Household indebtedness estimations and simulations using microdata canadian case
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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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Household Indebtedness: Estimations and Simulations using Microdata (Canadian case)

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Household indebtedness estimations and simulations using microdata canadian case

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

Shubhasis Dey, Ramdane Djoudad and Yaz Terajima

Bank 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

Motivation


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”

How do we assess the risks associated with the HH sector at the Bank?


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

What do we need?


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

Objective of this work


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.

Literature Survey


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.

The Data


Estimations

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

Estimations


Simulation

Given:

Estimated equations

A macroeconomic scenario for the path of some economic variables (income, debt, interest rates, house prices)

We evaluate

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

The implicit household’s propensity to be delinquent

Simulation


Stress testing exercise

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

Stress-testing exercise


Debt over income increases at trend over the simulation period

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

Caveats / Limitations


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.

Conclusion


Future work

Robustness check of estimated delinquency equation coefficients

Endogeneity of DSR for household’s delinquency equation

Future work


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