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Title: Forecasting low-cost housing demand in Johor Bahru, Malaysia using artificial neural networks (ANN)
Authors: Zainun, Noor Y.B.
Rahman, Ismail A.
Eftekhari, Mahroo
Keywords: Low-cost housing
Artificial neural networks
Principal component analysis
Issue Date: 2010
Publisher: © Canadian Center of Science and Education (CCSE)
Citation: ZAINUN, N.Y.B., RAHMAN, I.A. and EFTEKHARI, M., 2010. Forecasting low-cost housing demand in Johor Bahru, Malaysia using artificial neural networks (ANN). Journal of Mathematics Research, 2 (1), pp. 14 - 19.
Abstract: There is a need to fully appreciate the legacy of Malaysia urbanization on affordable 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.
Description: This article was published in the Journal of Mathematics Research [© Canadian Center of Science and Education (CCSE)] and licensed under a Creative Commons Attribution 3.0 License. The definitive version is available at: http://www.ccsenet.org/journal/index.php/jmr/article/view/1059
Version: Published
URI: https://dspace.lboro.ac.uk/2134/11440
Publisher Link: http://www.ccsenet.org/journal/index.php/jmr/article/view/1059
ISSN: 1916-9795
Appears in Collections:Published Articles (Architecture, Building and Civil Engineering)

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