Prague warehouse listings: is 119 days really the market pace?

S

sol.miles

First-time buyer
I would like to use 119 days as a realistic planning figure, but my saved Prague warehouse listings do not form a clean sample. They range from CZK 6,125,000 to CZK 9,187,000, move at very different speeds and often differ substantially in condition. The slower properties also tend to show poorer energy performance, although that may not be the real cause.

Would recent completed transactions provide a better baseline than the active stock? I suspect the live listings overstate normal marketing time because withdrawn properties disappear from view, but completed sales may hide earlier reductions or long periods at an unrealistic price.

Which records would settle this most reliably: original listing dates and asking prices, price-change histories, withdrawal dates, or evidence of when an offer was accepted?
 
You are probably giving the active listings too much weight. They are the unsold remainder, so slow or overpriced stock naturally becomes overrepresented. Completed deals would be more informative, but also track withdrawals and when price cuts occurred. A warehouse listed for 119 days after a late reduction tells a different story from one correctly priced from day one.
 
The 119-day figure is only useful if the properties are genuinely comparable. A sound warehouse in one part of Prague and a building needing substantial work elsewhere can produce very different outcomes even when their asking prices look similar.

Poor energy performance may be delaying buyers, but it could also be a visible sign of deferred maintenance. Conversely, a completed sale can show a long transaction period even when the seller accepted an offer quickly, simply because the buyer’s financing took time.

I would narrow the location boundaries, record condition separately and look for the offer-accepted date where it is available. That gives you a better test of whether the delay happened during marketing or after a buyer had already committed.
 
I’d split the sample into new listings, reduced listings, unchanged older stock and withdrawn stock. Then note condition and location separately rather than trying to explain everything through energy performance. I slightly disagree that completed sales alone solve it: without knowing the original asking price and marketing history, the final transaction can hide months of seller resistance. Revisit the saved listings monthly and record each status change.
 
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