Forecasting growth-at-risk of the United States
Housing price versus housing sentiment or attention
Publication date
2025-09-09
Document type
Forschungsartikel
Author
Organisational unit
Scopus ID
Publisher
Springer Science and Business Media
Series or journal
The Journal of Real Estate Finance and Economics
ISSN
Is a version of
Peer-reviewed
✅
Part of the university bibliography
✅
Language
English
Keyword
Connectedness
Economic activity
Forecasting
Housing price
Housing sentiment and attention
Quantile predictive regressions
Abstract
We examine the predictive power of national housing market-related behavioral variables, along with their connectedness at the state level, in forecasting US aggregate economic activity (such as the Chicago Fed National Activity Index (CFNAI) and real Gross Domestic Product (GDP) growth), as opposed to solely relying on state-level housing price return connectedness. Our results reveal that while standard linear regression models show statistically insignificant differences in forecast accuracy between the connectedness of housing price returns and behavioral variables, quantile regression models, which capture growth-at-risk, demonstrate significant forecasting improvements. Specifically, state-level connectedness of housing sentiment enhances forecast accuracy of the CFNAI at lower quantiles of economic activity, indicative of downturns, whereas connectedness of housing attention is more effective at upper quantiles, corresponding to upturns. The results for GDP growth suggest that, while both sentiment and attention contribute to forecasting performance at lower quantiles, only attention improves forecasting performance at upper quantiles. In terms of statistical significance, the results for GDP growth, however, are less conclusive than those for the CFNAI. Taken together, these findings underscore the importance of incorporating regional heterogeneity and behavioral aspects in economic forecasting.
Description
This article is licensed under a Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/).
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Online first
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