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- W3083323256 abstract "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." @default.
- W3083323256 created "2020-09-11" @default.
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- W3083323256 date "2020-08-01" @default.
- W3083323256 modified "2023-10-03" @default.
- W3083323256 title "Recent trends in real estate research: a comparison of recent working papers and publications using machine learning algorithms" @default.
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- W3083323256 doi "https://doi.org/10.1007/s11573-020-01005-w" @default.
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