Drawing from the findings in this chapter and the country papers, this section discusses four implications regarding household indebtedness and financial stability for SEACEN policymakers.
6.1 Balancing Costs and Benefits of Increased Household Indebtedness
Increases in household indebtedness have many benefits. Many supervisors tend to view robust increases in consumer loans with skepticism. While the concern is well placed, they tend to overlook the fact that parts of increases in consumer loans, or household debt more generally, are natural consequences of economic development and financial deepening. In fact, too low debt-to-GDP ratios and too low household loan penetration rates reflect a country’s financial underdevelopment.
Increased household indebtedness improves the quality of life of many households by making consumption smoothing and housing purchases easier. It also contributes to portfolio diversification and improved profitability of the banking sector. Over the longer term, greater household financial access is closely associated with economic growth (Townsend and Ueda, 2006). In the present post-crisis context, a robust household financial market will also facilitate a shift towards domestic demand that will help growth rebalancing.
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7. The Thai figure also encompasses business loans but debt to disposable income in Thailand is lower than Malaysia and Taiwan.
In light of these benefits, greater household debt access should be encouraged and financial innovations such as new consumer finance products and the securitisation of household debt should be promoted. The recent global financial crisis has given a bad name to securitisation and stalled the region’s nascent securitisation market. However, the criticism has been mostly on complex securities such as CDOs and ABCPs. Plain-vanilla, pass-through products will be beneficial for mortgage and consumer loan market deepening as well as risk management of financial institutions.
On its downside, excessive household indebtedness puts significant strains on households’ balance sheets and debt service capacity particularly when interest rates and/or unemployment are on a rise. In addition, a rapid, above-trend increase in housing loans is generally an important factor fuelling a harmful property price bubble. Given these negative repercussions, policymakers will need to strike a balance between greater household credit access and heightened threats to financial stability. This leads to the rest of the policy implications
6.2 Enhancing Household Debt Information
The ability to detect and assess the threats to financial stability arising from developments in household debt early on depends critically on the availability of timely and comprehensive information on household debt. Three areas of data enhancement – centralised household credit information system, better loan categorisation, and micro (household-level) data – make up a priority list for improved surveillance in the SEACEN region.
The importance of centralised credit information is vividly illustrated by the episodes leading up to Thailand’s 1997 financial crisis and Korea’s 2003 credit card crisis. In the Thai case, incidences of “double mortgages” where a single property was used as collateral for borrowings from more than one financial institution were parts of the destructive real estate market bubble. In the Korean case, many multiple credit card holders used proceeds from one card to pay off another card’s debt. These incidences could have been prevented had a centralised credit information system existed then. In countries that have established centralised credit information systems in the form of credit bureau or credit registry, borrower information is routinely used for loan screening. For policymakers, such a system also provides information on the exposure of the banking system, or of the financial institution system more broadly, to a particular household segment and its quality.
Finally, depending on the extent of data sharing, the vast data collected by the centralised credit information system can be used to improve the quality of financial institutions’ household credit risk assessment and household credit risk modeling. Given that several SEACEN countries now have credit bureaus or credit registries in operation for a while, much can be gained from knowledge and experience sharing between countries with and without such a system in place.
Data collection for this study reveals the need for expanded loan categorisation for a number of SEACEN countries. This issue goes beyond the existence of household loans for non-consumption purpose. Even within the scope of consumer loans, publicly available data in a number of countries do not have sub-categorisation or only distinguish between mortgage loans and non- mortgage loans. Even in countries that do have finer loan classification, the data on non-performing loans are available only for aggregate consumer loans. For certain countries, this is just a matter of public disclosure. Conversely in a few countries, the data simply do not exist.
Having finer data classification is important for financial stability analysis because different loan sub-categories may have different determinants, as illustrated by the Philippines country paper, different payment patterns (fixed or adjustable monthly payments, short or long maturity, for example), as well as different underlying risk factors. For example, mortgage default is more sensitive to house price boom-bust cycles than credit card default.
Finally, an analysis of household debt vulnerability based solely on aggregate data may mask important information regarding the distribution of leverage and debt service burden across households. This calls for the use of micro data where variables such as debt-to-income, debt-to-asset, and debt service ratios can be matched to household income levels, occupations, age groups, and so on. For example, using such data, Thailand’s country paper shows that even though Thai households on average are financially sound; low-income and less financially literate households are more likely to experience financial difficulties in times of economic shocks as their debt service ratio is more than twice as high as the average debt service ratio calculated from the aggregate data.
Beyond the three aforementioned areas of data enhancement, analysis of household debt can be greatly improved with relevant data collection. Such data include, but not limited to, household assets, delinquency rates, loss-given default, and various loan characteristics. A good example of the latter is Coleman et al. (2005) which maps mortgage default probabilities across variables such as the original loan-to-value ratio, loan age, and loan types (owner-occupied, investment, mortgage-insured, and large loans).
6.3 Strengthening Household Credit Risk Assessment
Because the market cannot always be counted on for accurate risk assessment, authorities with concerns for the maintenance of financial stability need to have some tools for vulnerability assessment of the household sector. At the minimum, the NPL and the delinquency rates of household loans of different types should be monitored regularly along with the behaviours of these loans relative to their trends. While up-to-date (in many countries, data on new loans, NPLs, and delinquencies are available to supervisors in quarterly, if not monthly, frequency and with only short time lags), these data are backward looking in nature. Two policy tools – sensitivity or scenario analysis and stress testing – offer ways for policymakers to get a glimpse into the future.
Scenario analysis is basically a broad term that encompasses both single- factor and multi-factor sensitivity analyses. Properly done, multi-factor sensitivity analysis must take into account the interactions among factors so that the assumed scenario is a coherent, realistic one.
Figure 6
Sensitivity Analyses of Household Loans
Examples of single-factor sensitivity analysis of household debt are shown in the two panels of Figure 6, taken from Ariyapruchya et al. (2004) and Nakornthab et al. (2004), respectively. Both analyses were carried out to answer in part the Bank of Thailand’s concern on the effect of an uptrend in the policy interest rate at the time. In Ariyapruchya et al. (2004), the focus was on the effect of interest rate increases on households’ interest service burden. In Nakornthab et al. (2004), the focus was on the sustainability of commercial banks’ mortgage payment contracts. In Thailand, mortgage payment terms are in fixed-amount monthly payments, most with low “teaser” fixed interest rates in the first couple of years and variable floating interest rates thereafter. As interest rates increase, a higher proportion of monthly payments will go into interest payment, leaving a smaller proportion for principal repayment. If interest rates are high enough, the sum of the fixed monthly payments over the loan life may not cover the original loan amounts. The right panel of Figure 6 traces time profiles of outstanding loan principal under different levels of interest rates for a hypothetical mortgage contract. The finding then was that most mortgage contracts on the market could withstand about 100-basis-point increases in reference lending rates without the need for contract extension or additional payments.
Stress testing is a special case of scenario analysis, with the assumed scenario being an extreme but plausible one. Like multi-factor scenario analysis, effective stress testing requires a sound empirical model that links a variable of interest to relevant risk factors. In bottom-up stress testing, the assumed scenario(s) are given to financial institutions by supervisors. The collected results are generally used to gauge vulnerabilities and capital shortfall of individual institutions as well as systemic vulnerabilities. Top-down stress testing is performed by supervisors using aggregate system-wide data. The results are generally coarser, but the analysis entails fewer data requirements and, therefore, can be done quickly. The Taiwan and the Thailand country papers feature such top-down analyses. Both the bottom-up and the top-down approaches complement each other and should be carried out regularly.
6.4 Macro-prudential Regulation and Supervision
The recent global financial crisis highlights the importance of macro-prudential regulation and supervision for the maintenance of financial stability. Broadly speaking, macro-prudential policy differs from traditional micro-prudential policy in that its concern is on the system as a whole rather than on individual institutions. Otherwise the underlying tools are similar. In fact, most of macro- prudential instruments currently in use and proposed thus far are adaptations,
re-calibrations, and re-orientations of existing micro-prudential instruments (Bank of England, 2009)
Assessments of household-sector credit risk in the preceding policy implication are an essential part of macro-prudential policy, but there are others. The most commonly used macro-prudential tool at present is loan-to-value ceilings on mortgage loans with an objective to guard against the buildup of imbalances in the mortgage market in times of booming property prices. Other examples include a cap on maximum loan amounts and minimum income requirements for credit card holders. In addition, qualitative tools such as Basel II pillar-2-type actions and moral suasion may also be used to take care of systemic concerns. Finally, the household sector section in financial stability reviews/reports serves as a public communication tool for central banks to articulate their views on financial conditions of the household sector.