Montreal units show +3.2%, yet sit 76 days—what is the market telling me?

W

WorthyPorch

Homeowner
The surprising part was not the apparent +3.2% movement but that the Montreal units still showed a median marketing period near 76 days. That made me question whether the price change says much about demand at all.

The advertised range is C$243,000 to C$364,500, with enough variation in condition to make a simple average unreliable. I have heard seasonality, buyer financing and overpricing offered as explanations. Rental regulation may also affect occupied units, but I cannot tell whether buyers respond with conditional or lower offers or simply leave those listings alone. Should I first compare recent completed sales and occupied versus vacant units, then narrow the analysis by neighbourhood and financing profile?
 
Start with completed sales, not asking prices. A +3.2% movement in listings can coexist with weaker negotiation if cheaper stock was withdrawn or the mix of neighbourhoods changed. I would also separate occupied units from vacant ones before trying to judge whether rental regulation affected buyer behaviour.
 
What changed my view was how easily a 76-day median could be produced by mixing nearby areas that buyers do not consider interchangeable. The completed-sale comparison only helps if the units sit in the same practical search market.

I would redraw the sample around genuine substitutes at this price level, then compare condition, occupancy and access to financing within each group. Neighbourhood grouping is easy to revise; relying on a blended +3.2% figure in an offer is harder to undo. If the pattern disappears after the boundaries are tightened, rental regulation probably was not the main explanation.
 
Also, is 76 days measured to an accepted offer, a completed sale, withdrawal, or simply today for active listings? Those produce very different stories. A listing that disappears without selling should not be treated as evidence of buyer acceptance.
 
I would not assume regulation is the decisive issue. Condition and financing can stop a deal before rental terms are seriously negotiated. If the slower units also need work, buyers may be discounting renovation uncertainty rather than anything connected with a tenant.
 
Rafael’s caveat is fair, but occupied status can still change the buyer pool. The useful comparison is within similar condition: occupied versus vacant, same narrow area and similar size. Otherwise every explanation remains possible.
 
Another missing piece is new-listing volume. If fresh supply arrived while older listings remained available, 76 days may reflect buyers having more choice. If supply was shrinking, the same marketing time would look less comfortable for sellers.
 
I would record the first price cut and the days before it. A unit listed ambitiously for 50 days and sold soon after a reduction is different from one priced consistently for 76 days with no result.
 
That method risks overreading price cuts. Some sellers start high deliberately, while others list near the amount they will accept. Seller motivation matters, and the listing history alone will not reveal it.
 
True, but the timing still gives liamm80 something testable. Grouping reductions at least shows whether long marketing periods cluster before sellers adjust. It does not prove motivation; it helps separate stale pricing from a broad lack of demand.
 
On the original question, buyers can negotiate or walk away; the sample needs a way to observe which happened. Completed sale prices may show negotiation. Withdrawals and repeated relistings may indicate that buyer and seller expectations never met.
 
Could buyer financing be filtered at all? In this price band, two otherwise similar units might attract different buyers depending on condition and anticipated work. A long financing condition or a failed transaction could inflate marketing time without showing up clearly in the headline figures.
 
One practical spreadsheet would be enough: original ask, final ask, completed price if known, listing date, first reduction date, status, condition, occupied or vacant, and tightly defined neighbourhood. Leave unknowns blank rather than guessing. Then compare small matched groups instead of one overall median.
 
I would add relistings under a new presentation where you can identify them reliably. Otherwise a property may appear fresh after already spending weeks on the market. Do not merge uncertain matches, though; that could create a bigger error than the one you are correcting.
 
And keep the +3.2% claim modest until the mix is controlled. If more renovated units entered the sample, the apparent increase may be composition rather than appreciation. Recent completed sales with similar condition are the stronger comparison.
 
For the rental-regulation question itself, ask for the actual occupancy and rental facts attached to each relevant unit, then have the implications checked for Montreal and the specific transaction. Forum-level generalisations will not resolve a property-specific issue. For market interpretation, the matched-sales approach suggested above should show whether buyers priced that concern or simply left.
 
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