Loading...
|
Please use this identifier to cite or link to this item:
https://nccur.lib.nccu.edu.tw/handle/140.119/136333
|
| Title: | 社群信心聲量與房市之關聯性
The Relationship between Internet Community Sentiment and Real Estate Market |
| Authors: | 張均綸
Chang, Chun-Lun |
| Contributors: | 陳明吉
Chen, Ming-Chi
張均綸
Chang, Chun-Lun |
| Keywords: | 房地產市場
情緒指數
社群聲量
交易量
議價空間
流動天數
internet sentiments
housing market
consumer confident index |
| Date: | 2021 |
| Issue Date: | 2021-08-04 14:45:09 (UTC+8) |
| Abstract: | 過去在研究影響不動產價格的因素中，除了常用的基本面因素以外，投資人的情緒也會影響房價的波動，例如隨著市場對於景氣和房市樂觀，可能會導致屋主惜售，進一步推升房市價格。而衡量市場投資人的指標有很多，包括消費者信心指數、Google搜索量指數、文字探勘技術等等。而本篇研究即是將兩種常見的方法進行結合和比較，即消費者信心指數和文字探勘。首先透過社群大數據平台Opview關鍵字設定功能建構情緒字典，以消費者信心指數的建構方式為參考依據，重新建構衡量消費者情緒的指標，稱之為社群信心聲量，並藉這個機會來比較透過社群所建構之指標，和傳統透過問卷調查方式所建立的消費者信心指標，有何異同。聲量指的是網路上的貼文和回文數，所以透過蒐集這些網路上的聲量，我們可以獲得比消費者信心指數更即時且大樣本的數據。本研究採集了2012年1月至2020年12月的資料，針對台北地區房屋的房價、成交量、流動天數、議價空間進行分析，並比較了兩種指標在不同應變數下的解釋力，而本篇研究發現，在房價和成交量的部分，社群聲量指標確實有更好的顯著水準，而在議價空間則是消費者信心指數表現較好，兩個指標各有所長，也就是說，未來在分析市場的有限情緒時，除了消費者信心指數，或許也可以把社群信心聲量納入考量，是不錯的研究工具。
In the past, there are many factors affecting real estate prices, in addition to the fundamental factors, investor sentiment will also affect the fluctuation of house prices. For example, as the market is optimistic about the boom and the housing market, it may cause homeowners to be reluctant to sell and further push up the housing market price. Therefore, there are many indicators to measure market investors, including consumer confidence index, Google search volume index, text mining technology and so on.
This study combines and compares the two methods. The keyword setting function of the social big data platform Opview is used to construct a sentiment dictionary, which is based on the consumer confidence index. The construction method is reconstructing an indicator to measure consumer sentiment, which is called the volume of community, and comparing whether the indicators constructed through the community can beat the traditional consumer confidence index. The volume of voice is the number of posts and palindromes on the Internet, so by collecting the volume of voice on these networks, we can obtain more real-time and larger sample data than the consumer confidence index. This research collects data from January 2012 to December 2020, and analyzes the housing prices, transaction volume, flow days, and bargaining space of the Taipei area. This research found that in the part of housing prices and transaction volume, the community volume index is indeed better than the consumer confidence index, and the consumer confidence index performs better in the bargaining space. In the future, when analyzing the limited sentiment of the market, in addition to the consumer confidence index, you can take internet community into account, it is possible that better predictive power can be obtained. |
| Reference: | 中文參考文獻
1.王韻怡、池祥萱、周冠男（2016）。行為財務學文獻回顧與展望: 台灣市場之研究。經濟論文叢刊,44(1):1-55。
2.許慶安（2017）。運用探勘技術於社會輿情以預測新竹市房地產市場之研究。未出版之碩士論文，國立勤益科技大學資訊管理系，台中市。
3.陳天德（1993）。消費者信心指數與房地產景氣相關之研究。未出版之碩士論文，國立政治大學企業管理研究所，台北市。
4.蔡宜君（2016）。消費者信心指數與房地產景氣之關聯性研究。未出版之碩士論文，世新大學財務金融學研究所(含碩專班)，臺北市。
5.朱芳妮、楊茜文、蘇子涵、陳明吉(2020)。情緒會影響房市嗎？指數編制與驗證。住宅學報,Vol.29, No.2, pp.35-68.
6.朱芳妮、楊茜文、黃御維、陳明吉(2020)。媒體傳播效應與房市變化關聯性之驗證。管理學報,Vol.37, No.3, pp.225-257.
7.鐘國應、曾萬益(2020)。我國2017至2019年「全民國防教育暑期戰鬥營」之網路輿情情勢－「以OpView社群口碑資料庫」為觀察視角。軍事社會科學專刊,Vol.17,pp.7-44
8.劉振隆、郭庭瑜、黃湋宸、蔡佳潔(2018)。以社群大數據基礎之台灣民眾國外旅遊概況與觀光行為模式。觀光與休閒管理期刊,Vol.6,pp.13-22
英文參考文獻
9.an de Meulen, P., Micheli, M., & Schmidt, T. (2014). Forecasting real estate prices in Germany: the role of consumer confidence. Journal of Property Research, 31(3), 244-263.
10.Asabere, P., Huffman, F., & Mehdian, S. (1993). Mispricing and optimal time on the market. Journal of real Estate research, 8(1), 149-155.
11.Baker, M., & Wurgler, J. (2007). Investor sentiment in the stock market. Journal of economic perspectives, 21(2), 129-152.
12.Beracha, E., Lang, M., & Hausler, J. (2019). On the relationship between market sentiment and commercial real estate performance—a textual analysis examination. Journal of real Estate research, 41(4), 605-638.
13.Bollen, J., Mao, H., & Zeng, X. (2011). Twitter mood predicts the stock market. Journal of computational science, 2(1), 1-8.
14.Case, K. E., & Shiller, R. J. (1990). Forecasting prices and excess returns in the housing market. Real Estate Economics, 18(3), 253-273.
15.Croce, R. M., & Haurin, D. R. (2009). Predicting turning points in the housing market. Journal of Housing Economics, 18(4), 281-293.
16.Dong, Z., Hui, E. C., & Yi, D. (2021). Housing market sentiment and homeownership. Journal of Housing and the Built Environment, 36(1), 29-46.
17.Hohenstatt, R., Käsbauer, M., & Schäfers, W. (2011). " Geco" and its potential for real estate research: Evidence from the US housing market. Journal of real Estate research, 33(4), 471-506.
18.Hott, C., & Monnin, P. (2008). Fundamental real estate prices: An empirical estimation with international data. The Journal of Real Estate Finance and Economics, 36(4), 427-450.
19.Im Tan, L., San Phang, W., Chin, K. O., & Anthony, P. (2015). Rule-based sentiment analysis for financial news. Paper presented at the 2015 IEEE International Conference on Systems, Man, and Cybernetics.
20.Ling, D. C., Naranjo, A., & Scheick, B. (2014). Investor sentiment, limits to arbitrage and private market returns. Real Estate Economics, 42(3), 531-577.
21.Loughran, T., & McDonald, B. (2011). When is a liability not a liability? Textual analysis, dictionaries, and 10‐Ks. The journal of Finance, 66(1), 35-65.
22.Malkiel, B. G., & Fama, E. F. (1970). Efficient capital markets: A review of theory and empirical work. The journal of Finance, 25(2), 383-417.
23.Marcato, G., & Nanda, A. (2016). Information content and forecasting ability of sentiment indicators: case of real estate market. Journal of real Estate research, 38(2), 165-204.
24.Rao, T., & Srivastava, S. (2012). Analyzing stock market movements using twitter sentiment analysis.
25.Rapach, D. E., & Strauss, J. K. (2007). Forecasting real housing price growth in the eighth district states. Federal Reserve Bank of St. Louis. Regional Economic Development, 3(2), 33-42.
26.Rouwendal, J., & Longhi, S. (2008). The effect of consumers` expectations in a booming housing market: space-time patterns in the Netherlands, 1999–2000. Housing Studies, 23(2), 291-317.
27.Shiller, R. J. (2007). Understanding recent trends in house prices and home ownership: National Bureau of Economic Research.
28.Soo, C. K. (2013). Essays in household finance and housing economics.
29.Stein, J. C. (1995). Prices and trading volume in the housing market: A model with down-payment effects. The Quarterly Journal of Economics, 110(2), 379-406.
30.Stevenson, S. (2008). Modeling housing market fundamentals: Empirical evidence of extreme market conditions. Real Estate Economics, 36(1), 1-29.
31.Sul, H., Dennis, A. R., & Yuan, L. I. (2014). Trading on twitter: The financial information content of emotion in social media. Paper presented at the 2014 47th Hawaii International Conference on System Sciences.
32.Tetlock, P. C. (2007). Giving content to investor sentiment: The role of media in the stock market. The journal of Finance, 62(3), 1139-1168.
33.Walker, C. B. (2014). Housing booms and media coverage. Applied Economics, 46(32), 3954-3967.
34.Walker, C. B. (2016). The direction of media influence: Real-estate news and the stock market. Journal of Behavioral and Experimental Finance, 10, 20-31.
35.Wheaton, W., & Nechayev, G. (2008). The 1998-2005 housing “bubble” and the current “correction”: What’s different this time? Journal of real Estate research, 30(1), 1-26.
36.Wu, L., & Brynjolfsson, E. (2015). The future of prediction: How Google searches foreshadow housing prices and sales Economic analysis of the digital economy (pp. 89-118): University of Chicago Press.
37.Zamani, M., & Schwartz, H. A. (2017). Using twitter language to predict the real estate market. Paper presented at the Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 2, Short Papers.
38.Zhou, Z.-g. (1997). Forecasting sales and price for existing single-family homes: a VAR model with error correction. Journal of real Estate research, 14(2), 155-167. |
| Description: | 碩士
國立政治大學
財務管理學系
108357026 |
| Source URI: | http://thesis.lib.nccu.edu.tw/record/#G0108357026 |
| Data Type: | thesis |
| DOI: | 10.6814/NCCU202100843 |
| Appears in Collections: | [財務管理學系] 學位論文
|
Files in This Item:
| File |
Description |
Size | Format | |
|---|
| 702601.pdf | | 2135Kb | Adobe PDF2 | 75 | View/Open |
|
All items in 政大典藏 are protected by copyright, with all rights reserved.
|
