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

That is the first thing I’d resolve. Match by stable property characteristics where possible, not listing wording alone, because descriptions and agents can change.
 
Be cautious with matching, though. Without a dependable identifier, two similar buildings could be merged incorrectly. Flag probable relistings rather than declaring them duplicates.
 
Fair. Use confirmed, probable, and unknown. The point is to stop every refreshed advertisement from resetting the clock automatically.
 
This is becoming a data-cleaning question before it is a Seoul market conclusion. Until status and relistings are sorted, 32 days is best described as a live-sample observation.
 
Exactly, and it may still be useful for screening. If Elena is choosing what to investigate now, current listing age matters even when it is not a completed-sales statistic.
 
Useful for screening, yes; less useful for saying what changed this month. A month-to-month claim requires the same collection method and comparable property mix.
 
That distinction helps: current buying shortlist versus market trend. The same data can support the first without being strong enough for the second.
 
For the shortlist, I’d investigate the exceptions: properties with manageable leases that still linger, and difficult leases that moved quickly. Those cases test the proposed explanation.
 
Good approach. For each exception, ask whether price, condition, neighbourhood, financing, or seller flexibility offers a better explanation.
 
A useful summary table could therefore have four sections: quick completed deals, slower completed deals, withdrawals, and current actives. Put unresolved removals in a note, not whichever section seems convenient.
 
Would price per building be enough? Differences in income or usable space could make two asking prices misleadingly similar.
 
Not enough, but it is the supplied filter. Elena could preserve that range while marking properties that are not genuinely comparable, rather than forcing a more complicated metric from incomplete listings.
 
Another question: are price cuts counted from the first listing or only the latest version? The seller’s true time on market begins before a cosmetic reset.
 
First observed listing, with a note if earlier marketing is suspected. That gives a defensible minimum without pretending the observation history is complete.
 
“Minimum observed exposure” is better wording than “time to sell” for those cases. It makes the limitation obvious.
 
The lease field might also need bands rather than exact values if the listings describe terms inconsistently. Exact-looking data can create false precision.
 
Yes. Use broad, clearly defined categories and retain an unknown group. Don’t translate vague descriptions into invented dates.
 
I’m still wary of overengineering this. Elena’s practical statement—more listings, few worth buying—may be answered by a rejection log showing why each candidate failed.
 
That log would complement the market table. If most rejections are lease-related, the original observation gains support; if condition or pricing dominates, the story changes.
 
Record only the primary rejection reason plus one secondary reason. Allowing five reasons per property will make every category appear equally important.
 
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