A Prediction Model for Forecasting the Trend of Macau Property Price Movements and Understanding the Influential Factors
Simon Fong1 and
Yap Bee Wah2
1. Faculty of Science and Technology, University of Macau, Macau SAR
2. Faculty of Computer and Mathematical Sciences, Universiti Teknologi MARA, Shah Alam, Selangor, Malaysia
2. Faculty of Computer and Mathematical Sciences, Universiti Teknologi MARA, Shah Alam, Selangor, Malaysia
Abstract—Property price and transaction activities may contain many factors. Data mining for property research provides a feasible way to analyze the trend and to understand the underlying influential factors. This paper addresses the issues and techniques on experiences in data mining for Macau property market. The original data for property price and factors are obtained as a multi-attribute dataset from the Statistics and Census Service of Macao SAR Government. The challenge is to apply different data mining methods and algorithms which include SVM, Neural Network, C&R Tree, Weka, SPSS, Multilayer Perception Model in order to identify hidden knowledge.
Index Terms—data mining, SVM, neural network, C&R tree, weka, SPSS, multilayer perception model
Cite: Simon Fong and Yap Bee Wah, "A Prediction Model for Forecasting the Trend of Macau Property Price Movements and Understanding the Influential Factors," Journal of Emerging Technologies in Web Intelligence, Vol. 5, No. 2, pp. 122-131, May 2013. doi:10.4304/jetwi.5.2.122-131
Index Terms—data mining, SVM, neural network, C&R tree, weka, SPSS, multilayer perception model
Cite: Simon Fong and Yap Bee Wah, "A Prediction Model for Forecasting the Trend of Macau Property Price Movements and Understanding the Influential Factors," Journal of Emerging Technologies in Web Intelligence, Vol. 5, No. 2, pp. 122-131, May 2013. doi:10.4304/jetwi.5.2.122-131
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