The Cost of Consumer Collateral: Evidence From Bunching

Q1. What is the setting and why does it cleanly identify the collateral shadow cost?

The Federal Disaster Loan Program creates a quasi-experimental threshold because collateral is the only loan term that changes at the $25,000 boundary — interest rate, maturity, approval probability, and all other terms remain identical on both sides. Households with uninsured disaster damages (median $51,000) can borrow up to their loss amount at a fixed 2.5% rate; those requesting above the threshold must post their home as collateral. Unlike mortgage or auto loan markets, which always require collateral, and credit card markets, which never do, this program generates a setting where the binary collateral requirement is the borrower’s own choice subject only to the threshold, eliminating the standard endogeneity between contract terms and borrower risk. The authors use administrative data covering over 1 million applications from 2005 to 2018 across all 50 states.

Q2. How do the bunching estimates identify the distribution of collateral aversion?

The traditional bunching estimator fits a polynomial to the distribution of loan amounts below the threshold, extrapolates it above the threshold to construct a counterfactual without the collateral requirement, and attributes the excess mass at the threshold (the “missing” mass above) to collateral aversion. The bunching region spans from the threshold to the upper loan amount beyond which essentially no borrower would give up to avoid collateral; for the $25,000 threshold, this region extends to $49,900. Across all three threshold regimes, 73–78% of borrowers within the bunching region move to the threshold, and the estimated mean private value of collateral ranges from $7,944 (at the $10,000 threshold) to $18,268 (at the $25,000 threshold), representing 37–44% of ideal loan amounts. The median borrower’s collateral aversion — 39–47% depending on the threshold — is robust across all three estimation methods.

Q3. What alternative bunching methods does the paper develop and what do they find?

Two alternative estimation approaches — a difference-in-bunching (DiB) estimator that compares borrowers with identical damage levels across different threshold regimes, and an originally-requested-loan estimator that uses the amount households requested before collateral salience increased — both yield median collateral aversion estimates consistent with the traditional method (40–47%), while suggesting considerably wider heterogeneity in the tails. The DiB approach exploits the fact that for a household with $20,000 in damages, the same loan amount was uncollateralized under the $25,000 regime but required collateral under the $10,000 regime, enabling consumer-level identification. The originally-requested-loan method shows that 70% of eventual bunchers initially requested an amount above the threshold, with the majority of the shift occurring after meeting with a loan officer when the collateral requirement became salient. Both methods are immune to the standard counterfactual mis-specification concern of the traditional bunching approach, and they suggest the traditional estimator substantially under-predicts the proportion of highly collateral-averse borrowers at the upper end of the distribution.

Q4. What is the mechanism behind collateral aversion and what does heterogeneity reveal?

Collateral aversion in this setting is driven by both financial incentives and behavioral/preference factors, as evidenced by the finding that roughly 30% of borrowers already underwater on existing mortgages — who have no real equity to lose — still bunch at the threshold to avoid adding a lien on their home. More creditworthy borrowers (higher credit scores, higher incomes) are actually more likely to bunch, consistent with an “advantageous selection” interpretation in which borrowers who are confident in their repayment capacity are especially averse to the stigma and risk of pledging their home. Interest rates also matter: borrowers bunch more when program interest rates are higher, suggesting that financial incentives amplify the existing aversion. The magnitude of bunching — giving up thousands of dollars of subsidized low-interest disaster recovery loans — indicates that the perceived cost of a lien on the primary residence extends far beyond the financial value of potential foreclosure.

Q5. How does collateral causally reduce default rates?

Using time variation in the collateral threshold as an instrument for whether a borrower’s loan is collateralized — borrowers are more likely to collateralize when the threshold is low (so their ideal loan amount exceeds the low threshold) than when it is high — the paper estimates that collateral causally reduces default rates by about 35%. This local average treatment effect applies to borrowers who would collateralize under the $10,000 threshold but not under the $25,000 threshold, and the magnitude is comparable to a 100-point FICO score improvement. The finding implies that collateral requirements address genuine moral hazard in consumer lending: when a primary residence is pledged, borrowers take repayment obligations substantially more seriously, reducing the probability of strategic or precautionary default. This provides causal evidence for the classic prediction of models like Bester (1985) and Chan and Thakor (1987) that collateral mitigates information asymmetries and expands efficient credit access.

Q6. What are the aggregate implications and contribution to methodology?

In aggregate, borrowers in the program have given up more than $1.1 billion in disaster recovery loans to avoid posting collateral, indicating that collateral requirements — standard in most large consumer credit markets — impose large implicit costs that are not captured in stated interest rates. The methodological contribution is threefold: the paper is among the first to apply bunching to consumer (rather than corporate) collateral; it develops two consumer-level alternative estimators that relax assumptions of the standard method and reveal greater heterogeneity in collateral aversion; and it separately identifies the moral hazard effect of collateral from adverse selection by exploiting threshold variation, extending bunching beyond tax compliance and into household finance.

Key Concepts

collateral shadow cost : the implicit cost a borrower assigns to pledging collateral beyond the direct financial cost; measured in this paper as the maximum loan amount a borrower forgoes to avoid posting their home, identified from bunching mass at the collateral threshold in the FDL program.

bunching estimator : an estimation strategy that infers a structural parameter — here collateral aversion — from the excess density of agents at a policy threshold, using a polynomial-extrapolated counterfactual distribution to identify the missing mass above the threshold.

difference-in-bunching (DiB) estimator : a consumer-level alternative to traditional bunching estimation that uses borrowers with identical damage levels but facing different threshold regimes over time to construct a within-person counterfactual for the ideal loan amount, avoiding assumptions about the counterfactual distribution shape.

advantageous selection : the pattern in which more creditworthy, higher-income borrowers are the ones most likely to avoid collateral requirements, the reverse of the adverse selection typically assumed in collateral models; consistent with these borrowers having strong repayment intent independent of the lien.