Forecasting low-cost housing demand in Pahang, Malaysia using artificial neural networks

Low cost housing is one of the government main agenda in fulfilling nation’s housing need. Thus, it is very crucial to forecast the housing demand because of economic implication to national interest. Neural Networks (ANN) is one of the tools that can predict the demand. This paper presents a work on developing a model to forecast lowcost housing demand in Pahang, Malaysia using Artificial Neural Networks approach. The actual and forecasted data are compared and validate using Mean Absolute Percentage Error (MAPE). It was found that the best NN model to forecast low-cost housing in state of Pahang is 1-22-1 with 0.7 learning rate and 0.4 momentum rate. The MAPE value for the comparison between the actual and forecasted data is 2.63%. This model is helpful to the related agencies such as developer or any other relevant government agencies in making their development planning for low cost housing demand in Pahan