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
Household Indebtedness: Estimations and Simulations using Microdata (Canadian case)
Shubhasis Dey, Ramdane Djoudad and Yaz Terajima
Bank of Canada
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
Aggregate indicators (Debt-service-ratio, arrears, personal bankruptcy rate etc.)
Previous indicators are useful but incomplete and “backward-looking”
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
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: 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.
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)
SFS data: not available on a regular and frequent basis.
CFM: no delinquency variable, only some of the explanatory variables identified in literature.
For simulations, we need to combine two parts:
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 variables
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 delinquent
Objective: assess how shocks would affect the distribution of the DSR, vulnerable households and the probability of delinquency
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
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.
Robustness check of estimated delinquency equation coefficients
Endogeneity of DSR for household’s delinquency equation