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Suggested Citation:"Chapter 1 - Introduction." National Academies of Sciences, Engineering, and Medicine. 2020. Improving Mid-Term, Intermediate, and Long-Range Cost Forecasting: Guidebook for State Transportation Agencies. Washington, DC: The National Academies Press. doi: 10.17226/25972.
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Suggested Citation:"Chapter 1 - Introduction." National Academies of Sciences, Engineering, and Medicine. 2020. Improving Mid-Term, Intermediate, and Long-Range Cost Forecasting: Guidebook for State Transportation Agencies. Washington, DC: The National Academies Press. doi: 10.17226/25972.
×
Page 2
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Suggested Citation:"Chapter 1 - Introduction." National Academies of Sciences, Engineering, and Medicine. 2020. Improving Mid-Term, Intermediate, and Long-Range Cost Forecasting: Guidebook for State Transportation Agencies. Washington, DC: The National Academies Press. doi: 10.17226/25972.
×
Page 3
Page 4
Suggested Citation:"Chapter 1 - Introduction." National Academies of Sciences, Engineering, and Medicine. 2020. Improving Mid-Term, Intermediate, and Long-Range Cost Forecasting: Guidebook for State Transportation Agencies. Washington, DC: The National Academies Press. doi: 10.17226/25972.
×
Page 4
Page 5
Suggested Citation:"Chapter 1 - Introduction." National Academies of Sciences, Engineering, and Medicine. 2020. Improving Mid-Term, Intermediate, and Long-Range Cost Forecasting: Guidebook for State Transportation Agencies. Washington, DC: The National Academies Press. doi: 10.17226/25972.
×
Page 5
Page 6
Suggested Citation:"Chapter 1 - Introduction." National Academies of Sciences, Engineering, and Medicine. 2020. Improving Mid-Term, Intermediate, and Long-Range Cost Forecasting: Guidebook for State Transportation Agencies. Washington, DC: The National Academies Press. doi: 10.17226/25972.
×
Page 6
Page 7
Suggested Citation:"Chapter 1 - Introduction." National Academies of Sciences, Engineering, and Medicine. 2020. Improving Mid-Term, Intermediate, and Long-Range Cost Forecasting: Guidebook for State Transportation Agencies. Washington, DC: The National Academies Press. doi: 10.17226/25972.
×
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1 Introduction 1.1 Introduction and Background “Although project cost escalation is usually caused by lack of project scope control and factors external to the state highway agency, it results in cost-estimation practice and cost- estimation management approaches that do not promote consistency and accuracy of cost estimates across the project development process” (Anderson et al. 2007). This quote effectively characterizes the reasons that motivated the research efforts that led to the development of this guidebook. There are always a number of cost-influencing factors that are beyond the control of state transportation agencies (STAs) and that generate unavoidable uncertainties that prevent these agencies from accurately estimating future construction costs. No cost estimating approach would ever provide a completely accurate and reliable construction cost estimate. However, there is still considerable room for improvement in current cost forecasting practices. The accuracy of cost forecasting can still be improved by better address- ing avoidable sources of uncertainty, and unavoidable risks can still be better modeled and understood to facilitate informed planning decisions. That is how this guidebook is intended to assist STAs. Construction cost forecasting is, and will always be, a challenging process and becomes increasingly challenging as the planning time horizon increases. For some transportation planning programs, the planning horizon could exceed 20 years. The existing literature is mainly focused on short-term cost forecasting (1 to 2 years) and leaves a knowledge gap for more extended forecasting periods, where more help is needed. This guidebook discusses cost forecasting practices for midterm (3 to 5 years), intermediate-range (up to 15 years), and long-range (more than 15 years) forecasting time horizons to help STAs select the cost fore- casting approaches that best meet their needs. It takes into consideration program-specific requirements, desired forecasting time horizons, data quality and availability, information technology (IT) and staff capabilities, and the risk tolerance of the STA. The guidebook recognizes that the most effective cost forecasting approach may not always align with the agency’s needs, preferences, or constraints. Thus, it provides guidance on a range of cost forecasting alternatives as well as a framework for selecting a cost forecasting approach to help to identify the most suitable approach for each agency. This guidebook is one of two deliverables from NCHRP Project 10-101, “Improving Midterm, Intermediate, and Long-Range Cost Forecasting: Guidance for State Departments of Trans- portation.” The other deliverable is a final report that summarizes all research efforts and major project findings. All cost forecasting approaches presented in this document are also discussed in the final report, but this guidebook is mainly directed to estimators. It explains the cost forecasting process at a higher technical level and includes a detailed description of mathematical and statistical procedures. The users of the guidebook are also encouraged to C H A P T E R 1

2 Improving Mid-Term, Intermediate, and Long-Range Cost Forecasting: Guidebook for State Transportation Agencies read the NCHRP Web-Only Document 283: Improving Mid-Term, Intermediate, and Long-Range Cost Forecasting for State Transportation Agencies (Rueda-Benavides et al. 2020) which includes additional background information that facilitates a better understating of the forecasting methodologies presented in the following chapters. 1.2 Overview of Guidebook This document provides guidance to STAs on the effective implementation of various mid- term (3 to 5 years), intermediate-range (up to 15 years), and long-range (more than 15 years) cost forecasting procedures. Guidance is provided on traditional practices as well as on novel cost forecasting procedures aimed to produce the ideal process discussed in Section 1.6. The framework for selecting a cost forecasting approach in Chapter 2 serves as a map to guide STAs through all available alternatives while assisting them in the identification of the set of practices that best meets their needs. This is a five-module framework. All five modules are presented in Chapter 2, but they refer guidebook users to additional information provided in Chapters 3 and 4. Module 1 starts by guiding STAs in the selection of a suitable cost indexing alternative. The alternatives considered by this module and discussed in detail in Chapter 3 include traditional construction cost indexes (CCIs), as well as an alternative cost indexing approach called mul- tilevel construction cost index (MCCI). Chapter 3 also presents a protocol for the quantitative comparative analysis of indexing alternatives. This protocol can be used to find the best MCCI configuration or to evaluate the suitability of traditional external and in-house CCIs. Thus, the STA that decides not to implement the proposed MCCI system could still use this methodology to identify the most suitable non-MCCI alternative. Module 2 provides standard inflation rates based on results from three case studies conducted under NCHRP 10-101. These inflation rates are expected to be used by STAs that prefer not to incur the effort of using a cost index to analyze the construction market to produce an appli- cable inflation rate—in other words, the users of Module 2 made the decision in Module 1 to not use any type of CCI. Modules 3 to 5 are intended to guide STAs in the selection of the mathematical method for producing an applicable inflation rate from the cost indexing alternative selected in Module 1. Each of these three modules corresponds to a different forecasting time horizon. These modules also provide guidance on the determination of lookback periods (number of years of index data) and the type of inflation rate that should be used for each forecasting period. The modules also indicate the levels of uncertainty to be expected from the different cost indexing alternatives included in Module 1. All the cost forecasting approaches considered in Modules 3 to 5 are further explained in Chapter 4. Modules 2 to 5 provide separate sets of guidelines and information for two different types of work: asphalt and concrete paving. Future research will aim to replicate the research efforts conducted under NCHRP 10-101 for other types of work. Finally, Chapter 5 describes a cost forecasting tool kit that consolidates most of the find- ings from NCHRP 10-101 into three spreadsheet-based tools. The tool kit is available to STAs on the TRB website (trb.org) on the summary web page for this guidebook. Estimators can use this tool kit to better understand the calculations behind the proposed cost forecasting techniques and to replicate the process in their own customized spreadsheets. The tool kit allows the use of both MCCIs and traditional CCIs and facilitates the generation and analysis of project- and program-specific CCIs from an MCCI, but it does not help with the actual creation of the MCCI system.

Introduction 3 1.3 Business Case for Implementation of Effective Cost Forecasting Programs Cost forecasting is just one part of the overall cost estimating process, but it is an essential component of an STA’s planning and programming processes. Accurate cost forecasting early during project development is critical to making sound financial decisions and optimizing the use of limited available resources (Anderson et al. 2007). Long-range transportation planning programs are intended to ensure that all investment decisions are part of a long-term strategic plan whose ultimate objective is maximizing the performance of transportation infrastructure and value for taxpayers’ money. The lack of effective cost forecasting methodologies to support transportation planning efforts may be preventing STAs from ensuring efficient use of public resources (Janacek 2006). Effective planning and cost forecasting seem to be more needed than ever due to the increasing gap between available resources and the level of resources required to maintain the national transportation infrastructure in a state of good repair. The 2013 Report Card for America’s Infrastructure, published by the American Society of Civil Engineers (ASCE), estimates that “32 percent of America’s major roads are in poor or mediocre condition, costing U.S. motorists more than $67 billion a year . . . in additional repairs and operating costs” (ASCE 2013). Likewise, ASCE found that roads and bridges across the country would need an average annual investment of about $170 billion to reach acceptable performance levels by 2028, but annual investment levels at that time were only around $91 billion. There is little STAs can do to improve their funding streams, but they can still improve their funding allocation procedures to attempt to use their limited available resources in the best possible manner. That can be achieved by improving cost forecasting procedures, which would translate into better funding allocation decisions (Byrnes 2002). Following are four possible negative scenarios that could be prevented with better cost forecasting practices (Pakalapati 2018): • Overrun budgets: When more funds than those originally estimated are required to success- fully complete construction activities anticipated in a program, an STA is forced to reallocate its budget, postponing, or even canceling, needed approved projects. • Underrun budgets: Although some may argue that under-budget situations are the result of effective management and budget control, at a program level, they could actually be a sign of inadequate cost forecasting procedures. Those situations reduce the ability of an STA to ensure value for taxpayers’ money, since more funds than required are allocated for planned construction activities, which prevents the STA from executing more projects with the same amount of resources. • Unreasonably high estimates: Poor cost forecasting could result in unreasonably high construction cost estimates, thereby inflating cost–benefit ratios and leading to the rejection of investments that should be accepted. • Unreasonably low estimates: Poor cost forecasting could also result in unreasonably low construction cost estimates, thereby understating cost–benefit ratios and leading to the acceptance of investments that should be rejected. 1.4 Inflation Rates and Cost Indexes There are two key elements usually involved in construction cost forecasting: inflation rates and cost indexes. Inflation refers to the overall increase in the price of goods and services at a microeconomic (for an individual, group, or industry) or macroeconomic (national economy) level (Munday 1996); inflation rates are an average measure for those price fluctuations during

4 Improving Mid-Term, Intermediate, and Long-Range Cost Forecasting: Guidebook for State Transportation Agencies a period of time (usually annual rates). A negative inflation rate is called deflation, and it corresponds to an overall decrease in the price of goods and services. Calculating a single inflation rate for a group of goods and services is a challenging process, since it usually involves a wide variety of inputs with different levels of relevance. For example, a transportation-related combined inflation rate calculated for asphalt and steel would be more affected by a 10% increase in the price of asphalt than by the same percentage increase in the price of steel. That is because asphalt is significantly more relevant for STAs than steel. Although that may be easily concluded from the hypothetical scenario, the quantification of the impact of the different inputs on the inflation rate is a more complicated process. The first step in quantifying an inflation rate with these two commodities would be determining how much more relevant asphalt is as compared with steel. After determining the relative relevance of each commodity, the agency would need to find a mechanism to facilitate an “apples-to-apples” integration of these two commodities into a single inflation rate. That mechanism is a cost index, which can then be used to generate the required inflation rate. Cost indexes can track prices for a single item or can be designed to integrate multiple cost inputs into a single eco- nomic indicator that takes into consideration the level of relevance (relative weight) of each item. In this example, a cost index developed with asphalt and steel prices could be used to estimate an overall combined inflation rate for both commodities, which in turn could be used to forecast a combined cost. Cost indexes developed with construction-related inputs are called CCIs. A CCI is a time series aimed to quantify average price fluctuations in the construction market or a specific sector of the construction industry. Although cost indexes are popular cost estimating tools among STAs, they are not commonly used for cost forecasting purposes. Most agencies seem to rely on standard inflation rates suggested by external entities, such as FHWA, other federal or state agencies, or financial consultants. Externally suggested inflation rates are most likely estimated from the quantitative analysis of cost indexes. However, that analysis is not internally performed by STAs, who decide to accept the suggested rates, ignoring their associated implica- tions and limitations. The 4% annual inflation rate proposed by FHWA is being used by a number of STAs. However, according to FHWA, preference should be given to the use of in-house or external CCIs to generate applicable inflation rates (FHWA 2017a). Some external CCIs available to STAs include the National Highway CCI (NHCCI) calculated by FHWA and CCIs published by the Engineering News-Record (ENR) and RSMeans. A number of STAs have developed their own CCIs, but only in a few cases have they used these to support forecasting efforts over long periods of time. As explained in Chapter 3, there are a number of limitations associated with the use of traditional in-house or external cost indexes. Chapter 3 of this guidebook provides information on the development and implementation of an MCCI system that has been designed to overcome the limitations of traditional CCIs and allow the implementation of the ideal cost forecasting process described in Section 1.6. NCHRP 10-101 found that MCCIs are significantly more effective at tracking price fluctuations in the construction market and, hence, are a more reliable source of inflation rates than traditional CCIs (Rueda-Benavides et al. 2020). 1.5 Factoring Inflation Rates into the Cost Forecasting Process Inflation rates are used to estimate future construction costs in “year of expenditure dollars.” Basically, when used in cost forecasting, an inflation rate is assumed to represent an anticipated future market behavior inferred from the analysis of relevant historical cost data. However,

Introduction 5 there are different approaches used to incorporate inflation rates into the cost forecasting process. There are two main types of inflation rates: • Simple annual inflation rate (not compounded) and • Compound annual inflation rate. An annual inflation rate represents the average expected annual growth in construction prices during the intended forecasting period. Some STAs use simple inflation rates, while others prefer compound rates. When a simple inflation rate is used, the projected cost is increased by the same number of dollars every year. The magnitude of the equal annual increase is equal to the cost estimate in current dollars multiplied by the simple annual infla- tion rate. For example, some agencies were found to use a simple annual inflation rate of 3% to forecast construction costs. Assuming that this inflation rate is reasonably accurate, the cost of a $10 million construction program (current-dollar estimate) would be expected to increase by $300,000 per year ($10 million × 0.03) for a total increase of $1.5 million in 5 years. Equations 1-1 and 1-2 show the calculations for this example. FCE CCE CCE Eq.1-1 FCE 10,000,000 10,000,000 0.03 5 $11,500,000 Eq.1-25 i nn ( ) ( ) = + × × = + × × = where n = length of forecasting time horizon in years, FCEn = forecast cost estimate over n years (in future dollars), CCE = current cost estimate (in current dollars), and i = fixed annual inflation rate. On the other hand, a compound annual inflation rate is applied every year to the cumulative inflation up to the previous year. Other agencies were also found to use a 3% annual inflation rate but compounded annually. Equations 1-3 and 1-4 show how a 3% compound annual inflation rate would be applied to develop a 5-year forecast for the same current-dollar estimate used in Equations 1-1 and 1-2. FCE CCE 1 Eq.1-3 FCE $10,000,000 1 0.03 $11,592,740.74 Eq.1-45 5 in n( ) ( ) = × + = × + = where n = length of forecasting time horizon in years, FCEn = forecast cost estimate over n years (in future dollars), CCE = current cost estimate (in current dollars), and i = fixed annual inflation rate. Figure 1-1 shows the difference between a 5% simple and a 5% compound inflation rate when applied to the same $10 million program over 20 years. Even though the two curves start deviating from each other after the first year, the difference between them starts becoming evident after the fifth year, which suggests that there is no significant difference in applying a simple or a compound inflation rate for midterm forecasts. The difference between these two types of inflation rates increases as the forecast time horizon is extended. Therefore, and given that the linear regression process is slightly more straightforward than the exponential regression approach, the framework for selecting a cost forecasting approach presented in the next chapter gives preference to the use of simple inflation rates for midterm forecasts and compound rates for intermediate and long-range time frames.

6 Improving Mid-Term, Intermediate, and Long-Range Cost Forecasting: Guidebook for State Transportation Agencies 1.6 Current Practice Versus Ideal Practice Figure 1-2 illustrates the typical cost forecasting process currently followed by STAs. Once the scope of a given program has been defined, a cost estimate in current dollars is performed and then projected into the future by using a given inflation rate to generate a single-value estimate. These types of outputs, also called deterministic outputs, tend to ignore the unavoid- able uncertainty inherent in the cost forecasting process. Ideally, inflation rates are determined as a function of the intended scope of work, but that is not usually the case. In fact, most agencies, if not all, use standard one-size-fits-all inflation rates to forecast costs for all transportation programs, regardless of the anticipated scope of work. Those rates are either externally suggested or internally calculated by STAs from an in-house CCI. Agencies supporting cost forecasting procedures with traditional external or in-house CCIs tend to assume that the selected CCI is applicable to all scopes of work. As discussed in Chapter 3, this assumption significantly affects the performance of cost fore- casting procedures. Results from NCHRP 10-101 allowed the definition of an ideal cost forecasting process, which is illustrated in Figure 1-3 (Rueda-Benavides et al. 2020). It was found that an ideal cost forecasting system should be able to handle different scopes of work at various levels of detail and for different forecasting time horizons. STAs are dealing with a certain degree of variability in the level of detail in the scope of construction activities forecast across long time $8,000,000 $10,000,000 $12,000,000 $14,000,000 $16,000,000 $18,000,000 $20,000,000 $22,000,000 $24,000,000 0 2 4 6 8 10 12 14 16 18 20 5% Simple Inflation Rate 5% Compounded Inflation Rate Number of Years Figure 1-1. Compound inflation rate versus simple inflation rate. Define Program Scope Estimate Construction Costs in Current Dollars Define Inflation Rate One-size-fits-all inflation rate or derived from a non-scope-based CCI Forecasted Cost Estimate (single value) Figure 1-2. Current typical cost forecasting process.

Introduction 7 periods. For example, long-range programs usually involve a broad scope of work. However, sometimes they could include specific capital projects defined at a higher level of detail and whose associated costs are forecast over 20–25 years. An ideal cost forecasting process should also provide decision-makers with a forecasting timeline showing the progression of the cost forecast as it moves across the desired forecasting time period. Likewise, traditional fore- casting practices need to evolve from deterministic into risk-based outputs to account for estimating uncertainty and to facilitate the communication of such uncertainty to different types of stakeholders and decision-makers. The combination of those ideal requirements led the research team to develop a methodology that facilitates the generation of reliable risk- based forecasting timelines, such as the one shown in Figure 1-3. That methodology, which is discussed in Chapter 4, is called moving forecasting error (MFE). From FHWA’s perspective, an ideal cost forecasting system would use in-house historical cost data as the main reference for the determination of applicable inflation rates. “Local historic cost data and experience with cost inflation are valuable data sources for use in projecting future rates” (FHWA 2017b). This logic explains the use of in-house historical cost data suggested in Figure 1-3. The ideal cost forecasting methodology also requires the implementation of flexible cost indexing techniques that allow the customization of CCIs to the specifics of each project, such as the scope-based CCIs shown in Figure 1-3, which in turn facilitate the generation scope-based inflation rates. Cost indexing systems with that level of flexibility have been developed by separate studies conducted for the Minnesota Department of Transportation (DOT) (Gransberg and Rueda-Benavides 2014) and the Alabama DOT (Pakalapati 2018). Those studies demonstrated the ability of an innovative cost indexing system to overcome the limita- tions of traditional CCIs. This innovative system is the MCCI mentioned earlier in this chapter. An MCCI consists of a group of indexes organized in a multilevel arrangement that allows the forecasting of each cost element in a program or project with the MCCI index that best matches its scope. Thus, costs for different programs or projects are forecast with different sets of indexes, which offers great flexibility in customizing the forecasting process to the specifics of each scope of work. As explained before, the framework for selecting a cost forecasting approach presented in the next chapter is intended to guide STAs on a wide range of cost forecasting options; however, not all these options would allow the ideal cost forecasting process shown in Figure 1-3. That ideal process offers the best cost forecasting performance, but it is also associated with greater staff and IT efforts and requirements that may discourage some STAs from taking that path. The ideal process in Figure 1-3 is mainly associated with the use of the proposed MCCI and MFE methodologies explained in Chapters 3 and 4, respectively. Define Program Scope Estimate Construction Costs in Current Dollars Define Scope-Based Inflation Rate Develop scope-based CCI Risk-Based Forecasting Timeline Collect and clean relevant in-house historical cost data Forecasting Time Horizon Estimate Range ($) Es tim at e Ra ng e ($ ) Figure 1-3. Ideal cost forecasting process.

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Because transportation investment programs have extended time horizons, state departments of transportation (DOTs) must forecast costs well into the future. This poses a serious challenge: the longer the time horizon, the more uncertainty and risk that forecasted costs will vary from actual, future costs.

The TRB National Cooperative Highway Research Program's NCHRP Research Report 953: Improving Mid-Term, Intermediate, and Long-Range Cost Forecasting: Guidebook for State Transportation Agencies presents a cost forecasting method for use by state transportation agencies that better accounts for cost variability and economic volatility over time.

Supplemental information to the report includes NCHRP Web-Only Document 283: Improving Mid-Term, Intermediate, and Long-Range Cost Forecasting for State Transportation Agencies, a cost forecasting toolkit, a guidebook presentation, and videos.

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