RDP 2014-13: Mortgage-related Financial Difficulties: Evidence from Australian Micro-level Data 3. Household-level Determinants of Missed Mortgage Payments

In this section, we identify household characteristics associated with missing a mortgage payment using household-level survey data. This complements the analysis in the previous section by allowing us to use a range of variables that are not available in the loan-level dataset, including the borrower's labour force status and income.

3.1 Data

As part of the 2006 and 2010 wealth modules, the HILDA Survey – an annual household-based longitudinal study – asked respondents if they had been unable to meet a payment by the due date on any housing or property loan in the previous 12 months because of financial difficulties. The share of households with owner-occupier mortgage debt that reported missing a mortgage payment was around 5½ per cent in 2006 and 6 per cent in 2010.^[12]

While missing a mortgage payment does not necessarily correspond to the borrower defaulting, it represents an early stage of the default process and provides a signal of financial difficulties. For example, around 14 per cent of households that missed a mortgage payment in 2010 reported being behind schedule on their mortgage payments at the time of the 2010 survey, compared with 2½ per cent of households that did not miss a payment. Additionally, around 5 per cent of households that missed a mortgage payment in 2006 reported selling their home due to financial difficulties at some point in the following four years, compared with 2 per cent of households that did not miss a payment. These statistics imply that mortgage-related financial difficulties are often temporary; only a small proportion of households that report missing a mortgage payment go on to report experiencing more serious financial difficulties.

3.1.1 Ability-to-pay factors

A common measure of a borrower's ability to comfortably make their mortgage payments is the debt-servicing ratio (DSR), defined as the percentage of household disposable income used to service mortgage debt. The measure of mortgage payments available in the HILDA Survey is based on households' reported ‘usual payments’ on owner-occupier housing debt, which may include regular and excess repayments of principal, as well as interest payments. The DSR may help to identify households that are particularly vulnerable to income or expenditure shocks.

The share of households that reported missing a mortgage payment tends to increase with the DSR (Figure 3). Borrowers that were unemployed or not in the labour force (NILF) were also more likely to miss a mortgage payment relative to those in employment. This may largely reflect correlation with income and DSRs; individuals that are unemployed or NILF tend to have lower incomes and higher DSRs.

Previous behaviour in servicing mortgage and other debt may also provide useful insight into the propensity for a household to miss a mortgage payment, potentially by capturing a household's ‘willingness to pay’. Households that missed a mortgage payment in 2006 were substantially more likely to miss a payment in 2010 than households that did not miss a mortgage payment, despite the substantial period of time between the two observations (Table 5). May and Tudela (2005) suggest three potential explanations for this:

1. The conditions relevant to a borrower meeting their debt obligations may be altered if they have previously missed a mortgage payment; this is sometimes referred to as state dependence. For example, missing a mortgage payment may make it more difficult to access credit in the future. If borrowers cannot costlessly renegotiate their mortgage terms (such as through refinancing) when facing payment difficulties, then payment problems may persist. Furthermore, the borrower may be less averse to missing payments if the associated stigma is lessened by their previous experience.
2. Characteristics that increase the propensity to miss a mortgage payment may be persistent (or invariant) over time. These may include observed characteristics, such as the DSR, as well as unobserved characteristics, such as financial literacy.
3. Persistence in mortgage-related financial difficulties may be observed if a single spell of mortgage-related financial difficulties tends to be long in duration. This explanation seems less applicable here, as our observations are four years apart.

The ongoing persistence of mortgage-related financial difficulties may also be observed through the relationship between a household's mortgage status – whether the household reports being ahead, behind or on schedule on their mortgage payments – and whether they miss a mortgage payment (Figure 4). Of households that reported being behind schedule on their mortgage payments in 2009, 16 per cent missed a mortgage payment in 2010; this is likely to reflect factors similar to those that cause persistence in missing mortgage payments (discussed above). By comparison, only 3 per cent of households that were ahead of schedule in 2009 missed a payment in 2010.

Credit card payment behaviour may also provide some information about mortgage payment behaviour. Households that reported always (or almost always) paying off the entire balance on their credit cards each month were less likely to miss mortgage payments than households that did not have a credit card or did not always pay off the entire balance of their credit card each month. This may be because these households are more financially literate or conscious of actively managing their finances; they could also have less variable income or expenditures.

3.1.2 Equity factors

The HILDA Survey data allow us to calculate a household's level of housing gearing using the value of their outstanding mortgage debt and their self-assessed home value. While Windsor, La Cava and Hansen (2014) show that there is considerable dispersion in the difference between home price beliefs and observed (hedonically adjusted) prices, home price beliefs appear to be unbiased on average and (as noted in Section 2.1) households' valuations are likely to be of greater relevance to mortgage default decisions than actual prices (Gerardi et al 2013).

There does not appear to be a particularly strong (or stable) relationship between housing gearing and missed payments (Figure 5). Taken at face value, this suggests that equity factors are less important than ability-to-pay factors as a determinant of missing mortgage payments; this is not particularly surprising given that housing lending in Australia is ‘full recourse’, meaning that, in the event of default, lenders have a claim on some assets other than the mortgaged property. On the other hand, looking at gearing and DSRs together provides some evidence for the importance of equity factors in missing mortgage payments; the incidence of missed mortgage payments among households that have both high gearing and high DSRs is greater than among households that have either high gearing or high DSRs, but not both. This suggests that double-trigger effects (described in Section 1) may play a role in households missing mortgage payments.

3.2 Modelling Framework

The preceding analysis of missed mortgage payments in the HILDA Survey dataset describes only unconditional correlations between missed payments and particular variables. In order to account for cross-correlations between these variables, and thus isolate their direct effects on the probability of missing a mortgage payment, we turn to regression methods. Since the dependent variable is binary – a household either missed a payment or did not – we employ a probit model:

Here, Y_i = 1 if household i missed a mortgage payment and Y_i = 0 if the household did not miss a payment, x_i is a vector of explanatory variables for household i, β is a vector of coefficients and Φ is the standard normal cumulative distribution function. Under this model, a household misses a mortgage payment when the continuous latent random variable exceeds some threshold (normalised to 0). Notable features of the model are that:

• We estimate the model for the 2010 cross-section and include a lag of the dependent variable to capture possible state dependence in missing mortgage payments. The lagged missed payments variable is a categorical variable (represented by a set of dummy variables), where one of the categories is for non-response.^[13] We also include other variables related to the mortgage status (i.e. whether the household is ahead, behind or on schedule) and credit card payment behaviour.^[14]
• Following May and Tudela (2005), we use lagged values of the explanatory variables (i.e. from the 2009 survey) instead of their contemporaneous values to capture the household's characteristics prior to missing a payment. This should help to minimise endogeneity problems related to reverse causality and allows us to better identify meaningful lead-lag relationships. We exclude households that bought their residence in 2010, as the DSR and gearing recorded in the 2009 survey would not correspond to the dwelling for which a mortgage payment was missed.
• DSRs and gearing are included as categorical variables to capture potential nonlinear relationships between these variables and missed payments.
• The household head's labour force status is included as an explanatory variable. In defining the labour force status, we differentiate between full-time wage earners, part-time wage earners and the self-employed. Self-employed households are likely to have relatively volatile incomes, which may affect their ability to repay loans.
• The state in which the property is located is used to control for geographic factors, possibly related to local conditions in the labour and housing markets.

3.3 Results

The results from our preferred probit model are shown in Table 6. Overall, the results indicate that ability-to-pay factors are strongly positively associated with the probability of missing mortgage payments. Additionally, there is also some evidence to suggest that borrowers with negative equity are more likely to miss a payment.

3.3.1 Ability-to-pay factors

The results suggest that having a DSR over 50 per cent is associated with a probability of missing a mortgage payment that is, on average, 10 percentage points higher than for a DSR under 30 per cent. Even after controlling for the DSR, we find evidence that the labour force status of the household head is correlated with the probability of missing a mortgage payment. Households with a household head that is NILF are 8 percentage points more likely, on average, to miss a mortgage payment than those with a household head that is a full-time wage earner. Full-time self-employed workers are also around 6 percentage points more likely to miss a mortgage payment, possibly reflecting these households' more volatile cash flows. The marginal effect of being unemployed is large in magnitude, at around 8 percentage points, but statistically insignificant, possibly reflecting the small sample of unemployed households (less than 1 per cent of the estimation sample). Replacing the labour force status variables with variables representing the change in labour force status yields qualitatively similar results.

Households that had missed a mortgage payment in 2006 are estimated to be around 22 percentage points more likely to miss a payment in 2010, on average. This effect is broadly consistent with aggregate data on non-conforming housing loans (many of which are to borrowers with blemished credit histories); arrears rates on non-conforming loans tend to be far greater than arrears rates on ‘prime’ lending. This result, however, should be interpreted with some caution, as it may reflect an endogeneity problem. In particular, the lagged dependent variable may be correlated with the latent error term (u_i) if there are persistent omitted factors that influence the probability of missing a mortgage payment, such as household wealth and financial literacy.^[15] However, we have potentially controlled for such factors by including variables relating to the mortgage status and credit card payment behaviour (discussed below). Even if the effect of state dependence on missing a mortgage payment is overstated by the coefficient on the lagged missed mortgage payment variable, these results still indicate that having previously missed a mortgage payment is a good predictor of subsequently missing another payment. This result supports the practice of lenders using credit scores and other information on previous debt payment behaviour in their credit assessment processes.

Also in relation to previous debt-servicing behaviour, we find that households that are ahead of schedule on their mortgage payments are, on average, 4 percentage points less likely to miss a payment than households that are on schedule. This could be the net result of several factors. First, households that are ahead of schedule on their mortgage payments probably tend to be better at managing their finances than other households. Second, if faced with temporarily lower income, households that are ahead of schedule can comfortably miss a scheduled payment without severe consequences. All else equal, this could make these households more willing to miss a payment. Finally, households that are ahead of schedule could avoid missing a payment by drawing down on existing offset account balances or mortgage redraw facilities. These considerations are complicated by uncertainty about whether households would report missing a payment if they are ahead of schedule at the time that they miss the payment. The marginal effect of being behind schedule is not statistically significant, possibly reflecting the small sample of such households (around 3 per cent of the estimation sample).

In terms of the payment of non-mortgage debt, households that do not pay off their entire credit card balance each month are, on average, 3 percentage points more likely to miss a payment than households with no credit card, while households that regularly pay off their credit card are around 2 percentage points less likely to miss a payment.

3.3.2 Equity factors

The estimation results provide weak evidence to suggest that equity factors play a role in missing mortgage payments: the coefficient on the negative equity variable (i.e. gearing greater than 100 per cent) is positive and significant at the 5 per cent level, although the marginal effect of 6 percentage points (relative to having gearing less than 60 per cent) is only significant at the 10 per cent level. The imprecision of this estimate could partly reflect sample size issues, as only around 4 per cent of households in the estimation sample have negative housing equity. It could also suggest that equity factors by themselves are not particularly important in driving missed payments; double-trigger theories of default suggest that a household experiencing negative equity would also need to experience an ability-to-pay shock before missing a mortgage payment. However, the very small sample of households with both a high DSR and negative equity, or that are unemployed and have negative equity, makes it difficult to estimate precisely the effects of double-trigger type interactions.

3.3.3 Demographics and other life events

Some studies of mortgage default find that demographic variables, such as age and education, and other life events, such as divorce and illness, play a significant role in mortgage payment behaviour. However, inclusion of variables related to these factors resulted in statistically insignificant effects and their inclusion in the model had little effect on the estimated marginal effects of the other variables. This is also the case when including changes in some of these variables, such as marital status and the household head's self-assessed health. Consequently, we have chosen to exclude these variables from our preferred model. The insignificance of these variables, particularly marital status and health, may reflect the wording of the missed payments question; the question asks whether the household had missed a payment due to financial difficulties. It is plausible that respondents may differentiate between missing payments due to purely financial difficulties and due to other problems, such as relationship breakdown or illness.

Footnotes

Here, ‘households with owner-occupier mortgage debt’ are defined as those that had an owner-occupier mortgage at the time of the ‘current’ or ‘previous’ survey (e.g. in 2009 or 2010). The reason for using this definition is outlined in Appendix E. [12]

Households that responded ‘no’ to missing a payment in 2006 but did not appear to have a property loan in the 2005 or 2006 surveys are treated as non-responders. For more information about this issue, see Appendix E. [13]

The mortgage status variable also includes a category for households that only have a ‘second mortgage’ (e.g. a home equity loan), as these households are not always asked about their mortgage payment status. [14]

Data on household wealth are only available every four years in the HILDA Survey's wealth modules. Including variables in the model related to household wealth in 2010 results in statistically significant marginal effects with the expected signs, and has little effect on the estimated marginal effects of the other explanatory variables. However, because these variables are observed after a mortgage payment has been missed, we are unable to disentangle the causal relationship between wealth and missed payments. [15]