Forecasting Growth-at-Risk of the United States: Housing Price versus Housing Sentiment or Attention

Oguzhan Cepni, Rangan Gupta and Christian Pierdzioch

No 202401, Working Papers from University of Pretoria, Department of Economics

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 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, however, are less conclusive. These findings underscore the importance of incorporating regional heterogeneity and behavioral aspects in economic forecasting.

Keywords: Housing price; Housing sentiment and attention; Connectedness; Economic activity; Forecasting; Quantile predictive regressions (search for similar items in EconPapers)
JEL-codes: C22 C32 C53 E30 R31 (search for similar items in EconPapers)
Pages: 29 pages
Date: 2024-01
New Economics Papers: this item is included in nep-rmg and nep-ure
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