Mexico City warehouses: is 19 days real or an active-listing illusion?

[mina.reese]

We’re narrowing a Mexico City search to two neighbourhoods based partly on commute time, and the citywide average is not helping. Among warehouses listed from MX$5,040,000 to MX$7,560,000, my current sample suggests roughly 19 days to find a buyer.

The apparent outliers mostly have weaker energy performance, but I may be overreading the stock still online. Before we adjust our expectations, how would you compare this with recent completed deals, withdrawals and price cuts?

 

[drawTheHarbor]

The listings still online are almost guaranteed to distort the picture: fast-moving properties disappear while unsuccessful ones remain visible. Also, how are you defining “find a buyer”? A listing going offline after 19 days could have sold, been withdrawn or simply been relisted. I’d separate those outcomes before treating 19 days as a market pace.

 

[kian_ash]

How many listings are in the sample, and are your neighbourhood boundaries exact? A warehouse just outside one boundary may have a similar commute but a very different pool of buyers. New-listing volume matters too: 19 days during a burst of fresh stock means something different from 19 days when very little is being listed.

 

[VeraDean]

I’m not convinced energy performance is necessarily driving the outliers. It may be standing in for overall condition: properties needing more work can also have poorer energy characteristics. Compare similar condition, size and location first, then see whether the energy pattern remains. Otherwise one visible feature gets credit for several differences.

 

[drawTheHarbor]

A practical approach is to track each property from the first day you see it. Record asking price, price-cut dates, status changes and whether a completed sale can later be identified. Keep withdrawals in a separate column rather than counting them as sales.

I’d also note whether financing appears relevant. Time to an accepted offer and time to completion are different things, and the online history may not tell you which one you’re observing.

 

[miro_east]

Seller motivation could explain part of the spread as well. A well-priced property from a motivated seller may move quickly despite average energy performance, while an efficient building with an ambitious asking price can sit. Can you divide the sample into no price cut, cut before day 19 and cut after day 19? That would make the timing more informative.

 

[miro_east]

One further caveat to my price-cut suggestion: don’t compare the cut date only with the final disappearance date. Keep the original ask and last ask visible, because a quick result after a reduction may really be a long marketing period at the wrong price. I’d build separate tables for each neighbourhood and add a third commute-based group for nearby properties outside the formal boundaries.