Affordability squeeze greatest for bottom 25%

CoreLogic’s Cameron Kusher has written an interesting article showing that the most expensive 25% of the market has experienced the strongest price growth over the past year, although over the long-run lower priced segments have risen the strongest in value [my emphasis]:

Over the 12 months to August 2017, the most affordable 25% of residential properties nationally have recorded value growth of 2.9% compared to growth of 8.0% across the middle 50% of suburbs and 11.4% growth across the 25% of most expensive suburbs…

Over the period shown, August 1987 to August 2017, the thirty year period has seen values across the most affordable 25% of properties rise 1,517% compared to increases of 580% across the middle 50% of suburbs and 432% across the most expensive 25% of suburbs.  This highlights how affordability has deteriorated substantially at the more affordable end of the housing market.

The second chart shows the change in values across the three broad segments throughout the combined capital cities.  Over the 12 months to August 2017, the most affordable 25% of properties have recorded growth of 4.6% compared to 9.3% growth across the middle 50% of properties and a 12.2% change across the most expensive 25% of the market.  Like the national figures, in the event of a housing market downturn it has been the more expensive housing which has tended to see the greater value falls…

The final chart shows the annual change in dwelling values across the three market segments throughout the capital cities.  Melbourne and Darwin are the only capital cities that have recorded the greatest change in values over the year across the most affordable suburbs and the slowest growth across the most expensive suburbs.

Full report here.


  1. This has been one of my many arguments against anti-sprawl planning and its perverse consequences, for years. It is grossly inequitable in every way. The median multiple as a measure of affordability, fails to identify the disparate impact.

    When the median multiple is a historically normal “3”, there tends to be a match-up between households at all income levels, and available housing stock. If you were to calculate a “bottom quintile multiple”, this would also be “3”.

    But when the median multiple has inflated to “6”, the bottom quintile multiple is likely to be 9+. When the median multiple has inflated to “9”, the bottom quintile multiple is likely to be 16+.

    This is because all the inflation is in the price of the land. Fringe land inflates in price so that a family-home section is $300,000 instead of $60,000. Then the small piece of land underneath a townhouse (in a redevelopment) in an inner suburb becomes $500,000 instead of $100,000. And the small piece of land under a dilapidated cottage built in the tramcar era, close to the central city, becomes $1,000,000 instead of $200,000.

    When you look at RE sites for US cities that still have a median multiple of 3, you will find tramcar-era cottages near the CBD for $150,000; new townhouses for $250,000 and depreciated townhouses for $150,000; depreciated family houses in lower socio-economic suburbs for $150,000; and “mobile” prefabricated cottages on serviced small sections, for $50,000. I am not kidding. You can get independently housed even on $20,000 per year if you cut your cloth according to your income. But anywhere there is a growth boundary or a proxy for it, you won’t find anything at all below $300,000 no matter how cramped or dilapidated or “lower socio-economic”. And many of the perfectly valid options in efficient locations – townhouses and fixer-upper old houses – for first home buyers – are now prohibitively expensive. Ironically this forces oncoming generations into less efficient, longer-commuting locations where the land is at least “less unaffordable”.

    • To my knowledge, there is one academic paper that identifies this effect in the UK:

      Matthew Keep (2012) “Regional house prices: affordability and income ratios”

      I base my comments above on my own observations. This should not be rocket science, everything about planning and house price inflation can be grasped with some basic common sense, and the ability to observe evidence.

      • Rather than either urban sprawl or increased densification, it would be far better to limit immigration. At least this issue is now being mentioned in the mainstream even though politicians are remaining silent and will do so for a while to come.

  2. Interesting figures. I am guessing that the big increase at the lower end from 1987 to 2007 is due to gentrification of terraced houses in inner city suburbs like Chippendale and Redfern, while the slower increase from 2012 to 2017 is due to a large number of new apartments being constructed in places where people don’t actually want to live.

    • Good observation. While containment policies for urban sprawl are intended to create efficiencies, the perverse consequences because of land value inflation, always worst near the city centre, are that development and population distribution ends up drifting to IN-efficient locations because they are the “least unaffordable”. Alain Bertaud has been producing graphs of “spatial distribution of population” for cities, since the late 1990’s and it is identifiable that cities with inflated land values because of growth-boundary type of planning, have unnaturally densely populated areas in inefficient locations, like near the urban fringe. Free-sprawling cities with competitive land values tend to have a population distribution that looks like Mt Fuji on a graph – spike in the centre, and gentle slope away from that. This is actually close to the classical economics “land rent curve”. Where the land rent curve is distorted upwards so it looks like a “Mesa” (with the growth boundary where the cliff-faces are) covered in mini-peaks, the population distribution tends to follow.

    • Also the cheapest detached houses are all built on the fringe, where new supply pops up after they are built and combined with the fact that not many people want to live there this slows the rate of their price rises. The people able to afford the cheapest homes will also see the least price growth.

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