A methodological framework can be used to evaluate public revenue financial risk exposure when transportation infrastructure is delivered 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 implementing 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 application of the methodology is demonstrated by two U.S. transportation PPP case studies.
Value capture refers to the process by which all or a portion of increments in land value attributed to community efforts rather than to landowner actions are recovered by the public sector. As such, it is a form of a public–private partnership. It is widely used across the country and around the world for transit applications; however, its applications to roadways have only recently emerged into discussions of roadway finance, out of motivation stemming from the transportation funding crisis. Two states have legislative provisions for enabling value capture for financing transportation. In Texas, this takes the form of a transportation reinvestment zone (TRZ). This paper presents specifications for a TRZ based on a case study approach and then applies a financial evaluation model based on those specifications to a case study corridor in El Paso, Texas, to assess preliminary revenue sources and cash flows that can be accrued for value capture bonding capacity.
This research presents a methodology for estimating freight flows along corridors serving international trade. A methodology for disaggregating regional flows from the FHWA Freight Analysis Framework (FAF3) to the state level was developed and applied to the state of Texas. To keep international trade moving in a timely and efficient manner, it is important to have accurate information identifying and anticipating capacity shortfalls and congestion nodes. As trade levels increase, the strain on existing infrastructure serving international trade only worsens; therefore, this information is important for improving strategic investment decisions. Findings from the literature are presented about the FAF3 structure and existing methodologies to estimate freight flows at statewide and regional levels. A methodology was developed to disaggregate national FAF3 data and then to assign and estimate the tons of international freight flows through statewide roadways and railroads. The international trade corridors in Texas are used as a case study to apply the methodology and estimate current and future freight demand. Results from the case study demonstrate encouraging findings about this methodology. Conclusions and recommendations to refine and improve this methodology and the FAF3 are provided.
Real estate property value analysis is used for municipal taxation and budgeting. Commercial properties make up a large percentage of the property tax base in many, if not most, taxing jurisdictions. Data constraints limit the number of analyses conducted on commercial property value patterns. This study employs a fairly extensive data set to address that problem in the context of El Paso, Texas, a large metropolitan economy located on the United States border with Mexico. The sample contains data for 105,611 commercial real estate parcels. Empirical analysis is conducted using geographically weighted regression analysis. Results confirm that parameter estimation for the commercial property data in this sample should be conducted using methodologies that allow for spatial autocorrelation and heteroscedasticity.
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