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    Title: 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
    Authors: 劉曉雲
    Liu, Hsiao-Yun
    Contributors: 林左裕
    Lin, Tsoyu Calvin
    劉曉雲
    Liu, Hsiao-Yun
    Keywords: 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
    Date: 2022
    Issue Date: 2022-03-01 17:44:31 (UTC+8)
    Abstract: 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 Most of them used outdated statistical information to analyze economic activities.decision-making before purchases.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.
    Reference: 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。
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    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。
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    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。
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    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。
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    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。
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    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」。


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    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/Home
    6.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。
    Description: 碩士
    國立政治大學
    地政學系碩士在職專班
    105923018
    Source URI: http://thesis.lib.nccu.edu.tw/record/#G0105923018
    Data Type: thesis
    DOI: 10.6814/NCCU202200298
    Appears in Collections:[Department of Land Economics] Theses

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