RDP 2023-01: The Effect of Credit Constraints on Housing Prices: (Further) Evidence from a Survey Experiment 6. Conclusion

The main result of this paper is that the effect of collateral constraints on the marginal buyer, and thus the price, is smaller than the effect on the average household. Constructing demand curves from individual WTP data shows that households with the most responsive demand for housing tend to have WTPs below the marginal buyer and so have little effect on prices. Looking through the lens of a heterogeneous user cost model, this result can be interpreted as households with higher discount rates having WTPs that are both low and responsive. The discount rate of a household at any given moment is a product of its preferences and circumstances. Empirical modelling in the paper shows that the financial resources available to a household, relative to the required down payment, is a good predictor of the household's responsiveness to collateral constraints. In essence, this means households that are constrained by collateral are relatively responsive to loosening that constraint. Although lower than the average effect, I estimate that the effect of collateral constraints on the marginal buyer is significantly greater than zero.

The analysis of market segments and heterogeneity in Section 6 considers how the effects of collateral constraints can vary depending on the responsiveness of marginal buyers in that segment. This type of heterogeneity is important when considering the effects of collateral constraints, both for macroprudential policy and any other changes in collateral constraint. That said, two aspects of the experimental data limit my contribution to this aspect. First, the limited sample size means I am unable to recreate local housing markets in any detail. Instead I construct illustrative or stylised market segments. Second, the experiment has no information about investor behaviour so I am limited to examining owner-occupier demand. A housing market with a high proportion of constrained (and thus responsive) households by definition has a high proportion of landlords. The effect of collateral constraints will depend on the responsiveness of housing investors, considered in Graham (2020), Greenwald and Guren (2021) and Cusbert (2022).

The heterogeneous user cost analysis helps connect the experimental data and implied demand curves to literature on the effects of collateral constraints in housing markets. The effective discount rate of the marginal buyer determines the market effect, and the rent versus buy preference influences whether a household is marginal. Models that show a large effect of credit conditions on prices typically have a strong market segmentation assumption at work. For example, in Justiniano et al (2019) and Garriga, Manuelli and Peralta-Alva (2019) households with patient discount rates, and thus higher WTPs, are assumed to be unable to own houses in the impatient market segment either as owner-occupiers or investors. Constrained households with relatively low WTPs are marginal by assumption and do affect prices. My paper suggests that even within market segments, heterogeneity acts to dampen the effect of collateral constraints on prices.

My results suggest models that find very small effects of collateral constraints on prices are likely to be an underestimate. For example, in Kaplan et al (2020) unconstrained households with ample financial resources set housing prices so collateral constraints have essentially zero effect on prices. Heterogeneity in the model is driven by idiosyncratic income shocks, which also leads to a distribution of wealth. This is akin to only the discount rate dimension of heterogeneity in the user cost model, where the underlying factors determining the effective discount rate are income, wealth and age. My analysis shows that conditional on that dimension of heterogeneity, there is also important heterogeneity in the rent versus own preference that also influences the effect of collateral constraints. This extra dimension of heterogeneity means the marginal buyer is typically partially constrained, has a modest preference to owning over renting, and is somewhat responsive to collateral constraints.

Aside from implications about the housing market, the method in this paper can serve as an example of how to map heterogeneity to market outcomes. For any data that includes WTPs it is important to consider the role of heterogeneity across the demand curve.