Predicting changes in construction cost indexes using neural networks

Construction cost indexes provide a comparison of cost changes from period to period for a fixed quantity of goods or services. Back-propagation neural-network models have been developed to predict the change in the ENR construction cost index for one month and six months ahead. A training set of macroeconomic data was developed for the period from 1967 to 1991. The neural-network models use inputs including recent trends in the index, the prime lending rate, housing starts, and the month of the year. Output from the neural-network models was compared with predictions made by exponential smoothing and simple linear regression. The prediction produced by the neural network gave a greater error than either exponential smoothing or linear regression. It can be concluded that the movement of the cost indexes is a complex problem that cannot be predicted accurately by a back-propagation neural-network model.