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

Noor Yasmin Zainun | Zendy; Ismail Abdul Rahman | Zendy; Mahroo Eftekhari | Zendy

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

Author(s) -

Noor Yasmin Zainun,

Ismail Abdul Rahman,

Mahroo Eftekhari

Publication year - 2010

Publication title -

journal of mathematics research

Language(s) - English

Resource type - Journals

eISSN - 1916-9809

pISSN - 1916-9795

DOI - 10.5539/jmr.v2n1p14

Subject(s) - artificial neural network , population , inflation rate , econometrics , unemployment rate , economics , statistics , mathematics , unemployment , agricultural economics , interest rate , computer science , finance , economic growth , demography , artificial intelligence , sociology

There is a need to fully appreciate the legacy of Malaysia urbanization on aordable housing since the proportions of urban population to total population in Malaysia are expected to increase up to 70% in year 2020. This study focused in Johor Bahru, Malaysia one of the highest urbanized state in the country. Monthly time-series data have been used to forecast the demand on low-cost housing using Artificial Neural Networks approach. The dependent indicator is the low-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 Analysis has been adopted to analyze the data using SPSS package. The results show that the best Neural Network is 2-22-1 with 0.5 learning rate and momentum rate respectively. Validation between actual and forecasted data show only 16.44% of MAPE value. Therefore Neural Network is capable to forecast low-cost housing demand in Johor Bahru, Malaysia.

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