Real Estate Data Scraping Services

Where the portal publishes an explicit status transition, yes. Where it does not, we are honest that disappearance is ambiguous — it can mean sold, withdrawn unsold, moved portals or a portal error, and those have opposite analytical meanings.

We never record disappearance as a sale. We confirm absence across multiple runs, check for the property reappearing under a different portal reference via address and media fingerprinting, and where the outcome stays unknown the record says disappeared_unconfirmed. How to treat that population is your modelling decision, not something our pipeline should quietly make for you.

Where the portal publishes them. Location granularity varies enormously: some portals publish full addresses, most publish street level, and some deliberately publish only an approximate area or an offset map pin.

We geocode to the finest granularity actually published and label which level it came from, because a record geocoded from a full address is not equivalent evidence to one geocoded from an area name. We do not cross-reference sources to unmask an address a portal chose to obscure — that is a privacy and terms problem, and a dataset built on it is fragile.

Price history is reconstructed from our own observation runs, so it begins when we started collecting that listing. For many major portals and markets we hold multi-year archives and can backfill; for a portal or geography we have not previously covered, history does not exist.

We report the archive start date per portal so you know exactly what a reduction count is measuring. Some portals also display their own price history, which we capture where published — but we label it as portal-reported rather than blending it with our own observations.

No. MLS systems are credentialed, licensed environments, and access requires membership and a data licence with obligations attached. Scraping is not a legitimate route, and any vendor offering MLS content via extraction is selling you a rights problem.

What we collect is publicly accessible portal and agency listings, which in many markets covers the large majority of marketed stock. If you need MLS specifically, the route is membership or an approved licensed feed, and we will say so rather than quote for it.

Through cross-portal matching on address, attributes and media fingerprinting, delivered with a confidence score. Duplicate listings across portals are extremely common and, if uncollapsed, inflate supply counts substantially.

We deliver both the portal-level records and the resolved property entity, so you can count supply correctly while still seeing per-portal pricing — which occasionally differs for the same property, and is itself informative about agent behaviour.

Yes, with hourly collection on defined geographies for new-listing alerting. This is common for acquisition teams where being first to a listing matters materially.

The honest constraint is portal publication timing rather than our collection speed: many listings appear on portals in batches from agency feeds, so a genuinely real-time signal does not exist upstream. Hourly collection typically puts you within an hour of portal appearance, which is as fast as the underlying publication allows.

We collect agency and branch as business entities — company name, branch, portfolio composition, instruction volume. We do not build individual agent profiles, and we do not collect direct personal contact details for named individuals as a deliverable.

Where a listing displays a named negotiator, that name is not part of the standard schema. Vendor and tenant details are never collected. This keeps the dataset firmly on the business-information side, which is what makes it straightforward for your DPO to approve.

Yes, where portals publish it, including offices, retail units, industrial and land. Commercial listings are structurally messier: pricing is frequently quoted per square foot or per annum, terms vary widely, and many listings say price on application rather than publishing a figure.

We capture the quoted basis explicitly rather than normalising it into a single number, because a per-square-foot annual rent and a capital value are different quantities and blending them silently is a common source of error in commercial datasets.

We quote individually. The main drivers are portal count, geographic scope, whether you need lifecycle tracking with history reconstruction, and refresh frequency.

Lifecycle tracking costs more than snapshot collection because it requires continuous coverage of the same listing population — you cannot reconstruct a reduction you never observed. A defined region across two or three portals at daily refresh sits at the lighter end; multi-country coverage with hourly new-listing alerting and cross-portal deduplication sits higher. One scoping call, a free pilot on your own geography within 48 hours, then a fixed monthly quote. Request a quote.