RDP 2023-01: The Effect of Credit Constraints on Housing Prices: (Further) Evidence from a Survey Experiment 2. The Role of Collateral Constraints is Contested

The effect of collateral constraints has been studied extensively in the context of the US housing boom and bust. More recently, collateral constraints in the form of loan-to-valuation (LTV) ratio limits have been the subject of research into macroprudential policy assessment. Part of the difficulty in figuring out cause and effect is that financing conditions vary endogenously with price expectations and many other variables. The experiment in Fuster and Zafar (2021) overcomes this difficulty by providing exogenous variation, at the cost of using stated preference responses rather than actual behaviour.

Across literature on both the US housing boom and macroprudential policy, the importance of household heterogeneity has become increasingly recognised. This paper finds that the effects of collateral constraint on prices depend crucially on household heterogeneity through its effect on the responsiveness of housing demand to credit constraints across the demand curve. There is no reason to expect the overall effect of credit constraints to be consistent over time or in different markets, which may explain some of the disagreement in the existing literature.

2.1 Collateral constraints in the US housing boom

Early research into the housing boom and bust in the United States attributed an important causal role to relaxed lending standards and collateral constraints. In the empirical micro literature, Mian and Sufi (2009) document that zip codes with more subprime borrowers in 1996 had disproportionately high mortgage growth in the boom and default rates in 2007 in the United States. They note that the expansion of credit in the 2002–2005 period was concentrated in areas with relatively low income growth. This was taken as evidence that the housing boom was caused by a credit supply shock enabled by increased securitisation. More recently, Foote, Loewenstein and Willen (2016) show that by looking at originations, rather than debt stocks, Mian and Sufi (2009) mistook a relative increase in turnover in low-income places for an increase in credit. Adelino, Schoar and Severino (2016) use loan-level data to show that there were large increases in credit in middle-income and prime borrowers. They argue this opposes the view put forward in Mian and Sufi (2009). Mian and Sufi (2017) argue that the income measure used in Adelino et al (2016) was artificially inflated due to fraudulent income reporting on mortgage applications, while tax data show that income growth amongst households with strong credit expansion was weak. Albanesi, De Giorgi and Nosal (2017) use individual-level data to show that almost all of the disproportionate growth in subprime zip codes can be explained by life-cycle effects as households in these zip codes aged. Further, prime borrowers in these zip codes also showed strong growth in mortgage debt.

The macro modelling literature is equally divided. Two recent prominent papers reach essentially opposite conclusions. Favilukis, Ludvigson and Van Nieuwerburgh (2017) use a model to attribute the boom and bust in US housing prices largely to changing collateral constraints. Kaplan, Mitman and Violante (2020) argue that the conclusion in Favilukis et al (2017) relies on unrealistic modelling assumptions, including one-period mortgages that are subject to refinancing risks and an absence of a rental market. The model in Kaplan et al (2020) makes more realistic assumptions about renting and mortgage markets, and attributes little of the price swings to collateral constraint changes. Empirical VAR studies also give conflicting results. Cox and Ludvigson (2021) attribute price swings to collateral constraints whereas Ben-David, Towbin and Weber (2019) attribute them to expectations.

2.2 Assessment of macroprudential policy

Changing the maximum allowable LTV ratio on housing loans is one common tool of macroprudential policy. Most research on LTV caps shows some dampening effect on prices and credit, but progress on estimating the size of the effects has been more difficult. In one recent example, Richter, Schularick and Shim (2019) use narrative identification and inverse propensity-weighted local projections to estimate the magnitude of the effect of LTV restrictions on housing prices in an international panel. They find a typical LTV tightening lowers prices by 1.5 per cent over the following year, growing to 8 per cent over four years. That is the average finding, with the effect larger in emerging economies and smaller in advanced economies.

Research on LTV caps has, to some extent, considered the heterogeneous effects on different households. Van Bekkum et al (2019) find that LTV caps have larger effects on the borrowing of constrained households, while Acharya et al (2020) find loan-level lending constraints tend to reallocate credit from low-income to high-income households. Armstrong, Skilling and Yao (2019) use the differential application of collateral constraint policy on existing and new houses to estimate a dampening effect of the policy on prices, although they find the effect depends on other factors such as the pace of recent house price growth.

As pointed out in Duca, Muellbauer and Murphy (2021), time series analysis of macroprudential policies in general, and LTV policies in specific, is made difficult by the endogeneity of policy actions, and the tendency for policymakers to coordinate different policy levers. The stated preference approach can introduce genuine exogeneity.

2.3 Household heterogeneity is important when considering collateral constraints

Several previous modelling studies have looked at heterogeneous responses of households to changes in collateral constraints. Saver–spender models of the housing market, such as Iacoviello (2005) and Justiniano, Primiceri and Tambalotti (2019), have two types of agents. The patient agent is unconstrained and their housing demand does not respond at all to changing collateral constraints, while the impatient agent is constrained and their housing demand responds substantially to collateral constraints. The actual effect on prices depends on the structure of the model, in particular market segmentation and the rental market (see Cusbert (2022) for details).

The model of the San Diego housing market in Landvoigt, Piazzesi and Schneider (2015) models richer household heterogeneity and takes the housing stock to be of varying quality over a continuum. In effect this means the housing market is partially and continuously segmented. Each buyer is marginal in its own segment, but there are spillovers between segments. There is no rental market in the model, which makes each household a marginal buyer. Household heterogeneity creates differing responses to collateral constraints, which flow through to price. Overall, they find the bottom quintile of housing prices respond relatively strongly to easing credit constraints.

Kaplan et al (2020) model households with heterogeneous responses to collateral constraints and the option to rent instead of own housing. Similar to Landvoigt et al (2015) and Justiniano et al (2019), only constrained households respond much to collateral constraints. But unlike in those models, in Kaplan et al (2020) the households who respond to collateral constraints are not typically marginal buyers so the effect on price is minimal.

The experimental approach allows a more direct empirical analysis of the interaction of household heterogeneity with collateral constraints that is less dependent on a particular model structure. The analysis in Fuster and Zafar (2021) shows heterogeneous responses to collateral constraints based on household characteristics, which is consistent with the models mentioned in this section. But as shown by the differences in results of the models discussed, heterogeneous responses to constraints is not sufficient to infer what happens to prices. This paper considers the heterogeneous response of households in the context of the market structure to explicitly estimate the changes in marginal buyer demand and thus prices. Broadly speaking, my results for the response of housing prices to collateral constraints are are closer to the zero response of Kaplan et al (2020) than the large responses in Landvoigt et al (2015) and Justiniano et al (2019). That said, the marginal buyer does show a significant response to credit constraints in the survey data. Households that respond most strongly to collateral constraints are not typically marginal, but marginal buyers appear to be somewhat constrained. The direct empirical analysis highlights that households seem to be constrained along a continuum rather than being classified as constrained or unconstrained.