Who’s driving consumer credit growth?

Ben Guttman-Kenney, Liam Kirwin, Sagar Shah

Consumer credit growth has raised concern in some quarters. This type of borrowing – which covers mainstream products such as credit cards, motor finance, personal loans and less mainstream ones such as rent-to-own agreements – has been growing at a rapid 10% a year. What’s been driving this credit growth, and how worried should policymakers be?

For many years regulators have relied on aggregated data from larger lenders to monitor which lenders and products are driving credit growth. These data are useful. But they also have important gaps. For example, they don’t include less-mainstream products that people with low incomes often rely on.

Such data do not show who is borrowing, or people’s overall debts across different lenders and products. This matters. If people borrow on many products, problems repaying one debt could rapidly spill over to others. Consumer surveys can offer some insights here. But surveys often have limited product coverage, are only available with a lag, and may suffer from misreporting.

To build a better, fuller picture of borrowing, the FCA requested credit reference agency (CRA) data for one in ten UK consumers. CRAs hold monthly data on most types of borrowing – including consumer credit, mortgages, and utilities. These data are really rich, going back six years, and can be studied at many different levels. For example, it is possible to scrutinise individual borrowing across products, or to focus on particular lenders or types of products.

We examined these data to assess possible risks from recent credit growth. This article summarises three particular insights which have emerged from this work:

  1. Credit growth has not been driven by subprime borrowers;
  2. People without mortgages have mainly driven credit growth;
  3. Consumers remain indebted for longer than product-level data implies.

Insight 1: Credit growth has not been driven by subprime borrowers

CRA data enables us to examine the distribution of credit scores among groups of borrowers. This is valuable because credit scores are excellent predictors of which types of borrowers are most likely to default or have high risks of suffering broader financial distress. A lower credit score indicates a greater risk of a person being unable to repay their debt. Those with very low credit scores are often referred to as ‘subprime’ borrowers.

In Figure 1 we show the share of outstanding consumer credit debt (net of repayments) by people’s credit scores. We divide the range of credit scores into ten buckets – the lowest bucket contains people with scores in the bottom tenth of the range (the riskiest borrowers).

Doing so reveals that a small proportion of all consumer credit debt is held by subprime consumers. There are some important differences when we compare people holding different credit products. Borrowing on credit cards with 0% offers and motor finance is concentrated among people with the highest scores. This contrasts with people borrowing on interest-bearing (non-0%) credit cards who more commonly have low scores.

Figure 1. Distribution of outstanding debt in November 2016 by credit score, split by types of consumer credit products

Given motor finance and 0% credit cards have accounted for a majority of consumer credit growth since 2012, this suggests much of the growth is going to the borrowers least likely to suffer financial distress. This story is consistent with high-cost credit markets used by subprime borrowers not rapidly expanding – on the contrary, some are contracting.

In Figure 2, we turn to how the distribution of borrowing has changed over time. Here we find little difference in credit scores over the recent period of rapid credit growth. This holds when looking at both the outstanding stock and the flow of new borrowing. At face value, this indicates that lenders have not dramatically relaxed their lending standards. But observing a similar credit score distribution when the macroeconomic environment has slightly improved may be better interpreted as a deterioration. The only product where we find an increased concentration of subprime borrowing is interest-bearing credit cards.

Figure 2. Distribution of consumer credit debt by credit score, split by outstanding stock and new accounts at two points in time

History also offers some caution on the relative importance of subprime lending. Recent research on the US mortgage crisis found the pre-crisis growth of subprime borrowing was less dramatic and important to explaining the crisis than earlier studies implied.

Insight 2: People without mortgages have mainly driven credit growth

The recent credit growth has followed a tightening of mortgage lending requirements. Did this tightening have the unintended side-effect of turning mortgage borrowers away from extracting home equity and instead towards consumer credit?

We assess the interaction between these two markets by splitting the growth and stock in borrowing between mortgagors and non-mortgagors. This is shown in Figure 3. About half of outstanding consumer credit is held by those with mortgages. However, this group accounts for a minority of growth in credit balances, with 60% of the growth in credit balances coming from non-mortgagors.

It is comforting that mortgagors do not appear to be bypassing tighter mortgage regulation by amassing consumer credit debt. But a key question going forward is how much of the growth is coming from renters and how much from outright owners.

We know that renters tend to spend a higher share of their income on housing than mortgagors, and so may have less income available for debt repayments. Rapid increases in indebtedness among renters could therefore be a vulnerability.

It is also possible that outright owners are taking out credit, even if they don’t need it. Survey data suggest around 40% of households with consumer credit debt hold savings in excess of such debt. If driven by outright owners, rapid credit growth among non-mortgagors may be less worrying.

Figure 3. Growth in consumer credit debt and outstanding stock of debt, split by consumers without and with mortgage

Insight 3: Consumers remain indebted for longer than product-level data implies

The Bank has previously argued that lenders’ consumer credit portfolios turn over relatively quickly, reflecting the short terms of consumer credit products (relative to mortgages). In theory, this rapid turnover means that the prudential risks from outstanding consumer credit could quickly increase (or decrease) if lending standards were to deteriorate (or improve).

While this may hold from a lender perspective, our analysis tells a different story from consumers’ perspective. We find that although a consumer may clear their debt on one credit product, it is not uncommon for them to remain in debt as they transfer balances, take out new credit products or draw down on existing credit lines (such as credit cards). As shown in Figure 4, 89% of the total outstanding stock of debt in November 2016 was held by people who also owed debt two years earlier. While approximately half of new borrowing is due to ‘new’ borrowers, these people are typically only able to access relatively small amounts of credit and therefore account for a small proportion of the overall stock of debt.

Figure 4. Growth in consumer credit debt and outstanding stock of debt, split by consumers without and with debt in November 2014

An implication of these findings is that regulators should not become too relaxed when they observe improvements in specific products at particular lenders. Unless the borrower population significantly changes, it is possible that the risk of consumer harm will simply be shifting from one part of the market to another rather than reducing. It is therefore important to regularly examine the financial health of people and their debts holistically using CRA (or similar) data.

Should policymakers be worried?

Credit growth not being disproportionately driven by subprime borrowers is reassuring. As is the lack of evidence that mortgage lending restrictions are pushing mortgagors towards taking on consumer credit.

But vulnerabilities remain. Consumers remain indebted for longer than previously thought. And renters with squeezed finances may be an increasingly important (and vulnerable) driver of growth in consumer credit. The FPC and PRC have taken actions in this space.

Benedict Guttman-Kenney works in the FCA’s Behavioural Economics & Data Science Unit. Sagar Shah works in the Bank’s Macro-Financial Risks Division. This post was written whilst Liam Kirwin was working in the Bank’s Policy, Strategy and Implementation Division.

This post is being published simultaneously on Bank Underground and FCA Insight

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Bank Underground is a blog for Bank of England staff to share views that challenge – or support – prevailing policy orthodoxies. The views expressed here are those of the authors, and are not necessarily those of the Bank of England, or its policy committees.

 

4 Comments

Filed under Financial Stability, Macroeconomics, Macroprudential Regulation, Microprudential Regulation

4 responses to “Who’s driving consumer credit growth?

  1. Karl Massey

    Thank you for raising this topic.

    May I make an observation based on anecdotal evidence?

    Credit Growth is strong because the majority of people’s residual incomes have been (and continue to) falling fast and without credit to supplement income they cannot maintain their lifestyle/standard of living.

    In other words, the “real” component is not being captured correctly and the inflation actually experienced by many people is much higher than official sources suggest.

    IMHO Why not do some micro-economic fieldwork on a sample of real people and their real experiences and behaviours instead of trying to aggregate official data and then impute the effects. This disconnect is part of the reason for many voters disengaging from official policy narratives.

  2. Alex Waters

    The many simplistic articles of ‘consumer debt is up’ have been in dire need of further analysis. Thank you for doing so, I hope this sort of context will be published more regularly in the mainstream.

  3. James Tebboth

    The comment above figure 2 that “observing a similar credit score distribution when the macroeconomic environment has slightly improved may be better interpreted as a deterioration” depends on whether the buckets into which you have partitioned credit score are fixed overall or recalculated by period. If the buckets are defined using fixed credit score boundaries, then the inference could well be correct; if however the buckets are calculated based on the overall score distribution observed at that point, then an alternative explanation is that the credit profile is improving alongside the economy, with the deciles themselves moving in tandem with an improving credit score profile, while the proportions in each decile unchanged.

  4. Ian Bright

    Thanks for this useful piece of work.
    Three observations.

    1. One potential interpretation of this analysis is that consumer credit is not as problematic as first thought, But that sits uncomfortably with reports of increased demand from debt advice groups for their services. For example, Step Change’s 2017 annual report notes increasing demand for their services ( see https://www.stepchange.org/PolicyandResearch/2017-personal-debt-statistics-mid-yearbook.aspx ). Although this charity works with those already in financial trouble (possibly low credit score?) it is difficult to square with this analysis.

    2. The CRA data goes back six years but the analysis covers only two. I cannot figure out why the analysis is over only two years. Further, six years does not cover a business cycle / period of increasing unemployment.

    3. There is a wider macro point. Some theorists argue increasing household debt (of which consumer credit is a part) increases macroeconomic and financial risk. This is irrespective of the credit quality of the individual households contributing to the aggregate growth in debt. If there is a system wide shock, the differentiation between the different credit score deciles is meaningless. It’s a bit like how the different tranches of RMBS became meaningless (or highly correlated) in the 2007-08 mortgage crisis.