Story overview

Australia has long been described as “the lucky country”, but housing affordability pressure is not shared equally. This visual story uses open data to show how housing costs affect renters, lower-income households, age groups and states differently.

Chart 1: Lower-income renters are carrying the heaviest burden

This chart shows the proportion of lower-income renter households paying more than 30% of their income on housing costs. A higher percentage means more households are under rental affordability stress.

Key message: Lower-income renters experienced persistent housing stress across the period. NSW stayed above the national average, showing that the housing squeeze is not only a national issue but also a state-level affordability problem.

Chart 2: Housing stress differs by state and territory

The housing squeeze does not look the same across Australia. This chart compares the most recent available rental stress levels for lower-income renter households across states and territories.

Key message: In 2019–20, rental stress remained high across Australia, but the burden differed between states and territories. This shows that affordability pressure is shaped by location, not only by national housing trends.

Chart 3: Younger households are far more likely to rent

Age changes the housing story. This chart compares renting and ownership patterns across age groups, showing how younger households are much more exposed to the rental market.

Key message: Housing pressure is strongly linked to age. Younger households are much more likely to rent, while older households are more likely to own their homes without a mortgage. This means younger Australians are more exposed to rental market pressure.

Chart 4: Some households spend a much larger share of income on housing

Housing affordability is not only about whether someone rents or owns. It also depends on household structure. This chart compares the median share of gross household income spent on housing costs across selected household types.

Key message: Housing costs take a larger share of income for renters and households with children, especially one-parent families. This shows that the housing squeeze is shaped by both tenure and household structure, not just national averages.

Chart 5: Lower-income renters face the sharpest affordability pressure

The final chart brings the story back to inequality. Housing stress matters most when it affects households with fewer financial resources.This chart compares available state and territory data for lower-income renter households paying more than 30% of their income on housing costs.

Key message: Lower-income renters face very different levels of affordability pressure depending on where they live. This reinforces the central story: Australia’s housing squeeze is both an income problem and a geographic problem.

Data notes

This visual story uses open data from the Australian Bureau of Statistics Housing Occupancy and Costs collection. The focus is on how housing affordability pressure is experienced differently across renters, lower-income households, age groups, household types and states.

Rental stress is understood as households spending more than 30% of their income on housing costs. This threshold is useful because it gives a clearer picture of when housing costs may start to place pressure on everyday living. Some charts use the most recent available data from the selected ABS tables, while the first chart uses time-series data from 2007–08 to 2019–20.

All visualisations were created in R using ggplot2 and made interactive with plotly. The interactive hover labels allow readers to check exact values without making the charts too crowded. Colours were selected carefully to avoid red and green combinations and support readability.

References

Australian Bureau of Statistics. (2022). Housing occupancy and costs, Australia, 2019–20. Australian Bureau of Statistics. https://www.abs.gov.au/statistics/people/housing/housing-occupancy-and-costs/latest-release

Acknowledgement

I acknowledge the use of ChatGPT to help me understand the assignment requirements, plan the visual story structure, troubleshoot R coding errors, and refine some written explanations. I reviewed the outputs, selected the final datasets and charts, checked the visualisations, and completed the final submission myself.