Improving Mid-Term, Intermediate, and Long-Range Cost Forecasting for State Transportation Agencies (2020) / Chapter Skim
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Pages 59-69
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From page 59... ...
52 5. Cost Forecasting Approaches 5.1 Introduction The protocol proposed in Chapter 4 for the comparative analysis of cost indexing alternatives is only intended to identify the index that most effectively represents the local construction market.
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53 simple or compounded rates. Section 1.4.2 in Chapter 1 explains the difference between simple and compounded inflation rates.
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54 capabilities of the MFE methodology. The figure shows the performance of a 4% compounded annual inflation rate on an asphalt paving scope of work in Colorado.
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55 noted that forecasting errors in this study do not include uncertainties associated with currentdollar estimates. Average errors in Figure 5.1 are only associated with forecasting risks.
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56 Results from the case studies not only showed significant differences in inflationary trends among geographic regions within the state, but also between agencies. The three case study agencies were found to represent inflation trends at three different levels of magnitude: low, medium, and high.
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57 Figure 5.3 Example of Risk-Based Forecasting Timeline with 4% Compounded Projection It seems evident from Figure 5.3 that a 4% compounded inflation rate is too high for the asphalt paving market in the Northeast Region of Colorado. The projection with that inflation rate increasingly deviates from the average forecast as the time horizon increases.
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58 Figure 5.4 Example of Risk-Based Forecasting Timeline with 3.1% Compounded Projection Risk-based forecasting timelines were developed and analyzed for both asphalt and concrete paving in all regions of the three case study agencies. The MFE methodology was applied using the most suitable MCCI, as well as the most suitable existing traditional CCI identified for each region.
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59 Table 5.2 Consolidated Forecasting Error Ranges from the Application of the MFE Method MCCI forecasting error ranges in Table 5.2 actually correspond to worst-case scenarios found among case study results on both ends of each range. An STA would obtain narrower forecasting error ranges after the actual application of the MFE method with its own data.
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60 analyses. NCHRP Research Report 953 provides guidance to calculate inflation rates from the typical outputs provided by those software packages.
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61 configuration of the available data, making them more appropriate for risk-seekers that decide to rely on a single scenario, underestimating cost forecasting uncertainties. However, the study also found that regression analysis techniques are more suitable to model the anticipated continuation of short-term abnormal price fluctuations.
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From page 69... ...
62 inflation rates start to appear as the forecasting time horizon extends over more than 5 years. There are no significant differences in the use of simple or compounded inflation rates for mid-term forecasts.
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