A New Modified Back-Propagation Algorithm for Forecasting Malaysian Housing Demand

Abstract

Over the past decade, the growth of the housing construction in Malaysia has been increase dramatically and the level of urbanization process in Malaysia is considered to be important in planning for low-cost housing needs. Unfortunately, there is a clear miss-match between the supply and the demand of low cost housing in Malaysia. Due to the problems faced, there have been several attempts in predicting housing demands using the artificial-neural networks (ANN) technique particularly back-propagation (BP). However, the training process of BP can result in slow convergence or even network paralysis and can easily get stuck at local minima. This paper presents a new approach to improve the training efficiency of BP algorithms to forecast low-cost housing demand in one of the states in Peninsular Malaysia. The proposed algorithm (BPM/AG) adaptively modifies the gradient based search direction by introducing the value of gain parameter in the activation function. The results show that the proposed algorithm significantly improves the learning process with more than 31% faster in term of CPU time and number of epochs as compared to the traditional approach. The proposed algorithm can forecast low-cost housing demand very well with 6.62% of MAPE value.