Is Property a Good Investment in Australia? It Depends on the Suburb.

Here’s a thought experiment. Two investors both buy Australian residential property in the same year. One buys in a suburb with strong affordability headroom and early boom signals. The other buys in a suburb that’s already run hard and sits well above the city median price.

Both can truthfully answer “yes, I invest in Australian property.” But their outcomes are not comparable. The backtest data makes this explicit.

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

The discrimination is perfectly monotonic. Strong Signal beats Good Signal, Good Signal beats Fair Signal, Fair Signal beats Weak Signal — with no exceptions. And the gap between the top and bottom tier is 13.9 percentage points of excess return per year.

That’s the number that should end the asset class debate. If you buy in a Weak Signal suburb, you’re underperforming the market by 6.4 percentage points annually and only beating the market 28% of the time. If you buy in a Strong Signal suburb, you’re outperforming by 7.5 percentage points and beating the market 71% of the time. Same asset class. Wildly different outcomes.

We ran five versions of a suburb-scoring formula before landing on one that passed rigorous backtesting. Most of what investors talk about — population growth, infrastructure spending, building approvals — failed to predict which suburbs would outperform. Not marginally. Completely.

Our v1 formula used seven factors including infrastructure spend, population and migration data, and building approvals. It tried to predict booms before they started. Backtest accuracy: 55%. A coin flip with spreadsheet overhead.

The pattern is consistent: macro-level signals that make intuitive sense at the national or state level don’t discriminate between suburbs within any given period. The market tide lifts all boats. What you need is a signal that identifies which boat will rise faster than the others once the tide comes in.

We eventually found two such signals. Both are free to check. Neither is what most investors focus on.

After backtesting across 12,360 postcode-months, applying tide cancellation (stripping out market-wide growth to isolate suburb-specific performance), and discarding everything that didn’t hold up, two signals remained.

Combine these two signals and you get the answer to both questions investors need: which suburb (affordability) and when to enter (timing). That’s the entire framework. Not seven factors. Not a complex model. Two signals, both derivable from free data sources.

Timing matters because booms are multi-year events with uneven gain distribution. The first 30% of affordability gap closure tends to drive the bulk of outperformance. An investor who enters at boom detection — 6–12 months after start — still captures most of the remaining upside. An investor who enters once the suburb is already talked about on property podcasts captures little.

Our current detection formula (v2.3) scores suburbs across five components:

Before a suburb is scored at all, it must pass four hard filters: annual growth above 5%, days on market below 45, vacancy below 2%, and a median price below $800K. Any suburb that fails these filters isn’t scored — not because the formula would give it a low score, but because the data quality at these extremes produces unreliable signals.

The detection thresholds from the backtest: a score of 80+ signals a boom, 65–79 signals an early boom, 50–64 is warming, and below 50 is no boom. The 20.2-point separation gap between real booms and false signals means the formula doesn’t just get the right answer — it gets it with enough margin to trust.

One nuance from the backtest worth flagging: how much a suburb gains during a boom varies significantly by era. Pre-2015, the median boom delivered around 1.3% above market. Post-2020, the median boom delivered 16.2% above market. The same detection formula, the same suburb characteristics — wildly different outcomes depending on the macro environment.

This matters for the “is property a good investment?” question. The honest answer is: the asset class question and the timing question are entangled. In a rising macro environment, almost everything goes up. The question is whether your suburb outperforms the average rise — and that’s where suburb selection determines the outcome.

The data also reveals a mean reversion dynamic: past outperformers tend to underperform going forward. A suburb that ran hard in the previous cycle is more likely to sit in the Weak Signal tier in the next one. Growth phase does not predict relative outperformance — the tide lifts all boats, but it doesn’t lift the same boats twice.

The two signals that survived backtesting are derivable from free public data. Here’s how to check them yourself for any suburb you’re evaluating.

The free data approach works. The hard part is doing it across thousands of suburbs, catching new booms within weeks of starting, filtering out thin markets where missing data produces false signals, and updating the scores fortnightly as conditions change. That’s the automation problem we’ve solved — not some proprietary insight the data doesn’t support.

Is property a good investment in Australia? The backtest says: it depends on the tier your suburb sits in. Strong Signal suburbs beat the market 71% of the time with +7.5pp excess returns. Weak Signal suburbs beat the market 28% of the time with −6.4pp excess returns. The spread between those outcomes — 13.9 percentage points annually — is not a rounding error. It’s the entire question.

The investors who ask “is property a good investment?” are asking a question that averages across all those tiers. The average includes the Strong Signals and the Weak Signals. The average answer is fine. But average property returns are not what people are hoping for when they buy an investment property.

This isn’t financial advice — it’s a data-driven framing of where the variation in property returns actually comes from. Suburb selection explains more of the return differential than any other factor in our backtest. Not the interest rate environment, not the asset class, not the state. Which suburb. When.

Full backtest methodology and tier discrimination results are published on our proof page. No email required. Check the numbers yourself.