Please use this identifier to cite or link to this item: http://hdl.handle.net/11455/10752
標題: 類神經網路應用於法拍不動產估價
Artificial Neural Network on Court Auction Houses
作者: 劉時旭
Liu, Shin-Hsu
關鍵字: 類神經網路;Artificial Neural Network;法拍不動產;法拍屋;不動產估價;超商數;Court Auction Real Estate;Court Auction Houses;Real Estate Appraisal;Number of Chain Convenience Store
出版社: 土木工程學系所
引用: 一、 中文 1. 張金鶚,范垂爐(1993),房地產真實交易價格之研究,住宅學報。 2. 蔡芬蓮(1997),法拍屋價格影響因素之研究-以台北市為例,政大地研所碩士論文。 3. 蔡瑞煌,高明志,張金鶚(1999),類神經網路應用於房地產估價之研究,住宅學報,第八期。 4. 王宋民(1999),都會地區房價之特徵價格分析-以台北市信義區為例,國立臺灣大學農業經濟學研究所碩士論文。 5. 趙子鑫(2002),法拍屋價格決定因素之研究-以台北市之中小型住宅為例,國立台北大學企業管理學系碩士班論文。 6. 李曉隆(2002),出租公寓之租金價格預測-複迴歸分析與類神經網路之比較,國立台灣科技大學碩士論文。 7. 葉怡成(2003),應用類神經網路。儒林。 8. 錢定家(2003),法拍屋底價訂定對拍定價格與拍賣結果影響之研究,朝陽科技大學財務金融研究所碩士論文。 9. 魏如龍(2003),類神經網路於不動產價格預估效果之研究,政大地研所碩士論文。 10. 林英彥(2004),不動產估價(十版),文笙書局。 11. 林致慶(2004),以住宅次市場觀點探討台南市法拍屋價格及影響因素之研究,長榮大學土地管理與開發研究所碩士論文。 12. 李佳璋(2004),重劃區住宅價格之調查研究-以台南市虎尾寮及鄭子寮為例,長榮大學碩士論文。 13. 吳富鶯(2007),影響法拍屋拍定價格之因素探討-以高雄市透天厝為例,高雄應用大學金融資訊所碩士論文。 14. 賴碧瑩(2007),應用類神經網路於電腦輔助大量估價之研究,住宅學報,第十六卷第二期。 15. 黃于祐(2007),台北市房價影響因素之空間分析-地理加權迴歸方法之應用,臺北大學都市計劃研究所碩士論文。 16. 葉怡成(2009),類神經網路模式應用與實作,儒林圖書。 17. 劉玉婷(2009),應用迴歸分析及類神經網路建構不動產估價模式-以台中市住宅為例,國立雲林科技大學營建工程系研究所碩士論文。 18. 謝富順、張巧宜(2010),臺灣法拍屋之拍定價格與面積關係之探究住宅學報,第十九卷第二期,學術論著。 二、 英文 1. Milgrom, P., and R. Weber., A Theory of Auctions and Competitive Bidding , Econometrica,1982. 2. Shilling, J.D., J.D. Benjamin and C.F. Sirmans, Estimating Net Realizable Value or Distressed Real Estate,Journal of Real Estate Research, 1990. 3. Hardin,W.G. and M.L. Wolverton, The Relationship between Foreclosure Status and Apartment Price, Journal of Real Estate Research, 1996. 4. Lusht , K. M.,A Comparison of Prices Brought by English Auction and Private Negotiations, Journal of Real Estate Economics,1996. 5. McCluskey, W. J.,R. A. Borst. An Evaluation of MRA, Comparable Sales Analysis and ANNs for the Mass Appraisal of Residential Property in Northern Ireland. Assessment Journal,1997. 6. Carroll, T, T. Clauritie and H. Neill, Effect of Foreclosure Status on Residential Selling Price: Comment, Journal of Real Estate Research, 1997. 7. Dotzour. G.. M., E. .Moorhead, and D. T.Winkler., The Impact of Auction on Residential Sales Pricing in New Zealand, Journal of Real Estate Research,1998. 8. Markham, I. S.,T. R. Rates. The Effect of Sample Size and Variability of Data on the Comparative Performance of Artificial Neural Networks and Regression. Computer Operation Research,1998. 9. Mayer, C. J., Assessing the Performance of Real Estate Auction, Journal of Real Estate Economics,1998. 10. Wong, K.C.,A. P. So,Y. C. Hung,K. Wang,M. L. Wolvertom (ed.).Real Estate Valuation Theory. American Real Estate Society (ARES) Monograph,2001. 11. Krishna. V., Auction Theory , USA: Elsevier Science,2002. 12. Quan, D. C., Market Mechanism Choice and Real Estate Disposition: Search vs Auction, Journal of Real Estate Economics,2002. 13. Christy, L. J, and J. L. Zaichkowsky., Bidding Behavior at the Auction, Psychology and Marketing,2003. 14. Radosveta, I., and T. C. Salmon., Bidder Preferences Among Auction Institutions, Economic Inquiry, 2004. 15. Visit L.,G. Christopher,M. Lee.House Price Predication: Hedonic Price Model vs. Artificial Neural Network. American Journal of Applied Science,2004. 三、 網頁 1. 臺中市政府:http://www.taichung.gov.tw/ 2. 臺中市北屯區公所:http://www.beitun.taichung.gov.tw 3. 地籍圖資網路便民服務系統:http://easymap.land.moi.gov.tw/K02Web/K02Land.jsp 4. 司法院法拍、主文、庭期查詢服務:http://www.judicial.gov.tw/db/alx.asp 5. 住商不動產房仲網:http://www.hbhousing.com.tw/ 6. 信義房屋:http://www.sinyi.com.tw/ 7. 維基百科:http://zh.wikipedia.org/zh-tw/Wiki
摘要: 
法拍屋乃是法院進行拍賣因債務抵押之不動產。一般而言,拍定價格會比市場行情價格為低,因而在房地產價格越來越高的近年來,吸引了大量投標者進入法拍屋市場。例如:單單就我國2010年法拍市場統計,法拍件數6萬630件,拍定金額高達1344億元。

法拍市場在近年來逐漸受到人們重視,然而目前針對法拍不動產估價做為研究之成果仍極度缺乏,由於不動產市場為一不完全市場,所得之資訊常會有不完整之處,對不動產的價格也僅能有一些基本概念性的推估,如果能從這些資訊中推估出較為準確的價格,將有助於降低買方的風險。因此本研究,應用類神經網路建立一個法拍不動產拍定價格預估模式,期能提供買方使用者做一參考使用。本研究收集法拍屋拍定價格之實際案例,並篩選評估可取得性,精挑重要資訊作為輸入變數,包含:(1)面臨路寬、(2)基地面臨道路數、(3)人口密度(流量)、(4)是否點交、(5)產權持份類型、(6)公告現值、(7)土地持份、(8)樓地板面積、(9)拍定價格、(10)第幾次拍賣,等數個變數。分析檢討與各項相關的輸入變數,透過類神經網路的學習訓練及參數改進修正,最後進行法拍不動產拍定價格預估。其中,「人口密度」此一選項,一般民眾無法有效取得此一資訊。本研究權宜採用:方圓五百公尺內之超商數目做為一分析指標。此一創新且便利準確之輸入變數,大幅提升研究成果之精確性。研究成果顯示:類神經網路確實可得到快速、精確的預估成果,建議非常適合作為法拍屋購買的決策評估使用。

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.
URI: http://hdl.handle.net/11455/10752
其他識別: U0005-2806201215375600
Appears in Collections:土木工程學系所

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