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Transportation Research Record: Journal of the Transportation Research Board, No. 2297, Transportation Research Board of the National Academies, Washington, D.C., 2012, pp. 88–96.DOI: 10.3141/2297-11

El Paso Office, Texas A&M Transportation Institute, Texas A&M University System, Suite 151, 4050 Rio Bravo Drive, El Paso, TX 79912. Corresponding author: R. Aldrete, r-aldrete@tamu.edu.

increasing use is the interdependent concepts of value for money (VfM) and the optimum allocation of project risks to the partner that is best able to manage them cost-effectively. The United Kingdom’s Treasury defines VfM as “the optimum combination of whole-of-life costs and quality (or fitness for purpose) of the good or service to meet the user’s requirement” (1). Because risk is often translated into costs, the allocation of project life-cycle risks (e.g., construction, operation, or maintenance risk, or all three) directly affects the ability of a PPP to deliver VfM. To truly assess the impact of private-sector involvement, transportation agencies need a comprehensive meth-odology to quantify not just short-term impacts of the project on the public budget but also the long-term potential cost of the risks the government chooses to retain and then need to incorporate all these factors into the VfM analysis.

ReseaRch Need

Countries with relatively longer experience in PPPs have devised dif-ferent ways to quantify public-sector risk exposure, and some have developed well-documented methodologies to assess VfM—such as the public-sector comparator (PSC) (2). In a PSC analysis, the government estimates the risk-adjusted costs and benefits of a proj-ect, comparing two alternative hypothetical scenarios: private-sector delivery and public-sector delivery.

U.S. transportation agencies currently lack a well-documented approach to consistently evaluate and account for the cost of public- sector risk exposure in PPPs and traditional project delivery mecha-nisms. Consequently, agencies lack systematic approaches to compare the risk-adjusted costs of delivering infrastructure projects as PPPs against the costs of using traditional delivery mechanisms. As the PPP trend continues to increase in the United States and the public demands more transparency in PPP processes, VfM analyses that incorporate the risk-adjusted costs of a project become increasingly necessary. In particular, transportation agencies need to develop systematic and methodological approaches to quantify the cost of public-sector risk exposure in both PPP and traditionally delivered projects.

ReseaRch Objective aNd appROach

This research developed a methodological framework to value the cost of public-sector revenue risk exposure that U.S. transportation agencies can readily apply to estimate the risk-adjusted costs of delivering a project by using a PPP or a traditional project deliv-ery mechanism. The framework proposed is based on international best practices in VfM analyses and tried and tested risk valuation

Valuing Public-Sector Revenue Risk Exposure in Transportation Public–Private Partnerships

Rafael Aldrete, Arturo Bujanda, and Gabriel A. Valdez

A methodological framework can be used to evaluate public revenue financial risk exposure when transportation infrastructure is deliv-ered through public–private partnerships (PPPs) in the United States. Transportation agencies worldwide and across the United States are increasingly using PPPs as a mechanism to deliver infrastructure. An analysis of international experience conducted for this research shows that countries with more extensive experience in PPPs than the United States have devised sophisticated methodologies to value and manage risk exposure in the context of value for money and optimum allocation of project risks. However, a review of major U.S. transportation PPP transactions reveals that U.S. states currently lack a well-documented methodology to quantify and incorporate the cost of public-sector risk into the evaluation of PPP projects. This evidence suggests that U.S. transportation agencies might benefit significantly from implement-ing more systematic approaches to incorporate the cost of risk in the evaluation of PPP projects. The framework proposed in this research provides a step-by-step methodology to quantify revenue risk exposure and is aimed at facilitating the estimation of the risk-adjusted costs of delivering a project as a PPP. The methodology is based on the concept of contingent liabilities and uses option pricing techniques. The applica-tion of the methodology is demonstrated by two U.S. transportation PPP case studies.

Transportation agencies worldwide and across the United States are increasingly using public–private partnerships (PPPs) as a mecha-nism to finance and deliver critically needed transportation infra-structure. Privately developed and operated projects promise to lessen the pressure on the public finances as well as generate substantial revenue in terms of the upfront payments and revenue-sharing agree-ments. Even though the implications of these new interfaces between the public and private sectors are important and far reaching, much of the research effort has been limited to investigation of project valu-ation models, project financing methods, and toll pricing techniques. Thus, the fundamental question in developing PPP projects has been neglected: What value does the public sector obtain by partnering with the private sector?

Governments embrace PPPs as project delivery mechanisms for many reasons. However, one of the key premises behind their

Aldrete, Bujanda, and Valdez 89

approaches that are already in use worldwide. More specifically, the framework provides a simple step-by-step methodology to quantify the value of public-sector revenue risk exposure by using a con-tingent liabilities approach. The valuation method for contingent liabilities is based on option pricing techniques and Monte Carlo simulation. The application of the framework is demonstrated with two U.S. transportation PPP case studies.

valuiNg pROject Risk iN ppps

PPPs achieve VfM by transferring some project risks traditionally borne by the public sector to a private partner. Optimal risk alloca-tion consists in the apportionment of risk between public and private parties (and third parties such as users) that minimizes the total cost of risk in a PPP and maximizes VfM. A private party will ultimately charge the cost of bearing this risk to the buyer of the service (i.e., the government or users). This mechanism is different from maxi-mum risk transfer to the private sector—a common misperception about PPPs that should be avoided. There would be no VfM in pay-ing the private party for bearing a risk that another party (e.g., the government or an insurance company) could bear at a lower cost (3). For example, if a transportation agency requires a private partner to retain a significant right-of-way acquisition risk in a PPP, the private partner will price the risk into its bid, which will require higher lev-els of revenue or a longer concession period. The agency will gener-ally be more familiar with the right-of-way acquisition process and in most cases will be able to handle this risk more cost-effectively.

Even though different governments apply different methods to perform the VfM assessment, one of the most frequently used to determine if a PPP will offer better VfM is the PSC, widely used in Australia, Canada, and Great Britain (1, 4, 5). The PSC method evaluates if a private investment proposal offers VfM in compari-son with the most efficient form of public procurement. Using the PSC as a decision-making tool, the government estimates the risk-adjusted costs and benefits of a project by comparing two hypotheti-cal scenarios: a private-sector delivery and a public-sector delivery (the PSC). The PSC analysis estimates the net present value of each scenario, adjusting both scenarios for the government’s cost of retaining risk (6). There will be some risks that the government may choose to retain in both scenarios (e.g., right-of-way acquisition),

and their cost is labeled “retained risk” in Figure 1 (7). In contrast, there will be risks that the government may choose to transfer (e.g., traffic demand), and their costs are labeled “transferable risk.”

The total cost of the PSC option is estimated as the sum of the base costs of the project (i.e., design, construction, operations, and maintenance) plus the costs of the retained and transferable risks. The total cost of the PPP option is estimated as the cost of the retained risk plus the cost of any service payments made by the gov-ernment to the private partner (e.g., availability payments) or costs incurred to set up the PPP (e.g., transaction costs). VfM would then be calculated as the difference between the total costs of the PSC option and the total costs of the PPP option (see Figure 1). Despite the fact that in Figure 1 the base costing for the public procurement approach is lower than the cost of service payments to the private provider, transferring the cost of bearing risk makes the PPP’s VfM superior and a better choice for the government.

FRamewORk tO value Risk expOsuRe

Countries with significant experience in implementing infrastruc-ture have developed sophisticated risk valuation methodologies in the context of VfM (2). The international experience shows that developing a rational process to determine which PPP projects merit the provision of public-sector support (e.g., a minimum revenue or noncompete guarantee) is critical to avoid excessive risk exposure.

Given that the public sector is in many cases the partner best placed to manage the risk at the lowest cost in a PPP, it is unrealistic to try to avoid the fiscal risk created by public support schemes altogether. In addition, it is important for governments to have a framework to value the monetary implications associated with retaining risk (e.g., providing a guarantee or other form of public support) before accepting the obligation (8). Valuing these contin-gent liabilities also allows governments to set additional reserve requirements in the budgeting process to account for the potential cost of meeting the obligation. Governments are better positioned to choose whether to bear a risk if they know its estimated cost (9).

In 2008, the U.S. Government Accountability Office released a report to Congress that reviewed the recent U.S. PPP experi-ence (10). Although the report noted the significant benefits of PPPs, its main focus was on the risks, costs, and trade-offs that

FIGURE 1 VfM comparison with PSC (NPV = net present value) [adapted from Harvey (7)].

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come along with private-sector participation. The report recognizes the systematic approaches that other countries have developed to identify and evaluate public-sector interest (including risk) in PPPs, such as the VfM and the PSC concepts, and the limited use of such approaches in the United States.

A review of major U.S. transportation PPP projects conducted confirmed the Government Accountability Office’s conclusion, revealing that a systematic, methodological approach to quantify public-sector risk exposure in transportation PPP projects in mon-etary terms does not exist in the United States (2). Although the proj-ects reviewed in the referenced study had a clear delineation of the risks that would be retained by the government, none appeared to have determined this risk allocation by using an approach based on a monetary measure of the cost of risk. The study also concluded that although there have been some isolated attempts at conduct-ing analyses somewhat similar to the PSC, these analyses have not included the cost of risk, fundamental to the VfM concept that drives PSC analyses, and hence the need to develop a risk valuation frame-work that U.S. transportation agencies can use to quantify the cost of risk exposure in PPP projects.

A case in point is the Sam Rayburn Tollway in the Dallas, Texas, area in 2007. The Texas Department of Transportation (DOT) awarded a 50-year concession for the project to the North Texas Toll-way Authority, a public-sector agency, over a private consortium led by the international toll operator Cintra. North Texas Tollway Author-ity’s offer of $3.3 billion in present value of payments was vastly superior to the $2.8 billion offer from Cintra. North Texas Tollway Authority presented an economic benefit analysis comparison of its offer and stated that its proposal had been prepared as a “public sector comparator” (11). However, this economic benefit analysis compari-son did not include any adjustments for the cost of the risk retained by the public agency, which the private concessionaire would have otherwise borne.

valuiNg public-sectOR ReveNue Risk expOsuRe as cONtiNgeNt liability

With the option-pricing technique, the value of a guarantee, or the contingent liability resulting from the public sector’s retaining all revenue risk in a project, can be analyzed by using a discounted cash flow model of the PPP project in question. The guarantee is interpreted as a put option giving the beneficiary (e.g., a private firm) the right to “sell” the project’s value and receive the minimum guaranteed income (MGI) every year over the life of the project.

When in a given year project revenue matches or exceeds the MGI, the government’s guarantee would have no value (G = 0). Conversely, when project revenues underperform and the MGI fails to materialize, the guarantee trigger becomes operative with a positive guarantee value equal to the difference between the actual annual income and the MGI, as follows:

Gd d= −MGI income ( )1

where

Gd = value of guarantee in a year when actual annual revenue is below MGI and

incomed = value of actual revenue in a year when actual revenue is below MGI.

Over the life of the guarantee (e.g., the revenue-producing part of the concession period), value of G is equal to the present value

(PV) of the guarantees expected to be triggered each year (t), if any, during the life of the guarantee (i.e., years 1 to n), as follows:

G Gr

dd

tt

n

= ( ) = −+( )=

∑PVMGI income

12

1

( )

where G is the value of guarantee and r is the risk-free interest rate.Given the unique characteristics and nature of infrastructure

projects, it is very unlikely that historic data will be available to determine the likelihood of a project’s upside and downside value of revenues in any given year. Consequently, Incomed is an unknown variable, and the value of G is nonpredictable. Rather, the value of G follows a probability distribution function (PDF).

The adopted approach to value PPP guarantees relies on using a computer spreadsheet model to develop a discounted cash flow model and Monte Carlo simulation to model the PDFs of the variables that affect Incomed. Once these PDF parameters have been developed for the input variables and incorporated into a discounted cash flow model that includes Equation 2, the Monte Carlo simulation can be used to develop a PDF of returns whose expected value (µ) would be used as the input for Incomed at time t. The expected annual rev-enue projections are calculated by using the expected value for all simulation variables (i.e., µ), producing a “static” pro forma cash flow.

The simulated output for the discounted cash flow model can then be used to determine the probability distribution for G, that is, the PV of the sum of the differences between the static annual cash flow values and the values simulated, as in Equation 2. A key issue is to determine which of the G statistics represents a fair value of the put option, that is, the guarantee or contingent liability. The literature indicates the use of statistics such as the mean and the median but recognizes that the actual percentile adopted will really depend on the negotiation and bargaining that take place among the parties involved (12, 13).

Figure 2 summarizes the methodology to analyze public-sector rev-enue risk exposure in a PPP project proposed in this research. A more detailed description of the methodology can be found elsewhere (2).

An important consideration in Step 3 of the methodology is that PDFs utilized to characterize variables can range from the simple triangular and beta distributions (often used to reflect subjective judgment) to the normal and lognormal PDFs that require historical or sample data and more elaborate analysis. In some cases these distributions can be estimated by using historical or statistical data, if available, or the outputs of a simulation model (e.g., a traffic simulation model). In other cases the analyst will have to rely on a combination of limited or no quantitative data and expert judgment. Comprehensive sources for identifying and selecting appropriate distributions for risk analysis are work by Roger and Petch (14) and Aldrete-Sanchez et al. (15).

case studies OF u.s. tRaNspORtatiON ppps

The application of the methodological framework in Figure 2 is shown with two U.S. case studies, both in El Paso, Texas: César Chávez toll road and the transportation reinvestment zone (TRZ) project. Despite their common locale, these case studies provide an opportunity to apply the methodology in two completely different sets of conditions and variables. Project information was available with sufficient detail to develop estimates for revenue streams and input variable PDFs in both cases. The triangular distribution was used as the PDF for sev-eral input variables because of its simplicity and flexibility. It is a PDF

Aldrete, Bujanda, and Valdez 91

that is considered suitable in cases where objective data are scarce and a simple intuitive understanding is paramount (14).

César Chávez Toll Road Project

The César Chávez Highway currently extends 9 mi along the U.S.–Mexico border in El Paso parallel to Interstate 10. This four-lane highway is an attractive alternative route to cross the city during peak hours and in consequence experiences congestion during the morning and afternoon peak hours. The proposed project includes rehabilitat-ing the existing four nontolled lanes and adding two toll lanes (for a total of six lanes). In coordination with Texas DOT, the Camino Real Regional Mobility Authority (CRRMA) will be conducting the market valuation process, which is aimed at determining the PV of the net cash flows that a toll project would generate. After the market valuation concludes, CRRMA will have the right of first refusal to develop, finance, construct, and operate the project once it is deemed

feasible. CRRMA in turn may or may not pursue a PPP through a comprehensive development agreement to implement the project.

This research assumed that CRRMA is considering pursuing the César Chávez toll road as either a PPP using a 20-year toll-concession type of comprehensive development agreement or as an in-house proj-ect retaining all project risks. In order to assess the benefits of pursuing the comprehensive development agreement option, CRRMA wants to estimate the cost of retaining toll revenue risk, a risk it is particularly sensitive to, given the lack of experience with toll roads in the region.

Toll Revenue Forecast Volatility and Simulation

Researchers developed a traffic simulation model using DynusT to estimate the volatility of revenues as a function of value of time (VoT) for the César Chávez toll road. DynusT is advanced dynamic traffic assignment software that is being used in tolling and congestion pricing situations. DynusT uses dynamic traffic

FIGURE 2 Methodology to value public-sector risk exposure in PPPs.

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assignment to overcome the limitations of traditional static assign-ment macroscopic models and capture the dynamics of congestion formation and dissipation (16). These characteristics allow the soft-ware to model driving behavior considering toll rates, VoT, and traffic congestion to estimate traffic demand.

This research estimated the volatility of toll traffic as a function of a triangular PDF of the users’ VoT, given an expected level of traffic congestion (modeled with DynusT) and a set of toll rates. In this case, the triangular PDF had a minimum value of $10.50/h, a most likely value of $14.00/h, and a maximum value of $17.50/h. The most likely value is the standard VoT used by Texas DOT and the El Paso metropolitan planning organization in toll project evaluations. Because of resource limitations, the minimum and the maximum val-ues for the VoT PDF had to be subjectively established in consultation with regional transportation experts.

The DynusT model was developed on the basis of the El Paso metropolitan planning organization’s regional travel demand model origin–destination matrices and network, to ensure that traffic flows were realistic. The tolls for the base year in the analysis (2010) were specified as $0.16/mi for passenger cars and $0.48/mi for trucks. A drawback of analyzing such a large network is that for each simula-tion DynusT required a significant amount of resources (i.e., com-puter memory and running time). Therefore, DynusT was run only for the years 2010, 2020, and 2030, and only for the morning and afternoon peak hours (6:00 to 9:00 a.m. and 4:00 to 7:00 p.m.). An analysis conducted during off-peak hours showed that the toll lanes carried little demand.

Toll Road Revenue Distributions

The iterative nature of DynusT allowed the development of PDFs for toll revenues by analyzing their frequency data. @Risk (a Microsoft Excel add-on) was used to find the best fit for the data in question. Based on the chi-square statistic, the beta general PDF appeared to be the best fit for the toll revenue data of all forecast years when compared with other PDFs such as normal, triangular, and lognormal distributions (17). Figure 3 shows the beta general PDF obtained for the base case scenario (2010).

Contingent Liability Analysis

A discounted cash flow model was developed to analyze CRRMA’s risk exposure to toll revenue risk on the César Chávez toll road. A working assumption to account for the fact that CRRMA would be retaining all revenue risk was that CRRMA would be providing a traffic revenue guarantee, or MGI, to a private concessionaire, equivalent to 100% of the expected toll revenue.

Because of the uncertainty associated with toll traffic volumes and revenue as a function of VoT, the value of G is not predictable and follows a probability distribution. This probability distribution was obtained from Monte Carlo simulation on the toll revenue, with the PDFs for revenue in 2010, 2020, and 2030 as the basis (interpolating for intermediate years). The output of the simulation was used to determine the probability distribution for G, which was defined earlier in Equation 2 as the PV of the sum of the differences between the MGI static annual cash flows and the cash flows pro-duced by using Monte Carlo simulation (see Figure 4). In addition, key statistics of G were calculated:

G-Statistic (at 10,000 iterations) Value

Mean $4,511,051Median $4,461,612Standard deviation $1,247,64990th percentile $6,141,313

With statistics most commonly recommended in the literature reviewed (i.e., the mean and the median), the value of CRRMA’s revenue guarantee G to the concessionaire would be around $4.5 million. This value would also be the cost to CRRMA of retain-ing the toll revenue risk if it were to pursue the project in-house.

Considering that the project is expected to yield almost $82 mil-lion in PV of revenues, the cost of retaining the toll revenue risk at $4.5 million is relatively significant (5.5% of the PV of revenues). In the context of VfM and a PSC analysis, CRRMA would consider this cost as part of the transferable risk in the PSC option, as shown in Figure 1. In order for the concession option to be more advantageous than the PSC, the net present cost of all payments made by CRRMA to set up the comprehensive development agreement (e.g., transaction

FIGURE 3 César Chávez toll road beta general PDF of revenue for base case scenario (Year 2010).

Aldrete, Bujanda, and Valdez 93

costs, right-of-way costs, capital cost contributions), plus any retained risk costs (e.g., right-of-way acquisition delays), would have to be less than the total cost of the PSC (including the cost of revenue risk).

tRZ project

TRZs 2 and 3 in the City of El Paso were used as case studies to con-duct a public-sector risk exposure analysis. Based on the concept of value capture, TRZs are an innovative financing instrument created in Texas. Value capture relies on leveraging the real estate potential brought by urban asset improvements and capturing it with a fis-cal instrument. In 2007, legislation adopted by Texas made specific provisions for the development of municipal TRZs, a concept that encapsulates the principles of value capture to supplement other tra-ditional and nontraditional sources of funds for roadway and transit project financing (18).

After a contiguous area along a corridor is designated as a TRZ, a local government entity (a city or county) can securitize the incre-mental tax revenues along with Texas DOT pass-through financing (shadow tolls paid by Texas DOT) to obtain the funds necessary to bring a project to fruition. Funds generated from the securitization will be used to pay for transportation infrastructure within the TRZ, and investors will be repaid from the combined revenue stream—the incremental tax revenues and Texas DOT shadow tolls.

With property tax revenues dependent on real estate prices and the pace of development in the region, there is a significant amount of uncertainty and risk associated with debt repayments. Figure 5 illustrates the flow of funds and risk allocation associated with TRZ finance.

City of El Paso TRZs 2 and 3

CRRMA entered into an agreement with Texas DOT and the City of El Paso to finance a series of transportation projects within TRZs 2 and 3 by using a mix of TRZ and shadow tolls. CRRMA will be the entity responsible for securitizing debt backed by property tax revenues from the city TRZs and Texas DOT shadow toll payments.

The total PV of the TRZ funding commitment of the City of El Paso to CRRMA is $70 million, which is to be repaid from the proceeds of the property tax increment collected by the city each year for 30 years. Given their dependence on the real estate market, these cash flows are by nature bound to be significantly uncertain. In contrast, the city has committed to deliver a certain cash flow stream to CRRMA. The city is therefore exposed to the risk that the property tax increment revenue expected does not materialize, whereas its obligation to CRRMA remains firm and is thus a contingent liability for the city.

Geographic Characteristics

Research shows that the improvement or addition of transportation capacity along a corridor has a positive effect on real estate values within buffer zones of up to 2 mi in radius from the corridor’s cen-terline (19). Research on metropolitan spatial densities concurs that transport project impacts tend to occur primarily in the vicinity of project areas (20, 21).

The boundaries of the TRZs were defined by estimating the minimum buffer from the centerline of the corridors that would allow collecting the tax increment revenue equivalent to a PV of about $70 million over the 30-year period (22). The buffers devel-oped along the corridors ranged from 1/16 of a mile to ½ mi from the centerline. The 2,481 real property parcels within both TRZs have a total of 9,947 acres with a total 2010 taxable value of about $1.2 billion; this area would generate $7.6 million in property tax revenues for the City of El Paso taxing entity. About 78% of this acreage (7,791 acres) is currently undeveloped land, holding the most potential for value capture over the 30-year analysis period as development takes place. All property and taxable value data for this analysis were obtained from official records.

TRZ Revenue Forecast Volatility and Simulation

The authors assisted the City of El Paso in conducting the analysis of TRZ revenue potential and in the implementation of TRZs 2 and 3

0.00%

1.00%

2.00%

3.00%

4.00%

5.00%

6.00%

$1,020 $2,740 $4,460 $6,180 $7,900

Pro

bab

ility

Guarantee Values in US$ Thousands

FIGURE 4 Probability distribution for total toll revenue shortfall guarantee (G).

94 Transportation Research Record 2297

(22). The analysis framework was based on key drivers of property value growth (including macroeconomic conditions) when a capacity improvement project is implemented along a transportation corridor. Properties located near an infrastructure project capitalize economic benefits over time. Only projects that generate large accessibility changes, cultivate economic development, and improve regional mobility foster the potential to generate TRZ revenues.

To analyze the revenue potential and define the TRZ boundaries, a discounted cash flow model was developed linking a geographic information system database with parcel information to key input parameters. These input parameters are documented in detail by Vadali et al. and include, among others, property price increases for different types of property (i.e., residential, commercial, industrial) as well as pace and timing of development (19). Because of the limited information available, most of these input parameters relied on a triangular PDF. Outputs from the revenue analysis included the annual cash flows for TRZ revenues over a 30-year analysis period and the resulting bonding capacity.

Contingent Liability Analysis

With the methodology in Figure 2, the value of the city’s TRZ guar-antee G to CRRMA was priced by applying Monte Carlo simulation on the TRZ discounted cash flow model and interpreted as a put option. In such a hypothetical put option, CRRMA has the right to “sell” the project’s value and receive the guaranteed amount (i.e., the TRZ annual cash flow).

When TRZ performance matches or exceeds projections, the city’s contingent liability or guarantee has no value (G = 0). Con-versely, when the TRZ underperforms and projected revenues fail to materialize, the guarantee trigger becomes operative with a positive guarantee value, as was shown previously in Equation 1. G will be equal to the PV of the total TRZ revenue shortfall (Gd) that the city guarantees over the life of the TRZ (30 years), as laid out previously in Equation 2.

Because of the uncertainties in property values and the pace of devel-opment of vacant land, G is not predictable and follows a probability distribution. This probability distribution was obtained by applying Monte Carlo simulation on the following three key variables in the TRZ financial model: (a) developed land, (b) pace of vacant land devel-opment, and (c) property value growth rates on commercial, industrial, and residential parcels located around transportation improvements. Vadali et al. detail the PDFs for each of the variables (19).

The simulated output from the financial model was used to deter-mine the probability distribution for G, that is, the present value of the sum of the differences between the static annual cash flow values and the values simulated. Figure 6 shows the resulting prob-ability distributions. In addition to the probability distribution of G, its key statistics were calculated as follows:

G-Statistic (at 10,000 iterations) TRZ 2 Value TRZ 3 Value

Mean $1,128,418 $220,697Median $547,188 $111,550Standard deviation $1,375,660 $259,35490th percentile $3,232,047 $620,050

With the median, the value of the guarantee G for the combined TRZs would be $658,738, whereas with the more conservative mean, the value of the guarantee would be about $1.3 million. In other words, the value of the city’s cost of risk exposure to TRZ revenue would be about $1.3 million.

Considering that the combined TRZs are expected to yield $74 million in PV of revenues, the guarantee value of $1.3 million (or 1.8%) is significantly less costly than in the case of the César Chávez toll road. In the context of a PSC analysis, the city would consider the guarantee cost as part of the cost of retained risk—increasing the cost of the PPP option and reducing its VfM.

analysis limitations

One of the limitations of the methodology as presented is that the Monte Carlo simulation approach does not address the predictive

FIGURE 5 Conceptual flow of funds for TRZ financing (RMA = regional mobility authority).

Aldrete, Bujanda, and Valdez 95

limitations inherent in property values and tolling feasibility models used to generate the analysis. It is simply a tool to attempt to char-acterize the uncertainty associated with the outputs of these models. Another limitation is that the analysis depends on estimates of the likely probability of the variables used in the analysis, which given the difficulty inherent in securing sufficient historical or quantita-tive data for a transportation project often carry a dose of subjectiv-ity. Despite these limitations, the methodology remains superior to approaches that simply rely on point value estimates or value ranges with no probability associated with them.

cONclusiONs aNd RecOmmeNdatiONs

Current international and U.S. practices in public-sector risk man-agement in the evaluation of infrastructure PPPs were reviewed and a methodological framework was presented to evaluate public-

sector revenue risk in U.S. transportation PPPs. This research found that countries with longer PPP history than that in the United States have developed advanced decision guidelines and tools in which the valuation of public-sector risk is a key step in the analysis of PPP proposals. Among the benefits of including risk-adjusted costs and benefits in the analysis of PPP proposals is the ability to maxi-mize VfM, a central tenet in the pursuit of PPPs. This research also found that although there have been isolated attempts at conduct-ing analyses similar to the PSC in the United States, these have not accounted for the cost of risk, a fundamental part of the VfM concept. U.S. states currently lack a systematic, methodological approach to quantify in monetary terms public-sector risk exposure in the analysis of PPP projects.

Given the expanding role that PPPs are playing in financing the development of the country’s transportation infrastructure, it is rec-ommended that U.S. transportation agencies formally incorporate the concept of VfM in their evaluations and adopt more methodological

FIGURE 6 Probability distribution for guarantees (G) of (a) TRZ 2 and (b) TRZ 3.

(a)

(b)

96 Transportation Research Record 2297

approaches to quantify the cost of public-sector risk. Use of objec-tive measures of the cost of risk-bearing in the process of defining the risks that agencies will retain in a PPP project should increase the likelihood that the preferred risk allocation maximizes VfM.

The framework to evaluate public-sector revenue risk presented here is aimed at addressing this recommendation. A step-by-step methodology was laid out to quantify the financial cost of risk expo-sure in a PPP and facilitate the estimation of the risk-adjusted costs of delivering a project as a PPP. To demonstrate its application in the U.S. context, the methodology was applied to two U.S. case studies.

ReFeReNces

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2. Aldrete, R., A. Bujanda, and G. Valdez. Valuing Public Sector Risk Expo-sure in Transportation Public–Private Partnerships. Texas Transportation Institute, College Station, 2010.

3. Guidelines for Structuring Public-Private Partnerships. Castalia Strategic Advisors, Washington, D.C., 2009.

4. Integrating Private Investment into Public Infrastructure. Partnerships Victoria, Department of Treasury and Finance, Victoria, Australia, 2010. http://www.partnerships.vic.gov.au.

5. Almeyda, M., and S. Hinojosa. Revision of the State of the Art Contingent Liability Management. World Bank, Washington, D.C., 2001.

6. Public Sector Comparator Technical Note. Partnerships Victoria, Department of Treasury and Finance, Victoria, Australia. http://www.partnerships.vic.gov.au/CA25708500035EB6/WebObj/TechnicalNote-PSCTechnicalNote1-Entire/$File/Technical%20Note%20-%20PSC%20Technical%20Note1%20-%20Entire.pdf. Accessed April 20, 2009.

7. Harvey, B. PSC and Value for Money. Ernst & Young, Australia, June 2010. http://www.bond.edu.au/prod_ext/groups/public/@pub-btsd-gen/documents/genericwebdocument/bd3_013342.pdf.

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The Transportation Economics Committee peer-reviewed this paper.