LAUSR

Owners need clear information about the cost at the early stage, while information about drawings and designs are limited in this stage. The purpose of this research is to propose a hybrid model using artificial neural networks and the regression analysis to estimate the cost of residential buildings in Egypt. Real data were collected from 174 real residential projects in Egypt. The Delphi technique was used to reach a consensus on the key factors affecting early stage cost estimation. Artificial neural network models were developed with various numbers of hidden layers and types of activation functions. The sensitivity analysis showed that the most effective factors in the cost estimate at the early stage are the number of floors and the area of the floors. The multiple linear regression, polynomial regression, gamma regression, and Poisson regression were used. The proposed hybrid model was extracted from the ANN model and the regression models using multiple linear regression. The mean absolute percentage error of the hybrid model was 10.64% which is less than the absolute percentage error of the ANN model and the regression models. The results of the hybrid model indicate that the model was successful in estimating the cost of residential projects and would be useful for decision-makers in the construction industry.