Suburb Growth Metrics, Key Targets and Where the Data comes from?

Why Precise Metric Targets Are Misleading

Jeremy explains that in over a decade of analysing this data, he has never seen a market where every single metric sits on the favourable side of its benchmark. Each metric has some approximate relationship to capital growth and can inform an overall model, but none are precise enough to be relied upon individually, this is precisely why they're combined into a single algorithm rather than assessed metric by metric.

Why Setting Too Many Thresholds Backfires

Using vacancy rate as an example, Jeremy explains the commonly cited 2% benchmark reflects the point at which a market is considered balanced (below 2%, rents tend to grow faster than inflation; above 2%, slower). He shares a live example (Marrickville, Sydney units), where vacancy sits at 2.22%, notably above the national average (just over 1%) given the current environment of historically low vacancy rates nationally.

He illustrates the danger of setting a strict vacancy threshold with a hypothetical: a suburb might excel on every other favourable metric, but have only 40 landlord-owned properties out of 200 total dwellings. If just one of those 40 becomes vacant in a given month, the vacancy rate jumps to 2.5%, technically exceeding a 2% cutoff, even though this could still be one of the strongest markets in the country. Jeremy's point: the more individual thresholds an investor manually sets, the more good opportunities get needlessly filtered out, since an AI-driven model doesn't work by applying hard cutoffs to individual metrics, it blends all favourable and unfavourable signals into a single overall score. He cautions that investors using the Suburb Data platform often apply too many individual filters (for example, requiring vacancy under 2%, auction clearance above 75%, and a minimum yield simultaneously), which can eliminate every genuinely strong market from the results entirely.

Why Vacancy Rate Specifically Can Be Volatile

Jeremy notes vacancy rate can swing dramatically in smaller or more thinly traded rental markets, where a change of just one or two properties can shift the calculated percentage significantly, reinforcing why it shouldn't be treated as a strict pass/fail filter on its own.

Which Metrics Should Investors Actually Consider?

Rather than trying to apply personal judgement across many individual metrics (DSR, statistical reliability, typical value, dwelling count, long-term growth rate, area size, and so on), Jeremy stresses that even a suburb narrowly missing the benchmark on several metrics, but scoring well overall, can still be the best-performing market in the country, and that trying to manually weigh each metric risks discarding it unnecessarily. His view is that human judgement simply can't compete with an algorithm that's absorbed years of nationwide data across dozens of metrics, and that personal filtering should be minimised in favour of trusting a properly weighted composite score.

Damien adds that a market can also show a comparatively low DSR or weak individual metrics like stock-on-market and still continue to grow, since these figures represent a probability of capital growth, not a certainty. He compares acting on a genuinely strong indicator to avoiding a lottery ticket with poor odds, using the available data to meaningfully shift the probability of a good outcome in an investor's favour, even though no individual purchase is ever guaranteed.

The Three Filters Jeremy Actually Recommends

Jeremy suggests personal filtering should be limited to just three genuinely personal, non-negotiable constraints, rather than applied broadly across every available metric:

1. Budget — narrowing suburbs to a realistic price range (for example, setting a lower limit like $650,000 alongside an upper limit, to avoid an overly broad, unmanageable shortlist).
2. Risk appetite, addressed via the statistical reliability score (a measure, out of 100, of how much confidence can be placed in a market's underlying data). Jeremy typically starts around 65, and would be cautious going much below 50, though acknowledges a smaller budget may force a lower reliability threshold in order to find any matching markets at all.
3. Cash flow requirements, addressed via a minimum rental yield, but only if cash flow is a genuine constraint (for example, if a mortgage broker has flagged tight serviceability). If cash flow isn't a limiting factor, Jeremy wouldn't apply a yield filter at all.

Beyond these three, Jeremy explicitly avoids placing any restriction on the DSR itself, instead simply sorting all markets that pass the budget, risk, and cash flow filters by DSR score from highest to lowest, and starting from the top of that list, regardless of whether it produces five matches or five hundred.

How the Algorithm Weighs Different Metrics

Damien asks whether a market can still score strongly overall even if several individual indicators are below average. Jeremy confirms this is entirely possible, since different metrics carry different weightings based on their historical correlation with capital growth (referred to as a correlation coefficient), highly predictive metrics carry more influence in the overall score, while weaker ones carry less, a distinction an individual investor has no practical way of replicating manually, since it depends on extensive historical data an algorithm has processed but a person hasn't.

Strategy First, Data Second

Both hosts stress that before applying any of this data, investors need to define their personal strategy first, understanding personal goals, life plans (buying an owner-occupied home, travel, family planning, upcoming major expenses) and maintaining an emergency buffer, rather than starting with a broker conversation about borrowing capacity. Damien cautions that buying purely based on data and current lending conditions, without factoring in how personal circumstances might change, can be risky, citing the shift from around 3% interest rates to significantly higher rates as an example of the kind of change that catches investors out if it isn't planned for in advance.

Closing Thoughts

Damien and Jeremy summarise that while data is central to good decision-making, balance matters just as much, understanding personal strategy first, then applying a small number of genuinely relevant personal filters (budget, risk, and cash flow), rather than attempting to manually evaluate every available metric. They close by encouraging listeners to like, comment, subscribe, and leave reviews on their podcast platform of choice