Rising home values also raise the cost of living, offsetting their impact on consumption. However, additional home equity collateral can loosen borrowing constraints, increasing spending for households that value their current endowment of housing highly. I use geographically linked microdata to exploit regional heterogeneity in housing markets and identify the causal effect of house price fluctuations on consumer spending. A $1 increase in home values leads to a $0.047 increase in spending for homeowners, but a negligible response for renters. Results reflect large responses among credit constrained households, suggesting looser borrowing constraints are a primary driver of the MPC out of housing wealth. (JEL D12, D14, E21, R31)
This survey and report were prepared by the Consumer and Community Development Research Section of the Federal Reserve Board's Division of Consumer and Community Affairs (DCCA). DCCA directs consumer-and community-related functions performed by the Board, including conducting research on financial services policies and practices and their implications for consumer financial stability, community development, and neighborhood stabilization.
Access to timely information on consumer spending is important to economic policymakers. The Census Bureau's monthly retail trade survey is a primary source for monitoring consumer spending nationally, but it is not well suited to study localized or shortlived economic shocks. Moreover, lags in the publication of the Census estimates and subsequent, sometimes large, revisions diminish its usefulness for real-time analysis. Expanding the Census survey to include higher frequencies and subnational detail would be costly and would add substantially to respondent burden. We take an alternative approach to fill these information gaps. Using anonymized transactions data from a large electronic payments technology company, we create daily estimates of retail spending at detailed geographies. Our daily estimates are available only a few days after the transactions occur, and the historical time series are available from 2010 to the present. When aggregated to the national level, the pattern of monthly growth rates is similar to the official Census statistics. We discuss two applications of these new data for economic analysis: First, we describe how our monthly spending estimates are useful for real-time monitoring of aggregate spending, especially during the government shutdown in 2019, when Census data were delayed and concerns about the economy spiked. Second, we show how the geographic detail allowed us quantify in real time the spending effects of Hurricanes Harvey and Irma in 2017.
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