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The purpose of this paper is to focus on the relationship between attitudinal data from the long-running Michigan Surveys of Consumers and US real GDP growth. One survey…
The purpose of this paper is to focus on the relationship between attitudinal data from the long-running Michigan Surveys of Consumers and US real GDP growth. One survey question asks, “Generally speaking, do you think now is a good time or a bad time to buy a house?” with the follow-up question “Why do you say so?” There are several factors for consumers to choose as reasons. Given the strong link between US housing market activity and business cycles, the authors ask whether the responses to the follow-up question explain the behavior of output growth.
The authors employ an augmented autoregressive model to investigate the relationship between output growth and the responses to the follow-up question for 1986–2007 and for 1986–2018, which includes the 2008 financial crisis. The authors follow the general-to-specific approach to obtain the final model estimates for interpretation. For a deeper analysis, the authors estimate the model using the responses of survey participants in the bottom 33 percent, middle 33 percent and upper 33 percent income categories, separately. While avoiding aggregation bias, this approach helps reveal important information embodied in the cross-sectional distribution of the data.
The follow-up question focuses on such factors as home prices, mortgage rates, houses as a good/bad investment, timing, uncertain future and affordability. The authors find that the majority of these factors chosen as reasons by consumers in the middle and upper 33 percent income categories explain the behavior of output growth. Among the factors chosen as reasons by consumers in the bottom 33 percent income category, only the mortgage rate and uncertain future explain output growth.
This study provides new insights into the usefulness of detailed consumer survey data in explaining the behavior of output growth and further underlines the usefulness of such measures across different income categories for revealing important information contained in the cross-sectional distribution of the data.