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

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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.