Continuing from the previous episode, Damien and Jeremy discuss how data and technology have changed property investing, and why some outdated advice still persists.
What Is "Old School" Investing?
Jeremy defines old school investing as simply not embracing available data and technology, noting this isn't necessarily about someone's age, some older investors have fully embraced modern data tools, while some newer ones haven't. Using the common advice to buy in high-income areas or close to the CBD as an example, Jeremy notes historical data shows neither has a meaningful correlation with capital growth, yet this belief persists, particularly among professionals who've built a public reputation (books, podcasts) around it, making it harder for them to walk back publicly. He also notes a practical incentive at play: a buyers agent operating in a single city or suburb has little reason to recommend elsewhere, since expanding into new markets requires more staff and effort.
Why Long Holding Periods Can Mask Poor Selection
Jeremy reiterates that the longer a property is held, the more its growth tends to converge toward the national average, meaning even a poorly selected purchase will eventually "come good" relative to a bog-standard benchmark, just not necessarily any better than that benchmark, regardless of how long it's held.Jeremy reiterates that the longer a property is held, the more its growth tends to converge toward the national average, meaning even a poorly selected purchase will eventually "come good" relative to a bog-standard benchmark, just not necessarily any better than that benchmark, regardless of how long it's held.
Why Data-Driven Investing Works Better
Jeremy outlines several advantages of a data-driven approach: it's completely objective and free of hidden agendas or personal bias (unlike a human assessment which can be skewed by something as trivial as being tired or having had a rude interaction at an earlier inspection that day); it can process a volume of market information (thousands of monthly auctions, for instance) no individual investor could physically track; and it draws on decades of historical data, far longer than most individual investors' or professionals' personal careers.
He also acknowledges a genuine limitation: a single data point like an auction clearance rate can't capture qualitative nuance (for example, two auctions might both technically clear at 100%, but one involved fierce competitive bidding while the other barely passed reserve with minimal interest), something a human observer on the ground might notice that raw data alone wouldn't reflect. Even so, he maintains the overall advantages of aggregated data outweigh this kind of limitation.
Why Filtering Out Markets by a Single Metric Can Backfire
Jeremy revisits his frequently made point about vacancy rate: research suggests around 2% marks the point where a rental market is roughly balanced, but he cautions that applying a strict "no more than 2%" filter can eliminate genuinely excellent markets purely due to small-sample volatility (for example, a suburb with only 40 rental properties, where a single vacant listing already equals 2.5%, purely because of how few total rentals exist there). His broader point: no market ever scores perfectly across every single metric (he suggests looking for something like 80+ out of 100 overall as a reasonable target), and manually filtering out markets based on any one imperfect metric risks discarding genuinely strong opportunities. He also notes those nearby suburbs and cities tend to move together in a "ripple effect" during a genuine boom, reinforcing the value of looking for clusters of strong data rather than fixating on any single suburb or metric in isolation.
How Technology Has Changed the Process
Jeremy recalls that before the DSR existed (pre-2010), his only real data source was the back pages of Australian Property Investor magazine, just a handful of published metrics that required manually scanning for any usable signal, one of the influences that led him to build a combined score in the first place. What used to take considerable manual effort can now be filtered in seconds using modern tools, compared to the guesswork, tips from the news, or informal anecdotal research that preceded it. Both note that using a specialised tool effectively still benefits from expertise (citing their own team's learning curve when onboarding a new staff member unfamiliar with their own platform), which is part of the value a consultation or expert guidance can add on top of self-service tools.
Why Trading Property Is Becoming More Viable
Jeremy explains the traditional "buy, hold forever" advice made sense historically because nobody could reliably forecast whether a new target market would genuinely outperform the one being sold, but improved forecasting capability now makes a more active trading approach increasingly viable, a trend he expects to grow. Both acknowledge the psychological and financial difficulty of trading in practice: the visible, immediate cost of capital gains tax on sale can feel much more painful upfront than the deferred, harder-to-visualise benefit of superior growth in a new market, even when the numbers genuinely favour making the switch. Jeremy shares that in his own portfolio, the properties he's held longest tend to be the ones where the capital gains tax liability has become large enough that selling and reallocating no longer makes sense, whereas underperforming assets with limited forecast growth are comparatively easier to justify selling and reallocating from.
Not All Data Is Equal
Asked about data lag, Jeremy notes property markets move far more slowly than something like the share market or crypto, comparing it to an oil tanker that can't turn quickly, meaning a data lag of a month or even a few months generally isn't a major practical issue, since the underlying nature of supply and demand in a market doesn't shift overnight. He reiterates that regardless of which platform or data source is used, understanding how to correctly apply filters (rather than accidentally excluding good opportunities, as in the earlier vacancy rate example) matters more than the platform itself.
What Smart Use of Data Actually Looks Like
Jeremy frames good data tools as fundamentally a shortlisting aid, helping direct limited personal research time toward genuinely promising areas rather than replacing due diligence entirely. He gives an example of how a greenfield estate can sometimes still score a high DSR (since demand can genuinely be strong even where oversupply risk exists), but a quick supplementary check (for instance, via Google Maps, or specifically checking building approval data, which DSR3 does factor in) can reveal that a seemingly attractive score doesn't account for the specific asset-level risks (depreciation on a new build, ongoing supply risk from nearby vacant land) that still make an established property nearby a better choice at a similar price point. He teases a future episode specifically covering examples of when buying near vacant land has and hasn't worked out.
Both note this doesn't necessarily apply the same way to a genuine owner-occupier prioritising lifestyle preferences (such as wanting a brand-new home to live in), acknowledging that not every property decision is purely about wealth-building.
An Example of Data Preventing a Poor Decision
Jeremy shares that he regularly fields questions from users pointing to a specific suburb with a mix of good and bad metrics, asking for guidance; in some cases, a quick check reveals an unremarkable overall DSR score despite one attractive individual figure, prompting him to flag the concern, though he notes he rarely receives follow-up feedback on what people ultimately decided to do.
On DIY Algorithms and Requests for Raw Data
Jeremy shares that he occasionally receives requests for raw data exports from people wanting to build their own independent analysis or algorithm. While he's cautious about directly saying so, his general view is that it's highly unlikely an individual, working alone, will meaningfully improve on an algorithm refined over 15 years of dedicated, full-time work, and that in his experience, providing a cost estimate for the data tends to end most of these inquiries. Both note the genuine risk of newer, unproven "proprietary algorithms" marketed by other providers primarily as a customer acquisition tool, without a demonstrated track record to support their claims, something that can only really be judged through back-testing over time.
DIY vs. Getting Help
Jeremy reiterates that the platform is designed to be usable independently, but acknowledges some users, after spending real time exploring it themselves, still want additional guidance, which is where engaging a buyers agent or a direct consultation can add value. Both note that requests to access new data slightly earlier than everyone else don't meaningfully matter, since a week or two's difference has negligible practical impact on a decision of this scale.
Closing Thoughts
Damien and Jeremy's closing advice: stay smart, check data from multiple sources (not just their own), and keep the relative cost of good data in proper perspective, spending even a couple of thousand dollars across several data providers is still trivial next to the tens of thousands of dollars a single percentage point of additional capital growth can represent on a typical property purchase. Jeremy notes that many investors research a property purchase with less rigour than they'd apply to buying a pair of shoes, sometimes relying on nothing more than a developer's own marketing material. They tease an upcoming, more provocative series without detailing it further, and close by encouraging listeners to like, subscribe, and share the episode with anyone who might be about to make an emotionally-driven property decision.