The aim of the study is to develop a project cost centre utility parameter-based
econometric model that incorporates econometric parameters using neural network.
Construction cost of residential building projects was used in this study. Random sampling
technique was used to select projects completed between 2009 and 2011 , and were
examined for their cost centres validity. Final construction cost (As-built cost) of selected
four hundred (400) projects were further modified with econometric factors like inflation
index, cost entropy and entropy factor and were used to form and train neural network
Back propagation neural network algorithm used. Probability technique was used to
generate risk impact matrix and influence of entropy on the cost centres. In this study a
parametric model similar hedonic models was generated using the utility parameters within
the early and late elemental dichotomy. The developed model was validated through
comparative analysis ofthe econometric loading attributes of the variables involved, using
Monte Carlo technique of SPSS software by extracting the resultant contingency
coefficient. This attribute would help client, project team and contractor manage cost of
construction, also, it would enable a builder or contactor load cost implication of an
unseen circumstance even on occasion of deferred cost reimbursement and hel
