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Forecasting low-cost housing demand in Johor Bahru, Malaysia using artificial neural networks (ANN)

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posted on 2013-01-14, 13:52 authored by Noor Y.B. Zainun, Ismail A. Rahman, Mahroo EftekhariMahroo Eftekhari
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.

History

School

  • Architecture, Building and Civil Engineering

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.

Publisher

© Canadian Center of Science and Education (CCSE)

Version

  • VoR (Version of Record)

Publication date

2010

Notes

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

ISSN

1916-9795

Language

  • en

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