Cost Modeling from the Contractor Perspective: Application to Residential and Office Buildings

Abstract

For the majority of the contractual arrangements used in construction projects, the owner is not responsible for the cost deviations due to the variability of labor productivity or material price, amongst many other aspects. Consequently, the cost performance of a project may be entirely distinct for the owner and the contractor. Since the majority of quantitative research on cost estimation and deviation found in the literature adopts the owners’ perspective, this research provides a contribution towards modeling costs and cost deviation from a contractor’s perspective. From an initial sample of 13 residential buildings and 10 office building projects, it was possible to develop models for cost estimation at the early stage of development, including both endogenous and exogenous variables. Although the sample is relatively small, the authors were able to fully analyze all the cost data, using no secondary sources of data (which is very frequent in cost modeling studies). The statistically significant variables in the cost estimation models were the areas above and below ground and the years following the 2008 financial crisis, including the international bailout (2011–2014) period. For estimating the unit cost, a nonlinear model was obtained with the number of underground and total floor, the floor ratio, and the years following the 2008 financial crisis, including the international bailout (2011–2014) period as predictors. For the office buildings, a statistically significant correlation was also found between the cost deviation and number of underground floors.

Comparing data mining methods for predicting cost construction projects: A case study of cost management datasets from Thailand

“…Nevertheless, the substantial accuracy of 98.14% demands careful consideration as show that in Figure 7. A pattern of such precision may be indicative of an overfitting anomaly (Chaitongrat et al, 2021;Monteiro et al, 2021;Abdul-Samad et al, 2022), in which the model's capability to generalize to novel data could be compromised (Plebankiewicz, 2018). Future research should rigorously investigate methods to alleviate this concern, possibly through cross-validation or regularization techniques.…”