Parametric time overrun estimation of building projects

Purpose Time overruns are commonplace within the construction industry. These result in deception because project managers critically assess the economic and financial viability of a project before implementation. Forecasting the likelihood of time overruns will not only lead to a reconsideration on the decision to build but also help put in place the necessary control measures – exactly what this research achieved. Design/methodology/approach The paper argues that rather than depending on the critical failure factors that are unknown at the pre-contract stage to forecast the likelihood of occurrence, it will be more useful to rely on project attributes that are known before contract signing. A multiple linear regression analysis is used for the model development based on ten independent variables. Findings About 86.6 per cent of all the projects experienced time overruns. The mean time overrun is 106.5 per cent. Initial contract sum, initial duration, gross floor area, contractor class D2K2, competitive tendering, sole sourcing and single-storey buildings explained about 44.7 per cent of the variations within time overruns, with a mean absolute percentage error of 60.7 per cent. Research limitations/implications The predictive accuracy of the model can, in practice, be tested after the completion of a project by comparing the actual project schedule with the planned schedule. Any disparity in the expected outputs should result in a reassessment of the significant independent variables to improve the forecasting abilities of the model. Practical implications The model is expected to be very useful at the pre-contract stage when detailed designs are unavailable. As a decision support system, it will help the practitioners and decision-makers make informed decisions while minimizing the time and resources spent to arrive at these decisions. Originality/value This research presents a unique opportunity to forecast the likelihood of time overruns within the building sector based on project attributes that are known before the contract-signing phase.