From Boomers to Zoomers: Housing in Crisis

A generational lens on Australia’s housing crisis: homeownership, wages, and social impact.

Housing affordability is a growing concern in Australia.
Is the dream of homeownership slipping away for younger generations?
How do house prices compare to wage growth?
What are the broader social impacts? (e.g. homelessness, crime)
As a teacher myself at RMIT, many of my students have told me they are struggling with homelessness and rent amidst this cost of living crisis
Let’s take a look at some interactive graphs that could explain the struggle and implications.

The annual median household income data comes from the Australian Bureau of Statistics (ABS) and is based on census surveys.
Data is collected every 2 years, and reports often take an additional 1–2 years to be published.
Household income represents equivalised disposable household income - adjusted for household size and composition to make incomes comparable.
The figures shown have been "adjusted using changes in the CPI." -ABS. Hence it shows Real Income (Purchasing Power) growth.

In Australia, housing affordability was an issue since the early 2000s but has only gotten worse.
House prices have grown ~190% since 2000, but household income only ~48.5%.
Housing affordability has drastically increased to extreme inaffordability.
Younger generations face significant challenges entering the housing market.
Leading to increased homelessness especially amongst the younger generation.

Plotly and revealjs prove extremely hard to work together with many issues including auto scaling, hover interactivity issues, etc.
ABS data is very messy and 40% of the time was spent trying to unpack/understand, extract, transforming the data to actually be used and plotted.
ABS lacked A LOT of data; median age of first home buyers for one (relatively simple data but ABS/Australia does not have this raw data). Unpublished recent data (most recent data seemed to be 2021 at most); missing/inconsistent gaps in data, etc.
Positcloud would literally shut me out every couple mins of working or so and give me an “500 internal server error” requiring me to wait 15-30 mins or so. Eventually locked me out telling me I’ve used 112% of its computing resources.
At times where it does shut me out with the error, I have to restart some part of my work.
All in all a not so fun 31 hours spent but I did learned a lot.

Australian Bureau of Statistics. (2019-20). Household Income and Wealth, Australia. ABS. Link.
Australian Bureau of Statistics. (2021, December). Residential Property Price Indexes: Eight Capital Cities. ABS. Link.
Australian Bureau of Statistics. (2021). Estimating Homelessness: Census. ABS. Link.
Plotly stacked bar chart error fix: Link.
Object not found error fix: Link.
Plotly and revealjs interactivity fix: Link.