Australia is often called “the lucky country”, but housing costs make that idea feel less realistic for many households. This visual story uses open data from the Australian Bureau of Statistics to show that housing pressure is not experienced equally. It looks at renters, lower-income households, age groups, household types and states to show where the pressure is strongest.
This chart shows the share of lower-income renter households spending more than 30% of their income on housing costs. A higher value means more renters are experiencing housing stress.
Key message: Lower-income renters experienced ongoing housing stress across the period. NSW remained above the national average, which shows that affordability pressure is both a national and state-level issue.
Housing stress is not evenly spread across Australia. This chart compares the latest available rental stress levels for lower-income renter households across states and territories.
Key message: Rental stress remained high in 2019–20, but the level of pressure differed by location. This shows that housing affordability is shaped by state-level conditions as well as national trends.
Age plays an important role in the housing story. This chart compares renting and ownership patterns across age groups, showing that younger households are more exposed to the rental market.
Key message: Younger households are much more likely to rent, while older households are more likely to own without a mortgage. This means younger Australians are more exposed to rental market pressure.
The final chart brings the story back to inequality. Housing stress has the greatest impact 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 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.
This visual story uses open data from the Australian Bureau of Statistics Housing Occupancy and Costs collection. I focused on variables that show housing pressure from different angles, including rental stress, housing costs as a share of income, age, household type and state or territory.
In this story, rental stress means households spending more than 30% of their income on housing costs. I used this measure because it gives a clear way to understand when housing costs may start affecting everyday financial comfort. Some charts use the most recent available ABS values, while the first chart uses time-series data from 2007–08 to 2019–20.
The charts were made in R using ggplot2 and then
converted into interactive visuals using plotly. The hover
labels allow readers to check exact values without crowding the charts.
I also avoided red and green colour combinations to make the visuals
easier to read.
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
I acknowledge that I used ChatGPT as a support tool while completing this assignment. It helped me understand the task requirements, plan the visual story, troubleshoot some R errors, and improve the clarity of selected written sections. I made the final decisions about the topic, datasets, chart types, visual design, interpretation, and submission. I also reviewed the final visualisations myself before publishing the work. ````