Innovation Economy Index: Technical Whitepaper
Full statistical method, bin performance analysis, temporal consistency testing, and regional results across 294,922 property sales. This index predicts 2-year growth, not 4-year.
1. Abstract
This paper presents a composite index that measures innovation and technology capacity in Australian suburbs. The index combines two employment sector variables related to technology and scientific services into a single score. Suburbs with high concentrations of workers in these sectors score higher. Suburbs with low concentrations score lower.
We trained a predictive model on historical property sales data to predict 2-year annualised growth rates. The model was evaluated out of sample and achieved an R-squared of 0.047. The top-scoring suburbs outperformed the national median by +1.9 percentage points over 2 years. The bottom-scoring suburbs underperformed by -2.9 percentage points.
The signal was tested across 187 quarterly periods, 26 individual sample dates, and 13 GCCSA regions. It held in 67% of quarters and was statistically significant at 19 of 26 sample dates. The spread was positive in 8 of 13 regions. But the signal breaks down after 2021 and inverts in 2022 to 2023. This paper documents both the strength and the failure modes.
2. Methodology
2.1 Feature Construction
The Innovation Economy Index is built from two employment sector variables related to technology and scientific services. These variables capture the concentration of innovation-oriented workers at the suburb level. We do not disclose the specific census field names. The combination represents proprietary research.
Each variable was standardised and combined into a single composite score using a weighted formula. The weights were derived from a predictive model trained to predict 2-year annualised property growth relative to the national median.
2.2 Model Training
The model was trained on property sales data spanning 2008 to 2023. Each observation is a single property sale. The target variable is the annualised 2-year growth rate from the sale date. The features are the two census-derived variables for the suburb where the sale occurred.
The model was trained using a strict out-of-sample framework. Training and test sets were split by time period to prevent data leakage. Properties sold in overlapping windows were excluded from the test set to ensure clean separation.
2.3 Performance Metric
The primary metric is the difference in median annualised 2-year growth between each bin and the national median. Statistical significance is assessed using a two-sided t-test against the null hypothesis that the bin's mean growth equals the national mean.
t-statistic = (mean(bin) - mean(national)) / SE(bin)
p-value from two-sided t-test
2.4 Out-of-Sample R-squared
The model achieved an out-of-sample test R-squared of 0.0128 and a training R-squared of 0.0397. The combined R-squared is 0.047. This means roughly 4.7% of variance in 2-year suburb-level growth can be explained by these two employment variables alone. Section 7 discusses why this level of explanatory power is still useful for investment decisions.
3. Bin Performance
The model sorts suburbs into three bins based on their Innovation Economy score. Each bin has a distinct growth profile.
N = 112,564 sales
Range: 62 to 100
N = 159,706 sales
Range: 8 to 62
N = 22,652 sales
Range: 0 to 8
| Bin | Score Range | Diff vs National | p-value | N (Sales) | Significant |
|---|---|---|---|---|---|
| Top | 62 to 100 | +1.9% | 2.5e-50 | 112,564 | Yes |
| Middle | 8 to 62 | -0.7% | 9.6e-08 | 159,706 | Yes |
| Bottom | 0 to 8 | -2.9% | 2.1e-25 | 22,652 | Yes |
4. Temporal Analysis
A signal that works at one point in time could be a fluke. We tested the Innovation Economy Index across every quarter from 2008-Q1 to 2023-Q3. The signal held in 126 of 187 quarters (67%). The separation is strongest during 2013 to 2017. But the signal inverts after 2021.
4.1 Date-by-Date Consistency
We tested the top bin's outperformance at 26 individual sample dates between 2012 and 2022. At each date, the model was evaluated independently. The result was statistically significant at 19 of 26 dates.
| Sample Date | Outperformance (2yr) | Significance |
|---|---|---|
| 2012-04 | +1.0% | Not Significant |
| 2012-10 | +1.9% | Significant |
| 2012-11 | +2.0% | Significant |
| 2013-06 | +2.7% | Significant |
| 2013-07 | +3.0% | Significant |
| 2014-02 | +3.2% | Significant |
| 2014-03 | +3.3% | Significant |
| 2014-07 | +2.9% | Significant |
| 2014-12 | +3.3% | Significant |
| 2015-04 | +3.5% | Significant |
| 2015-09 | +3.0% | Significant |
| 2016-07 | +2.7% | Significant |
| 2016-08 | +2.6% | Significant |
| 2017-04 | +1.4% | Significant |
| 2017-09 | +1.6% | Significant |
| 2018-08 | +0.6% | Not Significant |
| 2018-09 | +0.6% | Not Significant |
| 2018-12 | +0.8% | Not Significant |
| 2019-09 | +1.9% | Significant |
| 2019-11 | +1.9% | Significant |
| 2020-01 | +2.1% | Significant |
| 2020-04 | +2.4% | Significant |
| 2020-09 | +2.2% | Significant |
| 2021-07 | -0.3% | Not Significant |
| 2022-02 | -1.1% | Not Significant |
| 2022-07 | -0.8% | Not Significant |
5. Regional Robustness
We tested the Innovation Economy Index across all 13 GCCSA regions in Australia. The signal produces a positive spread in 8 of 13 regions. This is weaker than other Microburbs indices.
| Region (GCCSA) | Top Q Growth | Bottom Q Growth | Spread | N (Sales) |
|---|---|---|---|---|
| Rest of WA | -2.94% | -6.62% | +3.68% | 5,305 |
| Rest of Qld | +0.17% | -2.90% | +3.07% | 14,077 |
| Greater Sydney | +1.36% | -0.94% | +2.30% | 5,570 |
| Rest of Vic. | +3.18% | +1.36% | +1.82% | 9,126 |
| Rest of NSW | +3.44% | +2.17% | +1.27% | 12,632 |
| Greater Perth | -4.28% | -5.52% | +1.24% | 3,923 |
| Rest of SA | -1.85% | -2.19% | +0.34% | 3,056 |
| Greater Brisbane | +0.81% | +0.75% | +0.06% | 4,937 |
| Greater Darwin | -5.84% | -5.71% | -0.13% | 312 |
| Greater Adelaide | -0.86% | -0.70% | -0.16% | 3,643 |
| ACT | +0.94% | +1.29% | -0.35% | 1,005 |
| Greater Melbourne | +0.32% | +0.72% | -0.40% | 5,678 |
6. Suburb-Level Evidence
Suburb-level comparisons for selected cities are available on the summary.
7. Defence of Method
7.1 Why a Low R-squared Still Matters
An R-squared of 0.047 means the model explains 4.7% of variance in 2-year suburb-level growth. This is low in absolute terms. It would be a poor result for a model trying to predict exact growth rates. But that is not how this index is intended to be used.
Property prices are shaped by hundreds of factors. Interest rate cycles, infrastructure investment, zoning changes, population growth, local employment, and macroeconomic conditions all play a role. A model that explained 50% of property growth from two census variables would be implausible and almost certainly overfitted.
The relevant questions are different. Does the signal exist? Is it statistically significant? Does it persist across time? The evidence says yes, with important caveats about the post-2021 period.
7.2 Statistical Significance
The top bin's outperformance has a p-value of 2.5e-50. This is not borderline. The probability of observing a +1.9% difference across 112,564 sales by random chance is effectively zero. A p-value below 0.05 is the standard threshold. The Innovation Economy Index exceeds this by 48 orders of magnitude.
7.3 Consistency Over Time (With Caveats)
The signal was significant at 19 of 26 sample dates. But the failures are not random. They cluster in two periods: 2018 (weak signal) and 2021-2022 (inverted signal). This suggests the relationship between innovation-economy employment and property growth is conditional on broader economic conditions.
When interest rates are stable or falling, suburbs with high tech and scientific employment concentrations tend to outperform. When rates rise sharply (as in 2022-2023), this relationship breaks down.
7.4 Geographic Breadth
The spread is positive in 8 of 13 GCCSA regions. This is weaker than other Microburbs indices. The signal works best in regional areas and in Greater Sydney. It fails in Greater Melbourne and the ACT.
7.5 Practical Use
This index works best as a secondary signal. It should not be the primary factor in suburb selection. When combined with other Microburbs indices, it adds value by identifying suburbs where knowledge-economy employment supports property demand. But it should be weighted lower than signals with broader consistency.
8. Limitations
8.1 Post-2021 Signal Inversion
This is the most important limitation. From 2022 onwards, the bottom quartile outperforms the top quartile. The three most recent sample dates all show negative or inverted spreads. This is not a random failure. It is a structural shift.
The likely explanation is the 2022-2023 interest rate cycle. The Reserve Bank raised rates 13 times between May 2022 and November 2023. Suburbs with high concentrations of tech and scientific workers tend to have higher house prices and larger mortgages. These suburbs were hit harder by rate rises.
8.2 Narrow Feature Set
The index uses only two employment sector variables. This makes it less stable than indices built from six or more variables. A single structural shift in one employment sector can flip the entire signal.
8.3 Census Data Is Point-in-Time
The index relies on Australian Census data from 2016 and 2021. Census data is collected every five years. Employment patterns can shift between census dates. The model cannot capture these shifts in real time.
8.4 Geographic Unevenness
The signal inverts in Greater Melbourne (-0.40%), the ACT (-0.35%), Greater Adelaide (-0.16%), and Greater Darwin (-0.13%). Only 8 of 13 regions show a positive spread. Investors in Melbourne or Canberra should not rely on this index.
8.5 Low R-squared
The model explains 4.7% of variance. The remaining 95.3% is driven by factors outside this index. It should carry less weight than broader, more predictive signals.
8.6 2-Year Horizon
Unlike other Microburbs indices that predict 4-year growth, this index uses a 2-year horizon. Shorter horizons are noisier. A 2-year growth rate is more sensitive to short-term market swings, interest rate movements, and seasonal effects. This partly explains the lower consistency rate of 67% compared to 80% or higher for 4-year indices.
8.7 No Causal Claim
This paper documents a correlation, not a causal mechanism. We hypothesise that suburbs with high concentrations of technology and scientific workers attract further investment and support property prices. But the data does not prove causation. The post-2021 inversion suggests the mechanism is more complex and condition-dependent than a simple causal story would allow.
Access Suburb-Level Scores
Get Innovation Economy scores for every suburb in Australia. Combine with other Microburbs signals to build a shortlist backed by data.
Generated 27 February 2026 at 14:32:07