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From Corporate Disclosure to Social Media – Understanding Real Estate Markets with Textual Analysis
Paulus, Nino
(2023)
From Corporate Disclosure to Social Media – Understanding Real Estate Markets with Textual Analysis.
PhD, Universität Regensburg.
Date of publication of this fulltext: 31 Aug 2023 07:50
Thesis of the University of Regensburg
DOI to cite this document: 10.5283/epub.54637
Abstract (English)
In the dissertation, Natural Language Processing techniques are applied to analyze indicators such as investor sentiment, informativeness, and reporting intensity in real estate-related texts and to show their relation to real estate markets. It becomes clear that corporate publications, newspapers, and social media provide valuable information for investors and enable inferences about market ...
In the dissertation, Natural Language Processing techniques are applied to analyze indicators such as investor sentiment, informativeness, and reporting intensity in real estate-related texts and to show their relation to real estate markets. It becomes clear that corporate publications, newspapers, and social media provide valuable information for investors and enable inferences about market developments. It also shows that the forecasting quality can be significantly increased with more advanced methods.
Translation of the abstract (German)
In der Dissertation werden Techniken des Natural Language Processings angewendet um Indikatoren wie die Investorenstimmung, Informativität und Berichterstattungsintensität in immobilienbezogenen Texten zu analysieren und deren Zusammenhang mit Immobilienmärkten aufzuzeigen. Es wird deutlich, dass Unternehmenspublikationen, Fachzeitschriften und Soziale Medien wertvolle Informationen für Anleger ...
In der Dissertation werden Techniken des Natural Language Processings angewendet um Indikatoren wie die Investorenstimmung, Informativität und Berichterstattungsintensität in immobilienbezogenen Texten zu analysieren und deren Zusammenhang mit Immobilienmärkten aufzuzeigen. Es wird deutlich, dass Unternehmenspublikationen, Fachzeitschriften und Soziale Medien wertvolle Informationen für Anleger bereitstellen und Rückschlüsse auf Marktentwicklungen ermöglichen. Dabei zeigt sich auch, dass die Prognosequalität mit fortschrittlicheren Methoden deutlich gesteigert werden kann.
Involved Institutions
Details
| Item type | Thesis of the University of Regensburg (PhD) |
| Volume: | 105 |
|---|---|
| Date | 31 August 2023 |
| Referee | Prof. Dr. Wolfgang Schäfers and Prof. Dr. Bertram Steininger |
| Date of exam | 19 July 2023 |
| Institutions | Business, Economics and Information Systems > Institut für Immobilienenwirtschaft / IRE|BS > Lehrstuhl für Immobilienmanagement (Prof. Dr. Wolfgang Schäfers) |
| Keywords | Real Estate Markets; Natural Language Processing; Textual Analysis; Sentiment; Topic Modelling |
| Dewey Decimal Classification | 300 Social sciences > 330 Economics |
| Status | Published |
| Refereed | Yes, this version has been refereed |
| Created at the University of Regensburg | Yes |
| URN of the UB Regensburg | urn:nbn:de:bvb:355-epub-546371 |
| Item ID | 54637 |
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