Milan warehouses: is the 8.0% movement really a supply signal?

D

daan_reads

Property investor
Before I use these figures to judge the next warehouse opportunity, I need to know whether they show more buyer choice or merely a group of difficult listings. The Milan sample runs from €794,900 to €1,192,000, with median marketing time near 81 days and an indicated downward movement of 8.0%.

Condition varies enough to make broad comparisons unreliable. The movement could reflect expanding supply, optimistic launch prices or weaker warehouses remaining visible for longer. Would you first split the data by neighbourhood, track when reductions occurred, or separate sold stock from withdrawn listings?
 
I would not treat 81 days alone as evidence of excess supply. Buyers can move on only when the alternatives are genuinely comparable in location, access, size and condition. Start by separating listings that received cuts from those marketed at one price. Early reductions suggest a pricing mismatch; later ones may say more about seller motivation.
 
Getting the boundary wrong could make the 8.0% movement look much broader than it is. I would first check whether the price and marketing-time figures cover the same dates and the same parts of Milan.

For example, a month containing more warehouses from a lower-priced district could move the sample without any individual property losing 8.0%. If the geographic mix is stable, then separate listings that sold from those that vanished without a recorded sale. If it changed, fix the neighbourhood split before treating withdrawn stock as a market signal.
 
Recent completed sales are the missing comparison. Asking-price movement and marketing time describe seller behaviour, not necessarily the amount buyers ultimately pay. If completed deals remain near their original expectations while stale listings are repeatedly reduced, your sample may mostly be capturing overpricing at launch.
 
I agree on completed sales, but I would not make withdrawn stock the centre of the analysis. Warehouses can be too property-specific for a withdrawn listing to represent supply that buyers considered interchangeable. Neighbourhood boundaries matter too: two nearby buildings may compete poorly if their condition or practical suitability differs.
 
Buyer financing could also separate “negotiating” from “moving on.” A buyer facing uncertainty over funding may favour a property needing less work, even if the headline price is higher. Can you divide the sample into usable condition, refurbishment needed, and unclear condition? That might explain more than the listing count.
 
Be careful not to classify condition only from polished listing language. Instead, note visible works, missing information and whether the price cut follows a long quiet period. If the rougher properties account for most of the 81-day median and most reductions, the 8.0% figure is not a clean Milan-wide signal.
 
A practical next pass would track each listing by first-seen date, original price, cut dates, current status and nearest credible substitutes. Then add new listings and withdrawals by week within the same boundaries. That gives you two useful patterns: whether alternatives are accumulating faster than they disappear, and whether sellers cut only after competing stock arrives.
 
Seller motivation may be the final piece. Two similar warehouses can behave differently if one owner is prepared to wait and another needs certainty. I would test the sample both with and without the longest-listed properties. If the 8.0% movement changes sharply, a few motivated or initially optimistic sellers are probably distorting the snapshot.
 
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