| dc.contributor | 政大地政系 | en_US |
| dc.creator (作者) | 沈育生;林秋瑾 | zh_TW |
| dc.creator (作者) | Shen,Yu-Sheng ;Lin,Chiu-Chin Vickey | en_US |
| dc.date (日期) | 2012-05 | en_US |
| dc.date.accessioned | 2013-09-13 | - |
| dc.date.available | 2013-09-13 | - |
| dc.date.issued (上傳時間) | 2013-09-13 | - |
| dc.identifier.uri (URI) | http://nccur.lib.nccu.edu.tw/handle/140.119/60780 | - |
| dc.description.abstract (摘要) | 在因次貸所引起的金融風暴後，金融界比過往更加重視信用風險的議題，而此風暴起源自房價的下跌導致房貸商品的大量違約。違約產生於兩個面向：無意願支付和無能力支付，有無意願支付和抵押品的價值有關；而有無能力支付則和家計的所得及月付額變動有關。過往的文獻多數是專注於分析前者，但事實上違約的發生，不僅牽涉於抵押品的價格，是否能還款的支付壓力更是一個重要的因素，換言之，估計房貸商品的信用風險，需要同時考慮還款支付壓力、房價趨勢、利率以及所得波動等因素。本研究對傳統型與非傳統型的房貸商品其信用風險進行比較，信用風險本身無形且難直接測量，所以必須找到一個衡量指標。本研究首先模擬這些房貸商品的現金流量，而後計算出兩個風險指標：負權益機率和月付額短缺機率，用以評估這些產品的信用風險。本研究結果顯示，次級房貸商品在壓力經濟情境下，將產生大量違約；而新金融商品住宅增值參與證券的發行則有穩定房屋市場的功能。 | en_US |
| dc.description.abstract (摘要) | Real estate prices affect the compensation of land acquisition, the cost and benefit of land development, and the investment of real estate. Thus, how to evaluate and predict the price of real estate precisely plays an important role in land economics research. This study uses both hedonic multiple regression method (MRA) and different artificial neural networks (ANN) to build models for evaluating and predicting on housing prices. We used the Year 2006 to 2008 data of housing transactions in Taipei City. The empirical results reveal that ANN can be a better alternative for predicting of housing prices. Among the different ANN housing prices models, the best predicting performance show at Multilayer Functional-Link Network (MFLN). In comparing network architecture, it indicates that more hidden layers and more attributes make the model more complicated and make the procedure converge slowly. In Back-Propagation Network (BPN), 2-layer model performs better than other network models in fitted-modeling and forecast accuracy, whereas it shows the performance of 1-layer model is better than 2-layer hidden model for both Multilayer Functional-Link Network (MFLN) and Radial Basis Function Network (RBFN). | en_US |
| dc.format.extent | 1340871 bytes | - |
| dc.format.mimetype | application/pdf | - |
| dc.language.iso | en_US | - |
| dc.relation (關聯) | 台灣土地研究, 15(1), 1-29 | en_US |
| dc.subject (關鍵詞) | 人工神經網路架構;不動產大量估價;多元迴歸;分析多層函數連結網路;倒傳遞網路;輻狀基底函數網路 | en_US |
| dc.subject (關鍵詞) | Real estate mass appraisal;Structures of artificial neural network;Back-propagation network;Multilayer functional-link network;Radial basis function network;Multiple regression analysis | en_US |
| dc.title (題名) | 不同人工神經網路架構在不動產大量估價之應用與比較 | zh_TW |
| dc.title.alternative (其他題名) | Complex Structures of Artificial Neural Network Comparison and Application on Real Estate Mass Appraisal | en_US |
| dc.type (資料類型) | article | en |
