Mexico City serviced apartments: is January’s 36-day pace meaningful?

[sam.archer]

The 36-day figure for January 2025 made me look beyond the asking prices. Well-presented serviced apartments seem to have sold much faster than units needing work, while the difference between advertised and completed prices was about 2.3%.

That could reflect more selective buyers, but it could just be a thin or seasonal January sample. Before acting, I would want to know how many transactions produced those figures, the exact period covered and whether the pattern holds in the two neighbourhoods we are considering. Has anyone seen completed-sale evidence at that level rather than a citywide average?

 

[garden.far]

One month is too little to separate seasonality from a change in buyer behaviour. The first thing I’d want is the number of completed deals behind both the 36 days and the 2.3% gap. A handful of polished apartments could move the January result substantially, especially if slower listings remained unsold and therefore never entered the completed-deal sample.

 

[selma_rain]

Which two neighbourhoods, and what counts as a serviced apartment in your figures? An operator-managed unit, a furnished apartment with some services, and an ordinary apartment marketed as turnkey may attract different buyers. I’d also confirm whether “36 days” runs to accepted offer or completed transaction. Those dates can tell quite different stories.

 

[theo_cedar]

I wouldn’t dismiss buyer selectivity just because the period is short. Fast movement for well-presented units alongside longer exposure for properties needing work is at least consistent with buyers paying for convenience. But the 2.3% needs a clear denominator: original asking price, latest asking price after reductions, or asking price at the time an offer was accepted?

 

[walksAndWay]

Transaction volume matters more than the citywide percentage here. Compare the two neighbourhoods separately and split the sample by condition, building and approximate size. If possible, keep January 2025 as a fixed snapshot and note later revisions. Completed-sale records can arrive after the first version of a monthly figure, making an apparent shift disappear or strengthen later.

 

[mapTheSignal]

There is also a timing trap. A deal recorded as completed in January may reflect a price decision made earlier, while a listing shown as active in January belongs to the current market. Before connecting the change to seasonality or any policy event, line up listing date, price-change date, offer date if available, and completion date.

 

[workshop.solid]

The missing neighbourhood names really are central. Mexico City is too varied for one average to guide a purchase in two specific areas. I’d narrow it further to genuinely comparable buildings and ask whether the sale price includes the same furnishing or service arrangement advertised in the listing. Otherwise part of the apparent discount may simply be a difference in what transferred.

 

[WorthyVale]

A practical approach: make one row per completed deal and record neighbourhood, initial ask, final visible ask, completed price, first listing date, condition and whether services were included. Keep withdrawn and still-active listings in a separate tab. Then calculate the gap both from initial ask and final ask. That should reveal whether 2.3% means firm pricing or merely earlier reductions.

 

[theo_cedar]

That table would help, but duplicate and relisted properties need attention. A unit can appear to have a short marketing period after being relisted even though it was exposed earlier. I’d preserve the earliest identifiable listing date and flag uncertain matches rather than forcing them into the 36-day calculation.

 

[WorthyVale]

Agreed on relistings. I’d also resist calling January a buyers’ market or sellers’ market from sold data alone. If transaction volume fell while only the best-presented units completed, the 36-day figure could improve even though overall demand weakened. The count of active, withdrawn and completed listings is needed to interpret the speed.

 

[celine.reed]

So the useful test is not whether 36 days or 2.3% is “good” citywide. It is whether those figures persist within each target neighbourhood after matching condition, service arrangement and building type, correcting relistings, and using comparable dates. Repeat the exercise for several monthly cohorts before changing your timing. If the split survives those checks, selectivity is a stronger explanation; if not, January seasonal noise is more plausible.