Lagos townhouses: is 28 days real, or an active-listing illusion?

G

green_garden

Property investor
I have compared the saved Lagos townhouse listings by asking price and remaining lease length, but I still cannot tell whether 28 days reflects genuine selling time or merely the age of active adverts. The properties span roughly NGN 1,333,000,000 to NGN 2,000,000,000, and they are moving very differently.

Would recent completed sales be the better basis for separating lease-length effects from condition and initial overpricing? I would also like to identify when reductions occurred, since a listing that sold soon after a cut tells a different story from one priced correctly at the outset. What records or agent evidence would you ask for before using 28 days as an assumption?
 
The active listings alone cannot tell you how long it takes to find a buyer. They exclude homes that sold quickly and overrepresent the ones taking longer. I would track every removal from your saved set, then separate confirmed sales from withdrawals, price changes and listings that simply reappear.
 
What does the 28 days represent: the median age of today’s listings, or the time between first appearance and removal? Also, are all the properties within tightly drawn neighbourhood boundaries? A broad Lagos label can combine townhouses competing in quite different local markets.
 
I would not assume lease length is causing the difference yet. It may be travelling with condition, exact location or seller motivation. Compare properties with similar condition and neighbourhood first; otherwise a less appealing unit with a shorter remaining lease can make lease length look more decisive than it is.
 
There is also a timing mismatch in the question. A deal completed this month may reflect a buyer found earlier, while a listing removed this month may have been withdrawn rather than sold. Recent completions are useful, but they will not necessarily describe what changed among this month’s new listings.
 
A simple table would help: first-seen date, last-seen date, initial and latest asking price, remaining lease length, condition, neighbourhood, and final status if known. I’d add weekly new-listing volume too. If supply rose during the sample, slower movement may have little to do with the individual properties.
 
That is useful, but I would resist turning a small saved-listing set into a precise market clock. At this price range, financing readiness and how urgently the seller wants to transact can produce very different timelines. A range of observed outcomes is more honest than saying a townhouse “needs 28 days.”
 
Price-cut timing may explain some of the split. Measure from both the original listing date and the date the asking price entered your NGN 1,333,000,000–NGN 2,000,000,000 band. A property advertised above the band for weeks should not be treated the same as one launched inside it.
 
I agree on redrawing the neighbourhood groups. Even without naming smaller areas, test whether the apparent 28-day result survives when each property is compared only with nearby alternatives of similar condition. If the sample becomes too thin, that itself is a sign that one Lagos-wide figure is unreliable.
 
For completed sales, I’d record only cases where the outcome can actually be confirmed. Keep unverified removals in an “unknown” category rather than treating them as sold or withdrawn. That will leave fewer observations, but it avoids building the conclusion on guessed outcomes.
 
So the defensible conclusion for now is narrower: your active sample is around 28 days, but it does not yet establish buyer-finding time. Track new supply, cuts, removals and confirmed completions for longer, while separating neighbourhood, condition and lease length. If the pattern remains after those splits, it becomes much more meaningful.
 
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