房地产交易中自动化尽职调查流程的基本原理

IF 1.6 Q3 BUSINESS, FINANCE Journal of Property Investment & Finance Pub Date : 2020-04-30 DOI:10.1108/jpif-09-2019-0130
Philipp Maximilian Müller, Philipp Päuser, Björn-Martin Kurzrock
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引用次数: 2

摘要

目的本研究为生成(部分)自动化标准化尽职调查报告提供了基础。基于(机构)投资者的原始数字建筑文件,展示了通过机器学习算法自动提取信息的潜力。介绍了技术尽职调查报告关键信息的首选来源。论文最后提出了在尽职调查过程中自动提取信息的挑战。设计/方法/方法综合建筑文件,包括14处房产的8339份数字文件和21份技术尽职调查报告,是确定关键信息的基础。为了构建尽职调查文件,衍生出410个文件类别,主要检查文件的机器可读性。根据文档的相关性和机器可读性,为具有优先级的文档类开发了通用规则。发现分析表明,所有相关的数字建筑文档中有很大一部分不适合自动提取信息。文档的可用性和内容因所有者而异,也因文档类而异。根据机器可读性对文档类别进行优先级排序,揭示了在尽职调查过程中使用人工智能的潜力。实际意义本文包括提高文件机器可读性的建议,并指出了(部分)自动化尽职调查流程的潜力。因此,对文档类进行派生、审查和优先级排序。交易风险可以通过自动检查相关文件的完整性来应对。原创性/价值本文是第一篇专门评估尽职调查报告自动化数字处理的已发表(实证)研究。这些发现有助于改进尽职调查流程,更广泛地说,有助于促进机器学习在房地产行业的使用。
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Fundamentals for automating due diligence processes in property transactions
PurposeThis research provides fundamentals for generating (partially) automated standardized due diligence reports. Based on original digital building documents from (institutional) investors, the potential for automated information extraction through machine learning algorithms is demonstrated. Preferred sources for key information of technical due diligence reports are presented. The paper concludes with challenges towards an automated information extraction in due diligence processes.Design/methodology/approachThe 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. To structure documents for due diligence, 410 document classes are derived and documents principally checked for machine readability. General rules are developed for prioritized document classes according to relevance and machine readability of documents.FindingsThe analysis reveals that a substantial part of all relevant digital building documents is poorly suited for automated information extraction. The availability and content of documents vary greatly from owner to owner and between document classes. The prioritization of document classes according to machine readability reveals potentials for using artificial intelligence in due diligence processes.Practical implicationsThe paper includes recommendations for improving the machine readability of documents and indicates the potential for (partially) automating due diligence processes. Therefore, document classes are derived, reviewed and prioritized. Transaction risks can be countered by an automated check for completeness of relevant documents.Originality/valueThis paper is the first published (empirical) research to specifically assess the automated digital processing of due diligence reports. The findings are helpful for improving due diligence processes and, more generally, promoting the use of machine learning in the property sector.
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来源期刊
CiteScore
3.50
自引率
23.10%
发文量
33
期刊介绍: Fully refereed papers on practice and methodology in the UK, continental Western Europe, emerging markets of Eastern Europe, China, Australasia, Africa and the USA, in the following areas: ■Academic papers on the latest research, thinking and developments ■Law reports assessing new legislation ■Market data for a comprehensive review of current research ■Practice papers - a forum for the exchange of ideas and experiences
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