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1.231 Planning and <strong>Design</strong> <strong>of</strong> <strong>Airport</strong> <strong>System</strong><br />

Term Project<br />

<strong>Flexible</strong> <strong>Design</strong> <strong>of</strong> <strong>Airport</strong> <strong>System</strong><br />

<strong>Using</strong> <strong>Real</strong> <strong>Options</strong> <strong>Analysis</strong><br />

Case Study <strong>of</strong> New Runway Extension Project<br />

<strong>of</strong> Tokyo International <strong>Airport</strong><br />

Dai Ohama<br />

Technology and Policy Program<br />

Engineering <strong>System</strong>s Division<br />

Massachusetts Institute <strong>of</strong> Technology<br />

December 14, 2007


Table <strong>of</strong> Contents<br />

1. Introduction .................................................................................................................1<br />

2. Outline <strong>of</strong> the Case Project.........................................................................................1<br />

2.1 Tokyo International <strong>Airport</strong>....................................................................................1<br />

2.2 Runway Extension Project......................................................................................2<br />

2.3 Current <strong>Design</strong> is Optimal? ....................................................................................3<br />

2.4 Project Capacity <strong>of</strong> <strong>Airport</strong>.....................................................................................5<br />

2.5 <strong>Flexible</strong> <strong>Design</strong>.......................................................................................................5<br />

3. <strong>Real</strong> <strong>Options</strong> <strong>Analysis</strong> .................................................................................................6<br />

3.1 Financial <strong>Options</strong> Theory .......................................................................................6<br />

3.1.1 Black-Sholes Option Pricing Model ................................................................7<br />

3.1.2 Binomial Lattice Model ...................................................................................8<br />

3.1.3 Monte Calro Simulation...................................................................................9<br />

3.2 <strong>Real</strong> <strong>Options</strong> <strong>Analysis</strong> ............................................................................................9<br />

3.2.1 <strong>Real</strong> <strong>Options</strong>.....................................................................................................9<br />

3.2.2 Types <strong>of</strong> <strong>Real</strong> <strong>Options</strong> ...................................................................................10<br />

3.3 Application <strong>of</strong> <strong>Real</strong> <strong>Options</strong> <strong>Analysis</strong> to Case Study...........................................10<br />

3.3.1 Which Method should be used for the Case Study? ......................................10<br />

3.3.2 <strong>Real</strong> <strong>Options</strong> <strong>Analysis</strong> <strong>Using</strong> Monte Calro Simulation .................................11<br />

4. <strong>Analysis</strong> <strong>of</strong> Case .........................................................................................................11<br />

4.1 <strong>Analysis</strong> Condition ...............................................................................................11<br />

4.1.1 Capital Investment .........................................................................................12<br />

4.1.2 Revenues and Costs .......................................................................................13<br />

4.1.3 Capacity <strong>of</strong> <strong>Airport</strong> ........................................................................................14<br />

4.1.4 Discount Rate.................................................................................................14<br />

4.2 Uncertainty in <strong>System</strong>...........................................................................................14<br />

4.3 Demand Forecasting .............................................................................................15<br />

4.4 Summary <strong>of</strong> <strong>Analysis</strong> Condition ..........................................................................17<br />

4.5 Result <strong>of</strong> <strong>Analysis</strong>.................................................................................................18<br />

5. Conclusion..................................................................................................................21<br />

6. References ..................................................................................................................22


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1.231 Planning and <strong>Design</strong> <strong>of</strong> <strong>Airport</strong> <strong>System</strong> Dai Ohama<br />

1. Introduction<br />

The <strong>Airport</strong> systems not only in the U.S. but also all over the world are now very<br />

complex. However, all <strong>of</strong> them are not always designed optimally. Some facilities are<br />

built excessively compared to their estimation during planning phases, others are required<br />

improvement since they are too large for the actual situations. [1] For example, the size <strong>of</strong><br />

the New Denver <strong>Airport</strong> is too big, and as a matter <strong>of</strong> fact there is an unnecessary passenger<br />

building. The Newark airport is unsuited for the actual transfer and international traffic,<br />

and major changes should be required. The reason for them is that the master plan <strong>of</strong> these<br />

airport systems did not anticipate future risks and uncertainties <strong>of</strong> possible changes in market<br />

conditions, and did not consider the countermeasure <strong>of</strong> those real risks. Eventually, it leads<br />

to losses or extra costs, and even losses <strong>of</strong> opportunities happen. Thus, the master plan is<br />

<strong>of</strong>ten inflexible and inherently cannot respond to the risks. In order to respond to this<br />

situation, it is essential to plan strategically by considering future risks and uncertainties and<br />

by applying flexibility into design.<br />

This project is to consider one <strong>of</strong> the possible effective ways to incorporate flexibility<br />

into design by using real options analysis. The goal <strong>of</strong> this project is to demonstrate how<br />

the flexible design is conducted and how it works. The case <strong>of</strong> “Tokyo International<br />

<strong>Airport</strong> New Runway Extension Project” is applied as a case study.<br />

2. Outline <strong>of</strong> the Case Project<br />

2.1 Tokyo International <strong>Airport</strong><br />

Tokyo International <strong>Airport</strong> or Haneda <strong>Airport</strong> (HND) is located in the bay area, near<br />

the center <strong>of</strong> Tokyo. Haneda <strong>Airport</strong> is the busiest and an important hub airport in Japan<br />

for domestic air traffic.[2] Haneda <strong>Airport</strong> is consistently ranked among the world's busiest<br />

passenger airports in terms <strong>of</strong> the number <strong>of</strong> passengers, and its ranking was fourth in 2006.<br />

Currently it has three runways (Runway A: 3,500m, Runway B: 2,500m, Runway C:<br />

3,000m), serving nearly 60 million passengers every year. The total capacity <strong>of</strong> Haneda<br />

<strong>Airport</strong> is 296,000 aircraft (a/c) per year.<br />

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1.231 Planning and <strong>Design</strong> <strong>of</strong> <strong>Airport</strong> <strong>System</strong> Dai Ohama<br />

Japan<br />

Tokyo<br />

Chiba<br />

Kyoto<br />

Osaka<br />

Tokyo<br />

Yokohama<br />

Tokyo Bay<br />

Haneda <strong>Airport</strong><br />

Figure 2-1 Location<br />

Figure 2-2 Tokyo Int’l <strong>Airport</strong><br />

Source: Ministry <strong>of</strong> Land, Infrastructure and Transport in Japan, Kanto<br />

Regional Development Bureau [2]<br />

2.2 Runway Extension Project<br />

However, its capacity has already reached a limit <strong>of</strong> airport capacity against the<br />

increasing demand, and it is necessary to respond to the demand as soon as possible. In<br />

order to solve this problem, Tokyo International <strong>Airport</strong> Extension Project was launched in<br />

2002 to build 4 th runway, which is called Runway D, to increase the total capacity <strong>of</strong> the<br />

airport. [2] This extension enables the airport to have the capacity from <strong>of</strong> 296,000 a/c per<br />

year to 407,000 a/c per year.<br />

Runway B<br />

Runway C<br />

Runway A<br />

Extension Project<br />

“Runway D”<br />

(Under Construction)<br />

Figure 2-3 Plan <strong>of</strong> New Runway Island in Haneda <strong>Airport</strong><br />

Source: Ministry <strong>of</strong> Land, Infrastructure and Transport in Japan, Kanto Regional Development Bureau [2]<br />

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1.231 Planning and <strong>Design</strong> <strong>of</strong> <strong>Airport</strong> <strong>System</strong> Dai Ohama<br />

The new runway will be constructed on the artificial island approximately 3,000 meters<br />

long and 500 meters wide, on the south side <strong>of</strong> the airport. As the new runway island is<br />

planned in the estuarine waters <strong>of</strong> the Tama River, the course <strong>of</strong> the river shall not be<br />

affected by reclaimed island. Therefore, pile elevated platform is applied for the part in the<br />

river mouth, and reclamation is applied for the part outside the alignments <strong>of</strong> the river mouth.<br />

Two taxiway bridges for aircraft connecting the existing airport to the new runway island,<br />

600 meters long and 60 meters wide, are planned as well. Pile elevated platform <strong>of</strong> the new<br />

runway island is 1,100 meters long and 500 meters wide in total. It is a jacket structure<br />

composed <strong>of</strong> approx. 200 steel jacket units 60 meters long and 45 meters wide. The water<br />

depth at the construction site is 15 to 19 meters, and the river has 30 to 40 meters thick<br />

sedimentary layers <strong>of</strong> s<strong>of</strong>t clayey soil. Thus, long steel pipe piles are used as bearing piles<br />

for the pile elevated platform.<br />

Figure 2-4 Structure <strong>of</strong> Runway D<br />

Source: Ministry <strong>of</strong> Land, Infrastructure and Transport in Japan, Kanto Regional Development Bureau [2]<br />

By the end <strong>of</strong> the fiscal year <strong>of</strong> 2006, the design has been completed, the construction<br />

started in March 2007, and it is supposed to be in service in December 2010.<br />

2.3 Current <strong>Design</strong> is Optimal?<br />

As discussed above, the new runway is expected not only to improve the airport<br />

capacity but also to bring about a major impact on the overall economy. However, is this<br />

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new project really optimal? Can the design <strong>of</strong> this project respond to the future<br />

uncertainty?<br />

The current design <strong>of</strong> Runway D consists <strong>of</strong> the Runway (R/W) and the Parallel<br />

Taxiway (PT/W). At the Runway D, airplanes depart from south to north and land from<br />

north to south, and the projected capacity is based on this operation. A parallel taxiway is<br />

usually necessary for a runway since airplanes have to move to a runway in order to take <strong>of</strong>f<br />

and to go to terminal after landing. But at this Runway D, a parallel taxiway will be used<br />

only when those airplanes which are running for take<strong>of</strong>f stop and return to the terminal.<br />

This possibility is very low. Because there is turning pad at the end <strong>of</strong> the runway, it is<br />

possible that those airplanes which are running for take<strong>of</strong>f return to the terminal by using<br />

this area. Therefore, the operation frequency <strong>of</strong> the PT/W is extremely low. In addition,<br />

the area at the south edge <strong>of</strong> the runway (S/E), is also not used since airplanes do not depart<br />

and land this way. Therefore, PT/W & S/E are not necessary for the currently planned<br />

operation, and it can be said that this runway island is not designed optimally. This project<br />

was based on taxpayers’ money (¥570 billion) and it is excessively invested, so it is<br />

indispensable to evaluate if the PT/W and S/E is worthwhile investing or not.<br />

North<br />

Departure<br />

Runway<br />

(R/W)<br />

South<br />

Arrival<br />

Parallel Taxiway<br />

(PT/W)<br />

South Edge<br />

(S/E)<br />

In the currently designed<br />

operation, these areas<br />

are not necessary.<br />

Existing<br />

<strong>Airport</strong><br />

Figure 2-5 Operation Concepts <strong>of</strong> Runway D<br />

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1.231 Planning and <strong>Design</strong> <strong>of</strong> <strong>Airport</strong> <strong>System</strong> Dai Ohama<br />

These areas such as PT/W and S/E would be used if airplanes depart from north to south<br />

and land from south to north. Thus, if they need this operation in the future, they should be<br />

constructed, otherwise they should not be constructed.<br />

2.4 Projected Capacity <strong>of</strong> <strong>Airport</strong><br />

So when are such departure and landing necessary? In the current plan, when north<br />

wind blows, runway B and D are used for departure and runway A and C are used for<br />

landing. When the south wind blows, runway A and C are used for departure and runway<br />

B and D are used for landing. In this planning, during south wind, only 12 a/c land at the<br />

runway D per hour, while during north wind 28 a/c depart from the runway D per hour.<br />

This is because runway usages are decided so that the entire capacity <strong>of</strong> the airport could be<br />

maximized. However, at the runway D, if airplanes depart from N to S and land from S to<br />

N, it is possible for the entire capacity <strong>of</strong> the airport to be increased.<br />

North Wind<br />

Departure: B-12 ac/hr<br />

D-28<br />

Landing : A-28 ac/hr<br />

C-12<br />

Total : 40 ac/hr<br />

South Wind<br />

Departure: A-22 ac/hr<br />

C-18<br />

Landing : B-28 ac/hr<br />

D-12<br />

Total : 40 ac/hr<br />

Figure 2-6 Currently <strong>Design</strong>ed Capacity<br />

Source: Ministry <strong>of</strong> Land, Infrastructure and Transport in Japan, Kanto Regional Development Bureau [2]<br />

2.5 <strong>Flexible</strong> <strong>Design</strong><br />

As a matter <strong>of</strong> fact, estimating this capacity is very complicated because all <strong>of</strong> four<br />

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1.231 Planning and <strong>Design</strong> <strong>of</strong> <strong>Airport</strong> <strong>System</strong> Dai Ohama<br />

runways’ capacities are related complexly to one another. So, in this project, estimating<br />

this capacity is outside <strong>of</strong> the scope <strong>of</strong> the goal. I assume that the opposite departure (from<br />

north to south) and landing (from south to north) could increase the total capacity <strong>of</strong> the<br />

airport. In other words, PT/W and S/E could increase the capacity. If the demand is more<br />

than the capacity, PT/W and S/E should be constructed. If the demand is less than the<br />

capacity, it’s not necessary to construct them. Therefore, PT/W and R/E should be<br />

constructed when they become necessary. In other words, they don’t have to be<br />

constructed until the demand <strong>of</strong> passengers exceeds the projected capacity. If such<br />

flexibility is incorporated into design, managers are able to respond to future risks and<br />

uncertainties such as demand <strong>of</strong> passengers. Therefore, flexible design can minimize the<br />

initial investment, reduce risks, respond to uncertainties, and maximize the value <strong>of</strong> the<br />

project. By doing so, this runway extension project would be optimized.<br />

3. <strong>Real</strong> <strong>Options</strong> <strong>Analysis</strong><br />

In order to evaluate those projects, one <strong>of</strong> the best possible ways is real options analysis,<br />

which is an evaluation method for those projects which involve decision opportunities.<br />

<strong>Real</strong> options analysis is the application <strong>of</strong> financial options theory to the actual projects such<br />

as infrastructure developments, real asset investments, research and development, and so on.<br />

In these projects, those who make decision generally have options such as the option to defer,<br />

the option to defer, and the option to abandon. In this case study, the option to expand<br />

should be applied.<br />

3.1 Financial <strong>Options</strong> Theory<br />

Financial options theory is a basis for the real options analysis. In finance, an option<br />

contract is an agreement where the owner has the right, but not the obligation, to buy or sell<br />

an “underlying asset,” such as a stock, at a pre-determined price on or before the expiration<br />

date. [3]<br />

There are two types <strong>of</strong> options in terms <strong>of</strong> the right. A call option refers to the right to<br />

buy a stock at the strike price, the pre-determined price. A put option refers to the right to<br />

sell a stock at the strike price. [3] <strong>Options</strong> are also divided into two categories in terms <strong>of</strong><br />

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expiration date. An American option is exercised when the owners are allowed to exercise<br />

them on or before the expiration date. A European option is exercised only on the<br />

expiration date. [3]<br />

Option is not the obligation but the right to buy or sell the underlying asset. So the<br />

owner would exercise the option only when it is favorable to do so. [3] For example, those<br />

who hold option would exercise a call option only if the price <strong>of</strong> the underlying asset (S), is<br />

higher than the strike price (K), and would earn a pr<strong>of</strong>it <strong>of</strong> S-K; otherwise, they would buy<br />

the asset not from the option, but from the market. A European put option is the right to<br />

“sell” the underlying asset at a certain price. So it can be exercised only when the market<br />

price <strong>of</strong> the asset (S) is less than the strike price (K), where those who hold the option can<br />

make the pr<strong>of</strong>it <strong>of</strong> K-S. Figure 3-1 shows the general pay<strong>of</strong>f <strong>of</strong> a European call option and<br />

put option. Although American options can be exercised at any time on or before the<br />

expiration date, they have the same pay<strong>of</strong>fs as European options.<br />

Pay<strong>of</strong>f ($)<br />

Underlying Assets<br />

S<br />

Pay<strong>of</strong>f ($)<br />

Underlying Assets<br />

S-K<br />

K-S<br />

Option<br />

0<br />

S=K<br />

Asset Price ($)<br />

Pay<strong>of</strong>f <strong>of</strong> European call = Max (S-K, 0)<br />

0<br />

S=K<br />

Asset Price ($)<br />

Pay<strong>of</strong>f <strong>of</strong> European put = Max (K-S, 0)<br />

Figure 3-1 Pay<strong>of</strong>f Diagram for a European Call Option (Left) and Put Option (Right)<br />

Source: R de Neufville Lecture notes <strong>of</strong> the <strong>MIT</strong> course <strong>of</strong> ESD.71 (Fall 2006)[4]<br />

3.1.1 Black-Scholes Option Pricing Model<br />

The Black-Scholes option pricing model is the most well-known method. It applies to<br />

those European call and put options that do not provide dividends. It can produce the<br />

theoretical value <strong>of</strong> the option by applying five factors such as the price <strong>of</strong> the underlying<br />

asset (S), the strike price (K), the time until expiration (T), the risk-free rate <strong>of</strong> interest (rf),<br />

and the volatility (i.e., standard deviation) <strong>of</strong> returns on the stock. [3] The following<br />

formula shows this model for a European call option. [3]<br />

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C = S ⋅ N<br />

−r T<br />

( d ) − K ⋅ e<br />

f<br />

⋅ N( )<br />

1<br />

d 2<br />

Where C: Theoretical value <strong>of</strong> a European call option with no dividends<br />

d<br />

d<br />

1<br />

2<br />

ln<br />

=<br />

2<br />

( S / K ) + ( r / 2)<br />

= d 1<br />

− σ<br />

T<br />

σ<br />

f<br />

+ σ ⋅T<br />

T<br />

N(x): Cumulative probability function for a standardized normal distribution<br />

The following formula shows the theoretical value <strong>of</strong> European put options.<br />

P = K ⋅ e<br />

−r f T<br />

⋅ N<br />

( − d ) − S ⋅ N( − )<br />

2<br />

d 1<br />

Where P: Theoretical value <strong>of</strong> a European put option with no dividends<br />

3.1.2 Binomial Lattice Model<br />

Binomial Lattice Model, which is also widely used, is a more simplified discrete-time<br />

approach to valuation <strong>of</strong> options compared to the Black-Scholes Option Pricing Model. [4]<br />

It assumes that the price <strong>of</strong> the underlying asset will change to one <strong>of</strong> the only two possible<br />

values during the next period <strong>of</strong> time. Figure 3-2 shows a three-stage binomial example.<br />

Figure 3-2 Binomial Tree Representations <strong>of</strong> Changes in Underlying Asset<br />

Source: R de Neufville Lecture notes <strong>of</strong> the <strong>MIT</strong> course <strong>of</strong> ESD.71[4]<br />

In the Binomial Lattice Model, the price <strong>of</strong> an underlying asset at a certain time can<br />

change to one <strong>of</strong> the only two possibilities, which are an upward movement with multiplier u<br />

with the probability <strong>of</strong> p, and a downward movement with multiplier d with the probability<br />

<strong>of</strong> 1-q. [4]<br />

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u = e<br />

d = e<br />

σ ΔT<br />

−σ<br />

ΔT<br />

p = 0.5 + 0.5<br />

= 1/ u<br />

( v / d ) ΔT<br />

3.1.3 Monte Calro Simulation<br />

Monte Calro Simulation is an analytical method that generates the stochastic<br />

distribution <strong>of</strong> possible outcome that correspond to probability-distributed sampled inputs.<br />

[5] Because <strong>of</strong> the development <strong>of</strong> computer technology, large computer simulation such<br />

as Monte Calro Simulation can be constructed easily. Spread sheet s<strong>of</strong>tware such as<br />

Micros<strong>of</strong>t Excel is the tool for conducting Monte Calro Simulation.<br />

3.2 <strong>Real</strong> <strong>Options</strong> <strong>Analysis</strong><br />

In the previous section, financial options theory including Black-Scholes option pricing<br />

model, Binomial Lattice Model, and Monte Calro Simulation were introduced. In this<br />

section, real options analysis, which was the application <strong>of</strong> financial options theory to the<br />

actual projects, is introduced.<br />

3.2.1 <strong>Real</strong> <strong>Options</strong><br />

<strong>Real</strong> option is the option to undertake some business decision under high uncertainty.<br />

In real options analysis, they are associated with flexibilities in designing systems or the<br />

evolution <strong>of</strong> projects. [4] Option - like flexibilities are included in systems and projects,<br />

represent opportunities to increase the value <strong>of</strong> the project through design or through<br />

management actions. [4] It is not obligation, and only when future asset price is preferable<br />

for you, you can exercise it, otherwise you don’t have to do it. This allows<br />

decision-makers to avoid downside losses as well as to obtain upside opportunities. These<br />

flexibilities are called “real” options. [4] Managers have applied real options analysis to<br />

business strategies including large-scale engineering systems. Although real options are<br />

very similar to financial options, they are different from financial options in terms <strong>of</strong> what<br />

should be assessed and the time span. [4] In financial options, the actual price such as<br />

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stock price is evaluated, while in real options, the value <strong>of</strong> the project itself is evaluated.<br />

Financial options are usually very short span such as two years, while the time span <strong>of</strong> real<br />

options is very long-term.<br />

3.2.2 Types <strong>of</strong> <strong>Real</strong> <strong>Options</strong><br />

There are several types <strong>of</strong> real options, such as deferral options, abandonment options,<br />

expansion options, growth options, and compound options. [6] Applying appropriate type<br />

<strong>of</strong> options enables managers to actively manage risks and uncertainties. For example, in<br />

this case study, expansion options is appropriate to be applied. In the current design, which<br />

is inflexible design, the whole facilities such as a runway, a parallel taxiway, and a south<br />

edge would be constructed at the initial construction. On the other hand, in flexible design<br />

that should be proposed in this study, only a runway would be constructed at the initial<br />

construction and then sometime after 10 years <strong>of</strong> operation, if the demand <strong>of</strong> passengers<br />

exceeds the capacity, a parallel taxiway and a south edge should be constructed; otherwise<br />

they would not be constructed. Thus, if uncertain demand is unfavorable, it is not<br />

necessary to exercise the option. It is possible to reduce upfront capital investment and<br />

therefore reduce losses. The Flexibility to expand them takes advantage <strong>of</strong> demand that is<br />

higher than expected in the deterministic projections.<br />

Pay<strong>of</strong>f<br />

Expansion<br />

No Expansion<br />

Value <strong>of</strong> Project<br />

Figure 3-3 Pay<strong>of</strong>f <strong>of</strong> Expansion Option<br />

3.3 Application <strong>of</strong> <strong>Real</strong> <strong>Options</strong> <strong>Analysis</strong> to Case Study<br />

3.3.1 Which Method should be used for the Case Study<br />

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As introduced in section 3.1, there are three types <strong>of</strong> evaluation method in financial<br />

options, and these are also applied to real options analysis. Black-Scholes option pricing<br />

model and Binomial Lattice Model are widely used in financial options, but because they are<br />

very complex and needs financial skills, they are not so widely used in real options in<br />

engineering practice as in financial field. [7] Even if those methods are used appropriately,<br />

it is very difficult to explain to those managers who are not familiar with financial skills. [7]<br />

On the other hand, Monte Calro Simulation can be used more easily by using spread sheet<br />

than those two methods. Compared to conventional methods using financial mathematics,<br />

real options analysis using Monte Calro Simulation has advantages such as user friendly<br />

procedure, being based on data availability in practice, and being easy to explain graphically.<br />

[7] Thus real options analysis based on Monte Calro Simulation can be the appropriate as a<br />

tool for the valuation method for this case study.<br />

3.3.2 <strong>Real</strong> <strong>Options</strong> <strong>Analysis</strong> <strong>Using</strong> Monte Calro Simulation<br />

In this case study, real options analysis using Monte Calro Simulation method is used<br />

and its procedure is: 1) to estimate cash flows pro forma including capital investment, future<br />

costs and revenues <strong>of</strong> the project, and calculate the economic value <strong>of</strong> currently designed<br />

case, 2) to explore the effects <strong>of</strong> uncertainty by simulating possible scenarios, each <strong>of</strong> which<br />

leads to a various NPVs, and the collection <strong>of</strong> each scenario generate both an “expected net<br />

present value” (ENPV) and the distribution <strong>of</strong> possible outcomes for a project by<br />

demonstrating cumulative distribution functions, and 3) to explore ways to avoid the<br />

downside risk and take advantage <strong>of</strong> upside potential by exercising options. [7]<br />

4. <strong>Analysis</strong> <strong>of</strong> Case - Tokyo Int’l <strong>Airport</strong> New Runway Extension Project<br />

4.1 <strong>Analysis</strong> Condition<br />

In order to conduct real options analysis, it is necessary to set up analysis conditions<br />

such as cash flow pro forma including capital investment, revenue scheme, operating and<br />

maintenance costs.<br />

In this case study, three types <strong>of</strong> design should be considered. Case A refers to the<br />

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current design that government actually planned and that would construct the whole runway<br />

island, including a parallel taxiway and a south edge. This design does not include<br />

flexibility.<br />

Case B refers to the design that constructs the whole runway island in the same way as<br />

Case A. But this design recognizes future uncertainty which is demand <strong>of</strong> passengers,<br />

while Case A considers deterministic projection <strong>of</strong> the demand <strong>of</strong> passengers.<br />

Case C refers to the design that constructs only runway area without parallel taxiway<br />

and south edge <strong>of</strong> the runway at the initial construction, and expands the parallel taxiway<br />

and south edge after 10 years operation if the demand exceeds the capacity <strong>of</strong> the airport for<br />

two consecutive years.<br />

Table 4-1 shows the summary <strong>of</strong> case <strong>of</strong> analysis.<br />

Table 4-1 Cases <strong>of</strong> <strong>Analysis</strong><br />

Case<br />

Initial Future<br />

Investment Uncertainty<br />

Flexibility<br />

Case A<br />

R/W,<br />

PT/W & SE<br />

N/A<br />

N/A<br />

Case B<br />

R/W,<br />

PT/W & SE<br />

Recognizing N/A<br />

Case C R/W Recognizing<br />

Future Expansion<br />

PT/W & SE<br />

Source: Applied R de Neufville,et al [7]<br />

4.1.1 Capital Investment<br />

Capital investment here means the initial construction fee and the expansion fee <strong>of</strong> the<br />

parallel taxiway and the south edge. In the “New Runway Extension Project”, the<br />

construction fee is ¥570 billion, [2] so the initial investment in Case A and Case B is ¥570<br />

billion. In Case C, I assume that the initial investment can be set by prorating the area<br />

where it needs. In the area <strong>of</strong> the taxiway (827,625m 2 ), the structure <strong>of</strong> the runway island<br />

is landfill (¥64,700/m 2 ), [2] so the construction fee is ¥53.55 billion. In the area <strong>of</strong> the<br />

south edge (45,120m 2 ), the structure <strong>of</strong> the runway island is piled pier (¥786,500/m 2 ), [2] so<br />

the construction fee is ¥35.48 billion. Therefore, considering the bank revetment area<br />

(assumed 5% extra) and the joint area for the both areas so that the expansion could be<br />

possible, the initial construction fee <strong>of</strong> Case C is ¥505 billion. ([570-(53.55+35.48)]*1.05)<br />

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1.231 Planning and <strong>Design</strong> <strong>of</strong> <strong>Airport</strong> <strong>System</strong> Dai Ohama<br />

The expansion cost <strong>of</strong> Case C is only the area <strong>of</strong> taxiway and the south edge, which is the<br />

same as the difference between the initial cost <strong>of</strong> Case A, B and that <strong>of</strong> Case C.<br />

Considering the extra fee <strong>of</strong> the joint area (assumed 10% extra), the expansion fee is ¥97.9<br />

billion. ((53.55+35.48)*1.05) The initial investment is assumed to be paid equally every<br />

year through the construction for 4 years, while the expansion investment is supposed to be<br />

paid equally every year for 2 years.<br />

Runway<br />

(R/W)<br />

Parallel Taxiway<br />

South Edge<br />

(Landfill)<br />

(Piled Pier)<br />

827,625m 2 45,120m 2<br />

Figure 4-1 Expansion Area <strong>of</strong> Runway D<br />

4.1.2 Revenues and Costs<br />

There are several types <strong>of</strong> revenues and costs in the airport system such as from<br />

aeronautical charges, non-aeronautical charges, and <strong>of</strong>f-airport or non-operate charges. [8]<br />

In the current operation <strong>of</strong> Tokyo Int’l <strong>Airport</strong>, these charges applies, but in terms <strong>of</strong> a<br />

runway, aeronautical charges, which is the charges for services or facilities directly related to<br />

the processing <strong>of</strong> aircraft and their passengers, is the main charge. [8] The aeronautical<br />

charges have several categories such as landing charge, terminal-area air navigation charge,<br />

passenger service charge in terminals, security charge, charges for airport noise, and so on.<br />

[8] In terms <strong>of</strong> a runway, landing charge is the main source <strong>of</strong> the revenue. Thus, I<br />

assume that the revenue is only from the landing charge. The current landing charge in<br />

Tokyo Int’l <strong>Airport</strong> as <strong>of</strong> 2006 is ¥490,000 for B747-400D, ¥350,000 for B777-200, and<br />

¥230,000 for B777-724, and so on. [9] I set the average landing fee <strong>of</strong> this airport as<br />

¥332,000 / aircraft by assuming the probabilities for each type <strong>of</strong> aircraft <strong>of</strong> 23% for<br />

B747-400D, 35% for B777-200D, and 41% for B767-300. I also assume that this landing<br />

charge is fixed for the next 20 years. For the simplicity, I also set the revenue for each year<br />

is landing charge multiplied by the minimum <strong>of</strong> the number <strong>of</strong> passenger or the capacity.<br />

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Costs for the operation and maintenance are categorized into the ones above. From the<br />

past disclosed information by the government, operating and maintenance cost in all airports<br />

in Japan in 2005 was ¥147.4 billion. [10] I assume that these costs depend on the scale <strong>of</strong><br />

air traffic. The whole air traffic in Japan in 2005 was 1,431,000 and 300,000 in Tokyo Int’l<br />

<strong>Airport</strong>, which is about 20% <strong>of</strong> the whole traffic, so the operating and maintenance costs for<br />

Tokyo Int’l <strong>Airport</strong> in each year is assumed ¥29.48 billion, which is also assumed equally for<br />

the current three runways. So the operating cost and maintenance costs for the one runway<br />

is one-third <strong>of</strong> ¥29.48, which is equal to ¥9.84 billion. In addition, in the current<br />

construction plan indicates that the specific maintenance cost for the runway D is ¥100<br />

billion for the next 30 years. Thus the maintenance cost <strong>of</strong> ¥3.33 billion should also be<br />

included in the operating and maintenance cost for the new runway. Therefore, the<br />

operating and maintenance costs for the runway D are set as ¥13.17 billion.<br />

4.1.3 Capacity <strong>of</strong> <strong>Airport</strong><br />

According to the Ministry <strong>of</strong> Land, Infrastructure and Transport in Japan, the improved<br />

maximum capacity <strong>of</strong> the airport by the runway extension in Tokyo Int’l <strong>Airport</strong> is 40<br />

aircrafts per hour, and it estimated this capacity can accommodate 87 million passengers per<br />

year. [2] So the capacity in terms <strong>of</strong> the number <strong>of</strong> passenger in this airport is 87 million.<br />

4.1.4 Discount Rate<br />

Discount rate is the opportunity cost <strong>of</strong> capital, and it can be calculated by the capital<br />

asset pricing model (CAPM). CAPM requires the risk-free rate <strong>of</strong> interest, the sensitivity<br />

<strong>of</strong> specific project or company to stock price, and the expected rate <strong>of</strong> return <strong>of</strong> the market.<br />

However, the government used the discount rate <strong>of</strong> 4% when it assessed not only this project<br />

but also any airport related project. [11] Thus, the discount rate <strong>of</strong> 4% is also used in this<br />

case study.<br />

4.2 Uncertainty in <strong>System</strong><br />

There are usually a lot <strong>of</strong> uncertainties in large-scale engineering systems which include<br />

technical change, economic change, regulatory change, industrial change, political change,<br />

and so on. [4] This case also includes a lot <strong>of</strong> uncertainties such as demand <strong>of</strong> the number<br />

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1.231 Planning and <strong>Design</strong> <strong>of</strong> <strong>Airport</strong> <strong>System</strong> Dai Ohama<br />

<strong>of</strong> passengers, capital investment in the future, the operating and maintenance costs, and<br />

unexpected events in the future. For the sake <strong>of</strong> the simplicity, I set only demand <strong>of</strong> the<br />

number <strong>of</strong> passengers as uncertainty.<br />

When forecasting the number <strong>of</strong> passenger in airports,<br />

the time span should be considered at most 20 years span and the volatility, which is the<br />

range <strong>of</strong> the chance the demand is higher or lower, should be considered by plus or minus<br />

50%. [12]<br />

4.3 Demand Forecasting<br />

According to the government estimation, it forecasted the demand <strong>of</strong> the number <strong>of</strong><br />

passengers as 73.2 million in 2012, 80.3 million in 2017, and 85.5 million in 2022. [2]<br />

90.00<br />

Past and Predicted Number <strong>of</strong> Passengers at Tokyo Int'l <strong>Airport</strong><br />

80.00<br />

Number <strong>of</strong> Passengers<br />

70.00<br />

60.00<br />

50.00<br />

40.00<br />

30.00<br />

20.00<br />

10.00<br />

0.00<br />

1983<br />

1985<br />

1987<br />

1989<br />

1991<br />

1993<br />

1995<br />

1997<br />

1999<br />

2001<br />

2003<br />

2012<br />

2022<br />

Year<br />

Figure 4-2 Demand Forecasting<br />

Source: Ministry <strong>of</strong> Land, Infrastructure and Transport in Japan [2]<br />

This estimation was based on the estimate <strong>of</strong> the total population in Japan, the estimate<br />

<strong>of</strong> GDP and other several factors. [13] However, forecast is always wrong. The table<br />

below is comparison <strong>of</strong> 10 years forecasts <strong>of</strong> international passenger to Japan with actual<br />

results. [12] These results clearly show that forecast has been always wrong. In addition<br />

the forecast <strong>of</strong> the total population in Japan and GDP are also very difficult to assess. Thus,<br />

the demand forecast above could be wrong.<br />

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1.231 Planning and <strong>Design</strong> <strong>of</strong> <strong>Airport</strong> <strong>System</strong> Dai Ohama<br />

Table 4-2 Comparison <strong>of</strong> 10-year forecasts <strong>of</strong> international<br />

passengers to Japan with actual results<br />

Forecast Passengers (millions) Percent error<br />

For Done in Actual Forecast Over actual<br />

1980 1970 12.1 20.0 65<br />

1985 1975 17.6 27.0 53<br />

1990 1980 31.0 39.5 27<br />

1995 1985 43.6 37.9 (13)<br />

Source: R. de Neufville, A. Odoni, <strong>Airport</strong> <strong>System</strong>s: Planning, <strong>Design</strong>, and Management[12]<br />

However, when assessing the real options analysis, it is necessary to rely on some<br />

forecasts, although they are nothing but estimation. Thus, in this case study, the demand<br />

curve that is based on the estimation <strong>of</strong> the total population and the estimation <strong>of</strong> GDP in<br />

Japan should be considered. The important thing is to recognize that this forecast is just an<br />

assumption and to evaluate this by using various demand scenarios with volatility.<br />

In this case study, the demand forecast is estimated based on the estimation <strong>of</strong><br />

population [14] and the estimation <strong>of</strong> GDP [15] in Japan by conducting regression analysis.<br />

[16] The table and the graph below show the estimation <strong>of</strong> the number <strong>of</strong> passenger for the<br />

next 20 years in Tokyo Int’l <strong>Airport</strong>.<br />

Year<br />

Population<br />

(Thousands)<br />

GDP<br />

(in trillion)<br />

Passengers<br />

(million)<br />

1985 121.05 ¥350.3 24.27<br />

1986 121.66 ¥360.8 25.88<br />

1987 122.24 ¥374.5 28.19<br />

1988 122.75 ¥400.0 30.28<br />

1989 123.21 ¥421.2 34.96<br />

1990 123.61 ¥443.1 38.09<br />

1991 124.10 ¥458.2 40.06<br />

1992 124.57 ¥462.7 39.98<br />

1993 124.94 ¥463.7 39.12<br />

1994 125.27 ¥468.8 41.44<br />

1995 125.57 ¥477.7 43.02<br />

1996 125.86 ¥489.9 45.08<br />

1997 126.16 ¥496.8 47.43<br />

1998 126.47 ¥488.0 49.91<br />

1999 126.67 ¥487.0 52.27<br />

2000 126.93 ¥501.3 54.77<br />

2001 127.32 ¥503.2 57.01<br />

2002 127.49 ¥503.9 59.49<br />

2003 127.69 ¥512.8 59.41<br />

2004 127.79 ¥524.6 59.05<br />

2005 127.77 ¥538.9 59.47<br />

2006 127.76 ¥544.8 61.08<br />

2007 127.69 ¥550.8 62.23<br />

2008 127.57 ¥556.9 63.37<br />

2009 127.40 ¥563.0 64.49<br />

2010 127.18 ¥569.2 65.59<br />

2011 126.91 ¥578.9 67.35<br />

2012 126.60 ¥588.7 69.11<br />

2013 126.25 ¥598.7 70.87<br />

2014 125.86 ¥608.9 72.63<br />

2015 125.43 ¥619.3 74.39<br />

2016 124.96 ¥629.8 76.14<br />

2017 124.46 ¥640.5 77.89<br />

2018 123.92 ¥651.4 79.64<br />

2019 123.34 ¥662.4 81.38<br />

2020 122.73 ¥673.7 83.12<br />

2021 122.10 ¥683.1 84.46<br />

2022 121.43 ¥692.7 85.80<br />

2023 120.74 ¥702.4 87.11<br />

2024 120.01 ¥712.2 88.42<br />

2025 119.27 ¥722.2 89.70<br />

2026 118.50 ¥732.3 90.98<br />

2027 117.71 ¥742.6 92.24<br />

2028 116.90 ¥753.0 93.48<br />

2029 116.07 ¥763.5 94.71<br />

2030 115.22 ¥774.2 95.92<br />

Demand (PAX in million)<br />

120<br />

100<br />

80<br />

60<br />

40<br />

20<br />

0<br />

Demand Forecasting<br />

Projected Demand (Government)<br />

Projected Demand (This Project)<br />

1982 1987 1992 1997 2002 2007 2012 2017 2022 2027<br />

Time (Year)<br />

PAX = 2 * A * { - 0.1818 + 0.0077 * B }<br />

(A: Estimated Population, B: Estimated GDP)<br />

Figure 4-3 Demand Forecast <strong>of</strong> Number <strong>of</strong> Passengers in Tokyo Int’l <strong>Airport</strong><br />

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The demand curve shown in Figure 4-3 is still deterministic projection, and this is<br />

usually used for the static analysis. However, it is essential to recognize and consider the<br />

uncertainty in this demand forecasting. Thus, uncertainty is recognized in the model by<br />

simulating possible scenarios. It indicates how fluctuations can be incorporated around<br />

deterministic projections based on the relevant probability distribution. [17] In this case<br />

study, 2,000 Monte Calro Simulations are generated where all <strong>of</strong> those simulations create<br />

each demand scenarios over the 20 years span. Figure 4-4 shows some <strong>of</strong> the examples <strong>of</strong><br />

simulations <strong>of</strong> the uncertain demand. All <strong>of</strong> these scenarios can be considered and<br />

incorporated into the calculation <strong>of</strong> the expected value <strong>of</strong> the plans statistically.<br />

120<br />

Demand Forecasting<br />

120<br />

Demand Forecasting<br />

Demand (PAX in million)<br />

100<br />

80<br />

60<br />

40<br />

20<br />

0<br />

Projected Demand (Government)<br />

Projected Demand (This Project)<br />

Demand scenario<br />

1982 1987 1992 1997 2002 2007 2012 2017 2022 2027<br />

Time (Year)<br />

Demand (PAX in million)<br />

100<br />

80<br />

60<br />

40<br />

20<br />

0<br />

Projected Demand (Government)<br />

Projected Demand (This Project)<br />

Demand scenario<br />

1982 1987 1992 1997 2002 2007 2012 2017 2022 2027<br />

Time (Year)<br />

120<br />

Demand Forecasting<br />

120<br />

Demand Forecasting<br />

Demand (PAX in million)<br />

100<br />

80<br />

60<br />

40<br />

20<br />

Projected Demand (Government)<br />

Projected Demand (This Project)<br />

Demand scenario<br />

Demand (PAX in million)<br />

100<br />

80<br />

60<br />

40<br />

20<br />

Projected Demand (Government)<br />

Projected Demand (This Project)<br />

Demand scenario<br />

0<br />

1982 1987 1992 1997 2002 2007 2012 2017 2022 2027<br />

Time (Year)<br />

0<br />

1982 1987 1992 1997 2002 2007 2012 2017 2022 2027<br />

Time (Year)<br />

Figure 4-4 Examples <strong>of</strong> Simulation <strong>of</strong> the Uncertain Demand<br />

4.4 Summary <strong>of</strong> <strong>Analysis</strong> Condition<br />

The Table 4-3 shows the summary <strong>of</strong> analysis condition.<br />

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1.231 Planning and <strong>Design</strong> <strong>of</strong> <strong>Airport</strong> <strong>System</strong> Dai Ohama<br />

Table 4-3 Summary <strong>of</strong> <strong>Analysis</strong> Condition<br />

Initial Future<br />

Investment Expansion<br />

Perspective Simulation Option<br />

<strong>Design</strong> A<br />

R/W,<br />

PT/W & SE<br />

N/A Deterministic No No<br />

<strong>Design</strong> B<br />

R/W,<br />

Recognizing<br />

N/A<br />

PT/W & SE<br />

Uncertainty<br />

Yes No<br />

<strong>Design</strong> C R/W PT/W & SE Incorporating<br />

Flexibility<br />

Yes Yes<br />

Source: Applied R de Neufville, S. Scholtes, T. Wang [7]<br />

4.5 Result <strong>of</strong> <strong>Analysis</strong><br />

Table 4-4, 4-5, 4-6 shows the cash flow pro forma for three cases. Table 4-4 shows the<br />

cash flow pro forma and calculating the NPV <strong>of</strong> the project assuming the demand <strong>of</strong> the<br />

number <strong>of</strong> passengers grows as projected. (Case A) In this case, the expected NPV<br />

(ENPV) <strong>of</strong> the project is ¥678.8 billion. However, this value is not realistic since the actual<br />

demand <strong>of</strong> the number <strong>of</strong> passengers can change from this deterministic value.<br />

Table 4-5 shows the cash flow pro forma and calculating the NPV <strong>of</strong> the project<br />

recognizing uncertainty. (Case B) In this case, Monte Calro Simulation is conducted and it<br />

produces 2,000 possible demand scenarios. The ENPV <strong>of</strong> this case is ¥638.3 billion. But<br />

this ENPV is just one <strong>of</strong> the 2,000 scenarios. This simulation can generate 2,000 ENPV in<br />

each scenario, and can also generate distribution for each scenario. The actual ENPV is<br />

distributed as shown in Figure 4-5. The average <strong>of</strong> ENPV is ¥569.3 billion, which is less<br />

than that <strong>of</strong> the Case A. Although this design assumes that there are equal chances that<br />

demand changes to higher and to lower, this design limits the higher value <strong>of</strong> the project<br />

since the capacity is fixed, while there are still lower chances to generate losses. [7]<br />

Table 4-6 shows the cash flow pro forma and calculating the NPV <strong>of</strong> the project<br />

recognizing uncertainty and holding the option to expand. (Case C) This case also<br />

recognizes uncertainty and Monte Calro Simulation is conducted, which produces the ENPV<br />

<strong>of</strong> ¥686.7 billion. But this ENPV is also just one <strong>of</strong> the 2,000 scenarios. In this design,<br />

the actual ENPV is distributed as shown in Figure 4-5, and the average <strong>of</strong> ENPV is ¥621.6<br />

billion, which is higher than that <strong>of</strong> Case B. Because the option to expand is exercised<br />

when the demand is higher than the current capacity, the ENPV is improved higher. The<br />

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1.231 Planning and <strong>Design</strong> <strong>of</strong> <strong>Airport</strong> <strong>System</strong> Dai Ohama<br />

estimated value <strong>of</strong> the option exercised in order to incorporate the flexibility into design can<br />

be calculated by the difference between the ENPV <strong>of</strong> Case C and that <strong>of</strong> Case B, which is<br />

¥52.3 billion. Table 4-7 shows the summary <strong>of</strong> this analysis. Case C, which holds<br />

flexibility in design has advantages over Case B in every aspect. Table 4-6 shows the<br />

summary <strong>of</strong> the analysis. [7] The initial investment in Case C is less than that <strong>of</strong> Case B,<br />

which means that the flexibility can reduce the initial investment. The ENPV, the<br />

maximum and minimum NPV in Case C is higher than that <strong>of</strong> Case B, so the flexibility can<br />

enhance the overall value <strong>of</strong> the project.<br />

Probability<br />

100.0%<br />

90.0%<br />

80.0%<br />

70.0%<br />

60.0%<br />

50.0%<br />

40.0%<br />

30.0%<br />

20.0%<br />

10.0%<br />

0.0%<br />

Cumulative distribution function<br />

Case B<br />

Average ENPV(B)<br />

¥569.3 B<br />

200,000 300,000 400,000 500,000 600,000 700,000 800,000 900,000<br />

Target value \ million<br />

Case C<br />

Case A<br />

(¥678.7 B)<br />

Option Value<br />

¥52.3 B<br />

Average ENPV(C)<br />

¥621.6 B<br />

Figure 4-5 Cumulative Distribution Function (Value at Risk)<br />

Table 4-7 Summary <strong>of</strong> the <strong>Analysis</strong><br />

Case B<br />

(No Flexibility)<br />

Case C<br />

(with Flexibility)<br />

Initial Investment (¥ in billion) 570.0 505.0<br />

ENPV (¥ in billion) 569.3 621.6<br />

Minimum NPV (¥ in billion) 339.3 338.1<br />

Maximum NPV (¥ in billion) 773.4 899.1<br />

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Table 4-4 <strong>Analysis</strong> Result (Case A)<br />

NPV Static Model (<strong>Design</strong> A)<br />

Construction Phase Operation Phase<br />

Year<br />

2010 2007 2008 2009 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022 2023 2024 2025 2026 2027<br />

Demand (Deterministic Projection) 62,233,839 63,366,512 64,486,139 65,592,131 67,353,149 69,114,281 70,874,568 72,633,340 74,389,968 76,143,790 77,894,119 79,640,235 81,381,152 83,116,111 84,463,179 85,795,633 87,113,387 88,416,454 89,704,914 90,978,530 92,237,068<br />

Capacity (Constant) 87,000,000 87,000,000 87,000,000 87,000,000 87,000,000 87,000,000 87,000,000 87,000,000 87,000,000 87,000,000 87,000,000 87,000,000 87,000,000 87,000,000 87,000,000 87,000,000 87,000,000 87,000,000 87,000,000 87,000,000 87,000,000<br />

Revenue (Yen in M) 107,696 110,512 113,326 116,139 118,948 121,752 124,551 127,343 130,126 132,900 135,054 137,185 139,111 139,111 139,111 139,111 139,111<br />

Operating & Maintenance Cost (Yen in M) 13,170 13,170 13,170 13,170 13,170 13,170 13,170 13,170 13,170 13,170 13,170 13,170 13,170 13,170 13,170 13,170 13,170<br />

Initial Capital Investment (Yen in M) 142,500 142,500 142,500 142,500 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0<br />

Cash Flow (Yen in M) -142,500 -142,500 -142,500 -142,500 94,526 97,342 100,156 102,969 105,778 108,582 111,381 114,173 116,956 119,730 121,884 124,015 125,941 125,941 125,941 125,941 125,941<br />

Present Value Cash Flow (Yen in M) -142,500 -137,019 -131,749 -126,682 80,801 80,008 79,155 78,248 77,291 76,288 75,245 74,164 73,051 71,907 70,385 68,861 67,241 64,655 62,168 59,777 57,478<br />

Net Present Value (Yen in M) 678,770<br />

Table 4-5 <strong>Analysis</strong> Result (Case B)<br />

NPV Model with Flexibility (<strong>Design</strong> B)<br />

Construction Phase Operation Phase<br />

Year<br />

2010 2007 2008 2009 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022 2023 2024 2025 2026 2027<br />

Demand (Deterministic ,131 Projection) 62,233,839 63,366,512 64,486,139 65,592 67,353,149 69,114,281 70,874,568 72,633,340 74,389,968 76,143,790 77,894,119 79,640,235 81,381,152 83,116,111 84,463,179 85,795,633 87,113,387 88,416,454 89,704,914 90,978,530 92,237,068<br />

Random Demand 62 ,233,839 57,868,139 65,908,629 38,319,447 62,649,153 96,258,574 105,906,007 107,792,690 40,892,745 70,852,191 107,971,158 67,571,602 102,961,943 115,202,209 103,423,185 54,083,607 108,556,015 89,944,244 51,261,467 122,025,498 82,989,813<br />

Capacity (Constant) 87,000,000 87,000,000 87,000,000 87,000,000 87,000,000 87,000,000 87,000,000 87,000,000 87,000,000 87,000,000 87,000,000 87,000,000 87,000,000 87,000,000 87,000,000 87,000,000 87,000,000 87,000,000 87,000,000 87,000,000 87,000,000<br />

Revenue (YenM) 100,174 139,111 139,111 139,111 65,386 113,291 139,111 108,045 139,111 139,111 139,111 86,478 139,111 139,111 81,966 139,111 132,698<br />

Operating & Maintenance Cost (Yen in M) 13,170 13,170 13,170 13,170 13,170 13,170 13,170 13,170 13,170 13,170 13,170 13,170 13,170 13,170 13,170 13,170 13,170<br />

Initial Capital Investment (Yen in M) 142,500 142,500 142,500 142,500 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0<br />

Cash Flow (Yen in M) -142,500 -142,500 -142,500 -142,500 87,004 125,941 125,941 125,941 52,216 100,121 125,941 94,875 125,941 125,941 125,941 73,308 125,941 125,941 68,796 125,941 119,528<br />

Present Value Cash Flow (Yen in M) -142,500 -137,019 -131,749 -126,682 74,372 103,514 99,533 95,705 38,154 70,343 85,081 61,629 78,662 75,637 72,728 40,705 67,241 64,655 33,959 59,777 54,551<br />

Net Present Value (Yen in M) 638,294<br />

Table 4-6 <strong>Analysis</strong> Result (Case C)<br />

NPV Model with Expansion Option (<strong>Design</strong> C)<br />

Ope ration Phase (No Expansion)<br />

Construction Phase Operation Phase with Expansion Optiton<br />

Year<br />

2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022 2023 2024 2025 2026 2027 2028 2029 2030<br />

Demand (Deterministic Projection) 62,233,839 63,366,512 64,486,139 65,592 ,131 67,353,149 69,114,281 70,874,568 72,633,340 74,389,968 76,143,790 77,894,119 79,640,235 81,381,152 83,116,111 84,463,179 85,795,633 87,113,387 88,416,454 89,704,914 90,978,530 92,237,068 93,480,029 94,706,438 95,915,430<br />

Random Demand 62,233,839 39,894,335 54,103,487 51,525,702 76,708,558 90,411,186 78,516,141 85,421,842 58,639,900 53,141,813 101,463,792 81,211,366 116,851,429 99,558,999 80,045,196 46,217,658 76,364,557 94,004,581 71,889,072 85,106,617 118,002,960 108,274,523 116,423,514 134,211,221<br />

Ca pacity (<strong>Flexible</strong>) 87,000,000 87,000,000 87,000,000 87,000,000 87,000,000 87,000,000 87,000,000 87,000,000 87,000,000 87,000,000 87,000,000 87,000,000 87,000,000 87,000,000 121,800,000 121,800,000 121,800,000 121,800,000 121,800,000 121,800,000 121,800,000 121,800,000 121,800,000 121,800,000<br />

Expansion Expand<br />

Build Extra Capacity 0 0 0 0 0 0 0 0 0 0 0 0 0 34,800,000 0 0 0 0 0 0 0 0 0 0<br />

Revenue (YenM) 122,655 139,111 125,545 136,587 93,764 84,972 139,111 129,855 139,111 139,111 127,990 73,901 122,105 150,311 114,949 136,083 188,683 173,128 186,158 194,755<br />

Operating & Maintenance Cost (Yen in M) 12,580 12,580 12,580 12,580 12,580 12,580 12,580 12,580 12,580 12,580 13,170 13,170 13,170 13,170 13,170 13,170 13,170 13,170 13,170 13,170<br />

Initial Capital Investment (Yen in M) 126,255 126,255 126,255 126,255 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0<br />

Expansion Expenditure (Yen in M) 0 0 0 0 0 0 0 0 0 0 0 48,967 48,967 0 0 0 0 0 0 0 0 0 0 0<br />

Cash Flow (Yen in M) -126,255 -126,255 -126,255 -126,255 110,075 126,531 112,965 124,007 81,184 72,392 126,531 68,308 77,564 126,531 114,820 60,731 108,935 137,141 101,779 122,913 175,513 159,958 172,988 181,585<br />

Present Value Cash Flow (Yen in M) -126,255 -121,399 -116,729 -112,240 94,092 103,999 89,278 94,235 59,320 50,862 85,480 44,372 48,446 75,991 66,306 33,722 58,161 70,404 50,241 58,340 80,102 70,195 72,993 73,674<br />

Present Value Expansion Cost (Yen in M) 0 0 0 0 0 0 0 0 0 0 0 31,808 30,584 0 0 0 0 0 0 0 0 0 0 0<br />

Net Present Value (Yen in M) 686,728<br />

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1.231 Planning and <strong>Design</strong> <strong>of</strong> <strong>Airport</strong> <strong>System</strong> Dai Ohama<br />

5. Conclusion<br />

There are a lot <strong>of</strong> facilities that are not designed optimally in airport systems. The<br />

master plan <strong>of</strong> those designs does not anticipate and consider future risks and uncertainties.<br />

Thus, inflexible design cannot manage risks and uncertainties, and it leads to losses. In<br />

order to solve this problem, it is essential to incorporate flexibility into design. As<br />

demonstrated in the case study in this paper, the real option analysis is the useful way to<br />

recognize uncertainty and incorporate flexibility into design. The case study demonstrated<br />

that flexibility can enhance the expected value <strong>of</strong> the project, and reduce the possible losses<br />

by using the option to expand. Also it can demonstrate that flexibility can keep the initial<br />

cost lower than that <strong>of</strong> the current design. Therefore, at the initial construction phase, only<br />

runway should be constructed and if the demand <strong>of</strong> the number <strong>of</strong> passenger exceeds the<br />

capacity after 10 years operation, a parallel taxiway and a south edge should be constructed.<br />

Furthermore, real options analysis using Monte Calro Simulation is very useful in that it just<br />

focuses on the ENPV <strong>of</strong> options and it does not require any financial skills unlike typical<br />

financial options approaches such as Black-Sholes option pricing model and binomial lattice<br />

model. Thus, those who are in charge <strong>of</strong> design can relatively easily use this method.<br />

<strong>Flexible</strong> design thus enables projects to be optimized and reduce excessiveness and losses in<br />

airport systems.<br />

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1.231 Planning and <strong>Design</strong> <strong>of</strong> <strong>Airport</strong> <strong>System</strong> Dai Ohama<br />

6. Reference<br />

[1] R. de Neufville, Lecture note <strong>of</strong> the <strong>MIT</strong> course <strong>of</strong> 1.231: Planning and <strong>Design</strong> <strong>of</strong> <strong>Airport</strong><br />

<strong>System</strong> (Fall 2007).<br />

[2] Ministry <strong>of</strong> Land, Infrastructure and Transport in Japan, Outline <strong>of</strong> Tokyo Int’l <strong>Airport</strong><br />

New Runway Extension Project, 2007.<br />

http://www.mlit.go.jp/koku/04_outline/01_kuko/02_haneda/index.html<br />

[3] R. A. Brealey, S.C. Myers, and F. Allen, Principles <strong>of</strong> Corporate Finance, 8th ed., (New<br />

York: McGraw-Hill Publishing Company, 2005).<br />

[4] R. de Neufville, Lecture notes <strong>of</strong> the <strong>MIT</strong> course <strong>of</strong> ESD.71: Engineering <strong>System</strong>s<br />

<strong>Analysis</strong> for <strong>Design</strong> (Fall 2006).<br />

[5] K. Hodota, “R&D and Deployment Valuation <strong>of</strong> Intelligent Transportation <strong>System</strong>s: A<br />

Case Example <strong>of</strong> the Intersection Collision Avoidance <strong>System</strong>s,” M.S. Thesis in Master<br />

<strong>of</strong> Science in Transportation, <strong>MIT</strong>, Cambridge, MA, 2006.<br />

[6] T. Copeland, T. Koller, J. Murrin, Valuation: Measuring and Managing the Value <strong>of</strong><br />

Companies, Fourth Edition (McKinsey & Company Inc., 2002).<br />

[7] R. de Neufville, S. Scholtes, T. Wang, Valuing <strong>Real</strong> <strong>Options</strong> by Spread Sheet : Parking<br />

Garage Case Example, January 2005.<br />

[8] A. Odoni, Lecture notes <strong>of</strong> the <strong>MIT</strong> course <strong>of</strong> 1.231: Planning and <strong>Design</strong> <strong>of</strong> <strong>Airport</strong><br />

<strong>System</strong> (Fall 2007).<br />

[9] Ministry <strong>of</strong> Land, Infrastructure and Transport in Japan, Civil Aviation Breau.<br />

http://www.mlit.go.jp/singikai/koutusin/koku/seibi/14/images/shiryou1_22.pdf<br />

[10] Ministry <strong>of</strong> Land, Infrastructure and Transport in Japan, Survey <strong>of</strong> the Air Traffic<br />

Condition in Japan, 2003, http://www.mlit.go.jp/kisha/kisha03/12/120523_3/05.pdf<br />

[11] Katsuya Hihara, Research for New Operation <strong>System</strong> <strong>of</strong> Transportation Policy<br />

Considering Uncertainty, 2004, Policy Research Institute for Land Infrastructure and<br />

Transport, https://www.mlit.go.jp/pri/houkoku/gaiyou/pdf/kkk9.pdf<br />

[12] R. de Neufville, A. Odoni, <strong>Airport</strong> <strong>System</strong>s: Planning, <strong>Design</strong>, and Management, 2nd<br />

ed., (New York: McGraw-Hill Publishing Company, 2003)<br />

[13] Ministry <strong>of</strong> Land, Infrastructure and Transport in Japan, Forecast <strong>of</strong> air traffic,<br />

http://www.mlit.go.jp/singikai/koutusin/koku/07_9/01.pdf<br />

[14] National Institute <strong>of</strong> Population and Social Security Research, Forecast <strong>of</strong> Population<br />

http://www.ipss.go.jp/syoushika/tohkei/suikei07/suikei.html#chapt1-1<br />

[15] Ministry <strong>of</strong> Land, Infrastructure and Transport in Japan, Transition and Forecast <strong>of</strong> GDP,<br />

http://www.mlit.go.jp/road/kanren/suikei/7-1.pdf<br />

[16] R. de Neufville, Forecasting Assignment <strong>of</strong> the <strong>MIT</strong> course <strong>of</strong> 1.231: Planning and<br />

<strong>Design</strong> <strong>of</strong> <strong>Airport</strong> <strong>System</strong> (Fall 2007)<br />

[17] Michel-Alexandre Cardin, “Facing <strong>Real</strong>ity: <strong>Design</strong>ing and Management <strong>of</strong> Flexibility<br />

Engineering <strong>System</strong>s,” M.S. Thesis in Master <strong>of</strong> Science in Technology and Policy,<br />

Massachusetts Institute <strong>of</strong> Technology, Cambridge, MA, 2007.<br />

Term Project: <strong>Flexible</strong> <strong>Design</strong> <strong>of</strong> <strong>Airport</strong> <strong>System</strong> Page 22 <strong>of</strong> 22

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