Loading...
|
Please use this identifier to cite or link to this item:
https://nccur.lib.nccu.edu.tw/handle/140.119/131170
|
| Title: | Google Trends搜尋數據與辦公室租金之關聯
The Correlation Between Google Search Volume Data and Office Rent |
| Authors: | 鍾之琦
Chung, Chih-Chi |
| Contributors: | 林左裕
Lin, Tso-Yu
鍾之琦
Chung, Chih-Chi |
| Keywords: | 搜尋行為
辦公室租金
自我迴歸時間落遲模型
Google Trends
SVI
Search Behavior
Office Rent
ARDL |
| Date: | 2020 |
| Issue Date: | 2020-08-03 18:09:38 (UTC+8) |
| Abstract: | 網路搜尋行為已改變不動產市場的運作模式，市場參與者得利用網路搜尋以輔助最適決策之完成，過往文獻有關於辦公室租金與總體經濟面之研究大多僅考量總體經濟變數，並且存在所參考資料發布時間之遲延問題，為提高預測之即時性，本研究利用國泰辦公室租金市場報告、Google關鍵字搜尋量（Search Volume Index, SVI）探討人們在網路搜尋之後可能產生的真實租賃行為，以彌補此研究缺口。
本研究透過自我迴歸時間落差分配模型（ARDL）進行實證分析，研究發現「出租關鍵字」的搜尋量與當期A、B級辦公室租金呈現正向顯著關聯性；其次，前一期之「出租關鍵字」搜尋量可作為B辦租金之領先指標，即當前一季搜尋次數上升時，將對後期之B辦租金產生顯著之正向影響，顯示潛在承租戶事前之網路搜尋行為確實轉化為真實世界中的租賃需求。
綜上所述，隨著長期追蹤紀錄人類搜尋軌跡，搜尋引擎之資料特性得完整地呈現當下搜尋者之心理特徵與潛在需求，得以補足過去基本面或總經變數無法對市場提出即時解釋之文獻缺口。應用本研究之結果，能藉由大數據資料的優勢提供一新型辦公室之有效指標，使市場參與者能更即時地掌握市場趨勢。
Internet search behavior helps real estate market participants to reduce the risk in the decision-making process. In the past literature, studies on office rents mostly only considered overall economic variables and there was a delay in the publication time of the reference materials. This study uses the Office Rent Market Report issued by Cathay Life Insurance Company and Google Search Volume Index (SVI) to explore whether the search behavior is leading the change in office rent.
This study uses the Auto-Regressive Distributed Lag (ARDL) model for empirical analysis, and the result suggest that the search volume of “Leasing Keywords” was positively and significantly related to the rents of Grade A and B offices in the selected period. Secondly, the search volume of "Leasing Keyword" in the previous period can be used as the leading indicator of rent for Grade B office. It shows that the online search behavior of potential tenants is indeed transformed into real-world rental needs.
In summary, the search data completely present the psychological characteristics and potential needs of current searchers. According to the results of this research, it is possible to use the advantages of big data to predicts the direction of monthly rent changes, so that market participants can grasp market trends more immediately. |
| Reference: | 參考文獻
一、 中文文獻
王信達，2010，「從兩岸總體經濟環境探討臺北市與上海市辦公市場租金影響之實證分析」，淡江大學中國大陸研究所碩士論文：臺北市。
林左裕，2019，「應用網路搜尋行為預測房地產市場」，『應用經濟論叢』，105 ：219-254。
林左裕、程于芳，2014，「影響不動產市場之從眾行為與總體經濟因素之研究」，『應用經濟論叢』，95：61-99。
周美伶，2005，「購屋者外部資訊搜尋管道選擇行為與搜尋期間之探討」，『住宅學報』，14（2）：1-25。
周美伶、張金鶚，2005，「購屋搜尋期間影響因素之研究」，『管理評論』，24：133-150。
曾翊偉、黃名義、張金鶚，2000，「租戶結構對辦公大樓租金與空置率之影響」，『都市與計畫』，37（4）：481-500。
黃智聰、梁儀盈（2013），計量經濟學（原著R. Carter Hill, William E. Griffiths, Guay C. Lim）。臺北：雙葉書廊有限公司。
張譯之，2005，「臺北市辦公不動產要價租金衡量因素之探討」，國立臺北大學不動產城鄉研究所碩士論文：新北市。
張曉慈，2000，「影響不動產報酬波動性之總體經濟因素」，國立政治大學地政研究所碩士論文：臺北市。
楊奕農，時間序列分析-經濟與財務之應用，二版，雙葉書廊有限公司，民國九十八年。
廖仲仁、張金鶚，2004，「搜尋成本與定錨效果對於購屋者價格貼水之影響」，『住宅學報』，13（2）：47－62。
薄有為，2011，「臺北市辦公大樓市場租金與總體經濟因素關聯性之研究─應用時間序列分析方法」，逢甲大學土地管理學系碩士在職專班碩士論文。
二、 英文文獻
Beracha, E., Wintoki, M.B., 2013, Forecasting Residential Real Estate Price Changes From Online Search Activity , Journal of Real Estate Research, 35 (3).
Choi, H. and Varian, H., 2012, Predicting the Present with Google Trends, The Economic Record, 88(1), 2-9.
Cornin, F. J., 1982, The Efficiency of Housing Search, Southern Economic Journal, 48(4), 1016-1030.
Darrat, Ali F., and John Glascock L., 1993, On the Real Estate Market Efficiency., The Journal of Real Estate Finance and Economics, 7(1), 55-72.
De Francesco, A. J., 2008, Time-series Characteristics and Long-run Equilibrium for Major Australian Office Markets, Real Estate Economics, 36(2), 371-402.
Demchenko Y., Grosso P., De Laat C., Membrey P., 2013, Addressing Big Data Issues in Scientific Data Infrastructure, International Conference on Collaboration Technologies and Systems (CTS), San Diego, CA, 2013, 48-55
Dietzel, M. A., Braun, N., Schäfers, W., 2014, Sentiment-based Commercial Real Estate Forecasting with Google Search Volume Data. Journal of Property Investment & Finance, 36(6), 540-569.
Dietzel, M.A., 2016, Sentiment-based predictions of housing market turning points with Google trends, International Journal of Housing Markets and Analysis, Vol. 9 No. 1, 108-136.
Henig, S., Tsolacos, S. and Nanda, A., 2019, Which sentiment indicators matter? An analysis of the European commercial real estate market. Journal of Real Estate Research.
Hohenstatt, R., M. Kasbauer, and W. Schaffers, ‘‘Geco’’ and its Potential for Real Estate Research: Evidence from the U.S. Housing Market, Journal of Real Estate Research, 2011, 33(4), 471-506.
Ginsberg, J., Mohebbi, M. H., Patel, R. S., Brammer, L., Smolinski, M. S. and Brilliant, L., 2009, Detecting Influenza Epidemics Using Search Engine Query Data, Nature., 457(7232), 1012-1014.
Jin, C., Soydemir, G. and Tidwell, A., 2014, The US Housing Market and the Pricing of Risk: Fundamental Analysis and Market Sentiment”, Journal of Real Estate Research, 36(2), 187-216.
Jun, S., Yoo, H., & Choi, S., 2018, Ten years of research change using Google Trends: From the perspective of big data utilizations and applications. Technological Forecasting and Social Change, 130, 69–87.
Jurgilas, M. and Lansing, K.J., 2012, Housing Bubbles and Expected Returns to Homeownership: Lessons and Policy Implications, Working Paper. http//ssrn.com/abstract=2209719, 2013.
Kulkarni, Rajendra and Haynes, Kingsley E. and Stough, Roger R. and Paelinck, Jean H. P., Forecasting Housing Prices with Google Econometrics. GMU School of Public Policy Research Paper No. 2009-10.
Lee, J. and M. C. Strazicich, 2003, Minimum Lagrange Multiplier Unit Root Test with Two Structural Breaks, Review of Economics and Statistics, 85, 1082-1089.
Nelson, P., 1970, Information and Consumer Behavior, Journal of Political Economy, 78 (2), 311-329.
Ng, B. F. and Higgins, D., 2007, Modelling the commercial property market: an empirical study of the Singapore office market, Pacific Rim Property Research Journal, 13, 176-193.
Preis, T., Moat, H. S. and Stanley, E., 2013, Quantifying trading behavior in financial markets using Google trends, Nature Scientific Reports, 3, 1684, 1-6.
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, 49–60.
Rochdi, K. and Dietzel, M. (2015), Outperforming the benchmark: online information demand and REIT market performance. Journal of Property Investment & Finance, Vol. 33 ,No. 2, 169-195.
Palm, R., 1976, Real estate agents and geographical information, Geographical Review, 66, 266–280.
Rae, A., 2015, Online Housing Search and the Geography of Submarkets, Housing Studies, 30 (3), 453-472.
Schmidt, J. B. and Spreng, R.A., 1996,“A Proposed Model of External Consumer Information Search”, Journal of the Academy of Marketing Science, 1996, 24(3), 246-256
Shiller, R. J., 2007, Understanding Recent Trends in House Prices and Home Ownership, Paper Presented at the Federal Reserve Bank of Kansas City’s Jackson Hole Symposium, Kansas, 31 August – 1 September.
Stravroski, B., 2004, Designing a New E-business Model for a Commercial Real Estate Enterprise: A Case Study, Online Information Review, 28 (2), 110-119.
Tierney, H. L. R. and Pan, B., 2012, A Poisson Regression Examination of the Relationship Between Website Traffic and Search Engine Queries, Netnomics: Economic Research and Electronic Networking, 13(3), 155-189.
Wu, L., and Brynjolfsson, E., 2015, The Future of Prediction: How Google Aearches Foreshadow Housing Prices and Sales. Technical report.
Yung K.,Nafar N., 2017, Investor attention and the expected returns of reits, International Review of Economics and Finance, 48 , 423-439.
Zumpano, L.V., Johnson, K.H. and Anderson, R.I., 2003, Internet Use and Real Estate Brokerage Market Intermediation, Journal of Housing Economics, 12(2), 134-51.
三、 網頁資料
1. Google Trends(2019)，How is the data derived?. Retrieved December1, 2019 from Google on the World Wide Web:http://support.google.com/trends/bin/answer.py?hl=en&answer=92768&topic=13975&ctx=topic
2. Google Trends(2019)，How is the data scaled?. Retrieved December1, 2019 from Google on the World Wide Web: http://support.google.com/trends/bin/answer.py?hl=en&answer=87282&topic=13975&ctx=topic
3. Google Trends(2019)，Is the data normalized?. Retrieved December1, 2019 from Google on the World Wide Web: http://support.google.com/trends/bin/answer.py?hl=en&answer=87284&topic=13975&ctx=topic.
|
| Description: | 碩士
國立政治大學
地政學系
107257023 |
| Source URI: | http://thesis.lib.nccu.edu.tw/record/#G0107257023 |
| Data Type: | thesis |
| DOI: | 10.6814/NCCU202000964 |
| Appears in Collections: | [地政學系] 學位論文
|
Files in This Item:
| File |
Description |
Size | Format | |
|---|
| 702301.pdf | | 6781Kb | Adobe PDF2 | 111 | View/Open |
|
All items in 政大典藏 are protected by copyright, with all rights reserved.
|
