Forecasting Low-Cost Housing Demand in Johor Bahru, Malaysia Using Artificial Neural Networks (ANN)

Abstract

There is a need to fully appreciate the legacy of Malaysia urbanization on aordable housing since the proportions ofurban population to total population in Malaysia are expected to increase up to 70% in year 2020. This study focusedin Johor Bahru, Malaysia one of the highest urbanized state in the country. Monthly time-series data have been usedto forecast the demand on low-cost housing using Artificial Neural Networks approach. The dependent indicator is thelow-cost housing demand and nine independents indicators including; population growth; birth rate; mortality baby rate;inflation rate; income rate; housing stock; GDP rate; unemployment rate and poverty rate. Principal Component Analysishas been adopted to analyze the data using SPSS package. The results show that the best Neural Network is 2-22-1 with0.5 learning rate and momentum rate respectively. Validation between actual and forecasted data show only 16.44% ofMAPE value. Therefore Neural Network is capable to forecast low-cost housing demand in Johor Bahru, Malaysia.