Machine learning in real estate:
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Citation: Björn-Martin Kurzrock, Phillipp Maximillian Müller, Jonathan Rothenbusch, Konstantin Schütz (2022). Machine learning in real estate: Digitisation and document classification in professional services.
London: Property Research Trust DOI: https://doi.org/10.52915/YJKO1005 |
Summary:
The digital revolution is affecting all our lives and every business, creating huge new opportunities, but also many challenges. Real estate is no exception. As a sector it has always relied heavily on documentation: everything from marketing material to valuations to building plans, legal contracts and service agreements. All of this is rapidly becoming digital, creating scope for much leaner and smarter ways of working, with the management of information becoming a core part of a company’s competitive offer. In particular, machine learning can hugely enhance the real estate sector’s offering: but only if we get the basics right first. So, I am delighted that the Property Research Trust is publishing this report by a distinguished team of academics and practitioners, looking at that challenge, and suggesting rules and guidelines to help us all in the sector to move forward with greater confidence. Keywords: Real estate, digitised documents, digital workflows, digital data, self-learning algorithms. |
Authors
Björn-Martin Kurzrock, Professor of Real Estate Studies, head of the degree program "Real Estate and Facilities – Management and Technology" at the Department of Civil Engineering, Technische Universität Kaiserslautern (renamed as Rheinland-Pfälzische Technische Universität Kaiserslautern-Landau, RPTU, effective from January 2023). Björn-Martin does research in Business Administration, in particular Real Estate Management and Real Estate Development. A special focus of his research team is on Digital Real Estate Management and includes automated data classification and data extraction using Machine Learning (ML) algorithms. Digital data will thus be used for due diligence processes and predictive maintenance of real assets, as well as other applications. The team is further interested in Principles of Real Estate Development that add to the sustainable development of cities and our built environment. Real Estate Studies - RPTU (uni-kl.de)
Philipp Maximillian Müller, Dipl.-Ing, Sustainability Analyst, LaSalle Investment Management, external PhD student at Technische Universität Kaiserslautern (Rheinland-Pfälzische Technische Universität Kaiserslautern-Landau, RPTU).
Jonathan Rothenbusch, MSc, Research Assistant, Real Estate Studies, Technische Universität Kaiserslautern R(Rheinland-Pfälzische Technische Universität Kaiserslautern-Landau, RPTU)
Konstantin Schütz, MSc, Research Assistant, Real Estate Studies, Technische Universität Kaiserslautern (Rheinland-Pfälzische Technische Universität Kaiserslautern-Landau, RPTU)
Philipp Maximillian Müller, Dipl.-Ing, Sustainability Analyst, LaSalle Investment Management, external PhD student at Technische Universität Kaiserslautern (Rheinland-Pfälzische Technische Universität Kaiserslautern-Landau, RPTU).
Jonathan Rothenbusch, MSc, Research Assistant, Real Estate Studies, Technische Universität Kaiserslautern R(Rheinland-Pfälzische Technische Universität Kaiserslautern-Landau, RPTU)
Konstantin Schütz, MSc, Research Assistant, Real Estate Studies, Technische Universität Kaiserslautern (Rheinland-Pfälzische Technische Universität Kaiserslautern-Landau, RPTU)