RDP 2020-03: The Determinants of Mortgage Defaults in Australia – Evidence for the Double-trigger Hypothesis 3. Data Description

3.1 Securitisation Dataset

The Reserve Bank of Australia (RBA) accepts residential mortgage-backed securities (RMBS) as collateral in its domestic market operations. Since June 2015, collateral eligibility has required detailed information about the security and its underlying assets to be provided to the RBA. These data, submitted on a monthly basis, form the Securitisation Dataset and as at June 2019 contained details on approximately 1.7 million residential mortgages with a total value of around $400 billion. This represents roughly one-quarter of the total value of housing loans in Australia and includes mortgages from most lenders. Around 120 data fields are collected for each loan, including loan characteristics, borrower characteristics and details on the property underlying the mortgage. Such granular and timely data are not readily available from other sources.

The loans are not, however, representative of the entire mortgage market across all of its dimensions (see Fernandes and Jones (2018) for more details). This partly reflects the securitisation process. For example, there can be lags between loan origination and loan securitisation; we typically cannot observe the first months of a loan's lifetime and recent loans are under-represented in the dataset. Issuers of securitisations may also face incentives to disproportionately select certain types of loans, such as through the credit rating agencies' ratings criteria. For example, the Securitisation Dataset contains a lower share of loans with original loan-to-valuation ratios (LVRs) above 80 per cent than the broader mortgage market, as well as a lower share of fixed-rate mortgages (Fernandes and Jones 2018). Issuers of some open pool self-securitisations also remove loans that enter arrears from the pool; to avoid selection effects, I remove deals that exhibit this behaviour from my analysis.^[2] While it appears unlikely that these differences would have a large effect on the model coefficients, aggregate arrears rates may differ to that of the broader mortgage market due to these compositional differences.

I use observations for 2.8 million individual loans that were reported in the Securitisation Dataset at any point between July 2015 and June 2019. Around 45,000 of these loans entered 90+ day arrears at some point during this period (around 1.5 per cent of loans) and around 3,000 loans proceeded to foreclosure. Further details on the construction of the samples used for the models are provided in Section 5. Summary statistics and variable definitions are provided in Appendix A.

3.2 Indexed Loan-to-valuation Ratios

I calculate indexed LVRs to estimate the equity position of mortgages, as per Equation (1).^[3] To capture changes in housing prices, I use regional housing price indices to update property valuations. This approach is standard within the literature, but does introduce some measurement error – it cannot account for changes to the quality of the property and may not be precise enough to account for highly localised changes in prices. It also does not account for borrowers' price expectations.

Hedonic regional housing price indices are sourced from CoreLogic. These data are available for Statistical Area Level 3 (SA3) regions (there are around 350 SA3 regions in Australia, each comprising between 20,000 and 130,000 residents). As at June 2019, housing prices had declined from their peaks in most regions (by around 8 per cent on average), but had fallen by as much as 70 per cent in some mining-exposed regions (Figure 1).

A loan is defined as having negative equity if its indexed LVR is above 100 (i.e. the estimated value of the property has fallen below the amount owing on the mortgage). The incidence of negative equity has been fairly rare in Australia, at around 4 per cent of the loans in the dataset in 2019.^[4] These loans were mostly located in the mining-exposed regions of Western Australia, Queensland and the Northern Territory, and many were originated between 2012 and 2016 (Figure 2; see RBA (2019) for further details). Many of these loans were located in metropolitan Perth and Darwin. Note that I classify SA3 regions as mining-exposed if they contain at least two coal, copper or iron ore mines or if at least 3 per cent of the labour force is employed in the mining industry.

The extent of negative equity has also been greater in mining-exposed regions, particularly in non-metropolitan regions (Figure 3). Since the risk of foreclosure may increase nonlinearly with the extent of negative equity, regional mining areas play an important role in identifying the relationship between negative equity and default risk.

3.3 Census Data

Regional economic data are sourced from the ABS Census. Key among these is the regional unemployment rate. I use a version of the unemployment rate that adjusts for internal migration; it records the unemployment rate of working-age individuals in 2016, based on the SA3 region in which they lived at the previous census in 2011. Adjusting for internal migration is important in the context of the winding down of the mining investment boom, as many unemployed workers had migrated from mining regions to other areas in search of employment, particularly to capital cities. Unadjusted regional unemployment rates are a poor proxy for the true probability that home owners from mining-exposed areas experienced unemployment.^[5]

Footnotes

Self-securitisations are held entirely by the originating banks for use as collateral in the RBA's market operations. Many of these deals have ‘open’, or ‘revolving’, pools; that is, loans can be added or removed from the pool. [2]

The scheduled loan balance differs from the current loan balance by abstracting from any additional repayments previously made, including those in redraw and offset accounts, which a borrower would be able to draw upon prior to defaulting. The calculation does not take into account additional debts, such as credit card debts or debts with other lenders. [3]

This figure is higher than estimates in RBA (2019) due to the use of scheduled balances in the LVR calculation. Estimates from the Securitisation Dataset may understate the incidence of negative equity due to the skew towards loans with lower LVRs at origination, or overstate it due to the prevalence of newer loans in the dataset. [4]

Using the unadjusted unemployment rate in my model produced smaller coefficients that were generally not statistically significant.[5]