5/7/2023 0 Comments Zeilgalerie abriss![]() ![]() The prioritization of document classes according to machine readability reveals potentials for using artificial intelligence in due diligence processes. The availability and content of documents vary greatly from owner to owner and between document classes. The analysis reveals that a substantial part of all relevant digital building documents is poorly suited for automated information extraction. General rules are developed for prioritized document classes according to relevance and machine readability of documents. To structure documents for due diligence, 410 document classes are derived and documents principally checked for machine readability. The comprehensive building documentation including n = 8,339 digital documents of 14 properties and 21 technical due diligence reports serve as a basis for identifying key information. The paper concludes with challenges towards an automated information extraction in due diligence processes. Preferred sources for key information of technical due diligence reports are presented. Based on original digital building documents from (institutional) investors, the potential for automated information extraction through machine learning algorithms is demonstrated. This research provides fundamentals for generating (partially) automated standardized due diligence reports. The full report can be received by the authors or downloaded at Purpose The research was funded by the Property Research Trust. The findings are helpful for improving digital building documentation and more generally using machine learning (ML) in real estate professional services. General rules are developed for prioritised document classes according to relevance and machine readability of documents. ![]() The paper outlines requirements for digital document archives, scanning rulesįurther, it shows how digitised documents can add value to digital workflowsĪnd applications in real estate professional services. Workflows and applications in real estate professional services. ![]() The digitisation of an analogous building documentation to support digital This research promotes the development of digital workflows and applications ![]()
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