Step 3 · Suburb, the 49-metric scorecard, factor by factor

Why Step 3 needs a scorecard, not a vibe

A typical Australian buyer evaluates a suburb on three or four signals: how nice the streets look, what the recent median sale prices were, what their local network says, and whether their agent has properties to show there. That's enough information to decide between two known suburbs. It isn't enough to decide between fourteen thousand.

propautopilot's suburb scorecard runs 49 metrics against every suburb in Australia weekly. This chapter walks the twelve metrics that move forward returns the most. Each has a threshold (the line at which the suburb passes or fails), a rationale (why that threshold and not another), a relaxation risk line (what happens if the buyer ignores the threshold), and a tightening risk line (what happens if the buyer over-applies the threshold).

The buyer who applies all twelve metrics in 90 minutes per suburb is doing the same data work a buyer's agent charges A$15-25k for. The Investor tier surfaces the live values; this chapter explains what the values mean.

Metric 1. Vacancy rate

Threshold: below 2.0%. Hard cap: 3.0%.

Why it matters. Vacancy rate is the share of rental properties on the market without a tenant for more than 30 days. Below 2.0% signals tight tenant demand and rising rents. Above 3.0% is oversupply territory where rent growth stalls and tenants become hard to find.

Vacancy is a trailing snapshot, so direction of change matters as much as level. A suburb at 2.5% trending down over six months is very different from one at 2.0% trending up. At Step 3 it pairs with renter proportion and days-on-market as the three short-term demand signals.

Why this is strict. Among the seven short-term Step 3 factors, vacancy is the single strongest predictor of near-term rental demand and therefore forward growth. Listings data is public and hard for agents to talk away. Relaxing vacancy materially in a softer market compounds tenant-demand risk.

Relax this and: softer tenant demand compounds in a cooling market. Tighten this past 1% and: the market is so tight it often precedes price spikes that narrow the buy window.

Source signal: state tenancy bonds registers (NSW Fair Trading, RTBA Victoria, RTA Queensland, etc.) report bonds-lodged + bonds-released weekly. propautopilot triangulates against rental-listings count to back out vacancy at the suburb level.

Metric 2. Demand-to-supply pressure index

Threshold: above 55, sustained for 6-9 months. Hard cap: below 45.

Why it matters. This is a composite index that triangulates several short-term demand and supply inputs (listings, online search interest, vacancy, building approvals, auction clearance) into a single 0-100 score. A suburb reading 55+ for 6-9 months means buy-side pressure is persistent, not a data blip.

The 55 threshold replaces the older 73 line that worked in the 2016-2020 bull market. After the 2022 cooling, 73 excluded almost every Australian suburb. 55 retained statistical signal while still filtering most of the market. The methodology requires the signal to persist 6-9 months before acting.

Why this is strict. Demand-to-supply pressure is the principal composite short-term signal. Most of the single-factor short-term checks feed into it. Accepting a suburb below the threshold undermines the statistical base of the method.

Relax this and: supply catches up within a quarter, compressing forward growth. Tighten this past 70 and: suburbs are typically already hot and competitive on price (late-cycle entry risk).

Pair with Metric 3 (36-month growth). A high demand-supply pressure reading is only meaningful when 36-month growth is still below the strict cap. High pressure in an already-run market signals mean-reversion risk rather than opportunity.

Metric 3. 36-month median growth

Threshold: below 50%. Hard cap: 60%.

Why it matters. Cumulative three-year median value growth. Below 50% keeps the suburb away from post-boom mean-reversion risk. Above 50% means the market has already priced in most of the forward growth and a buyer is arriving late.

The 36-month window is wide enough to smooth quarterly noise but tight enough to reflect the current cycle. A suburb at 20-40% over 36 months is usually mid-cycle, with demand signals (demand-supply pressure, vacancy, renter mix) often still strong. Past 50%, statistical base rates show forward growth slowing as affordability ceilings bite and investors rotate.

The 50% threshold replaced an older 15% line after the 2020-2022 national bull run made 15% unattainable nationwide. The principle is unchanged: buy before the market has fully priced.

Why this is strict. The empirical base rate for suburbs over 50% 36-month growth is weak forward growth. Mean-reversion is the default outcome. Relaxing this is effectively a bet against the methodology's core thesis.

Relax this and: mean-reversion risk rises sharply. Tighten this past 30% and: the buyer eliminates many mid-cycle suburbs that still have meaningful runway.

Pair with Metric 4 (10-year growth). A suburb past both the 36-month cap and the 10-year cap is the clearest mean-reversion setup.

Metric 4. 10-year median growth

Threshold: below 80%. Hard cap: 110%.

Why it matters. Captures whether the suburb is mid-cycle or late-cycle on the longer horizon. Below 80% over 10 years signals mid-cycle territory. Affordability runway remains, mean-reversion risk is moderate. Above 80% the suburb is typically late-cycle: affordability is stretched, forward returns are usually below historical average, and the buyer is more exposed to rate-cycle and policy-cycle risk.

10-year growth pairs differently with strategy: - Growth-strategy buyers want sub-80% (runway). - Cashflow-strategy buyers can accept up to 110% (priced-in growth is consistent with stable yields). - Mixed-strategy buyers want a clear bias toward sub-80%.

Relax this and: late-cycle entry risk rises; affordability ceiling hits within the typical hold window.

Metric 5. Renter proportion

Threshold: between 25% and 35%. Hard cap above: 45%.

Why it matters. The share of dwellings rented vs owner-occupied. Below 25% signals limited tenant pool: the suburb runs on owner-occupier demand and a future investor exit becomes thinly traded. Above 35% the suburb becomes tenant-dependent. A small downturn in tenant demand moves rents and vacancy disproportionately.

The 25-35% band is the empirical sweet spot. Tenant pool is deep enough for resilient yield, owner-occupier demand is strong enough for resilient capital growth.

Pair with vacancy (Metric 1). Suburbs above 35% renter proportion need vacancy below 2% to compensate for the tenant-dependence. Suburbs at 25-30% can tolerate 2.0-2.5% vacancy.

Metric 6. Days on market

Threshold: below 50 days. Hard cap: 75 days.

Why it matters. The median time a property listing sits on the market before sale. Below 50 days signals strong buyer demand. Above 75 days is a buyer's market where sellers concede on price.

Days on market is a real-time signal that leads price moves by roughly 3-6 months. A suburb with rising days-on-market is a leading indicator of softening price growth, even before the median sale price reflects it.

Pair with vendor discount (Metric 8). Rising DOM + rising vendor discount = sellers are making concessions. This is the signal a buyer waits for if their strategy is value-buy at cycle bottom.

Metric 7. Auction clearance rate

Threshold: above 65% (sustained 4+ weeks). Hard cap: below 55%.

Why it matters. The share of auctioned properties that actually sell at auction (excluding withdrawn). Above 65% signals strong buyer demand. Below 55% is buyer's-market territory.

Auction clearance only matters in suburbs where auction is the dominant sale method (predominantly inner-Sydney + inner-Melbourne + parts of inner-Brisbane). In private-treaty-dominant suburbs (most regional + most outer suburbs) the metric is noise; use days-on-market and vendor discount instead.

Auction clearance is a high-frequency signal where week-on-week changes carry information. A suburb dropping from 70% to 55% over 6-8 weeks is a more reliable softening signal than the equivalent move in any monthly metric.

Metric 8. Vendor discount

Threshold: below 4%. Hard cap: 8%.

Why it matters. The percentage gap between original asking price and actual sale price, averaged across recent transactions. Below 4% means sellers hold the upper hand. Above 8% means buyers are negotiating prices down materially. Vendor discount is the cleanest read on negotiation leverage at the suburb level.

Pair with days on market (Metric 6). Both moving the same direction (DOM up + discount up) confirms a softening market. Diverging signals (DOM down but discount up, or vice versa) usually indicates a single agent or developer skewing the data; check the underlying transaction list.

Metric 9. Building approvals (forward supply)

Threshold: below 5% of existing dwelling stock per year. Hard cap: 10%.

Why it matters. Building approvals are the forward supply signal. A suburb with high approvals will see new dwellings hitting the market 12-24 months later, which moves vacancy and median price.

A suburb running 10%+ approvals as a share of dwelling stock is in a supply-shock setup. Even strong current demand-supply pressure can reverse within 18 months as the new stock completes. The threshold is most relevant for suburbs with a high unit-to-house ratio (Metric 11), since unit supply is more elastic than house supply.

Metric 10. Gentrification velocity

Threshold: between 1.5% and 3.5% pa over 5 years. Hard cap above: 5%.

Why it matters. Gentrification velocity captures the rate at which a suburb's demographic, income, and dwelling-mix profile is shifting toward higher socioeconomic strata. Sub-1.5% suburbs are static. The 1.5-3.5% band is where mid-cycle gentrifiers sit (the highest-return band historically). Above 5% the suburb has typically already gentrified and forward returns slow.

The signal triangulates ABS Census income deciles, dwelling-type mix shifts, and renovation activity from public planning records. Mid-cycle gentrifiers are often the highest-return suburbs the playbook surfaces, but only when paired with sub-50% 36-month growth (Metric 3) and sub-80% 10-year growth (Metric 4), to avoid catching the late-cycle gentrification overshoot.

Metric 11. Unit-to-house ratio

Threshold: below 60% units. Hard cap: 75%.

Why it matters. The share of dwelling stock that is units / apartments rather than detached houses. Suburbs above 60% units are unit-dominant. Their cycles run on different drivers (international migration, student demand, infrastructure) than house-dominant suburbs. Above 75% the suburb is functionally a unit market and house-cycle growth analysis doesn't apply.

For yield-prioritised investors, unit-dominant suburbs can deliver higher gross yields (4-5.5% vs 3-4% house-dominant). For capital-growth investors, house-dominant suburbs tend to deliver more durable long-term growth, because land value compounds and units are subject to forward supply shocks.

Metric 12. Rent growth (12-month + 5-year)

Threshold: above 3.5% pa (12-month). 5-year: above 4% pa.

Why it matters. Suburb-level rent growth is the cleanest signal of underlying tenant-demand strength. Sub-3.5% 12-month rent growth in a suburb claiming low vacancy is usually a data-quality issue. Verify against the state tenancy bonds register before trusting the vacancy reading.

5-year rent growth is the income-side counterpart to 10-year price growth (Metric 4). A suburb with strong 5-year rent growth and below-threshold 10-year price growth is the cashflow-strategy sweet spot, where yield is rising while affordability runway remains.

How to use the scorecard, sequential or composite

Two ways to apply the twelve metrics:

Sequential filter. Walk the metrics in order. Eliminate any suburb that fails on the strict-cap side of two or more metrics. Eliminate any suburb that fails on more than four metrics overall. The remaining shortlist is the candidate set for Step 4 (Pocket-level).

Composite score. Each metric contributes to a 0-100 score. propautopilot's 49-metric scorecard rolls all 49 into a single score per suburb (the Pap Score). Suburbs scoring above 75 with no individual factor in the strict-cap zone are the highest-confidence picks. Suburbs scoring 60-75 with one factor in the strict-cap zone are second-tier, workable for buyers who understand the specific tradeoff.

The Investor tier surfaces both views. The composite is faster. The sequential filter teaches the buyer where each suburb's specific strengths and risks live.

Common mistakes at Step 3

• Picking the highest-yield suburb. Yield without checking renter proportion + vacancy + 10-year growth selects for thin tenant pools, supply shocks, or late-cycle entry.
• Picking the highest-growth suburb. Same problem in reverse. High 36-month growth without 10-year context catches mean-reversion.
• Reading any single metric in isolation. The factor library is built around metric-relationship graphs (vacancy compensates renter proportion, demand-supply pressure prerequisites 36-month growth, gentrification amplifies rent growth). The combinations carry the signal, not the individual numbers.
• Skipping building approvals. Forward supply is the most under-read signal at Step 3. A suburb passing every present-tense metric but with 12% approvals as share of stock is a supply-shock setup.
• Conflating units and houses. A unit-dominant suburb with strong house-cycle metrics is still a unit market. The metrics that matter for unit returns are different (international migration, student demand, infrastructure).
• Ignoring data quality. Where state tenancy bonds data is thin, vacancy estimates carry larger error bars. Treat any single metric as a lower-confidence signal in suburbs with under 200 rental bonds.

Now do this on your shortlist, pull the live scorecard

The propautopilot suburb-page at /suburb/[id] surfaces the live 49-metric scorecard for every Australian suburb. Curious tier covers 3 free pulls. Investor tier unlocks unlimited.