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Fundamentals for automating due diligence processes in property transactions
Abstract
Purpose
This 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/approach
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. 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.
Findings
The 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 implications
The 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/value
This 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.
Supplementary resource (1)
... Darrough and Deng (2019) analysed information from due diligence process during obtaining a credit and revealed that a debtor is more motivated to subsequently participate in a sub-optimal investment decision when a creditor lacks a motivation to disclose such information. The research conducted by Mueller et al. (2021) explored construction documentation by content analysis to find out that a substantial part of all relevant digital construction documents is not suitable for the automated extraction of information. In his study, Wiejak-Roy (2023) analysed the value of due diligence and determined a theoretical construct viewed from the perspective of game theory, it was discovered that the construct adapts to the feature although due diligence broadly increases information effectiveness in the market. ...
Motivation: The topic of the article is of paramount importance in today’s ever-evolving business landscape. As companies strive to expand their operations and stay ahead of the competition, mergers and acquisitions have become a popular strategy. However, the risks and complexities associated with these transactions cannot be overlooked. Understanding the role of due diligence is essential for any organization considering a business takeover. It serves as a powerful tool to uncover potential risks, assess the viability of the transaction, and make well-informed decisions. By conducting a thorough due diligence process, companies can identify financial discrepancies, legal liabilities, operational inefficiencies, and hidden risks that may impact the success of the transaction.Aim: The goal of the research was to analyze to what extent is the resulting value of a business is affected by improperly performed due diligence. The method used for data collection was in-debt literature research based on the Web of Science database. Subsequent content analysis was used to identify the 10 most common errors that can occur during the process of due diligence. The following research used the data obtained from answering the first research question with. Ishikawa fishbone diagram was used as a specific method of causal analysis.Results: The main findings include the identification of the most common errors occurring during the performance of due diligence, such as wrong financial analysis, incomplete financial accounts, Incomplete accounting statements, or adjusting entries for receivables. The main causes include excessive work pressure on auditors, past due receivables, poor supplier vetting, or failure to document the application of prices. The research is beneficial for auditors or audited companies so that they could avoid such errors. However, there are also several limitations as it is very time and cost-consuming to deal with due diligence on a company-by-company basis. Also, the application of Ishikawa fishbone diagram and brainstorming prior to diagram depends on the number of experts involve. Another limitation is that the research results applied under current accounting legislation only, and there are different laws and regulations in different countries.
Purpose
We demonstrate the practical application of machine learning (ML) techniques in document processing, addressing the increasing need for digitalization in the real estate industry and beyond. Our focus lies on identifying efficient algorithms for extracting individual documents from multi-page PDF files. Through the implementation of these algorithms, organizations can accelerate the digitization of paper-based files on a large scale, eliminating the laborious process of one-by- one scanning. Additionally, we showcase ML-powered methods for automating the classification of both digital and digitized documents, thereby simplifying the categorization process.
Design/methodology/approach
We compare two segmentation models that are presented in this paper to analyze the individual pages within a bulk scan, identifying the starting and ending points of each document contained in the PDF. This process involves extracting relevant features from both the textual content and page design elements, such as fonts, layouts and existing page numbers. By leveraging these features, the algorithm accurately splits multi-document PDFs into their respective components. An outlook is provided with a classification code that effectively categorizes the segmented documents into different real estate document classes.
Findings
The case study provides an overview of different ML methods employed in the development of these models while also evaluating their performance across various conditions. As a result, it offers insight into solutions and lessons learned for processing documents in real estate on a case-by-case basis. The findings presented in this study lay the groundwork for addressing this prevalent problem. The methods, for which we provide the code as open source, establish a solid foundation for expediting real estate document processing, enabling a seamless transition from scanning or inbox management to digital storage, ultimately facilitating machine-based information extraction.
Practical implications
The process of digitally managing documents in the real estate industry can be a daunting task, particularly due to the substantial volume of documents involved, whether they are paper-based, digitized or in digital formats. Our approach aims to streamline this often tedious and time-consuming process by offering two models as simplified solutions that encourage companies to embrace much-needed digitization. The methods we present in this context are crucial for digitizing all facets of real estate management, offering significant potential in advancing PropTech business cases. The open-source codes can be trained further by researchers and practitioners with access to large volumes of documents.
Originality/value
This study illustrates effective methods for processing paper-based, digitized and digital files, along with tailored ML models designed to enhance these methods, particularly within the real estate sector. The methods are showcased on two datasets, and lessons learned are discussed.
Purpose
The transaction process of an office building is known to be time consuming and inefficient, in part due to the lack of market transparency. The purpose of this paper is to focus on the development of a blockchain application that can improve the transaction process of office buildings in the Netherlands.
Design/methodology/approach
Conducting design science research, the current transaction process of an office building and status quo of blockchain technology in real estate is investigated. Subsequently, multiple parties are interviewed to define major pain points within the process. The interview findings are used to design a blockchain solution which overcomes the aforementioned pain points. After designing, the interviewees are asked again to pragmatically validate the proposed model.
Findings
One of the major pain points identified concerning the transaction process of an office building is that it is difficult to define the characteristics of a property, due to lack of data structure and quality. The proposed application improves the way specific assets are understood by structuring physical and contractual information in one place and guarantees the quality of the data by using the blockchain mechanisms.
Practical implications
A blockchain application is proposed, which can improve the transaction process of an office building.
Originality/value
Recent studies indicate that blockchain technology could lead to improvements in efficiency, transparency and therefore trust within the transaction process. Therefore, the proposed application is of value for the future of real estate data management and the transaction process.
The condition of a building is an important factor for real estate valuation. Currently, the estimation of condition is determined by real estate appraisers which makes it subjective to a certain degree. We propose a novel vision-based approach for the assessment of the building condition from exterior views of the building. To this end, we develop a multi-scale patch-based pattern extraction approach and combine it with convolutional neural networks to estimate building condition from visual clues. Our evaluation shows that visually estimated building condition can serve as a proxy for condition estimates by appraisers.
Purpose
This explorative case study focuses on property investment decision making from a behavioural perspective at the very microlevel. The study contributes to an understanding of how property investors manage the decision‐making process, including organizational aspects, property valuation, and financial management.
Design/methodology/approach
Applying a qualitative approach, the authors analyse a very large transaction that occurred in the Swedish property market in 2008. In an open bid transaction, properties of Vasakronan Corporation were sold for SEK41.1 billion (€4.3 billion). Managers in both the purchasing company and the consortium making the second highest bid were interviewed. The authors were encouraged to speak freely, but also used an interview guide with a number of themes as well as specific questions.
Findings
The findings reveal the characteristics of two types of property investment decision‐making behaviour with respect to how actors organize the work, use external consultants, value the properties, and secure the financing necessary for a final bid.
Practical implications
Creditors, analysts, and appraisers may benefit from the insight that property investment decision makers can use different approaches in determining their final bids.
Originality/value
The authors use a qualitative empirical approach in analysing an extraordinarily large property transaction from a buyer's point of view and presents detailed information about this transaction as well as general insights into actual behaviour rarely examined in the property investment literature.
Real estate represents a major share of economic activities and wealth in all economies. Due to the lack of widely acknowledged standards, however, the structuring, providing and managing of a life cycle-comprehensive building documentation yet remain challenging. Based on the empirical analysis of 8965 digital documents from 14 properties of 8 different owners, the article presents a model that will unify existing approaches and lead to the development of a document classification standard. This provides the basis for software systems to process relevant data and create timely information over the entire life cycle of a building. Further, it is shown that automated information extraction through artificial intelligence will become instrumental for enhanced and innovative business models and products in real estate such as automated data validation and data evaluation, documentation review, benchmarking and other analytical applications.
This book presents an overview of all four elements of a real estate due diligence: judicial, financial, technical and tax issues.
The decision of buying residential property may be one of the most important transactions people will ever make, and the emotional attachment when houses become homes is inevitable. Nevertheless, this stylized fact appears to be underrepresented in the literature. While its importance is undeniable, the consumption function, as well as the social and emotional perspective of real estate are often neglected. We provide an overview of the current state of affairs of the main themes in which the behavioral approach intersects with real estate to gain a deeper understanding of the built environment. It seems to be a general consensus that behavioral studies can help to provide insight into property markets, but that a large component of behavioral decision making is left undefined.
Purpose
– Real estate market transparency is an important factor in real estate investment and occupier decision making. The purpose of this paper is to assess real estate transparency over 2004-2014 to determine whether the European real estate markets have become more transparent in a regional and global context.
Design/methodology/approach
– Using the JLL real estate transparency index over 2004-2014, changes in real estate market transparency are assessed for 102 real estate markets. This JLL real estate market transparency index is also assessed against corruption levels and business competitiveness in these markets.
Findings
– Improvements in real estate transparency are clearly evident in many European real estate markets, with several of these European real estate markets seen to be the major improvers in transparency from a global real estate markets perspective.
Practical implications
– Institutional investors and occupiers see real estate market transparency as a key factor in their strategic real estate investment and occupancy decision making. By assessing changes in real estate transparency across 102 real estate markets, investors and occupiers are able to make more informed real estate investment decisions across the global real estate markets. In particular, this relates to both investors and occupiers being able to more fully understand the risk dimensions of their international real estate decisions.
Originality/value
– This paper is the first paper to assess the dynamics of real estate market transparency over 2004-2014, with a particular focus on the 33 European real estate markets in a global context to facilitate more informed real estate investment and occupancy decision making.
Argues that the knowledge management process can be categorized into knowledge creation, knowledge validation, knowledge presentation, knowledge distribution, and knowledge application activities. To capitalize on knowledge, an organization must be swift in balancing its knowledge management activities. In general, such a balancing act requires changes in organizational culture, technologies, and techniques. A number of organizations believe that by focusing exclusively on people, technologies, or techniques, they can manage knowledge. However, that exclusive focus on people, technologies, or techniques does not enable a firm to sustain its competitive advantages. It is, rather, the interaction between technology, techniques, and people that allow an organization to manage its knowledge effectively. By creating a nurturing and “learning-by-doing” kind of environment, an organization can sustain its competitive advantages.
This review article identifies and discusses some of main issues and potential problems - paradoxes and pathologies - around the communication of recorded information, and points to some possible solutions. The article considers the changing contexts of information communication, with some caveats about the identifi- cation of 'pathologies of information', and analyses the changes over time in the way in which issues of the quantity and quality of information available have been regarded. Two main classes of problems and issues are discussed. The first comprises issues relating to the quantity and diversity of information available: infor- mation overload, information anxiety, etc. The second comprises issues relating to the changing information environment with the advent of Web 2.0: loss of identity and authority, emphasis on micro-chunking and shallow novelty, and the impermanence of information. A final section proposes some means of solution of problems and of improvements to the situation.
Global market perspective
- Jones Lang Lasalle
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Drooms Due-Diligence-Index™
- Drooms
Drooms (Ed.) (undated), Drooms Due-Diligence-Index™, Frankfurt, available at: https://drooms.com/
de/due-diligence-checkliste?whitepaper51671 (accessed 06 December 2019).
Information extraction
- D Jurafsky
- H M James
Jurafsky, D. and James, H.M. (2019), "Information extraction", in Jurafsky, D. and James, H.M. (Eds.),
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Due-diligence-prozess
- P Fischer
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Fischer, P. and Teufelsdorfer, H. (2015), "Due-diligence-prozess", in Teufelsdorfer, H. (Ed.), Handbuch
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Getting ahead of the market
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Urban Land Institute, available at: https://urbanland.uli.org/economy-markets-trends/gettingahead-of-the-market (accessed 05 December 2019).
Gif-IDA -Guideline of Real Estate Data Exchange
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Standards for Configuring a Property Data Room and Document Management System 2.0
gif, Society of Property Researchers, Germany (Ed.) (2019), Standards for Configuring a Property Data
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European Commission -Directorate-General for Research and Innovation
- P Hatto
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