Buyer’s Market vs Seller’s Market in Australia — The Two Numbers That Actually Define It

At its most basic, a seller’s market exists when buyers are competing for limited supply. A buyer’s market exists when sellers are competing for limited demand. Neither is a vibe or a sentiment score — both are observable in the transaction data a suburb produces.

Two metrics capture this directly:

These two numbers answer the same question from different angles. DOM measures how quickly the sales market is clearing. Vacancy measures how tightly the rental market is absorbing. A suburb that scores tight on both is under genuine pressure from both owner-occupier buyers and investors — a seller’s market by any definition that matters.

DOM is the easiest number to misread. A figure under 20 days sounds like urgency. A figure above 60 days sounds like a soft market. The thresholds that actually matter come from backtesting, not from intuition.

In BoomAU’s detection formula, DOM contributes to the tightness component alongside vacancy. The hard filter threshold — the minimum requirement for a suburb to even be scored — is 45 days or fewer. Above that, the suburb doesn’t qualify regardless of how strong its other signals look.

These thresholds reflect the actual transaction pattern in boom-identified suburbs vs controls across the 78-suburb backtest. They are not arbitrary.

The vacancy rate adds a dimension DOM alone can’t see. A suburb might have fast-moving sales but a soft rental market — or vice versa. The vacancy rate tests whether underlying demand is real or driven by a short-term spike in one segment.

In BoomAU’s formula, the hard filter threshold is 2% or below. Any suburb with a vacancy rate above 2% is excluded from scoring entirely — not penalised, excluded. The reasoning is straightforward: vacancy above 2% means the rental market has loosened, suggesting that demand for housing in that suburb is not generating the pressure a genuine boom requires.

SQM Research publishes postcode-level vacancy rates for free, with 16 years of monthly history behind each suburb. This is one of the few genuinely free datasets that gives you suburb-level rental demand over time — not just a snapshot but a trend. A suburb with a vacancy rate that has been falling for 18 months tells a different story than one that just dipped below 2% for a single month.

Knowing whether a suburb is a seller’s market is necessary but not sufficient. Plenty of suburbs maintain tight conditions for years without producing exceptional growth — the market is balanced at a price level that reflects full value. The question that matters for an investor isn’t just “is it tight?” but “is it tight and early?”

That’s the logic behind BoomAU’s detection formula. Tightness — measured by DOM and vacancy — carries 20% of the suburb score. It is one of five components:

Tightness is the third-highest weighted component. A suburb can score well on tightness and still fail to qualify for the Strong Buy tier if momentum is absent or affordability headroom is exhausted. Conversely, a suburb with strong momentum and deep affordability headroom gets a significant boost if tightness confirms the demand pressure.

The hard filters — DOM 45 days or fewer, vacancy 2% or below, annual growth above 5%, and median price under $800K — must all be satisfied before a suburb is scored at all. Think of them as minimum entry conditions: a suburb that fails any one of them doesn’t get a score, it gets excluded. Currently, 393 suburbs pass all four filters and receive fortnightly scores.

The case for paying attention to tightness isn’t theoretical. It’s in the data. The walk-forward backtest across 12,360 postcode-months shows a clear relationship between suburb tier and realised excess returns — where excess return is each suburb’s 12-month growth minus the market median growth in the same period.

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

The tier discrimination is perfectly monotonic. Every step down the tier ladder produces worse outcomes — and the spread between the top and bottom is 13.9 percentage points per year. That’s the difference between a tightly scored seller’s market suburb with affordability headroom and a soft buyer’s market suburb that has already run its course.

It also shows why DOM and vacancy alone aren’t enough. The Strong Buy tier requires tight conditions plusthe other signals — momentum, growth strength, and affordability headroom — to align. A suburb that is tight but priced above the city median is unlikely to reach the Strong Buy tier regardless of how fast properties are selling.

One of the clearest findings from the 78-suburb backtest is that every boom was led by suburbs priced well below the city median. That’s not a coincidence — it’s demand displacement in action. As prices in inner-ring suburbs climb, buyers are pushed outward toward suburbs they can still afford. Those suburbs absorb the overflow demand and tighten fast.

This is why affordability headroom is the only cross-suburb ranking signal that survived backtesting when the market tide was cancelled out. The effect is monotonic: suburbs priced below the city median consistently outperform, suburbs priced above 1.5x the city median consistently underperform.

Tightness — DOM and vacancy — is where you see this demand show up in real time. When affordability-priced suburbs start tightening, it’s often a leading indicator that the overflow has arrived. The detection formula catches booms 6–12 months after they start, while 60–85% of the total gains are still ahead.

BoomAU’s current detection formula was validated across 78 suburbs — 28 that boomed, 50 controls that didn’t. The results:

The 20.2-point separation gap between boom-classified suburbs and controls is the number that matters most. The formula doesn’t just identify the right answer — it identifies it with enough conviction that a misclassification would require the formula to be badly wrong, not just slightly off. No false positives means every suburb the formula called a boom turned out to be one.

Tightness is a necessary condition for that separation. Suburbs that boomed had, without exception, tight DOM and low vacancy at the point of detection. The hard filters capture this: a suburb that can’t clear the tightness threshold doesn’t get a boom call, even if every other signal is pointing up.

Every data point you need is available for free. Here’s where to find each one:

Run through all three and you’ve done a reasonable first-pass tightness check on any suburb in Australia. The hard part is doing this systematically across hundreds of suburbs every fortnight, combining the tightness signals with momentum, growth, and affordability into a consistent score, and filtering out the noise from thin markets where DOM figures aren’t reliable. That’s what BoomAU automates.

Full backtest methodology and the 78-suburb validation results are published on the proof page. No gating, no email required.