FORECASTING LOW COST HOUSING DEMAND IN MALAYSIA: COMPARISON BETWEEN ANN AND ARIMA METHOD

Abstract

One of Malaysias longstanding development objectives is the provision of affordable housing for Malaysian, with a focus on lower-income groups. It is very crucial to predict low-cost housing demand to match the demand and supply so that the government can plan the allocation of low cost housing based on the demand. Thus the aim of this study is to forecast low-cost housing demand in Johor, Malaysia using ARIMA model. Time series data on low-cost housing demand have been converted to Ln before develop the model. Three ARIMA model were used; ARIMA (1,0,1); ARIMA (1,0,0) and ARIMA (2,0,0). The performance of models was validated using Mean Absolute Percentage Error (MAPE). The results show that ARIMA (1,0,1) is the best model with MAPE value 3.9%. It can be conclude that ARIMA method can forecast low cost housing demand in Johor slightly better than ANN.