on passenger demand and aviation activity at U.S. airports based on individual airport projections. The document (and its sources) is available for free download at the FAA’s website. The TAF model can be accessed from the Internet so that model users can relatively easily generate their own forecast scenarios. The principal input for the TAF is the FAA Aerospace Forecasts (Fiscal Years 2013 – 2033 just being released) which are developed from econometric models intended to explain the relationships and emerging trends for all major segments of air transportation. Typical of econometric models, the FAA Forecasts assume unconstrained capacity. They also assume no further contractions of the industry through bankruptcy, consolidation, or liquidation. Even since publication, these assumptions have likely already been significantly compromised and the FAA wisely filled its narrative with cautions accentuating the recent unpredictability of commercial aviation. Both the FAA Aerospace Forecasts and the TAF are repositories of economic data that may be useful in conducting regression analyses. They also possess forecasts for passenger activities useful in considerations of potential belly capacity available for cargo. The air cargo element of the FAA Aerospace Forecasts (in revenue ton miles – RTMs) assumes that security restrictions on air cargo transportation will remain in place and that most of the shift from air to ground transportation has already occurred. Finally, the forecasts assume that long-term cargo activity will continue to be tied to economic growth. While obviously uncertain, these assumptions are defensible. The forecasts of RTMs were based on models linking cargo activity to GDP with domestic cargo RTMs linked to real U.S. GDP as the primary driver and international cargo RTMs based on world GDP growth (adjusted for inflation). Distribution between belly and all-cargo carriers was forecasted on the basis of historic trends in market shares, changes in industry structure, and market assumptions. The International Air Transport Association (IATA) produces an annual cargo-specific forecast that is stratified into more narrow market segments than any of the preceding forecasts. Its liabilities include that the detailed version must be purchased (unlike the three free downloads previously cited) and it is only completed in 5-year increments. In fairness, it should be noted that IATA only forecasts in 5- year increments due to the belief that forecasts beyond that horizon are so seriously compromised as to be virtually meaningless. That is an assessment with which many industry observers agree.

Page 7-8 Potentially among the most illuminating sources of forecasts would be the air carriers which commonly develop in-house forecasts with 5-year increments being common for traffic and 5-10 year increments for fleet forecasts. Particularly at hub airports where a single carrier has a commanding share of belly cargo and at the many airports where FedEx and UPS may have combined market shares in excess of 90%, carrier forecasts would be invaluable. Unfortunately, these forecasts are considered commercially sensitive and therefore rarely shared with airport operators and/or their consultants. However, the preferred collaborative process of developing forecasts should present the opportunity to at least test the airport's own forecasts against perceptions of the carrier-tenants. Moreover, the carriers will typically provide input into operations forecasts pursuant to fleet expectations for the near to mid-term. Operations Forecasts Airports' cargo operations forecasts are principally derived from tonnage forecasts. As much as tonnage is a critical input for planning warehouse capacity, operations are critical for planning ramp capacity. Airport planners need as much feedback as possible pursuant to carriers' fleet and route planning. While the gauge of aircraft is critical to calibrate aircraft capacity, it is also critical to know how much of the payload is dedicated to the local market. If the aircraft continues to other cities to build/break loads before returning to the hub, partial loads decrease throughput anticipated for the warehouse and may shorten the time the aircraft will be on the ground. A thorough understanding of airline schedules may allow airport planners to maximize the use of aircraft ramp positions by getting multiple turns on a single position when schedules are compatible. Moreover, a carrier may be able to double or triple its local tonnage without adding another operation if its current payload dedicated to the local market is small. These considerations are particularly important at international gateways such as ATL and DFW where international freighter operators commonly share multi-stop service with other gateways. Clearly, this information must also be reconciled with the actual capacity of each ramp position in terms of the maximum gauge of aircraft that can be accommodated. Airport planners can extract current fleet and flight operations data from landing reports and flight schedules from proprietary sources such as OAG Cargo Flights. Industry-wide fleet information can also be gained from Airbus and Boeing, as well as from outstanding secondary sources such as Cargo Facts produced by Air Cargo Management Group. Both OAG and Cargo Facts are available on a subscription basis. No matter how credible the secondary sources, interviews with cargo carriers (and handlers where applicable) are indispensable to verify potentially outdated secondary sources, as well as to gain unique forward-looking insights into prospective future operations on a specific market basis. In order to derive operations from tonnage, airport planners must first determine the market share presently transported by passenger carriers (therefore not contributing to freighter operations) and then make assumptions about future trends pursuant to that distribution. The FAA Aerospace Forecast provides such forecasts for both domestic and international cargo on a national airport system basis. Once that belly cargo has been deducted from total cargo to isolate the tonnage that specifically drives demand for freighter operations, planners must make assumptions about the carriers' payload limits that would trigger either additional frequencies or a change in gauge of aircraft. Again, it is also critical to know how the local market is presently served by the carriers – as a stand-alone destination or as part of a multi-stop routing – in order to evaluate how much capacity is available before another frequency would

Page 7-9 be required. Unlike passenger service that very often is daily, freighter service at many U.S. airports may only be weekday with perhaps partial service on weekends. Consequently, airport planners may use an annual standard of 282 annual cargo days (5.5 days/week), adjusting according to local schedules which may only have weekday (5 days/week or 260 days/year) or alternatively full calendar (7 days/week) service. Operations will typically be forecasted on a three-tier basis compatible with tonnage forecasts on a low, base and high case scenarios. Additional matrices can easily be formed to create alternative forecasts on the basis of a range of load factors. While the more nuanced approach for deriving operations from tonnage just described is appropriate for airports served by a variety of carriers, planners at airports with relatively modest cargo operations may opt for a simpler approach comparable to the "market share" methodology described earlier. Applied to operations, the approach would entail simply calculating the tons/operation that the airport has recently experienced and then applying that average to future tonnage forecasts. On an applied basis, airport planners may combine the tons/operation with the airport's number of ramp positions (recognizing variable capacity) and aircraft turns per day per position, in order to determine total ramp capacity in tonnage terms. METHODS USED IN RECENT AIRPORT PLANNING EFFORTS During this study's Literature Review, the consultants reviewed twelve airport master plans completed between 2005 and 2011, analyzing the methodologies used in air cargo volume forecasts and air cargo operations forecasts. For air cargo volume forecasts, the master plans primarily used traditional methodologies dependent upon statistical models and factors such as the airport's historic air cargo volumes (time series), and global, national, and local air cargo, as well as socioeconomic trends and forecasts. However, four master plans used market share approaches or probabilistic forecasting. A minimal number of airport master plans used unique approaches to forecasting air cargo aircraft operations. As described in this section's earlier methodology briefs, standard techniques involved consideration of historical air cargo tonnage per aircraft operation, existing and future aircraft sizes, as well as global and national forecasts prepared by Boeing, Airbus, and the FAA. Among the rare exceptions, one airport (FedEx western regional hub Oakland) used a methodology that involved development of average annual day cargo schedules and another used probabilistic forecasting. Forecasting methodologies of the twelve airports are summarized in Table 7-2 below.

Page 7-10 Table 7-2 Airport Master Planning Documents Reviewed and Analyzed. Airport City ACI Cargo Volume Rank 2011 Cargo Activity Prime Consultant Year Cargo Volume Forecast Method Boise Airport, BOI Boise, ID 74 Non hub Ricondo & Associates 2010 National and historic trends. Capital Region International Airport, LAN Lansing, MI 94 Non hub RS&H 2006 Historical trends. Cincinnati/ Northern Kentucky International Airport, CVG Cincinnati, OH 17 Non hub (at time of analysis) Landrum & Brown 2005 None. Dallas / Fort Worth International Airport, DFW Dallas Fort Worth TX 11 Hub URS 2009 Blended growth rates. Dona Ana County Airport, 5T6 Santa Teresa, NM NA Non hub WHPacific 2008 Blended growth rates and market share based on El Paso cargo activity. George Bush Intercontinental Airport, IAH Houston, TX 15 Gateway DMJM Aviation 2006 Local and national economic trends. Kansas City International Airport, MCI Kansas City, MO 45 Non hub Landrum & Brown 2009 Historical trends. Memphis International Airport, MEM Memphis, TN 1 Hub Jacobs Consultancy 2010 FedEx trends and historic trends. Oakland International Airport, OAK Oakland, CA 12 Hub 2006 Average Annual Day. Piedmont Triad International Airport, GSO Greensboro, NC 46 Hub Jacobs Consultancy 2010 Boeing and Airbus, Historic belly and mail. Portland International Airport, PDX Portland, OR 28 Gateway Jacobs Consultancy 2010 Econometric model & probabilistic forecasts. San Antonio International Airport, SAT San Antonio TX 33 Non hub AECOM 2010 Blended growth rates. SOURCE: CDM Smith Analysis. Typical of all of the airport forecasts was a pronounced tendency to assume that the air cargo industry's challenges were already past and that recovery was imminent. In Boise's case, the forecasts prepared in 2006 (using 2005 as the base year) predicted an accelerated CAGR of 4.0% for the first ten years, falling slightly to a CAGR of 3.8% per annum through 2030. Instead cargo fell almost 2% per annum for the first six years of the period. Boise's relative isolation may keep it from losing dedicated all- cargo service but its dependence on FedEx and UPS may portend fewer operations but with larger aircraft. Lansing's tonnage forecasts were informed by several regression analyses with the strongest correlation between the population of the state of Michigan and air cargo growth at Capital Region International Airport. Aircraft operations were then formulated based on historical relationships and forecasted air cargo tonnage. Again, Lansing's forecasts projected a CAGR of 3.5% from 2000's 65.2 million pounds of total cargo but the airport's cargo actually fell more than 18% for the period through 2010.

Page 7-11 Cincinnati/Northern Kentucky International Airport's forecasts provided relatively scarce detail pursuant to forecast methods. As the international gateway for DHL, the airport's cargo fortunes have tracked closely with the all-cargo carrier, especially since the demise of the former Delta Airlines hub at CVG. The last decade found DHL first abandoning its CVG hub in favor of the former Airborne Express hub at Wilmington, OH, then returning to CVG as a leaner, international forwarder and consolidator rather than the former domestic integrator it had principally been. While an extreme case, CVG's reliance on DHL accentuates the volatility, producing a growth pattern (Figure 7-2 below) that no analyst likely would have anticipated. Figure 7-2 DHL Legacy Hub at CVG: Total Annual Cargo (Metric Tons): CY 2000 – 2010. (SOURCE: Airports Council International, Webber Air Cargo Analysis.) Dallas / Fort Worth International Airport (DFW) completed forecasts during its master planning process, prepared in 2007 but using 2006 as the base year. Unusually, the forecasts were based on only four years (2003 – 2006) of historical data due to the assumption that market conditions had changed so completely after 9/11 to render previous experience meaningless. Baseline forecasts were prepared for all- cargo carriers, integrators, and belly cargo carriers, as well as an alternate high-growth scenario assuming expansion of the UPS regional cargo hub at DFW. Apart from the unusually slight four years of historical data, other aspects of the forecasts were conventional, meshing assumptions about regional market share and Boeing's institutional forecasts. DFW's forecasters assumed an immediate industry recovery that still has not happened and as a result, (as of 2010) DFW's total cargo was already 31% below forecast: all- cargo carrier tonnage was 21% below, integrator cargo was 30% below and belly cargo was 47% below forecast, just four years into the forecasted period. As forecasted tonnage was converted into operations, no recognition was given that all of DFW's international freighters were shared on multi-city routes and therefore may require adjustment of tons/operation. Dona Ana County Airport (DOCA), located in Santa Teresa, New Mexico, is a general aviation airport with a service area shared with El Paso International Airport. The airport is only 21 miles from downtown El Paso, TX. With the larger airport as the principal commercial gateway for the region, the general aviation airport's principal function has been ad hoc charters. Its 2005 annual cargo total of 270 tons was its highest since 1990. Its tonnage forecasts were prepared based upon reviews of historical air cargo data and previous air cargo forecasts prepared by Boeing and the FAA, as well as econometric analysis using projections of population, employment and earnings in the service area. The forecasts also used a model borrowed from the 2003 New Mexico Airport System Plan. - 100,000 200,000 300,000 400,000 500,000 M et ri c To ns

Page 7-12 Appropriately, the tonnage forecasts acknowledged the dominant role of ELP in the area but in spite of finding that ELP could accommodate forecasted demand through 2025, the consultants still produced a cargo forecast for DOCA anticipating that the airport could capture growing maquiladora and local demand, regardless of the superiority of facilities and air service at ELP. The forecast estimated that 11,100 tons of annual enplaned air cargo could be captured by 2025 – equated to two daily Boeing 737- 300SF freighter operations. The forecast of 11,100 annual tons seems modest until it is compared with the airport's current record year of 270 annual tons. As detailed in an earlier section on the state of the air cargo industry (detail repeated in Figure 7-3 below), George Bush Intercontinental Airport (IAH) has been one of very few U.S. international gateways to experience air cargo growth between 2000 and 2010, enabled by its energy-based local economy, growth by its hub passenger carrier and network adjustments by integrators. Figure 7-3 U.S. International Gateways: Total Annual Cargo Growth: CY 2000 – 2010. (SOURCE: Airports Council International, Webber Air Cargo Analysis.) Completed in 2006, IAH's Master Plan's cargo forecasts were based on a review of historical trends at the airport, regional economic indicators, and evolving industry trends. Key assumptions included: • Air cargo growth will be primarily driven by local and national economic trends. • Passenger airlines will continue to account for the majority of the air freight and mail activity. • Local market consolidation (e.g., UPS's move from Ellington Field in 2003) will force more air cargo traffic through the integrators' local hubs. • Improved international air service will further garner forwarders' consolidations. Based on these assumptions and the fleet mix forecast developed in the Master Plan, forecasts of cargo pounds per departure were developed. Air carriers' cargo operations were projected to grow by 2.8% per annum through 2025, from 9,186 operations in 2003 to 17,000 operations in 2025. Cargo commuter operations were projected to grow at an accelerated rate. By virtue of IAH's total cargo having grown during this challenging period, its forecasts have likely tracked more closely with actual 11.8% -14.3% -6.3% -26.1% -21.0% -24.1% -28.5% -51.1% 14.9% -13.4% -38.0% -45.4% -60.0% -50.0% -40.0% -30.0% -20.0% -10.0% 0.0% 10.0% 20.0% M ia m i ( M IA ) Lo s A ng el es (L AX ) Ch ic ag o (O RD ) N ew Y or k (J FK ) N ew ar k (E W R) At la nt a (A TL ) D al la s/ Ft . W or th (D FW ) Sa n Fr an ci sc o (S FO ) H ou st on (I AH ) W as hi ng to n DC (I AD ) Se at tle (S EA ) Bo st on (B O S)

Page 7-13 experience even while using very similar methodologies to those used at U.S. airports with more typical losses. Kansas City International Airport (MCI) serves an area larger for its geographic breadth than its presence of population or industry. Kansas City's central location has made it a dominant gateway for rail and trucking but a marginal air cargo airport. The airport has no significant international cargo capacity, no integrator hub and no passenger hub. Tellingly, the most notable cargo facilities growth has been for a trucking company specializing in hauling international cargo to/from major gateways to feeder markets. Its Master Plan (completed in 2006 and accepted in 2008) acknowledged the airports' limitless capacity for development but the market's limitations in generating demand. At the time, the integrators had a 92% cargo market share at MCI. The forecasts took account of potential effects of air cargo security requirements, historical trends (local and national), consumer demand, local business investment and modal competition. The forecasts assumed that air cargo growth would resume over the forecast period but that the airport would not likely secure international cargo service during the planning period. In essence, MCI would continue to serve the origin and destination market but would not assume the kind of regional and national role that Kansas City serves in other modes. The forecasts projected a modest CAGR of 2.1% for the period, discounting institutional forecasts (such as Boeing's) higher growth rates perceived as more likely accruing to hubs, rather than pure feeder markets. All-cargo operations were forecasted based on assumptions that netted projected belly cargo, then applying MCI's existing and projected aircraft fleet mix based on interviews with the tenants. Local cargo tenants and the airport operator readily accepted the modest expectations. With its FedEx hub accounting for a 98.7% market share of airport cargo, Memphis International Airport (MEM) has led U.S. airports in annual cargo for many years. Consequently, MEM's cargo outlook is tied to the network activity of only one carrier (even more as its former passenger hub has been downsized in the wake of Delta's acquisition of Northwest) and should have a greater functional relationship to national and international economic conditions, than to the local or even regional markets. For its last Master Plan update, MEM had two forecasts prepared: A forecast exclusively for FedEx Express based on market drivers unique to that carrier and another for all other cargo carriers that use MEM more for local demand. At least in terms of distinguishing between demand drivers, the two-faceted approach seems like a reasonable accommodation. While typical low, baseline and high forecasts were created, following the FAA's review only the baseline scenario was used in the Master Plan Update. In this scenario, FedEx Express' air cargo tonnage was forecast to increase at a CAGR of 2.1%, while air cargo handled by all other carriers was forecast with a CAGR of 4.6%. The disparity is understandable, given that the growth for FedEx Express at MEM applies to an existing base likely without peer for one carrier at any other airport in the world. Translation from cargo tonnage to freighter operations was completed conventionally in terms of methodology but with the unconventional context of being so heavily driven by a single carrier and its fleet expectations. On a much smaller scale, Oakland International Airport (OAK) parallels Memphis in that OAK is a Western regional hub for FedEx which had about an 80% market share (with UPS controlling another 15%) when OAK's last Master Plan was finalized in 2006. Unlike MEM, OAK shares its own regional market with the area's dominant international gateway, San Francisco International Airport, and to a

Page 7-14 lesser degree with San Jose. Forecasts were prepared through 2025 using 2003 as the base year for air cargo tonnage. The methodology considered potential markets for future air cargo growth and identified an air cargo growth scenario based on historical growth rates at OAK, SFO and Norman Y. Mineta San Jose International Airport, as well as the maturity of the air cargo market at OAK. The forecasts also reviewed air cargo forecasts from the 2000 Regional Airport System Plan (RASP) and the ADP's 2003 Supplemental Environmental Impact Report (SEIR), as well as growth rates from historical data, RASP, and SEIR to generate growth rates for OAK, ranging from 3.59 to 7.84% with the former (low rate) used to project future cargo tonnage. The use of the low rate acknowledged the unlikelihood that OAK would successfully capture greater regional market share and that the Port of Oakland would not aggressively encourage rapid air cargo growth due to external influences. Air cargo operations forecasts were prepared through development of Average Annual Day (AAD) air cargo schedules for 2003 and 2010. In the document, the preference for AAD over average day peak month (ADPM) was explained because unlike airline passenger activity, air cargo volumes were perceived to be relatively constant throughout the year. The regional approach to airport cargo forecasting may apply in some circumstances, as long as it is at least adequately explained in the forecast's narrative. As represented in Figure 7-4, it is unlikely that any methodology would have forecasted a roughly 25% decrease in annual cargo at OAK, a 51% drop at SFO and a 70% drop at SJC between 2000 and 2010 (inclusive). Figure 7-4 Bay Area Airports: Total Annual Cargo (Metric Tons): CY 2000 – 2010. (SOURCE: Airports Council International, Webber Air Cargo Analysis.) Piedmont Triad International Airport (GSO) in Greensboro, NC was another exceptional case among U.S. airports in that – due to the opening of a FedEx regional hub during the period – it experienced 24% net growth in air cargo between CY 2000 and 2010. While relatively impressive, that growth still left GSO considerably smaller than other regional hubs in the FedEx Express network and was substantially smaller than projected when the GSO was first announced as FedEx's Mid-Atlantic hub. GSO's latest Master Plan Update was finalized in September 2010 with forecasts for the period 2007 to 2030. However because the FedEx expansion was ongoing at the time, only all-cargo volumes for GSO's other all-cargo carriers were forecast in the Master Plan Update – excluding the dominant carrier. The forecasts considered Airbus and Boeing's forecasts for U.S. domestic air cargo, arriving at an average of 3.1% used to forecast all-cargo tonnage at GSO (again, segregating FedEx's dominant contribution). - 200,000 400,000 600,000 800,000 1,000,000 M et ri c To ns OAK SFO SJC

Page 7-15 GSO's limited belly cargo (900 MTs/year) were forecasted to merely be maintained. The forecasters did include FedEx Express and Mountain Air Cargo in the forecasts of all-cargo operations. Forecasts for the new sort hub used estimates from the Environmental Impact Statement for a recent runway extension. Forecasts for all other all-cargo carriers were based on historical ratios of tonnage/operation, adjusted for future fleets. Portland International Airport (PDX) is a major commercial airport in the Pacific Northwest with limited international service, including transpacific freighter flights operated with a subsidy to Asiana. Subsidies to international carriers have been a staple among gateways in that region, given competition between Portland, Seattle and Vancouver. PDX experienced a 32.6% decrease in annual total air cargo, falling from around 282,000 MTs in 2000 to only a little more than 190,000 MTs in 2010. A Master Plan Update was begun in 2007 and finalized in 2010. Unconstrained air cargo volume forecasts were prepared using 2006 as the base year and 2035 as the planning horizon year. Given references to the extraordinary influence of base years in forecasts, it bears noting that 2006 was actually PDX's peak cargo year and its tonnage fell about 33% in just the four following years. The forecasts entailed development of an econometric model that related cargo tonnage to total personal income for the Portland-Vancouver region, as well as development of probabilistic forecasts of air cargo tonnage. Probabilistic forecasting allows for assessment of the uncertainty associated with future aviation demand. PDX air cargo was projected to grow at 3.3% through 2035 but has shrunk by roughly 33% since the base year. Estimates of shares between aircraft size were completed and then total air carrier cargo operations were calculated by dividing the cargo tonnage to be carried by all-cargo carriers by estimates of cargo tons per departure for both air carrier and commuter aircraft. San Antonio International Airport is the primary commercial service airport serving its own metropolitan area but competes with Austin for regional traffic. After a period during which Austin's high-tech industry had swung the balance toward Austin, San Antonio's superior heavy industry base has recaptured some of its losses. Comparatively speaking, SAT's having only experienced a 0.7% decrease in total cargo between 2000 and 2010 could be considered a triumph, especially compared with Austin's roughly 57% decrease for the same period. Unconstrained air cargo volume forecasts were prepared for SAT for the period 2008 through 2050, using forecasts by the FAA, Airbus and Boeing as benchmarks. The forecasts assumed: • Future growth in GDP would largely determine future cargo tonnage. • U.S. domestic air cargo growth would lag international growth. • The now mature Express market would slow from its 1980's and 1990's growth rates. • High fuel and other operating costs would continue to support diversion to truck transport. • Belly cargo would continue to decline with only some shifting to all-cargo carriers. • Air freight would grow at a high rate than mail According to the baseline, cargo tonnage was forecasted to grow at 3.3% through 2050. The Master Plan provided little information pursuant to how all-cargo aircraft operations forecasts were derived but projected only a 2.5% growth rate, possibly suggesting larger aircraft to accommodate a slightly higher growth rate in tonnage than in operations.

Page 7-16 RISK ASSESSMENT A variety of forecast risks have already been referenced in preceding descriptions of individual methodologies. Rarely have such risks been more obvious than in recent years when air cargo forecasts have routinely diverged dramatically from actual results. Not only was the collapse of the last decade relatively unforeseen but analysts have repeatedly misidentified what was perceived as the low point of the industry recession. Consequently, the usefulness of a conventional time series (trend analysis) has been greatly compromised as a predictor since it somewhat requires a belief that "past is prologue" even as most industry participants hope to never experience another decade like the last. Time series can also be wildly skewed by anomalies at either the start or finish year of the historical period under review. Econometric modeling also requires faith that the past is a reliable indicator of future activity, typically linking historical relationships between cargo growth rates and other economic variables – such as GDP. Typically, such models assume unconstrained capacity but the ability of airlines to shift between modes and service points greatly undercuts the reliability of that assumption. Using institutional forecasts such as those of Airbus, Boeing and the FAA to calibrate local growth rates is certainly defensible. However, each of those forecasts is national in scope while the U.S. is comprised of individual markets that have tended not to move in economic lock-step. Moreover, the forecasts are actually in revenue-ton-miles, rather than strictly tonnage. Should any of these forecasts be used, airport planners should consider adapting the national forecasts to recognize local trends. Because air cargo operations are typically derived from tonnage forecasts, any inaccuracies in the latter will carry through the former. Moreover, airport planners must recognize when carriers are splitting payloads among multiple markets and similar schedule and equipment nuances. Split service may result in quicker turns – since only a portion of the aircraft is loaded and unloaded – allowing for more frequencies per ramp position. The carrier has the potential to grow tonnage without adding frequencies simply by increasing the share of payload dedicated to the market, potentially growing from split to fully- dedicated service. Alternatively, a carrier may choose to eliminate split service at the weaker of two service points in favor of trucking to/from another gateway. The preceding also underscores the fluidity of freight flows in that demand growth may be accommodated in more ways than simply additional flight operations. In addition to all of the preceding, cargo forecasts could be dramatically affected toward the negative by increased diversion of intercontinental cargo from air to ocean transport, as well as any acceleration of the recent mass migration of domestic cargo from air to trucks. Cargo operations may also be affected by spikes in fuel prices that could make freighters more challenging to operate profitably, while a decrease in fuel prices may cause carriers to resurrect freighters that have been parked due to fuel inefficiency but which still had utility otherwise. ACRP Report 76 Addressing Uncertainty about Future Airport Activity Levels in Airport Decision Making, completed in June 2012, provided a guidebook and systems analysis methodology to (1) identify and characterize risks and opportunities; and (2) enable airport management to address risks and opportunities in their business models.

Page 7-17 SUMMARY Both in descriptions of methodologies and in examples from a dozen U.S. airports, this section summarized a variety of techniques used to forecast airports' cargo tonnage, as well as to derive all-cargo operations from that forecasted tonnage. Forecasts for cargo tonnage and flight operations have direct implications for warehouse and ramp capacity planning, respectively. Distinctions arise on a case-by-case basis. Warehouse throughput may be closely tied to freighter operations at the vast majority of U.S. airports where all-cargo carriers dominate. At international gateways, belly cargo often accounts for more significant market shares. When freighter service is shared among multiple stops on a single aircraft, warehouse space required to accommodate only a partial freighter payload is reduced but an entire freighter position, as well as ground service equipment, is still required on-ramp. The length of time that position will be required may be reduced by a quicker turn from unloading/loading only part of a freighter's payload. Rather than depend upon a single forecast methodology, airport planners should adapt and incorporate a variety of techniques suited to the individual market. Planners must avail themselves of ample primary data sources through interviews and surveys of current air cargo tenants and potential prospects, in order to introduce local conditions into what can otherwise be completely secondary, remote sources. Cargo forecasts cited for specific airports in this section typically diverged dramatically from actual experience in the forecast period to-date. Nothing in the Forecast Techniques Task should be misconstrued as criticism of either the methodologies or the forecasters. Industry veterans suggest that they have never experienced a period as challenging as the period since Calendar Year 2000 and the data substantiates that claim. With an expectation that forecasts may miss the mark – dramatically, in many cases – analysts must be especially attentive to documenting their assumptions and describing their methodologies so that interim updates can be completed without the need for comprehensive Master Plans or Updates to be completed on a continuous basis. The focus of next chapter is to develop planning metrics and functional relationships that enable a translation from forecasted demand to specific air cargo facility requirements.