What actually drives Melbourne house prices

Everyone has a theory about what makes a Melbourne suburb expensive: it's all location, it's the schools, it's the trains. We built a statistical pricing model on 342 Greater Melbourne suburbs with a reliable house market to actually test it — learning, from real sale prices, how much buyers pay for each ingredient of a suburb once everything else is held equal. The results are surprising: the single strongest driver isn't distance to the city at all.

The headline numbers

• 342 Greater Melbourne suburbs with a reliable house market, used to fit the model
• 75% of the price differences between suburbs the model can explain from measurable fundamentals alone
• +12% the effect of a highly educated population — the single strongest driver we measure
• -5% the effect of higher local crime — the strongest downward pull

The full ranking: what moves a Melbourne house price, and by how much

Each row shows the effect on a suburb's median house price of that suburb having meaningfully more of the feature than a typical Melbourne suburb, with every other factor held constant. "Confidence" reflects how statistically reliable that effect is across the 342 suburbs studied — a "strong" effect is one we're confident is real and not noise; "weak" means the direction is probably right but we're less sure of the exact size.

Data as of 2026 Q2 (VGV medians) · ABS 2021 Census · Victoria in Future 2023. Effects are estimated holding all other listed features constant; the model also includes a broader spatial "location and prestige" term not broken out feature-by-feature above (see the worked examples below, where it appears as "Location").

The biggest surprise: it's not distance to the city

Ask most people what drives a Melbourne suburb's price and they'll say location — how close to the CBD. It matters, but once you account for everything else, distance to the CBD on its own is one of the weaker effects we measure (-1.6%, and not statistically reliable on its own). The single biggest driver of a Melbourne suburb's house price is education: the share of residents with a bachelor's degree or higher. Suburbs full of degree-holders command a large, statistically strong premium — +12.1%, more than double the next-strongest effect.

Close behind is the work-from-home share (+10.5%) — a reasonable proxy for concentrations of knowledge-work income — and tram access (+5.0%), a genuinely Melbourne-specific premium that doesn't show up the same way in car-dependent cities. On the downside, crime (-5.0%) and distance from the coast (-3.0%) are the strongest negative pulls. Distance to the CBD matters, just less than most people assume once education, transport and safety are already accounted for — a lot of what looks like a "location premium" in raw prices is really these other factors clustering near the city, not distance itself.

What you're paying for, in dollars: three worked examples

Because the model is transparent, we can break any suburb's modelled value into the dollar contribution of each feature — not just the percentage effect, but what it's actually worth in that specific suburb. Three examples, all currently undervalued relative to their own fundamentals:

Richmond — here's roughly what its fundamentals add or subtract versus a typical Melbourne suburb:

Kensington — a different mix, dominated even more by education and remote-work demographics:

Seddon — tram access is actually a small negative here (Seddon has fewer tram stops than the model expects for a suburb with its other characteristics), showing these effects genuinely vary suburb to suburb rather than being a fixed formula:

These are the largest factors for each suburb, not the complete picture — smaller effects and a base "typical Melbourne suburb" starting value make up the rest of each suburb's modelled value. When a suburb has strong fundamentals like these but a below-average asking price, it shows up on our most undervalued suburbs list. When it's the reverse — a price the fundamentals don't fully justify — buyers are paying a prestige premium on top of the fundamentals, which we unpack in undervalued bargain or value trap?

How we measured this

A statistical pricing model — the same kind of technique used behind official house-price indices — trained on 342 reliable Greater Melbourne house markets (suburbs with at least 10 recorded sales). Each effect in the table above is the typical impact on median house price of a suburb having meaningfully more of that feature than average, with every other listed factor held equal. Altogether, the model accounts for about 75% of the price differences between Melbourne suburbs — a solid fit, not a perfect one, which is exactly why some effects above are marked "weak" rather than treated as certain.

This is a suburb-level, correlational model: it explains what tends to move prices across hundreds of suburbs, not what any single house is worth, and correlation in a model like this doesn't prove any one factor causes the price effect on its own (education and income, for instance, are related to several other things this model doesn't directly measure). Treat it as a well-grounded estimate of general tendencies, not a formula for any specific address.

Frequently asked questions

What most affects house prices in Melbourne?
In our model, the strongest driver is the share of residents with a university degree or higher, followed by the work-from-home share and tram access. Higher local crime and greater distance from the coast are the strongest downward pulls. Distance to the CBD matters, but is a weaker effect than commonly assumed once these other factors are accounted for.

Does a tram or train line add value to a suburb?
Tram access carries a measurable, statistically strong premium in Melbourne (+5.0%) — one of the clearest and most reliable amenity effects in the data. Train-commute share, by contrast, shows a small negative effect once other factors are held equal, likely because it's correlated with outer, more car-dependent areas in this dataset rather than being a driver in its own right.

Why doesn't the model use school NAPLAN or VCE results?
Per-school VCE and NAPLAN results aren't published as open data at the suburb level, so the model uses distance to the nearest school and the strongly related education profile of the surrounding population instead. It's a known limitation, and one reason "distance to school" and "% bachelor+ degree" should be read together rather than in isolation.

How accurate is this model?
It explains about 75% of the price differences between the 342 Greater Melbourne suburbs studied — a solid fit for a suburb-level model, but not a complete explanation. The remaining 25% reflects factors the model doesn't capture (street-level variation, individual property condition, momentum and sentiment) plus genuine randomness in a market.

Can I use this to work out what my street is worth?
No — this is a suburb-level model, not a property valuation tool. It's useful for understanding why suburbs differ in price on average, and for comparing a suburb's actual price against what its fundamentals suggest (see our undervalued suburbs ranking), but it isn't a substitute for an actual valuation of a specific property.

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See also: Melbourne's most undervalued suburbs in 2026 · undervalued bargain or value trap? · browse all Melbourne suburbs

Delora provides general information, not legal or financial advice, and is not a substitute for a licensed conveyancer, solicitor or financial adviser. Public-record figures are suburb-level indicators — always confirm the specific parcel. Always obtain professional advice before signing a contract of sale.