Build-operate-transfer (BOT) is a public-private partnership (PPP) project delivery system for the financing, development, and operations of highway projects around the world. Uncertainty about future traffic demands is one of the most important risk factors in the operations phase of a BOT project. There is a considerable amount of evidence indicating that the improper consideration of this uncertainty contributes to the financial failure of BOT projects. The inherent limitation of conventional economic analysis methods contributes to this uncertainty; most notably the net present value (NPV) approach that is typically used in the economic valuation of BOT projects. In addition, the NPV approach is insufficient to determine the correct market value of minimum revenue guarantee (MRG) options. The government offers MRG options to the concessionaire as a revenue risk-sharing strategy in BOT projects. The authors apply the real options theory from finance/decision science to explicitly price MRG options in BOT projects. This real options model has several prominent attributes that make it different from NPV models. It uses a market-based option pricing approach called risk-neutral valuation method to determine the correct value of MRG options. Unlike the other models, this approach treats the risk of underestimating future traffic demands internally and adjusts for the traffic market risk in the valuation of MRG options. The authors’ approach also describes a procedure for characterizing the concessionaire’s economic risk profile under uncertainty about future traffic demands. In addition, it uses real options analysis to price MRG and traffic revenue cap (TRC) options as compound options and determines their effects on the concessionaire’s economic risk profile. The probability distributions of when the concessionaire may request MRG and when the public sector may receive additional revenues as TRC options are also presented. Further, the distributions of the number of times that the concessionaire may request the MRG option and the number of times that the public sector may receive additional revenue are characterized. Finally, this model identifies the probability distributions of the present value of MRG options and the present value of total additional revenues recalled by the public sector. The proposed model can help public and private sectors better analyze and understand the economic risk of BOT projects under uncertainty about future traffic demands. The private sector can use this proposed model to make better entry decisions to BOT highway projects considering the level of support provided by the government. The government can also use this proposed model to identify the appropriate MRG levels to encourage private investments without comprising future budgetary strength.

Every month, Engineering News-Record (ENR) publishes the construction cost index (CCI), which is a weighted aggregate index of the 20-city average prices of construction activities. Although CCI increases over the long term, it is subject to considerable short-term variations, which make it problematic for cost estimators to prepare accurate bids for contractors or engineering estimates for owner organizations. The ability to predict construction cost trends can result in more-accurate bids and avoid under- or overestimation. This paper summarizes and compares the applicability and predictability of various univariate time series approach for in-sample and out-of-sample forecastings of CCI. It is shown that the seasonal autoregressive integrated moving-average model is the most-accurate time series approach for in-sample forecasting of CCI, while the Holt-Winters exponential smoothing model is the most-accurate time series approach for out-of-sample forecasting of CCI. It is also shown that several time s...

The construction cost index (CCI), which has been published monthly in the United States by Engineering News-Record (ENR), is subject to significant variations. These variations are problematic for cost estimation, bid preparation, and investment planning. The accurate prediction of CCI can be invaluable for cost estimation and budgeting of capital projects, and can result in accurate bids. The research objective of this paper is to create appropriate multivariate time series models for forecasting CCI based on a group of explanatory variables that are identified by using Granger causality tests. The results of cointegration tests recommend vector error correction (VEC) models as the proper type of multivariate time series models to forecast CCI. Several VEC models are created and compared with existing univariate time series models for forecasting CCI. It is shown that the CCI predicted by these VEC models is more accurate than that predicted by the previously proposed univariate models (i.e., se...

Highway construction costs are subject to significant variations over time. These fluctuations are apparent in trends of indices, such as the national highway construction cost index (NHCCI). These variations are problematic for highway contractors because they can result in bid loss or profit loss. They are also problematic for owner organizations, such as State Departments of Transportation (State DOTs), because they can result in hidden price contingencies, delayed or cancelled projects, inconsistency in budgets, and unsteady flow of projects. These problems can be prevented if highway construction costs are forecasted accurately. Existing literature lacks appropriate models to accurately forecast NHCCI. The objective of this research is to identify the leading indicators of NHCCI (i.e., the explanatory variables of NHCCI that are useful to predict NHCCI), and create appropriate multivariate time series models for forecasting NHCCI through utilizing information available from the identified lea...