Montreal townhouses: does +3.9% reflect the market or the sample?

[small_quill]

The 19-day median is the number driving my uncertainty, because condition varies sharply across this small Montreal townhouse sample. The asking prices run from C$1,539,000 to C$2,308,000, and that spread makes the apparent 3.9% price increase difficult to interpret.

Renovated properties seem to attract buyers quickly, while dated ones stay available longer and later receive cuts. What I cannot see is whether that is mainly a condition effect or a response to new competing listings and buyer financing limits. Completed prices, first-cut dates and new-listing volume within tighter neighbourhood boundaries would help test the impression.

 

[AishaSlate]

I would not trust the +3.9% until you compare completed sales rather than asking prices. In that price band, one unusually renovated townhouse could shift a small sample substantially. Also separate days before the first price cut from total marketing time. A 19-day median can conceal stale properties that were withdrawn and relisted.

 

[tradeTheGlass]

Narrowing the areas creates another question: how many genuinely competing townhouses remain in each group? A broad Montreal label can make supply look deeper than buyers experience when the neighbourhoods are not practical substitutes.

New-listing flow matters here, not just the active count. Homes that appear briefly, attract little interest and are withdrawn can otherwise vanish from the analysis, distorting both the 19-day figure and the timing of price cuts. I would track those entries and exits alongside the condition categories.

 

[openTheKite]

I agree on boundaries, but I am not convinced that unrenovated automatically means negotiable. Some sellers may prefer to wait rather than accept a discount. Buyer behaviour depends on whether the asking price already reflects the work required and whether financing leaves enough room for renovations.

 

[openTheKite]

One way to test that: divide the sample into renovated, livable but dated, and needing major work. Then compare the first price-cut timing and completed-sale outcome within each group. It will not solve the small-sample problem, but it should show whether condition is driving the 19-day figure more than supply.

 

[eden_roofs]

Seller motivation is another hidden variable. A quick reduction after limited interest says something different from a listing that sits unchanged. Can you see whether the withdrawn stock later returns at a new price? If it disappears from the data entirely, supply may look tighter than buyers actually experienced.

 

[lina.sage]

There is a caveat with treating every withdrawal as shadow supply: some homes may no longer be available at all. I would track them separately rather than adding them to active inventory. For the practical buyer question, recent completed sales and the number of genuinely comparable new listings matter more than a broad stock total.

 

[eden_roofs]

Yes—three separate buckets would be cleaner: active, withdrawn, and withdrawn then relisted. I would also record whether each sale occurred before or after a price cut. That gives you a better basis for saying buyers negotiated, waited, or moved on, without assuming the seller accepted less simply because the listing vanished.

 

[frame.sunny]

The C$1,539,000 to C$2,308,000 range may itself be too broad for a small sample. Financing constraints and renovation costs can affect the upper and lower ends differently. I would narrow by neighbourhood and condition first, then use +3.9% only as a descriptive result—not evidence of a general Montreal movement.

 

[frame.sunny]

A useful final table could have one row per property: neighbourhood, condition category, original ask, price-cut date, final status, completed price where available, and days to each event. Then rerun the comparison without the most expensive and least expensive properties. If +3.9% disappears, the mix drove it; if it remains across comparable groups, the signal is more credible.