Many of us will be fortunate enough never to fall into distressed debt – watching the unpaid bills mount up, the County Court letter arrive, and the debt collectors knocking at the door.
What went wrong for those people who do suffer financial distress? Are there symptoms that could be warning signs for those who would later become financially unwell?
In a first step to try to answer these questions we have researched the credit files of almost half a million individuals in the UK. Our aim has been to study the financial lives of those people who find themselves in distress and identify how they differ from those whose finances keep on an even keel..
The research was based on credit files drawn from one UK Credit Reference Agency and covered records for the period from January 2015 to February 2018.
The study excluded people who had no debt and no regular bills to pay, and also excluded individuals with business loans. Finally, the sample excluded people who were already persistently in arrears on financial commitments – the aim being to study those whose circumstances changed.
The final research was able to draw on the credit files of 428,097 individuals – anonymised of course.
The files provided a wealth of data beyond an individual’s credit score including age, total debt balances, mortgage balances and their use of other types of credit, including standard cost credit (such as a personal loan or credit card) or high cost credit (such as certain payday loans, doorstep lending or rent to own products). They also contained data for average monthly current account turnover, which may be reasonably used as a proxy measure monthly income.
What is distress?
There is no universally agreed definition of financial distress and different organisations use the term in slightly different ways. On the personal scale, of course, financial problems cause feelings of distress to people in different ways and human misery is something that is felt rather than measured.
But for the purposes of such a large scale study using impersonal financial information, we needed to settle on some unambiguous criteria that would show up clearly in the data available. So in the context of this research, individuals were deemed to have entered financial distress if they met one or more of the following criteria:
- They reached 90 days (or a default) on any credit product or bill
- A County Court Judgement (CCJ) is issued against them
- One or more of their credit accounts was passed to a debt collector
- They were declared bankrupt
By this measure approximately 12% of our sample fell into financial distress during the period covered by the study.
Our aim was to compare the financial pasts of those suffering financial distress and those who did not in order to isolate any distinguishing differences.
Looking across the sample, we can see that those who go on to experience distress tend to share some common characteristics six months prior to hitting problems.
They are typically younger, lower income, have a lower credit score, higher total debt balances, and tend to hold more expensive forms of debt. They also tend to have used up more of their available credit.
There is also on average a fall in income among those who enter financial distress. This shows up as a small change in the averages, but underneath this is a range including some people who suffer major falls in income and other who enter distress despite having suffered no such drop.
Distressed debtors also tend to be disproportionately concentrated in major urban centres (London, West Midlands, the urban North West and North East and Southern Scotland).
But while these trends hold true across the study, a closer analysis and a break-down of borrowers into different groups, or archetypes, revealed a more complex picture.
Each of these characteristics (age, income and so forth) were more relevant for some types of borrower than others. And in some groups the correlations were even the opposite of the average.
Using clustering techniques – a statistical method for dividing a sample into meaningful groups based on common characteristics – we identified four distinct groups or borrower archetypes.
The analysis used a range of measures including age, total debt and credit score. Data on income was not directly available, but what could be measured was average monthly turnover in an individual’s current account. Though not perfect this is a reasonable proxy for income.
The other key measures were type of borrowing used by different individuals and it was the type of borrowing that emerged from the cluster analysis as the clearest characteristic for each of our archetypes.
Put plainly, the people in each group typically hold the vast majority (95%+) of their debt in one particularly form of borrowing and so we have labelled each group according to this designation.
It is important to note that we have labelled groups according to their main form of borrowing.
For example, not all mortgage holders are in the ‘mortgage holder’ group. There are a tiny number of mortgage holders in other groups, but their mortgages are typically extremely small and are dwarfed by their other debts.
For the purposes of research into debt, an individual with a very small mortgage, but with high levels of personal loans and credit card debt is not best-described as a mortgage-holder.
Mortgage-holders – 162,000 individuals (c.38% of the sample).
Members of this group have average total debts of £136,000 - 95% of which is mortgage debt. Unsurprisingly they have the highest average monthly current account turnover (our proxy for income) at £2,465. They also have the highest average credit score at 613, slightly higher than the average credit score across all individuals in our sample of 601.
The average age of this group is 44, and the distribution of ages is an approximate bell-curve, echoing closely the typical mortgage debt life-cycle of most home-owners.
Mortgage-holders – age profile
Standard-cost borrowers – 147,000 individuals (c.34% of sample).
This group have average total debts of £5,095. The vast bulk of this debt (95%) is in standard-cost borrowing typically credit cards, motor and personal loans. This group has the second highest average monthly current account turnover of £1,992 and the second highest credit score of 607.
This group will be particularly mixed since it will include those who have no mortgage and rent a property, a small number whose mortgage is very small in comparison with their other debts, and many who own their home outright. This is reflected in the age data. The average age of members of this archetype is 50, shown by the black line in the graph below.
But the age profile shows two age peaks – the younger cohort has a peak at around 30 years of age and we see a second peak corresponding to an older cohort in their mid to late 60s.
Standard-cost borrowers – age profile
High-cost borrowers - 80,000 individuals (c.19% of sample).
Average total debts among this group are £1,179 and 95% of their debts are in high cost credit – this includes current account overdrafts, pay-day loans, store cards, home-collection lending or other borrowing with interest rates higher than that of a typical credit card. The average monthly current account turnover in this group is £1,328. They have the lowest average credit scores of our four groups at 572.
This is also the youngest of our groups with an average age of 40, and even this masks the youthfulness of this set of borrowers. Although 40 is the mean average, most are markedly younger with the majority being in their 20s or 30s.
High-cost borrowers – age profile
Household bills only - 39,000 individuals (c.9% of sample).
This archetype is particularly notable in that it’s members have very little in the way of credit, with on average 99% of its debt being outstanding household bills – typically utility and telecoms services bills. Average total debts for this group are a very modest £91. This average credit scores for this group are 595.
However, of all the archetypes this is one where averages may be especially misleading. The group will include young renters with few assets and low earnings, but with very little debt. But it will also include the retired, including relatively affluent homeowners who have paid off their mortgage. Their average age is 51, but the age profile graph shows this in in fact a group dominated by two distinct age cohorts – boomers and millennials.
Household bills – age profile
So that is our universe and those are our groups defined by their main type of borrowing.
It is important to note that our groups are based on the lives and financial statistics of people 6 months before any of them fell into financial distress.
The credit files enabled researchers to identify those who later suffered financial distress then look backwards to their situation six months prior. The aim of this approach was to assess whether there were any features that distinguished those who would later suffer financial distress from those who would not.
Those in distress
The first obvious question is whether any of these archetype groups is more likely to fall into financial distress than the others.
On this point there is an extremely clear answer, as the proportion of each group who enter financial distress six months later demonstrates.
Percentage who fell into financial distress:
- Mortgage holders 6%
- Standard-cost borrowers 8%
- High-cost borrowers 18%
- Household bills only 5%
It is also possible to see that certain groups remain in distress for longer.
Average length of period of distress:
- Mortgage holders - 5.9 months
- Standard-cost borrowers - 6.5 months
- High-cost borrowers - 7.8 months
- Household bills only - 8.3 months
High-cost borrowers are far more likely to suffer financial distress than other groups. Household bills only types have the lowest rate of distress but they are likely to stay in distress longer, although high-cost borrowers are close behind them in this measure.
Of course, the patterns we have highlighted are not necessarily causal.
Household bills only individuals may not spend more time in distress because their debts are mostly to utility companies. Instead it could be that such people are usually very stable financially and have few debts. For these people to fall into distress at all might require a more significant life shock, which may then take longer to resolve.
Equally, the fact that distress is far more common among those using mainly high-cost credit may be a sign that they have been struggling for some time before entering financial distress, rather than indicating that high-cost credit being the cause of financial distress.
The research data cannot answer these questions, though doubtless some readers have their own hypotheses.
In part 2 we will dive deeper into each of the groups. We will put some flesh on the types of people to whom these archetypes refer and explain what we found in terms financial distress in each group.
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