Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/45657
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dc.contributor.authorChew, Kelvin Yuan Sheng.
dc.date.accessioned2011-06-16T01:19:44Z
dc.date.available2011-06-16T01:19:44Z
dc.date.copyright2011en_US
dc.date.issued2011
dc.identifier.urihttp://hdl.handle.net/10356/45657
dc.description.abstractThe property market is a safe and appreciating asset class in many cities, hence represents an excellent investment opportunity for investors. With vibrant yet volatile activities in the property sector, it is crucial for investors to time their entry and exit into the property market for higher rate of returns. The report investigated the effectiveness of a number of neural network architectures in predicting property housing prices. The most accurate architecture found was the general regression network with the ability to predict public housing prices with a small error of less than 4%, hence revealing the effectiveness of neural network in predicting housing prices.en_US
dc.format.extent45 p.en_US
dc.language.isoenen_US
dc.rightsNanyang Technological University
dc.subjectDRNTU::Engineering::Electrical and electronic engineering::Control and instrumentationen_US
dc.titleModeling property markets using neural networken_US
dc.typeFinal Year Project (FYP)en_US
dc.contributor.supervisorQuah Tong Sengen_US
dc.contributor.schoolSchool of Electrical and Electronic Engineeringen_US
dc.description.degreeBachelor of Engineeringen_US
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Appears in Collections:EEE Student Reports (FYP/IA/PA/PI)
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