Forecasting low-cost housing demand in pahang, malaysia using artificial neural networks
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 o...
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| Main Authors: | , , |
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| Format: | Article |
| Published: |
Penerbit UTHM
2010
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| Subjects: | |
| Online Access: | http://penerbit.uthm.edu.my/ojs/index.php/IJSCET http://penerbit.uthm.edu.my/ojs/index.php/IJSCET http://eprints.uthm.edu.my/2108/1/IJSCETv2n1p7.pdf |
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| Summary: | 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 |
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