Property Market Cycle: Where Are We Now? — The Wrong Question to Ask

Sydney might be cooling while Perth is still running. Adelaide could be mid-boom while regional Victoria is flat. A national “cycle phase” averages these together, which means it describes no single suburb accurately.

This isn’t a theoretical point. It has direct financial consequences. If you decide not to buy because “the market is at the top of the cycle,” you may be sitting out the strongest run in a Perth suburb that just cleared every boom signal. If you rush in because “the market has corrected,” you might land in a postcode where vacancy is climbing past 3% and days on market is above 60.

National cycle commentary is useful context. It tells you whether rates are rising or falling, whether credit is tightening, whether the tide is coming in or going out. What it cannot tell you is which suburbs are actually moving within that environment — and by how much.

Backtesting across 12,360 postcode-months confirmed this directly: growth phase does not predict relative outperformance between suburbs. The tide lifts all boats. What does predict outperformance is suburb-level affordability headroom — a signal that works regardless of where the national cycle sits.

BoomAU scores every qualifying suburb on a 0–100 scale across five weighted components: price momentum, growth strength, market tightness (days on market and vacancy rate), sustainability (rental yield and vacancy trend direction), and affordability headroom. The score reflects what the numbers show today — not a forecast, not an opinion.

The score places each suburb into one of four phases. Here’s what each one means in market terms:

Of the 8,417 suburbs in the national sweep, 393 currently clear all four hard filters. Those are the only suburbs where conditions are tight enough for the detection signal to be meaningful. The remaining suburbs aren’t hidden opportunities — they’re suburbs where the data doesn’t support a boom conclusion.

Within the 393, the phase distribution is what makes the “national cycle” question so inadequate. A suburb scoring 82 and a suburb scoring 51 are in the same national market but in completely different phases of their individual cycles. Treating them the same because “we’re mid-cycle nationally” is the mistake most investors make.

Even if two suburbs both score “Boom” today, the size of the gains available depends heavily on which era of the national cycle you’re entering. This is where national context actually does matter — not for identifying which suburb to buy, but for calibrating what “boom” means in dollar terms.

Across the booms in BoomAU’s backtest dataset, boom size splits sharply by era:

The same suburb-level detection signal fired in two different eras produces radically different outcomes. A boom in a low-amplitude national cycle might mean 1–3% gains above baseline. The same signal in a high-amplitude cycle might mean 15–20%. The national cycle sets the amplitude. Suburb-level detection finds the suburbs actually running within that amplitude.

This also explains why “timing the national cycle” is a losing strategy on its own. Even if you correctly identify that we’re in a high-amplitude era, buying in the wrong suburb within that era will still underperform. The spread between a Strong Buy suburb and a Pass suburb is 13.9 percentage points of excess return — that gap exists inside every era, boom or bust.

The most common anxiety about cycle timing is this: what if I’m already late? The suburb has been running for months. Has the opportunity closed?

Almost certainly not. Booms at the suburb level are multi-year events, not monthly spikes. A detected boom in the Early Boom phase — score 65–79, less than 30% of the affordability gap closed — means the suburb has started moving but most of the runway is intact.

BoomAU’s formula detects booms 6–12 months after they start. That’s by design. Early confirmation requires enough transaction volume and consistent data across multiple months before the signal clears the threshold. The trade-off is intentional: you give up the first 6–12 months in exchange for dramatically higher confidence that the signal is real.

The alternative — trying to predict booms before they start using leading indicators like infrastructure spending, population inflows, and building approvals — returned 55% accuracy in backtesting. A coin flip. No usable edge. The confidence gain from waiting for detection to confirm is worth more than the first few months of gains.

The 20.2-point separation gap between real booms and non-booms means the formula doesn’t just get the answer right — it gets it with conviction. A suburb scoring 82 is not a borderline case. The gap is wide enough that noise in the underlying data doesn’t flip conclusions.

The Early Boom phase is often the strongest entry point for exactly this reason. You’re not first in — first in is the riskiest position, where you’re buying a prediction rather than a confirmed signal. You’re early enough that substantial upside remains, but late enough that the data has confirmed the boom is real.

Whether the national cycle is at its peak, trough, or mid-run, one factor consistently separates outperforming suburbs from underperforming ones: how the suburb’s median price compares to the capital city median.

Suburbs priced below the city median have consistently outperformed after accounting for the national tide. Suburbs priced above 1.5x the city median have consistently underperformed. The effect is monotonic — it held across every sub-sample tested, across every year in the dataset.

Every boom in BoomAU’s backtest was led by suburbs priced well below the city median. Not one boom in the dataset originated in an above-median suburb. Affordability isn’t just a component of the score — it’s the structural precondition for a boom to exist at all.

The mechanism is buyer behaviour under any market condition. When prices rise nationally, buyers who get priced out of premium suburbs look for the next tier down. That demand displacement flows into affordable suburbs which have more room to run before hitting the ceiling. Mean reversion runs in the other direction: suburbs that already closed their affordability gap tend to underperform in the next cycle, regardless of how strong their recent growth was.

The tier discrimination data across 12,360 postcode-months captures this directly:

Walk-forward backtest, 12,360 postcode-months, 2012–2026. No lookahead. Excess return = suburb 12-month growth minus market median growth. Full methodology →

Perfectly monotonic. Strong Buy outperforms Buy outperforms Watch outperforms Pass. The 13.9pp spread between the top and bottom tier is present in boom eras and quiet eras alike. The national cycle changes the absolute return numbers — a Strong Buy in a post-2020 boom era might return 22%, the same signal in a pre-2015 era might return 8% — but the relative outperformance holds.

You can run a simplified version of this analysis using free, publicly available data. Here’s what to check and where to find it:

That’s the framework. The difficult part is doing it across hundreds of suburbs, fortnightly, filtering out noise from thin markets, data gaps, and temporary aberrations from single unusual transactions. That’s what BoomAU automates: 393 qualifying suburbs scored on a consistent formula every two weeks, each one labelled Strong Buy, Buy, Watch, or Pass — broken out by budget band (under $400K, under $600K, under $800K).

The full backtest methodology, the 78-suburb validation set, and the walk-forward tier discrimination results are published on our proof page. No gating, no email required. Check the numbers yourself.