Free Data vs Paid

Responding to Listener Feedback

Damien reads out a comment from a viewer, Luke Powalski, responding to Episode 6's discussion on whether to buy one larger property or split a budget across two cheaper ones. Luke's comment raised several points in favour of buying a single, larger asset: less maintenance, lower management fees, fewer things to go wrong, less insurance and fewer tenants to manage, and the view that demand should increase over time alongside population growth and rising wealth.

Damien and Jeremy work through each point. On maintenance, management fees, and fewer things going wrong, both agree these are valid, practical advantages of holding a single property rather than two. Jeremy shares that during his period of holding 16 investment properties, the administrative burden (particularly around tax time) was genuinely significant, though he notes that during an active acquisition and capital growth phase, holding more properties was still preferable to holding fewer, even if concentrating that many properties in a single area would have been unwise. Both agree the practical cost of this kind of "headache" is real, but can be substantially reduced with a good property manager and reliable tenants.

On the claim that more affluent, higher-demand areas naturally attract more capital growth as population and wealth increase over time, Jeremy pushes back firmly. He distinguishes between genuine market demand, which reflects buyers with the financial capacity to actually push up prices, and simple aspiration or desire, which doesn't. He uses the example of a expensive mansion in Point Piper: many people might like to live there, but that widespread desire doesn't translate into actual demand capable of moving prices, in the same way a teenager wanting a Ferrari doesn't influence Ferrari pricing. He notes that historically, more affluent and expensive areas, particularly those closer to the CBD, have not shown higher capital growth, and that population growth and rising incomes have not been shown to have a significant impact on capital growth either.

Managing Risk in Cheaper Areas

Turning to a common concern about buying in cheaper areas, the worry that a lower socioeconomic tenant demographic might lead to property damage or unreliable tenancies, Jeremy outlines his approach to managing that risk: holding landlord insurance, using a good property manager, and avoiding being too aggressive with rent pricing. He suggests listing a property slightly under the property manager's suggested rent (for example, $485 instead of a suggested $500) to attract more applicants, then selecting the tenant with the strongest, most reliable rental history. Damien shares his own experience owning an older established property purchased for $489,000 (built in 1962, on a 650 sqm block), where after an initial $15,000 spent on cleanup and building and pest work, total repair costs over three and a half years came to just $1,300. Jeremy notes this aligns with what he'd typically budget for an established property, around $1,500 a year, and that with a good property manager and reliable tenants, cheaper established properties tend to hold up well.

Why Use Data at All

Moving to the main topic, Jeremy explains that data-driven investing means basing decisions on historical evidence of what actually drives property prices, rather than opinion, hearsay, or guesswork. Damien shares that he first encountered the concept of data-driven investing around 2014, discovering it through Ryan McLean's OnProperty podcast (where he also first came across Jeremy), at a time when relying on data was still relatively uncommon in the industry compared to today, when the term "data-driven" is used widely, sometimes more as a marketing phrase than a genuine practice.

Free vs Paid Data

Jeremy explains he strongly favours using the best available data, generally paid data, while acknowledging some free data (such as ABS data) is reliable and well vetted. At the other end of the spectrum, he's seen free data so poor that basing an investment decision on it could cost an investor tens of thousands of dollars, and finds it notable that investors will hesitate to spend a couple of hundred dollars on good data before committing hundreds of thousands, or millions, to a property purchase.

Both discuss how free data is often used deliberately as a marketing lead magnet to bring potential clients into a broader, more profitable relationship (mortgage broking, buyers agent services, financial planning referrals, and so on). Jeremy notes that while Suburb Data itself sells data and has its own bias in promoting its value, the underlying message is to remain appropriately sceptical of any provider and do proper due diligence, rather than assuming any one source, paid or free, is automatically trustworthy.

Putting a Value on Data

Jeremy illustrates the value of good data with an example: if a single additional metric improved a property's performance by just 1% in a single year, on a $500,000 property, that's worth $5,000. Against that, he'd happily pay $1,000 for that one metric, a fourfold, or 400%, return on that spend, which he argues is a far better return than many alternative uses of that money. Damien shares a personal experience of providing his email to a free data provider and receiving 15 emails within three days, and a separate case where insisting on his phone number led to ongoing spam calls.

The Trouble with Some Free Data Providers

Jeremy notes that some free data is genuinely useless, while other data is technically usable but tainted by the interests of the provider, citing property developer reports that highlight only positive aspects of an area, are focused only on the specific market being marketed at the time, and stem from feasibility research repurposed for marketing rather than genuine forward-looking analysis.

On evaluating any data provider (free or paid), Jeremy points to the level of care evident in the data itself, sharing an example of a provider whose published yield figures for various suburbs seemed clearly implausible once checked against listings on realestate.com.au and Domain, suggesting the data existed purely to generate leads rather than to be genuinely useful. Both note that a further limitation of relying on any single data snapshot is that figures like vacancy rate can shift significantly month to month, particularly in thinly traded markets with few landlords, so checking historical trends and looking for broader clusters of similarly performing suburbs (rather than one isolated hot suburb) provides a more reliable picture.

Why Data Is Comparatively Cheap

Both agree that given the scale of money involved in a typical property purchase, the cost of good data, whether from Suburb Data or a competitor, is genuinely small in comparison to the value it can add, and that investors going through a buyers agent should feel comfortable asking what data and methodology is actually being used to support a recommendation.

Jeremy shares his frustration with providers who market highly branded, proprietary-sounding algorithms aggressively without transparency, noting that no algorithm can predict the future with certainty, but a good one can meaningfully improve the probability of a good outcome. He acknowledges that earlier in his own investing journey (around 2008), he was persuaded by a confident, well-marketed but ultimately unreliable source into an investment that underperformed for close to 15 years and remains, to this day, still below its 2008 purchase price, an experience he estimates cost him around a million dollars in opportunity cost. Both note that the individual behind that advice faced no real consequences and remains active and well regarded in the industry, which they see as reflecting a broader lack of accountability in the space.

Using Data to Match Your Own Needs

Jeremy outlines three practical ways investors can use data to tailor their search: filtering by budget (identifying suburbs at or below an affordable price point), filtering by yield or cash flow needs (particularly relevant if a mortgage broker has flagged tight serviceability), and using data availability itself as a risk indicator, since a lack of data on a given suburb represents a form of risk in itself, since unknowns are what tend to cause harm.

Balancing Free and Paid Data

Asked how investors should balance free and paid data, Jeremy suggests that, particularly with more advanced algorithms, supplementing a good data-driven recommendation with independently sourced free data risks undermining the value of the algorithm's own analysis. Damien offers a slightly different take, suggesting free data can still serve as a useful sense check, comparing outputs across two or three different data sources (their own, a competitor's, and free data) to see whether they broadly agree, particularly for a significant, long-term purchase.

Trends

On the topic of trends, Jeremy shares his view that the longer a trend continues, the closer it likely is to ending, and generally recommends aligning with long-term trends while being cautious of short-term ones. Damien agrees, noting his preference for entering a market once the DSR has already been picking up for some time, rather than jumping in purely off a single data point, since that pattern offers some protection against volatility. Jeremy adds that reviewing the historical chart behind any sudden spike in the DSR helps distinguish a genuine emerging trend from a one-off fluctuation.

Reviewing Existing Properties, Not Just New Purchases

Damien notes that data isn't only useful for shortlisting new purchases, it's also valuable for reviewing an existing portfolio. He pushes back on the common advice to "never sell," arguing that if a property has shown little to no growth (or negative growth) over five to eight years, it's worth consulting a professional about whether to hold or sell, rather than assuming it will eventually turn around. He shares examples of clients holding underperforming one-bedroom apartments in areas like Melbourne's Docklands, Fitzroy, and West Footscray, where in some cases cutting losses and reallocating capital elsewhere made more sense than continuing to hold. Both caution that this decision requires careful analysis, since selling at the wrong time can also mean losing the ability to re-enter the market altogether. Jeremy shares that he ultimately sold the underperforming property from his 2008 investment mistake, describing it as a difficult but ultimately necessary decision once he was able to acknowledge the original error.

Closing Thoughts

Damien and Jeremy close with a simple takeaway: respect the data, regardless of the source, rather than assuming quality is guaranteed just because information is labelled as "data-driven." They encourage listeners to share feedback on the episode and subscribe for future content.