Artificial Neural Network on Court Auction Houses

• Main Authors: Shin-Hsu Liu, 劉時旭
• Other Authors: Machine Hsie
• Format: Others
• Language: zh-TW
• Published: 2012
• Online Access: http://ndltd.ncl.edu.tw/handle/52612806418017603818

碩士 === 國立中興大學 === 土木工程學系所 === 100 === Auction houses are the real estate of the Foreclosure. Generally speaking, the auction price is lower than general market price. In recent years, the real estate price getting higher and higher, and it attracts a large number of bidders entering the foreclosure market. For example, in 2010, the market statistics number for foreclosure is 60630, the amount of money is NT.134.4 billion . In recent years, auction houses had been subjected to people''s attention. But the information for auction houses is still extremely lacking. Due to the real estate market is an imperfectly competitive market, the proceeds of the information is often not be complete. The price can only be estimated by some fundamental concepts. By using the information to estimate a more accurate price will help reducing the risk for buyers. Therefore, this study use Artificial Neural Network to establish a model to estimate auction houses .By collecting cases of auction houses, we select the finest information as input variables, including: (1)the width of facing road (2)numbers of facing road (3) population density, (4)delivered by the court or not, (5) property rights, (6) current assessed land value, (7) land possession, (8) floor area, (9) bid price, (10) how many times the auction, etc. Analyze and review the relevant input variables Improvement Amendments through the training and the parameters of the artificial neural network learning, we can estimate the price. In particular, the population density is often that can’t be effectively achieved. In this study, we use the number of chain convenience store within a radius of 500 meters as an analysis of indicators. This innovative and facilitate accurate input variables, significantly increasing the accuracy of the research results. The research results show that: the artificial neural network is indeed available fast, accurate forecast of the result, so we recommend that this is good for assessment of auction houses.