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).

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