The question behind this project

How do places to stay differ across Mexico City?

Looking for a place to stay means comparing neighborhoods, rooms, and prices. We explore Airbnb listings to see where the supply is concentrated and who offers it, without assuming every listing gets booked.

Read the analysis
Painting of apartment listings and room interiors being compared beside a street map with location pins in Mexico City.

Market research An analytical case study

Airbnb listings, prices, and hosts in Mexico City

How do location, room type, and host scale shape the visible listing market?

Airbnb listings in Mexico City

The finding

Listing prices and host concentration describe the supply on display. They do not directly measure bookings, occupancy, or realized revenue.

Analytical work

Connect price distributions, borough comparisons, and host portfolios while separating observed supply from unobserved demand.

FIG. 01Retained evidence

Listings are concentrated in a few boroughs

Cuauhtémoc12,514
Miguel Hidalgo4,573
Benito Juárez3,087
Coyoacán1,727
Álvaro Obregón978

Listings · five largest borough counts in the retained artifact

Inside Airbnb-derived borough counts. Artifact updated: 2026-03-02. This timestamp is not a verified collection date. Listing supply does not measure bookings, occupancy, or host income.

01

Show the distribution before the average

A few expensive listings can move the mean away from the typical listing. Compare distributions within room type and location, and inspect missing-price coverage before ranking boroughs on price.

02

Count listings and hosts separately

A host can operate multiple listings. Comparing each host segment’s share of hosts with its share of listings reveals the structure of supply. The map shows locations from a sample; borough totals come from the broader processed listing data.

03

Treat availability as an ambiguous signal

An unavailable calendar day may be booked or blocked by the host. A high advertised price may reflect room mix rather than stronger demand. These distinctions determine which questions the snapshot can answer and which require booking or longitudinal data.

Decision supported

What to do with this evidence

Use the snapshot to describe market composition and select comparable listing groups. Validate any pricing or expansion hypothesis with occupancy, realized prices, and repeated snapshots.

What this analysis cannot establish

  • Price and review fields contain missing values. The geographic scatter is sampled, and location summaries are at borough level.
  • A single listing snapshot cannot establish demand strength, an underserved market, or a causal effect on housing.

Continue the investigation

Inspect the evidence behind the story.