Toronto inventory shifted in September 2025 — what are you seeing?

G

gia_reese

Property investor
I’m tracking Toronto listings from September 2025 and the mix appears to be changing. Well-presented townhouses seem to move in roughly 27 days, while properties needing work remain available longer. The gap between visible asking prices and completed deals looks close to 5.1%.

Does this suggest buyers are becoming more selective, or is it ordinary seasonal noise? I’m particularly interested in how people define the asking price, account for relistings, and separate citywide patterns from individual neighbourhoods.
 
The 27-day figure alone cannot answer it. How many townhouse sales are behind that number, and are they concentrated in one price band or area? A small group of renovated homes can make the market look faster than it is. I’d compare transaction volume and the distribution of selling times, not just one average.
 
Which asking price produced the 5.1% gap: the original list price or the last price displayed before sale? A property can be reduced, terminated, or relisted, so those two calculations may tell very different stories. Also, completed deals exclude listings that never sold, which may understate the divide between sellers and buyers.
 
The 27-day result would mean more if we knew how many completed sales produced it and where they were. A citywide figure can hide very different townhouse markets, especially when condition and price range vary. If September 2025 had a small sample, a few fast deals could move the average noticeably. Can you break the data into neighbourhoods and show weekly sale counts alongside the asking-to-sold gap?
 
There is another timing problem: “27 days” may not be comparable if some listings were previously on the market under another entry. I would track total exposure where possible, alongside days attached to the final listing. Otherwise, relisted properties can appear fresh even though buyers have been passing over them for longer.
 
Good points. My 5.1% observation was based on the visible asking price rather than a fully reconstructed original-price history, so I shouldn’t treat it as a clean market-wide measure. I also need to separate areas instead of blending every townhouse together. What would be the simplest table for testing this without overcomplicating it?
 
Start one row per completed deal: neighbourhood, property subtype, condition category, original ask if available, final ask, sold price, first listing date, final listing date, and whether a prior listing is apparent. Then calculate both sold-to-original and sold-to-final differences. Keep unsold September listings in a second table rather than quietly dropping them.
 
I agree with that layout, but I disagree that the sold-price gap necessarily measures buyer selectivity. It can also reflect sellers deliberately pricing above the level they expect to accept. Selectivity is more convincing if homes needing work have rising exposure or a lower completion rate compared with similar well-presented homes.
 
Condition also needs a reasonably consistent definition. “Needs work” could mean dated finishes, major repairs, or simply poor presentation in the photos. Those are not equivalent to buyers. I’d use broad, repeatable categories and compare within neighbourhoods; otherwise the condition label may absorb regional and price differences.
 
For policy timing, I wouldn’t assign a cause after one month. Add a dated note for any financing or policy event you think might matter, but first see whether activity changed before or after it. September can also contain a back-to-market rush after summer, so new inventory and completed volume should be read together.
 
A fixed cohort would reduce some of the revision problem. Save the listings first observed during September 2025, then follow that same group through sale, withdrawal, reduction, or continued availability. If the figures are recalculated later from only the surviving online records, the apparent gap and selling time may change because the underlying set changed.
 
One month is too narrow for a seasonal conclusion. Compare the same September weeks across available years, but keep the property and neighbourhood filters identical. Also compare September with the surrounding months. If the pattern appears only in one short window, I’d describe it as noise rather than a shift.
 
The practical test seems to be three views: September’s completed sales, all listings first observed that month, and neighbourhood-level subsets. Report the count beside every 27-day or 5.1% result, distinguish original from final asking price, and preserve relisting notes. If well-presented homes remain faster across several adequately sized subsets, the selectivity argument becomes much stronger.
 
You must log in or register to reply here.
Back
Top