Forecasting low cost housing demand in urban area in Malaysia using Artificial Neural Networks (ANN)
The forecasted proportions of urban population to total population in Malaysia are steadily increasing from 26% in 1965 to 70% in 2020. Therefore, there is a need to fully appreciate the legacy of the urbanization of Malaysia by providing affordable housing. The main aim of this study is to focus on...
| Main Authors: | , , |
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| Format: | Thesis |
| Published: |
2012
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| Subjects: | |
| Online Access: | http://eprints.uthm.edu.my/2870/ http://eprints.uthm.edu.my/2870/1/FRGS_0381.pdf |
| Summary: | The forecasted proportions of urban population to total population in Malaysia are
steadily increasing from 26% in 1965 to 70% in 2020. Therefore, there is a need to fully
appreciate the legacy of the urbanization of Malaysia by providing affordable housing.
The main aim of this study is to focus on developing a model to forecast the demand of
low cost housing in urban areas. The study is focused on eight states in Peninsular
Malaysia, as most of these states are among the areas predicted to have achieved the
highest urbanization level in the country. The states are Kedah, Penang, Perlis, Kelantan,
Terengganu, Perak, Pahang and Johor. Monthly time-series data for six to eight years of
nine indicators including: population growth, birth rate; child mortality rate;
unemployment rate; household income rate; inflation rate; GDP; poverty rate and
housing stocks have been used to forecast the demand on low cost housing using
Artificial Neural Network (ANN) approach. The data is collected from the Department
of Malaysian Statistics, the Ministry of Housing and the Housing Department of the
State Secretary. The Principal Component Analysis (PCA) method has been adopted to
analyze the data using SPSS 18.0 package. The performance of the Neural Network is
evaluated using R squared (R~a)n d the accuracy of the model is measured using the
Mean Absolute Percentage Error (MAPE). Lastly, a user friendly interface is developed
using Visual Basic. From the results, it was found that the best Neural Network to
forecast the demand on low cost housing in Kedah is 2-16-1, Pahang 2-15-1, Kelantan
2-25-1, Terengganu 2-30-1, Perlis 3-5-1, Pulau Pinang 3-7-1, Johor 3-38-1 and Perak 3-
24-1. In conclusion, the evaluation performance of the model through the MAPE value
shows that the NN model can forecast the low-cost housing demand 'very good' in
Pulau Pinang, Johor, Pahang and Kelantan, where else 'good' in Kedah and Terengganu
while in Perlis and Perak it is 'not accurate' due to the lack of data. The study has
successfully developed a user friendly interface to retrieve and view all the data easily. |
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