Improving Mid-Term, Intermediate, and Long-Range Cost Forecasting for State Transportation Agencies (2020) / Chapter Skim
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46 4. Identification of Suitable Construction Cost Index 4.1 Introduction "Garbage In, Garbage Out" (GIGO)
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47 4.3 Overview of Comparative Suitability Analysis Protocol for Cost Indexes It should be noted that the proposed protocol for the assessment of cost indexes is not intended to evaluate their cost forecasting capabilities. Those capabilities are ultimately given by the forecasting methodologies discussed in the next chapter.
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48 Table 4.1 Selected Relevant Items for Comparison Analysis Agency Item ID Description Relative Weight Minnesota DOT 2106507/00010 Excavation - Common 17% 2301507/00010 Structural Concrete 33% 2360509/23200 Type SP 12.5 Wearing Course Mixture (3,B) 21% 2360509/23300 Type SP 12.5 Wearing Course Mixture (3,C)
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49 4.3.3 Base Power Regression Curves and Base Unit Price Estimates The base power regression curves used in the comparative analysis of cost indexing alternatives are built with bid data from projects awarded during the first year of the analysis period. For the case studies, one power regression curve was developed for each of the items listed in Table 4.1.
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50 protocol was conducted at the regional level in order to identify the most suitable cost indexing alternative for each of the regions shown in Figures 3.7 to 3.9. Table 4.2 shows the top three cost indexing alternatives identified for each region.
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51 better performance of regional indexes was Colorado's Northeast Region. This could be due to the fact that this is the most densely populated region in Colorado, leading the Colorado Department of Transportation (CDOT)
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