Edinburgh inventory shifted in October 2025 — what are you seeing?

T

timo_taxes

First-time buyer
Established
The 57-day figure is what caught my attention in Edinburgh during October 2025. Well-presented serviced apartments appear to be moving around that pace, while properties requiring work seem slower. I am also seeing an apparent difference of roughly 1.3% between asking figures and completed prices, although I do not know whether the sample is large or consistent enough to mean much.

Could this be ordinary seasonal variation, or are buyers becoming more selective? Please identify the neighbourhood and property segment you are watching, and keep listing evidence separate from completed transactions. If the pattern survives a consistent sample and revision history, it may indicate a change; if not, it is probably mix or timing noise.
 
One month cannot separate seasonality from selectivity. The 57-day figure is interesting, but only if the same property definition and start-to-finish dates were used throughout.
 
How many serviced apartments sit behind that figure? A median from a small cluster could shift sharply when one unusually quick or slow property enters the sample.
 
Also, which asking price produces the 1.3% gap: the original price or the last advertised price? Reductions before agreement would make those comparisons tell different stories.
 
Exactly. And does “moving” mean marked under offer, removed from advertising, or legally completed? Comparing a listing milestone with completed-sale data could manufacture the apparent change.
 
I’d split the question into liquidity and pricing. Days advertised may show liquidity; the ask-to-sold gap may show pricing. They run on different timelines, so October listings need not match October completions.
 
My cautious answer is buyer selectivity within a seasonal period, not necessarily across Edinburgh. Condition appears relevant in the opening, but the evidence supplied does not yet establish a citywide shift.
 
A practical test: keep serviced apartments, ordinary flats, and houses needing work in separate rows. Then compare their listing dates, price changes, agreement dates, and completion dates without blending cohorts.
 
I’d add neighbourhood before interpreting anything. A citywide average can change because more properties appeared in one area, even if behaviour within every area stayed broadly similar.
 
Could “well-presented” be assigned only after seeing which properties sold quickly? That would create hindsight bias. The condition category needs to be recorded when the listing first appears.
 
Good point. Presentation is also subjective. A repeatable classification—ready to occupy, cosmetic work, or substantial work—would be more useful than relying on listing language.
 
Adding transaction volume would answer a new question: does the 1.3% difference persist across a meaningful set of completions, or is it being driven by a few properties? I can see why the percentage looks like a pricing signal, but a changing mix could produce the same result.

I would first group completed properties using the condition categories suggested above, then compare the figures within those groups. A consistent gap would deserve attention. If it disappears after that split, the headline percentage should not guide a pricing decision.
 
There is another timing issue: completed deals reflect decisions made earlier. If buyers changed behaviour during October 2025, completion records may show it only later.
 
Were withdrawn listings excluded? If only successful listings enter the 57-day calculation, it measures speed among movers rather than the experience of the full inventory.
 
For direct observations to be comparable, contributors should include area, property type, condition, original ask, latest ask, outcome stage, and the dates used. Otherwise anecdotes will pull in opposite directions.
 
Yes, although exact addresses are unnecessary. A neighbourhood-level entry with those fields would preserve privacy while revealing whether the pattern is concentrated.
 
Serviced apartments may need their own treatment throughout. Their presentation, intended use and buyer pool can differ from homes needing work, so the comparison may reflect segment mix rather than changing sentiment.
 
That is the main caveat for me. The opening compares two ends of a condition spectrum. It does not yet show whether similar properties became more selective month to month.
 
Would asking-price data include listings that were repeatedly removed and relisted? Resetting the advertised date could make marketing periods look shorter than they really were.
 
A stable property identifier would help with relistings, but even a manual note for suspected duplicates is better than counting each appearance as fresh inventory.
 
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