臺灣博碩士論文加值系統

根據金管會最新統計顯示，房貸逾放比由2014年底的0.15%，至2015年底的0.18% ，到2016年4月的0.19%，顯示逾放比持續攀升的問題，已影響國內金融體系的穩定運作。
由於近年來國內經濟衰退以及銀行業的激烈競爭，使得逾放比及逾放金額的節節攀升，金管會公布到今年2月底止，本國銀行逾期放款為766億元，較1月底增加60億元，國內不良債權(法拍屋)金額有日益升高的趨勢，如此一來金融機構貸款意願便轉趨保守，金融業為加速去化逾期放款，因而造成法拍案件激增，鉅額之不良債權導致金融機構貸款意願轉趨保守，為處理龐大的不良債權，金融機構將不良資產以法拍、銀拍、金拍、等方式執行。
本研究以內政部不動產資訊平台所提供法拍屋實價登錄的歷史成交案例作討論，蒐集並予以彙整分析出可能影響法拍屋價格之初步因子選項再利用統計軟體SPSS對初步因子與輸出變數(拍定價格)進行相關性分析，客觀挑選出影響法拍屋投標價格之重要因子作為研究模型的輸入參數，並應用不同的人工智慧理論，進行案例資料庫的學習訓練，再以各種推論模式進行測試，得到法拍屋投標價格之預測成果值。
為驗證各種人工智慧(AI)推論模式之預測準確性，本研究展現出各種模型預測成果比較，並分別以平均絕對百分比誤差(Mean Absolute Percent Error， MAPE)、均方根誤差 (Root Mean Square Error， RMSE)、平均絕對誤差(Mean Absolute Error， MAE)、線性相關係數(Linear Correlation Coefficient， R)，進行預測準確性之誤差衡量，最後再以參考索引指數(Reference Index, RI)做為整體成果評估標準而得到驗證結論，結果以「 SOS-LSSVM 」可得最佳預測。

Based on the latest statistical data from the Financial Supervisory Commission (FSC), the continuous increase in the mortgage ratio (from 0.15% at the end of 2014 to 0.18% at the end of 2015, and to 0.19% in April 2016) is a problem that already affects the stability of the national financial system.
In recent years, economic recession and fierce competition in the banking industry have caused the mortgage ratio and mortgage amount to increase constantly. According to the FSC, the total amount of nonperforming loans (NPLs) at the end of February 2017 was NTD76.6 billion, an increase of NTD6 billion from the previous month. The rising bad debt (foreclosures) in the country has caused financial institutions to be conservative in granting loans. To quickly recover NPLs, the finance industry has been issuing foreclosures on defaulters, causing a rapid increase in the number of foreclosure cases. Foreclosures involve auctioning off the mortgaged asset under the supervision of the judicial court, bank, or the Taiwan Financial Asset Service Corporation.
This study discusses the transaction history of foreclosure cases and their registered prices. Data from the Real Estate Information Platform of the Ministry of the Interior are analyzed to determine which antecedents could affect the foreclosure price. SPSS is employed for a correlation analysis on the antecedents and output variable (auction price), and critical factors affecting the bidding price in the auction are objectively selected as the input parameters for the research model. Various artificial intelligence (AI) theories are adopted to train the case database, and various inference models are applied to test and obtain the predicted outcome value of the auctioned house bidding price.
To validate the predictive accuracy of the AI models, the predicted outcomes are compared using the mean absolute percent error (MAPE), root mean square error (RMSE), mean absolute error (MAE), and linear correlation coefficient (R) to evaluate the error measure of the predictive accuracy. Finally, a reference index (RI) is derived to serve as the overall evaluation criterion to validate conclusion. The optimal forecast can be obtained using a symbiotic organisms search-least squares support vector machine (SOS-LSSVM).

第一章 緒論 1
1.1研究動機 1
1.2研究目的 3
1.3研究範圍與限制 4
1.4研究方法與流程 5
1.4.1研究方法 5
1.4.2研究流程 6
1.5論文架構 9
第二章 文獻回顧 10
2.1法拍屋的由來 10
2.1.1何謂法拍屋 11
2.1.2成為法拍屋的原因 12
2.1.3法院拍賣不動產之作業流程 12
2.2不動產的估價方法 24
2.2.1比較法 24
2.2.2成本法 25
2.2.3收益法 27
2.3不動產的價格影響因素 30
2.3.1不動產價格影響因素之相關文獻 32
2.3.2一般因素 34
2.3.3區域因素 35
2.3.4個別因素 35
2.3.5內政部不動產資訊平台法拍屋案例搜尋 36
2.4人工智慧推論模式 36
2.4.1類神經網絡(ANN) 36
2.4.2倒傳遞類神經網路(BPNN) 38
2.4.3支持向量機(SVM) 39
2.4.4最小平方差支持向量機(LS-SVM) 41
2.4.5生物共生演算法最小平方差支持向量機(SOS-LSSVM) 43
第三章 法拍價之影響因子確立 50
3.1 建立資料庫 50
3.2確立法拍價格影響因子 51
3.2.1因子評估 51
3.2.2因子篩選 53
3.2.3因子確立 54
3.3 正規化 54
第四章 法拍價推論模式建立與驗證 56
4.1推論模式選用 56
4.2模式訓練與測試 57
4.3預測成果比較 60
4.3.1誤差衡量 60
4.3.2各模式結果比較 63
第五章 結論與建議 65
5.1結論 65
5.2建議 66
參考文獻 67

1.趙子鑫，「法拍屋價格決定因素之研究-以台北巿之中小型住宅為例」，國立臺北大學碩士論文，2003
2.林致慶，「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」，長榮大學碩士論文，2004。
3.邱國隆，「影響法拍屋市場價格因素之研究-以台中市透天屋為個案分析」，朝陽科技大學碩士論文，2006。
4.周佳穎，「法拍屋拍定機率之研究-以台北市為例」，世新大學碩士論文，2008。
5.黃于祐，「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」，國立台北大學碩士論文，2008。
6.陳治勳，「應用模糊類神經網路於房地產價格之研究─以北、高兩市為例」國立屏東商業技術學院碩士論文，2008。
7.劉玉婷，「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」，國立雲林科技大學碩士論文，2009。
8.楊謙柔，「都市住環境設施評價模式之研究」，中國文化大學博士論文，2009。
9.黃韋憲，「都會區住宅用地價格評估模式之建構」，國立中央大學碩士論文，2010。
10.張志弘，「影響新北市永和區不動產價格因素分析-以法拍屋市場為例」，國立台灣大學碩士論文，2011。
11.蔡爾逸，「應用支撐向量機(SVM)於都市不動產價格預測之研究」，國立中央大學碩士論文，2012。
12.李宣佑，「顧客需求導向對都會區不動產特徵價格影響之研究」，國立中央大學碩士論文，2014。
13.鄭明安，「不動產估價理論與方法」，五南書局，2000。
14.A.-K. Jain, J. Mao and K.-M. Mohiuddin, Artificial neural networks: a tutorial, Computer, vol. 29(3), pp. 31-44, 1996.
15.Cortes and V. Vapnik, “Support-vector network”, Machine Learning, Vol.20, No.3, pp.273-297, 1995.
16.Suykens, J., et al., 2002, Least Square Support Vector Machines. World Scientific Publishing Co. Pte. Ltd.
17.Min-Yuan Cheng and Nhat-Duc Hoang, 2012, Evolutionary Least Squares Support Vector Machine – Userguide, Technical Report, CIC Lab, National Taiwan Univ. of Sci. and Tech.
18.Min-Yuan Cheng and Doddy Prayogo, “Symbiotic Organisms Search: A new metaheuristic optimization algorithm”, Computers & Structures, Vol. 139, pp. 98-112, 2014.
19.G.G. Tejani, V.J. Savsani, Patel V. K., 2016, Adaptive symbiotic organisms search (SOS) algorithm for structural design optimization. Journal of Computational Design and Engineering.
20.M.-Y. Cheng, D. Prayogo, D.-H. Tran, 2015, Optimizing Multiple-Resources Leveling in Multiple Projects Using Discrete Symbiotic Organisms Search. Journal of Computing in Civil Engineering: 04015036.
21.D.-H. Tran, M.-Y. Cheng, D. Prayogo, 2016, A novel Multiple Objective Symbiotic Organisms Search (MOSOS) for time–cost–labor utilization tradeoff problem. Knowledge-Based Systems, 94: 132-145.
22.M.-Y. Cheng, C.-K. Chiu, Y.-F. Chiu, Y.-W. Wu, Z.-L. Syu, D. Prayogo, C.-H. Lin, 2014, SOS optimization model for bridge life cycle risk evaluation and maintenance strategies. Journal of the Chinese Institute of Civil and Hydraulic Engineering, 26(4): 293-308.
23.S. Duman, 2016, Symbiotic organisms search algorithm for optimal power flow problem based on valve-point effect and prohibited zones. Neural Computing and Applications: 1-15.
24.H. Kamankesh, V.G. Agelidis, A. Kavousi-Fard, 2016, Optimal scheduling of renewable micro-grids considering plug-in hybrid electric vehicle charging demand. Energy, 100: 285-297.
25.E. Ruskartina, V.F. Yu, B. Santosa, A.A.N.P. Redi, 2015, Symbiotic Organism Search (SOS) for Solving the Capacitated Vehicle Routing Problem. International Journal of Mechanical, Aerospace, Industrial, Mechatronic and Manufacturing Engineering, 101: 857-861.
26.A. Panda, S. Pani, 2016, A Symbiotic Organisms Search algorithm with adaptive penalty function to solve multi-objective constrained optimization problems. Applied Soft Computing, 46: 344-360.
27.Min-Yuan Cheng, Doddy Prayogo and Yu-Wei Wu, (in preparation), “Predicting the Pavement Rutting Behavior of Asphalt Mixtures Using Symbiotic Organisms Search - Least Squares Support Vector Machine Inference Model”, Construction and Building Materials.
28.Lewis, C. D., 1982, International and Business Forecasting Methods. London: Butterwo.
29.Jui-Sheng Chou, Kuo-Hsin Yang, Jusieandra Pribadi Pampang and Anh-Duc Pham, Evolutionary metaheuristic intelligence to simulate tensile loads in reinforcement for geosynthetic-reinforced soil structures, Computers and Geotechnics 66 , 1-15,2015.
30.中國科技大學官網www.cute.edu.tw
31.CopyRight 法院公告黃頁，網址：http://www.yp123.tw
32.銀行局全球資訊網，網址：https://www.banking.gov.tw