Techniques to develop forecasting model on low cost housing in urban area

Abstract

The number of people who will live in urban areas is expected to double to more than five billion between 1990 to 2025. Therefore, accurate predictions of the level of aggregate demand for housing are very important. Various forecasting techniques have been developed using probabilistic, statistics, simulation or artificial intelligent. Hence, there is a need to identify different techniques, in terms of accuracy, in the prediction of needs for facilities. This paper discusses the Artificial Neural Networks (ANN) technique and compaes it with other techniques in forecasting needs of housing in urban area. Investigation on previous research and literature materials will be derived and compared in terms of errors in the accuracy of the technique. The findings of this study indicates that the ANN model performs best overall