Free Property Data Sources Australia — What Each One Gives You

URL: yourinvestmentpropertymag.com.au. Backed by CoreLogic — the same data provider behind most paid property platforms in Australia. For a free source, it’s unusually complete.

Search a suburb name and you get a data card covering the metrics that matter most for growth and rental analysis:

That single page covers four of the five components in BoomAU’s detection formula — momentum, growth strength, tightness (partially), and sustainability — plus the suburb median price you need to calculate affordability headroom against the city median. For free research, it’s the best starting point in Australia.

But there’s one number on that page most investors ignore, and it matters more than any of the headline figures: annual sales volume.

Suburbs that transact fewer than roughly 30 homes per year produce unreliable days-on-market figures. The median is just a reflection of a handful of transactions. One unusually fast sale pulls DOM to 10 days. One drawn-out listing pushes it to 150. Neither number tells you anything meaningful about buyer urgency across that suburb — it tells you about those two sellers specifically.

Below about 15 annual sales, the DOM figure is effectively noise. A suburb showing 12 days on market with 11 annual transactions isn’t necessarily a tight, fast-moving market. It just doesn’t sell enough homes to produce a meaningful median. This distinction matters when you’re using DOM as a tightness signal — which is exactly how it functions in BoomAU’s detection formula.

Growth rate and median price hold up better even in thin markets — a suburb with 12 annual sales can still show a reliable annual growth rate if the transactions are spread evenly through the year. But DOM and yield figures require more scrutiny the lower the annual sales count goes.

URL: sqmresearch.com.au. This is the only free source with granular vacancy rate data at postcode level going back 16 years.

Vacancy rate is one of the most direct measures of rental demand versus rental supply in any given suburb. When vacancy is low, properties rent quickly, rents hold up or grow, and landlords carry less void risk. When vacancy climbs, the reverse applies. The direction of that change — whether vacancy is falling or rising over the past 12 months — is often more informative than the current number in isolation.

The 16 years of monthly history is the part most investors don’t use — and it’s where SQM earns its place in the research process. A suburb sitting at 1.2% vacancy looks tight in isolation. But whether that figure is improving from 2.5% two years ago, or deteriorating from 0.5%, changes the analysis entirely. SQM makes that trend visible.

In BoomAU’s formula, vacancy feeds into two separate components. The current vacancy level is part of Tightness (weight 0.20 in the total detection score), sitting alongside days on market as a measure of how much buyer and tenant pressure the suburb is under. The vacancy trend direction feeds into Sustainability (weight 0.15) — a suburb can have low vacancy but rising vacancy, and that trend is a warning sign for how long the current conditions will hold.

The hard filter BoomAU applies before any suburb gets a score at all: vacancy must be at or below 2%. Above that, the rental market isn’t tight enough to support the kind of demand pressure that underpins a genuine boom. Most suburbs fail this filter — which is part of how 393 qualifying suburbs are narrowed down from the 8,417 that htag covers nationally.

One limitation worth noting: SQM data is at postcode level, not always at individual suburb level. In dense city areas where multiple suburbs share a postcode, the vacancy figure blends their rental markets together. In regional areas where a single suburb occupies a full postcode, the figure is precise. The more mixed a postcode, the more carefully you need to interpret SQM vacancy numbers.

URL: domain.com.au. Best known as a listings portal, but what Domain exposes about active supply and recent sales activity is genuinely useful for understanding buyer-versus-seller dynamics at suburb level.

What you’re looking for on Domain is the relationship between properties currently listed for sale and properties that recently sold. A suburb where new listings appear slowly and the sold volume is high relative to current stock is one where buyers are absorbing supply quickly. That imbalance — demand outpacing supply — is what tightness looks like before it shows up in days-on-market or price growth figures.

The sold rate — what fraction of active listings are clearing within a reasonable window — is the most useful signal for understanding buyer urgency. A suburb where 80% of listings sell within 30 days looks fundamentally different to one where listings sit for months before vendors start cutting prices. Both markets might show a low current for-sale count, but the behaviour of the listings already on market tells you which one buyers are genuinely competing over.

The limitation is that Domain doesn’t publish this as a clean, directly readable number. You can see for-sale counts and sold listings, but calculating an actual sold rate requires cross-referencing the active listings against what sold in the same period. It’s useful directional data rather than a figure you can plug straight into a scoring formula without further work.

URL: htag.com.au. The broadest coverage of any free Australian suburb data source: 8,417 suburbs in a single national dataset.

Where YIP and SQM Research are best used for going deep on a specific suburb, htag is best used for going wide. If you want to identify which suburbs across an entire state or price band are worth investigating — before committing the 15–20 minutes of detailed YIP and SQM research each suburb requires — htag gives you a starting point at national scale.

The limitation of htag is depth. It’s a wide-angle lens rather than a magnifying glass. Once you’ve used it to generate a shortlist of suburbs showing growth activity in your budget band, you still need YIP for growth and DOM detail, SQM for vacancy trend, and Domain for listing activity before you have a complete picture. htag gets you to the starting line quickly — the detailed research still needs to happen.

The data exists. The problem is the time required to combine it — and the consistency required to combine it correctly.

Working through all four sources for one suburb — YIP for growth, DOM, yield, and annual sales; SQM for vacancy level and trend; Domain for listing activity; htag for national context — takes roughly 15–20 minutes if you’re being thorough. Then you need to apply the hard filter criteria before doing any meaningful scoring. A suburb that passes growth but fails vacancy, or passes vacancy but sits above the price cap, gets eliminated before you invest more time in it.

The hard filters BoomAU applies before any suburb receives a score:

A suburb must pass all four before it gets scored on the five weighted components. Most suburbs fail at least one. The ones that pass across all four represent a small fraction of the 8,417 that htag sweeps nationally — which is exactly why filtering matters before scoring.

Multiply the research time across the number of suburbs worth checking in your budget band:

If you’re working in the under-$600K budget band, that’s 149 suburbs that already pass the basic filters and deserve a closer look. At 15 minutes each, that’s over 37 hours of research before you’ve done any suburb deep-dives or arranged any inspections. And the list moves. Suburbs that passed last month may not pass this month. Suburbs that didn’t appear before may have crossed the growth threshold. Staying current requires repeating the exercise regularly.

There’s also the consistency problem. When you’re researching manually, the suburb you check on a motivated Tuesday evening will get more rigorous scrutiny than the one you glance at on a Friday afternoon. A formula applied consistently to every suburb doesn’t have that variance. The score a suburb gets this fortnight reflects the same methodology as the score it gets next fortnight — so you can track changes over time without questioning whether the comparison is apples-to-apples.

BoomAU combines all four sources to score every suburb that passes the hard filters. Each of the five scored components draws from a specific source — or a combination:

Domain’s for-sale count and sold rate feed into the Tightness component alongside YIP’s DOM figure. htag provides the national sweep that identifies which suburbs reach the filter stage in the first place.

This combination — backtested across 78 suburbs, 28 that genuinely boomed and 50 controls that didn’t — produces 85.7% detection accuracy, 0% false positives, and a 20.2-point separation gap between real booms and the suburbs that looked similar but weren’t.

The data behind those numbers is identical to what you can access for free on the four sources above. No proprietary feeds, no exclusive data agreements. Every figure BoomAU scores on is publicly available. The difference is doing it for all 393 qualifying suburbs every fortnight, applying consistent thresholds rather than subjective judgement, and surfacing the result as a simple label: Strong Buy, Buy, Watch, or Pass.

The walk-forward backtest across 12,360 postcode-months shows what suburbs in each tier deliver relative to the broader market:

Walk-forward backtest, 12,360 postcode-months. Excess return = suburb 12-month growth minus market median growth. Perfectly monotonic across all four tiers. Full methodology →

The 13.9-point spread between Strong Buy and Pass comes entirely from the same free data sources described in this guide — organised consistently, weighted by the components that actually survived backtesting, and applied identically to every suburb. The four sources are the inputs. The formula is what turns them into a usable signal.

Full backtest methodology and the 78-suburb validation results are published on our proof page. No registration required — check the maths yourself.