Seoul serviced apartments: is 110 days a real market signal?

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DaanGale

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I may be too close to this sample to judge it clearly. Seoul serviced apartments listed between ₩899,800,000 and ₩1,350,000,000 appear to need roughly 110 days to find a buyer. The outliers mostly seem connected to property-tax concerns.

There are more listings this month, but not many I would actually buy. Before deciding whether to wait or bid, should I trust that 110-day figure, or are recent completed sales likely to tell a different story from the stock still online?
 
The listings still online will naturally contain more stubborn properties, so 110 days may describe the unsold remainder rather than normal selling time. I would compare recent completed sales, withdrawn listings and active stock separately. Otherwise, an overpriced unit that sits indefinitely can distort the whole sample.
 
How are you defining the start and finish? First advertised date to contract, first advertised date to completion, or simply the current age of an active listing? Relisted units are another problem because their visible age can reset even when the seller has been trying for much longer.
 
Neighbourhood boundaries matter too. A citywide range this broad could mix locations that buyers do not view as substitutes. I would rather see several small local groups, even if each has fewer observations, than one Seoul figure that looks precise but combines unlike properties.
 
Completed sales are necessary, but I would not treat them as the full answer. They exclude sellers who withdrew rather than accept the market price. If withdrawals have increased alongside new listings, completed deals alone could make conditions look healthier than they are.
 
A useful split would be: sold, withdrawn and still active. Then keep original asking price, latest asking price and days until the first reduction. That would show whether 110 days reflects genuine buyer hesitation or sellers spending two months testing an ambitious price.
 
Condition could explain why the expanded inventory still feels unbuyable. Two units in the same price band may differ sharply in fit-out, maintenance needs or how readily the space can be used. Listing count is not the same as the count of credible alternatives.
 
I would also pull the property-tax outliers into their own group rather than letting them define the trend. If buyers are making different tax assumptions about those units, their marketing periods are not clean comparisons with otherwise similar serviced apartments.
 
The missing piece for me is whether the new volume is genuinely new stock. Duplicate advertisements, relaunches and small price edits can all make supply appear to rise. Tracking the same physical unit instead of each advertisement would make the month-to-month comparison much more useful.
 
Buyer financing may be part of the delay, especially at the upper end, but it should not be assumed from listing age alone. Is there any way to distinguish offers that failed to proceed from properties that received no acceptable offers? Those are very different signals.
 
Agreed, although I would be cautious about assigning too much to financing without deal-level information. Seller motivation may be easier to infer: repeated cuts, vacant possession language, or withdrawal after no reduction each suggest different behaviour. The final sale price matters less without that path.
 
For a practical sheet, I would record one row per unit: neighbourhood, first-seen date, relisting dates, original and current price, first-cut date, condition notes, tax concern, and final status. Even incomplete entries should expose whether the 110-day result is concentrated in one subgroup.
 
That would also answer my concern about the meaning of “more listings.” If the additional units are mostly relisted or poor-condition stock, buyers have not really gained much choice. If they are fresh, comparable units, then waiting becomes a more defensible option.
 
I would match completed sales to listings by neighbourhood, condition and price position rather than calculate one overall average. A small number of attractive units selling quickly can coexist with a large stale tail. Both observations may be true without either describing the whole market.
 
The neighbourhood split should be quite tight. Buyers can reject a property over a boundary that appears trivial on a citywide map because the immediate surroundings and convenience differ. Start with the areas already acceptable to you, then compare only realistic substitutes.
 
One more timing issue: a price reduction may renew buyer attention, but it should not restart the marketing clock in your analysis. Keep both total days since first appearance and days since the latest cut. That separates stale exposure from the response to a more realistic price.
 
There is still a caveat with completed deals: they show the price at which agreement became possible, not necessarily the asking level buyers currently face. If sellers now have different expectations, older completions can anchor the analysis to conditions that are no longer available.
 
So the provisional reading is not “Seoul serviced apartments take 110 days.” It is closer to “the currently visible sample has accumulated about 110 days of exposure.” That is useful as a warning about seller expectations, but not yet enough to estimate how quickly a well-priced, suitable unit would sell.
 
What decision would change if the corrected number were much shorter? If the choice is whether to bid now or wait, the better question may be whether any current unit is acceptable at a price supported by close completed sales, rather than whether the citywide clock says 80 or 110 days.
 
Exactly. Set the acceptable property criteria first, then look at the selling history of that narrow group. If none of the extra listings meets those criteria, higher volume does not improve the buying opportunity. It only gives more evidence about what the market is struggling to absorb.
 
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