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題名 網站搜尋點擊次數與房市指標因果關係之研究－以桃寶網為例
Causality Between Website CTR (Clicks Through Rate) and Real Estate Market Index - The Study Based On taobao.tycg.gov.tw作者 劉曉雲
Liu, Hsiao-Yun貢獻者 林左裕
Lin, Tsoyu Calvin
劉曉雲
Liu, Hsiao-Yun關鍵詞 點擊次數
搜尋行為
網路大數據
房價
共整合
時間序列
Granger 因果關係
誤差修正
CTR
Click through rate
Searching behavior
Big data
Housing price
Cointegration
Time series
Granger Causality
Error correction日期 2022 上傳時間 1-Mar-2022 17:44:31 (UTC+8) 摘要 我們正處於網路大數據的時代，新科技新技術改變了人們的習慣，食衣住行日常活動都可以上網進行且分秒都被記錄著，相關網路大數據的應用近年來也如雨後春筍的發展，包含房地產領域。過去不動產研究主要著重於房價之探討與預測，多半採用落後之統計資訊分析經濟活動，此類資料缺乏即時性，無法完全反映不動產市場趨勢，而房地產市場有資訊不透明的特性，交易金額龐大，消費者於消費前會進行搜尋行為以輔助決策，近期已有文獻指出模型中納入網路搜尋指標對不動產市場交易量及交易價格有預測能力，因此本研究想探討潛在使用者網站搜尋點擊次數能否作為房價領先指標，並進一步探討網站搜尋次數、交易量、房價及總體經濟指標間之長期均衡及變數間之因果關係。本研究以「桃園住宅及不動產資訊桃寶網」（簡稱：桃寶網）網站桃園區及中壢區 2016 年 1 月至 2020 年 3 月共 51 個月熱門搜尋點擊次數與實價價錄交易量、房價指數及消費者物價指數及營造工程物價指數為變數，建立時間序列誤差修正模型(VECM)，分別進行共整合分析及 Granger 因果關係檢定，以檢視桃園區及中壢區桃寶網站點擊次數與房市指標是否存在共整合關係及 Granger 領先-落後關係。研究結果顯示不動產網站搜尋點擊次數可作為房價領先指標、不動產市場有量先價行之現象，而本研究之變數間也存在長期均衡關係及 Granger因果關係，此外 VECM 模型最佳落後期數之長短與不動產交易時程相符。綜上所述，納入網站搜尋指標之模型能使政府透過觀察與房價、交易量、及消費者物價指數變數之領先-落後關係，能夠更有效率的掌握不動產市場潛在動向。
We are in the era full of big data from internet. New technologies have changed people`s habits. Daily activities in all aspects can be carried out and recorded online every second. The relative applications of internet big data have also sprung up in recent years, including in the real estate field. In the past, real estate research mainly focused on the discussion and prediction of housing prices. Such data lacked timeliness and could not fully reflect the real estate market trend. The real estate market has the characteristics of not being transparent and involving huge transaction amounts. Consumers will conduct researches to assist decision-making before consumption. Most of them used outdated statistical information to analyze economic activities.decision-making before purchases. The recent study paper has pointed out that the embedded internet search engine in the model has the ability to predict the transaction volume and transaction price of the real estate market. Therefore, this study will further explore the potential users’ website search clicks as a leading indicator of housing prices. We will also analyze the long-term equilibrium and the causality in between variables: (1) website click through rate (2) transaction volume (3) housing prices, and (4) Macroeconomic index.This research is based on CTR(clicks through rate) of popular searches and the actual transaction volume of the Taoyuan District and Zhongli District on the "Taoyuan Residential and Real Estate Information website (http://taobao.tycg.gov.tw/Home) from January 2016 to March 2020. Use house price index, consumer price index (CPI) and construction cost index as variables to establish a time series Vector Error Correction Model (VECM), to conduct co-integration analysis and Granger Causality test respectively, and to check whether there is a co-integration relationship between CTR (clicks through rate) and the housing market indicators and the Granger leading-lagging relationship. The research results show that CTR on real estate websites can be used as a leading indicator of housing prices. The real estate market has a phenomenon of quantity leading prices. Long-term equilibrium relationship and Granger Causality exist between variables. In addition, the length of the optimal lag period of the VECM model is consistent with the real estate transaction interval. To sum up, the model incorporated into the website search indicators enables the government to grasp the potential trends of the real estate market more efficiently by observing the leading-lagging relationship with house prices, transaction volume, and consumer price index.參考文獻 中文參考文獻專書1.余清祥、顏貝珊，2016，『大數據知識經濟與實務應用』，台中市：滄海書局。2.林左裕，2014，『不動產投資管理』五版，臺北市：智勝文化。3.城田真琴著、鐘慧真、梁世英譯，2013，『Big Data 大數據的獲利模式』，臺北市：經濟新潮社。4.陳旭昇，2020，『時間序列分析:總體經濟與財務金融之應用』二版，臺北市：東華書局。5.楊奕農，2017，『時間序列分析：經濟與財務上之應用』三版，臺北市：雙葉書廊。期刊論文1.余孝先、趙祖佑，2015，「巨量資料應用，打造資料驅動決策的智慧政府」，『國土及公共治理季刊』，3(4)：27-37。2.花敬群、張金鶚，1997，「住宅市場價量波動之研究」，『住宅學報』，5 (4)：1-15。3.林左裕、程于芳，2014，「影響不動產市場之從眾行為與總體經濟因素之研究」，『應用經濟論叢』，95：61-99。4.林左裕，2018，「從總經因素及大數據分析不動產市場－兼論桃園市住宅市場之分析」，『土地問題研究季刊』，17(4)：30-48。5.林左裕，2019，「應用網路搜尋行為預測房地產市場」，『應用經濟論叢』，105：219 - 254。6.林秋瑾、黃佩玲，1995，「住宅價格與總體經濟變數關係之研究—以向量自我迴歸模式（VAR）進行實證」，『政治大學學報』，71：143-159。7.林秋瑾、王健安、張金鶚，1996，「房地產景氣與總體經濟景氣於時間上領先、同時、落後關係之探討」，『國科會人文及社會科學彙刊』，7(1)：35-56。8.林進益、林元興，2018，「不動產市場在資訊時代的革新」，『土地問題研究季刊』，17(2)：8-18。9.林恩從、高斐蘭，1998，「台灣地區房地產景氣與經濟、金融變數之共整合研究」,『東吳大學經濟商學學報』:21-46。10.周美伶與張金鶚，2005，「購屋搜尋期間影響因素之研究」，『管理評論』，24（1）：133-150。11.梅強、林尚毅，2017，「臺灣總體經濟變數對六都房價之影響分析」，『亞太經濟管理評論』，21(1):33-48。12.彭建文、張金鶚，2000，「總體經濟對房地產景氣影響之研究」，『國科會人文及社會科學彙刊』，10(3)：330-343。13.彭建文，2004，「台灣出租住宅市場與自有住宅市場價格調整關係之研究」，『都市與計劃』，31(4)：391-412。14.鄭美幸、康信鴻，2002，「台商赴大陸投資與重大非經濟事件對我國房地產景氣的影響」，『住宅學報』，11(2)：101-119。15.賴碧瑩，2002，「經濟結構轉變後之地價變動分析」，『 臺灣土地研究』，5:1-22。其他桃園市政府，2016，「桃園住宅及不動產資訊網(桃園不動產資訊桃寶網)第七期擴充計畫建置案」。外文參考文獻1.Beatty, S. 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Francke., 2018, “Internet Search Behavior, Liquidity and Prices in the Housing Market”, Real Estate Economics, 46(2):368-403.9.Erevelles, S., Fukawa, N. and Swayne L., 2016, “Big Data Consumer Analytics and the Transformation of Marketing”, Journal of Business Research, 69(2) :897- 904.10.Fu, Y., Ng, L. K., 2001, “Market Efficiency and Return Statistics: Evidence from Real Estate and Stock Markets Using a Present-Value Approach”, Real Estate Economics, 29(2):227-250.11.Goel, S., Hofman, J.M., Lahaie, S., Pennock, D. M. and Watts, D. J., 2010, “Predicting Consumer Behavior with Web Search”, PNAS , 107(41).12.Hoskins, N., Higgins, D., Cardew, R., 2004, “Macroeconomic Variables and Real Estate Returns: An International Comparison”, Appraisal Journal, 72(2):163-170.13.Kulkarni, R., K. Haynes, R. Stough, and J. Paelinck, 2009, “Forecasting Housing Prices with Google Econometrics”, George Mason University School of Public Policy Research Paper, No. 2009-10.14.Lin, T. C.and Hsu, S.H.,2020, “Forecasting Housing Markets from Number of Visits toActual Price Registration System”,International Real Estate Review ,23(4): 505-536.15.McCue, T. E., Kling, J. L., 1994,“Real Estate Returns and the Macro Economy: Some Empirical Evidence from Real Estate Investment Trust Data”, The Journal of Real Estate Research, 9(3):277-288.16.Nelson, P., 1970, “ Information and Consumer Behavior”,Jourmal of Political Economy, 78 (2) :311-329.17.Reichert, K. A.,1990, “The Impact of Interest Rates, Income and Employment upon Regional Housing Prices”, Journal of Real Estate Finance and Economics, 3(4):373-391.18.Rae, A., 2015, “Online Housing Search and the Geography of Submarkets＂, Housing studies, 30(3):453-472.19.Sims, C. A., 1980, “Macroeconomics and Reality”,Econometrica, 48:1-48.20.Stigler, G. J., 1961, “ The Economics of Information”, Journal of Poitical Economy, 69 (3) :213-225.21.Safer. A., M., 2002, “ The Application of-Neural Networks to Predict Abnormal Stock Returns Using Insider Trading Data”, Applied Stochastic Models in Business and Industry, 18(4):381-389.22.Witkiewicz, W., 2002, “The Use of the HP-filter in Constructing Real Estate Cycle Indicators”, Journal of Real Estate Research, 23(1/2):65-87.23.Wu, L, and E. Brynjolfsson, 2015, “The Future of Prediction: How Google Searches Foreshadow Housing Prices and Sales”, in Goldfarb, A., S. M. Greenstein, and C. E. Tucker, ed, Economic Analysis of the Digital Econony, 89-118. Chicago: University of Chicago Press參考網頁1.內政部不動產交易實價查詢服務網：https://lvr.land.moi.gov.tw/2.台灣網路資訊中心網址：https://report.twnic.tw/2020/，取用日期:2021年12月18日。3.好時價（House+）網站：https://www.houseplus.tw/，取用日期:2021年12月18日。4.好房網News：https://news.housefun.com.tw/tsoyulin/article/205058309176，取用日期:2022年1月31日。5.桃寶網網址http://taobao.tycg.gov.tw/Home6.國發會網址：https://www.ndc.gov.tw/Content_List.aspx?n=0C669D9634F511BC，取用日期:2022年1月20日。7.經濟日報:https://udn.com/news/story/7238/5944969，取用日期:2022年1月31日。 描述 碩士
國立政治大學
地政學系碩士在職專班
105923018資料來源 http://thesis.lib.nccu.edu.tw/record/#G0105923018 資料類型 thesis dc.contributor.advisor 林左裕 zh_TW dc.contributor.advisor Lin, Tsoyu Calvin en_US dc.contributor.author (Authors) 劉曉雲 zh_TW dc.contributor.author (Authors) Liu, Hsiao-Yun en_US dc.creator (作者) 劉曉雲 zh_TW dc.creator (作者) Liu, Hsiao-Yun en_US dc.date (日期) 2022 en_US dc.date.accessioned 1-Mar-2022 17:44:31 (UTC+8) - dc.date.available 1-Mar-2022 17:44:31 (UTC+8) - dc.date.issued (上傳時間) 1-Mar-2022 17:44:31 (UTC+8) - dc.identifier (Other Identifiers) G0105923018 en_US dc.identifier.uri (URI) http://nccur.lib.nccu.edu.tw/handle/140.119/139253 - dc.description (描述) 碩士 zh_TW dc.description (描述) 國立政治大學 zh_TW dc.description (描述) 地政學系碩士在職專班 zh_TW dc.description (描述) 105923018 zh_TW dc.description.abstract (摘要) 我們正處於網路大數據的時代，新科技新技術改變了人們的習慣，食衣住行日常活動都可以上網進行且分秒都被記錄著，相關網路大數據的應用近年來也如雨後春筍的發展，包含房地產領域。過去不動產研究主要著重於房價之探討與預測，多半採用落後之統計資訊分析經濟活動，此類資料缺乏即時性，無法完全反映不動產市場趨勢，而房地產市場有資訊不透明的特性，交易金額龐大，消費者於消費前會進行搜尋行為以輔助決策，近期已有文獻指出模型中納入網路搜尋指標對不動產市場交易量及交易價格有預測能力，因此本研究想探討潛在使用者網站搜尋點擊次數能否作為房價領先指標，並進一步探討網站搜尋次數、交易量、房價及總體經濟指標間之長期均衡及變數間之因果關係。本研究以「桃園住宅及不動產資訊桃寶網」（簡稱：桃寶網）網站桃園區及中壢區 2016 年 1 月至 2020 年 3 月共 51 個月熱門搜尋點擊次數與實價價錄交易量、房價指數及消費者物價指數及營造工程物價指數為變數，建立時間序列誤差修正模型(VECM)，分別進行共整合分析及 Granger 因果關係檢定，以檢視桃園區及中壢區桃寶網站點擊次數與房市指標是否存在共整合關係及 Granger 領先-落後關係。研究結果顯示不動產網站搜尋點擊次數可作為房價領先指標、不動產市場有量先價行之現象，而本研究之變數間也存在長期均衡關係及 Granger因果關係，此外 VECM 模型最佳落後期數之長短與不動產交易時程相符。綜上所述，納入網站搜尋指標之模型能使政府透過觀察與房價、交易量、及消費者物價指數變數之領先-落後關係，能夠更有效率的掌握不動產市場潛在動向。 zh_TW dc.description.abstract (摘要) We are in the era full of big data from internet. New technologies have changed people`s habits. Daily activities in all aspects can be carried out and recorded online every second. The relative applications of internet big data have also sprung up in recent years, including in the real estate field. In the past, real estate research mainly focused on the discussion and prediction of housing prices. Such data lacked timeliness and could not fully reflect the real estate market trend. The real estate market has the characteristics of not being transparent and involving huge transaction amounts. Consumers will conduct researches to assist decision-making before consumption. Most of them used outdated statistical information to analyze economic activities.decision-making before purchases. The recent study paper has pointed out that the embedded internet search engine in the model has the ability to predict the transaction volume and transaction price of the real estate market. Therefore, this study will further explore the potential users’ website search clicks as a leading indicator of housing prices. We will also analyze the long-term equilibrium and the causality in between variables: (1) website click through rate (2) transaction volume (3) housing prices, and (4) Macroeconomic index.This research is based on CTR(clicks through rate) of popular searches and the actual transaction volume of the Taoyuan District and Zhongli District on the "Taoyuan Residential and Real Estate Information website (http://taobao.tycg.gov.tw/Home) from January 2016 to March 2020. Use house price index, consumer price index (CPI) and construction cost index as variables to establish a time series Vector Error Correction Model (VECM), to conduct co-integration analysis and Granger Causality test respectively, and to check whether there is a co-integration relationship between CTR (clicks through rate) and the housing market indicators and the Granger leading-lagging relationship. The research results show that CTR on real estate websites can be used as a leading indicator of housing prices. The real estate market has a phenomenon of quantity leading prices. Long-term equilibrium relationship and Granger Causality exist between variables. In addition, the length of the optimal lag period of the VECM model is consistent with the real estate transaction interval. To sum up, the model incorporated into the website search indicators enables the government to grasp the potential trends of the real estate market more efficiently by observing the leading-lagging relationship with house prices, transaction volume, and consumer price index. en_US dc.description.tableofcontents 第一章 緒論 1第一節 研究動機與目的 1第二節 研究範圍、內容與限制 6第三節 研究方法、架構與流程 8第二章 文獻回顧 11第一節 搜尋行為與網路大數據 11第二節 影響房地產市場之總體經濟因素 16第三章 研究設計與變數說明 19第一節 變數說明及檢定 19第二節 研究設計 28第四章 實證結果分析 33第一節 變數說明及檢定 33第二節 時間序列資料因果關係檢定結果 47第三節 小結 55第五章 結論與建議 56第一節 結論 56第二節 建議 58參考文獻 60附錄 64 zh_TW dc.format.extent 3879540 bytes - dc.format.mimetype application/pdf - dc.source.uri (資料來源) http://thesis.lib.nccu.edu.tw/record/#G0105923018 en_US dc.subject (關鍵詞) 點擊次數 zh_TW dc.subject (關鍵詞) 搜尋行為 zh_TW dc.subject (關鍵詞) 網路大數據 zh_TW dc.subject (關鍵詞) 房價 zh_TW dc.subject (關鍵詞) 共整合 zh_TW dc.subject (關鍵詞) 時間序列 zh_TW dc.subject (關鍵詞) Granger 因果關係 zh_TW dc.subject (關鍵詞) 誤差修正 zh_TW dc.subject (關鍵詞) CTR en_US dc.subject (關鍵詞) Click through rate en_US dc.subject (關鍵詞) Searching behavior en_US dc.subject (關鍵詞) Big data en_US dc.subject (關鍵詞) Housing price en_US dc.subject (關鍵詞) Cointegration en_US dc.subject (關鍵詞) Time series en_US dc.subject (關鍵詞) Granger Causality en_US dc.subject (關鍵詞) Error correction en_US dc.title (題名) 網站搜尋點擊次數與房市指標因果關係之研究－以桃寶網為例 zh_TW dc.title (題名) Causality Between Website CTR (Clicks Through Rate) and Real Estate Market Index - The Study Based On taobao.tycg.gov.tw en_US dc.type (資料類型) thesis en_US dc.relation.reference (參考文獻) 中文參考文獻專書1.余清祥、顏貝珊，2016，『大數據知識經濟與實務應用』，台中市：滄海書局。2.林左裕，2014，『不動產投資管理』五版，臺北市：智勝文化。3.城田真琴著、鐘慧真、梁世英譯，2013，『Big Data 大數據的獲利模式』，臺北市：經濟新潮社。4.陳旭昇，2020，『時間序列分析:總體經濟與財務金融之應用』二版，臺北市：東華書局。5.楊奕農，2017，『時間序列分析：經濟與財務上之應用』三版，臺北市：雙葉書廊。期刊論文1.余孝先、趙祖佑，2015，「巨量資料應用，打造資料驅動決策的智慧政府」，『國土及公共治理季刊』，3(4)：27-37。2.花敬群、張金鶚，1997，「住宅市場價量波動之研究」，『住宅學報』，5 (4)：1-15。3.林左裕、程于芳，2014，「影響不動產市場之從眾行為與總體經濟因素之研究」，『應用經濟論叢』，95：61-99。4.林左裕，2018，「從總經因素及大數據分析不動產市場－兼論桃園市住宅市場之分析」，『土地問題研究季刊』，17(4)：30-48。5.林左裕，2019，「應用網路搜尋行為預測房地產市場」，『應用經濟論叢』，105：219 - 254。6.林秋瑾、黃佩玲，1995，「住宅價格與總體經濟變數關係之研究—以向量自我迴歸模式（VAR）進行實證」，『政治大學學報』，71：143-159。7.林秋瑾、王健安、張金鶚，1996，「房地產景氣與總體經濟景氣於時間上領先、同時、落後關係之探討」，『國科會人文及社會科學彙刊』，7(1)：35-56。8.林進益、林元興，2018，「不動產市場在資訊時代的革新」，『土地問題研究季刊』，17(2)：8-18。9.林恩從、高斐蘭，1998，「台灣地區房地產景氣與經濟、金融變數之共整合研究」,『東吳大學經濟商學學報』:21-46。10.周美伶與張金鶚，2005，「購屋搜尋期間影響因素之研究」，『管理評論』，24（1）：133-150。11.梅強、林尚毅，2017，「臺灣總體經濟變數對六都房價之影響分析」，『亞太經濟管理評論』，21(1):33-48。12.彭建文、張金鶚，2000，「總體經濟對房地產景氣影響之研究」，『國科會人文及社會科學彙刊』，10(3)：330-343。13.彭建文，2004，「台灣出租住宅市場與自有住宅市場價格調整關係之研究」，『都市與計劃』，31(4)：391-412。14.鄭美幸、康信鴻，2002，「台商赴大陸投資與重大非經濟事件對我國房地產景氣的影響」，『住宅學報』，11(2)：101-119。15.賴碧瑩，2002，「經濟結構轉變後之地價變動分析」，『 臺灣土地研究』，5:1-22。其他桃園市政府，2016，「桃園住宅及不動產資訊網(桃園不動產資訊桃寶網)第七期擴充計畫建置案」。外文參考文獻1.Beatty, S. 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