In my last post I suggested that the claim that Melbourne rents were cheaper than Adelaide was a silly statistic – basically not true, and obviously so. That was the first of a series of posts on silly housing statistics and highlighted a problem with commercially-produced housing statistics. This week’s silly housing statistic comes from my world of not-for-profit advocacy for better housing.

A leading body in this NFP advocacy is the Everybody’s Home campaign which is a coalition claiming over 500 organisations, businesses and councils, and more than 43,000 individuals across Australia, aiming to tackle the systemic drivers of housing insecurity and inequality.

Last year they produced their Priced Out report which included a common, silly statistic used by our sector. I pick this report as an exemplar because of the organisation’s prominence and the importance of the recommendations in the report (which I support), but also because the methodology is repeated across other reports.

The Priced Out Statistics

The report uses SQM Research’s data on asking rents as its key statistics.[1] As I argued in my previous post, these are probably an inflated starting point for rent, and at times the Priced Out report slips into the language of representing these asking rents as all rents (rather than just new tenancies). But this is not the really silly statistic in the report. That comes in the key table (excerpted below) which compares the income of various low-income households with the SQM weekly rental data, and then calculates the percentage of income that would be required to pay the rent on a housing unit. All the results are well over the usual housing stress measure of rent above 30% of income.

These results would be concerning, but for the fact they are silly housing statistics. They are an asymmetric comparison of a very low income with a median rental price. It is an apples-to-oranges comparison resulting in the grand claim that people on low-income households can’t afford houses that are affordable for someone on twice their income.

Think about it: I can’t afford a beachside house, but I could if I had twice the income. That does not mean that I am at risk of homelessness or even in housing stress. It simply means that I need to have a house that is affordable on my income. The comparator to a market average does not give me data on that. Arguably, the relevant comparator for someone in the bottom 20% of the income spectrum is whether they can afford the cheapest 20% of houses, not the median market price.

It would be theoretically possible for all people in the lowest income bands to afford housing if the rental market spread reflected the income spread. Obviously it doesn’t, which is why the work of Everybody’s Home is so important – I just wish they wouldn’t use silly housing statistics to make otherwise good arguments.

Similar but better options

In my SACOSS role I have done similar income-rent comparisons, but the comparison is to median rent in the cheapest suburbs. In the September Quarter of 2024 the median rent for a 2-bedroom unit in the cheapest suburbs in Adelaide was $60 p.w. below the figure across the city as a whole. By my calculation, an Age Pensioner would require 60% of their income to rent a 2-bedroom unit in the cheaper suburbs of Adelaide, while the Everybody’s Home report puts the Adelaide figure at 74%. Similarly, the Priced Out estimate of 101% of a single JobSeeker’s income for a housing unit is reduced to 82% using the SACOSS methodology – still alarming, and grossly unaffordable, but a more accurate and defensible statistic.

However, the SACOSS methodology is arbitrary (defining the cheapest suburbs as the bottom half) and clunky (in that it is a median of suburb medians rather than a true median of cheapest suburbs).

A far better alternative is provided by the annual Anglicare Rental Affordability Snapshot. Its methodology is simple (but no doubt resource-intensive for them), but for me, it is probably the best of the NFP housing reports. It simply surveys all the rental properties listed online on a given weekend and compares them to the established income-types (Minimum wage, JobSeeker, Pension etc) to see how many are affordable (using the standard 30% of income measure). There remains the issue (discussed in my previous post) of non-advertised rentals, but there is no comparison in the Snapshot to a median price or to properties which would never be expected to be in the price range. It is a scrape to see if any rentals are affordable. Sadly, the answer is usually that there are none or next to none.

This is not a silly housing statistic – it is a damning one! And again, it shows why we need well researched advocacy for changes to the housing system.

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[1]              The SQM methodology is more complicated than a simple data scrape, but still relies largely on real estate agent data (the limitations of which are in my last post). Curiously, they also claim their results closely align with ABS CPI rental data – which in the next post I will argue is a government-produced silly housing statistic!