Household Indebtedness: Estimations and Simulations using Microdata (Canadian case)

Shubhasis Dey, Ramdane Djoudad and Yaz TerajimaBank of Canada

May 2008

Motivation

• Household credit (HH) have considerably grown over 2000-2007

• HH credit represents about 70% of total Cdn$ loan exposure of commercial banks in Canada.

• Assessing credit risk associated with HH sector is an important part of the assessment of risks in the Canadian financial system

How do we assess the risks associated with the

HH sector at the Bank?

• Aggregate indicators (Debt-service-ratio, arrears, personal bankruptcy rate etc.)

• Microdata indicators

• Previous indicators are useful but incomplete and “backward-looking”

What do we need?

– A simulation tool for HH Debt-service-ratio

– A measure of the probability of delinquency for Cdn HHs

– Need to link both indicators and to be forward-looking

Objective of this work

• Develop a tool for performing stress testing simulations on microdata to:

– Assess the future path as well as the distribution of the DSR given macroeconomic scenarios

– Estimate the probability of delinquency for Canadian households

Literature Survey

• Literature: few empirical studies on household delinquency and simulation exercises for Canada.

• Reason: lack of household-level data

• Existing studies: Pyper, 2002; Domowitz and Sartain,1999; Stavins, 2000; Fay, Hurst and White, 2002; Gross and, Risto Souleles,2002; Li and Sarte, 2006; Herrala and Kauko, 2007.

The Data

In practice we use two datasets

• Advantages of 2 datasets:– SFS: delinquency and explanatory variables identified

in literature. (16 000 HH in 2005, 5000 in 1999)– CFM: available on a frequent and regular basis (12

000 every year)

• Data limitations:– SFS data: not available on a regular and frequent

basis.– CFM: no delinquency variable, only some of the

explanatory variables identified in literature.

Estimations

For simulations, we need to combine two parts:

1. On one hand we estimate equations for household credit (total and mortgage credit), to distribute aggregate debt among different households according to: age, education, working status, region of residence, indebtedness, income, interest rates, house prices, wealth.

2. On the other hand we estimate equations for household’s propensity to be delinquent according to similar sets of variables

Simulation

Given:

1. Estimated equations2. A macroeconomic scenario for the path of some economic

variables (income, debt, interest rates, house prices)

We evaluate

1. The implied average DSR along with its distribution over the forecasting horizon

2. The implicit household’s propensity to be delinquent

Stress-testing exercise

Objective: assess how shocks would affect the distribution of the DSR, vulnerable households and the probability of delinquency

Debt-over-income increases at trend over the simulation period

Caveats / Limitations

• We limit explanatory variables to those included in CFM (fewer than in SFS)

• Implicit assumption: coefficients of the equations are stable over time

• Liquid assets scenario: change similar for all households

• Stress-test: some variables kept unchanged but it might not be the case

Conclusion

• Objective: simulate the DSR and the probability of default for Cdn households.

• Stress-test results: probability of default for most vulnerable households would significantly increase subsequently to negative developments in DSR and liquid assets.

Future work

• Robustness check of estimated delinquency equation coefficients

• Endogeneity of DSR for household’s delinquency equation