Modular construction cost forecasting design to support continuous construction system improvements

Open Access
Article
Conference Proceedings
Authors: Martins DanusevicsLiga BraslinaDaine SkiltereAnda BatragaGirts Braslins

Abstract: Problem statement. The development of construction projects requires a relatively long period of time, during which, according to historical data, construction costs change significantly even on an annual basis, which significantly affects the commercial opportunities of participants in the construction industry. Sometimes an inaccurate forecast can lead to the liquidation of a company or the suspension of a construction project. Many construction forecasting methods have been developed in recent decades, but their accuracy is fragmented. The latest machine learning methods are not yet able to produce accurate predictable calculations due to the high degree of uncertainty and variability in cost factors in the construction industry.Research aim. To further improve the quality of construction forecasting systems, the aim of the study was to develop and test a new modular prediction model of construction costs dynamics, which can forecast changes in the next 12M as accurately as possible, as well as to provide precise guidelines for five-year forecasts.Research methods used. The combined methodology of the study was developed in 2018 and for 3 years was approbated to assess changes in the costs and volume of the Latvian, EU member state, construction sectors. Each year during the summer period, a forecast was developed, and after a year, the results were compared, and the methodology was adjusted. The methodology is modular - it includes the use of both statistical and expert methods, a combination of methods. In total, more than 200 experts were interviewed over the 3-year survey, while the 2020 phase survey includes the opinions of 59 experts from 56 organizations according to the Quadruple Helix methodological approach. The quantitative data array consists of statistical data blocks by historical changes in the costs and volume of construction sub-sectors.Main results and findings of the study. Based on the improved methodology, the 2020 projections differed from the actual changes in 2020 by 0.4 percentage points in volume and 1.4 percentage points in costs in context where changes over the last decade have fluctuated in double-digit ranges. The results of the study identify high-precision construction cost and volume methodology.Theoretical and practical implications of the work and conclusions. The developed methodology can be applied in practical forecasts of construction costs and volume in both the public and private sectors. The developed methodology can be applied to the improvement of future construction forecasting, including the development of machine learning algorithms. The proposed forecasting method offers a new direction for construction cost forecasting research and will provide construction planners with an additional effective tool to manage the risks associated with construction project costs.

Keywords: Modular Forecasting, Construction Costs, Prediction Methods

DOI: 10.54941/ahfe1001066

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