Is energy performance really separating Copenhagen’s quick and stale listings?

A

alba_questions

Homeowner
I would like to identify what makes certain Copenhagen listings move sooner, but the sample may simply be too young. It covers properties asking from DKK 3,370,000 to DKK 5,055,000, mostly described as coastal homes, and the midpoint for listing visibility is only 12 days.

Energy performance is my leading explanation, though it could be masking condition, buyer financing or differences between nearby streets. Should I first anchor each property to completed sales, or track asking-price reductions, withdrawn listings and new-listing volume until there is enough history to separate those effects?
 
Twelve days sounds too early to label much of the sample stale. Energy performance may matter, but asking price versus recent completed sales is the first comparison I’d make. Condition can also blur the result: a renovated home with weaker energy performance may still attract more interest than an efficient property needing substantial cosmetic work.
 
Fair point on the 12 days—I’m using it as a visibility midpoint, not a definition of stale stock. I also need to tighten “coastal,” because several neighbourhoods can look comparable on a map while appealing to different buyers. Would you include withdrawn listings in the same analysis, or keep them separate until there is evidence they were withdrawn rather than sold?
 
Keep withdrawn listings separate. A disappearance does not tell you the outcome, and mixing it with completed sales could make the faster end of the market look stronger than it is. I’d also divide the sample by a clear neighbourhood boundary before testing the energy theory. Street, outlook and access can overwhelm a broad coastal label.
 
Buyer financing is another possible split. Two properties at similar asking prices may create very different total commitments once condition and likely improvement costs are considered. That does not prove buyers are pricing the energy result itself; they may simply be reacting to the amount of cash and work required after purchase.
 
A practical table would have one row per listing and separate columns for initial asking price, current price, first-seen date, neighbourhood, apparent condition, energy performance, and current status. Add completed-sale evidence only when available. Then look at whether price cuts cluster by energy performance after controlling, as far as your small sample permits, for location and condition.
 
Energy performance may have an effect of its own. The concern is that this sample cannot yet separate it from the features that often accompany it.

Older construction, dated interiors, improvement costs and an ambitious asking price can all influence buyer financing and time on market. I would add seller motivation and the volume of competing new listings to the table already suggested, then watch what changes as the sample ages. If similar homes in the same area behave differently after those factors are accounted for, the energy result becomes a stronger explanation.
 
The cleanest next step is to let the sample age. Record changes rather than taking another snapshot: which homes cut price, after how long, which are withdrawn, and which reach a confirmed sale. If the quicker listings still have stronger energy performance within the same neighbourhood and condition band, the theory becomes more persuasive. Until then, 12 days mainly describes listing age, not market resistance.
 
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