Property Market Indicators Explained — What Each One Measures, and Which Ones Actually Work

Days on market is simply how long the average property sits listed before going under contract. When DOM is low, buyers are competing hard. When it’s high, buyers have time and options.

YIP publishes suburb-level DOM figures backed by CoreLogic data. Domain shows time-on-market for individual listings, which you can use to build a rough picture if you’re researching a specific suburb.

BoomAU treats DOM as both a hard gate and a scored signal. A suburb must have DOM at or below 45 days to qualify for scoring at all. Inside the formula, low DOM combines with low vacancy to form the Tightness component — which measures whether buyer demand is genuinely outrunning available supply.

One important caveat that almost no reports mention: DOM figures become unreliable in thin markets. In a suburb that sells fewer than about 30 homes per year, a single fast sale can drag the median DOM down to 10 days. A single property that sits for six months can push it to 150. Below around 15 annual sales, the figure stops telling you anything meaningful about market conditions — you’re just reading the echo of a handful of transactions.

Vacancy rate is the percentage of rental properties that are currently sitting empty. The lower it is, the more competition there is among renters — which means landlords have pricing power and demand for housing in that suburb is real, not just speculative.

SQM Research publishes free postcode-level vacancy charts going back 16 years. It’s one of the best free data sources available to Australian property investors and covers almost every postcode in the country.

Like DOM, vacancy appears in BoomAU’s formula in two places. It’s a hard filter: if vacancy is above 2%, the suburb doesn’t score. It also feeds into the Tightness component alongside DOM, and the vacancy trend direction feeds into the Sustainability component.

A suburb where vacancy is low and falling is signalling something different from a suburb where vacancy is low but rising. The direction matters almost as much as the level.

Annual growth rate is the percentage change in a suburb’s median price over the past 12 months. It’s the most widely reported property statistic in Australia and the one that dominates “best performing suburbs” lists.

Growth rate works for one specific question: is this suburb currently booming? BoomAU requires growth of at least 5% before a suburb qualifies for scoring, and the Growth Strength component scores the rate directly with a weight of 0.25.

What growth rate cannot do: tell you which of two growing suburbs will outperform the other. When we tested whether higher-growth suburbs went on to produce stronger returns after controlling for the overall market tide, the answer was no. Mean reversion dominates at suburb level — suburbs that have already outperformed tend to underperform going forward. The market tide lifts all boats in a rising cycle, which makes it impossible to rank suburbs by growth rate alone.

Rental yield is annual rent divided by the property’s price, expressed as a percentage. A suburb where the median price is $500,000 and the median annual rent is $26,000 has a gross yield of 5.2%.

YIP publishes median rental yield alongside median prices at suburb level. It’s based on CoreLogic data and is one of the more reliable free sources for this figure.

Yield is in BoomAU’s formula as a sustainability check, not a growth predictor. A suburb where prices are accelerating but yield is collapsing may be running ahead of what the rental market can support. Combined with vacancy trend direction, it tells you whether the growth has a fundamental income floor under it.

High yield alone is not a boom signal. Some of Australia’s highest-yielding markets are thin regional towns where capital growth is flat or negative. Yield filters for quality; it doesn’t select for outperformance.

Median price is the midpoint sale price across all transactions in a period — the number that gets quoted in every property report. On its own it’s a useful size anchor. But the more powerful version of this indicator is the ratio: how does a suburb’s median price compare to its capital city’s median?

That gap — what BoomAU calls affordability headroom — turned out to be the most important signal in the entire backtest.

Every boom in the 78-suburb backtest was led by suburbs priced well below their capital city median. Not one exception. The effect is monotonic: the lower the suburb sits relative to its city’s median, the stronger the historical outperformance. Suburbs priced above 1.5 times the city median consistently underperformed after controlling for the overall market cycle.

This is the only cross-suburb ranking signal that survived after cancelling the market tide. When we held the broader market cycle constant and asked which suburbs beat their peers, affordability headroom was the answer. Everything else we tested — momentum, acceleration, 5-year history — did not predict which suburb would outperform within any given period.

The formula also tracks how much of that gap has already been consumed. A suburb that was 40% below the city median and has closed half that gap has less remaining upside than one where the full gap is intact. Detection catches booms 6–12 months after they start — when less than 30% of the affordability gap has been closed — and still captures 60–85% of total gains.

Clearance rate is the percentage of properties that sell at or before auction, out of all properties that went to auction. Domain and REA both publish this weekly. A 75% clearance rate means buyers are active and competitive; a 50% rate means sellers are under pressure.

Clearance rate is a useful barometer for city-level market mood. The problem is resolution: it tells you how competitive buyers are across an entire city, not which suburb within that city is tightening.

A city-wide 75% clearance rate masks enormous variation. Some suburbs in that same week are selling in under two weeks with multiple offers. Others are languishing at 120 days on market. The clearance rate can’t tell you which is which. For suburb-level analysis, DOM and vacancy rate — both of which are available at postcode granularity — do the job that clearance rates can’t.

Vendor discount measures how much below the initial asking price properties eventually sell for, on average. In a hot market, properties sell above asking; in a soft market, sellers progressively cut. A negative vendor discount (properties selling below ask) signals buyer hesitation.

Vendor discount sounds like a strong signal in theory. In practice, the data barely exists at suburb level from free sources that cover the whole Australian market. The figures that are available are based on thin transaction counts — particularly in smaller suburbs — and produce medians too unreliable to rank against each other.

It was one of the seven factors in BoomAU’s first formula version. That formula delivered 55% accuracy — exactly what you’d expect from a coin flip. When the data for a metric doesn’t reliably exist at the granularity you need, you can’t build a formula around it.

Population growth tracks how many new residents are moving into an area. Infrastructure spending covers planned investment in roads, rail, hospitals, and public amenities. Both are widely cited as drivers of property price growth.

The logic behind these indicators is sound at a macro level. More people moving to a city pushes up demand for housing. Better infrastructure makes suburbs more liveable and accessible. The problem is resolution and timing:

• ×Infrastructure affects whole corridors over decades, not individual suburbs over the 1–3 year investment horizons most property investors care about.
• ×Population growth data from the ABS arrives too slowly and at too coarse a resolution to time a suburb entry. By the time official figures confirm an inflow, the market has usually already moved.
• ×Building approvals, another commonly cited leading indicator, are available at state level but not reliably at suburb level from free sources — the granularity needed for a suburb comparison doesn’t exist.

The BoomAU formula version that relied on these factors hit 55% accuracy across 78 suburbs. Exactly what you’d get from a coin flip. These metrics are useful context for understanding a region broadly, but they have near-zero ability to tell you which suburb will outperform its peers over a property investment timeframe.

Every major indicator, its free source, and its backtesting verdict.

You don’t need our formula to act on this. The four indicators that survived backtesting are all publicly checkable:

That’s the honest version. Four indicators, all free. The difficult part is running this consistently across hundreds of suburbs every fortnight, filtering out thin-market noise where the numbers lie, and catching booms within the first few months of starting while 60–85% of gains are still ahead. That’s what BoomAU automates.

The full detection formula, the 78-suburb backtest results, and the walk-forward tier discrimination data are published on the proof page. No email required. Check the methodology yourself.

Walk-forward backtest, 12,360 postcode-months. Excess return = suburb 12-month growth minus market median growth. Full methodology →

The spread between Strong Buy and Pass tiers is 13.9 percentage points. Same asset class, same country, same time period — just different suburbs. The indicator choice is the entire investment.