Analysis of Airbnb in Málaga (2024)

The problem

Málaga is one of the fastest-growing tourist destinations in Spain. Airbnb concentrates thousands of listings, but 65.8% of properties are in the City Centre. Saturation has turned that area into a highly competitive market with tight margins.

Is it still profitable to invest in the City Centre? Are there better opportunities in peripheral neighbourhoods? How does demand behave throughout the year?

This project analyses 9,420 properties from Inside Airbnb (2024 dump) using exploratory, geospatial and temporal analysis techniques, aimed at investors, hosts and tourism consultants.

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The approach

I designed a three-phase analysis.

Phase 1 · Cleaning and preprocessing — Python

I loaded the data with pandas. I normalised prices (range €15 to €410), handled missing values and removed outliers by neighbourhood using the interquartile range (IQR). I imputed missing values with the neighbourhood mean.

• Total records: 9,420
• Unique hosts: 3,504
• Neighbourhoods: 11
• Most common property type: entire home (86.8%)
• Marginal types: hotel room (4 records) and shared room (2 records)

Phase 2 · Exploratory and geospatial analysis — Python

I calculated key statistics and created heatmaps with Plotly and Folium.

Phase 3 · Temporal analysis — Python

I used review frequency as a proxy for occupancy to detect seasonality. I analysed monthly distribution and calculated the correlation between reviews and availability (coefficient = 0.10, very weak).

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The findings

Finding 1 · The City Centre concentrates supply, but not the best price opportunities

Campanillas, Puerto de la Torre and Churriana have higher average prices than the City Centre. Tourist saturation in the City Centre and competition from hotels and other channels push prices down.

The City Centre has the most supply, but it is not the most expensive for guests. Saturation has flattened its prices.

Finding 2 · Western peripheral neighbourhoods show better profitability potential

Those same neighbourhoods offer the best estimated profitability (average price × estimated occupancy —estimated from calendar availability). Lower supply and the presence of spacious accommodation —near the Technology Park— drive prices up.

Finding 3 · Demand shows strong seasonal concentration

December accounts for 66.4% of the year's reviews. Demand spikes at Christmas; the first half of the year represents barely 3% of the total.

The correlation between reviews and actual occupancy is weak (0.10), so reviews should not be used as the sole metric for estimating occupancy.

Finding 4 · The market is already dominated by professional operators

Companies such as I Loft Málaga, Remy and Living4Malaga manage hundreds of properties each. The peer-to-peer collaborative economy model is now a minority.

The three largest hosts account for 4.9% of properties. Competition is no longer between individuals, but with specialised companies.

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The conclusion

The analysis yields four strategic implications:

1. Invest in the West (Campanillas, Puerto de la Torre, Churriana) offers better profitability than the saturated City Centre.
2. Adjust prices dynamically: raise them in December, apply discounts from January to June.
3. Do not blindly trust reviews as an occupancy indicator; the correlation is very low (0.10).
4. The market is professionalised. Competition is no longer between individuals, but with specialised companies.

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Stack and methodology

Dataset: 9,420 properties · 11 neighbourhoods · 3,504 hosts

Geospatial analysis: heatmaps with folium · official shapefiles

Seasonality: occupancy proxy = reviews · correlation with availability = 0.10

Professionalisation: the three largest hosts account for 4.9% of properties