Modeling property markets using neural network - Chew, Kelvin Yuan Sheng. (2011)
The 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.