In this episode, Damien and Jeremy discuss data accuracy and reliability for days on market (DOM), prompted by a listener's question about conflicting figures between data sources.
A Quick Update on Suburb Data
Before getting into the main topic, Damien shares a brief update on the upcoming Suburb Data platform, noting ongoing work behind the scenes on a new website and DSR algorithm, with a landing page available for anyone wanting to register interest ahead of launch.
Where Does DSR Source Its Data?
Responding to a listener question about where different data providers source their figures, Jeremy confirms DSR draws on Google, the Australian Bureau of Statistics, CoreLogic, realestate.com.au, domain.com.au, and a small number of other sources, with plans to continue expanding these sources as the platform grows.
What Is Days on Market?
Jeremy explains that days on market measures how long a property is advertised for sale, tracked by monitoring listing portals like realestate.com.au and domain.com.au from the moment a property is first listed until it sells, then aggregating that figure across all properties sold in a given market over a given month.
A Listener's Discrepancy: Townsville Data
A listener, Mac, asks whether Suburb Data's figures for Townsville are accurate, noting a specific suburb's days on market was showing as 50 days on the platform, while another tool showed just 14–15 days for the same period.
Jeremy explains that differences like this generally come down to variations in how a metric is measured or published across providers, not necessarily inaccuracy. He gives examples of common sources of variation: some providers publish vacancy rate at the postcode level rather than suburb level (which can be misleading, since some postcodes span over 100 different suburbs), some split figures by house versus unit or by bedroom count, some apply different rules around date boundaries, and some (in the case of vacancy rate specifically) exclude properties that were only listed for rent briefly before being snapped up, a practice Jeremy says Suburb Data doesn't follow, since it includes all such listings.
Why Data Accuracy Matters
Jeremy explains that days on market is a key read on the balance of supply and demand in a market: properties selling quickly point to a market where demand exceeds supply (encouraging buyers to act fast rather than risk missing out), while longer selling times suggest the opposite. Suburb Data updates this figure monthly, based on all properties that sold within the most recent calendar month (data updated by around the 7th of the following month), whereas Jeremy notes realestate.com.au measures days on market on a trailing 12-month basis, a difference in time frame that alone doesn't fully explain a gap as large as 50 versus 15 days.
Digging Into the Numbers
To properly investigate Mac's question, Jeremy pulled a sample of 16 actual property sales from the relevant Townsville suburb for January 2024 (the month in question), listing the date each property was first advertised and the date it was removed from sale, in order to calculate an actual days-on-market figure directly from the raw listing data.
The results: only three properties in the sample sold in under 15 days (one at 15 days, two at 9 days), while the rest all took longer than 15 days, with some as long as 216 days. Based on this sample, Jeremy finds it implausible that the suburb's genuine days on market for that month could reasonably be reported as just 15 days.
A Possible Explanation
Jeremy raises one plausible technical explanation: some platforms may stop counting days on market once a property goes under contract, even if that contract later falls through (for example, due to financing issues, an unsatisfactory building and pest inspection, or a low valuation during a cooling-off period), rather than waiting until the sale becomes fully unconditional and the listing is formally removed. He notes Suburb Data doesn't stop the clock until a sale is unconditional and removed from sale listings altogether.
Even accounting for this possibility, Jeremy remains sceptical it fully explains a figure as low as 15 days given the sample he reviewed. He also raises a broader consideration: since agents are the paying customers of major listing platforms, and a high days-on-market figure can make a market look sluggish (potentially making a property harder to sell), it's worth questioning whether some platforms apply calculation methods that present a more favourable picture for agents, though he stops short of asserting this with certainty, noting the only way to properly verify the true figure is to manually track listings over time, as he did for this analysis.
Closing Thoughts
Jeremy reiterates his view that no data is ever truly free, since acting on inaccurate free data can be far more costly than paying for reliable data in the first place. Damien notes that even resolving one metric's accuracy, as done in this episode, still leaves the broader challenge of combining multiple data sources into something genuinely useful for decision-making, which they suggest may be worth covering in a future episode. Both close by encouraging listeners to like, comment, and subscribe if they'd like to see more content like this.