Data Guide
Vacancy Rate by Suburb Australia — Where to Find the Data and What It Actually Means
Vacancy rate is one of the most reliable signals in the suburb analysis toolkit — and one of the most misread. Investors quote it constantly, but most are looking at state-level or city-level averages that smooth out everything interesting. The useful data is at the postcode level, it’s free, and almost nobody uses it correctly.
This guide covers where to find suburb-level vacancy data, how to read it, what different levels actually mean for property investment, and how vacancy rate fits into BoomAU’s boom detection formula as part of the Tightness component.
Where to Find Suburb Vacancy Rate Data for Free
There is one source worth knowing: SQM Research (sqmresearch.com.au). It publishes free postcode-level vacancy charts with 16 years of monthly history — by far the deepest free vacancy dataset available for Australian suburbs.
SQM defines a vacancy as any property that has been listed for rent on a major portal for at least 21 days. This is a methodologically sound definition: it filters out normal lease turnovers (which are typically listed and filled within a week or two) and captures genuine supply overhang. The 21-day threshold is what separates SQM vacancy data from a raw listing count.
SQM Research — what you get for free
- ✓Postcode-level vacancy rate, updated monthly
- ✓16 years of monthly history per postcode
- ✓Absolute vacancy count alongside the percentage
- ✓Downloadable CSV for any postcode
The history is the valuable part. A suburb at 1.2% vacancy today reads very differently depending on whether it was at 3.5% twelve months ago (trend tightening fast — a supply squeeze is building) or whether it has been between 1.0% and 1.4% for five years (stable, low demand but not accelerating). The direction of the trend matters as much as the current number.
The limitation of SQM data is coverage. Suburbs with thin rental markets — fewer than 20–30 rentals — produce noisy figures. A single large landlord listing three properties at once can move the vacancy rate by a full percentage point. For sparse suburbs, treat the trend over 6–12 months as more reliable than any single month’s reading.
Key point
SQM Research is the only free source for postcode-level vacancy data with meaningful history in Australia. For suburb-level analysis, there is no comparable alternative. The 16-year monthly history lets you read trend direction, not just a single snapshot.
We check vacancy for 393 suburbs every fortnight
BoomAU pulls SQM vacancy data and scores it in context of boom detection. Join the wishlist to see the data.
What Different Vacancy Rates Actually Mean
The conventional benchmarks thrown around — “below 3% is healthy, above 3% is oversupplied” — are blunt instruments. They were calibrated on balanced markets and they miss the spectrum of signals that matter for property investment. Here is a more granular reading.
Demand is dramatically outpacing supply. Landlords hold all the power. Rents are likely rising fast. For investors, this signals strong rental income but also raises the question of sustainability — vacancy can't stay this low indefinitely. It often precedes a price acceleration as owner-occupiers also compete for scarce housing.
This is the range that appears consistently in suburbs that go on to sustain growth. Supply is constrained relative to demand, rents are firm, and the market is not oversupplied. BoomAU's hard filter requires vacancy at or below 2% — suburbs above this threshold are excluded from scoring entirely.
Tenants have some choice. Landlords may need to negotiate on rent. Growth is possible but the rental market is not a tailwind. Suburb scoring starts to penalise vacancy in this range through the Tightness component.
More rental stock than tenants want. Rents are under pressure. This typically reflects either a construction pipeline that got ahead of demand, population outflows, or economic weakness. The market needs time to absorb supply before fundamentals improve.
A structural supply problem. Common in mining towns after a boom cycle, in CBD apartment markets after large construction waves, and in regional areas experiencing population decline. Recovery timelines are long and uncertain.
These are directional readings, not hard rules. A suburb at 2.5% vacancy with a tightening trend over the past 12 months reads differently from the same suburb with a loosening trend. Context is everything.
How Vacancy Feeds Into BoomAU’s Formula
Vacancy rate appears in BoomAU’s v2.3 detection formula in two distinct places: as a hard filter and as a scored component. Both roles matter, and they operate differently.
v2.3 formula — vacancy’s two roles
Role 1: Hard filter (pass/fail)
Vacancy must be ≤ 2% for a suburb to be scored at all. This is a binary gate, not a gradient. A suburb at 2.1% vacancy is excluded entirely, regardless of how strong its other signals are. The rationale: a loose rental market is an early warning that the housing demand story isn’t as tight as growth numbers might suggest.
Role 2: Tightness component (scored, 0.20 weight)
For suburbs that pass the hard filter, vacancy is scored within the Tightness component alongside days on market. Lower vacancy = higher tightness score. The Tightness component carries a 0.20 weight in the overall formula — the third largest of the five components.
| Component | Weight | What it measures |
|---|---|---|
| Momentum | 0.30 | Price growth acceleration |
| Growth Strength | 0.25 | Annual growth scored directly |
| Tightness | 0.20 | DOM + vacancy rate |
| Sustainability | 0.15 | Rental yield + vacancy trend |
| Headroom | 0.10 | Price relative to capital city median |
BoomAU v2.3 formula — 85.7% accuracy, 0% false positives on 78-suburb backtest. Full methodology →
Note that vacancy also appears indirectly in the Sustainability component (weight 0.15), which tracks vacancy trend direction alongside rental yield. A suburb with stable 1.5% vacancy reads differently from one trending from 3.0% down to 1.5% over six months. The latter has tightening momentum — a more bullish sustainability signal.
Why two roles?
The hard filter and the scored component do different work. The filter protects against loose-market false positives — suburbs with strong price growth driven by something other than genuine housing demand. The scored component then ranks the tightness of suburbs that passed the filter. You need both: the filter for quality control, the score for discrimination.
Why Trend Direction Matters More Than the Current Rate
A vacancy rate is a snapshot. A vacancy trend is a story. The most useful way to read SQM data is to look at the direction of travel over the past 6–12 months, not just the current month’s figure.
Three patterns are worth distinguishing:
Sustained low vacancy (chronically tight)
A suburb that has sat at 1–2% vacancy for multiple years has a structural supply shortage. This is a different kind of signal from a suburb that recently tightened. It suggests a chronic mismatch between the housing stock and the number of people who want to rent there. Reliable for long-term hold; less useful for timing entry.
Tightening fast (falling vacancy trend)
Vacancy has been falling for 6–12 months. This is often an early signal of demand exceeding supply before price growth shows up in the data. In BoomAU’s Sustainability component, a falling vacancy trend is the most bullish vacancy signal. The best booms typically show this pattern in the 6–18 months before prices accelerate.
Loosening (rising vacancy trend)
Vacancy has been rising. Even if the current rate is still low, a rising trend is a warning. It means supply is growing faster than demand. The Sustainability component scores this negatively. A suburb with a rising vacancy trend is more likely to see rental income pressure and may be later in its cycle.
SQM’s 16 years of monthly history per postcode is precisely what makes trend analysis possible. You can see whether the current reading is a local minimum, a plateau, or a turning point. Most investors look only at the current number and miss the trajectory entirely.
Vacancy trend direction is scored fortnightly across 393 suburbs
BoomAU’s Sustainability component tracks vacancy trend alongside rental yield. Join the wishlist.
Why BoomAU Uses a Hard 2% Vacancy Filter
The backtesting result that drove the hard filter is simple: every suburb that boomed in the 78-suburb validation set had vacancy below 2% during the boom period. Not “most” of them — all of them. A suburb with loose rental supply was not a suburb whose housing demand story was strong enough to sustain price growth.
The filter is deliberately conservative. Suburbs in the 2.0%–2.5% range may well be worth investigating — but they don’t meet the confidence threshold for a BoomAU score. The cost of this conservatism is some false negatives (genuine booms that start with slightly looser vacancy). The benefit is a cleaner signal set with 0% false positives in the backtest.
The 0% false positive rate matters. False positives in property investment aren’t just statistical noise — they’re capital allocated to the wrong suburb. A formula that occasionally fires on loose-vacancy suburbs would erode the value of the signal entirely. The hard filter is the mechanism that prevents this.
The data quality caveat
Missing vacancy data is a silent bug. In BoomAU’s data pipeline, a suburb with no SQM vacancy figure cannot be allowed to default to zero — a zero vacancy rate would pass the hard filter and score as the tightest market possible, producing a false strong signal. Handling missing data correctly is as important as the filter threshold itself.
How to Check Suburb Vacancy Rate Manually
If you want to check vacancy for a specific suburb before BoomAU’s scored output is available, SQM Research is the right starting point. Here’s how to get the most out of the free data:
1. Look up the postcode, not the suburb name
SQM organises vacancy data by postcode. A single postcode sometimes covers multiple suburbs, which can blur the signal for smaller areas. If the postcode is large and mixed, treat the vacancy figure as a proxy rather than a precise suburb reading.
2. Pull at least 12 months of history
A single month’s vacancy figure is nearly useless. You want to see the trend over the past year. Is vacancy falling, stable, or rising? SQM’s chart view makes this easy to read. Download the CSV for precise numbers.
3. Check the absolute count alongside the percentage
SQM shows both the vacancy rate and the raw count of vacant properties. A rate of 1.5% on 8 properties is far less reliable than 1.5% on 200. For thin markets (under 30 vacancies), treat the trend with caution and look for corroborating signals from days on market data.
4. Cross-reference with days on market
Vacancy rate and days on market (from YIP or CoreLogic) are complementary signals. A suburb with low vacancy and short days on market is genuinely tight — both the rental and the sales market are showing demand pressure. If they diverge, investigate why.
Doing this manually for a handful of suburbs is reasonable. Doing it across hundreds of postcodes fortnightly is what BoomAU automates — pulling SQM vacancy data, checking the trend direction, and scoring it in context of the full detection formula.
What Vacancy Rate Doesn’t Tell You
Vacancy rate is a useful signal, but it is not a standalone buy indicator. A few important limitations:
Low vacancy doesn’t guarantee price growth
A suburb can have persistently low vacancy because nobody is building new rentals there, not because demand is strong. Some regional and fringe markets maintain tight vacancy with flat or declining values because the owner-occupier demand simply isn’t there. Vacancy tightness needs to be combined with price momentum to distinguish genuine boom conditions from structural illiquidity.
It doesn’t predict which booming suburb outperforms others
BoomAU’s backtesting found that vacancy, as part of the Tightness component, works well for detecting whether a suburb is in boom conditions. What it doesn’t do is rank one booming suburb above another with meaningful accuracy. The signal that survived cross-suburb ranking after cancelling the market tide was affordability headroom — price relative to the capital city median — not vacancy. Low vacancy is a necessary condition, not a sufficient one.
Data sparseness can mislead
As noted, thin rental markets produce noisy vacancy figures. A suburb with 15 rental properties and a vacancy rate of 0.8% has one or two properties vacant. That’s a single landlord decision away from a different figure next month. The same vacancy rate in a suburb with 400 rental properties is a much more reliable signal. Always check the absolute count.
The honest summary
Vacancy rate is a necessary component of any rigorous suburb analysis — but it needs to work alongside price momentum, growth strength, affordability, and rental yield to mean anything. On its own, a low vacancy number tells you something about rental supply and demand. In combination with four other signals, it’s part of an 85.7%-accurate boom detection formula.
393 suburbs. Vacancy scored in context.
BoomAU applies the hard 2% filter and scores vacancy trend direction alongside four other signals. Join the wishlist for fortnightly suburb ratings.
Putting It Together: Vacancy in the Boom Detection System
BoomAU scores 393 suburbs fortnightly. Every suburb in that set passes four hard filters: annual growth at or above 5%, days on market at or below 45, vacancy at or below 2%, and median price at or below $800K. Suburbs that fail any filter are excluded from scoring entirely.
Of the 393 scored suburbs, the budget distribution is:
The vacancy filter is one reason the under-$800K set is 393 suburbs rather than the full 8,417 suburbs in the national sweep. Most Australian suburbs are either above the price cap, below the growth threshold, or above the vacancy threshold. The filter is doing meaningful work.
Within those 393 scored suburbs, the four tiers — Strong Signal, Good Signal, Fair Signal, and Weak Signal — are derived from the five-component formula. A suburb in the Strong Signal tier has passed the vacancy filter and scored well on vacancy within the Tightness and Sustainability components. Vacancy alone can’t put a suburb into Strong Signal, but a loose vacancy market can prevent it from getting there.
The tier discrimination from the walk-forward backtest across 12,360 postcode-months shows what the system produces when vacancy (and the other components) are working together:
| Tier | Excess return | Beat market | n |
|---|---|---|---|
| Strong | +7.5pp | 71% | 2,103 |
| Good | +1.3pp | 55% | 3,349 |
| Fair | −0.7pp | 47% | 5,788 |
| Weak | −6.4pp | 28% | 1,120 |
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. The tier system works because each component — vacancy included — is doing its part. Remove the vacancy filter and the false positive rate rises. Remove vacancy from the Tightness score and the tier discrimination narrows. The formula is a system, not a collection of independent signals.
If you want to check vacancy manually for a suburb you’re researching, start with SQM Research, pull 12 months of postcode history, and look at the trend direction before the current rate. That alone will tell you more than the single-number benchmarks quoted in most investment content.
Full backtest methodology and the 78-suburb validation results are on the proof page. No email required.
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- ✓Fortnightly Strong / Good / Fair / Weak signal labels per suburb
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- ✓Built on a backtest of 12,360 postcode-months