Valencia Airbnb Analytics (SQL + Metabase)

Scope note: learning project, not a production system. I iterate in small steps, write down assumptions, and call out trade‑offs.

Goal

Show how short-term rentals intersect with Valencia’s housing market: where the supply is concentrated, how quickly demand recovers after shocks, and which districts carry the greatest pressure per resident.

Approach

Stage Inside Airbnb listings in SQL, cleaning status flags and hosts to surface active-stock metrics by borough.
Join municipal population counts and Idealista rent indices, aligning geography and month so trends are directly comparable.
Publish derivative KPIs (per-capita listings, review velocity, rent deltas) as Plotly charts for quick exploration.

Guiding questions

Which barrios host the densest Airbnb supply once adjusted for population?
Do review volumes move in step with long-term rent changes across the city?
How does the spatial footprint of listings expand beyond the historic core over time?
Are compact boroughs disproportionately saturated compared with larger districts?

Data

Inside Airbnb – Valencia snapshot: raw listings, hosts, calendars.
Idealista rental index: €/m² benchmarks by district.
Ayuntamiento de València statistics: population per barrio for per-capita scaling.
Processing notebooks and exports live in the project repository.

Question

Which Valencia neighbourhoods host the most Airbnb listings?

Counts aggregated by neighbourhood captured from the JSON export produced during the modelling phase.

Insight: CABANYAL-CANYAMELAR leads with 7.95 × 10² listings.

Data sources: Inside Airbnb – Valencia, GitHub – SQL & data pipeline

Question

How have advertised rents moved alongside Airbnb demand?

Median asking rent per m² across Valencia. Tracking the Idealista rent index helps contextualise Airbnb activity.

Insight: Advertised rents moved from 5.8 × 10⁰ €/m² to 1.34 × 10¹ €/m² (up 7.6 × 10⁰ €/m²), while monthly reviews peaked at 1.32 × 10⁴.

Data sources: Idealista – Historical rent index (Valencia), Inside Airbnb – Valencia, GitHub – SQL & data pipeline

Question

How has Valencia’s Airbnb supply evolved by room type?

Separate traces for entire homes, private rooms, shared rooms, and hotel-style listings show how inventory mix has shifted since 2010.

Insight: Entire home/apt now leads with 4.28 × 10³ listings, up from 1 × 10⁰ at the start of the period.

Data sources: Inside Airbnb – Valencia, GitHub – SQL & data pipeline

Question

Where do Airbnb listings cluster across Valencia over time?

Monthly heatmap of listing counts aggregated to spatial bins; drag the slider to watch hotspots emerge and shift across the city.

Insight: May 2025 recorded the highest concentration with 4.01 × 10³ active listings across the grid.

Data sources: Inside Airbnb – Valencia, GitHub – SQL & data pipeline

Question

Do smaller boroughs exhibit higher Airbnb saturation than larger districts?

Bubble size represents active listings while positioning contrasts resident population with listings per 1,000 residents.

Insight: EL MERCAT tops the saturation chart with 7.39 × 10¹ listings per 1k residents across 4.03 × 10³ residents.

Data sources: Inside Airbnb – Valencia, Ayuntamiento de València – Estadística de barrios, GitHub – SQL & data pipeline

Question

How have Airbnb listings per 1k residents shifted across key boroughs?

Yearly density trajectories highlight which historic centre boroughs are accelerating fastest. Only listings with at least one review are counted, so densities are conservative.

Insight: EL MERCAT now leads with 5.5 × 10¹ listings per 1k residents, up from 3.51 × 10⁰ at the start of the period. Only listings with at least one review contribute to these densities.

Data sources: Inside Airbnb – Valencia, Ayuntamiento de València – Estadística de barrios, GitHub – SQL & data pipeline

Conclusion

El Mercat, La Seu, and adjacent Ciutat Vella barrios still host the most active listings per resident; post-2021 both review counts and advertised rents climb back sharply, signalling demand pressure on the historic core.
New hotspots break outward toward Ruzafa and the waterfront, while compact seaside districts reach similar per-capita saturation as the centre—these neighbourhoods need the closest monitoring if policy limits tighten.

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