Where Innovation Workers Live,
Prices Rise
Suburbs with high concentrations of IT, media, science and technical services workers see stronger 2-year house price growth. A two-variable model. Simple and measurable.
“They make money. They can charge more rent and they’ll pay more.”
IT and science workers earn above-average incomes. They cluster in suburbs with good amenity. Their presence signals economic resilience and upward price pressure. This is not speculation. It shows up in the numbers across 26 measurement dates.
The top bin outperforms the national average at 19 of 26 measurement dates. It underperforms at only 3.
Three Performance Zones
The model splits suburbs into three bins based on their Innovation Economy Readiness score.
High Innovation
Score 62 to 100
112,564 suburb-date observations. 56.3% outperform the national average. Statistical significance: p = 2.5 x 10-50.
Moderate Innovation
Score 8 to 62
A neutral zone. These suburbs track close to the national average but lean slightly below it. Not a strong signal in either direction.
Low Innovation
Score 0 to 8
22,652 suburb-date observations. These suburbs consistently underperform. Statistical significance: p = 2.1 x 10-25.
What Does the Model Actually Measure?
The Innovation Economy Readiness Index uses exactly two census variables. That is the entire model.
Variable 1: IT and Media Workers (Importance 0.618)
The suburb-level rank of workers in IT and media industries. This is the dominant variable. It captures the concentration of high-income knowledge workers who tend to cluster in well-serviced suburbs with strong amenity.
Variable 2: Science and Technical Services Workers (Importance 0.382)
The suburb-level rank of workers in science and technical services. The second variable adds a complementary signal. These workers overlap with IT in income but skew toward different employers and different suburb preferences.
Why two variables work. Most models try to pack in dozens of features. This one works because the signal is clean. Innovation workers earn more, spend more locally, and create upward price pressure. The model identifies a threshold at a science and technical services rank of 0.624. Suburbs above that threshold are far more likely to outperform.
Model Statistics
R-squared: 0.047. Test R-squared: 0.0128. These are low numbers in isolation. But property prices are driven by hundreds of factors. Extracting a consistent, statistically significant signal from just two variables is the point. The model does not try to explain all price movement. It identifies one reliable pattern.
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Microburbs can show you which suburbs score highest on the Innovation Economy Readiness Index. Street-level data. Backtested to 1990.
Book a 15-Minute CallWhere Does This Signal Work Best?
The Innovation Economy Readiness Index performs unevenly across regions. It works best in Queensland and Sydney. It does not work well in Melbourne or Perth.
Strongest Markets (Top Bin Outperformance)
Rest of Queensland
Strongest outperformance nationally
Rest of WA
Strong signal in regional WA
Greater Brisbane
Consistent metro signal
Greater Sydney
Reliable in the largest market
Rest of Victoria
Works in regional Victoria
Weak or Negative Markets
Greater Melbourne
Signal inverts in metro Melbourne
Greater Perth
Weak negative signal
ACT
Does not work in Canberra
Melbourne is the outlier. The signal inverts in Greater Melbourne. Innovation workers are present, but they do not drive the same price premium seen elsewhere. This is an active area of investigation. One hypothesis: Melbourne’s innovation workforce is more evenly distributed across suburbs, reducing the clustering effect that drives outperformance in other cities.
Why Two Variables Beat Twenty
Most property models pack in dozens of variables. They overfit. They find patterns in noise. Then they fail on new data.
The Innovation Economy Readiness Index takes the opposite approach. Two variables. One clean signal. The test R-squared (0.0128) is lower than the training R-squared (0.047). That gap is small. It means the model generalises. It works on data it has never seen.
Think of it like a weather forecast. A model that uses temperature, humidity and wind direction will often beat a model that uses 50 variables including the colour of the sunrise. More inputs mean more noise. The signal gets buried.
Property prices are driven by hundreds of forces. No model explains all of them. The goal is not a perfect model. The goal is a reliable one. This model identifies suburbs where innovation economy workers create upward price pressure. Not always. Not everywhere. But consistently enough to matter.
What We Do Not Know Yet
The Melbourne inversion needs more investigation. We have not yet tested whether the signal strengthens or weakens over longer time horizons (3-year, 5-year growth). And we do not know whether the signal has changed since the 2021 census data. These are open questions.
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