In this episode, Damien and Jeremy tackle a deceptively simple question: is there such a thing as a perfect suburb for investment? Their short answer is no, but the discussion covers why that's actually fine, and how to invest confidently anyway.
What "Perfect" Actually Means for an Investor
Jeremy clarifies upfront that this isn't about lifestyle appeal (tree-lined streets, parks, community feel), it's about investment potential specifically: high growth, low risk, and reasonable cash flow. Both note that new investors are often drawn in by "pretty" reports promoting a suburb based on nearby infrastructure (a new train station, airport, or shopping centre), without that necessarily translating into genuine investment potential.
Has a Truly "Perfect" Suburb Ever Existed in the Data?
Jeremy is unequivocal: in the entire history of the data he's analysed, no suburb has ever beaten the benchmark on every single metric simultaneously, even looking at just a dozen metrics makes this virtually statistically impossible. He notes that any report claiming a suburb is "perfect" for investment is either using an unreasonably narrow set of metrics or has selectively excluded less flattering data.
Does Recent Growth Rule a Suburb Out?
Jeremy cautions against dismissing a suburb purely because it's already had strong recent growth, since a market's short-term run (the last 2–3 years) doesn't necessarily reflect a longer 10-year cycle, and doesn't preclude continued growth going forward. He points to Perth as a market that continued to surprise many observers by sustaining growth (and strong yields) well beyond when some expected it to slow.
How Many Data Points Feed Into DSR3?
Jeremy estimates roughly 30+ underlying data points feed into the new DSR3 algorithm (noting some, like vacancy rate, are themselves derived from combining around a dozen other data points, effectively an "algorithm of algorithms"). He stresses that more metrics isn't automatically better, adding metrics that don't genuinely improve predictive performance can actually weigh an algorithm down rather than help it; each new metric is only included if it demonstrably improves overall results.
Which "Common Sense" Factors Actually Matter?
Jeremy confirms unemployment rate and job vacancies do factor meaningfully into the algorithm, but explicitly excludes infrastructure, since it can just as easily work against an area (added noise, more traffic, disruption) as in its favour, and its effects are inherently difficult to predict reliably in advance. On other commonly cited concerns (flood zones, bushfire risk, traffic noise, flight paths, proximity to high-voltage power lines), he agrees these matter for determining a fair purchase price, but says they generally don't meaningfully affect an asset's long-term capital growth once that price reflects the relevant risk.
Should You Compromise on the Suburb or the Property?
Asked directly whether it's better to buy the best available property in a mediocre suburb, or a more compromised property in a genuinely strong suburb, both agree without hesitation: pick the stronger suburb, since it tends to lift most properties within it, whereas an individually excellent property in a weak suburb doesn't get the same benefit.
Why "Undesirable" Suburb Traits Rarely Matter as Much as People Think
Jeremy reiterates that factors like a higher crime rate or nearby public housing can affect a suburb's perception and, in the short term, a buyer's comfort level, but don't show the impact on actual capital growth outcomes that many investors assume. On tenant-related concerns specifically, he outlines three practical protections: landlord insurance, choosing a strong property manager, and avoiding overly aggressive rent pricing (advertising slightly under the suggested top rent to attract more applicants and better select for a reliable tenant).
A Real 2014 Example: Western Sydney
Jeremy shares a genuine March 2014 back-test of the DSR3 algorithm, at a time he admits he was personally stuck in analysis paralysis, showing strong scores across much of Western Sydney (Canterbury, Blacktown, Penrith, Parramatta) alongside strong yields and low, tight vacancy rates. In hindsight, suburbs like Blacktown (then around $422,000, now a million-dollar-plus market) delivered extraordinary growth over the following years, part of a broader Sydney boom that saw around 80% growth citywide between 2012 and 2017. Jeremy notes he was personally holding an underperforming Gold Coast property throughout this same period, missing out on this run entirely, a real, lived example of opportunity cost.
Common Pitfalls When Chasing the "Perfect" Suburb
Both flag over-analysing individual metrics (something Jeremy admits he was guilty of early on) as a key trap, since a metric can look concerning in isolation while representing normal statistical noise rather than a genuine warning sign. They also caution against chasing "hotspot" hype uncritically, and against holding out for an "under market value" or off-market bargain, arguing it's often better to buy into a market that's already showing consistent signs of movement (even at a fair or slightly elevated price) than to wait for a discount that may never come, since the opportunity cost of sitting on the sidelines while other markets grow can easily run into tens of thousands of dollars.
Can the Data Always Be Trusted?
Jeremy shares a real example from December 2023 test data showing several suburbs with strong DSR scores but very low statistical reliability, extremely cheap properties (one around $108,000) or improbably high yields (around 11%), both signs of thin, unreliable underlying data rather than genuine opportunity. His practical filter: rule out any suburb with weak statistical reliability regardless of how attractive the headline score looks. By contrast, he shows a genuine example (effectively describing Perth during its recent boom) where both the DSR3 score and statistical reliability were strong simultaneously, alongside very tight vacancy rates (under 6% at the highest) and striking rental growth figures (in the 20–32% range over 12 months), a case where the underlying data quality genuinely supported the investment case.
Is There a "Perfect" Property, Once You've Found the Right Suburb?
Just as with suburbs, Jeremy says no individual property is ever perfect either, but the goal is finding one within roughly the top 5% for a given suburb. He outlines his own priorities: maximising land-to-asset ratio (using an example of wanting $400,000 of a $600,000 purchase price attributed to land, not the reverse), structural soundness, and genuine value-add potential (renovation upside, achievable rental uplift), while treating layout as a secondary, but still relevant, consideration when choosing between several similar options. He reiterates his consistent view on "under market value" purchases: in a genuinely strong market, this kind of deal shouldn't really be achievable at all, and chasing one is often a sign of looking in the wrong market rather than genuine skill. He also confirms proximity to a school specifically isn't something he separately factors in, since it's already reflected in the property's price.
Closing Thoughts
Jeremy sums up simply: there's no such thing as a perfect suburb or property, and the "right fit" depends entirely on an individual investor's strategy, budget, and goals. Their shared advice: define your strategy, buy with a set-and-forget mindset once you've done your due diligence, ignore marketing noise, and rely on genuine data rather than chasing an unattainable ideal. They close by encouraging listeners to like, comment, subscribe, and share the episode.