Forecasting low-cost housing demand in pahang, malaysia using artificial neural networks

Noor Yasmin , Zainun and Ismail , Abdul Rahman and Mahroo , Eftekhari (2010) Forecasting low-cost housing demand in pahang, malaysia using artificial neural networks. International Journal of Sustainable Construction, 2 (1). pp. 1-6. ISSN 2180-3242

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Low cost housing is one of the government main agenda in fulfilling nation’s housing need. Thus, it is very crucial to forecast the housing demand because of economic implication to national interest. Neural Networks (ANN) is one of the tools that can predict the demand. This paper presents a work on developing a model to forecast lowcost housing demand in Pahang, Malaysia using Artificial Neural Networks approach. The actual and forecasted data are compared and validate using Mean Absolute Percentage Error (MAPE). It was found that the best NN model to forecast low-cost housing in state of Pahang is 1-22-1 with 0.7 learning rate and 0.4 momentum rate. The MAPE value for the comparison between the actual and forecasted data is 2.63%. This model is helpful to the related agencies such as developer or any other relevant government agencies in making their development planning for low cost housing demand in Pahang

Item Type:Article
Uncontrolled Keywords:Low-cost housing demand, ANN
Subjects:H Social Sciences > HD Industries. Land use. Labor > HD72-88 Economic development. Development economics. Economic growth
T Technology > TA Engineering (General). Civil engineering (General)
Divisions:Faculty of Civil and Environmental Engineering > Department of Construction and Building Engineering
ID Code:2108
Deposited By:Nurhafiza Hamzah
Deposited On:28 Mar 2012 08:24
Last Modified:28 Mar 2012 08:24

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