Exploring Regression Models for Forecasting Early Cost Estimates for High-Rise Buildings



Construction projects with inaccurate early cost estimates are burdened with excessive financial and contractual risks. Subsequently, construction investors and professionals demand accurate early cost estimates even when there are limited or no construction documents, because these estimates become the basis of project funding. Concerns that estimating accuracy during the early stages of construction projects lie between ±25% and ±50% present a dire need for more accurate forecasting models for specific building types. Forecasting models for early cost estimates for high-rise buildings are particularly of interest due to their complexity, high levels of investments and the paucity of related research. In order to determine the most accurate model for forecasting early costs for high-rise buildings in this present study, regression analysis methods were used to explore several cost drivers, cost estimating relationships and cost models. The results revealed that the five key cost drivers are gross floor area, location, structural material, height, and completion date. With an improved R2 of 64% and an error rate of approximately 9%, the most accurate model indicated that power functions best described cost estimating relationships. Also, the natural log of building cost per square foot was the most reliable dependent variable for modeling costs. This early cost forecasting model works best for high-rise buildings with gross floor areas ranging between 88,000 and 6,500,000 square feet. Key results from this study are most useful to construction professionals and investors during the early phases of financial and economic feasibility decision making for high-rise building development. The proposed model has an improved level of accuracy, simplicity and should increase the ease of forecasting early cost estimates for high-rise projects, when there are limited or no construction contract documents. Academicians may use the processes outlined to educate their students on alternative early cost estimating methods. Future research should explore other methods and models for improving the accuracy of early cost estimates for specific building types. Improved cost forecasting models should reduce detrimental construction project results such as losses, delays, strife, and litigation. Accurate early cost forecasting models should improve overall construction project success and client satisfaction.

Keywords: cost estimating, cost forecasting, cost modeling, regression analysis, high-rise buildings

How to Cite: Ofori-Boadu, A. N. (2015) “Exploring Regression Models for Forecasting Early Cost Estimates for High-Rise Buildings”, The Journal of Technology, Management, and Applied Engineering. 31(5).