How to Scrape Pisos.com Spain Property Listings in 2026

Pisos.com is Spain’s number-three property portal, owned by media group Vocento, and it carries a deep inventory of apartments, houses, attics, duplexes, studios, lofts, offices, garages and storage units across every Spanish province. While Idealista and Fotocasa get more attention, Pisos.com is valuable precisely because it’s a distinct dataset — different agencies, different exclusives, different coverage — so anyone building a complete picture of the Spanish market needs it in the mix. This guide covers what Pisos.com exposes, how to extract it without a browser, and how to turn it into clean price-per-m² data for any region in Spain.

What’s worth extracting

For each Pisos.com listing, two structured sources on the page combine into a rich record:

• Identity — listing ID and detail URL (from the page’s JSON-LD), plus title.
• Classification — operation (sale / rent / new construction) and property type (apartment, house, attic/ático, duplex, studio, loft, office, garage, storage).
• Pricing — price in EUR and price per m² (reported or computed from price ÷ area).
• Physical — built area in m², room count, bathroom count, floor, and condition (new, good, to-reform).
• Energy — energy-efficiency classification where the listing declares it.
• Location — full hierarchy (country / province / city / district / neighborhood) and street address, plus precise GPS coordinates read from JSON-LD.
• Advertiser — name and type (so you can split agency listings from private sellers).
• Amenities — a checklist of features: elevator, parking/garage, terrace/balcony, AC/heating, garden, pool, storage room, fitted wardrobes, sea view, furnished, luxury.
• Media — the main image URL.

That’s a 30+ field record per listing, enough to build serious market analytics and amenity-aware filtering.

Two data sources on one page

Pisos.com pages carry their data in two complementary places, and a good scraper reads both:

• JSON-LD structured data. Like most modern real-estate sites, Pisos.com embeds schema.org markup in a <script type="application/ld+json"> block. This is where the stable identifiers and precise GPS coordinates live — the most reliable, least-likely-to-shift fields.
• Listing-card HTML. The visible card markup carries the pricing, area, room/bathroom counts, condition, advertiser and amenity checklist. This needs careful HTML parsing because the layout varies by property type.

Reading JSON-LD for identity and geography, then parsing the card HTML for the rest, gives you the best of both: stable keys from structured data and full detail from the rendered card. Crucially, all of this is in the server-delivered HTML — no headless browser is required. The scraper issues direct HTTP requests, reads the embedded JSON-LD and parses the card HTML.

The anti-bot reality is lighter than on the biggest portals, but Pisos.com still rate-limits and geo-gates. A proxy configuration — ideally a Spanish residential IP — smooths regional access and reduces friction, while the architecture stays HTTP-only and fast.

How search and pagination work

You drive the scraper by search scope and filters:

• Geographic scope — Spanish region (comunidad autónoma), province, or city
• Operation — sale, rent, or new construction
• Property type — apartment, house, attic, duplex, studio, loft, office, garage, storage
• Numeric filters applied client-side — price range (EUR), area, rooms
• Keyword filters and sort order

The scraper discovers and fetches the search-results pages, handles pagination with a cap and loop detection (so a misbehaving “next page” link can’t spin forever), and emits one structured row per listing. You can also feed the resulting listing URLs into a downstream detail crawler when you need phone numbers, full feature lists or energy certificates that aren’t on the card.

▶ Run the Pisos.com Property Scraper — extract apartments, houses, attics, duplexes and more by Spanish province or city. Returns address, GPS, district, amenities and price-per-m² data. No browser.

Schema design for downstream use

When the data lands in your warehouse, you want it shaped for EUR/m² analysis across Spanish geography. A clean per-listing row:

{
  "listing_id": "pisos-9912034",
  "url": "https://www.pisos.com/comprar/piso-...",
  "operation": "sale",
  "property_type": "apartment",
  "price_eur": 285000,
  "price_per_m2": 3958,
  "area_m2": 72.0,
  "rooms": 3,
  "bathrooms": 2,
  "floor": 4,
  "condition": "good",
  "energy_rating": "C",
  "province": "Valencia",
  "city": "Valencia",
  "district": "Ruzafa",
  "neighborhood": "Russafa",
  "address": "Calle de Cádiz",
  "lat": 39.4622,
  "lon": -0.3705,
  "advertiser": "Inmobiliaria XYZ",
  "advertiser_type": "agency",
  "features": ["elevator", "ac", "terrace", "fitted_wardrobes"],
  "main_image_url": "https://...",
  "scraped_at": "2026-05-24T12:00:00Z"
}

A few schema choices worth making early:

• Store both reported and computed price_per_m2. Some listings declare it; compute it from price ÷ area for the rest, and record which is which so your heatmaps stay honest.
• Keep the full geographic hierarchy. Province → city → district → neighborhood lets you aggregate at any zoom level and is essential for Spain’s neighborhood-driven pricing.
• Keep features as an array, not flattened booleans. Amenity presence (pool, sea view, parking) drives buyer interest and price premiums; an array is easy to filter and easy to count.
• Capture energy_rating when present. Spanish listings increasingly declare it, and it’s a real price factor — but it’s optional, so allow nulls.

Typical use cases

What customers actually do with Pisos.com data:

• Bulk inventory snapshots — pull all sale, rent or new-build listings across a province or the whole country.
• Price-per-m² datasets — aggregate by province, city and neighborhood to build market analytics and valuation models.
• Map-based discovery — use the JSON-LD GPS coordinates to plot inventory and run location-based search.
• Price tracking — re-scrape on a schedule and monitor price changes and reductions over time.
• Amenity-aware filtering — slice inventory by pool, parking, sea view, condition or advertiser type for niche buyer segments.
• Pipeline seeding — feed listing URLs and summary metadata into a detail crawler for phone numbers, full feature lists or energy certificates.

The common thread is a second independent view of the Spanish market. Combined with other portals, Pisos.com fills coverage gaps and surfaces exclusives the bigger sites miss.

Cost math for the managed approach

Because the scraper is HTTP-only — reading JSON-LD and parsing card HTML rather than rendering pages — compute per listing is minimal. Under this actor’s pricing, results are emitted at no per-row charge, so the dominant cost is the small per-run start fee plus proxy bandwidth, and JSON/HTML fetches use far less bandwidth than full browser page loads.

A realistic pipeline — all for-sale apartments across the five largest Spanish provinces, refreshed daily — lands in the tens of thousands of rows per run while staying in the low-cost range. A browser-based equivalent would multiply both compute and proxy bandwidth several times over.

What you avoid by using a managed actor rather than building your own:

• Writing JSON-LD extraction plus per-property-type card parsing
• Building pagination with caps and loop detection
• Sourcing and rotating a Spanish residential proxy pool
• Normalizing EUR pricing, the geographic hierarchy and the amenity checklist

Common pitfalls

A few things to know before committing to a Pisos.com pipeline, whether you build or buy:

• Card layout varies by property type. A garage card and a luxury villa card don’t carry the same fields. A parser tuned only to apartments silently drops data on other types.
• GPS lives in JSON-LD, not the card. If you only parse visible HTML you’ll miss precise coordinates. Read the structured data block.
• Price-per-m² source matters. Mixing reported and computed values without tracking the source corrupts neighborhood comparisons.
• Pagination loops. Some search configurations return a “next” link that points back into the set. Without loop detection, a run spins indefinitely.
• Cross-portal duplicates. The same property often appears on multiple Spanish portals under different agencies. Dedup on listing ID within Pisos.com, and consider address+area matching when blending sources.

Wrapping up

Pisos.com is the Spanish market’s valuable third opinion — distinct inventory you can’t get from the bigger portals alone. Its data sits in server-delivered JSON-LD and card HTML, so no browser is needed; the work is reading both sources, parsing every property type correctly, paginating safely and normalizing EUR pricing across Spain’s neighborhood-level geography. If you need refreshed Spanish property data for analytics, valuation or discovery, a managed HTTP-only actor delivers it cheaply and keeps the parsers green as the site evolves.

▶ Open the Pisos.com Property Scraper on Apify — fast, browser-free, proxy-backed. Filter by province, price and amenities; get clean per-listing rows with GPS and price-per-m². Start with Apify’s free monthly credit.