Recent Trends in Real Estate Research: A Comparison of Recent Working Papers and Publications using Machine Learning Algorithms
Wolfgang Breuer () and
Bertram Steininger ()
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Wolfgang Breuer: RWTH Aachen University, Department of Finance, Aachen, Germany, Postal: RWTH Aachen University, Department of Finance, Templergraben 64, 52056 Aachen, Germany
No 20/15, Working Paper Series from Royal Institute of Technology, Department of Real Estate and Construction Management & Banking and Finance
This paper is organized as follows. In Section 1, we describe the economic relevance of the real estate sector and its recent dynamics. Then, we identify the most mentioned keywords of working papers presented at the real estate conferences between 2015 and 2019 and showing network figures for them in Section 2. In order to identify the newest trends, we rely on working papers since they have an average lead time of at least 1 to 2 years before they are published. In addition, we give a short overview of the articles published in this special issue. To get a better overview of the relevance of real estate related topics in finance, we analyzed the most relevant finance conferences and journals between 2015 and May 2020 in Section 3. To find the topics, we apply the text mining approach Latent Dirichlet Allocation (LDA), an unsupervised machine learning method. The real estate trends (retail, e-commerce) and the potential impact of COVID-19 is described in Section 4.
Keywords: Recent trends; Real estate; Machine learning; Latent Dirichlet Allocation; LDA (search for similar items in EconPapers)
JEL-codes: C45 C80 R30 (search for similar items in EconPapers)
Pages: 20 pages
New Economics Papers: this item is included in nep-big and nep-cmp
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Journal Article: Recent trends in real estate research: a comparison of recent working papers and publications using machine learning algorithms (2020)
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