How to Scrape Real Estate Listings (With GPS Coordinates and Agent Phone Numbers)

Property analysis is a data problem before it is a judgement problem. Whether you are pricing a listing, hunting yield, or building a proptech product, the first requirement is the same: a few thousand current listings with consistent fields, refreshed often enough to still be true.

Portals publish all of it. Getting it out reliably is the part that stops most people.

Which property portals can be scraped?

Three actors cover Greece and Quebec today:

All three sit in the real estate category. Spitogatos is the most-used actor in my entire store, which says something about how much appetite there is for structured property data.

What fields do you get per property?

• Commercial — asking price, price per square metre where derivable, listing date, listing URL
• Physical — size in m², number of rooms and bedrooms, floor, year built, condition
• Location — full address, neighbourhood, and on XE.gr latitude and longitude
• Features — amenities and characteristics (parking, heating type, lift, balcony, and so on)
• Media — every image in the gallery, not just the thumbnail
• Contact — advertiser or agent name, and phone number where the listing publishes it

The Greek actors work in both English and Greek, which matters more than it sounds: field labels differ by language, and an actor that only handles one will silently drop half your data.

Why do GPS coordinates matter so much?

Because addresses are unreliable and geocoding them afterwards is expensive and lossy. Getting latitude and longitude straight from the listing means you can immediately:

• Plot inventory on a map and see clusters that a table hides
• Run radius analysis — average price per m² within 500 m of a metro station
• Join against any other geographic dataset: schools, transport, flood zones, tourism density
• Detect the same property listed twice at different prices by proximity plus size

That last one is quietly valuable. Duplicate listings distort every average you compute, and coordinates are the most reliable way to catch them.

How do you run one?

1. Search on the portal. Filter by area, price band, property type and size — whatever defines your market.
2. Copy the results URL. Every filter is encoded in it, so you configure the search rather than the scraper.
3. Paste it in and set a result limit. The limit controls your cost. Leave it open to take every page.
4. Run, then export. Pagination and anti-bot handling happen in the cloud. Download CSV, JSON or Excel.

For tracking a market over time rather than taking one snapshot, add a weekly schedule and append each run to the same sheet. Price history per listing is what lets you see reductions — and a listing that has been cut twice is a negotiation opportunity that a single snapshot never reveals.

What do people do with property data?

Pricing a listing defensibly

Pull every comparable in the neighbourhood — same size band, same room count, same condition — and price against the actual distribution. "Here are the 47 comparable properties currently listed and where yours sits" is a very different conversation with a seller than an opinion.

Yield hunting

Combine sale listings with rental listings for the same area and compute gross yield per neighbourhood. The areas that look best are rarely the ones that feel best.

Agency lead lists

Private-seller listings with published phone numbers are the classic source for agency prospecting. Filter for properties listed over 60 days ago and you have a list of people whose current approach is not working.

Proptech products

If you are building a portal, valuation tool or alerting product, scheduled runs into your own database are the supply side of the product. Start with one market and prove the pipeline before adding more.

The honest caveats

• Asking price is not sale price. Portals publish what sellers want, not what buyers paid. For actual transaction data you need a land registry, and that is a different problem.
• Duplicates are real. The same property is often listed by several agencies with slightly different photos and sizes. Deduplicate on coordinates plus size before computing any average.
• Contact data is personal data. Phone numbers published on listings are still personal data under GDPR, which very much applies in Greece and has an analogue in Canada. Have a lawful basis, honour opt-outs, and do not build a permanent database of individuals because you can.
• Coverage is currently three portals. If your market is not Greece or Quebec, the actor you need may not exist yet.

Frequently asked questions

Start with one neighbourhood

Do not try to scrape a country on day one. Filter one portal to one area you know well, run it, and check the output against listings you can verify by eye. Once you trust the data on a market you understand, scaling it up is just a bigger result limit.