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| Hedonic Housing Price Model Via BP Neural Network |
| SI Ji-Wen, HAN Ying-Ying, LUO Xi |
| Huazhong University of Science and Technology, Wuhan, China |
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Abstract In this paper, hedonic pricing model is used to assess the housing price in Washington, USA. For the pricing model, in this paper, the crime variables around the house are included. The model is built by hedonic pricing method through using traditional OLS method and neural network to simulate and with data modified by Boxcox transformation. The result shows the change in criminal rate makes the housing price change, and as the distance of crime to the housing and the types of crimes changes, the house price changes from 5.78% to 2.08%. In July of 2007 and the whole 2008,the influences of crime on housing price are different. It also shows that neural network is more accurate than the traditional OLS method with 5.74% higher degree of approximation, and shows better features.
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Received: 20 May 2012
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2012, Vol. 9 
