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Models used to guide policy, as well as some empirical studies, suggest that the effect of housing wealth on consumption is large and greater than the wealth effect on consumption from stock holdings. Recent theoretical work, in contrast, argues that changes in housing wealth are offset by changes in housing consumption, meaning that unexpected shocks in housing wealth should have little effect on non-housing consumption. We reexamine the impact of housing wealth on non-housing consumption, employing the Case-Quigley-Shiller data on U.S. housing wealth that have been used in prior studies to estimate a large housing wealth effect. Existing empirical work fails to control for the fact that changes in housing wealth may be correlated with changes in expected permanent income, biasing the resulting estimates. Once we control for the endogeneity bias resulting from the correlation between housing wealth and permanent income, we find that housing wealth has a small and insignificant effect on consumption. Additional analysis of time-series results provides further support for that view.
Models used to guide policy, as well as some empirical studies, suggest that the effect of housing wealth on consumption is large and greater than the wealth effect on consumption from stock holdings. Recent theoretical work, in contrast, argues that changes in housing wealth are offset by changes in housing consumption, meaning that unexpected shocks in housing wealth should have little effect on non-housing consumption. We reexamine the impact of housing wealth on non-housing consumption, employing the Case-Quigley-Shiller data on U.S. housing wealth that have been used in prior studies to estimate a large housing wealth effect. Existing empirical work fails to control for the fact that changes in housing wealth may be correlated with changes in expected permanent income, biasing the resulting estimates. Once we control for the endogeneity bias resulting from the correlation between housing wealth and permanent income, we find that housing wealth has a small and insignificant effect on consumption. Additional analysis of time-series results provides further support for that view.
We provide new, time-varying estimates of the housing wealth effect back to the 1980s. We exploit systematic differences in city-level exposure to regional house price cycles to instrument for house prices. Our main findings are that: 1) Large housing wealth effects are not new: we estimate substantial effects back to the mid 1980s; 2) Housing wealth effects were not particularly large in the 2000s; if anything, they were larger prior to 2000; and 3) There is no evidence of a boom-bust asymmetry. We compare these findings to the implications of a standard life-cycle model with borrowing constraints, uninsurable income risk, illiquid housing, and long-term mortgages. The model explains our empirical findings about the insensitivity of the housing wealth effects to changes in the loan-to-value (LTV) distribution, including the dramatic rise in LTVs in the Great Recession. The insensitivity arises in the model for two reasons. First, impatient low-LTV agents have a high elasticity. Second, a rightward shift in the LTV distribution increases not only the number of highly sensitive constrained agents but also the number of underwater agents whose consumption is insensitive to house prices.
This paper presents a simple new method for estimating the size of 'wealth effects' on aggregate consumption. The method exploits the well-documented sluggishness of consumption growth (often interpreted as 'habits' in the asset pricing literature) to distinguish between short-run and long-run wealth effects. In U.S. data, we estimate that the immediate (next-quarter) marginal propensity to consume from a $1 change in housing wealth is about 2 cents, with a final long-run effect around 9 cents. Consistent with several recent studies, we find a housing wealth effect that is substantially larger than the stock wealth effect. We believe that our approach is preferable to the currently popular cointegration- based estimation methods, because neither theory nor evidence justifies faith in the existence of a stable cointegrating vector.
"Current estimates of housing wealth effects vary widely. We consider the role of omitted variables suggested by economic theory that have been absent in a number of prior studies. Our estimates take into account age composition and wealth distribution (using poverty rates as a proxy), as well as wealth shares (how much of total wealth is comprised of housing vs. stock wealth). We exploit cross-state variation in housing, stock wealth and other variables in a newly assembled panel data set and find that the impact of housing on consumer spending depends crucially on age composition, poverty rates, and the housing wealth share. In particular, young people who are more likely to be credit-constrained, and older homeowners, likely to be "trading down" on their housing stock, experience the largest housing wealth effects, as suggested by theory. Also, as suggested by theory, housing wealth effects are higher in state-years with higher housing wealth shares, and in state-years with higher poverty rates (likely reflecting the greater importance of credit constraints for those observations). Taking these various factors into account implies huge variation over time and across states in the size of housing wealth effects"--National Bureau of Economic Research web site
Empirical studies have estimated a big range of consumption response sizes to changes in house prices. Using a quasi-experiment, we estimate a shock of -19.4 percent to house prices in the area surrounding an airport in Stockholm after its operations were unexpectedly continued as a result of political bargaining behind closed doors. This source of price divergence is ideal for identifying housing wealth effects since it is local and unrelated to variations in macroeconomic conditions. Using a household data set with information on the location of primary residence relative to the airport, we find a short-run elasticity with respect to new car purchases of 0.39, corresponding to a one-year marginal propensity for car expenditures of 0.12 cents per dollar lost in housing wealth. Households with high loan-to-value ratios and little bank deposits respond the most. A quantitative model is consistent with the empirical findings and pinpoints important determinants of the response size, which may explain the variation in previous estimates.
We exploit a quasi-experiment to provide new evidence on the magnitude of the housing wealth effect. We estimate an immediate shock of approximately $-$15% to house prices close to one of Stockholm's airports after its operations were unexpectedly continued. This source of price variation is ideal for identifying housing wealth effects since it is unrelated to macroeconomic conditions. We estimate a micro elasticity of 0.45 among purchasers of new cars. The implied aggregate MPC on cars is however only 0.13 cents per dollar. The response is entirely concentrated to homeowners with a loan-to-value ratio between 0.6 and 0.8.