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1.231 Planning and Design of Airport SystemTerm Project
Flexible Design of Airport SystemUsing Real Options Analysis
Case Study of New Runway Extension Projectof Tokyo International Airport
Dai OhamaTechnology and Policy ProgramEngineering Systems Division
Massachusetts Institute of Technology
December 14, 2007
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Table of Contents
1. Introduction .................................................................................................................1
2. Outline of the Case Project.........................................................................................1
2.1 Tokyo International Airport....................................................................................12.2 Runway Extension Project......................................................................................2
2.3 Current Design is Optimal? ....................................................................................3
2.4 Project Capacity of Airport.....................................................................................52.5 Flexible Design.......................................................................................................5
3. Real Options Analysis .................................................................................................6
3.1 Financial Options Theory .......................................................................................63.1.1 Black-Sholes Option Pricing Model................................................................7
3.1.2 Binomial Lattice Model ...................................................................................83.1.3 Monte Calro Simulation...................................................................................9
3.2 Real Options Analysis ............................................................................................93.2.1 Real Options.....................................................................................................9
3.2.2 Types of Real Options ...................................................................................10
3.3 Application of Real Options Analysis to Case Study...........................................103.3.1 Which Method should be used for the Case Study? ......................................10
3.3.2 Real Options Analysis Using Monte Calro Simulation .................................11
4. Analysis of Case .........................................................................................................11
4.1 Analysis Condition ...............................................................................................11
4.1.1 Capital Investment .........................................................................................124.1.2 Revenues and Costs .......................................................................................13
4.1.3 Capacity of Airport ........................................................................................14
4.1.4 Discount Rate.................................................................................................144.2 Uncertainty in System...........................................................................................14
4.3 Demand Forecasting .............................................................................................15
4.4 Summary of Analysis Condition ..........................................................................174.5 Result of Analysis.................................................................................................18
5. Conclusion..................................................................................................................21
6. References ..................................................................................................................22
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1.231 Planning and Design of Airport System Dai Ohama
1. Introduction
The Airport systems not only in the U.S. but also all over the world are now very
complex. However, all of them are not always designed optimally. Some facilities are
built excessively compared to their estimation during planning phases, others are required
improvement since they are too large for the actual situations. [1] For example, the size of
the New Denver Airport is too big, and as a matter of fact there is an unnecessary passenger
building. The Newark airport is unsuited for the actual transfer and international traffic,
and major changes should be required. The reason for them is that the master plan of these
airport systems did not anticipate future risks and uncertainties of possible changes in market
conditions, and did not consider the countermeasure of those real risks. Eventually, it leads
to losses or extra costs, and even losses of opportunities happen. Thus, the master plan is
often inflexible and inherently cannot respond to the risks. In order to respond to this
situation, it is essential to plan strategically by considering future risks and uncertainties and
by applying flexibility into design.
This project is to consider one of the possible effective ways to incorporate flexibility
into design by using real options analysis. The goal of this project is to demonstrate how
the flexible design is conducted and how it works. The case of Tokyo International
Airport New Runway Extension Project is applied as a case study.
2. Outline of the Case Project
2.1 Tokyo International Airport
Tokyo International Airport or Haneda Airport (HND) is located in the bay area, near
the center of Tokyo. Haneda Airport is the busiest and an important hub airport in Japan
for domestic air traffic.[2] Haneda Airport is consistently ranked among the world's busiest
passenger airports in terms of the number of passengers, and its ranking was fourth in 2006.
Currently it has three runways (Runway A: 3,500m, Runway B: 2,500m, Runway C:
3,000m), serving nearly 60 million passengers every year. The total capacity of Haneda
Airport is 296,000 aircraft (a/c) per year.
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1.231 Planning and Design of Airport System Dai Ohama
Japan
KyotoTokyo
Chiba
Haneda AirportYokohama
Tokyo
Tokyo Bay
Osaka
Figure 2-1 Location
Figure 2-2 Tokyo Intl AirportSource: Ministry of Land, Infrastructure and Transport in Japan, Kanto
Regional Development Bureau [2]
2.2 Runway Extension Project
However, its capacity has already reached a limit of airport capacity against the
increasing demand, and it is necessary to respond to the demand as soon as possible. In
order to solve this problem, Tokyo International Airport Extension Projectwas launched in
2002 to build 4th
runway, which is calledRunway D, to increase the total capacity of the
airport. [2] This extension enables the airport to have the capacity from of 296,000 a/c per
year to 407,000 a/c per year.
Extension Project Runway D
(Under Construction)
Runway A
Runway B
Runway C
Figure 2-3 Plan of New Runway Island in Haneda AirportSource: Ministry of Land, Infrastructure and Transport in Japan, Kanto Regional Development Bureau [2]
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1.231 Planning and Design of Airport System Dai Ohama
The new runway will be constructed on the artificial island approximately 3,000 meters
long and 500 meters wide, on the south side of the airport. As the new runway island is
planned in the estuarine waters of the Tama River, the course of the river shall not be
affected by reclaimed island. Therefore, pile elevated platform is applied for the part in the
river mouth, and reclamation is applied for the part outside the alignments of the river mouth.
Two taxiway bridges for aircraft connecting the existing airport to the new runway island,
600 meters long and 60 meters wide, are planned as well. Pile elevated platform of the new
runway island is 1,100 meters long and 500 meters wide in total. It is a jacket structure
composed of approx. 200 steel jacket units 60 meters long and 45 meters wide. The water
depth at the construction site is 15 to 19 meters, and the river has 30 to 40 meters thick
sedimentary layers of soft clayey soil. Thus, long steel pipe piles are used as bearing piles
for the pile elevated platform.
Figure 2-4 Structure of Runway DSource: Ministry of Land, Infrastructure and Transport in Japan, Kanto Regional Development Bureau [2]
By the end of the fiscal year of 2006, the design has been completed, the construction
started in March 2007, and it is supposed to be in service in December 2010.
2.3 Current Design is Optimal?
As discussed above, the new runway is expected not only to improve the airport
capacity but also to bring about a major impact on the overall economy. However, is this
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1.231 Planning and Design of Airport System Dai Ohama
new project really optimal? Can the design of this project respond to the future
uncertainty?
The current design of Runway D consists of the Runway (R/W) and the Parallel
Taxiway (PT/W). At the Runway D, airplanes depart from south to north and land from
north to south, and the projected capacity is based on this operation. A parallel taxiway is
usually necessary for a runway since airplanes have to move to a runway in order to take off
and to go to terminal after landing. But at this Runway D, a parallel taxiway will be used
only when those airplanes which are running for takeoff stop and return to the terminal.
This possibility is very low. Because there is turning pad at the end of the runway, it is
possible that those airplanes which are running for takeoff return to the terminal by using
this area. Therefore, the operation frequency of the PT/W is extremely low. In addition,
the area at the south edge of the runway (S/E), is also not used since airplanes do not depart
and land this way. Therefore, PT/W & S/E are not necessary for the currently planned
operation, and it can be said that this runway island is not designed optimally. This project
was based on taxpayers money (570 billion) and it is excessively invested, so it is
indispensable to evaluate if the PT/W and S/E is worthwhile investing or not.
Existing
Airport
Runway(R/W)
Parallel Taxiway(PT/W)
In the currently designedoperation, these areas
are not necessary.
Departure
Arrival
North South
South Edge(S/E)
Figure 2-5 Operation Concepts of Runway D
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1.231 Planning and Design of Airport System Dai Ohama
These areas such as PT/W and S/E would be used if airplanes depart from north to south
and land from south to north. Thus, if they need this operation in the future, they should be
constructed, otherwise they should not be constructed.
2.4 Projected Capacity of Airport
So when are such departure and landing necessary? In the current plan, when north
wind blows, runway B and D are used for departure and runway A and C are used for
landing. When the south wind blows, runway A and C are used for departure and runway
B and D are used for landing. In this planning, during south wind, only 12 a/c land at the
runway D per hour, while during north wind 28 a/c depart from the runway D per hour.
This is because runway usages are decided so that the entire capacity of the airport could bemaximized. However, at the runway D, if airplanes depart from N to S and land from S to
N, it is possible for the entire capacity of the airport to be increased.
North Wind South WindDeparture: B-12 ac/hr
D-28
Landing : A-28 ac/hrC-12
Total : 40 ac/hr
Departure: A-22 ac/hrC-18
Landing : B-28 ac/hrD-12
Total : 40 ac/hr
Figure 2-6 Currently Designed CapacitySource: Ministry of Land, Infrastructure and Transport in Japan, Kanto Regional Development Bureau [2]
2.5 Flexible Design
As a matter of fact, estimating this capacity is very complicated because all of four
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1.231 Planning and Design of Airport System Dai Ohama
runways capacities are related complexly to one another. So, in this project, estimating
this capacity is outside of the scope of the goal. I assume that the opposite departure (from
north to south) and landing (from south to north) could increase the total capacity of the
airport. In other words, PT/W and S/E could increase the capacity. If the demand is more
than the capacity, PT/W and S/E should be constructed. If the demand is less than the
capacity, its not necessary to construct them. Therefore, PT/W and R/E should be
constructed when they become necessary. In other words, they dont have to be
constructed until the demand of passengers exceeds the projected capacity. If such
flexibility is incorporated into design, managers are able to respond to future risks and
uncertainties such as demand of passengers. Therefore, flexible design can minimize the
initial investment, reduce risks, respond to uncertainties, and maximize the value of the
project. By doing so, this runway extension project would be optimized.
3. Real Options Analysis
In order to evaluate those projects, one of the best possible ways is real options analysis,
which is an evaluation method for those projects which involve decision opportunities.
Real options analysis is the application of financial options theory to the actual projects such
as infrastructure developments, real asset investments, research and development, and so on.
In these projects, those who make decision generally have options such as the option to defer,
the option to defer, and the option to abandon. In this case study, the option to expand
should be applied.
3.1 Financial Options Theory
Financial options theory is a basis for the real options analysis. In finance, an option
contract is an agreement where the owner has the right, but not the obligation, to buy or sell
an underlying asset, such as a stock, at a pre-determined price on or before the expiration
date. [3]
There are two types of options in terms of the right. A call option refers to the right to
buy a stock at the strike price, the pre-determined price. A put option refers to the right to
sell a stock at the strike price. [3] Options are also divided into two categories in terms of
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1.231 Planning and Design of Airport System Dai Ohama
expiration date. An American option is exercised when the owners are allowed to exercise
them on or before the expiration date. A European option is exercised only on the
expiration date. [3]
Option is not the obligation but the right to buy or sell the underlying asset. So the
owner would exercise the option only when it is favorable to do so. [3] For example, those
who hold option would exercise a call option only if the price of the underlying asset (S), is
higher than the strike price (K), and would earn a profit of S-K; otherwise, they would buy
the asset not from the option, but from the market. A European put option is the right to
sell the underlying asset at a certain price. So it can be exercised only when the market
price of the asset (S) is less than the strike price (K), where those who hold the option can
make the profit of K-S. Figure 3-1 shows the general payoff of a European call option and
put option. Although American options can be exercised at any time on or before the
expiration date, they have the same payoffs as European options.
Asset Price ($)
Payoff($)
Underlying Assets
S
S-K
S=K0
Option
Payoff of European call = Max (S-K, 0)
Asset Price ($)
Payoff($)
Underlying Assets
K-S
S=K0
Payoff of European put = Max (K-S, 0)
Figure 3-1 Payoff Diagram for a European Call Option (Left) and Put Option (Right)Source: R de Neufville Lecture notes of the MIT course of ESD.71 (Fall 2006)[4]
3.1.1 Black-Scholes Option Pricing Model
The Black-Scholes option pricing model is the most well-known method. It applies to
those European call and put options that do not provide dividends. It can produce the
theoretical value of the option by applying five factors such as the price of the underlying
asset (S), the strike price (K), the time until expiration (T), the risk-free rate of interest (rf),
and the volatility (i.e., standard deviation) of returns on the stock. [3] The following
formula shows this model for a European call option. [3]
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( ) ( )21 dNeKdNSCTrf=
Where C: Theoretical value of a European call option with no dividends
( ) ( )T
TrKSd f
++= 2//ln
2
1
Tdd = 12
N(x): Cumulative probability function for a standardized normal distribution
The following formula shows the theoretical value of European put options.
( ) ( )12 dNSdNeKPTrf
=
Where P: Theoretical value of a European put option with no dividends
3.1.2 Binomial Lattice Model
Binomial Lattice Model, which is also widely used, is a more simplified discrete-time
approach to valuation of options compared to the Black-Scholes Option Pricing Model. [4]
It assumes that the price of the underlying asset will change to one of the only two possible
values during the next period of time. Figure 3-2 shows a three-stage binomial example.
Figure 3-2 Binomial Tree Representations of Changes in Underlying AssetSource: R de Neufville Lecture notes of the MIT course of ESD.71[4]
In the Binomial Lattice Model, the price of an underlying asset at a certain time can
change to one of the only two possibilities, which are an upward movement with multiplieru
with the probability ofp, and a downward movement with multiplierdwith the probability
of1-q. [4]
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( ) Tdvp
ued
eu
T
T
+=
==
=
/5.05.0
/1
3.1.3 Monte Calro Simulation
Monte Calro Simulation is an analytical method that generates the stochastic
distribution of possible outcome that correspond to probability-distributed sampled inputs.
[5] Because of the development of computer technology, large computer simulation such
as Monte Calro Simulation can be constructed easily. Spread sheet software such as
Microsoft Excel is the tool for conducting Monte Calro Simulation.
3.2 Real Options Analysis
In the previous section, financial options theory including Black-Scholes option pricing
model, Binomial Lattice Model, and Monte Calro Simulation were introduced. In this
section, real options analysis, which was the application of financial options theory to the
actual projects, is introduced.
3.2.1 Real Options
Real option is the option to undertake some business decision under high uncertainty.
In real options analysis, they are associated with flexibilities in designing systems or the
evolution of projects. [4] Option - like flexibilities are included in systems and projects,
represent opportunities to increase the value of the project through design or through
management actions. [4] It is not obligation, and only when future asset price is preferable
for you, you can exercise it, otherwise you dont have to do it. This allows
decision-makers to avoid downside losses as well as to obtain upside opportunities. These
flexibilities are called real options. [4] Managers have applied real options analysis to
business strategies including large-scale engineering systems. Although real options are
very similar to financial options, they are different from financial options in terms of what
should be assessed and the time span. [4] In financial options, the actual price such as
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stock price is evaluated, while in real options, the value of the project itself is evaluated.
Financial options are usually very short span such as two years, while the time span of real
options is very long-term.
3.2.2 Types of Real Options
There are several types of real options, such as deferral options, abandonment options,
expansion options, growth options, and compound options. [6] Applying appropriate type
of options enables managers to actively manage risks and uncertainties. For example, in
this case study, expansion options is appropriate to be applied. In the current design, which
is inflexible design, the whole facilities such as a runway, a parallel taxiway, and a south
edge would be constructed at the initial construction. On the other hand, in flexible design
that should be proposed in this study, only a runway would be constructed at the initial
construction and then sometime after 10 years of operation, if the demand of passengers
exceeds the capacity, a parallel taxiway and a south edge should be constructed; otherwise
they would not be constructed. Thus, if uncertain demand is unfavorable, it is not
necessary to exercise the option. It is possible to reduce upfront capital investment and
therefore reduce losses. The Flexibility to expand them takes advantage of demand that is
higher than expected in the deterministic projections.
Value of Project
PayoffExpansion
No Expansion
Figure 3-3 Payoff of Expansion Option
3.3 Application of Real Options Analysis to Case Study
3.3.1 Which Method should be used for the Case Study
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As introduced in section 3.1, there are three types of evaluation method in financial
options, and these are also applied to real options analysis. Black-Scholes option pricing
model and Binomial Lattice Model are widely used in financial options, but because they are
very complex and needs financial skills, they are not so widely used in real options in
engineering practice as in financial field. [7] Even if those methods are used appropriately,
it is very difficult to explain to those managers who are not familiar with financial skills. [7]
On the other hand, Monte Calro Simulation can be used more easily by using spread sheet
than those two methods. Compared to conventional methods using financial mathematics,
real options analysis using Monte Calro Simulation has advantages such as user friendly
procedure, being based on data availability in practice, and being easy to explain graphically.
[7] Thus real options analysis based on Monte Calro Simulation can be the appropriate as a
tool for the valuation method for this case study.
3.3.2 Real Options Analysis Using Monte Calro Simulation
In this case study, real options analysis using Monte Calro Simulation method is used
and its procedure is: 1) to estimate cash flows pro forma including capital investment, future
costs and revenues of the project, and calculate the economic value of currently designed
case, 2) to explore the effects of uncertainty by simulating possible scenarios, each of which
leads to a various NPVs, and the collection of each scenario generate both an expected net
present value (ENPV) and the distribution of possible outcomes for a project by
demonstrating cumulative distribution functions, and 3) to explore ways to avoid the
downside risk and take advantage of upside potential by exercising options. [7]
4. Analysis of Case - Tokyo Intl Airport New Runway Extension Project
4.1 Analysis Condition
In order to conduct real options analysis, it is necessary to set up analysis conditions
such as cash flow pro forma including capital investment, revenue scheme, operating and
maintenance costs.
In this case study, three types of design should be considered. Case A refers to the
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current design that government actually planned and that would construct the whole runway
island, including a parallel taxiway and a south edge. This design does not include
flexibility.
Case B refers to the design that constructs the whole runway island in the same way as
Case A. But this design recognizes future uncertainty which is demand of passengers,
while Case A considers deterministic projection of the demand of passengers.
Case C refers to the design that constructs only runway area without parallel taxiway
and south edge of the runway at the initial construction, and expands the parallel taxiway
and south edge after 10 years operation if the demand exceeds the capacity of the airport for
two consecutive years.
Table 4-1 shows the summary of case of analysis.
Table 4-1 Cases of Analysis
CaseInitial
Investment
Future
UncertaintyFlexibility
Case AR/W,
PT/W & SEN/A N/A
Case BR/W,
PT/W & SERecognizing N/A
Case C R/W RecognizingFuture Expansion
PT/W & SE
Source: Applied R de Neufville,et al [7]
4.1.1 Capital Investment
Capital investment here means the initial construction fee and the expansion fee of the
parallel taxiway and the south edge. In the New Runway Extension Project, the
construction fee is 570 billion, [2] so the initial investment in Case A and Case B is 570
billion. In Case C, I assume that the initial investment can be set by prorating the area
where it needs. In the area of the taxiway (827,625m2), the structure of the runway island
is landfill (64,700/m2), [2] so the construction fee is 53.55 billion. In the area of the
south edge (45,120m2), the structure of the runway island is piled pier (786,500/m
2), [2] so
the construction fee is 35.48 billion. Therefore, considering the bank revetment area
(assumed 5% extra) and the joint area for the both areas so that the expansion could be
possible, the initial construction fee of Case C is 505 billion. ([570-(53.55+35.48)]*1.05)
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The expansion cost of Case C is only the area of taxiway and the south edge, which is the
same as the difference between the initial cost of Case A, B and that of Case C.
Considering the extra fee of the joint area (assumed 10% extra), the expansion fee is 97.9
billion. ((53.55+35.48)*1.05) The initial investment is assumed to be paid equally every
year through the construction for 4 years, while the expansion investment is supposed to be
paid equally every year for 2 years.
Runway(R/W)
Parallel Taxiway
(Landfill)827,625m2
South Edge
(Piled Pier)45,120m2
Figure 4-1 Expansion Area of Runway D
4.1.2 Revenues and Costs
There are several types of revenues and costs in the airport system such as from
aeronautical charges, non-aeronautical charges, and off-airport or non-operate charges. [8]
In the current operation of Tokyo Intl Airport, these charges applies, but in terms of a
runway, aeronautical charges, which is the charges for services or facilities directly related tothe processing of aircraft and their passengers, is the main charge. [8] The aeronautical
charges have several categories such as landing charge, terminal-area air navigation charge,
passenger service charge in terminals, security charge, charges for airport noise, and so on.
[8] In terms of a runway, landing charge is the main source of the revenue. Thus, I
assume that the revenue is only from the landing charge. The current landing charge in
Tokyo Intl Airport as of 2006 is 490,000 for B747-400D, 350,000 for B777-200, and
230,000 for B777-724, and so on. [9] I set the average landing fee of this airport as
332,000 / aircraft by assuming the probabilities for each type of aircraft of 23% for
B747-400D, 35% for B777-200D, and 41% for B767-300. I also assume that this landing
charge is fixed for the next 20 years. For the simplicity, I also set the revenue for each year
is landing charge multiplied by the minimum of the number of passenger or the capacity.
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Costs for the operation and maintenance are categorized into the ones above. From the
past disclosed information by the government, operating and maintenance cost in all airports
in Japan in 2005 was 147.4 billion. [10] I assume that these costs depend on the scale of
air traffic. The whole air traffic in Japan in 2005 was 1,431,000 and 300,000 in Tokyo Intl
Airport, which is about 20% of the whole traffic, so the operating and maintenance costs for
Tokyo Intl Airport in each year is assumed 29.48 billion, which is also assumed equally for
the current three runways. So the operating cost and maintenance costs for the one runway
is one-third of 29.48, which is equal to 9.84 billion. In addition, in the current
construction plan indicates that the specific maintenance cost for the runway D is 100
billion for the next 30 years. Thus the maintenance cost of 3.33 billion should also be
included in the operating and maintenance cost for the new runway. Therefore, the
operating and maintenance costs for the runway D are set as 13.17 billion.
4.1.3 Capacity of Airport
According to the Ministry of Land, Infrastructure and Transport in Japan, the improved
maximum capacity of the airport by the runway extension in Tokyo Intl Airport is 40
aircrafts per hour, and it estimated this capacity can accommodate 87 million passengers per
year. [2] So the capacity in terms of the number of passenger in this airport is 87 million.
4.1.4 Discount Rate
Discount rate is the opportunity cost of capital, and it can be calculated by the capital
asset pricing model (CAPM). CAPM requires the risk-free rate of interest, the sensitivity
of specific project or company to stock price, and the expected rate of return of the market.
However, the government used the discount rate of 4% when it assessed not only this project
but also any airport related project. [11] Thus, the discount rate of 4% is also used in this
case study.
4.2 Uncertainty in System
There are usually a lot of uncertainties in large-scale engineering systems which include
technical change, economic change, regulatory change, industrial change, political change,
and so on. [4] This case also includes a lot of uncertainties such as demand of the number
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of passengers, capital investment in the future, the operating and maintenance costs, and
unexpected events in the future. For the sake of the simplicity, I set only demand of the
number of passengers as uncertainty. When forecasting the number of passenger in airports,
the time span should be considered at most 20 years span and the volatility, which is the
range of the chance the demand is higher or lower, should be considered by plus or minus
50%. [12]
4.3 Demand Forecasting
According to the government estimation, it forecasted the demand of the number of
passengers as 73.2 million in 2012, 80.3 million in 2017, and 85.5 million in 2022. [2]
Past and Predicted Number of Passengers at Tokyo Int'l Airport
0.00
10.00
20.00
30.00
40.00
50.00
60.00
70.00
80.00
90.00
1983
1985
1987
1989
1991
1993
1995
1997
1999
2001
2003
2012
2022
Year
NumberofPassengers
Figure 4-2 Demand Forecasting
Source: Ministry of Land, Infrastructure and Transport in Japan [2]
This estimation was based on the estimate of the total population in Japan, the estimate
of GDP and other several factors. [13] However, forecast is always wrong. The table
below is comparison of 10 years forecasts of international passenger to Japan with actual
results. [12] These results clearly show that forecast has been always wrong. In addition
the forecast of the total population in Japan and GDP are also very difficult to assess. Thus,
the demand forecast above could be wrong.
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Table 4-2 Comparison of 10-year forecasts of international
passengers to Japan with actual results
Forecast Passengers (millions) Percent error
For Done in Actual Forecast Over actual
1980 1970 12.1 20.0 65
1985 1975 17.6 27.0 531990 1980 31.0 39.5 27
1995 1985 43.6 37.9 (13)Source: R. de Neufville, A. Odoni, Airport Systems: Planning, Design, and Management[12]
However, when assessing the real options analysis, it is necessary to rely on some
forecasts, although they are nothing but estimation. Thus, in this case study, the demand
curve that is based on the estimation of the total population and the estimation of GDP in
Japan should be considered. The important thing is to recognize that this forecast is just an
assumption and to evaluate this by using various demand scenarios with volatility.
In this case study, the demand forecast is estimated based on the estimation of
population [14] and the estimation of GDP [15] in Japan by conducting regression analysis.
[16] The table and the graph below show the estimation of the number of passenger for the
next 20 years in Tokyo Intl Airport.
YearPopulation
(Thousands)
GDP
(in trillion)Passengers
(million)
1985 121.05 350.3 24.271986 121.66 360.8 25.881987 122.24 374.5 28.191988 122.75 400.0 30.281989 123.21 421.2 34.961990 123.61 443.1 38.091991 124.10 458.2 40.061992 124.57 462.7 39.981993 124.94 463.7 39.121994 125.27 468.8 41.441995 125.57 477.7 43.021996 125.86 489.9 45.081997 126.16 496.8 47.431998 126.47 488.0 49.911999 126.67 487.0 52.272000 126.93 501.3 54.772001 127.32 503.2 57.012002 127.49 503.9 59.492003 127.69 512.8 59.412004 127.79 524.6 59.052005 127.77 538.9 59.472006 127.76 544.8 61.08
62.2363.3764.4965.5967.35
69.1170.8772.6374.39
76.1477.89
79.6481.3883.1284.4685.80
87.1188.4289.7090.9892.2493.48
94.71
2007 127.69 550.8
2008 127.57 556.9
2009 127.40 563.0
2010 127.18 569.2
2011 126.91 578.9
2012 126.60 588.7
2013 126.25 598.7
2014 125.86 608.9
2015 125.43 619.3
2016 124.96 629.82017 124.46 640.5
2018 123.92 651.4
2019 123.34 662.4
2020 122.73 673.7
2021 122.10 683.1
2022 121.43 692.7
2023 120.74 702.4
2024 120.01 712.2
2025 119.27 722.2
2026 118.50 732.3
2027 117.71 742.6
2028 116.90 753.0
2029 116.07 763.5
2030 115.22 774.2 .
Demand Forecasting
0
20
40
60
80
100
120
1982 1987 1992 1997 2002 2007 2012 2017 2022 202Time (Year)
Demand(PAXinmillion)
Projected Demand (Government)
Projected Demand (This Project)
PAX = 2 * A * { - 0.1818 + 0.0077 * B }
(A: Estimated Population, B: Estimated GDP)
Figure 4-3 Demand Forecast of Number of Passengers in Tokyo Intl Airport
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1.231 Planning and Design of Airport System Dai Ohama
The demand curve shown in Figure 4-3 is still deterministic projection, and this is
usually used for the static analysis. However, it is essential to recognize and consider the
uncertainty in this demand forecasting. Thus, uncertainty is recognized in the model by
simulating possible scenarios. It indicates how fluctuations can be incorporated around
deterministic projections based on the relevant probability distribution. [17] In this case
study, 2,000 Monte Calro Simulations are generated where all of those simulations create
each demand scenarios over the 20 years span. Figure 4-4 shows some of the examples of
simulations of the uncertain demand. All of these scenarios can be considered and
incorporated into the calculation of the expected value of the plans statistically.
Demand Forecasting
0
20
40
60
80
100
120
1982 1987 1992 1997 2002 2007 2012 2017 2022 2027
Time (Year)
Demand(PAXinmillion)
Projected Demand (Government)Projected Demand (This Project)Demand scenario
Demand Forecasting
0
20
40
60
80
100
120
1982 1987 1992 1997 2002 2007 2012 2017 2022 2027
Time (Year)
Demand(PAXinmillion)
Projected Demand (Government)Projected Demand (This Project)Demand scenario
Demand Forecasting
0
20
40
60
80
100
120
1982 1987 1992 1997 2002 2007 2012 2017 2022 2027
Time (Year)
Demand(PAX
inmillion)
Projected Demand (Government)Projected Demand (This Project)Demand scenario
Demand Forecasting
0
20
40
60
80
100
120
1982 1987 1992 1997 2002 2007 2012 2017 2022 2027
Time (Year)
Demand(PAXinmillion)
Projected Demand (Government)Projected Demand (This Project)Demand scenario
Figure 4-4 Examples of Simulation of the Uncertain Demand
4.4 Summary of Analysis Condition
The Table 4-3 shows the summary of analysis condition.
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Table 4-3 Summary of Analysis Condition
Initial
Investment
Future
ExpansionPerspective Simulation Option
Design AR/W,
PT/W & SEN/A Deterministic No No
Design B R/W,PT/W & SE
N/A RecognizingUncertainty
Yes No
Design C R/W PT/W & SEIncorporating
FlexibilityYes Yes
Source: Applied R de Neufville, S. Scholtes, T. Wang [7]
4.5 Result of Analysis
Table 4-4, 4-5, 4-6 shows the cash flow pro forma for three cases. Table 4-4 shows the
cash flow pro forma and calculating the NPV of the project assuming the demand of thenumber of passengers grows as projected. (Case A) In this case, the expected NPV
(ENPV) of the project is 678.8 billion. However, this value is not realistic since the actual
demand of the number of passengers can change from this deterministic value.
Table 4-5 shows the cash flow pro forma and calculating the NPV of the project
recognizing uncertainty. (Case B) In this case, Monte Calro Simulation is conducted and it
produces 2,000 possible demand scenarios. The ENPV of this case is 638.3 billion. But
this ENPV is just one of the 2,000 scenarios. This simulation can generate 2,000 ENPV in
each scenario, and can also generate distribution for each scenario. The actual ENPV is
distributed as shown in Figure 4-5. The average of ENPV is 569.3 billion, which is less
than that of the Case A. Although this design assumes that there are equal chances that
demand changes to higher and to lower, this design limits the higher value of the project
since the capacity is fixed, while there are still lower chances to generate losses. [7]
Table 4-6 shows the cash flow pro forma and calculating the NPV of the project
recognizing uncertainty and holding the option to expand. (Case C) This case also
recognizes uncertainty and Monte Calro Simulation is conducted, which produces the ENPV
of 686.7 billion. But this ENPV is also just one of the 2,000 scenarios. In this design,
the actual ENPV is distributed as shown in Figure 4-5, and the average of ENPV is 621.6
billion, which is higher than that of Case B. Because the option to expand is exercised
when the demand is higher than the current capacity, the ENPV is improved higher. The
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estimated value of the option exercised in order to incorporate the flexibility into design can
be calculated by the difference between the ENPV of Case C and that of Case B, which is
52.3 billion. Table 4-7 shows the summary of this analysis. Case C, which holds
flexibility in design has advantages over Case B in every aspect. Table 4-6 shows the
summary of the analysis. [7] The initial investment in Case C is less than that of Case B,
which means that the flexibility can reduce the initial investment. The ENPV, the
maximum and minimum NPV in Case C is higher than that of Case B, so the flexibility can
enhance the overall value of the project.
Cumulative distri bution function
0.0%
10.0%
20.0%
30.0%
40.0%
50.0%
60.0%
70.0%
80.0%
90.0%
100.0%
200,000 300,000 400,000 500,000 600,000 700,000 800,000 900,000
Target value \ mill ion
Probability
Case CCase B
Average ENPV(B)569.3 B
Average ENPV(C)
621.6 B
Option Value52.3 B
Case A678.7 B
Figure 4-5 Cumulative Distribution Function (Value at Risk)
Table 4-7 Summary of the Analysis
Case B
(No Flexibility)
Case C
(with Flexibility)
Initial Investment ( in billion) 570.0 505.0ENPV ( in billion) 569.3 621.6
Minimum NPV ( in billion) 339.3 338.1
Maximum NPV ( in billion) 773.4 899.1
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5. Conclusion
There are a lot of facilities that are not designed optimally in airport systems. The
master plan of those designs does not anticipate and consider future risks and uncertainties.
Thus, inflexible design cannot manage risks and uncertainties, and it leads to losses. In
order to solve this problem, it is essential to incorporate flexibility into design. As
demonstrated in the case study in this paper, the real option analysis is the useful way to
recognize uncertainty and incorporate flexibility into design. The case study demonstrated
that flexibility can enhance the expected value of the project, and reduce the possible losses
by using the option to expand. Also it can demonstrate that flexibility can keep the initial
cost lower than that of the current design. Therefore, at the initial construction phase, only
runway should be constructed and if the demand of the number of passenger exceeds the
capacity after 10 years operation, a parallel taxiway and a south edge should be constructed.
Furthermore, real options analysis using Monte Calro Simulation is very useful in that it just
focuses on the ENPV of options and it does not require any financial skills unlike typical
financial options approaches such as Black-Sholes option pricing model and binomial lattice
model. Thus, those who are in charge of design can relatively easily use this method.
Flexible design thus enables projects to be optimized and reduce excessiveness and losses in
airport systems.
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6. Reference
[1] R. de Neufville, Lecture note of the MIT course of1.231: Planning and Design of Airport
System (Fall 2007).
[2] Ministry of Land, Infrastructure and Transport in Japan, Outline of Tokyo Intl Airport
New Runway Extension Project, 2007.http://www.mlit.go.jp/koku/04_outline/01_kuko/02_haneda/index.html
[3] R. A. Brealey, S.C. Myers, and F. Allen, Principles of Corporate Finance, 8th ed., (NewYork: McGraw-Hill Publishing Company, 2005).
[4] R. de Neufville, Lecture notes of the MIT course ofESD.71: Engineering Systems
Analysis for Design (Fall 2006).
[5] K. Hodota, R&D and Deployment Valuation of Intelligent Transportation Systems: ACase Example of the Intersection Collision Avoidance Systems, M.S. Thesis in Master
of Science in Transportation, MIT, Cambridge, MA, 2006.
[6] T. Copeland, T. Koller, J. Murrin, Valuation: Measuring and Managing the Value of
Companies, Fourth Edition (McKinsey & Company Inc., 2002).
[7] R. de Neufville, S. Scholtes, T. Wang, Valuing Real Options by Spread Sheet : Parking
Garage Case Example, January 2005.
[8] A. Odoni, Lecture notes of the MIT course of1.231: Planning and Design of Airport
System (Fall 2007).
[9] Ministry of Land, Infrastructure and Transport in Japan, Civil Aviation Breau.
http://www.mlit.go.jp/singikai/koutusin/koku/seibi/14/images/shiryou1_22.pdf
[10] Ministry of Land, Infrastructure and Transport in Japan, Survey of the Air Traffic
Condition in Japan, 2003, http://www.mlit.go.jp/kisha/kisha03/12/120523_3/05.pdf
[11] Katsuya Hihara,Research for New Operation System of Transportation PolicyConsidering Uncertainty, 2004, Policy Research Institute for Land Infrastructure andTransport, https://www.mlit.go.jp/pri/houkoku/gaiyou/pdf/kkk9.pdf
[12] R. de Neufville, A. Odoni,Airport Systems: Planning, Design, and Management, 2nd
ed., (New York: McGraw-Hill Publishing Company, 2003)
[13] Ministry of Land, Infrastructure and Transport in Japan, Forecast of air traffic,http://www.mlit.go.jp/singikai/koutusin/koku/07_9/01.pdf
[14] National Institute of Population and Social Security Research, Forecast of Population
http://www.ipss.go.jp/syoushika/tohkei/suikei07/suikei.html#chapt1-1
[15] Ministry of Land, Infrastructure and Transport in Japan, Transition and Forecast of GDP,http://www.mlit.go.jp/road/kanren/suikei/7-1.pdf
[16] R. de Neufville, Forecasting Assignmentof the MIT course of1.231: Planning and
Design of Airport System (Fall 2007)
[17] Michel-Alexandre Cardin, Facing Reality: Designing and Management of Flexibility
Engineering Systems, M.S. Thesis in Master of Science in Technology and Policy,Massachusetts Institute of Technology, Cambridge, MA, 2007.
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