Vienna serviced apartments: are 67 days and high service charges misleading me?

A

AbleLens

Property investor
Established
I’m tracking Vienna serviced apartments listed between €651,400 and €977,000. The current sample suggests roughly 67 days to find a buyer, with many of the slower outliers also carrying higher service charges.

Is that pattern visible in recent completed sales, or is the live stock distorting the picture through withdrawals, relistings and unsold properties? I’m trying to decide whether 67 days reflects this month’s market or mainly the listings left behind.
 
Live stock will usually overrepresent difficult listings, so I wouldn’t treat 67 days as a completed-sale timeline without matching the two groups. How are you handling withdrawn and relisted units? Also, are all Vienna neighbourhoods combined? At this price level, location and condition could explain both the time online and some of the apparent service-charge effect.
 
At the moment I’m counting listings still online, so withdrawals and relistings are the weak point. I also combined Vienna rather than using strict neighbourhood boundaries. That probably makes the 67-day figure more of an “age of available stock” measure than a true time-to-sale result. I’ll separate completed, withdrawn and still-active properties before drawing a conclusion about the charges.
 
Either removing the higher-service-charge buildings or treating their charges as the main cause of delay feels unsatisfactory. The first option may discard a real part of this market, while the second overlooks differences in condition, layout, location and ambitious initial pricing.

I do not think separating active, withdrawn and completed properties will by itself establish that the charges drive the 67-day figure. As a narrower test, record when each reduction occurred and what happened next. A unit that finds a buyer soon after one cut points toward price resistance; one that remains available after repeated cuts suggests the charge or some other property-specific issue may matter more.
 
A simple comparison table could help: neighbourhood, condition, initial asking price, latest asking price, service charge, first-listing date and outcome. Then group new listings by week. If fresh supply was unusually high this month, the active sample could look younger; if few new properties arrived, the remaining stock could push the apparent age upward.
 
Seller motivation and buyer financing also matter. Two similar units can have very different timelines if one seller holds firm while another needs certainty, or if a prospective buyer needs longer to arrange funding. Completed sales may clarify the eventual price, but they won’t fully reveal failed negotiations. I’d focus on whether high-charge units require earlier or larger price adjustments, rather than days alone.
 
One more practical point: preserve the earliest date you can verify when a property disappears and returns. Otherwise a relisted unit may look like new supply and hide its real marketing history. I’d also report a range alongside the 67-day figure; a single average can be pulled around by a few long-running outliers.
 
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