Forecasting U.S. REIT Returns: Leveraging GenAI-Extracted Sentiment

Julian Lütticke, Lukas Lautenschlaeger and Wolfgang Schäfers

ERES from European Real Estate Society (ERES)

Abstract: The role of investor sentiment in real estate investment trust (REIT) markets is well-documented. However, traditional sentiment indicators often fail to fully capture real-time market dynamics. This study explores the potential of GenAI-extracted sentiment in forecasting U.S. REIT returns by leveraging large language models (LLMs) to analyze textual data from news media sources. The hypothesis underpinning this study is that LLMs can process textual data in a manner analogous to that of humans. The novel sentiment score is integrated into a machine learning model to predict REIT returns. The analysis differentiates between overall index returns and sector-specific REIT performance, thereby providing a more granular view of sentiment-driven market behavior. In addition to traditional statistical metrics the model performance is assessed by evaluating an active trading strategy based on sentiment signals. This strategy is benchmarked against a buy-and-hold approach to determine whether sentiment-based predictions can systematically outperform the market. The findings contribute to the growing field of AI-driven financial forecasting and offer valuable insights for investors and policymakers in the indirect real estate sector.

Keywords: Generative AI; Large Language Model; News Sentiment; REIT (search for similar items in EconPapers)
JEL-codes: R3 (search for similar items in EconPapers)
Date: 2025-01-01
New Economics Papers: this item is included in nep-ain, nep-big, nep-cmp and nep-for
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Persistent link: https://EconPapers.repec.org/RePEc:arz:wpaper:eres2025_242

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