臺灣博碩士論文加值系統

不動產交易量的波動與趨勢是探討住宅價格趨勢的重要因素，甚至有領先價格波動的現象，價格與交易量是決定市場供需的兩大因素，但不動產經濟研究多以價格為分析對象，關於交易量的研究相對缺乏。市場情緒指數代表著一個市場內參與者對該市場的態度，一般要衡量市場情緒，多使用情緒代理指標去揣測市場情緒的波動，而這類指標又可分為直接調查而得或者間接取得，但這些指標可能存在時間落後、代表性不足、報告偏倚，而Google Trends提供之搜尋量指數(SVI)存在著消費者關注程度的改變並避免情緒代理指標常有的問題，讓我們對於現在正要發生的經濟活動能有更好的預測能力。
本篇以台灣2004年1月至2016年12月，156筆月資料及2013年1月至2016年12月208筆週資料。以自我迴歸遞延分配模型(ARDL)建立月資料傳統模型、月資料搜尋關鍵字熱度模型以及週資料搜尋關鍵字熱度模型，探討Google Trends提供之SVI與台灣房屋交易量的關聯性，分析SVI作為解釋中長期及短期房屋市場交易量變動參考指標之適切性。
實證分析後發現，由SVI組成之搜尋關鍵字熱度模型對於房屋需求量變動有相當好的解釋能力及預測能力以週資料組成之搜尋關鍵字熱度模型對房屋需求量變動之解釋能力，優於月資料組成之傳統模型及搜尋關鍵字熱度模型。在搜尋熱度模型中不論中長期或短期，「房屋」搜尋熱度對房屋需求量變動皆為正向影響且相關性最高；中長期而言，「房屋租金」搜尋熱度與房屋購買需求呈正相關，房屋購買需求與租屋需求呈現同步上揚或下降趨勢；短期而言，「房屋租金」搜尋熱度與房屋購買需求呈負相關，房屋購買需求與租屋需求呈現反向變動趨勢。

The fluctuations and trends of real estate transaction volumes are the two important factors that can be used to forecast the future trend of housing prices and serve as the leading index of housing prices. Most previous literature have been focused on the price of real estate, there has been relatively few studies on the transaction volume of real estate. Market sentiment index represents the attitude of market participant, it has been widely used as an emotional agent index to speculate fluctuations in real estate market sentiment. In general, market sentiment indices can be divided into direct surveys or indirect acquirement, however, these indices often suffer from problems such as time lags, lack of representation, and reporting bias. On the contrary, search volume index (SVI) provides by Google Trends contains the information about consumer attention without the disadvantages of most emotional agent indices. Therefore, SVI is considered as a more adequate tool than other market sentiment indices in the forecast of future transaction volume of real estate.
In this study, two real estate related dataset of Taiwan were collected for our empirical analysis: (1) 156 medium-long term monthly data from January 2004 to December 2016; (2) 208 short term weekly data from January 2013 to December2016. Autoregressive distributed lag (ARDL) models were used to establish monthly traditional econometric model, monthly SVI model, and weekly SVI model to investigate the relationship between SVI and Taiwan housing transaction volume and the suitability of SVI as a reference index to explain the change of transaction volume in medium-long term and short term housing market.
The empirical results show that SVI model is performed better than traditional econometric model at explaining and predicting the demand fluctuations in housing market. Furthermore, high frequency weekly SVI model is performed much better than monthly traditional econometric model and monthly SVI model. Among the search keywords, “housing” is the most important keyword that positively correlated to housing demand both in the medium-long term and short term SVI models. In the medium-long term SVI model, the keyword "housing rent" is also positively correlated with housing demand because rental housing demand is usually co-movement with housing demand. In the short term SVI model, however, the keyword "housing rent" is negatively correlated with housing demand because increases in rental housing demand may lead to decreases in own house demand in the short run.

摘要 i
目錄 iii
圖目錄 iv
表目錄 v
第一章 緒論 1
第一節 研究背景 1
第二節 研究動機與目的 2
第三節 研究範圍 7
第四節 研究架構與流程 7
第二章 文獻回顧 9
第一節 台灣房地產購買過程簡述 9
第二節 影響房地產景氣相關因素與指標 13
第三節 Google Trends相關文獻 20
第三章 研究方法 27
第一節 單根檢定 28
第二節 共整合檢定 30
第三節 自我迴歸遞延分配邊界檢定 31
第四節 模型適合度與預測誤差 34
第四章 實證分析 36
第一節 資料來源及說明 36
第二節 實證模型 42
第三節 實證結果分析 44
第五章 結論與建議 59
第一節 結論 59
第二節 政策建議 60
第三節 未來研究方向 60
參考文獻 62
中文部分 62
英文部分 63
網路資源 66

中文部分
宇真德 (2011)，「經濟發展過程所得分配對住宅需求影響之變化趨勢」，國立暨南國際大學經營管理學程碩士在職專班碩士論文。
林勝益 (1992)，「房地產景氣預測探討」，國立成功大學企業管理研究所碩士論文。
洪麗芬 (2010)，「影響房地產市場交易量之系統動態模擬-以政府階段性利率政策為例」，國立高雄應用科技大學商務經營研究所碩士論文。
陳文祥 (1984)，「住宅房屋市場區隔之研究」，國立台灣大學商業管理研究所碩士論文。
陳昱州 (2016)，「投資人關注與股票報酬的影響 」，國立東華大學財務金融學系碩士論文。
張金鶚 (1996)，「房地產投資與決策分析-理論與實務」。
張金鶚，王健安 & 林秋瑾 (1997)，「房地產景氣與總體經濟景氣於時間上領先，同時，落後關係之探討」《人文及社會科學研究彙刊》，7(1)，35-56。
張金鶚，林秋瑾(1996)，「台灣地區房地產景氣指標電腦輔助系統之建立與應用」。
張慈佳 (2000)，「政治景氣循環現象對房地產價格影響之研究」，國立政治大學地政學研究所博士論文。
黃俊英 (1999)，「行銷研究: 管理與技術」，第六版，華泰書局。
黃俊英 (2004)，「行銷管理-策略性的觀點」，第二版，台北：華泰文化。
黃浚紘 (2013)，「Google 是否能預測台灣股票報酬率?」，臺灣大學財務金融學研究所碩士論文。
詹任偉 (2003)，「台灣房地產景氣動向預測之準確度研究」，國立政治大學地政研究所碩士論文。
鄭惠如 (2005)，「人口結構變遷對房地產價格影響之研究」，東吳大學國際貿易學系研究所碩士論文。
謝宗憲 (2014)，「亞馬遜公司股價與Google Trends關鍵字搜尋之探討」，世新大 學財務金融學研究所碩士論文。
戴鎰家 (1995)，「影響建築業房地產投資決策因素分析理論與實務之研究-以新竹地區為例」，中華大學工業研究所碩士論文。
顏郁峰 (2010)，「台北市、全國房地產市場與總體經濟連結度-台北市房地產是否為總體優良指標」，國立台灣大學經濟學研究所碩士論文。
英文部分
Banerjee, A., Dolado, J. J., Galbraith, J. W., & Hendry, D. (1993). Co-integration, error correction, and the econometric analysis of non-stationary data. OUP Catalogue.
Beatty, S. E., & Smith, S. M. (1987). “External search effort: An investigation across several product categories, ” Journal of consumer research, 14(1), 83-95.
Beracha, E., & Wintoki, M. B. (2013). “Forecasting residential real estate price changes from online search activity,” Journal of Real Estate Research, 35(3), 283-312.
Bokhari, S., & Geltner, D. (2011). “Loss aversion and anchoring in commercial real estate pricing: Empirical evidence and price index implications,” Real Estate Economics, 39(4), 635-670.
Choi, H., & Varian, H. (2009). Predicting initial claims for unemployment benefits. Google Inc, 1-5.
Choi, H., & Varian, H. (2012). “Predicting the present with Google Trends,” Economic Record, 88(1), 2-9.
Dickey, D. A., & Fuller, W. A. (1979). “Distribution of the estimators for autoregressive time series with a unit root,” Journal of the American statistical association, 74(366), 427-431.
Davenport, T. H. (2006). “Competing on analytics,” Harvard Business Review, 84(1), 98.
Engel, J. F., Blackwell, R. D., & Miniard, P. W. (1993). Consumer behavior, Chicago: Dryden
Engle, R. F., & Granger, C. W. (1987). “Co-integration and error correction: representation, estimation, and testing,” Econometrica, 251-276.
Genesove, D., & Mayer, C. (2001). “Loss aversion and seller behavior: Evidence
from the housing market, ” National bureau of economic research, (No. w8143).
Granger, C. W., & Newbold, P. (1974). Spurious regressions in econometrics,” Journal of Econometrics, 2(2), 111-120.
Gwin, C. (2009). “International Comparisons of Real Estate E-nformation on the Internet,” Journal of Real Estate Research.
Hohenstatt, R., Käsbauer, M., & Schäfers, W. (2012). “" Geco" and its potential for real estate research: Evidence from the US housing market,” Journal of Real Estate Research.
Horrigan, J. B., & Vitak, J. (2008). The Internet and consumer choice: online Americans use different search and purchase strategies for different goods: Pew Internet
& American Life Project.
Johansen, S. (1988). “Statistical analysis of cointegration vectors,” Journal of Economic Dynamics and Control, 12(2-3), 231-254.
Kahneman, D. (1973). Attention and effort (Vol. 1063). Englewood Cliffs, NJ:Prentice-Hall
Kahneman, D., & Tversky, A. (1979). “Prospect theory: An analysis of decision under risk,” Econometrica, 263-291.
Klein, L. R., & Ford, G. T. (2003). “Consumer search for information in the digital age: An empirical study of prepurchase search for automobiles,” Journal of Interactive, 17(3), 29-49.
Kuruzovich, J., Viswanathan, S., Agarwal, R., Gosain, S., & Weitzman, S. (2008). “Marketspace or marketplace? Online information search and channel outcomes in auto retailing,” Information Systems Research, 19(2), 182-201.
Leung, C. K. Y., Lau, G. C. K., & Leong, C. F. Y. (2003). “Testing alternative theories of the property price-trading volume correlation,” Available at SSRN 409080.
McAfee, A., Brynjolfsson, E., Davenport, T. H., Patil, D., & Barton, D. (2012). “Big data. The management revolution,” Harvard Business Review, 90(10), 61-67.
Moe, W. W., & Fader, P. S. (2004). “Dynamic conversion behavior at e-commerce sites,” Management Science, 50(3), 326-335.
Pentland, A., & Heibeck, T. (2010). Honest signals: how they shape our world: MIT press.
Pesaran, M. H., Shin, Y., & Smith, R. P. (1999). “Pooled mean group estimation of dynamic heterogeneous panels,” Journal of the American Statistical Association, 94(446), 621-634.
Pesaran, M. H., Shin, Y., & Smith, R. J. (2001). “Bounds testing approaches to the analysis of level relationships,” Journal of Applied Econometrics, 16(3), 289-326.
Preis, T., Moat, H. S., Stanley, H. E., & Bishop, S. R. (2012). “Quantifying the
advantage of looking forward,” Scientific Reports, 2.
Rangaswamy, A., Giles, C. L., & Seres, S. (2009). “A strategic perspective on search engines: Thought candies for practitioners and researchers,” Journal of Interactive Marketing, 23(1), 49-60.
Said, S. E., & Dickey, D. A. (1984). “Testing for unit roots in autoregressive-moving average models of unknown order,” Biometrika, 71(3), 599-607.
Seiler, M. J., Seiler, V. L., & Lane, M. A. (2012). “Mental accounting and false reference points in real estate investment decision making,” Journal of Behavioral Finance, 13(1), 17-26.
Shefrin, H., & Statman, M. (1985). “The disposition to sell winners too early and ride losers too long: Theory and evidence,” The Journal of Finance, 40(3), 777-790.
Stigler, G. J. (1961). “The economics of information,” The Journal of Political Economy, 213-225.
Wu, L., & Brynjolfsson, E. (2009). “The future of prediction: how Google searches housing prices and quantities,” ICIS 2009 Proceedings, 147.
Wu, L., Waber, B. N., Aral, S., Brynjolfsson, E., & Pentland, A. (2008). Mining face-to-face interaction networks using sociometric badges: Predicting productivity in an it configuration task.
網路資源
台灣法人台灣網路資訊中心(TWNIC)
< http://www.twnic.net.tw/>
591房屋交易
< https://www.591.com.tw/>
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