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    政大機構典藏 > 商學院 > 企業管理學系 > 期刊論文 >  Item 140.119/125425
    Please use this identifier to cite or link to this item: http://nccur.lib.nccu.edu.tw/handle/140.119/125425

    Title: 應用網路搜尋行為預測房地產市場
    Predicting Housing Markets through the Searching Behavior on Internet
    Authors: 林左裕
    Lin, Tsoyu Calvin
    Contributors: 地政系
    Keywords: 搜尋引擎;搜尋行為;住宅價格;住宅交易量;大數據;Google 搜尋趨勢
    Search Engine ; Searching Behavior ; Housing Price; Housing Volume; Big Data; Google Trend
    Date: 2019-06
    Issue Date: 2019-09-04 10:38:00 (UTC+8)
    Abstract: 隨著網路時代的來臨,人們透過網路搜尋資訊及交易的頻率大為增加,相關數據之 數量及種類也大幅成長,這些數據雖細碎龐雜,卻記錄著人們自搜尋、瀏覽至交易行為 決策之過程。以往我國之不動產研究或政策擬定,資料來源多來自官方、民間企業或學 術統計為主,這些資訊雖給予政府或企業分析、預估市場一定程度之基礎,然其資料背 景不同且發布時間落後,於資料使用上仍存有諸多限制。而搜尋引擎具有即時性、隱含 潛在經濟活動意象之特性,應可補足過去統計資料之不足,提供不動產市場更即時、精 確之預估。本研究以自 Google Trends 搜集之搜尋引擎指數作為自變數,加入傳統不動產市場之
    計量模型,同時探討其與住宅交易量與價格間之關係。結果發現 Google Trends 指數對於
    With the increasing popularity of the internet, people acquire information frequently via internet. Data of searching and trading activities on internet accumulate sharply. These data, although fragmented, record people from search, browse and decision-making process. Traditional analysis of real estate market or policy making rely on statistics published by governments, companies, or academic institutions. Although these data provide some basis information, there are still some restrictions, i.e., limited sample and delayed posting. Data on search engines may complement these flaws. They are extremely timely and cover a great variety of economic activities, providing prompt and accurate forecast for the markets. This study applies the search engine index provided by Google Trends to explore the relationship between the search engine index and housing markets. Results show that search engine index significantly leads housing prices and transaction volume, indicating that people tend to search and obtain information before entering real estate markets. By understanding the leading effect of "big data" on search engine to the housing markets, we can forecast real estate markets more accurately, and provide more timely implications for governments, enterprises and the public.
    Relation: 應用經濟論叢, No.105, pp.219-254
    Data Type: article
    DOI 連結: https://doi.org/   10.3966/054696002019060105006
    DOI: 10.3966/054696002019060105006
    Appears in Collections:[企業管理學系] 期刊論文

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