學術產出-學位論文
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題名 機器學習與房地產估價
Machine learning and appraisal of real estate作者 蔡育展
Tsai, Yu Chang貢獻者 蔡瑞煌
Tsaih, Rua Huan
蔡育展
Tsai, Yu Chang關鍵詞 房地產估價
機器學習
類神經網路
張量流
圖形處理器運算日期 2017 上傳時間 28-八月-2017 13:49:14 (UTC+8) 摘要 近年來，房地產之投資及買賣廣為盛行，而房地產依舊為人們投資的方向之一。屬於人工智慧範疇之類神經網路，其具有學習能力，可以進一步的歸納推演所要預估的結果，也適合應用於非線性的問題中，但以往類神經網路的機器學習模型，皆採用中央處理器(CPU)進行運算，在計算量龐大時往往耗費大量時間於訓練上。而圖形處理器(GPU)之崛起，將增進機器學習的速率。本研究利用穩健學習程序搭配信封模組的概念，建立一類神經網路系統，利用GPU設備及機器學習工具–Tensorflow實作，針對民國一零四年之台北市不動產交易之住宅資料，並使用1276筆資料，隨機選取60%資料作為訓練範例並分別進行以假設有5%為可能離群值及沒有之情況做學習，並選取影響房地產價格之11個變數做為輸入變數，對網路進行訓練，實證結果發現類神經網路的速度有顯著的提升；且在假定有5%離群值之狀況下學習有較好的預測水準；另外在對資料依價格進行分組後，顯示此網路在對中價位以上的資料有較好的預測能力。就實務應用方面，藉由類神經網路適合應用於非線性問題的特性，對未來房地產之估價系統輔助做為參考。
Real estate investment and transcation prevails in recent year. And it is still one of the choices for people to invest. The Neural Network which belongs to the category of Arificial Intelligence has the ability to learn and it can deduce to reach the goal. In addi-tion, it is also suitable for the application of non-linear problems. However, the machine learning model of the Neural Network use CPU to operate before and it will always spend a lot of time on training when the calculation is large.However, the rise of GPU speeds up the machine learing system. This study will implement resistant learning procedure with the concept of Enve-lope Bulk focus to built a Neural Network system. Using TensorFlow and graphics pro-cessing unit (GPU) to speed up the original Arificial Intelligence system. According to the real estate transaction data of Taipei City in 2015, 1276 data will be used. We will pick 60% of the data in a random way as training data of our two experiment , one of it will assume that there are 5% of outlier and another won’t. Then select 11 variables which may impact the value of real estate as input. As the experiment result, it makes the operation more efficient and faster , training of the Neural Network really speed up a lot. The experiment which has assume that there are 5% of outlier shows the better effect of predicting than the another. And we got a better prediction on the part of the higher price after we divided the data into six groups by their price.In the other hand, Neural Network is good at solving the problem of non-linear. 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國立政治大學
資訊管理學系
104356018資料來源 http://thesis.lib.nccu.edu.tw/record/#G1043560182 資料類型 thesis dc.contributor.advisor 蔡瑞煌 zh_TW dc.contributor.advisor Tsaih, Rua Huan en_US dc.contributor.author (作者) 蔡育展 zh_TW dc.contributor.author (作者) Tsai, Yu Chang en_US dc.creator (作者) 蔡育展 zh_TW dc.creator (作者) Tsai, Yu Chang en_US dc.date (日期) 2017 en_US dc.date.accessioned 28-八月-2017 13:49:14 (UTC+8) - dc.date.available 28-八月-2017 13:49:14 (UTC+8) - dc.date.issued (上傳時間) 28-八月-2017 13:49:14 (UTC+8) - dc.identifier (其他 識別碼) G1043560182 en_US dc.identifier.uri (URI) http://nccur.lib.nccu.edu.tw/handle/140.119/112279 - dc.description (描述) 碩士 zh_TW dc.description (描述) 國立政治大學 zh_TW dc.description (描述) 資訊管理學系 zh_TW dc.description (描述) 104356018 zh_TW dc.description.abstract (摘要) 近年來，房地產之投資及買賣廣為盛行，而房地產依舊為人們投資的方向之一。屬於人工智慧範疇之類神經網路，其具有學習能力，可以進一步的歸納推演所要預估的結果，也適合應用於非線性的問題中，但以往類神經網路的機器學習模型，皆採用中央處理器(CPU)進行運算，在計算量龐大時往往耗費大量時間於訓練上。而圖形處理器(GPU)之崛起，將增進機器學習的速率。本研究利用穩健學習程序搭配信封模組的概念，建立一類神經網路系統，利用GPU設備及機器學習工具–Tensorflow實作，針對民國一零四年之台北市不動產交易之住宅資料，並使用1276筆資料，隨機選取60%資料作為訓練範例並分別進行以假設有5%為可能離群值及沒有之情況做學習，並選取影響房地產價格之11個變數做為輸入變數，對網路進行訓練，實證結果發現類神經網路的速度有顯著的提升；且在假定有5%離群值之狀況下學習有較好的預測水準；另外在對資料依價格進行分組後，顯示此網路在對中價位以上的資料有較好的預測能力。就實務應用方面，藉由類神經網路適合應用於非線性問題的特性，對未來房地產之估價系統輔助做為參考。 zh_TW dc.description.abstract (摘要) Real estate investment and transcation prevails in recent year. And it is still one of the choices for people to invest. The Neural Network which belongs to the category of Arificial Intelligence has the ability to learn and it can deduce to reach the goal. In addi-tion, it is also suitable for the application of non-linear problems. However, the machine learning model of the Neural Network use CPU to operate before and it will always spend a lot of time on training when the calculation is large.However, the rise of GPU speeds up the machine learing system. This study will implement resistant learning procedure with the concept of Enve-lope Bulk focus to built a Neural Network system. Using TensorFlow and graphics pro-cessing unit (GPU) to speed up the original Arificial Intelligence system. According to the real estate transaction data of Taipei City in 2015, 1276 data will be used. We will pick 60% of the data in a random way as training data of our two experiment , one of it will assume that there are 5% of outlier and another won’t. Then select 11 variables which may impact the value of real estate as input. As the experiment result, it makes the operation more efficient and faster , training of the Neural Network really speed up a lot. The experiment which has assume that there are 5% of outlier shows the better effect of predicting than the another. And we got a better prediction on the part of the higher price after we divided the data into six groups by their price.In the other hand, Neural Network is good at solving the problem of non-linear. It can be a reference of the sup-port system of real estate appraisal in the future. en_US dc.description.tableofcontents 第一章 緒論 1第一節 研究背景與動機 1第二節 研究目的 3(一) 樣本數的增加 4(二) 演算法之複雜度 5(三) 圖形處理器及張量流加速 5第二章 相關理論及文獻回顧 6第一節 房地產估價方法 6(一) 大量估價法－特徵價格法 6(二) 類神經網路於不動產估價應用 7第二節 機器學習與類神經網路 10第三節 相關演算法 12(一) 理解神經網路 12(二) 穩健學習程序 12第四節 GPU與Tensorflow於機器學習 15(一) 圖形處理器(GPU) 15(二) 張量流(Tensorflow) 17第五節 影響房地產價格之因素 22第三章 實驗 31第一節 實驗設計 31(一) 機器學習之變數選取 31(二) 資料來源及使用 34(三) 系統建置 36第四章 實驗結果 41第一節 結果與討論 41(一) 訓練階段 41(二) 測試階段 43第五章 結論與未來方向 51參考文獻 54中文參考文獻 54英文參考文獻 56 zh_TW dc.format.extent 1877368 bytes - dc.format.mimetype application/pdf - dc.source.uri (資料來源) http://thesis.lib.nccu.edu.tw/record/#G1043560182 en_US dc.subject (關鍵詞) 房地產估價 zh_TW dc.subject (關鍵詞) 機器學習 zh_TW dc.subject (關鍵詞) 類神經網路 zh_TW dc.subject (關鍵詞) 張量流 zh_TW dc.subject (關鍵詞) 圖形處理器運算 zh_TW dc.title (題名) 機器學習與房地產估價 zh_TW dc.title (題名) Machine learning and appraisal of real estate en_US dc.type (資料類型) thesis en_US dc.relation.reference (參考文獻) 1. 何友鋒、吳綱立，(1993)，台中市住宅價格與屬性關係之研究，建築學報，8，59-84。2. 吳峻安，(2010)，大眾運輸系統的可及性對房地產價格之影響-以新北市板橋都會區與高雄左營、鼓山都會區為例，臺灣大學農業經濟學研究所碩士論文。3. 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，土地管理與開發學系碩士論文。4. 李怡婷，(2005)，大眾運輸導向發展策略對捷運站區房地產價格之影響分析，成功大學都市計劃研究所碩士論文。5. 李春長、游淑滿、張維倫，(2012)，公共設施、環境品質與不動產景氣對住宅價格影響之研究─兼論不動產景氣之調節效果，住宅學報，21(1)，67-87。6. 李嘉淵，(2005)，應用演化式類神經網路、灰關聯分析及類神經模糊於不動產估價之研究，朝陽科技大學財務金融系碩士論文。7. 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，國立台灣科技大學企業管理系碩士論文。8. 汪駿旭，(2004)，不動產估價人員受客戶影響之研究，政治大學地政研究所碩士論文。9. 林英彥，(2006)，不動產估價第十一版，台北： 文笙書局。10. 林祖嘉、林素菁，(1992)，臺灣地區環境品質與公共設施對房價與房租影響之分析，住宅學報，1，21-45。11. 林祖嘉、馬毓駿，(2007)，特徵方程式大量估價法在台灣不動產市場之應用，住宅學報，16(2)，1-22。12. 林朝揚，(2013)，捷運對不動產價格之影響-以台中都會區為例，嶺東科技大學行銷與流通管理研究所碩士論文。13. 張怡文、江穎慧、張金鶚，(2009)，分量迴歸在大量估價模型之應用-非典型住宅估價之改進，都市與計劃，36(3)，281-304。14. 張金鶚、范垂爐，(1993)，房地產真實交易價格之研究，住宅學報，1，75-97。15. 張能政，(2004)，不動產估價行為研究-行為理論之應用，國立台北大學地政學系碩士論文。16. 陳力維，(2001)，台灣房地產價格變動因素之研究，私立淡江大學財務金融研究所碩士論文。17. 陳奉瑤，(2008)，不動產估價師之教育、考試與執業分析，國家菁英季刊，4(4)，141-158。18. 陳奉瑤，(2011)，不動產估價行為研究，台北: 財團法人中國地政研究所。19. 陳奉瑤、楊依蓁，(2007)，個別估價與大量估價之準確性分析，住宅學報，16(2)，67-83。20. 陳冠穎，(2016)，大眾運輸系統對周邊不動產價格之影響-以高雄輕軌為例.，國立成功大學都市計劃學系碩士論文。21. 游適銘，(2011)，不動產估價最終估值之形成-權重模式、估值差異與市場景氣之影響，物業管理學報，2(1)，21-30。22. 游適銘、張金鶚，(2011)，買賣實例比較法實例權重之分析與模式建立，都市與計劃，38(2)，147-169。23. 黃鈺雯，(2010)，以區位可及性與區位的市場需求訂定容積率之研究，政治大學地政研究所碩士論文。24. 楊珮欣，(2007)，住商混合使用對房價之影響-台北市經驗，國立政治大學地政研究所碩士論文。25. 劉秀玲，(1992)，臺北市住宅品質對住宅價格影響關係之探討，國立中興大學都市計畫研究所碩士論文。26. 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， 8: 1-20。27. 鄭惟元，(2013)，經由CPU/GPU加速實現支持向量機與類神經學習之模糊系統，中興大學電機工程學系所碩士論文。.28. 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，16(2)，43-65。29. 顏愛靜，(2006)，由知識經濟談不動產估價師專業技術人才證照制度，國家菁英季刊，2.2，81-106。30. 魏如龍，(2002)，類神經網路於不動產價格預估效果之研究，國立政治大學地政研究所碩士論文。英文參考文獻1. 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