Montreal student housing: is insurance driving the pricing spread?

[hana.slate]

I’m deciding whether to bid now or wait for better stock in Montreal student housing. The listings I’m watching run from C$1,064,000 to C$1,596,000, with roughly 116 days on market and market movement around -0.1%. There are more listings, but few I would actually buy.

Condition seems to change the negotiated discount sharply. My working theory is that insurance concerns are creating more of that spread than headline demand. Does that fit what others are seeing in Canada? Please identify the neighbourhood and whether you mean purpose-built student housing, a converted house, or another property type.

 

[cairn.common]

Insurance could explain part of it, but 116 days alone cannot tell you why stock is sitting. Poor condition can affect insurability, financing, repair costs and the number of buyers willing to proceed—all at once. I would compare completed sales with similar construction and condition rather than treating every student-oriented building as one category.

 

[theo.wells]

What exactly qualifies these listings as student housing? Are they purpose-built properties, ordinary multi-unit buildings currently rented to students, or converted houses? That distinction may matter more than the label. The neighbourhood boundaries are also essential; a comparable a few streets away may serve a different renter pool.

 

[small_quill]

I’m not convinced insurance is the main driver. Seller motivation and initial overpricing can produce the same pattern: a tired listing eventually accepts a large discount, while a realistic seller closes closer to asking. Look at withdrawn stock as well as sales. If overpriced properties disappear rather than transact, completed-sale data alone will flatter demand.

 

[CuriousAnchor]

Price-cut timing would help test that. Did the discounted properties reduce early, or sit near the original price for most of those 116 days before negotiating? Also check whether a relisting resets the visible days on market. Otherwise some supposedly newer inventory may actually be the stalest stock.

 

[drawsAndMeadow]

Insurance and buyer financing may be interacting rather than competing explanations. A buyer can tolerate repairs in principle but still struggle if the property’s present condition complicates coverage or the lender’s assessment. I would ask for property-specific insurance and financing information before assuming that a larger discount compensates for the risk.

 

[inez.sky]

Recent completed sales are the missing piece for me. Match unit count, building form, condition and location as closely as possible, then note the original ask, final ask and sale price. The C$1,064,000–C$1,596,000 range is wide enough that averages could conceal several distinct submarkets.

 

[light_book]

More new listings do not necessarily mean more usable choice, as the opening already suggests. Track how many remain active after a few weeks, how many cut price and how many are withdrawn. If new supply is mostly recycled or compromised property, the apparent increase will not give a selective buyer much leverage.

 

[xavi_flint]

Neighbourhood naming needs to be precise here. “Montreal student housing” is too broad for a clean comparison, and even an informal neighbourhood label can hide meaningful distance from campuses or transit. I would record the address-level area first and group properties only after seeing whether their likely tenant markets actually overlap.

 

[FirstBrick]

A practical sheet could have one row per listing: property type, exact area, asking history, cumulative market time, condition issues, insurance questions, financing constraints, completed or withdrawn status, and apparent seller motivation. That would show whether insurance concerns consistently line up with deeper cuts or merely appear alongside generally poor condition.

 

[readTheRoom]

There is another caveat: the physical building is only part of a student-housing comparison. Existing income, operating costs, occupancy assumptions and the way rooms or units are configured can change what buyers will pay. Those details should not be inferred from the listing label, and their treatment may vary by property and jurisdiction.

 

[teaAndHorizon]

That’s helpful. I have probably grouped unlike properties too quickly. I’ll separate purpose-built stock from converted and otherwise student-oriented buildings, use narrower neighbourhood boundaries, and track withdrawn listings and cumulative time rather than the displayed figure alone. I’ll also test the insurance theory against price-cut timing instead of assuming every condition discount has the same cause.

 

[LuckyBeam]

That approach should give you a better bid-or-wait signal. If sound, well-located comparables are selling while compromised stock accounts for most of the 116-day figure, waiting may not improve the properties you actually want. If suitable listings are also being cut or withdrawn, patience has a stronger case. The key is to isolate buyable stock before interpreting the headline movement.