Strata vs House Investment Australia — What the Data Actually Says

Most investors approach this debate as a product comparison. Strata has pros and cons. Houses have pros and cons. You weigh them, pick a side, and go looking for properties in that category.

The problem is that this frames the decision as being primarily about the asset type, when backtesting consistently shows that suburb selection is the dominant variable. A house in a Weak Signal suburb underperforms a unit in a Strong Signal suburb. The container matters less than the location.

Our walk-forward backtest across 12,360 postcode-months produced a stark result: Strong Signal suburbs delivered a +7.5 percentage point excess return with 71% of suburbs beating the market. Weak Signal suburbs delivered −6.4pp with only 28% beating the market. That’s a 13.9-point spread between best and worst tier — driven entirely by suburb selection, with no regard for what type of property sits in those suburbs.

Walk-forward backtest, 12,360 postcode-months, 2012–2026. No lookahead. Excess return = suburb 12-month growth minus market median growth. Full methodology →

Here’s where property type becomes directly relevant to the data. Our formula applies a hard filter: suburbs with a median price above $800K are excluded entirely. They don’t get scored.

This isn’t arbitrary. Every boom in our 78-suburb backtest was led by suburbs priced well below the city median. The affordability headroom signal — how a suburb’s median price compares to its capital city median — is the only cross-suburb ranking signal that survived tide cancellation in our backtesting. Suburbs priced above 1.5× the city median consistently underperformed. Suburbs priced below the city median consistently outperformed.

The practical consequence: in most capital cities, the suburbs that pass the $800K filter and sit below the city median are often unit-dominated or mixed-stock suburbs. Not because units are intrinsically better investments, but because they’re what drives the suburb median down into the headroom zone.

That distribution matters. The 35 suburbs under $400K are almost exclusively regional and outer-suburban — areas where units and smaller houses exist at sub-$400K medians. The 149 suburbs under $600K include both unit-heavy inner-ring suburbs in cheaper cities and house-dominated outer suburbs in expensive ones. In either case, it’s the price point relative to the city median that drives the signal, not the property type itself.

BoomAU’s v2.3 detection formula has five components. None of them explicitly measure property type — but each one interacts with the strata vs house question in ways worth understanding.

Take Momentum (0.30 weight). The formula measures price growth acceleration at the suburb level. If a suburb’s median is unit-dominated, momentum reflects what units are doing there. If it’s house-dominated, same logic. The formula doesn’t care which type drives the median — it cares whether the median is accelerating.

Tightness (0.20) combines days on market and vacancy rate. Neither metric is specific to property type. A suburb with low DOM and low vacancy is a tight market regardless of whether the stock is mainly units or houses. Both property types trade faster in a tight market. Both benefit from low vacancy on the rental side.

Sustainability (0.15) incorporates rental yield and vacancy trend. This is where units have a structural argument: they typically carry higher gross yields than houses at the same price point, particularly in inner-ring suburbs. A suburb with strong unit yield can score well on sustainability even if house yields in the same suburb are more modest. But the formula measures the suburb, not the individual property — so mixed-stock suburbs will produce blended signals.

Headroom (0.10) is where the property type connection is clearest. Units bring the suburb median down. A suburb where most stock is units priced at $450K has more headroom against a $700K city median than a house-dominated suburb where detached homes push the median to $650K. Both of those suburbs might have similar growth prospects from a demand perspective — but the headroom component scores the first one higher.

Before any suburb gets scored, it must pass four hard filters. Fail any one of them and the suburb isn’t evaluated at all.

The vacancy filter has a sharp implication for the strata debate. High vacancy is a chronic problem in certain unit markets — particularly new apartment developments where supply came online faster than demand could absorb it. Suburbs with vacancy above 2% don’t pass the filter. This naturally weeds out oversupplied unit precincts: CBDs with thousands of new apartments, inner-Brisbane high-rise corridors that absorbed a wave of interstate completions, off-the-plan developments that settled into a softening market.

The suburbs that do pass tend to be those where unit supply is more constrained — established inner-ring suburbs with limited new development, or outer suburbs where the unit stock is relatively thin and vacancy stays low because rental demand is stable. This isn’t a rule against units. It’s a rule against oversupply. Units in tight markets clear the filter just as reliably as houses.

Our 78-suburb backtest covered 28 suburbs that boomed and 50 controls that didn’t. The formula achieved 85.7% accuracy and 0% false positives, with a 20.2-point separation gap between genuine booms and false signals.

The single strongest finding from that dataset: every boom was led by suburbs priced well below their city median. Not some booms. Not most booms. Every one. Suburbs priced above 1.5× the city median did not boom in the dataset — consistent with the monotonic underperformance pattern we see in the tier discrimination backtest.

This finding does not say “buy units.” It says that the suburbs most likely to boom are those with intact affordability headroom. Whether that headroom exists because the suburb is unit-dominated, geographically peripheral, or simply less fashionable than its neighbours is a secondary question. The headroom is what matters.

There’s also a timing dimension to consider. The formula detects booms 6–12 months after they start — still capturing 60–85% of total gains. Pre-2015, the median boom delivered 1.3% outperformance. Post-2020, that figure rose to 16.2%. The era matters: the same suburb at a different point in the macro cycle produces very different returns, regardless of property type.

The boom size variance is enormous. An investor buying into the same suburb at the same boom stage in 2013 vs 2021 would experience dramatically different outcomes. This is why infrastructure spending, population growth, and building approvals failed as predictors in our testing: they attempt to identify structural drivers that operate over decades, when the more significant variable is often the macro cycle you happen to be entering in.

Based on what the data shows, here is the most accurate framing of the strata vs house question for Australian property investment:

None of this constitutes financial advice. What it does constitute is a data-driven framework for thinking about the question differently. The most productive reframe: instead of asking “strata or house?” ask “which suburbs currently have affordability headroom, tightening conditions, and growth momentum?” Then ask what property types are available in those suburbs at what price points.

That sequence — suburb first, property type second — is what the backtesting supports. The conventional sequence (property type first, then suburb) is what produced the coin-flip results of our v1 formula.