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Asiedu, R O, Frempong, N K and Alfen, H W (2017) Predicting likelihood of cost overrun in educational projects. Engineering, Construction and Architectural Management, 24(01), 21-39.

  • Type: Journal Article
  • Keywords: cost overruns; model; educational projects; ghanaian construction industry; multi linear regression; predictive variables
  • URL: https://doi.org/10.1108/ECAM-06-2015-0103
  • Abstract:
    Purpose 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. Design/methodology/approach 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. Findings 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. Research limitations/implications 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. Practical implications 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. Originality/value 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.