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研究生:高郁雯
研究生(外文):KAO, YU-WEN
論文名稱:網路搜尋對台灣區域房價之影響
論文名稱(外文):The Effects of Internet Search on Regional Housing Prices in Taiwan
指導教授:簡美瑟簡美瑟引用關係
指導教授(外文):CHIEN, MEI-SE
口試委員:張嘉倩呂書屏簡美瑟
口試委員(外文):CHANG, CHIA-CHIENLIU, SHU-BINGCHIEN, MEI-SE
口試日期:2020-06-29
學位類別:碩士
校院名稱:國立高雄科技大學
系所名稱:金融資訊系
學門:商業及管理學門
學類:財務金融學類
論文種類:學術論文
論文出版年:2020
畢業學年度:108
語文別:中文
論文頁數:59
中文關鍵詞:房價網路搜尋不動產租屋追蹤資料迴歸
外文關鍵詞:Housing PricesInternet SearchReal EstateRentPanel Regression
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本研究旨在探討Google Trends之搜尋指數對於台灣房地產市場之影響。本研究分別以全國房價以及六都房價兩不同樣本群組來進行實證,本文的實證結果可歸納如下:第一、全國房價報酬率之OLS估計結果顯示,當期之不動產搜尋指數(RE)會顯著正向影響房價,顯示民眾進行不動產搜尋行動與購屋行動並無明顯的時間差;租屋搜尋指數(RENT)在落後一期顯著為負,表示在短期下有租屋取代購屋之「替代效果」。第二、六都房價異常報酬率之追蹤資料實證結果顯示,不動產搜尋指數(RE)於落後二、三期顯著為正,顯示六都房價相對較高,網路搜尋至實際買房有三個月以上較長之時間落差;就租屋搜尋指數(RENT)而言,在落後三、四期顯著為正表示有租屋帶動購屋之「投資效果」。第三、當購屋者處於正向情緒時,係數在落後一期顯著為負,此為「羊群效應」的現象,而在負向購屋者情緒時並無「羊群效應」的現象。第四、冷熱市場對於六都房價超額報酬之影響,在熱市場下,不動產搜尋指數(RE)短期顯著為正;在冷市場下,租屋搜尋指數(RENT)短期顯著為負,代表冷、熱市場在短期下確實會強化網路搜尋指數對六都房價異常報酬率之影響力。最後、因果關係檢定顯示網路搜尋關鍵字會領先六都房價。
The aim of this study is to investigate the effects of Google trends search queries on the volatilities of housing prices in Taiwan. Based on two data samples of Taiwan’s housing prices, the country-level data and six megacities’ data, the empirical results are as the followings: First, according to the OLS results for the country-level data, Google trend of real estate at the current period can significantly and positively affect housing prices, implying no time lag between internet search and buying houses; Google trend of rent at lagged one quarter can significantly and negatively affect housing prices, displaying the existence of a substitution effect between rent and buy a house in the short run. Second, based on the results of the housing prices’ abnormal returns of six megacities’ data, Google trend of real estate at lagged two and three quarters can significantly and positively affect housing prices, implying longer time lag between internet search and buying houses; Google trend of rent at lagged three and four quarters can significantly and positively affect housing prices, displaying the existence of a investment effect between rent and buy a house. Third, the results of the model of buyer sentiment show that herding effect exist for the model of positive buyer sentiment but does not for the model of negative buyer sentiment. Forth, the effect of Google trend of real estate is significantly positive in the hot market and the effect of Google trend of rent is significantly negative in the cold market, meaning the effect of internet search intensity on housing prices’ abnormal returns will be strengthened. Finally, the results of Granger causality tests show that internet search intensity can lead housing prices’ abnormal returns.
摘要 i
Abstract ii
誌謝 iii
目錄 iv
表目錄 v
圖目錄 vi
第一章 緒論 1
第一節 研究動機與目的 1
第二節 研究架構 4
第二章 文獻回顧 6
第三章 研究方法 12
第一節 理論模型 12
3.1.1網路搜尋對全國房價報酬率影響之模型 12
3.1.2網路搜尋對六都房價異常報酬率影響之模型 14
3.1.3考量購屋者情緒之模型 15
3.1.4考量市場冷熱之模型 16
3.1.5網路搜尋與六都房價異常報酬率之因果關係模型 17
第二節 Google Trends資料處理 19
第三節 追蹤資料模型(Panel Data) 20
第四節 因果關係檢定 22
第四章 實證結果 24
第一節 資料來源與處理 24
第二節 敘述性統計與相關係數分析 26
第三節 全國房價報酬率之實證結果分析 29
第四節 六都房價異常報酬率之實證結果分析 32
4.4.1 網路搜尋對於六都房價異常報酬率之影響 32
4.4.2 考量購屋者之情緒對於六都房價超額報酬之影響 35
4.4.3 考量市場冷熱對於六都房價超額報酬之影響 40
4.4.4 網路搜尋與六都房價異常報酬率之因果關係 44
第五章 結論與建議 45
參考文獻 47


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