Seoul small multifamily: is 32 days real, or a live-listing illusion?

There is another timing trap: completion dates reflect the full process, while your 32 days appears to describe time until a buyer. Keep marketing time and post-agreement time separate.
 
I’m less convinced the exact day count is useful with mixed definitions. The stronger observation may simply be that lease compatibility separates the obvious candidates from the rest.
 
But that still needs testing against neighbourhood and condition. Otherwise “lease compatibility” becomes the explanation for anything that sits online.
 
A simple matched comparison would help: similar price, nearby location, comparable condition, different remaining lease periods. Even a few imperfect pairs could expose whether the pattern survives basic controls.
 
Matched pairs are useful, though the result should remain descriptive. Small multifamily properties can differ in ways that listing text does not reveal.
 
Would floor area or unit count be available consistently? Calling everything small multifamily may still leave materially different buildings in one group.
 
If those details are inconsistent, record missing rather than filling gaps from assumptions. Missing information may itself contribute to longer marketing.
 
I’d graph each property as a timeline: initial listing, price cuts, removal, relisting, and confirmed completion. Patterns will be easier to see than in one 32-day average.
 
The median may also be more informative than the mean if lease-related cases create the outliers Elena mentioned. Show both rather than choosing whichever looks cleaner.
 
And include the range. Two samples can share a 32-day average while one is tightly grouped and the other contains quick sales plus very stale stock.
 
One caution: active listings do not yet have final marketing times. A median calculated as though today were their endpoint will understate how long they eventually remain available.
 
Right. My suggested timeline would have one panel for completed or withdrawn spells and another for current age. No pretending they measure the same thing.
 
Neighbourhood grouping needs consistency too. Decide the boundaries before looking at which grouping supports the lease theory, or the exercise becomes subjective.
 
Could transport access be folded into location rather than adding endless columns? The aim is a usable comparison, not a model with more variables than properties.
 
That’s sensible. Start with price segment, defined neighbourhood, condition, and remaining lease period. Add other fields only when they explain a specific mismatch.
 
I’d put seller price changes alongside those four. Condition may be acceptable, but an unchanged ambitious asking price can keep a property active regardless of lease length.
 
There may be two seller strategies: price for the current lease situation, or wait for a buyer who accepts it. Their marketing times should not be read as identical demand signals.
 
Can the new-listing increase be separated into genuinely new properties and returning stock? Otherwise the apparent rise in choice could just be recycled inventory.
 
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