Tag Archives: housing statistics

Revisiting Issues of Affordability, Income and Inequality

This post updates and collects in one place my previous writings about how policy arguments around inequality which are based solely on income data (e.g. income percentiles) fundamentally misunderstand and misrepresent inequality.

The basic argument is that standard income deciles/percentiles are misleading because they create a picture of a continuous income distribution spectrum, rather than differential flows of income to certain parts of the economy. More specifically, they ignore fundamental differences in income arising from housing tenure/ownership, income and capital gains on wealth, and social transfers in kind.

In short, such analysis reflects a 1980s world – before housing costs ate household budgets and superannuation turned wage earners into stock holders.

Housing

Housing tenure matters because it creates differences in effective household income (i.e. actual purchasing power) and living standards. In an earlier post I compared the effective income of a renter and homeowner with identical annual salaries. The homeowner (without a mortgage) ended up nearly $30,000 a year better off than the renter on the same $100,000 p.a. income. This result was driven by:

  • differences in housing costs (imputing rental income to homeowners for the value of housing services received)
  • income from investing the cash that would otherwise have gone to rents, and
  • tax advantages that go with that investment.

This comparison did not take account of capital gains which could heighten the gap, and the renter/homeowner difference is probably worse now with rent prices going up faster than income (so proportionately higher imputed income for homeowners) and a booming housing market seeing higher capital gains.

This is all pretty obvious, and echoes why poverty studies tend to focus on “after-housing” income. However, it does suggest that plotting an income spectrum just based on cash incomes is very misleading when it comes to understanding difference in purchasing power and standards of living. At a minimum, we need to be basing analysis on the intersection of income and housing tenure.

Wealth

While housing is the primary form of wealth for most Australian households, the issues above are magnified when all forms of wealth are taken into account. Capital gains and tax advantages are increased, while wealth also creates additional ability to invest in money-saving technologies (e.g. energy efficient devices) which in turn increases future purchasing power without a change in income. And there are financial, health and psychological benefits of having savings/wealth to fall back on in emergencies.

But what is important here is that we can’t simply assume that income and wealth go hand-in-hand. The last ABS data (before the national statistician created an inequality data black-hole), shows that just under a third (32%) of low-income households also had low wealth, but 23% had moderate wealth (probably owning their own home), while 11% of low-income households had high wealth (See the graph below).

Column graph showing percent of households in lowest, middle and high income brackets that have low, moderate and high wealth.
Source: ABS, Household Income and Wealth

A concrete example of this wealth-income divergence emerges from the government’s data on age pensioners. The data for the September Quarter 2025 shows that 72% of pensioners own their own home, and around two-thirds of those homeowner pensioners have more than $100,000 in financial assets beyond their home. These pensioners have low-moderate incomes (otherwise they would not be eligible for the pension), but substantial enough capital to be protected against poverty and to have a better standard of living than many renters on higher incomes.

In short, low income does not necessarily mean low wealth or low purchasing power, and an income spectrum based solely on income figures misleads as to who is likely to be struggling.

Social Transfers in Kind

The final piece of the puzzle would be the inclusion of social transfers in kind – that is, the receipt of public services such as education, public health care, child care subsidies as well as a range of rebates and concessions. Many of these transfers go disproportionately to those on lower incomes, which then increases their effective consumption and standard of living. In turn, this decreases inequality – which was the finding of a leading Australian scholar in this field, Yuvisthi Naidoo, whose work I have summarised here.

However, the analysis is more complicated. A very useful recent briefing paper from the e61 Institute shows that while social transfers in kind are generally progressive (i.e. disproportionately benefit those on lowest incomes), there are significant differences between different transfers. The graph below from their report shows the distribution of transfers across both income and wealth quintiles. We can see, for instance, that pharmaceutical concessions are one of the more progressive transfers when plotted against income, with about two-thirds going to those in the lowest two income quintiles. However, those pharmaceutical benefits are far less progressive when plotted by wealth – in part because older people have more needs and eligibility, and are also likely to have accumulated more wealth (mostly in the form of home ownership).

Bar graph showing the percent of each of 15 different government transfers going to each income quintile. Social/public housing is the most progressive, while community health services and private health insurance rebate are the least progressive.
Source: e61 Micronote: Welfare for the Well Off?

It is worth tracking the comparison of progressivity in this graph for each transfer, and there is further discussion below on energy concessions, but the main point here is simply that inequality looks different when wealth and social transfers in kind are considered.

Why Does It Matter?

Overall, all this matters because it means that the level of inequality we see in standard income spectrums may be misleading, but also because actual households will be in different places on the income spectrum when extended incomes are taken into account. Naidoo’s research showed that nearly a half of all older people (65+) were in the lowest standard (money) income quintile, but the inclusion of imputed rent and social transfers in kind reduced that to 22.5% (because older Australians are disproportionately more likely to own homes and benefit from health services). By the time imputed wealth annuities were included in the analysis, only 17% of older people were in the lowest income quintile. At the other end of the spectrum, accounting for extended income meant that 26% of older households were in the highest quintile, up from 7.1% when based on standard income alone. (Naidoo, Appendix Tables C8-11) .

These issues have very direct implications for policy fairness. My attention was recently drawn to this in relation to energy affordability, where there is a legitimate concern to alleviate and avoid energy costs for low-income households. Obviously we don’t want households to go without power, or be bankrupted by power bills, but targeting energy assistance to those on low incomes may be poor targeting. Worse, a focus simply on income might mean imposing more network and other costs on those least able to pay.

Consider the pensioner households noted above. The nearly one-half of age pensioners who own their own home and have more than $100,000 in financial assets can easily afford solar power and energy saving technologies (if they have not got them already). They are far less likely to be facing energy hardship than renters on the age pension without access to the same technologies (who, incidentally, would be seen to have a higher income due to receipt of Commonwealth Rent Assistance). This is important because both pensioner households would receive the same energy concessions (at least in states like SA where the concession is a flat rate) because concession eligibility is based on income rather than ability to pay.

Further, those homeowner pensioners are also far less likely to be in energy hardship than renter families in waged poverty, yet the age pensioner will get an energy concession while those in waged poverty may not qualify. This is a different type of income fetishism (based on income type rather than quantum), but again we see income as an unreliable indicator of affordability and need for support.

More broadly, we can see in the e61 graph above that energy concessions are more progressive by income than by wealth. Nearly half of all concessions go to those in the lowest income quintiles, but only around a quarter go to those in lowest wealth quintile.

There are lots more intricate issues around who bears (and should bear) the necessary costs of the energy transition and how network costs are paid for (apportioned between customers). But what is clear is that a distributional analysis of energy costs based on a simple income spectrum would be misleading in terms of both ability to pay (income) and access to energy-saving technology (cost).

The Way Forward

Energy is just one area where there is a need for a far more sophisticated analysis of income inequality. We need an analysis of affordability for a range of essential expenditures that takes account of housing tenure, but also extended incomes and the real ability to pay for essential consumption.

Ultimately what I would like to see is, firstly, for the ABS to get themselves resourced and organised to do another Household Expenditure Survey (the last one was 2025-16!), and then to be able to analyse those expenditures based on an extended income spectrum combining wealth, housing and income. Only then will we really know which expenditures are genuinely regressive (have disproportionately highest impact on those with the least ability to pay) and where and how to target support.

In the meantime, caution and an analysis based on housing tenure is advised.

Silly Housing Statistics 3: The CPI – Official silliness, or just misunderstood and misused data?

In previous posts I have criticised housing statistics produced by industry and the NFP sector. In this post I want to question the official housing statistics in the Consumer Price Index (CPI), which it turns out are not particularly useful in tracking housing costs. This is true even though the CPI includes specific data on housing and rent and is widely used for indexing and updating less frequent housing data.

CPI and Home Owners

The first thing to note about the CPI housing data is that, while it includes an item for house purchase prices, the actual CPI item is “New dwelling purchase by owner-occupiers”. In any given period, relatively few people purchase a house, and those purchasing a new dwelling is an even smaller subset, so the CPI figure here may not reflect prices for many home owners. But more importantly, the CPI figure for new dwelling purchases does not include land prices. In that sense, it measures the increase in the cost of building a new dwelling – which may be very different from changes in the market price of housing (particularly where there is significant speculation-based inflation).

Further, while the CPI includes this (limited) house sale price measure, it does not include mortgage payments (as they are not prices). Yet mortgage payments impact far more on weekly household budgets than house sale prices, and it has been mortgage increases which have driven much of what we now (mistakenly) call a “cost of living crisis”.

The absence of mortgage payments in the CPI is not the fault of the ABS or a problem in the CPI itself. It is a problem in the way the data is sometimes used. The CPI is about measuring price changes experienced by households – a measure of price inflation, rather than the cost of living. The inflation measure is important as a tool of economic management because of the (theoretical) macro-economic relation between production, money, aggregate price levels and jobs. It is particular measure for a particular purpose. In that sense, the statistical silliness is not in the CPI, but in its misuse as a cost of living measure.

The ABS recognises this and produces (a week after each quarterly CPI data release) Selected Living Cost Indexes, which include mortgage payments and are a much better reflection of the impact of housing costs on households. However, these living cost indexes are mostly ignored by the media (presumably out of ignorance) and economists (because their focus is on models rather than real households). Further, the Living Cost Indexes are produced for different household types (based on income-source) and don’t give a single headline figure which can be used conveniently for indexation in the way that CPI is used.

The bottom line is that using the CPI data is not useful in tracking housing prices.

CPI and Rent Prices

And it turns out that that the CPI rent data is not much more use in relation to rent. Unlike the commercial “asking rent” data discussed in my earlier post, the CPI does at least cover increases in existing rents not just new tenancies. However, the CPI significantly underestimates the increases in the market price of rentals (and so is less than helpful for indexing changes to other rent data). There are several reasons for this.

The CPI rent category includes public and community housing rents, which are income rather than market based. In South Australia, this accounts for around one-in-five rentals, but if these (predominantly Centrelink) incomes go up by less than private rents – as is likely in a tight rental market – the smaller increases in public housing rents will lower the overall CPI for rent.

Further, for those in the private rental market the data, the CPI is adjusted for increases in Commonwealth Rent Assistance (because the CPI is designed to capture the price paid by the consumer not the price charged in the market). The rent increases recorded in the CPI for tenants receiving CRA are therefore less than the market price increases.

This CRA adjustment to CPI is even more important because in recent years there have been two significant “above indexation” increases in the CRA. These were welcome increases for those struggling to pay increasing rents, but given that more than half of all tenants receive CRA it means that the CPI rent data further underestimated the actual rent price increases in the market. According to the ABS, without the changes to CRA, rents would have increased by 7.8% over the 12 months to the December 2024 quarter – as opposed to the 6.4% recorded in the CPI.

This is particularly important because it means that it is problematic to use the CPI to update other intermittent rent data. The graph below shows the census data on median weekly rent for the Greater Adelaide area. The census is one of the few sources which captures all rents paid (rather than just new rental agreements), and if the CPI accurately reflected increases in rent prices, the line would be a smooth increase from one census to the next. However, the sharp increases at each census point show that the actual rents actually increased quicker than CPI for rent.

Line graph showing median rents in the census data from 2006, 2011, 2016 and 2021, updated in between by CPI.

Conclusion

The result of all of the above is that, if we want to focus on all rents – rather than just the prices in new tenancy agreements – then we would need three different index series: one for public housing, one for tenants getting CRA, and one for unassisted private renters. While the CPI provides a weighted average index of these, it does not accurately reflect what is happening for each particular group. It exaggerates increases in public housing rents (if market rents are going up faster than income), but underestimates rent increases in the market overall (as shown above).

However, this shortcoming does not legitimise or provide a reason to use the “asking rent” data or the bond data for new tenancies as surrogates for actual rents or for measuring rent increases. As the graph below shows, there is a big difference between the rent increases in those data sets and the CPI. I suspect the real average rent increase is somewhere between these lines .

Line graph showing the difference between increases in the CPI rent and in the SA government rental bond data for 2-bedroom units and 3-bedroom houses - a 40 point difference in less than four years.

This brings us back to where I started this series on silly housing statistics: there is actually no data that shows average rents in capital cities, and in this case, no way to unproblematically track or index rent increases. Yet we can’t just abandon the data, the housing crisis is too important. But we can try to understand and qualify the data to avoid using silly housing statistics.

Silly Housing Statistics 2: I am Priced Out of a renting a beachside house

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.

Table 2 from the Priced Out report showing national comparison of various weekly incomes, average weekly rents and percentage of income required for rent.

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.

[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!

Silly Housing Statistics: Is it more expensive to rent in Adelaide than in Melbourne?

This is the first of a series of articles on “silly housing statistics”, highlighting how bizarre claims are made with inaccurate or mis-used statistics. We begin with the claim made at the end of January that Adelaide rents had surpassed those in Melbourne for the first time. The story came from housing market analysts PropTrack and was covered in the Advertiser (see below) and elsewhere.

An example of a silly housing statistic - photo of the story from the Adelaide Advertiser, 31 January 2025, headed "Rent costs more here than in Melbourne"

Now I like a “killer stat” – a statistic which sums up an issue and grabs attention for a policy argument. I try to get such stats in most reports I write, but I also try to get the statistics right! So, is the claim about Adelaide and Melbourne rents right?

Turns out, the answer is complicated – and a bit of a primer on housing data.

A Silly Housing Statistic

The PropTrack data says that Adelaide’s median weekly rent in the December quarter of 2024 was $580, although this figure for the “asking” or advertised rent was considerably above the official government data on the median amounts actually agreed and paid – which was $550 per week. (The differences here are discussed further below).

Unfortunately, the official Victorian government data won’t come out until the middle of the year (which is why commercial data like PropTrack gets the headlines!). However, the PropTrack data suggests no change in the Melbourne median rent in the December Quarter – which if applied to the official data from the September Quarter would put the median rent at $560 p.w. – that is, above the Adelaide median.

We might also get a hint of the silliness of the “Adelaide more expensive than Melbourne” claim from another commercial data source. The SQM analysis for mid-quarter (12 November) showed Adelaide median rent at $609 per week, but Melbourne at $627 – both significantly higher than the PropTrack figures, but with Melbourne clearly higher than Adelaide.

But really, all these are silly statistics because the comparison is meaningless without also considering the mix of housing in the data. For instance, if rental houses are more expensive than units, then if one market has a greater proportion of houses than the other, its median rent price will be higher even if the comparable prices are lower. That is particularly relevant here because, at least in the September Quarter, only 40% of Melbourne rentals that quarter were houses by comparison with 55% of Adelaide rentals. That would make the overall median rental in Adelaide relatively higher, even if the rent for particular property types is lower.

Similarly, we would need to add location into the housing mix because if the majority of available rentals are in inner-city locations the median price would be higher than if the majority were in outer suburbs.

A further level of complexity here is provided by the government data for the September Quarter. It shows that Melbourne rental prices for units were significantly above Adelaide prices, but $25 a week lower for a 3-bedroom home. The housing mix clearly matters to the overall average – although it should be noted that the SQM data for September showed both Adelaide house and unit prices significantly below those in Melbourne.

A Silly Endeavour?

It is all a bit messy, and in that sense, the silliness of the Adelaide-Melbourne statistic is as much about making the simplistic comparison in the first place as it is about the conclusion of which jurisdiction is more expensive.

But even if you can sort through the comparison issues, there are also some pretty big questions about the usefulness of the base data.

The sad truth is that, despite a flood of housing data, there is actually no data that usefully gives average rents paid in each city, and no real basis from which we can say which city is more expensive – or more generally, what is happening “for renters”.

Navigating Rents, Rent Prices and Asking Rent Data

The first issue is that, all of the above data is in fact not comparing rents in Melbourne and Adelaide – it is comparing rent prices for new tenancies. But new tenancies are less than 10% of the total rental market (my calculation from SA bond data and 2021 census data). So it is simply a misrepresentation (and a silly housing statistic) to say, as in the Tiser headline, that these numbers refer to “rents”.

That said, there may be good reasons to focus on price of new tenancies. These prices represent the current market price faced by people looking to rent, and it is arguably a lead indicator which other rentals will follow. However, even with this focus, and even if what was being measured was accurately described (which it is usually not), there would still be problems.

There are two key data sources of data on rental prices for new tenancies: official state government data based on rental bonds paid, and the “asking rent” (i.e. advertised rent prices) data which is produced by a range of commercial firms (e.g. PropTrack, CoreLogic, SQM) that scrape real estate sites on the web to produce data and spruik their analytical tools.

As we saw above, there are data differences between the companies even on what the asking rent was, but the asking rents may also not be the price the property is rented at (which is what is captured in the official data). For instance, rent-bidding may mean actual prices go beyond the advertised price, but landlords may also wildly overestimate the rent they can get (the Tiser headline could also have read “Adelaide landlords more optimistic than Melbourne counterparts”!).

Further, web sites like realestate.com.au and the other commercial sources are dominated by advertisements from real estate agents, but real estate agents only manage about two-thirds of Adelaide’s private rental properties. The other third may not be advertised or captured by the web-scraping data, and rents tend to be below the real estate agent’s “market price” (see Census GCP4GADE – Table 40).

The difference between asking and actual rents is clear in the table below. It compares rental bond data for Adelaide metropolitan area for each quarter last year with the SQM asking price data for the week in the middle of each quarter. The SQM data (from their National Vacancy Rate reports) is used because it is well-respected and accessible, but as the table shows, the asking price data is 6% to 17% higher than the median rents actually paid by new tenants.

Data table comparing SQM Adelaide rents with rental bond data for units and houses for each quarter of 2024.

Silly Housing Statistics

So, in summary, the data behind the fairly counter-intuitive claim that Adelaide median rents have surpassed those in Melbourne refers to only a fraction of rents being paid across the market, and is inaccurate even in relation to new rentals because it only relates to advertised prices rather than the rent prices actually agreed and paid.

This makes the headline claim about Adelaide prices surpassing Melbourne a particularly silly statistic – and the media’s uncritical reporting of it even sillier.

However, our beloved Tiser is not alone here. A lot of media and even so-called expert commentary, either blindly or lazily, blurs the differences or makes leaps from asking price to rent prices to rents in general. The result is analysis which is inaccurate, over-generalised or at least questionable given the limitations of the data.

Beware the silly housing statistic.