Methodology: Passenger Forecast for the planned Savaria International Airport. CENTRAL EUROPE Programme Project CHAMPIONS

Transcription

1 Methodology: Passenger Forecast for the planned Savaria International Airport CENTRAL EUROPE Programme Project CHAMPIONS October 2011

2 Methodology: Passenger Forecast for the planned Savaria International Airport CENTRAL EUROPE Programme Project CHAMPIONS This report has been prepared by: Burchardkai Hamburg Tel.: +49 (0) Fax: +49 (0) uniconsult@uniconsult-hamburg.de Web: Copyright by UC September/October 2011

3 Methodology Passenger Forecast for SIA Airport iii Contents Seite 1. INTRODUCTION 4 2. OUTLINE OF A METHODOLOGY FOR A PASSENGER FORECAST FOR SAVARIA INTERNATIONAL AIRPORT Delineation of the Catchment Area Regular Demand Forecast Demand Influencing Factors Base Value for the Air Transport Passenger Potential Macro Economic Determinates Gross Domestic Product Wages and Consumption Human Development Index Economic Sector Distribution Inbound Tourism Micro Economic Determinates Inter Airport Competition Potential Hub Development Total adjusted regular Passenger Demand Additional Passenger Demand Accumulated Passenger Demand Identification of relevant Destinations Destinations from airports in the vicinity conclusion by analogy for SIA Minimum demand for at least one flight per week Forecast of Passenger Potential Forecast of annual Passenger Aircraft Movements Peak Analysis FINAL REMARKS 24

4 Methodology Passenger Forecast for SIA Airport 4 1. INTRODUCTION The SIA-PORT Ltd. was established in 2006 with the aim to realize a greenfield airport project in the West-Pannon Region. The planned aircargo focused airport will be situated in the area of Vát and Porpác, in the region of Szombathely, about 12 km from the Austrian border. In order to meet the expected growing demand for air transportation both in terms of quality and quantity, an additional airport in the region is considered to be required. Due to expected aircraft sizes in the air freight transport business, a runway of meter length is planned. An industrial and logistic park with direct and fast motor way and railway connections should additional increase attractiveness. The base traffic of the airport should be secured by such flights which represent longdistance, large-scale goods delivery. Additional aircraft movements at SIA are expected to result from aircrafts, which are using the planned maintenance service center at the site. Negotiations with large global players offering such aircraft maintenance services have been already started. Another source for aircraft traffic is seen in passenger air transport, which could also contribute to the overall traffic volumes. In order to assess the passenger market potential of SIA, it has been decided to commission UNICONSULT Universal Transport Consulting GmbH, Hamburg, to develop a general forecast methodology for the prognosis of the likely air passenger demand. Based on this methodology, the local Hungarian project partner ICEG European Center 1 will carry out the calculations for the forecast. The analysis of the air passenger demand has to be based on a passenger potential forecast for the dedicated region s airport, enabling the approach to attract airlines for improved regional accessibility already on the short run in line with the schedule planning of airlines. This following report comprises a comprehensive forecast methodology, which can be directly applied for the prognosis of air passenger traffic at the planned SIA. 1 ICEG European Center ( is an independent research institute based in Budapest, Hungary, providing economic research and consultancy services.

5 Methodology Passenger Forecast for SIA Airport 5 2. OUTLINE OF A METHODOLOGY FOR A PASSENGER FORECAST FOR SAVARIA INTERNATIONAL AIRPORT The objective of the first chapters is the establishment of the passenger demand potential for SIA as it would be in The question to be answered is How many passengers would use SIA if the airport is open now? The goal is to create a starting point for the forecast which will be subsequently performed by ICEG. The methodology to forecast the likely passenger demand until 2040 will be based on the calculated passenger demand potential 2011 and is described in later chapters. 2.1 Delineation of the Catchment Area The initial stage of the demand analysis is the delineation of a reasonable catchment area of SIA. Theoretically, the SIA catchment area can be characterized by the two following qualities: Outgoing air transport passengers from that area will choose SIA over other alternative airports. Incoming air transport passengers to that area will choose SIA over alternative airports. In practice, however, the delineation of a catchment area can be somewhat tricky: While the term catchment area implies a geographical notion based on distance from an airport passenger decisions about airport selection tend to be much more based on airport access and egress time2. These time considerations can be rather complex, as they may involve dynamic factors, such as expected traffic congestion. In the case of overlapping catchment areas a multitude of factors come into play. 2 Jarach, D.: AVIATION-RELATED AIRPORT MARKETING IN AN OVERLAPPING METROPOLITAN CATCHMENT AREA: THE CASE OF MILAN'S THREE AIRPORTS, Journal of Air Transportation, 2005

6 Methodology Passenger Forecast for SIA Airport 6 Figure 1 Passenger airport choice in regions with overlapping catchment areas Source: UNICONSULT Hamburg Obviously, in the case of airports with overlapping catchment areas there is no physical boundary beyond which passengers will entirely switch from using airport A to using airport B. In fact, there is a catchment area overlap where passengers will select their airport based on other criteria than mere distance/travel time: Availability of destinations (incl. flight time/price differences) at the competing airports Service level considerations at the competing airports (i.e. parking space and prices, general state of infrastructure) Habitual use of one or another airport ( has worked well in the past. ) Accessibility by means of public transport, especially in regions with lower individual mobility (i.e. low number of vehicles per persons). These considerations apply to both outgoing and incoming passengers. Notwithstanding the intricacies pointed out above, a general rule of defining an airport catchment area by one hour of driving time to/from the airport has been commonly established for reasons of reduction of complexity 3. Bearing in mind the fairly developed availability of airport infrastructure (and scheduled air services) around SIA, it can be reasonably argued that the accepted driving time for airport access and egress is about one hour in this region. For this reason, a catchment area with a radius of roughly 100 km (translating approximately into 1 hour driving time) will be assumed. In the following, this area will be referred as the SIA catchment area. The following picture depicts in a 100 km radius airports with commercial airline services within the greater SIA region. 3 Jarach, D.: AVIATION-RELATED AIRPORT MARKETING IN AN OVERLAPPING METROPOLITAN CATCHMENT AREA: THE CASE OF MILAN'S THREE AIRPORTS, Journal of Air Transportation, 2005

7 Methodology Passenger Forecast for SIA Airport 7 Figure 2 Catchment areas (100 km radius) of Airports in the Region Source: Google Earth; UNICONSULT Hamburg Five commercial airports can be identified within this radius around SIA site. The SIA 100 km radius overlaps with all other airport radii. SIA s potential competitors are in Hungary the Airport Sarmellek and Budapest, on the Austrian side the Airports in Vienna and Graz, in Slovakia Bratislava Airport Since all affected countries (catchment area of SIA) are members of the EU, border crossing waiting times can be neglected. The borders are considered to be no limitation of any relevant catchment area. 2.2 Regular Demand Forecast The total demand for air transport at SIA will derive from a number of sources. In the following, the SIA demand model dissects total demand into different categories for further analysis. Total SIA demand will be the sum of the following addends: Regular demand describes the more conventional type of demand associated with an airport. Regular demand for SIA draws from the people living in SIA s catchment area wishing to travel to another place by aircraft for business or leisure reasons, or vice versa for foreign people to travel to the SIA catchment area. Generally, as the SIA catchment area overlaps with the catchment areas of other airports, it includes demand from all this regions.

8 Methodology Passenger Forecast for SIA Airport 8 Regular demand for SIA traffic can be classified into the following categories: Air transport demand currently served by other airports and unsatisfied surplus demand (latent demand) for air transport from / to the SIA region that currently does not translate into actual traffic, as people refrain from flying due to the lack of adequate flights. Additional demand derives from the construction and creation of supplemental facilities and services. It includes, for instance, conference visitors attending meetings in a conference center. The volume and type (business/leisure, incoming/outgoing) of demand will obviously depend on the types and target groups of supplemental facilities and services provided at SIA. In the following, an analysis of the determinants for SIA s regular demand and additional demand will be described. Finally the findings will be used to derive the total demand for air transport via SIA, including Passenger demand Resulting aircraft movements. It should be noted that the results of the analysis refer to the current situation 4. These results are the starting point for forecasts that predict future demand for SIA within the upcoming years. 2.3 Demand Influencing Factors Regular demand will be evaluated by establishing a passenger potential base value (2.3.1) that will be adjusted with respect to macro and micro economic determinates as well as competition with other means of transport Base Value for the Air Transport Passenger Potential The passenger potential base value (BV) for SIA air transport demand can be established with the help of the catchment area inhabitants and the propensity to fly (PTF), whereas propensity to fly represents the average number of flights per person per year. Base Value = Population Catchment Area * Propensity to Fly Considering the catchment area as delineated above the population within the 100 km radius around SIA should be calculated. Since the catchment area includes areas of Austria, Croatia, Slovenia and Slovakia, theoretically the population in all these regions has to be calculated accordingly. Total population in the SIA catchment area is the sum of the inhabitants (IN) of all regions within the catchment area of SIA. 4 In other words How much demand for air transport via SIA does exist at present?

9 Methodology Passenger Forecast for SIA Airport 9 Since only marginal parts of Croatia, Slovenia and Slovakia are covered by the 100 km radius around SIA, it can be assumed that the number of passengers attracted from these countries is very limited. Thus the relevant catchment area of SIA in the context of this methodology is limited to regions in Austria and Hungary. Actual values for the propensity to fly (PTF) have to be extracted from flight and population statistics. This leads to the following base value (BV): BV = IN HU * PTF HU + IN AU * PTF AU = passengers per year The above calculation preliminarily suggests a passenger potential base value of passengers per year. However, this value should be treated with caution: by using the PTF values for a country, it implies that on national average the country exploits its air transport passenger potentials to the full extent. Taking into account the analysis of a countries air transport sector, this assumption is improbable. Just as the SIA catchment area is - compared to areas in direct vicinity to existing airports - currently slightly underserved by appropriate airport infrastructure other areas of Hungary, Austria and Slovakia (apart from the capitals) might be as well. This means that the PTF derived from actual flight statistics does not represent the actual air transport passenger potential but lies below it. Therefore, the share of the air transport passenger potential in the catchment area that is not realized due to lack of (or e.g. economic distance to) infrastructure (striped area in following figure) needs to be added. Figure 3 Total air transport passenger demand Source: UNICONSULT Hamburg One way to approximate the share of the passenger potential which is not realized is to consider PTF values from countries, where airport infrastructure is not a limiting factor of air transport. This is typically the case in economies where airport infrastructure is abundant. PTF mod1 = PTF * Adjustment Factor infrastructure

10 Methodology Passenger Forecast for SIA Airport 10 The PTF mod1 is obviously higher than the original PTF, as it includes the hidden potential that is not reflected in the PTF due to lack of adequate infrastructure and, consequently, airline connection availability. However, by adjusting the Hungarian and Austrian PTF by using PTF values from other countries, the PTF mod1 now also incorporates numerous other factors beyond infrastructure availability that determine air transport in these countries, most importantly different economic potencies of these countries. For example, Norwegians (PTF Norway: 5.6) do not fly 47 times (= PTF Norway / PTF Ukraine ) as much as Ukrainians (PTF Ukraine: 0,12) only due to better airport infrastructure, but also due to higher economic potency of the Norwegian economy. Therefore, the PTF mod1 needs readjustment to filter out the differences in economic potency. This is done by multiplying with the ratio of Hungary s (et alt.) and the foreign country s GDP per capita. PTF mod2 = PTF mod1 * Adjustment Factor economy Finally, PTF mod2 will be used to calculate a more realistic passenger potential base value than by using the original unadjusted PTF. Figure 4 Passenger Base Value Adjustment Source: UNICONSULT Hamburg The technique described above will be applied to a set of 2-3 countries, which should be selected in a way that they represent prototypes of criteria such as: Country with aviation market liberalized due to EU membership, amidst catchup process to Western European standard (as e.g. Poland);

11 Methodology Passenger Forecast for SIA Airport 11 Country with aviation market liberalized due to EU membership, in an earlier stage of catch-up process and smaller economic power (as e.g. Romania); Full-scale western type market economy, fully liberalized air transport market (as e.g. UK). The double adjustment of Hungary s and Austria s PTFs for infrastructure availability and economic potency relative to the given set of comparison countries, result in three corridors for values for a realistic propensity to fly. Returning to the original formula with the PTF_mod2 values a new base value for the passenger potential can be calculated: BV = IN HU * PTF HU_mod2 + IN AU * PTF AU_mod2 = passengers per year (mod2) This number of passengers per year will be the base value for the SIA passenger potential that will be adjusted in the further course of the study Macro Economic Determinates Gross Domestic Product The gross domestic product (GDP) 5 is one of the most commonly used indicators to measure the economic strength of a country or region. When comparing the economic strength of different countries two details require consideration: As different countries vary significantly in the size of their population, the comparison of GDP per capita (= per person, short: p.c.) yields much more meaningful results than a mere comparison of aggregated GDPs. Comparing, for instance, the total GDP of the United States and San Marino is almost meaningless due to their different population sizes. Assuming that two individuals in different countries wish to purchase two identical given baskets of goods and services, another detail requires consideration: the price levels of these articles may vary strongly between these countries. For instance, a US dollar exchanged at nominal price rate and spent in India will buy more haircuts than a dollar spent in the US. In order to rectify this distortion, the concept of purchasing power parity (PPP) is introduced, and GDP is corrected for these price level differences 6. 5 GDP (defined by expenditure method) = consumption + gross investment + government spending + (exports imports) 6 First introduced by Gustav Cassel (Swedish economist) in 1920

12 Methodology Passenger Forecast for SIA Airport 12 In conclusion, we will whenever possible compare GDP values in terms of PPP and per capita. The total GDP values and the GDP per capita of the countries within the catchment area of SIA (Hungary, Slovakia and Austria) should be compared with the respective world average GDP values. A high ranking indicates a relative strong economical base. Apart from the absolute value of a country s GDP, its annual growth (or decline) expressed in percent is frequently seen as the most relevant indicator for a country s economic dynamic. By comparing the compound annual growth rates (CAGR) of Hungary and Austria with world average growth rates, conclusions regarding likely impacts on air transport demand in the region can be derived. Impact Assessment regarding Air Transport Passenger Potential: The base value as calculated in chapter already reflects the economic backlog (in terms of GDP (PPP) per capita). However, strong recent GDP growth rates put air transport demand in a particularly good position, while low growth rates do not. This will be reflected through an estimated upward/downward adjustment of the base value (e.g. in the range or plus/minus 10 20%) Wages and Consumption Rising wages grant citizens more disposable income, allowing them to take leisure trips more often or to more distant places. Rising salaries fuel domestic consumption. The respective annual average growth rates of disposable income of the inhabitants within the catchment area should be assessed. It is, however, questionable for how long trends can be maintained. To predict consumption growth rates, relevant forecasts - as published by e.g. The World Bank for individual countries - should be used. Impact Assessment regarding Air Transport Passenger Potential: Rapidly growing consumption may contribute to private travel demand, i.e. for leisure trips. It remains, however, questionable, for how long growth rate can be maintained. Qualified estimations about the future development can be translated into upward/downward adjustments of the base value (e.g. in the range of plus/minus 5 10% ) Human Development Index The Human Development Index (HDI) developed in 1990 by economists from Pakistan, the United Kingdom, and the United States combines normalized measures of countries life expectancy, literacy, educational attainment, and GDP per capita. Its central objective is the comparison of different countries standards of living

13 Methodology Passenger Forecast for SIA Airport 13 by means of utilizing more diversified basic data than only relying on a classical GDP per capita comparison. HDI values theoretically range between 0 (lowest) and 1 (highest). In practice, however, values are between e.g (Zimbabwe) and 0.94 (Norway) in the year While the HDI itself does not influence the passenger potential, it can be shown that countries with HDI values around 0.75 to about 0.9 show disproportionately high air transport passenger growth numbers. Hungary and Austria fall into this subset of countries, thus over-average air transport passenger growth can be expected for SIA. Impact Assessment regarding Air Transport Passenger Potential: No direct positive or negative impact on current passenger potential is expected. There are, however, over-average passenger potential growth prospects that will be considered in the demand prognosis in forthcoming chapters Economic Sector Distribution The economic sector distribution 7 describes the division of value creation between the primary sector (agriculture, mining, fishing and forestry), the secondary sector (industry, manufacturing) and the tertiary sector (services, including transport). Economic activity in the three sectors generates different levels of demand for business-related air transport. Primary sector activity is typically associated with low passenger demand. Secondary sector activity can be associated with moderate to high businessrelated air transport demand levels. However, the degree of secondary sector air transport depends on a number of variables, including o Location of upstream (purchase of raw materials) and downstream (sales of finished goods) markets nearby or international o Degree of international integration of industry branches (i.e. power generation with global division of labor vs. consumer staples with local production and consumption) o Existence or absence of an international fair and meeting tradition (i.e. Hannover Messe for engine construction industry) o Incentive travel in high value creation branches, i.e. for high potentials 7 See also Fourastié, J.: Great Hope of the 20iest Century, 1954

14 Methodology Passenger Forecast for SIA Airport 14 Tertiary sector activity often entails over-average air transport demand. This pertains particularly to finance, consulting, and professional technical services. Again, the level of value creation plays an important role with regard to generation of air transport demand. Business-related transport is particularly attractive for airlines as business travelers typically are less price sensitive than leisure travelers and thus more profitable for airlines. However, business travelers are also more demanding in terms of airport and airline quality levels, frequencies, and destination portfolios. An assessment of the SIA catchment with regards to the distribution of economic sectors should be carried out. Likely impacts on air transport demand for SIA should be derived and roughly quantified. Impact Assessment regarding Air Transport Passenger Potential: Over-average passenger potential of high-yield business travelers due to strong industrial and service sector within the catchment area would justifies an upward adjustment of the base value and vice versa (e.g. in the range of plus 10 20%) Inbound Tourism The chapter is focused on deriving values for the incoming tourism only, since effects from outgoing tourism are considered to be reflected in the propensity to fly value as derived in chapter Inbound tourism could be a major driver for air passengers, if SIA could become the gateway for e.g. Spa-Tourism in the area or the Lake Balaton Region. Based on total volumes of tourism in the region, a realistic share of air travel tourism should be derived. For the estimations, tourism related indicators such as origin of the visitors, capacity and utilization of hotel rooms, seasonal variations, number of visitors by origin per year, average time spent in the region, lengths of stay, and the like should be applied. Impact Assessment regarding Air Transport Passenger Potential: Inbound tourism could become an important driver of the passenger numbers at SIA. International outbound tourism could also play a role. With growing disposable incomes, more flight connections (provided by e.g. low cost carriers) available, and a better accessibility of air transportation in the region, an increasing number of air passengers can be expected. A positive adjustment of the base value is justified to which extend should be estimated based on an in-depth analysis to be carried out based on the results from the questionnaires developed by ICEG European Center. As an indication: The number of potential incoming air tourists could be estimated as 10% of all tourists visiting the region per year (by car, bus or rail). From the perspective of airlines, a regular service to a tourism-destination is generally feasible, if there is sufficient demand for at least 1-2 flights (150 seat aircraft at an average utilization rate of 80%) per week.

15 Methodology Passenger Forecast for SIA Airport Micro Economic Determinates Inter Airport Competition As a service provider for airlines and passengers SIA will be in a competitive situation with other airports in the region (see chapter 2.1). Airport competition only comes into play whenever passengers have a choice of more than one airport to fly from/to in order to reach their destination (inbound) or leave their hometown (outbound). The availability of commercial airport infrastructure in the SIA region is rather high, with many airports overlapping with their 100 km catchment radius the respective catchment radius of SIA. Based on results from recent investigations carried out for potentials of regional airports in Germany, it can be assumed that about 40% of the demand from the SIA (= regional airport) region would actually use SIA, since the catchment areas of main international airports (Vienna, Graz, Bratislava and Budapest) overlap with the catchment area of SIA. The remaining 60% will likely prefer the established international airports in the vicinity of SIA, depending on the distance and the attractiveness of destinations and frequencies they are offering. The share of potential air passengers in the catchment area of SIA, which will prefer SIA in the future, will increase with the number/frequencies/destinations of flight connections to/from SIA. At airports like Frankfurt or Munich, the share of air passengers within the catchment area, which are choosing Frankfurt or Munich, is in the range of 95%. Impact Assessment regarding Air Transport Passenger Potential: The 100 km catchment area is assumed to be largely disputed. It can be assumed that initially 60% of air passenger potential of SIA will use competing airports in the vicinity. Depending on the development of SIA with regard to offered regular flights, destinations and frequencies, SIA would be able to draw passengers from these competing airports. In conclusion, a total base value adjustment is necessary (i.e. in the range of -60% of the air passenger potential (base value) in the catchment area) Potential Hub Development SIA will be located relatively close to already well established major international airports. While, over a longer time period, the development of a hub function at SIA may be possible, this will not play a role in the initial phase of airport operations. Impact Assessment regarding Air Transport Passenger Potential: Due to an expected limited local demand and low frequencies of regular flights, SIA will most likely not develop into an international hub. An adjustment of the base value is not necessary.

16 Methodology Passenger Forecast for SIA Airport Total adjusted regular Passenger Demand The current total regular demand for air passenger transport within the SIA catchment can be calculated by adjusting the base value (mod2) as derived in chapter by deploying the estimated/calculated adjustment values (2.3.2 and 2.3.3). Figure 5 SIA current regular air transport passenger potential Base Value Macro Economics Determinates Gross Domestic Product Wages and Consumptions Human Development Indes Economic Sector Distribution Tourism Micro Economics Determinates Inter Airport Competition Hub Development Total Passengers per Year BV = IN HU * PTF HU_mod2 + IN AU * PTF AU_mod2 +/- GDP adjustment value +/- Wages and Consumption adjustment value +/- HDI adjustment value +/- Economic Sector Distribution adjustment value +/- Tourism adjustment value +/- Inter Airport Competition adjustment value +/- Hub Development adjustment value Regular Passenger Demand (total adjusted Base Value) Source: UNICONSULT Hamburg 2.4 Additional Passenger Demand Additional passenger demand is largely unrelated to the characteristics of the catchment area but is rather induced by supplemental facilities and services at the SIA site. Such facilities and services currently considered include a conference and business center. The common denominator of the concept is the objective to enhance the value proposition of the SIA project, to increase the attractiveness of the region and to induce additional traffic for the airport. In the initial phase of operations, the additional passenger potential generated by the availability of supplemental facilities and services is expected to be somewhat limited in terms of absolute passenger numbers. The following table provides a methodology to estimate the approximated passenger potential generated by individual supplemental facilities and services in their first year of operations.

17 Methodology Passenger Forecast for SIA Airport 17 Figure 6 Scheme to estimate demand for supplemental facilities and services at SIA (Example for a conference center) Events per Year Average No. of Participants % arriving and leaving by plane Passenger demand Major Conferences % Midsize Conferences % Company Meetings % Source: UNICONSULT Hamburg In this example, a Conference and Business Center is expected to draw about additional passengers. Potentials from other additional planned facilities and services can be calculated accordingly. However, absolute passenger numbers do not tell the whole truth about the benefit when it comes to supplemental facilities and services. A number of points especially deserve consideration: A Conference and Business Center attracts business-type passengers. This passenger category is typically less price-sensitive than private passengers and tends to fly on corporate accounts. Thus, they are considered high yield passengers by airlines, and play an important role in attracting more scheduled air service connections. Supplemental facilities may raise the profile and contribute to the reputation of SIA as e.g. the most modern and technologically-advanced airport in the region. This, in turn, may induce additional passenger potential that are attracted even beyond the borders of the regular catchment area. Impact Assessment regarding Air Transport Passenger Potential: At this time, it remains unclear which supplemental facilities and services will be offered to passengers/visitors/customers. Assuming that a Conference and Business Center will be built, the initial additional passenger potential should be adjusted (e.g. in the range of 20,000 passengers per year). 2.5 Accumulated Passenger Demand Accumulated current passenger demand for the SIA catchment area is the sum of regular passenger demand (2.3.4) and additional passenger demand (2.4). The value describes ex post the approximated total passenger potential for the SIA catchment area for today (year 2011), assuming perfectly demand-oriented airport infrastructure and airline connections 8. 8 Evidently, in order to exploit this full potential SIA requires appropriate (in terms of destinations, frequencies, aircraft size and type) airline connections, which in turn require certain infrastructure.

18 Methodology Passenger Forecast for SIA Airport Identification of relevant Destinations Destinations from airports in the vicinity conclusion by analogy for SIA Relevant destinations with air transport potential can be derived e.g. from the assessment of the regular flight schedules of neighboring airports. The assumption is that destinations that generate a sufficient demand from Budapest, Vienna, Granz and/or Bratislava would generate a certain demand from SIA as well. Further, it is assumed, that only European destinations would generate sufficient demand for regular air services. SIA is a greenfield project, therefore no historical data can be exploited to identify feasible connections. The methodology presented here is based on plausibility assumptions and uses public available data from e.g. airports, national air transport statistics and EUROSTAT 9. It can be used to develop a first indication of most promising destinations possibly served from SIA. The first step is to collect data about annual air passengers by destinations travelling from the neighboring airports. The data should be used to set up a table as shown below. Please note: all numbers in the following tables are dummies and only used as examples! Figure 7 Annual Air Passengers to/from the TOP-20 Destinations (as served by Airport Budapest) Examples only! Airport Budapest Airport Graz Airport Bratislava Airpot Vienna total passengers to/from Top-20 destinations at neighbouring airports London Brussels Paris Milano Frankfurt Munich Antalya x y z Madrid Hamburg Stockholm total Source: UNICONSULT Hamburg Please note: all numbers are examples only! If intercontinental destinations should show up in this Top-20, these destinations should be skipped. SIA is considered to serve air passenger demand to/from European destinations only See for example Today Budapest has only very few intercontinental destinations. We assume that a second airport in Hungary will not intercontinental passenger traffic.

19 Methodology Passenger Forecast for SIA Airport 19 In a next step, assumptions about the comparability and similarity of the passenger behaviors and preferences in the catchment areas of neighboring airports and the catchment area of the SIA catchment area have to be made. E.g. one could assume that the travel preferences in the SIA region are most similar to the preferences in the Budapest area (resulting in a weighing factor (WF) of e.g. 50%), followed by Graz (WF of 20%), Bratislava (WF 15%) and Vienna (WF 15%). The actual annual passenger numbers in the table above will be now multiplied with the assumed weighting factors of the airports. Figure 8 Weighted Passenger Values to/from the TOP-20 Destinations (as served by Airport Budapest) Examples only! Airport Budapest Airport Graz Airport Bratislava Airpot Vienna total weighted value for passengers to/from Top-20 destinations at neighbouring airports London Brussels Paris Milano Frankfurt Munich Antalya X Y Z Madrid Hamburg Stockholm total Source: UNICONSULT Hamburg Please note: all numbers are examples only! By ranking the results (by size of the weighting values of each destination) a likely pattern of Top-20 flight destinations to be served from SIA can be derived. However, smaller regional destinations, which also could have a potential are not identified by the described method. The demand for such regional connections should be estimated by other means (e.g. market research).

20 Methodology Passenger Forecast for SIA Airport 20 Figure 9 Ranking of weighted Top-20 destination passenger values Examples only! weighted passenger value % on total 1 London ,6% 2 Frankfurt ,5% 3 Brussels ,9% 4 Paris ,3% 5 Palma de Mallorca ,1% 6 Milano ,2% 7 Munich ,2% X Y Z 18 Madrid ,0% 19 Hamburg ,7% 20 Stockholm ,5% Total weighted passenger value ,0% Source: UNICONSULT Hamburg Please note: all numbers are examples only! For a first feasibility check, the share of the weighted passenger value for each destination on the total weighted passenger value can be calculated and applied to the modified total passenger demand (adjusted base value) as derived in chapter For example: is the calculated adjusted base value for SIA e.g passengers per year, about passengers per year could be expected on a regular connection to London (17,6% in the example above) and on a connection to Frankfurt (15,5%) Minimum demand for at least one flight per week In the classical scheduled air transport sector a regular connection is considered to be generally feasible, if a daily service (6 per week) of one aircraft with 70 seats would reach a utilization rate of at least 70%. From the perceptive of a holiday or lowcost carrier, a regular connection is considered generally feasible, if a weekly service of one aircraft with 180 seats would reach an utilization rate of at least 80%. By applying the estimated passenger values of each identified destination, which could be served from SIA, the likely utilization rates of aircrafts for each service can be calculated. The results provide a first indication, if the expected demand generated within the SIA catchment area will be sufficient to operate a particular connection.

21 Methodology Passenger Forecast for SIA Airport Forecast of Passenger Potential In forthcoming chapters it has been shown, how the potential passenger demand of a greenfield airport project can be estimated. The calculated base passenger value provides an indication, how many air passengers would use SIA, if it would be available today. It serves as input for the forecast. The forecast differentiates passenger potential in three scenarios: optimistic (O), most likely (ML), and pessimistic (P). The time frame for the passenger potential prognosis is 2011 to In order to calculate the future development of the passenger potential, some plausible assumptions regarding the main driving parameter for air transport demand in general and the specific situation at SIA have to be made. Since 3 alternative scenarios should be calculated, 3 sets of parameters have to be derived and finally translated into growth rates. Generally, air passenger forecast methodologies are based on the assumption that there are main drivers for future air transport demand. These drivers include: the development of the European air transport growth rate; the development of European Low-Cost air transport market; the GDP developments in Europe. Base data for deriving these drivers can be found in public available statistics and forecasts as published by organizations such as e.g. EUROSTAT, Worldbank or Boeing/Airbus 11 (for world wide air passenger forecasts). The growth rates to be applied for the SIA project should be initially based on the Boeing forecast values for the European air passenger markets 12. This value is taking into account a wide range of parameter and is considered to be a solid basis for the prognosis of passengers at SIA in the most ML-scenario. To derive growth rates for the O-scenario, the rate applied for the ML-scenario should be increased by up to 20%; to derive respective growth rates for the P-scenario, the rate applied for the MLscenario should be reduced by up to 20%. If special local developments and circumstances can be identified, which are considered to have a strong impact on the main drivers of regional air transport demand and consequently on the air passenger demand for SIA, the Boeing forecast value for European air passenger markets could be adjusted (and the values for O and P-scenario accordingly). The escalation of the 2011 passenger demand on a year-by-year basis finally shows the estimated air passenger demand for each year until the year E.g. for long-term world air transport forecast see current market outlook at It is suggested to use the Boeing forecast due to its more in-depth description and documentation.

22 Methodology Passenger Forecast for SIA Airport Forecast of annual Passenger Aircraft Movements Since SIA is considered to serve only air passenger demand to/from Europe, the forecast of aircraft movements (ACM) will be based on two type-sizes, which are typically used for passenger air transports within Europe: Type 1: 150 seats passenger capacity at 75% load factor for the forecast of a minimum number of movements Type 2: 75 seats passenger capacity at 75% load factor - for the forecast of a maximum number of movements As for example: is the current passenger base value for SIA e.g passengers per year, the resulting aircraft movements are ACM 1 = / 150 (seats) * 75% (loading factor) = per year of aircraft Type 1 or ACM 2 = / 75 (seats) * 75% (loading factor) = per year of aircraft Type 2. Under realistic conditions, there will be a mix of the aircraft types operating from SIA. Therefore the number of aircraft movements in the example would be in the range of to movements per year or 20 to 38 per day. For the prognosis of the aircraft movements, the year-by-year values as calculated for each scenario in the passenger demand prognosis should be applied in the formulas accordingly. Please note that the total aircraft movements at SIA are the sum of passenger and cargo aircraft movements. The methodology shown only applies for passenger aircraft movements. 2.9 Peak Analysis Passenger volumes and aircraft movements are typically not spread evenly over the year or any other given period of time (month, week, etc.). However, it is important to get a good estimate for the maximum short term passenger volumes and aircraft movements at an airport, as these are the base for the airport s design capacity, i.e. size of gate waiting rooms, passenger processing capacity at check-in and security, and baggage handling systems. Following figure schematically illustrates traffic peaks and valleys.

23 Methodology Passenger Forecast for SIA Airport 23 Figure 10 Schematic illustration of passenger peaks and valleys Source: UNICONSULT Hamburg For each scenario, the passenger volumes and aircraft movements during passenger peak days can be estimated. The approximation is based on the results from the passenger potential forecasts and assumed variations of passenger volumes. The variations of the passenger volumes and aircraft movements at SIA can be based on the pattern of actual passenger volume/aircraft movement distributions at the e.g. Airports Vienna and Budapest (i.e. using the average of both values). For an indication of likely peak volumes, the following factors can be applied to the passenger volumes as calculated above: Peak month with approximately 10.0% of yearly total passenger volume (compared to mathematical average of 8.3%) Peak day with approximately 0.41% of yearly total passenger volume (compared to mathematical average of 0.27%) The following factors can be applied to the calculated aircraft movements: Peak month with approximately 9.5% of yearly total aircraft movements (compared to mathematical average of 8.3%) Peak day with approximately 0.36% of yearly total aircraft movements (compared to mathematical average of 0.27%)

24 Methodology Passenger Forecast for SIA Airport 24 By applying these factors to the forecast values, a qualified estimation of the utilization of the airport infrastructure for the entire forecast period can be derived. This may also support the initial planning of the SIA infrastructure. 3. FINAL REMARKS The methodology for the prognosis of air passengers presented in this report is meant to be used like a manual. It can only provide a description of one possible way to estimate the likely air passenger demand for the SIA project there are alternative methods existing. However, UNICONSULT has been applying the described methodology with good results. As a matter of fact, the quality of the final results depends on the quality of the input data, which have to be collected and implemented faithfully. In the phase of setting up the calculation model, a number of assumptions have to be made and the input data have to be adjusted in order to adjust the general approach to the specific needs of SIA. Since - by nature - there is always a high degree of uncertainty in forecasts, all assumptions should be made comprehensible and by best knowledge and belief. Finally: a plausible and transparent forecast methodology facilitates discussions about forecast results with the stakeholders.