Following widespread acceptance by researchers that the effects of qualitative/managerial construction time-influencing factors need to be considered in project scope-based construction time predicting models, several multivariate models combining project scope and qualitative/managerial factors have been developed. However, it has been shown in literature that the applicability of these models is clearly limited to the regions/countries where they were developed. This study was therefore aimed at developing a multivariate construction time predicting model that will be applicable to the Nigeria construction industry. A self-administered questionnaire survey was used to source information on the quantitative (project scope) factors considered in the study as well as to assess the extent of influence of the qualitative factors on construction time. Principal component regression was used for the data analysis and model development, using SPSS 16.0 for windows. Following a non-normal distribution of errors and a low R2 value obtained when multiple linear regression analysis was first conducted, the study’s data set was double log transformed and then partitioned/reclassified to account for public and private sector projects. Three models were developed following the multiple linear regression analysis repeated after transforming and partitioning/reclassifying the study’s data set. Two of these models (the public sector model and the private sector model) had high R2 values and were found after testing and validation, to be suitable for predicting construction time, while one of the models (the all projects model) had a low R2 value and was consequently found to be unsuitable for predicting construction time. The models with high R2 values serve as a useful

tool to project managers and contractors for predicting construction time, thereby facilitating effective planning.

The importance of ensuring accuracy and reliability of construction time estimates at the tendering stage cannot be overemphasized. Accurate early estimates of construction time typically provides clients and contractors with a basis for evaluating the success of a project and the efficiency of the project organisation (Nkado, 1995). They also provide them with a basis for ascertaining logistical and cash flow implications for feasibility, budgeting, planning, monitoring and even litigation purposes (Skitmore and Ng, 2003). Furthermore, they serve as a criteria in determining the best combination when performing time-cost optimization (Que, 2002). It is therefore clear that construction time has become a vital tool used by clients and contractors to ensure the success of construction projects. This success will however, only be achieved when construction time is accurately predicted.

Construction time/periods are often calculated on the basis of the planner’s own previous experience on similar projects (Choudhury and Phatak, 2004). However, as pointed out by Skitmore and Ng (2003), the need to reduce the problem of subjectivity associated with the planner’s experience and judgement to correctly interpret project and site information and make the best possible decisions, has long resulted in the development of construction time predicting models.

The development of construction time predicting models commenced with the use of project scope factors. Project scope is a measure of project size, which can be described as construction cost, project duration, gross floor area, number of storey, building type and procurement method (Walker, 1995).