arXiv 30 Nov 2025 · Econometrics
arXiv:2512.01139 · PDF · DOI · OpenAlex · Extracted main text
Australian house prices have risen strongly since the mid-1990s, but growth has been highly uneven across regions. Raw growth figures obscure whether these differences reflect persistent structural trends or cyclical fluctuations. We address this by estimating a three-factor model in levels for regional repeat-sales log price indexes over 1995-2024. The model decomposes each regional index into a national Market factor, two stationary spreads (Mining and Lifestyle) that capture mean-reverting geographic cycles, and a city-specific residual. The Mining spread, proxied by a Perth-Sydney index differential, reflects resource-driven oscillations in relative performance; the Lifestyle spread captures amenity-driven coastal and regional cycles. The Market loading isolates each region's fundamental sensitivity, beta, to national growth, so that a city's growth under an assumed national change is calculated from its beta once mean-reverting spreads are netted out. Comparing realised paths to these factor-implied trajectories indicates when a city is historically elevated or depressed, and attributes the gap to Mining or Lifestyle spreads. Expanding-window ARIMAX estimation reveals that Market betas are stable across major shocks (the mining boom, the Global Financial Crisis, and COVID-19), while Mining and Lifestyle behave as stationary spreads that widen forecast funnels without overturning the cross-sectional ranking implied by beta. Melbourne amplifies national growth, Sydney tracks the national trend closely, and regional areas dampen it. The framework thus provides a simple, factor-based tool for interpreting regional growth differentials and their persistence.
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The works this paper leans on most, across its whole bibliography — not restricted to papers in our corpus. Ranked by composite intensity, which combines how often a work is mentioned, how many sections mention it, and how much of that falls in the main text rather than the appendix.
| Reference | Intensity | Mentions | Sections | Main text | |
|---|---|---|---|---|---|
| 1 | Otto, Glenn (2007) The Growth of House Prices in Australian Capital Cities: What Do Economic Fundamentals Explain? | 1.000 | 5 | 3 | 100% |
| 2 | Sijp, Willem P. and Panagiotelis, Anastasios and Francke, Marc K (2025) Data-sparse price indexes by spatio-temporal regularization and PCA: An application to the Australian housing market self | 0.965 | 10 | 4 | 90% |
| 3 | (2019) Hot Property: The Housing Market in Major Cities | 0.737 | 3 | 2 | 100% |
| 4 | Yanotti, Maria and Kangogo, Moses and Wright, Danika and Sarkar, Som… (2023) House Price Dynamics and Internal Migration Across Australia | 0.737 | 3 | 2 | 100% |
| 5 | Downes, Peter and Hanslow, Kevin and Tulip, Peter (2014) The Effect of the Mining Boom on the Australian Economy | 0.644 | 2 | 2 | 100% |
| 6 | Kohler, Marion and van der Merwe, Michelle (2015) Long-run Trends in Housing Price Growth | 0.644 | 2 | 2 | 100% |
| 7 | Sijp, Willem P. and Marc K. Francke (2025) A Hedonic Regularized Random Effects House Price Model Based on Graph Encodings of Neighborhood Structures self | 0.511 | 2 | 2 | 50% |
| 8 | Abelson, Peter (1994) HOUSE PRICES, COSTS AND POLICIES: AN OVERVIEW | 0.511 | 2 | 1 | 100% |
| 9 | Case, Karl E. and Cotter, John and Gabriel, Stuart A (2010) Housing Risk and Return: Evidence from a Housing Asset-Pricing Model | 0.511 | 2 | 1 | 100% |
| 10 | Himmelberg, Charles and Mayer, Christopher and Sinai, Todd (2005) Assessing High House Prices: Bubbles, Fundamentals, and Misperceptions | 0.511 | 2 | 1 | 100% |
Showing the top 10 of 43 scored citations.