Thirty years of Australian housing: A PCA-ARIMAX econometric study
The question
Australian house prices have risen strongly since the mid-1990s, but that growth has been anything but uniform. Melbourne has consistently amplified national movements. Sydney tracks the national market closely. Regional areas tend to dampen it. Perth swings wildly depending on where the mining cycle sits.
The question this paper sets out to answer is: how much of the difference in regional house price growth reflects persistent structural trends, and how much is just cyclical noise?
The approach
The paper builds a three-factor model applied to regional repeat-sales log price indexes spanning 1995 to 2024, covering roughly 3 million house transactions across Australia.
The three factors are:
Market: The national price trend common to all regions, proxied by the national house price index. Each region’s sensitivity to this factor is captured by a loading coefficient βr. A region with βr > 1 amplifies national movements; βr < 1 dampens them.
Mining: A mean-reverting spread constructed from the Perth-Sydney price differential, capturing the resource-sector cycle. Perth loads positively; Sydney negatively. This explains a lot of why these two cities behave so differently over time.
Lifestyle: A spread capturing amenity-driven demand in coastal and regional areas, reflecting internal migration patterns toward sea-change and tree-change destinations, a pattern that intensified sharply during and after COVID-19.
These three factors are grounded in PCA evidence from Sijp et al. (2025), with the leading principal components mapping almost perfectly to the Market, Mining, and Lifestyle interpretations. The model is estimated via ARIMAX, which accounts for the autocorrelated structure of housing price residuals.
Key findings
The Market loadings are stable across major economic shocks including the mining boom, the Global Financial Crisis, and COVID-19. This stability is the central finding: it means the cross-sectional ranking of cities by sensitivity to national growth is persistent and informative for scenario planning.
Among the major cities, Melbourne amplifies national growth the most, Brisbane follows, Sydney tracks the national market closely, and regional areas dampen it. Perth is the most volatile, driven by its large positive exposure to the Mining factor.
My contribution
This paper is the work of Dr Willem Sijp at Neoval Pty Ltd and the University of Technology Sydney. I contributed as a research assistant in the following ways:
- Data processing and preparation of the underlying house price dataset
- Text editing and refinement of the manuscript
- Stimulating discussions on methodology and interpretation
- Writing Appendix D in full - a technical analysis of the stationarity of the Mining factor, including structural break detection using the ruptures algorithm and regime-adjusted ARIMA modelling
Appendix D addresses an important methodological concern: The raw Mining factor does not reject a unit root under standard ADF tests, which appears inconsistent with the ARIMAX specification. The appendix shows that this is a consequence of structural breaks around the mining boom rather than genuine non-stationarity, and that once break regimes are accounted for the factor behaves as a persistent but mean-reverting cycle, consistent with the model’s assumptions.
What I took from it
Working closely with a chief scientist on a live research project is a different experience from coursework. The methodology here is sophisticated: PCA, ARIMAX, expanding-window stability testing, uncertainty decomposition, but what struck me most was how much of the work is about making the right simplifying assumptions and being able to defend them rigorously.
The stationarity question in Appendix D is a good example. It would have been easy to difference the Mining factor and move on. Instead we took the time to understand why the ADF test was failing, identify the structural breaks, and show that the mean-reverting interpretation was defensible. That kind of rigour is something I carry into every analysis I do now.