The Foundations of High-Performing Property Selection

Suburb Selection Comes First

Jeremy reiterates that suburb selection is fundamentally a data problem, the DSR was originally built to answer exactly that question. He notes the growing number of competing data providers and algorithms now on the market, and reinforces his long-standing preference for cluster analysis: looking for a genuine cluster of well-performing suburbs in a region, rather than trusting an isolated, one-off standout suburb with no supporting neighbours. He also makes the case for spending on additional data sources as a strong return on investment in its own right, using an example where a $1,000 data subscription that improves capital growth by just 1% on a $500,000 property nets a $5,000 benefit within a year.

Property Selection: Asset-Level Considerations

Once a suburb is chosen, Jeremy's core asset-selection principle is maximising land-to-asset ratio in dollar terms (not just land size), aiming to have as much of the purchase price as possible allocated to land rather than the building. Damien adds that freshly renovated properties often carry a visible price premium (since a buyer pays not just for the improvement, but a proportionally higher stamp duty too), and suggests that for borderless investors buying in an unfamiliar city, a good property manager can help identify simple ways to lift rental return on an older, structurally sound but slightly dated property, rather than paying a premium for someone else's renovation.

Jeremy reiterates a consistent theme: since suburb selection does most of the heavy lifting, even an average property choice in the right suburb will typically outperform an excellent property choice in the wrong one. He'd rather pay above perceived fair value in a genuinely hot, well-selected market (since it can look like a bargain in hindsight) than secure an apparent "under market value" deal in the wrong market, which he considers a red flag for a cooling or declining market overall.

Common Emotional Pitfalls

Jeremy identifies analysis paralysis and its opposite, fear of missing out, as the two most common emotional traps, along with an overly cautious avoidance of lower socioeconomic or higher-crime suburbs that may actually represent a missed opportunity, since he considers this fear less damaging than rushing into an ill-considered purchase, but still a source of real opportunity cost. Damien notes the most common mistake he's seen in his own experience is investors rushing a decision (such as an off-the-plan purchase) without properly accounting for changing personal circumstances (like starting a family), and observes that many people research a holiday far more thoroughly than a property purchase worth many times more. Both also flag the influence of curated "financial freedom" lifestyle content on social media as an increasingly common driver of rushed decision-making.

Learning From His Own Mistakes

Jeremy shares a personal example of cross-collateralising loans early in his investing journey (using multiple properties as combined security for a loan to reduce costs), which later caused complications when he wanted to sell just one or two of those properties. Damien adds a related example he's seen often: investors funding a new deposit by withdrawing personal offset savings rather than first completing a proper equity release, missing out on tax deductibility, sometimes simply because their broker prioritised speed and convenience over the most tax-effective structure. Both stress that a mortgage broker isn't necessarily an expert in overall investment strategy, and that getting the right structure advice matters as much as picking the right property.

What "Success" Actually Looks Like

Jeremy stresses that outperforming the national growth benchmark, not simply achieving any capital growth at all, is the real measure of a successful purchase; an investor who's only achieved 3% growth over a decade when the national average was 6% hasn't had a good outcome, even if it feels like one in isolation.

How the DSR Differs From Simpler Individual Metrics

Jeremy explains that individual metrics commonly published elsewhere (median values, auction clearance rates, vacancy rates, days on market, stock on market) are typically derived from just two or three raw data points, making them prone to significant month-to-month volatility. The DSR, by contrast, combines a much larger set of these individual metrics (auction clearance rate, vacancy rate, stock on market percentage, days on market, vendor discounting, online search interest, and more) into a single composite score, assessing demand relative to supply from multiple angles simultaneously rather than relying on any single lens.

How New Metrics Get Tested

Asked how a new candidate metric is evaluated, Jeremy explains it requires testing across a wide range of suburbs and a long historical window, since a metric can appear to correlate strongly with capital growth in one era and not in another. He shares an example of someone once presenting a striking correlation between typical ownership hold periods and future capital growth, based on only five years of data; when Jeremy tested the same relationship against roughly 27 years of history, the correlation didn't hold up consistently across different eras. He also notes some metrics with no direct standalone correlation to capital growth can still add value in combination with other metrics, and that newer or smaller data sets are sometimes revisited later once enough history has accumulated to test properly.

How Often Should Investors Recheck Their Own Property's DSR Score?

For an existing portfolio (rather than searching for a new purchase), Jeremy suggests checking in roughly every six to twelve months, getting a fresh valuation and reviewing whether the local market's demand-to-supply balance has shifted. If a market has clearly slowed, this creates a decision point: draw out equity to buy again, sell and reallocate elsewhere, or pay down non-deductible debt, though he acknowledges the practical hassle (selling costs, capital gains tax, buyers agent fees) understandably deters some investors from acting on this regularly. Both agree there's a legitimate role for a more active, trading-oriented approach to property for investors willing to take on that extra complexity, a topic they intend to explore further in future episodes. Jeremy also notes that many investors fail to properly benchmark their own portfolio's performance against the national average at all, sometimes being satisfied with modest growth without realising it has actually underperformed a reasonable benchmark.

What to Check on the Ground Once a Suburb Is Shortlisted

Jeremy's first practical check is confirming actual property prices align with expected budget for that suburb, followed by checking Google Maps for nearby vacant land that could represent future oversupply risk, work he says can mostly be done remotely without needing to physically visit the area. He notes that if a buyer's budget doesn't match a shortlisted suburb's typical price point, the choice becomes whether to compromise on the specific property within that suburb or shift budget expectations to a different, better-matched area altogether, rather than treating individual property selection as the primary decision point.

Why Jeremy Generally Ignores Infrastructure Projects

Jeremy explains infrastructure is inherently difficult to forecast reliably: a new project can add value (job creation, convenience) or detract from it (noise, traffic), and can also be delayed or cancelled entirely due to funding or political changes, citing a past Commonwealth Games relocation as an example. His preferred approach is waiting to see actual, observable demand materialise in the data before acting, rather than speculating on an infrastructure project's eventual impact ahead of time, since getting the timing wrong on an unbuilt or delayed project can mean a substantial opportunity cost compared to simply investing where demand is already provable. Both agree the right time to invest is generally once early signs of upswing are already visible in the data, rather than trying to pre-empt a boom that may never eventuate as expected.

Has a Strong DSR Score Ever Led to Underperformance?

Jeremy is candid that the DSR isn't perfect and has had failures against the national growth benchmark, sometimes for reasons that remain genuinely unclear, and sometimes due to identifiable external shocks: infrastructure project cancellations, changes in government or policy (citing the 2018 federal election and negative gearing uncertainty as an example that dampened investor sentiment in some areas at the time), interest rate movements, or immigration policy shifts. He notes COVID had the opposite effect to widely predicted "property Armageddon" forecasts, likely driven by historically low interest rates rather than the disruption itself.

Damien recalls one specific example he tracked closely: the DSR remained strong for Brisbane for an extended period before growth actually materialised, a delay he found notable at the time. Jeremy attributes some of this uncertainty to the imperfect, still-developing nature of the algorithm, while both note that in hindsight, an ideal, perfectly-timed investor could have captured Hobart's earlier upswing, then Adelaide, then Brisbane, in sequence, following where DSR strength appeared over time, illustrating the value of geographic diversification and willingness to reallocate rather than concentrating in a single market and waiting.

Closing Thoughts

Both reiterate their layered approach to shortlisting: start broad (state, then city, then local government area, then suburb), looking for confidence at each level via genuine clusters of strong data rather than isolated pockets, gradually narrowing down with more confidence at each step, rather than jumping straight to an individual suburb or property based on a single strong signal. They close by encouraging listeners to like, comment, subscribe, and share the episode.