臺灣博碩士論文加值系統

不動產交易在任何國家都是一項重要的經濟活動，其單筆交易金額是鉅大的，學者及業界人士都積極投入研究，買賣雙方對於大筆金額交易也會特別在意價格，關於價格預測通常以回歸分析方法來做連續型資料的預測。
政府於2012年實行不動產交易實價登錄制度，提供公開且免費的交易資料，因此本研究以台中市2016~2019年的實價登錄資料為基礎，但由於資料中的地址欄位並非精準揭露，因此使用地理資訊圖資雲服務平台(TGOS，Taiwan Geospatial One-Stop)，將模糊性的區段位置轉換成較精確性的地理坐標資訊，再將其他資料做資料預處理(Data preprocessing)以利建立模型分析，本研究應用最小二乘支持向量回歸(LSSVR)進行不動產價格預測，並與其他機器學習模型及多元線性回歸相比較，實驗結果最小二乘支持向量回歸(LSSVR)皆優於倒傳遞神經網路(BPNN)、廣義回歸神經網路(GRNN)、分類及回歸樹(CART)及多元線性回歸(MLR)。

Real estate transaction is an important economic activity in many countries and has been of great research interest to scholars and people in the industry because of the huge amount of money involved.
In 2012, the government implemented the real-value transaction registration system for real estate transactions to provide open and free transaction information. This study is based on the actual price registration data in Taichung City from 2016 to 2019. Because the address information of the data is not accurately disclosed, the Geographic Information Map Cloud Service platform (TGOS, Taiwan Geospatial One-Stop) is used to convert the ambiguous locations into more accurate geographic coordinate information, and other data are preprocessed so as to establish a model analysis. In this study, I use least squares support vector regression (LSSVR) to predict real estate prices. The results of the experiment show that the prices predicted by LSSVR are more accurate than those predicted by back propagation neural network (BPNN), generalized regression neural network (GRNN), classification and regression tree (CART) and multiple linear regression (MLR).

致謝辭 i
摘要 ii
Abstract iii
目次 iv
表目次 v
圖目次 vi
第一章 緒論 1
第二章 文獻探討 2
第一節　不動產預測議題之相關文獻回顧 2
第二節　以最小二乘支持向量回歸預測之相關文獻回顧 9
第三節　遺傳演算法(GA) 16
第四節　實價登錄 20
第三章 研究方法 33
第一節　研究架構 33
第二節　資料預處理 35
第三節　最小二乘支持向量回歸(LSSVR) 37
第四節　交叉驗證(Cross Validation) 45
第五節　誤差指標 46
第四章 實驗結果與分析 47
第一節　資料來源與預處理 47
第二節　實驗結果及討論 53
第五章 結論與未來展望 55
參考文獻 56
一、中文部分 56
二、英文部分 57
三、網路部分 62

表目次
表一 不動產預測議題之相關文獻回顧 5
表二 以最小二乘支持向量回歸預測之相關文獻回顧 13
表三 遺傳演算法之相關文獻回顧 18
表四 國外各國不動產交易資訊登錄制度之比較 21
表五 實價登錄欄位說明 31
表六 研究資料之變數說明 36
表七 單價元/平方公尺四分位距表 49
表八 總價元四分位距表 50
表九 預處理轉換後欄位對照 52
表十 最小二乘支持向量回歸預測結果 53
表十一 倒傳遞類神經網路預測結果 53
表十二 廣義回歸神經網路預測結果 53
表十三 分類及回歸樹預測結果 54
表十四 多元線性回歸預測結果 54
表十五 各模型平均預測結果 54

圖目次
圖一 最佳超平面示意圖 9
圖二 過適(overfitting)示意圖 10
圖三 最適(appropriate -fitting)示意圖 10
圖四 英國開放資料平台 22
圖五 英國不動產交易資訊網站 23
圖六 美國開放資料平台 24
圖七 美國不動產交易資訊網站 25
圖八 德國開放資料平台 26
圖九 歐洲不動產交易資訊網站 27
圖十 UROPEAN PROPERTY 德國版 27
圖十一 香港「土地註冊處」網站 28
圖十二 日本「不動産取引価格情報検索」網站 29
圖十三 內政部不動產交易實價查詢服務網 30
圖十四 研究流程架構圖 34
圖十五 交叉驗證示意圖 45
圖十六 單價元/平方公尺直條圖 49
圖十七 刪除離群值前單價元/平方公尺分布圖 49
圖十八 刪除離群值後單價元/平方公尺分布圖 50
圖十九 總價元直條圖 50
圖二十 刪除離群值前總價元分布圖 51
圖二十一 刪除離群值後總價元分布圖 51

一、中文部分
1.湯祐任(2018)，應用資料探勘於臺北市房地產實價登錄資料，臺北市立大學數學系數學教育研究所碩士論文。
2.顏嘉伶(2018)，我國實價登錄制度與不動產交易資訊透明度之問題研析，新北市政府地政局地價科。
3.黃惠芬 (2017)，以類神經網路方法建構房價估價模型-以高雄市實價登錄資料為例，國立高雄應用科技大學金融資訊研究所碩士論文。
4.謝孟勳(2017)，實價登錄資料庫結合類神經網路推估房地產市價，國立中興大學土木工程學系碩士論文。
5.徐沛曛(2017)，不動產實價登錄法制之研究—以實價登錄地政三法之修正為中心，東吳大學法學院法律學系碩士論文。
6.柯采宜(2017)，應用支援向量回歸建構台北市實價登錄房價預測系統，輔仁大學資訊管理學系碩士論文。
7.歐陽榆(2015)，從各國不動產制度看我國實價登錄，新北市汐止地政事務所。
8.花敬群(2014)，實價登錄實施成效與改善建議，台灣地區2014房地產年鑑。
9.邱司杰(2014)，基於實價登錄的房價模型研究，國立交通大學網路工程研究所碩士論文。
10.陳珍華(2014)，巨量資料 : 公開資料與房仲網的房價分析，交通大學資訊學院資訊學程碩士論文。
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三、網路部分
1.德國房地產市場概況與公證人角色
https://blog.xuite.net/noeljou/twblog/122403375-%E5%BE%B7%E5%9C%8B%E4%B8%8D%E5%8B%95%E7%94%A2%E4%BA%A4%E6%98%93%E8%88%87%E5%85%AC%E8%AD%89%E4%BA%BA，存取時間：2020/03。
2.他山之石，各國實價登錄做法比較，好房網雜誌，第57期，2018.Jun，https://news.housefun.com.tw/mag/hf/57/article/1/441203197496.html，存取時間：2020/03。
3.經驗風險最小化
https://zh.wikipedia.org/wiki/%E7%BB%8F%E9%AA%8C%E9%A3%8E%E9%99%A9%E6%9C%80%E5%B0%8F%E5%8C%96，維基百科，存取時間：2020/03。
4.內政部不動產交易實價查詢服務網https://lvr.land.moi.gov.tw/，存取時間：2020/03。