IDEAS home Printed from https://ideas.repec.org/p/arz/wpaper/eres2021_65.html
   My bibliography  Save this paper

Document Classification for Machine Learning in Real Estate Professional Services – Results of the Property Research Trust Project

Author

Listed:
  • Philipp Maximilian Mueller
  • Björn-Martin Kurzrock

Abstract

Due to numerous documents and the lack of widely acknowledged standards, the capture and provision of information in transaction processes frequently remains challenging. Since construction and maintenance come with substantial costs, the evaluation of the structural condition and maintenance requirements as well as the assessment of contracts and legal structures are important in real estate transactions. The quality and completeness of digital building documentation is increasingly becoming a factor as deal maker and deal breaker. Artificial intelligence can well assist in the classification of documents and extraction of information This research provides fundamentals for generating a (semi-)automated standardized technical and legal assessment of buildings. Based on a large building documentation set from (institutional) investors, the potential for digital processing, automated classification and information extraction through machine learning algorithms is demonstrated. For this purpose, more than 400 document classes are derived, reviewed, prioritized and principally checked for machine readability. In addition, key information is structured and prioritized for technical and legal due diligence. The paper highlights recommendations for improving the machine readability of documents and indicates the potential for partially automating technical and legal due diligence processes. The practical recommendations are relevant for investors, owners, users and service providers who depend on specific real estate information as well as for companies that develop or use software tools. For policymaking, the research offers some guidance for standardizing documents to support digital information processing in real estate. The recommendations are helpful for improving information processing and in general, promoting the use of automated information extraction based on machine learning in real estate.

Suggested Citation

  • Philipp Maximilian Mueller & Björn-Martin Kurzrock, 2021. "Document Classification for Machine Learning in Real Estate Professional Services – Results of the Property Research Trust Project," ERES eres2021_65, European Real Estate Society (ERES).
    • Handle: RePEc:arz:wpaper:eres2021_65
    as

    Download full text from publisher

    File URL: https://eres.architexturez.net/doc/oai-eres-id-eres2021-65
    Download Restriction: no
    ---><---

    More about this item

    Keywords

    digital building documentation; Due diligence; Machine Learning; property research trust;
    All these keywords.

    JEL classification:

    • R3 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - Real Estate Markets, Spatial Production Analysis, and Firm Location

    NEP fields

    This paper has been announced in the following NEP Reports:

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:arz:wpaper:eres2021_65. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Architexturez Imprints (email available below). General contact details of provider: https://edirc.repec.org/data/eressea.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.