Introduction

Between 2019 and 2023, the median price of an established house rose faster than median income in 429 of the 476 councils (90.1%) where both can be measured. This report uses official Australian Bureau of Statistics (ABS) data for Australia’s 547 local government areas (councils) to examine three things:

  1. how house prices compare with income across councils;
  2. whether prices and incomes have grown at the same pace;
  3. whether population growth is linked to either.

The five visualisations move from the national picture to individual councils. The final section lets you find your own council.

Data and methods

Source. All data come from the ABS Data by region, 2011-25 release, published 26 May 2026 (Australian Bureau of Statistics [ABS], 2026). The measures are council-level (Local Government Areas, 2021 boundaries) plus the state and Australia figures the ABS publishes.

Measure ABS item Unit Reference period
House price Median price of established house transfers (HOUSES_3) $ Years ended 30 June 2019-2023
House sales Number of established house transfers (HOUSES_2) count Years ended 30 June 2019, 2023
Income Median total income, excluding government pensions and allowances (INCOME_17; Australian Taxation Office data) $ per year Years ended 30 June 2019-2023
Population Estimated resident population (ERP_P_20) persons 30 June 2020-2023

Derived measures (calculated in scripts/08_build_report_data.R):

  • Price-to-income ratio = median house price ÷ median income, same year. How many years of median income a median house costs.
  • Growth, 2019 to 2023 = percentage change between the two years, for house price and for income. Gap = price growth minus income growth, in percentage points.
  • Population growth, 2020 to 2023 = percentage change in ERP. This release begins its ERP series in 2020.
  • Percentile (chart 5) = a council’s rank among all councils with a value, from 0 (lowest) to 100 (highest).

Data treatment.

  • Missing values. ABS shows “-” for values that are not available, not published, nil or rounded to zero. These are treated as missing, never as zero. 71 councils have no house-price growth for this reason. They are mostly very small councils (median population 923, against 17,662 for the rest), 65 of them in Western Australia, Queensland and the Northern Territory.
  • Low-sales councils. A median from a handful of sales is unstable. 53 councils with fewer than 20 house sales in 2019 or 2023 are shown with hollow symbols and excluded from the headline statistics; every headline result was checked with and without them.
  • No re-aggregation. State and national values are the ABS’s own published figures, never averages of councils.
  • Inflation. Dollar values are nominal (not adjusted for inflation). Ratios and comparisons of price growth with income growth are not affected, because inflation applies to both.

1. How many years of income does a house cost?

Each dot is a council; states are ordered by their 2023 ratio. Use the slider or Play to compare 2019 with 2023. Source: ABS (2026).

Text description of chart 1 Dot plot of the price-to-income ratio for each council, grouped by state. In 2023 the median council ratio was 9.0 years of income, up from 7.3 in 2019. Using ABS-published state figures, the 2023 ratio ranged from 8.3 (NT) to 16.0 (NSW); Australia’s was 12.5.

What it shows. In the median council, a house cost 9.0 years of median income in 2023, up from 7.3 in 2019. The ratio rose in 388 of the 423 councils with reliable sales data. At state level, the ABS-published figures put New South Wales highest (16.0 years). Western Australia (8.7) and the Northern Territory (8.3) are lowest.

Why it matters. This sets the baseline for the question: across most councils, houses became more expensive relative to local incomes, not just in dollars.

Caveat. Income here is personal income of people who lodge tax returns. It excludes most government payments, so the ratio is higher than ratios based on household income and should be read as indicative.

2. Did house prices keep pace with income?

Each point is a council with data in both years (n = 476); the dashed line marks equal growth. A few extreme councils lie outside the plotted range: use the zoom-out button (top right) to see them. Click a point, or a row in the table in section 5, to highlight a council. Source: ABS (2026).

Text description of chart 2 Scatterplot of the change in median income (horizontal) against the change in median house price (vertical), 2019 to 2023, for 476 councils. 429 councils (90.1%) lie above the equal-growth line, meaning house prices grew faster than income. Excluding councils with fewer than 20 sales, the share is 91.7% (388 of 423); median price growth was 46.5% against median income growth of 17.5%.

What it shows. Prices outpaced income in 90.1% of councils (429 of 476). The result is almost unchanged when low-sales councils are removed (91.7%, 388 of 423). In the median reliable council, house prices rose 46.5% while income rose 17.5%: a gap of about 28 percentage points over four years.

Why it matters. This is the most direct answer to whether housing “kept pace”: by these measures, in most councils it did not.

Caveat. The comparison is between areas. It does not show that the same households faced the gap, and it does not show that one change caused the other. Income growth also varied less across councils than price growth.

3. The pattern by state

Small multiples: each panel is a state (or Australia), using ABS-published state medians indexed to 2019 = 100. Panels are ordered by the size of the gap. Source: ABS (2026).

Text description of chart 3 Small multiples of house price and income indexed to 2019 for Australia and each state, 2019 to 2023. Australia: house price 33.4%, income 18.6%. Largest gap: Tas (house price 67.9%, income 18.8%). Smallest: NT (house price 16.8%, income 15.9%); WA (house price 20.0%, income 19.9%).

What it shows. Nationally, the median house price rose 33.4% against 18.6% for income. The gap was largest in Tas (67.9% vs 18.8%). In NT and WA, prices and income grew at almost the same rate. Income growth was similar everywhere (15.9% to 19.9%); the differences between states come mainly from house prices.

Why it matters. “Keeping pace” is not uniform. Whether housing outran income depends heavily on the state.

Caveat. National growth (33.4%) is lower than the median council’s (46.5%) because each council counts once in the council figures regardless of population, and many small councils recorded large rises.

4. Is population growth part of the story?

Councils with at least 20 house sales in 2019 and 2023 (n = 423), split into five equal groups (quintiles) by population growth. Source: ABS (2026).

Text description of chart 4 Dumbbell chart of median income growth and median house price growth for five groups of councils ranked by population growth. Median house price growth ranges from 41.7% to 50.0% and median income growth from 17.1% to 18.6% across the five groups, with no consistent direction. The median 2023 price-to-income ratio rises from 7.3 in the slowest-growing group to 11.5 in the fastest.

What it shows. Across the five population-growth groups, house prices grew much faster than income in every group: median price growth 41.7% to 50.0%, median income growth 17.1% to 18.6%. There is no consistent link between population growth and price growth (Spearman ρ = -0.01, n = 423) or the size of the gap (ρ = -0.01). Faster-growing councils did, however, start from higher levels: the 2023 price-to-income ratio rises from 7.3 to 11.5 across the groups (ρ = 0.25).

Why it matters. Over 2019-2023, population growth does not explain where prices outpaced income; the gap appears in slow- and fast-growing councils alike. Population growth is associated instead with how expensive housing already is relative to income.

Caveat. Correlations are between council-level figures and do not establish cause. The population window (2020-2023) is one year shorter than the price and income window.

5. Find your council

Every council appears once in each row. Search for a council in the table below and click its row, or click a point, to see where it ranks on all four measures; it is also highlighted in chart 2. Source: ABS (2026).

Text description of chart 5 Strip plot of every council’s percentile rank on four measures: house price growth, income growth, population growth and the 2023 price-to-income ratio. Selecting a council in the table below, or clicking a point, highlights it in all four rows and in chart 2. The table gives every council’s values as text.

What it shows. This view hands control to the reader. It places any council on all four measures used in this report, so a reader can see whether their area follows the national pattern.

Summary of findings

  • Prices outpaced income almost everywhere. In 90.1% of councils with data, house prices grew faster than income between 2019 and 2023 (91.7% excluding low-sales councils). The median gap was about 28 percentage points.
  • Houses became less affordable relative to income. The median council’s price-to-income ratio rose from 7.3 to 9.0 years.
  • The size of the gap depends on the state. Nationally the gap was 14.8 points. It was largest in Tas and close to zero in NT and WA.
  • Population growth did not explain the gap. Councils grew in house prices well beyond income whether their population grew quickly or slowly. Faster-growing councils already had higher price-to-income ratios.
  • Prices and incomes go together, but loosely. Councils with higher incomes tend to have higher house prices (Spearman ρ = 0.33, n = 490, 2023), but the association is weak.

Conclusion

This analysis highlights a widening gap between housing costs and local incomes across Australia. Between 2019 and 2023, median house prices grew by 46.5%, compared with 17.5% growth in median income across councils with reliable sales data. The price-to-income ratio increased in most of these councils, suggesting that purchasing an established house became less affordable relative to local earnings.

However, the pattern varies across Australia, and population growth alone does not explain differences in house-price growth. These findings suggest that local housing affordability is shaped by multiple factors, requiring more than population trends alone to understand the pressures facing communities.

Although the analysis has limitations, including differences in sales volumes and income measurement, the overall message is clear: housing prices are outpacing income growth in most councils examined. Understanding where this gap is greatest is an important step towards identifying local affordability challenges and informing more targeted housing policy.

Limitations

  • Areas, not people. All figures describe councils. Patterns between councils do not show that the same households experienced them (ecological fallacy), and no result establishes cause.
  • Income measure. ATO personal income excludes most government pensions and allowances and people who do not lodge a tax return, so it under-represents lower-income residents. It is personal, not household, income.
  • House prices. Medians of established house sales reflect the homes that sold, not all homes. Councils with few sales are volatile; this is mitigated by the 20-sale threshold and hollow symbols.
  • Time period. 2019-2023 includes the COVID-19 period. Results may differ over other periods. Population data start in 2020.
  • Missing data. 71 councils, mostly very small ones in WA, Queensland and the NT, lack house-price growth, so they are under-represented.
  • Nominal values. Dollar amounts are not adjusted for inflation; comparisons of growth rates and ratios are unaffected.
  • Revisions. The ABS updates this release periodically. A Data by region update was scheduled for 10 November 2026, and figures may be revised.

About the interactivity

This report is a standalone HTML page with no server behind it. It keeps:

  • hover details on every point;
  • zoom, pan and legend toggles;
  • a year slider (chart 1);
  • linked council search and highlighting across chart 2, chart 5 and the table.

The companion Shiny app in the project folder offers features that need a running server, and so are not in this report:

  • choosing any of 18 indicators and years;
  • the log-scale toggle;
  • a recomputed council profile across all indicators;
  • the CSV download.

References

Australian Bureau of Statistics. (2025). Data by region methodology, 2011-25. https://www.abs.gov.au/methodologies/data-region-methodology/2011-25

Australian Bureau of Statistics. (2026). Data by region, 2011-25 [Data set]. https://www.abs.gov.au/methodologies/data-region-methodology/2011-25

Cheng, J., Sievert, C., Schloerke, B., Chang, W., Xie, Y., & Allen, J. (2023). htmltools: Tools for HTML (Version 0.5.9) [Computer software]. https://CRAN.R-project.org/package=htmltools

Cheng, J., & Sievert, C. (2023). crosstalk: Inter-widget interactivity for HTML widgets (Version 1.2.2) [Computer software]. https://CRAN.R-project.org/package=crosstalk

R Core Team. (2024). R: A language and environment for statistical computing (Version 4.6.1) [Computer software]. R Foundation for Statistical Computing. https://www.R-project.org/

Sievert, C. (2020). Interactive web-based data visualization with R, plotly, and shiny. Chapman and Hall/CRC. https://plotly-r.com

Wickham, H. (2016). ggplot2: Elegant graphics for data analysis. Springer-Verlag New York. https://ggplot2.tidyverse.org

Xie, Y., Cheng, J., & Tan, X. (2023). DT: A wrapper of the JavaScript library ‘DataTables’ (Version 0.34.0) [Computer software]. https://CRAN.R-project.org/package=DT

Acknowledgements

ABS material is used under a Creative Commons Attribution 4.0 International licence (© Commonwealth of Australia 2026). Some series in the release are compiled by the ABS from Australian Taxation Office personal income data and Cotality property sales data.

Generative AI.

This assignment was completed independently by the author. Generative AI tools were used only as a supporting resource to clarify concepts, understand errors encountered during the development process, troubleshoot technical issues, and provide guidance when needed. The analysis, implementation, interpretation of results, and final presentation of the work were carried out by the author. The author reviewed and verified the final submission and takes responsibility for its accuracy and integrity.