Australia’s runaway rents
Entry type: Single project
Publishing organisation: ABC News
Organisation size: Big
Publication date: 2022-09-20
Authors: Inga Ting, Katia Shatoba, and Alex Palmer
Inga Ting is a data journalist, Katia Shatoba is a developer and Alex Palmer is a designer. All three are in ABC News’ Digital Story Innovations team.
This project used two decades of data from property portal Domain to delve into Australia’s worst rental crisis in at least a generation. It showed that advertised house rents hit record highs in 2022 in 85 per cent of suburbs with reliable data — a figure that is itself unprecedented in 20 years of data.
The figures, supplied exclusively to the ABC, track median asking rents in nearly 4,000 Australian suburbs, roughly one-third outside capital cities, from 2002 to 2022. It is the first time rental price data has been published at this level of detail, for this many neighbourhoods.
This project put Australia’s rental crisis into historic perspective, combining exlusive data with expert analysis and gripping case studies to explain how the pandemic triggered the worst rental crisis in recent memory.
It injected much needed facts and figures into a highly emotional public debate, and helped shape public policy debates about the kind of interventions that would have the greatest (and fastest) benefit.
The story contributed to public debate around tenant rights, housing tax policy, investment in affordable housing and the effectiveness of rental assistance in Australia.
Our audience metrics show it achieved the highest average engagement time of any story in our team’s history.
From the outset, our aim was to tell a data-driven story about the human impact of Australia’s rental crisis. While many stories had been written (and continue to be written) about Australia’s ongoing rental crisis, this project stands apart for the seamless way it combines powerful case studies with a dataset of unrivalled breadth and depth.
A key difference was the project’s focus on smaller, regional areas, as well as major cities. Many stories about rental prices tend to aggregate the data into larger areas to simplify both the analysis and the storytelling. However, we felt that the data’s granularity revealed a richer, more nuanced, and more accurate picture of the crisis.
For example, the divide not only between city and country, but also between smaller regions and towns (eg. commuting towns and so-called lifestyle areas) — was among the critical themes uncovered by the data. This kept this “front and centre” of not only our reporting and but also our visualisations, which honed in on smaller towns and regions often left out of national-level analyses.
We also built two interactive databases allowing users to explore “hyper-local” rental price data stretching back as far as records were available.
Tools and technologies used included:
– Excel, Google Sheets, Tableau Prep, Tableau and Datawrapper for data cleaning, data blending, data analysis, mapping and charts
– Figma and Adobe Illustrator for UI + dataviz design
– Adobe Photoshop for the header illustrations
– HTML, CSS + JS and Illustrator for the comparative cap city maps
– HTML, CSS, JS with D3 for interactive line charts.
– HTML, CSS, JS with Deck.gl for interactive maps.
– HTML, CSS, JS with Deck.gl and custom Vector tiles for Australian geographical areas for interactive map.
Context about the project:
One unexpected challenge of working with such a long historical period was that neighbourhood names and boundaries had changed sometimes multiple times over the span of the dataset. This meant that the rental price data for a single suburb could be separated across multiple suburb names. Each of these names might match to a different set of geographic boundaries, even though locals of that area would recognise all these names as referring to the same place, even if the boundaries of that place had changed slightly over time).
On top of this, the regions used in Domain’s proprietary dataset did not match the regions used in the Australian Statistical Geography Standard, the mapping standard for geographic data.
To overcome this data quirk (read: headache), we had to match the rental price data to multiple geographic datasets, and then consolidate each of these datasets into a single “master” dataset. This required weeks of extra work, none of which is apparent from the story itself.
What can other journalists learn from this project?
This project demonstrates how data can be used to put current events into historical perspective. It also shows the truth behind the idea that “behind every data point is a person”. Rather than trying to simplify this large, detailed dataset, we saw its granularity as its key attribute, and allowed the data to guide us in telling a compelling, nuanced and people-centred story.