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    Title: 以網路搜尋點擊次數分析臺北市租屋市場之租金
    Analyzing the rents of rental market in Taipei by the number of visits on the website platform
    Authors: 劉敏
    Liu, Min
    Contributors: 林左裕
    劉敏
    Liu, Min
    Keywords: 平均點擊次數
    租金
    搜尋行為
    網路大數據
    Average visiting numbers
    Rent
    Searching behavior
    Big data
    Date: 2019
    Issue Date: 2019-08-07 16:45:09 (UTC+8)
    Abstract: 不動產市場與國民的生活息息相關,然而過去研究主要著重於房價之探討與預測,對租金市場之討論較為缺乏,惟現今台灣租屋市場規模已不容忽視,需要社會之關注。此外,過去在進行不動產市場分析時,一般多依據過去落後之統計資訊分析經濟活動,該類資料通常缺乏即時性,無法完全反映不動產市場趨勢。惟現今全世界邁入網路世代,人們進行消費決策前經常會透過網路進行市場資訊之搜尋,且過去便已經有文獻指出透過在模型中納入網路搜尋指標能夠增進對房屋交易價格與成交量的預測能力。故本研究欲探討平均點擊次數與租金之關聯,以及平均點擊資料是否能夠作為租金之領先指標。
    本研究之資料為臺北市十二個行政區自2013年至2017年四種租屋類型的租金與租屋網平均點擊次數資料,共960筆資料,採用追蹤資料固定效果模型進行分析。研究結果顯示平均點擊次數與租金有正向顯著的關係,且平均點擊次數可以作為租金之領先指標。另外,實證結果亦顯示,平均點擊次數所建立之搜尋指標領先程度在整層住家與套房有所不同。綜上所述,納入搜尋指標之租金模型能使政府透過觀察平均點擊次數的變化,提前發現租金之潛在變動趨勢,使政府能夠更有效率的監控租金市場潛在動向、因應租屋市場的變化。
    Real estate market is closely related to people’s life. In the past, previous research was mainly focused on the discussion and forecast of housing prices, only a few papers paid attention to the rental market. However, the scale of the rental housing market in Taiwan has gradually expanded over time, which needed the attention from the society. In addition, researchers usually use outdated statistical data while conducting research related to real estate economic activities in the past. However, with the worldwide progress of technology, consumers often search for information through the Internet before making decisions. In addition, there have been reports pointed out that by adding in online searching indicators in the model can improve the ability to predict housing price and the amount of transaction in the real estate market. Therefore, this study is to discuss the relationship between the average visiting numbers of the rental housing platform and the rent, and whether the average visiting numbers could be used as a leading indicator of the rent.
    By using the average visiting numbers data of Taipei City’s 12 administrative districts from 2013 to 2017 to analyze housing rent with fixed effect model. The result shows that the average visiting numbers have a positive relationship with the rent, and the average visiting numbers can be used as a leading indicator of the rent. In addition, the empirical results also demonstrate that the searching period’s difference between common residence and suite. In conclusion, by adding online searching indicator in the model can improve the ability of forecasting the trend of housing rent, and enable the government to monitor the rental housing market and also react faster to the change of the rental housing market.
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    Description: 碩士
    國立政治大學
    地政學系
    106257004
    Source URI: http://thesis.lib.nccu.edu.tw/record/#G0106257004
    Data Type: thesis
    DOI: 10.6814/NCCU201900568
    Appears in Collections:[地政學系] 學位論文

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