Brussels apartments: is 114 days real or a live-listing distortion?

F

friendly_kite

Seller
Established
I’m tracking Brussels apartments listed between €765,400 and €1,148,000, particularly around the school catchment we are considering. The current sample suggests roughly 114 days to find a buyer, but the longest-running listings often appear vacant.

I’m trying to decide whether to treat that as a realistic selling period or as distortion from stale stock. Would recent completed sales, withdrawn listings and the timing of price cuts tell a different story? Agents have also given conflicting explanations about seasonality.
 
The listings still online will naturally make the market look slower because the successful ones disappear. How are you handling relisted properties and anything withdrawn without a sale? Also, does 114 days run from the original listing date or the latest appearance? Those details could materially change it.
 
That is the weakness in my calculation. I can’t reliably connect every relisting to its earlier appearance, and withdrawn stock is largely invisible once it goes offline. I’ve also pooled listings near the catchment rather than separating each neighbourhood boundary. So 114 days is better described as the age of the visible sample, not a completed-sale figure.
 
In that case I would stop using it as an estimate of time to buyer. Take a group of new listings, then follow each one as live, reduced, withdrawn or apparently sold. Record the first appearance and first price cut separately. It takes longer, but it avoids mixing fresh listings with properties that have already failed at another price.
 
I’m not convinced vacancy itself explains the outliers. An empty apartment may be easier to view. Vacancy could instead be standing in for condition, an unrealistic asking price or a seller who can afford to wait. Compare vacant and occupied homes only after matching them reasonably closely on location, condition and price.
 
The catchment element may also be obscuring things. Two apartments that look close on a map can appeal to different buyer groups if they fall on different sides of the boundary. I would divide the sample into the exact area you would accept, nearby alternatives, and everything else. Otherwise a neighbourhood effect may be mistaken for seasonality.
 
One month cannot tell you much about seasonality on its own. New-listing volume matters too: a burst of fresh stock lowers the average age even if buyer demand has not changed. Completed deals also reflect decisions made earlier, while current listings reflect today’s seller expectations. They answer related but different questions.
 
For a purchase decision, I’d make a short comparison table rather than chase one market-wide number: exact neighbourhood, condition, vacant or occupied, original ask, current ask, first-seen date and current status. Ask agents for examples of recent completed sales in the same narrow area, while recognising that financing and seller motivation can make superficially similar cases move very differently. The 114-day figure is still useful as a warning about stale listings, just not as a clean forecast.
 
You must log in or register to reply here.
Back
Top