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    Please use this identifier to cite or link to this item: https://nccur.lib.nccu.edu.tw/handle/140.119/131170


    Title: Google Trends搜尋數據與辦公室租金之關聯
    The Correlation Between Google Search Volume Data and Office Rent
    Authors: 鍾之琦
    Chung, Chih-Chi
    Contributors: 林左裕
    Lin, Tso-Yu
    鍾之琦
    Chung, Chih-Chi
    Keywords: 搜尋行為
    辦公室租金
    自我迴歸時間落遲模型
    Google Trends
    SVI
    Search Behavior
    Office Rent
    ARDL
    Date: 2020
    Issue Date: 2020-08-03 18:09:38 (UTC+8)
    Abstract: 網路搜尋行為已改變不動產市場的運作模式，市場參與者得利用網路搜尋以輔助最適決策之完成，過往文獻有關於辦公室租金與總體經濟面之研究大多僅考量總體經濟變數，並且存在所參考資料發布時間之遲延問題，為提高預測之即時性，本研究利用國泰辦公室租金市場報告、Google關鍵字搜尋量（Search Volume Index, SVI）探討人們在網路搜尋之後可能產生的真實租賃行為，以彌補此研究缺口。
    本研究透過自我迴歸時間落差分配模型（ARDL）進行實證分析，研究發現「出租關鍵字」的搜尋量與當期A、B級辦公室租金呈現正向顯著關聯性；其次，前一期之「出租關鍵字」搜尋量可作為B辦租金之領先指標，即當前一季搜尋次數上升時，將對後期之B辦租金產生顯著之正向影響，顯示潛在承租戶事前之網路搜尋行為確實轉化為真實世界中的租賃需求。
    綜上所述，隨著長期追蹤紀錄人類搜尋軌跡，搜尋引擎之資料特性得完整地呈現當下搜尋者之心理特徵與潛在需求，得以補足過去基本面或總經變數無法對市場提出即時解釋之文獻缺口。應用本研究之結果，能藉由大數據資料的優勢提供一新型辦公室之有效指標，使市場參與者能更即時地掌握市場趨勢。
    Internet search behavior helps real estate market participants to reduce the risk in the decision-making process. In the past literature, studies on office rents mostly only considered overall economic variables and there was a delay in the publication time of the reference materials. This study uses the Office Rent Market Report issued by Cathay Life Insurance Company and Google Search Volume Index (SVI) to explore whether the search behavior is leading the change in office rent.
    This study uses the Auto-Regressive Distributed Lag (ARDL) model for empirical analysis, and the result suggest that the search volume of “Leasing Keywords” was positively and significantly related to the rents of Grade A and B offices in the selected period. Secondly, the search volume of "Leasing Keyword" in the previous period can be used as the leading indicator of rent for Grade B office. It shows that the online search behavior of potential tenants is indeed transformed into real-world rental needs.
    In summary, the search data completely present the psychological characteristics and potential needs of current searchers. According to the results of this research, it is possible to use the advantages of big data to predicts the direction of monthly rent changes, so that market participants can grasp market trends more immediately.
    Reference: 參考文獻
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    二、 英文文獻
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    三、 網頁資料
    1. Google Trends(2019)，How is the data derived?. Retrieved December1, 2019 from Google on the World Wide Web:http://support.google.com/trends/bin/answer.py?hl=en&answer=92768&topic=13975&ctx=topic
    2. Google Trends(2019)，How is the data scaled?. Retrieved December1, 2019 from Google on the World Wide Web: http://support.google.com/trends/bin/answer.py?hl=en&answer=87282&topic=13975&ctx=topic
    3. Google Trends(2019)，Is the data normalized?. Retrieved December1, 2019 from Google on the World Wide Web: http://support.google.com/trends/bin/answer.py?hl=en&answer=87284&topic=13975&ctx=topic.
     
    Description: 碩士
    國立政治大學
    地政學系
    107257023
    Source URI: http://thesis.lib.nccu.edu.tw/record/#G0107257023
    Data Type: thesis
    DOI: 10.6814/NCCU202000964
    Appears in Collections:[Department of Land Economics] Theses

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