Forecasting low cost housing demand in urban area in Malaysia using a modified back-propagation algorithm

Over the past decade, the growth of the housing construction in Malaysia has been increase dramatically. The level of urbanization process in the various states in Peninsular Malaysia is considered to be important in planning for low-cost housing needs. Recent studies have found the potential applications of Artificial Neural Networks (ANN) particularly back propagation neural network (BPNN) as a successful forecasting tool to forecast low-cost housing demand. However, the training process of BPNN can result in slow convergence or even network paralysis, where the training process comes to a standstill or get stuck at local minima. This paper presents a new approach to improve the training efficiency of BPNN 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.