Document Classification and Key Information for Technical Due Diligence in Real Estate Management

Philipp Maximilian Mueller and Björn-Martin Kurzrock

ERES from European Real Estate Society (ERES)

Abstract: In real estate transactions, the parties generally have limited time to provide and process information. Using building documentation and digital building data may help to obtain an unbiased view of the asset. In practice, it is still particularly difficult to assess the physical structure of a building due to shortcomings in data structure and quality. Machine learning may improve speed and accuracy of information processing and results. This requires structured documents and applying a taxonomy of unambiguous document classes. In this paper, prioritized document classes from previous research (Müller, Päuser, Kurzrock 2020) are supplemented with key information for technical due diligence reports. The key information is derived from the analysis of n=35 due diligence reports. Based on the analyzed reports and identified key information, a checklist for technical due diligence is derived. The checklist will serve as a basis for a standardized reporting structure. The paper provides fundamentals for generating a (semi-)automated standardized due diligence report with a focus on the technical assessment of the asset. The paper includes recommendations for improving the machine readability of documents and indicates the potential for (partially) automated due diligence processes. The paper concludes with challenges towards an automated information extraction in due diligence processes and the potential for digital real estate management.

Keywords: digital building documentation; Document Classification; Due diligence; Machine Learning (search for similar items in EconPapers)
JEL-codes: R3 (search for similar items in EconPapers)
Date: 2021-01-01
New Economics Papers: this item is included in nep-big, nep-cmp, nep-isf and nep-ure
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