A Study Affecting Factors for the Residential Property Value using Artificial Neural network models

• Main Authors: Lee,Tsung-Lin, 李宗霖
• Other Authors: Hung, Shin-Lin
• Format: Others
• Language: zh-TW
• Published: 2016
• Online Access: http://ndltd.ncl.edu.tw/handle/5fj68h

碩士 === 國立交通大學 === 工學院工程技術與管理學程 === 104 === Taiwan housing prices in most rely expertise judgment, but on the basis of subjective cognitive conditions of each person are not the same. But also because the transaction information is not transparent, resulting in prices is not perfect. So how to find the factors that affect housing prices, many research direction. In this study, back-propagation neural network to predict the price of residential houses Nangang District, Taipei City. By way of literature review to identify the impact of residential housing prices 15 factor as input variables. Before entering back-propagation neural network, the first for data filtering and normalization, after 164 cases and 39 sets of training after a set of test cases, reached 81.81% accuracy, showing the price is associated with the existence of a factor. Finally, the training results then sensitivity analysis to identify the real impact factor Taipei Nangang District house price. The results show, for the Nangang District has an impact on property prices rose by more factors have three, respectively, where the total number of floors, the ratio of public buildings, and the distance between the two countries. However, in the 15 factors in the sensitivity of the two relatively low number of factors, the deletion of the small distance and the climate index two factors, the input type of neural network to complete the convergence, showing the remaining 13 house price impact factors for the class of neural networks The road has an important influence.