Household Indebtedness: Estimations and Simulations using Microdata (Canadian case). Shubhasis Dey, Ramdane Djoudad and Yaz Terajima Bank of Canada May 2008. Household credit (HH) have considerably grown over 2000-2007
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Shubhasis Dey, Ramdane Djoudad and Yaz Terajima
Bank of Canada
Household credit (HH) have considerably grown over 2000-2007 Microdata (Canadian case)
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 systemMotivation
Aggregate indicators (Debt-service-ratio, arrears, personal bankruptcy rate etc.)
Previous indicators are useful but incomplete and “backward-looking”How do we assess the risks associated with the HH sector at the Bank?
A simulation tool for HH Debt-service-ratio bankruptcy rate etc.)
A measure of the probability of delinquency for Cdn HHs
Need to link both indicators and to be forward-lookingWhat do we need?
Assess the future path as well as the distribution of the DSR given macroeconomic scenarios
Estimate the probability of delinquency for Canadian householdsObjective of this work
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.Literature Survey
In practice we use two datasets and simulation exercises for Canada.
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)
SFS data: not available on a regular and frequent basis.
CFM: no delinquency variable, only some of the explanatory variables identified in literature.The Data
For simulations, we need to combine two parts: and simulation exercises for Canada.
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.
On the other hand we estimate equations for household’s propensity to be delinquent according to similar sets of variablesEstimations
Given: and simulation exercises for Canada.
A macroeconomic scenario for the path of some economic variables (income, debt, interest rates, house prices)
The implied average DSR along with its distribution over the forecasting horizon
The implicit household’s propensity to be delinquentSimulation
Objective: assess how shocks would affect the distribution of the DSR, vulnerable households and the probability of delinquencyStress-testing exercise
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 caseCaveats / Limitations
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.Conclusion
Robustness check of estimated delinquency equation coefficients
Endogeneity of DSR for household’s delinquency equationFuture work