related financial difficulties : evidence from australian micro - level data ’, rba research discussion paper no 2014 – 13. 10 some analysis we have conducted on the representativeness of the securitisation dataset suggests that it has fewer high lvr loans than the broader population of loans. there is also a tendency to include loans in securitisation pools only after they have aged somewhat ( i. e. become more ‘ seasoned ’ ). 11 the share of new investor loans with very high lvrs ( above 90 per cent ) at the time of origination has been declining for a few years and is below that for owner - occupier loans ( reserve bank of australia ( 2017 ), financial stability review, april ). this feature is not apparent in the data i ’ ve shown here, which is based on the current lvr for the stock of outstanding securitised loans, including those that are well advanced in age. 12 apra have instructed lenders to implement stricter underwriting standards for interest - only loans with lvrs greater than 80 per cent ( see : ). 13 the 90 + days arrears rate refers to the share of loans that have been behind the required payment schedule or missed payments for 90 days or more but not yet foreclosed. 14 these regions are defined in terms of the abs ’ s statistical areas level 4 ( sa4s ), which are geographic boundaries defined for the labour force survey. the boundaries for most sa4s cover at least 100 000 persons. the securitisation dataset identifies loans according to the location of the mortgaged property. 13 / 13 bis central bankers'speeches

about one - quarter of the total value of home loans outstanding in australia. nature of the data let me make a few brief remarks about the nature of the data. for each housing loan, we collect ( de - identified ) data on around 100 fields including : loan characteristics, such as balances, interest rates, loan type ( e. g. principal - and - interest ( p & i ), interest - only ), loan purpose ( e. g. owner - occupier, investor ) and arrears status ; borrower characteristics, such as income and the type of employment ( e. g. pay as you go ( payg ), self - employed ) ; details on the collateral underpinning the mortgage, such as the type of property ( e. g. house or apartment ), its location ( postcode ) and its valuation. 4 1 / 13 bis central bankers'speeches the dataset is updated each month with a lag of just one month. the frequency and timeliness of the data allow us to observe changes in interest rates, progress on repayments ( i. e. the current loan balance ) and the extent of any redraw or offset balances ( just to name a few ) without much delay. i should note that, while the dataset covers a significant share of the market for housing loans, it may not be entirely representative across all its dimensions. in particular, the choice of assets in the collateral pool may be influenced by the way that credit ratings agencies assign ratings and by investor preferences. also, in practice it may take quite a while until new loans enter a securitised pool. i ’ ll mention one important example of this later. now let ’ s look at some interesting things we have learnt from this dataset. 1. interest rates in the years prior to 2015, banks would generally advertise only one standard variable reference rate for housing loans. 5 there was no distinction, at least in advertised rates, between investors and owner - occupiers, or between principal - and - interest and interest - only loans. that changed when the banks responded to requirements by the australian prudential regulation authority ( apra ) to tighten lending standards, with a particular focus on investor loans. then, earlier this year, apra and the australian securities and investments commission ( asic ) further tightened lending standards : this time the focus was on interest - only lending. a key concern has been that interest -