Valencia Airbnb Analytics (SQL + Metabase)

Surfaced booking, pricing, and occupancy patterns for Valencia neighbourhoods.

Interactive analysis of Airbnb listings in Valencia powered by SQL transformations and exploratory datasets.

Goal

Quantify how Airbnb supply interacts with Valencia’s long-term rental market and which boroughs carry the greatest hosting pressure, so policy-makers and hosts can gauge sustainability risks from short-term rentals.

Workflow

  1. Ingest Inside Airbnb listings for Valencia and clean them with SQL pipelines to expose borough-level metrics.
  2. Blend municipal population stats and Idealista rent indices, aligning them on time and geography.
  3. Model derived KPIs (per-capita listings, review velocity, price trends) and surface them via embedded charts.

Key Questions

  • Which boroughs concentrate the most active Airbnb supply per resident?
  • How does monthly Airbnb demand move relative to the long-term rent market?
  • Where do Airbnb hotspots emerge across the city as new listings appear month to month?
  • Are smaller boroughs proportionally more saturated than larger districts?

Airbnb metrics are sourced from the Inside Airbnb Valencia dataset, historic rent figures come from Idealista's price index, borough demographics are compiled by the Ayuntamiento de València statistical service, and the full SQL workflows plus prepared datasets are available on GitHub.

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 795 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 5.80 €/m² to 13.40 €/m² (up 7.60 €/m²), while monthly reviews peaked at 13,240.

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 4,280.0 listings, up from 1.0 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 4,012 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 73.9 listings per 1k residents across 4,034 residents.

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

Conclusion

Valencia’s Airbnb market remains centred in the historic core: boroughs such as El Mercat and La Seu show the highest per-capita density of active listings, even when counting only properties with at least one guest review. Meanwhile, Idealista’s rent index recovered steadily after 2020 while review volumes surged, suggesting renewed tourism pressure alongside rising long-term housing costs.

Taken together, the datasets imply that housing stress is driven by both intense visitor demand in compact boroughs and a broader, citywide recovery in nightly stays. The spatio-temporal heatmap shows hotspots radiating from Ciutat Vella toward Ruzafa and the waterfront after 2016, underscoring how tourism pressure spread beyond the historic core. Sustained monitoring of per-resident listing counts and price trends will help the city balance tourism with resident affordability. All SQL scripts, notebooks, and exported datasets powering these visuals are documented in the analysis repository.

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