Household indebtedness and financial vulnerability

1. Dawid Żochowski Directorate Macroprudential Policy and Financial Stability Macro-Financial Linkages Division Household indebtedness and financial vulnerability ‘The Bank of Italy’s Analysis of Household Finances’3-4 December 2015Bank of Italy, Rome The views expressed in this presentation do not necessarily represent the official stance of the ECB

2. “Over-indebtedness in Italy: how spread and persistent is it?” Giovanni D’Alessio (Bank of Italy) and Stefano Iezzi (Bank of Italy) “Households’ vulnerability in the euro area” Laura Bartiloro (Bank of Italy), Valentina Michelangeli (Bank of Italy) and Cristiana Rampazzi (Bank of Italy) “The determinants of household debt: a cross-country analysis” Massimo Coletta (Bank of Italy), Riccardo De Bonis (Bank of Italy) and Stefano Piermattei (Bank of Italy) Discussion

3. Well motivated and very rich analysis: 12 various indicators Compare indebtedness in IT to other euro area countries Comment 1: Figure 1 yields very different results – what explains this? Only the indicators based on arrears correlate with self-reported stress. Comment 2: Is the indicator a good predictor of vulnerability: PD, NPL? Ideally if households linked with the credit register, matching via HH characteristics? Comment 3: Search for the definition of excessive indebtedness: legal definitions? Economic definition: ability/willingness to pay Comment 4: Is excessive leverage of some individual households sufficient to raise financial stability concerns? When the problem becomes systemic? A need for a link to supply-side and macro variables Comment 5: Determinants of over-indebtedness: use panel regression instead “Over-indebtedness in Italy: how spread and persistent is it?” Giovanni D’Alessio and Stefano Iezzi

6. Solid multivariate analysis of determinants of overindebtednens using DSR>40% (for HH with income below median) and other indicators + PC Find that self employment and LTV impact determine the ability to repay debt Comment 1: Vulnerability analysis but comparing the performance of the indicator to other indicators. Why not to macro credit indicators such as NPLs, defaults to demonstrate the predictive power of the indicator? Comment 2: Why focus only on the half of the sample (income below median)? Argument: higher income HH have financial resources to smooth out repayments. In fact, vulnerability decreases with higher financial assets => Consider financial resources in the regressions. Comment 3: Banking sector variables could include credit conditions, such as credit spreads or qualitative indicators from the BLS. In particular, as high LTVs may reflect loose lending policies. Comment 4: Expand on the PCA “Households’ vulnerability in the euro area” Laura Bartiloro, Valentina Michelangeli and Cristiana Rampazzi

7. Panel study of demand- and supply-side determinants of household debt Find that debt higher in countries with higher per capita GDP and better quality bankruptcy laws, while lower debt the longer the resolution process Comment 1: Effective interest rate affects the level of debt via the size of the interest instalment, via the value of collateral and via inter-temporal shifts of consumption (discount factor) => include IR and BLS in the regressions. Link with financial stability: IR and bank lending policy affect debt Comment 2: Endogeneity: Hausman-Taylor estimates but lagged variables may not be sufficient instruments: Countries with higher per capita GDP have higher HH debt. In a similar fashion life expectancy is likely to be correlated with GDP per capita. => Look for other instruments as a robustness check. Comment 4: Split the sample into emerging and developed economies. Perhaps you will find further insight into the determinants of debt. “The determinants of household debt: a cross-country analysis” Massimo Coletta, Riccardo De Bonis and Stefano Piermattei

8. The use of HFCS data Data on household balance sheets, cash flow and debt, including collateral for 60,000+ households (150,000+ household members) from 15 EU countries. Twofold objective: • For stress testing on consistent macro scenarios or selected sensitivity analyses (e.g. with respect to unemployment rate, house prices, income). • For assessing impact of borrower-based macroprudential policy measures.

9. Metric of distressa proposal First, define a financial margin, as: = income – tax – debt service – basic living costs Miguel Ampudia, Has van Vlokhoven, Dawid Żochowski (2014), Financial Fragility of Euro Area Households, ECB WP 1737

10. Metric of distresscalibration Calibration strategy: match EAD with the NPL ratios

11. NPLs and exposure at default for different metrics of distress Notes: DSI means debt debt-service service-to to-income ratio, BLC stands for basic living costs as a percentage of median income. The last 3 columns follow from specific questions asked in the HFCS. The column inability to meet expenses considers households with expenses above income, and who finance this by means of a loan, asking help from friends or relatives or leaving some bills unpaid, as in distress. Sources: HFCS, various Central Banks, Consolidated Banking Data & own calculations.

12. Sensitivity to the interest rate shock Percentage of indebted households in distress for various changes to the interest rate Sources: HFCS & own calculations. Notes: the dashed lines represent the 95% confidence intervals.

13. Impact on banks: Loss given default

14. Effect of an LTV ratio cap on banks’ losses Banks’ losses and revenues (index) Note: the index is set to 100 in case there is no LTV ratio cap (i.e. the baseline).

15. IDHBS model byMarco Gross, Javier Poblacion C.1 The IDHBS model – Schematic overview

16. The IDHBS modelThe questions we can address with the model What impact has the imposition of an LTV cap at x% in country Y on that country’s household sector PDs/LGDs/LRs? What macro impact would the LTV cap have and how would the macro feedback in turn influence PDs/LGDs/LRs?  quantify 2nd round effects What cross-border effects would the imposition of LTV/DSTI caps in country Y? What is the impact of the LTV cap at x% in country Y on the banks’ CET1? How would a move of the unemployment rate / income / house price by +/-X pp/% impact the household PDs/LGDs/LRs in that country? What is the impact of a combined LTV + DSTI cap? What is more effective – an LTV or a DSTI cap?

17. The IDHBS modelImpact on households PDs, LGDs, LRs – 1st and 2nd round impact 2nd round effects, starting from policy-induced negative loan demand shock, driving PDs and LGDs a bit up (not much but still): Loan demand ↓  construction ↓  unemployment ↑  PDs ↑  House prices ↓  collateral value ↓  LGDs ↑

18. Dawid Żochowski Directorate Macroprudential Policy and Financial Stability Macro-Financial Linkages Division Household indebtedness and financial vulnerability ‘The Bank of Italy’s Analysis of Household Finances’3-4 December 2015Bank of Italy, Rome The views expressed in this presentation do not necessarily represent the official stance of the ECB