Platform · Fotocasa

Fotocasa Data Scraping

One property, several agencies, several listings, sometimes several prices. A listing count here is not a property count.

Fotocasa data scraping collects Spanish property listings, asking prices, status and agency attribution. The structural feature of this market: the same property is frequently listed by several agencies simultaneously, sometimes at different prices. So listing count and property count are different numbers, and supply figures built on listings overstate.

Our Idealista page covers the Spanish market's pricing conventions. This one is about counting, which the Spanish market makes unusually hard.

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fotocasa.jsonl LIVE FEED

{"portal":"fotocasa","listing_id":"fc-44120", "property_cluster_id":"pc-0812","cluster_confidence":0.79, "agency_name":"agency-a", "asking_price":289000,"currency":"EUR", "address_precision":"area_as_published"} {"listing_id":"fc-88120","property_cluster_id":"pc-0812", "agency_name":"agency-b","asking_price":299000, "note":"same property, different agency, 10k apart. merging would destroy that"} {"listing_count":1204880,"estimated_property_count":918400, "disappearance_reason":"not_determined", "caution":"a supply figure from listings overstates by ~24% here"}

3 of 1,204,880 listing rows · Spainlistings NEVER merged · both counts reported · schema v1.0

Independence and trademarks. Actowiz Solutions is not affiliated with, endorsed by or connected to Fotocasa or its owners. Fotocasa and related marks belong to their respective owners, used here only to name the publicly accessible source this service collects from.

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Fotocasa at a glance

How we handle Fotocasa specifically

Platform-specific handling, not a generic retail template pointed at a different domain.

Portal: Fotocasa — Spain
The feature: Multi-agency listing is common
Consequence: Listing count ≠ property count
And: Prices can differ between agencies for one property
So: Clustered, never merged, with both counts reported
Address: Frequently approximate. Precision recorded
Asking price: Not a transaction price
Refresh: Daily; status changes are the events worth catching

Platform specifics

Counting properties in a multi-agency market

These are the reasons a Fotocasa dataset needs its own handling rather than a shared retail schema.

Both counts, and the estimate labelled

Spanish residential marketing frequently runs through several agencies at once, each producing its own listing with its own photographs, description and sometimes its own price.

A supply figure from listings overstates, sometimes substantially in dense urban markets.
Price dispersion within one property is real and informative, and merging destroys it.
Photographs and descriptions differ, so text similarity alone is a weak clustering signal.
Addresses are frequently approximate, which weakens the strongest signal.

We deliver listing_count and estimated_property_count both, with the estimate labelled and built from property_cluster_id carrying cluster_confidence and cluster_basis.

Listings are never merged. Each stays its own record, because the price difference between two agencies on one property is a finding rather than noise — the same position our ghost kitchen page takes on brand versus kitchen counts.

Where clustering is weak, we say so

Confidence travels per cluster. A low-confidence cluster is delivered as low confidence rather than dropped or promoted, so an analysis can set its own threshold.

Spanish conventions, and what we do not collect

Address precision

Spanish listings frequently give an area rather than a street address, particularly for higher-value property. address_precision is recorded as published and we do not sharpen it by triangulating from images or map approximations.

Price conventions

Asking price flagged as asking. Where a listing shows price per square metre we capture it and compute our own alongside, flagging disagreement — as our Idealista page sets out.

Status

Transitions as timestamped events, retained rather than overwritten. A listing disappearing may mean sold, withdrawn or an agency mandate ending, and the portal does not say which — disappearance_reason is not_determined.

What we do not collect

Vendor, buyer or enquirer data. Never.
Agent individuals. Agency is a commercial entity; the person listing is not collected.
A sold price. Not published on a portal.
An address sharpened beyond publication.

Scope

What we collect on Fotocasa, and what we do not

The right column matters more than the left. Anyone can list fields; the limits are what tell you whether the dataset will hold up.

✅ What we collect

listing_count and estimated_property_count both reported
property_cluster_id with confidence and basis, listings never merged
Low-confidence clusters delivered as low confidence, not dropped
address_precision as published, never sharpened
price_is_asking as a constant true
Price per square metre captured and computed, with disagreement flagged
Status transitions as timestamped events
disappearance_reason as not_determined
Agency attribution as a commercial entity

❌ What we do not, and why

A listing count presented as a property count
Duplicate listings merged into one record
An address resolved beyond the precision published
A disappearance attributed to a sale
Vendor, buyer, enquirer or individual agent data

Core Fotocasa fields

The full dictionary is agreed during scoping. These are the fields specific to this platform.

Field	What it is on this platform
portal / listing_id / property_cluster_id	The listing, and its cluster
cluster_confidence / cluster_basis	A cluster, not a fact
listing_count / estimated_property_count	Both. The estimate is labelled
asking_price / price_is_asking / currency	A marketing figure, flagged
price_per_sqm_displayed / price_per_sqm_computed	Theirs and ours
floor_area_sqm / area_basis	As published, with the basis
address_precision	As published. Never sharpened
agency_name	A commercial entity
status / status_changed_at / disappearance_reason	Events, and what we cannot know
property_attributes	Rooms, type, condition as published
observed_at	Timestamp

Use cases

What teams do with Fotocasa data

Spanish supply counting done honestly

Listing count and an estimated property count both reported, so a supply figure says whether it is counting listings or properties in a market where the two diverge substantially.

Intra-property price dispersion

Listings kept separate within a cluster, so the price difference between two agencies marketing one property is visible rather than merged away.

Agency activity analysis

Attribution per listing as a commercial entity, showing which agencies list where and at what price level.

Status lifecycle tracking

Transitions retained as events with the disappearance reason left undetermined, since a portal does not say whether a listing sold or a mandate ended.

The 24-hour sample — run on your sources, not ours

Send us a Fotocasa item or category list. We run real collection against it and return the output within 24 hours, with the platform-specific fields populated so you can check them yourself rather than take our word for it.

Real extraction from your actual sources
Returned within 24 hours
Coverage and QA note included
You keep the data either way
No card, no trial clock
Named engineer on the call

Get my free sample Book a 20-min scoping call Reply within one business day. Reference calls available under NDA.

How we engage

Three ways to engage us

Same collection pipeline and QA underneath. The difference is who holds the schedule and how the data reaches you.

Managed service (most common)

We own the collection, the QA and the delivery. You receive clean data on a schedule and never touch a scraper.

Dedicated engineer assigned to your account
Site changes fixed by us, not reported to you
Scheduled delivery to your warehouse or S3
Named contact on Slack or email

Best fit: Teams who need the data, not the infrastructure.

API access

The same collection pipeline exposed as an authenticated REST endpoint your systems query directly.

On-demand and scheduled endpoints
Rate limits agreed to your load profile
Sandbox keys for integration testing
Versioned schema with deprecation notice

Best fit: Product and engineering teams building on live data.

One-time or project extraction

A defined pull for a specific question — market sizing, diligence, a pitch, a one-off audit.

Fixed scope agreed in writing upfront
Single delivery with full QA report
Methodology documented for your records
Converts to managed if you want continuity

Best fit: Research, strategy and diligence work with a deadline.

Pricing

Every engagement is quoted individually, because the honest answer depends on your scope: how many sources, how many records, how often, and how the data reaches you. We scope it with you, run a free pilot on your own sources, and then quote a fixed monthly figure — no per-request metering and no overage billing when volumes move. Request a quote and you will have a number after one call.

Fotocasa is usually collected alongside its competitors

Almost nobody buys a single platform in isolation. Fotocasa data becomes useful when it sits next to the competitor set on one schema, refreshed on one schedule, so a price index or availability comparison is genuinely like-for-like.

That is what real estate data covers, and a Fotocasa-only engagement can be expanded into it without rebuilding. If you already know you need several platforms, start there instead — it is the same pipeline and usually the better scoping conversation.

FAQ

Fotocasa data scraping: frequently asked questions

Platform-specific questions, including what cannot be collected here.

Because the same property is frequently listed by several agencies at once, each producing its own listing with its own photographs, description and sometimes its own price.

A supply figure from listings overstates, sometimes substantially in dense urban markets. We report both counts and label the estimate.

Because the price difference between two agencies marketing one property is a finding rather than noise, and merging destroys it.

We cluster with a confidence and a stated basis, and every listing stays its own record.

It varies, and the confidence travels per cluster. Photographs and descriptions differ between agencies so text similarity is weak, and Spanish addresses are frequently approximate, which weakens the strongest signal.

Low-confidence clusters are delivered as low confidence rather than dropped or promoted.

Only at the precision published. Spanish listings frequently give an area rather than a street address, particularly for higher-value property, and we do not sharpen that by triangulating from images or maps.

Not necessarily. It may mean sold, withdrawn, or an agency mandate ending — and the portal does not say which.

We record the disappearance with its timestamp and leave the reason as not determined.

We quote individually on geography, listing volume and refresh. Clustering adds cost beyond raw extraction, and it is what makes the property count meaningful.

One scoping call, a free pilot within 24 hours including cluster confidence on your geography, then a fixed monthly quote. Request a quote.

See real Fotocasa data before you commit to anything

Send us an item or category list. We return the output within 24 hours with the platform-specific fields populated.

Get a free sample Talk to an engineer

Free pilot, no card, no obligation. If we cannot collect a field you need on this platform, the sample shows you that too.

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