Search results

Being able to predict the likelihood of a project to overrun its cost before the contract signing phase is crucial in developing the required mitigating measures to avert it. Known parameters that permit the timely prediction of cost overrun provide the basis for such predictions. Therefore, the purpose of this paper is to develop a model for forecasting cost overruns.

Ten predictive variables known before the contract signing phase of a project are identified. Based on a survey approach, information on 321 educational projects completed are compiled. A multiple linear regression analysis is adopted for the model development.

Five variables – initial contract sum, gross floor area, number of storeys, source of funds and contractors’ financial classification are observed to influence cost overruns. The model, however, yields a fairly weak coefficient of determination with a mean absolute percentage error of 30.22 and 138 per cent, respectively.

The model developed focussed on data only educational projects sampled from three out of the ten administration regions in Ghana based on a purposive sampling approach.

Policy makers and construction managers working on public projects stand to gain tremendous assistance in formulating and strengthening their own in-house cost forecasting at the precontract phase based on “what if” analysis to generate various alternative predictions of cost overruns.

Considering the innate nature of cost overruns within the Ghanaian construction industry often resulting to project abandonment, this research presents a unique dimension for tackling cost overruns based on a predictive approach.

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.

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.

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.

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.

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.

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.