English  |  正體中文  |  简体中文  |  Post-Print筆數 : 27 |  Items with full text/Total items : 113311/144292 (79%)
Visitors : 50929363      Online Users : 1009
RC Version 6.0 © Powered By DSPACE, MIT. Enhanced by NTU Library IR team.
Scope Tips:
  • please add "double quotation mark" for query phrases to get precise results
  • please goto advance search for comprehansive author search
  • Adv. Search
    HomeLoginUploadHelpAboutAdminister Goto mobile version
    政大機構典藏 > 商學院 > 財務管理學系 > 學位論文 >  Item 140.119/141019
    Please use this identifier to cite or link to this item: https://nccur.lib.nccu.edu.tw/handle/140.119/141019


    Title: 行銷文字對不動產成交價之影響與預測:文字探勘法
    The Influence and Prediction of Marketing Texts on Real Estate Transaction Price:Text Mining Approach
    Authors: 陳奕全
    Chen, Yi-Chuan
    Contributors: 陳明吉
    Chen, Ming-Chi
    陳奕全
    Chen, Yi-Chuan
    Keywords: 廣告行銷文字
    文字探勘
    不動產成交價
    Marketing Text
    Text Mining
    Real Estate Transaction Price
    Date: 2022
    Issue Date: 2022-08-01 17:18:22 (UTC+8)
    Abstract: 為了提升自家的產品形象及銷售表現,多數賣家皆會運用廣告行銷手法來達成此目的。透過廣告行銷,賣方能將資訊傳達給閱聽人,改變其對於特定產品的認知態度,並有機會進一步影響其後續購買行為。在不動產市場中,最常見的行銷方式莫過於在實體的看板以及網路上對待售標的進行列點式的特色整理,而這些特色整理基本以文字方式呈現。文字資料與與傳統銷售量等數字資料不同,屬於非結構資料。本文統整過往的研究,使用文字探勘的方式將不動產廣告行銷文字轉換為不同形式的有效變數,來探討在不動產市場中,廣告行銷文字的運用能否對房屋的成交價產生影響以及能否對不動產成交價的預測提供一定的幫助,進而增加預測的準確性。
    本文利用特徵價格法,建立半對數迴歸模型,得到行銷文字確實能對不動產成交價產生顯著正負程度不等的影響,並且透過將文字變數分組後,觀察到行銷性文字、房屋特性文字以及房屋狀態文字對於不動產成交價較能產生正面影響。本文另外發現,公寓的購屋者相對於大樓的購屋者,更在意與房屋內部資訊相關的行銷文字。研究的最後也發現加入文字變數能夠有效提升對於房屋成交價的預測能力,幫助進行更精準的房屋大量估價,且預測能力好壞與加入的文字變數數量有正比關係。
    In order to improve product image and sales performance, almost all sellers use marketing strategies. Through marketing, sellers can convey information to readers, changing their cognition and attitude towards a specific product and having the opportunity to further influence subsequent purchase behavior. In the real estate market,the most common way of marketing is to list the features of the houses to be sold on the physical billboards and on the Internet, and most of these features are shown in the form of text (written words). However, text data, unlike traditional digital data such as sales volume, is non-structured. This thesis integrates past research and uses text mining to convert real estate marketing text into different forms of effective variables to explore in the real estate market whether the use of marketing text can have an influence on the transaction price of houses and whether it can further provide some help in the prediction of real estate transaction prices.
    This thesis uses the hedonic price method to establish a semi-logarithmic regression model, and finds that marketing words can indeed have significant positive and negative effects on real estate transaction prices. In addition, this thesis also finds that apartment buyers are more concerned about marketing text related to information about the interior of the house than building buyers. At the end of this study, it is also found that adding text variables can effectively increase the accuracy in predicting the transaction prices of houses and thereby helps when conducting the mass appraisal of house prices, and that the accuracy is proportional to the number of text added.
    Reference: 一、中文文獻
    李吉弘、楊宗憲,2010,「預售屋與成屋價差比關係之研究-以台北市和台北縣為例」,『建築與規劃學報』,11(1) : 1-14。
    林秋瑾、楊宗憲、張金鶚,1996,「住宅價格指數之研究—以台北市為例」,『住宅學報』,4 : 1-30。
    邱志聖,2015。《行銷研究:實務與理論應用》。元照出版。
    二、英文文獻
    Abdallah, S. and Khashan, D. A., 2016, “Using Text Mining To Analyze Real Estate Classifieds”, Proceedings of the International Conference on Advanced Intelligent Systems and Informatics, Pages 193–202.
    Adiyanto, O. and Jatmiko, H. A., 2019, ” Development Of Food Packaging Design With Kansei Engineering Approach”, International Journal of Scientific & Technology Research, 8(12) : 1778-1788.
    Albion, M. S. and Farris, P. W., 1981, “The advertising controversy : evidence on the economic effects of advertising”, Boston, Mass. : Auburn.
    Aune, M., 2012, “Making energy visible in domestic property markets: The influence of advertisements”, Building Research and Information, 40(6) : 1-11.
    Berrar, D., 2018, “Cross-Validation”, In book: Reference Module in Life Sciences.
    Boyd, D., and Crawford, K., 2012, “Critical questions for big data: Provocations for a cultural, technological, and scholarly phenomenon”, Information, Communication & Society, 15(5) : 662–679.
    Chen, G., Wan, Y and Xu, X., 2016, “An Analysis of the Sales and Consumer Preferences of E-cigarettes Based on Text Mining of Online Reviews”, Conference: 2016 3rd International Conference on Systems and Informatics.
    Cheung, S. C. H. and MA, E. K. W., 2007, “Advertising Modernity: Home, Space and Privacy”, Visual Anthropology, 18:1,65-80.
    Chung, Y. and Sarnikar, S., 2021, “Understanding Host Marketing Strategies on Airbnb and Their Impact on Listing Performance: A Text Analytics Approach”, Information Technology & People.
    Colley, R. H., 1961, “Defining advertising goals for measured advertising results”, New York : Association of National Advertisers.
    Collins, D. and Kearns, R., 2008, “Uninterrupted Views: Real-Estate Advertising and Changing Perspectives on Coastal Property in New Zealand”, Environment and Planning A: 40(12):2914-2932.
    Frew, J. and Jud, G. D., 2003, “Estimating the Value of Apartment Buildings”, Journal of Real Estate Research, 25(1):77-86.
    Goo, P., 2010, “A Study on the Meaning and Strategy of Keyword Advertising Marketing”, Journal of Distribution Science 8-3:49-56.
    Guan, J., Zurada, J. and Levitan, A. S., 2008, “An Adaptive Neuro-Fuzzy Inference System Based Approach to Real Estate Property Assessment”, Journal of Real Estate Research, 30(4):395-422.
    Halseth, G., Hall, C. M. and Muller, D. K., 2004, “The ‘Cottage’ Privilege: Increasingly Elite Landscapes of Second Homes in Canada”, Tourism, Mobility and Second Homes: Between elite landscape and common ground, 35-54.
    Holbrook, M. B. and Batra, R., 1987, “Assessing the Role of Emotions as Mediators of Consumer Responses to Advertising”, Journal of Consumer Research 14(3):404-420.
    Isa, I. G. T., 2018, “Kansei Engineering Approach in Software Interface Design”, Journal of Science Innovare, 1(01) : 22-26.
    Isen, A. M. and Means, B., 2011, “The Influence of Positive Affect on Decision-Making Strategy”, Social Cognition 2(1).
    Kiel, K. A. and Zabel J. E., 2008, “Location, location, location: The 3L Approach to house price determination”, Journal of Housing Economics, 17: 175-190.
    Kotler, P. and Gertner, D., 2002, “Country as Brand, Product, and Beyond: A Place Marketing and Brand Management Perspective”, The Journal of Brand Management, 9, 249-261.
    Lau, K. N., Lee, K. H. and Ho, Y., 2005, “Text Mining for the Hotel Industry”, Cornell Hospitality Quarterly, 46(3):344-362.
    Lawson, G., 2013, “A rhetorical study of in-flight real estate advertisements as a potential site of ethical transformation in Chinese cities”, Cities, 31:85-95.
    Li, K., Zhang, L., Wang, D. and Pan, D., 2021, “The Effects of Online Information on E-Book Pricing Strategies: A Text Analytics Approach”, Mathematical Problems in Engineering, 2021(2):1-11.
    Little, J. D., 1979, “Decision Support Systems for Marketing Managers”, Journal of Marketing, Summer 43(3): 9.
    Lyu, F. and Choi, J., 2020, “The Forecasting Sales Volume and Satisfaction of Organic Products through Text Mining on Web Customer Reviews”, Sustainability, 12(11) : 4383.
    Morgan, N. A., Whitler, K. A., Feng, H. and Chari, S., 2018, “Research in marketing strategy”, Journal of the Academy of Marketing Science, 47:4-29.
    Novgorodov, S., Guy, I., Elad, G. and Radinsky, K., 2019, “Generating Product Descriptions from User Reviews”, The World Wide Web Conference, Pages 1354-1364.
    Nowak, A. and Smith, P., 2017, “Textual Analysis in Real Estate”, Journal of Applied Econometrics, 32(4) : 896-918.
    Peladeau, N. and Davoodi, E., 2018, “Comparison of Latent Dirichlet Modeling and Factor Analysis for Topic Extraction: A Lesson of History”, Conference: Hawaii International Conference on System Sciences.
    Pryzant, R., Chung, Y. and Jurafsky, D., 2017, “Predicting Sales from the Language of Product Descriptions”, International Journal of Engineering and Technical Research, 9(04).
    Shahrokh, Z. D. and Pourhosseini, A. H., 2013, “Performance Implications of Sales & Marketing Strategy”, Journal of Business Management, 5(1) : 61-84.
    Shen, L., 2021, “Information value of property description: A Machine learning approach”, Journal of Urban Economics, 121 : 103299.
    Weiss, R. F., 1969, “Repetition of Persuasion”, SAGE Journals, Psychological Reports.
    Description: 碩士
    國立政治大學
    財務管理學系
    109357011
    Source URI: http://thesis.lib.nccu.edu.tw/record/#G0109357011
    Data Type: thesis
    DOI: 10.6814/NCCU202200784
    Appears in Collections:[財務管理學系] 學位論文

    Files in This Item:

    File Description SizeFormat
    701101.pdf2276KbAdobe PDF265View/Open


    All items in 政大典藏 are protected by copyright, with all rights reserved.


    社群 sharing

    著作權政策宣告 Copyright Announcement
    1.本網站之數位內容為國立政治大學所收錄之機構典藏,無償提供學術研究與公眾教育等公益性使用,惟仍請適度,合理使用本網站之內容,以尊重著作權人之權益。商業上之利用,則請先取得著作權人之授權。
    The digital content of this website is part of National Chengchi University Institutional Repository. It provides free access to academic research and public education for non-commercial use. Please utilize it in a proper and reasonable manner and respect the rights of copyright owners. For commercial use, please obtain authorization from the copyright owner in advance.

    2.本網站之製作,已盡力防止侵害著作權人之權益,如仍發現本網站之數位內容有侵害著作權人權益情事者,請權利人通知本網站維護人員(nccur@nccu.edu.tw),維護人員將立即採取移除該數位著作等補救措施。
    NCCU Institutional Repository is made to protect the interests of copyright owners. If you believe that any material on the website infringes copyright, please contact our staff(nccur@nccu.edu.tw). We will remove the work from the repository and investigate your claim.
    DSpace Software Copyright © 2002-2004  MIT &  Hewlett-Packard  /   Enhanced by   NTU Library IR team Copyright ©   - Feedback