A New Modified Back-Propagation Algorithm for Forecasting Malaysian Housing Demand


Article Preview

Over the past decade, the growth of the housing construction in Malaysia has been increase dramatically and the level of urbanization process in Malaysia is considered to be important in planning for low-cost housing needs. Unfortunately, there is a clear miss-match between the supply and the demand of low cost housing in Malaysia. Due to the problems faced, there have been several attempts in predicting housing demands using the artificial-neural networks (ANN) technique particularly back-propagation (BP). However, the training process of BP can result in slow convergence or even network paralysis and can easily get stuck at local minima. This paper presents a new approach to improve the training efficiency of BP algorithms to forecast low-cost housing demand in one of the states in Peninsular Malaysia. The proposed algorithm (BPM/AG) adaptively modifies the gradient based search direction by introducing the value of gain parameter in the activation function. The results show that the proposed algorithm significantly improves the learning process with more than 31% faster in term of CPU time and number of epochs as compared to the traditional approach. The proposed algorithm can forecast low-cost housing demand very well with 6.62% of MAPE value.



Edited by:

Amanda Wu




N. M. Nawi et al., "A New Modified Back-Propagation Algorithm for Forecasting Malaysian Housing Demand", Applied Mechanics and Materials, Vol. 232, pp. 908-912, 2012

Online since:

November 2012




[1] Government of Malaysia. The Ninth Malaysia Plan, 2006-2010. Kuala Lumpur: Percetakan Nasional Malaysia Berhad, (2006).

[2] Ong, Han Ching and Lenard, D. Partnership between Stakeholders in the Provision of an Access to Affordable Housing in Malaysia., FIG XXII International Congress. USA, Washington D.C., (2002).

[3] Syafiee Shuid. Low Medium Cost Housing in Malaysia: Issues and Challanges, Department of Urban and Regional Planning. International Islamic University Malaysia, September (2004).

[4] N Bakhary, K Yahya, N Ng Chin. Univariate Artificial Neural Network In Forecasting Demand of Low Cost House in Petaling Jaya (ANN),. Jurnal Teknologi, Universiti Teknologi Malaysia, June 2004. 40: pp.1-16.

DOI: https://doi.org/10.11113/jt.v40.406

[5] Noor Yasmin Zainun, Ismail Abdul Rahman, Mahroo Eftekhari. Forecasting Low-Cost Housing Demand in Urban Area in Malaysia using Artificial Neural Networks: Batu Pahat, Johor,. Journal of Mathematics Research, February 2010. Vol 2, No. 1: pp.14-19.

DOI: https://doi.org/10.5539/jmr.v2n1p14

[6] D.E. Rumelhart, G.E. Hinton, and R.J. Williams, Learning internal representations by error propagation. in D.E. Rumelhart and J.L. McClelland (eds), Parallel Distributed Processing, 1986. 1: pp.318-362.

DOI: https://doi.org/10.1016/b978-1-4832-1446-7.50035-2

[7] Zweiri, Y.H., Seneviratne, L D., Althoefer, K.: Stability Analysis of a Three-term Back-propagation Algorithm. J. Neural Networks. 18, 1341- -1347 (2005).

DOI: https://doi.org/10.1016/j.neunet.2005.04.007

[8] Bishop C. M., Neural Networks for Pattern Recognition. 1995: Oxford University Press.

[9] Codrington C. and Tenorio M. (1994). Adaptive Gain networks., Proceedings of the IEEE International Conference on Neural Networks (ICNN94) 1: 339-344.

DOI: https://doi.org/10.1109/icnn.1994.374186

[10] Nazri, M. N., Ransing, M. S., and Ransing, R. S., An improved learning algorithm based on the Broyden-Fletcher-GoldfarbShanno (BFGS) method for back propagation neural networks, Sixth International Conference on Intelligent Systems Design and Applications. Volume 1, 2006, Pages 152-157 (2006).

DOI: https://doi.org/10.1109/isda.2006.95

[11] Nawi, N.M., Ransing, R.S., Salleh, M.N.M., Ghazali, R., and Hamid, N.A. An improved back propagation neural network algorithm on classification problems, International Conferences on Database Theory and Application, DTA 2010. Volume 118 CCIS, 2010, Pages 177-188.

DOI: https://doi.org/10.1007/978-3-642-17622-7_18