The objective of this study is to develop a computer based manpower demand forecasting model at occupational levels in the construction industry. The output of the model is aimed at assisting manpower planners and policy makers in the preparation of training needs of future occupational and skill demand in the industry. It will also help in redressing manpower imbalances. The model hinges on the hypothesis that there is a close relationship between Employment (E) and Gross Domestic Product (GDP) of the industry. The coefficient of Determination, R2, of both variables are close to unity. In the study, it was found that R2 is 0.95, showing a very close relationship among the two variables. This clearly supports the hypothesis of the model. There are 4 components of the model, namely Output/GDP target, Employment Estimation, Occupational Matrix and Occupational Projection. A change in any of the components would affect the output result of the model. The model enables a number of methods to be used in manpower forecasting. In the study, 2 methods are used. They are Regression Method and Elasticity of Employment. In the latter, 3 elasticity coefficients are used, namely : (a) annual elasticity coefficient of 1970 to 1981 (b) average elasticity coefficient of 1970 to 1981 (c) fixed elasticity coefficient of 0.5. The results of the projections vary with the elasticity used. The use of the different elasticity depends on the future scenarios predicted and government policies. In the study, the model was simulated to forecast occupational employment in the construction industry. There are many advantages in using the model to forecast manpower demand. It is cost effective compared to the present technique and provides speedier output since it eliminates much of the calculation which currently done manually. The model is flexible and adaptable to incorporate other variables with ability to simulate alternative scenarios. The model is also able to undertake the manpower forecast on the micro basis. In its present form, the model takes into account 2 variables i.e GDP and Employment. In reality there are other factors that affect employment such as wages, education and technology that determine the structure and type of employment in the industry. These variables may be included in the model to improve its usefulness and validity. In addition, it tends to concentrate on numerical calculations without investigating the demand and supply of labour in the labour market. Despite its limitations, the model is useful as a manpower forecasting technique. To complement its results, other approaches such as international comparison and qualitative forecasting could be used to compare with the forecast results of the model.