Modelling property markets using neural network

• Main Author: Phang, Kok Chiang.
• Other Authors: Wang Lipo
• Format: Final Year Project
• Language: English
• Published: 2012
• Subjects: DRNTU::Engineering::Electrical and electronic engineering::Control and instrumentation
• Online Access: http://hdl.handle.net/10356/49595
• Institution: Nanyang Technological University

This paper aims to look at property market in Singapore and the factors that affect the property prices for both private and public resale housing. It uses Neural Network techniques to analyse the impact of macroeconomic factor, supply and demand factors, government policy and land usage on price movement of the various housing sectors locally. The paper investigates the effectiveness of a number of neural network architectures in predicting property housing prices. With selected economic indicators, the study aim to establish the private property and public resale housing prices in Singapore with the help of Artificial Neural Networks (ANN). The predictive and generalization ability of the Artificial Neural Networks (ANN) will be used to explore private property price in Singapore. ANN is used in this forecast particularly due to its ability to handle non linear problem and give a good prediction. The historical data of these indicators which are found to be ideal will be used as input and will be used to train the Artificial Neural Networks (ANN), the trained Artificial Neural Networks (ANN) will be able to infer from the training based on the input indicators. This predictive ability of the trained ANN can be used by the government or economic planners in Singapore for further studies such as modelling the likely effects of new economic policies to adjust the property prices when there is a need to intervene the property market.