Sentiment Analysis Within a Deep Learning Probabilistic Framework – New Evidence from Residential Real Estate in the United States

Cathrine Nagl

Journal of Housing Research, 2024, vol. 33, issue 1, 25-49

Abstract: This paper is devoted to the relationship between news sentiment and changes in housing market movements. It provides a novel and straightforward approach to account for heterogeneous expectations of market actors within a probabilistic framework utilizing machine learning. Our novel sentiment index shows a persistent and statistically significant explanatory power for the prediction of the housing market, in contrast to common dictionary approaches. This holds for news headlines and abstracts and different definitions of sentiment indices. Our results can be regarded as the first sentiment-based evidence of heterogeneous actors in the housing market and underline the importance of different expectations for measuring non-fundamental drivers.

Date: 2024
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DOI: 10.1080/10527001.2023.2210776

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