Property Data Tools Australia — A Comparison of the Free Sources That Actually Matter

There’s a widespread assumption in property investment circles that the best research requires paid tools — CoreLogic subscriptions, RP Data access, or expensive buyer’s agent reports. That assumption is wrong, at least at the suburb-analysis level.

When we backtested five versions of a suburb-scoring formula across 78 Australian suburbs and 12,360 postcode-months, the metrics that survived were all available from free sources. Infrastructure spending data, population migration statistics, building approval numbers — these are the premium datasets investors often pay for. They delivered 55% accuracy in backtesting. A coin flip.

What worked was simpler: price growth, days on market, vacancy rate, rental yield, and affordability headroom. All free. The expensive data didn’t survive. The free data did.

That said, free data has real limitations. Sample size problems, stale figures, coverage gaps, and missing suburbs are all genuine issues that affect how much you can trust any individual data point. Understanding each source’s strengths and weaknesses is how you use free data well.

YIP (yourinvestmentpropertymag.com.au) is backed by CoreLogic data and is the most comprehensive free source for suburb-level growth and transaction statistics in Australia. It’s the closest thing to a free CoreLogic subscription for the metrics that matter to investors.

BoomAU uses YIP as the primary source for annual growth, DOM, yield, rent, median price, and annual sales. Four of the five scored components in the detection formula draw from YIP data. It’s the backbone of the analysis.

The data is updated quarterly, which is the main constraint for a fortnightly scoring run. YIP figures reflect the most recent quarterly CoreLogic update — meaning the DOM and growth numbers can lag by up to three months. That’s acceptable for boom detection, where signals persist across months, but it matters for understanding whether a suburb that was tight six months ago is still tight today.

SQM Research (sqmresearch.com.au) is the definitive free source for rental vacancy data at the postcode level in Australia. Nothing else comes close for vacancy history, and vacancy is one of the two hard filters every suburb must pass before BoomAU scores it.

Vacancy rate matters for two reasons in suburb analysis. First, it confirms demand: a suburb where landlords are struggling to find tenants (vacancy above 3%) is not a suburb where buyers are competing aggressively. Second, vacancy trend direction is a sustainability signal — a falling vacancy in a suburb that already had low vacancy is a different proposition to one that just crossed below 2% from 4%.

BoomAU uses SQM’s vacancy data as both a hard filter (vacancy must be under 2% to qualify for scoring) and as part of the Sustainability component (weight 0.15 in the formula), where the vacancy trend direction contributes alongside rental yield.

Domain (domain.com.au) provides something the other sources can’t: real-time listing counts and sold counts at suburb level. That means you can measure market absorption — how quickly properties are being sold relative to how many are listed.

Domain’s listing data is particularly useful for catching early-stage booms. The detection formula catches booms 6–12 months after they start — still capturing 60–85% of total gains — but current listing counts can sometimes signal tightening conditions before they appear in quarterly growth figures.

htag.com.au is the least-known of the four sources but arguably the most important for systematic analysis. It covers 8,417 Australian suburbs in a single location, making it the starting point for any national suburb sweep.

For individual investors doing manual research, htag is useful as a discovery layer — a way to find suburbs you hadn’t considered that match a rough profile. For systematic analysis, it’s the source that defines the starting universe before filters are applied.

Each source covers a different piece of the picture. Put them together and you have most of what backtesting found to be useful:

Hard filters (must pass all): growth ≥5%, DOM ≤45 days, vacancy ≤2%, median price ≤$800K. Full methodology →

The combination works because each source covers a distinct signal. YIP provides the growth, tightness, and headroom data. SQM provides vacancy history and trend. Domain provides real-time supply context. htag provides the national suburb universe to start from.

The gaps are also consistent across all four sources. None of them give you reliable suburb-level data on infrastructure spending, population growth projections, or building approvals at fine granularity. Backtesting found those signals didn’t predict suburb-level outperformance anyway — but if you were trying to use them, free data couldn’t get you there.

There is one data quality problem that affects every free source and every paid source: transaction volume. Small-volume suburbs produce unreliable medians regardless of where the data comes from.

A suburb that sells fewer than 30 homes per year has a DOM median that can move dramatically based on a handful of transactions. One property listed for 180 days waiting for the right buyer, and the DOM median might jump from 22 to 45. One suburb-record sale that goes under contract in three days, and the same median drops to 12. Neither number tells you anything real about buyer demand.

Below 15 annual sales, the DOM figure is not usable. Median prices have the same problem: three premium sales in a quarter can shift the median house price of a small suburb by $100,000 or more, creating apparent growth or decline that has nothing to do with underlying demand.

This is a particular concern for regional suburbs and outer-ring areas, which often show attractive affordability headroom but produce medians based on 8–15 annual transactions. The free data is technically there. It just isn’t reliable enough to act on.

The four free sources described above give you most of what you need. But having access to data and knowing which data matters are two different things.

YIP publishes everything from vendor discounts to median days advertised to auction clearance rates. SQM publishes asking price indices, asking rent indices, and stock on market levels. The data is there. The question is whether it predicts anything.

BoomAU’s approach is to only use metrics that survived a formal backtest. The current formula (version 2.3) was tested against 78 suburbs — 28 that boomed and 50 that didn’t — and reached 85.7% accuracy with zero false positives and a 20.2-point separation gap between real booms and false signals.

The formula has five components: Momentum (0.30), Growth Strength (0.25), Tightness (0.20), Sustainability (0.15), and Headroom (0.10). Every number in those components comes from the free sources above. None of it is proprietary data. The difference is that each component was included because backtesting showed it contributed to accuracy — not because it sounds logical or appears in buyer’s agent slide decks.

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

The tier discrimination is perfectly monotonic: Strong Buy outperforms Buy outperforms Watch outperforms Pass. A 13.9-percentage-point spread between the top and bottom tier — from the same freely available data, sorted by a backtested formula.

If you want to run your own suburb analysis with these tools, here is the order of operations that reflects the backtested formula:

Done manually, this process takes roughly 20–30 minutes per suburb once you know the sources. Across 393 qualifying suburbs, fortnightly, that’s a significant research effort. Which is exactly what BoomAU automates.

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