Dublin warehouses: is financing behind +0.7% movement and 43 days on market?

NimblePlan

First-time buyer
Established
Averages were obscuring more than they explained, so here are the actual parameters. I’m tracking Dublin warehouse listings from €736,000 to €1,104,000. The snapshot shows +0.7% movement and roughly 43 days on market, but negotiated discounts appear to change sharply with condition.

My working theory is that financing costs are creating more of that spread than headline demand. Does that hold up, or am I underestimating seller motivation and withdrawn stock? Please be specific about the Dublin neighbourhood and warehouse type.
 
Forty-three days is only meaningful if relisted and withdrawn properties are handled consistently. A warehouse can disappear, return with a revised price and look new again.

I’d compare recent completed sales with original asking prices, then record when each price cut occurred. Financing may matter, but condition creates both renovation costs and lending uncertainty, so the two effects may be difficult to separate.
 
You also need tighter boundaries. Ballymount industrial units, Finglas warehouses and stock around the north Dublin logistics areas should not automatically sit in one group. Access, yard space, loading arrangements and vacant versus occupied status can outweigh a modest citywide movement.

Is the €736,000–€1,104,000 range normalised by floor area, or is it simply the asking-price band?
 
It’s currently just the asking-price band, not adjusted for floor area, so that is a weakness. The +0.7% is the movement within my snapshot rather than a claim about every Dublin warehouse.

I’ll split the properties geographically and add floor area, occupancy, loading/yard provision and visible condition. Mateo’s relisting point is important: would you keep total exposure time as well as the latest listing date?
 
Keep both dates. Latest listing date tells you how buyers encounter the property now; total known exposure gives a better picture of seller difficulty.

I’m less convinced by the financing explanation, though. Without knowing whether likely buyers require debt, the pattern could just as easily reflect motivated sellers accepting reductions while better-positioned owners wait. Price-cut timing and completed-sale evidence should come before attributing the spread to borrowing costs.
 
A practical table could have one row per warehouse and columns for area, floor area, original ask, current ask, first-seen date, latest-listing date, withdrawal periods, condition and occupancy. Add the completed price only when available.

Then compare like with like inside the €736,000–€1,104,000 band. Otherwise one poor-condition unit with major works can make “discount” look like a financing signal.
 
I wouldn’t discard the 43-day figure completely. Used consistently, it can still show whether fresh supply is being absorbed or accumulating. Pair it with weekly new-listing volume and the number withdrawn, rather than treating days on market as a standalone measure.

Also separate warehouses from smaller industrial units with substantial office content. Buyers may price those layouts differently even within the same neighbourhood.
 
There may be an interaction rather than a choice between condition and finance. A buyer funding both acquisition and remedial work faces a different calculation from one buying a usable warehouse. That can widen offers even if underlying demand is unchanged.

Do you know whether the properties are vacant, occupied or being marketed with possession delayed? That could reveal more about the discounts than the headline +0.7%.
 
The next useful test is to group by neighbourhood, warehouse configuration and occupancy, then compare three timings: first appearance, first reduction and completion or withdrawal. If reductions cluster early, pricing expectations may be the issue; if stock sits unchanged and later disappears, seller motivation or financing constraints become more plausible.

With the current information, financing is a reasonable hypothesis, but not yet the strongest conclusion.
 
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