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From page 15... ... 15 Cost Indexing Alternatives 3.1 Introduction This chapter discusses cost indexing alternatives available to state transportation agencies (STAs) to support cost forecasting procedures. Read the entire page →
From page 16... ... 16 Improving Mid-Term, Intermediate, and Long-Range Cost Forecasting: Guidebook for State Transportation Agencies impact that a drastic change in asphalt prices would have on the construction industry. Likewise, some STAs have found that "construction inflation generally outpaces consumer inflation" (Duncan et al. Read the entire page →
From page 17... ... Cost Indexing Alternatives 17 bid data to develop in-house CCIs enables STAs to indirectly account for the unique conditions of the local construction market (to a certain extent) : "Pricing changes in any single state can be affected by influences that are muted or lost in national prices and price indexes. Read the entire page →
From page 18... ... 18 Improving Mid-Term, Intermediate, and Long-Range Cost Forecasting: Guidebook for State Transportation Agencies Figure 3-1 shows two curves that represent the market conditions for two commodities for a given STA: asphalt and concrete. These curves were created with historical cost data from a given indexing period (Period 1) Read the entire page →
From page 19... ... Cost Indexing Alternatives 19 The matching principle refers to the degree of similarity between the components used in the calculation of a CCI and the scope to be forecast. Once the matching principle has been reasonably met, the proportionality principle comes into play. Read the entire page →
From page 20... ... 20 Improving Mid-Term, Intermediate, and Long-Range Cost Forecasting: Guidebook for State Transportation Agencies 3.6.1 Collection and Cleaning of Historical Bid Data If the intended MCCI is anticipated to be used for long-range forecasting purposes, the STA should make efforts to collect and clean at least 20 years of historical bid data, since that is the suggested lookback period for long-range forecasts. To the maximum extent possible and practical, efforts should be made to collect data from all unit price projects awarded during that period of time. Read the entire page →
From page 21... ... Figure 3-3. Excerpt of Minnesota DOT's tidy data. Read the entire page →
From page 22... ... 22 Improving Mid-Term, Intermediate, and Long-Range Cost Forecasting: Guidebook for State Transportation Agencies Equation 3-4 is used to apply the modified Z-score method. The reason behind the use of this method is that outliers are identified by using the sample median (x̃) Read the entire page →
From page 23... ... Cost Indexing Alternatives 23 3.6.2 Defining Basket of Pay Items for the Multilevel Construction Cost Index When the data collection and cleaning are completed, the process of developing the MCCI continues with the selection of the pay items that will become the foundation of the indexing system. The larger the basket of MCCI pay items, the better. Read the entire page →
From page 24... ... 24 Improving Mid-Term, Intermediate, and Long-Range Cost Forecasting: Guidebook for State Transportation Agencies Sub-Division Level 2, and so on, until the top level was reached, where a single general index was calculated at the agency level. All indexes were developed with a semiannual updating frequency, with index values updated twice every year, once on June 30 and again on December 31. Read the entire page →
From page 25... ... Cost Indexing Alternatives 25 curves are commonly used to explain the reduction in construction prices as the quantities of work increase (Rueda-Benavides 2016, Pakalapati 2018, Molenaar et al. Read the entire page →
From page 26... ... 26 Improving Mid-Term, Intermediate, and Long-Range Cost Forecasting: Guidebook for State Transportation Agencies in the available data would facilitate a future development of scope-based cost indexes. In the Minnesota DOT case study, the first indexing period was P1-1999. Read the entire page →
From page 27... ... Cost Indexing Alternatives 27 MCCIs developed for different agencies would have different configurations from the one shown in Table 3-3, but the bottom-up calculation process and upward ramifications would follow the same general principles. Each STA's MCCI configuration should be adjusted to its unique pay item classification system. Read the entire page →
From page 28... ... 28 Improving Mid-Term, Intermediate, and Long-Range Cost Forecasting: Guidebook for State Transportation Agencies their corresponding index at Sub-Division Level 1 (230-000) Read the entire page →
From page 29... ... Cost Indexing Alternatives 29 The final project-specific CCI is just the weighted average of the selected MCCI indexes. The weighted average calculation is similar to the one shown in Figure 3-8 for the bottom-up calculation process. Read the entire page →
From page 30... ... 30 Improving Mid-Term, Intermediate, and Long-Range Cost Forecasting: Guidebook for State Transportation Agencies example, the asphalt paving project in Table 3-4 was initially identified as a good representative of the Minnesota DOT's typical asphalt paving activities. Thus, a planning program focused only on asphalt paving could use the index shown in Table 3-5 to determine a program-specific inflation rate. Read the entire page →
From page 31... ... Cost Indexing Alternatives 31 3.6.5 Regional Considerations and Price Inputs for Multilevel Construction Cost Index One of the objectives of NCHRP 10-101 was testing the hypothesis that different geographic regions within a state could be affected by different inflationary trends, so that different inflation rates should be applied to different regions. The study confirmed that hypothesis and found that different MCCI versions would better represent local construction markets for different regions within the case study agencies. Read the entire page →
From page 32... ... 32 Improving Mid-Term, Intermediate, and Long-Range Cost Forecasting: Guidebook for State Transportation Agencies Figure 3-11. Colorado DOT geographic regions. Read the entire page →
From page 33... ... Cost Indexing Alternatives 33 the fact that statewide indexes are developed with larger data sets that allow for a more effective representation of construction market changes. The only region that showed an overall better performance of regional indexes was Colorado's northeast region. Read the entire page →
From page 34... ... 34 Improving Mid-Term, Intermediate, and Long-Range Cost Forecasting: Guidebook for State Transportation Agencies relevant construction division. Table 3-7 shows the items selected for the Minnesota DOT's case study. Read the entire page →
From page 35... ... Cost Indexing Alternatives 35 Minnesota DOT case study, one power regression curve was developed for each of the items listed in Table 3-7. The analysis period for that case study started in January 2007 and ended in December 2018; therefore, base power regression curves were created with historical bid data from 2007. Read the entire page →
From page 36... ... 36 Improving Mid-Term, Intermediate, and Long-Range Cost Forecasting: Guidebook for State Transportation Agencies data and the index-based data point clouds and the more suitable the cost indexing alternative. It should be noted that corresponding points have the same letting date and awarded quantity. Read the entire page →

From page 15...

... 15 Cost Indexing Alternatives 3.1 Introduction This chapter discusses cost indexing alternatives available to state transportation agencies (STAs) to support cost forecasting procedures.