How to Scrape xe.gr Greek Property Listings in 2026

xe.gr is the default classifieds portal for Greek real estate — 1.5M+ monthly visitors and 500K+ active ads covering apartments, houses, land, commercial space and parking across Athens, Thessaloniki and the islands. For anyone modeling the Greek property market — €/m² heatmaps, yield screening, agency tracking — it’s the source. And there’s a pleasant technical surprise: xe.gr is a Next.js site that ships its data as a server-rendered JSON blob, so you can extract clean structured listings over plain HTTP without a browser or anti-bot fight. This guide covers how that extraction works and how to turn it into analysis-ready data.

What’s worth extracting

xe.gr embeds rich per-listing data in its Next.js payload. Flattened, each property carries:

• Identity — a stable listing UUID and the detail-page URL.
• Classification — property type (apartment, house, land, commercial, parking) and transaction type (buy/rent).
• Price — numeric price and currency, plus price-per-square-meter.
• Size & layout — area in m², bedroom and bathroom counts, floor information (often a multi-floor array for maisonettes), year built.
• Condition flags — renovation and furnishing flags, derived where the site encodes them in image alt text.
• Location — a hierarchy of city / area / neighborhood.
• Branding — main image URL, agent logo URL, agent attribution where present.
• Meta — multi-listing indicators (the same property listed by several agents), relative posting age, and crawl metadata.

For market research you center on price, €/m², area and the location hierarchy. The multi-listing flag is unusually useful here — it lets you de-duplicate the same physical property advertised by competing agencies.

Why the embedded JSON beats both HTML and a browser

xe.gr is built on Next.js with server-side rendering, which changes the extraction strategy:

• Server-rendered JSON blob — Next.js serializes the page’s data into an embedded JSON payload (the hydration data). Extracting from that blob gives you clean, typed fields directly — no fragile DOM scraping.
• Cheerio fallback — when the blob shape shifts or a field isn’t serialized, fall back to parsing the listing cards and detail links from the HTML. Belt-and-suspenders.
• No browser, no anti-bot — because the data is in the server-rendered response, a plain HTTP fetch gets everything. No headless Chrome, no residential proxy, no challenge page. This is what makes xe.gr fast and cheap to scrape relative to JS-heavy portals.

The strategy — prefer the embedded JSON, fall back to Cheerio — is the durable approach for Next.js SSR sites generally. A managed actor implements both paths so a payload tweak doesn’t silently break your feed.

▶ Run the xe.gr Real Estate Scraper — extracts Greek property listings by area, price, size and bedrooms from xe.gr’s Next.js payload. Returns price, €/m², floors, year, location, agent and images. Direct HTML, no browser, no anti-bot.

How the URL structure works

xe.gr encodes the property type, transaction and geography in the search path, with filters as query params:

https://www.xe.gr/property/results
  ?transaction_name=buy           # or rent
  &item_type=re_residence         # apartment/house/land/commercial...
  &geo_place_id=...               # Athens, Thessaloniki, a neighborhood
  &price_min=80000
  &price_max=300000
  &size_min=50

You seed the scraper with a transaction + property-type + area (optionally price/size bounds), and it walks the result pages, pulling each listing from the embedded JSON. Re-running the same seed on a schedule and diffing gives you a price-tracking feed.

Build it yourself vs. use a managed scraper

• Roll your own — locating and parsing the Next.js JSON blob for one page is a good afternoon’s work. The tail: handling the blob’s nested shape, the Cheerio fallback for when it shifts, the multi-floor arrays, deriving renovation/furnishing flags from image alt text, the location hierarchy, pagination, and re-finding the blob path whenever xe.gr ships a Next.js update (the hydration shape changes with framework upgrades).
• Managed actor — running in minutes, both extraction paths implemented, fields normalized including €/m² and floor arrays.

For a one-off neighborhood pull, a script works. For an ongoing €/m² tracking feed across Greek cities, the dual-path extraction and the field normalization are exactly the maintenance you want offloaded.

Schema design for downstream use

A clean per-listing row:

{
  "listing_id": "a1b2c3d4-...",
  "url": "https://www.xe.gr/property/d/...",
  "property_type": "apartment",
  "transaction": "buy",
  "price": 185000,
  "currency": "EUR",
  "price_per_m2": 2466,
  "area_m2": 75,
  "bedrooms": 2,
  "bathrooms": 1,
  "floors": [3],
  "year_built": 2004,
  "renovated": true,
  "furnished": false,
  "city": "Athens",
  "area": "Kypseli",
  "neighborhood": "Fokionos Negri",
  "agent": "XYZ Real Estate",
  "agent_logo": "https://...",
  "main_image": "https://...",
  "multi_listing": false,
  "posted_age": "3 days ago",
  "scraped_at": "2026-06-03T09:00:00Z"
}

Schema choices worth making early:

• Store price_per_m2 even though it’s derivable. It’s the central metric for Greek property analysis; precomputing it makes heatmaps and screens trivial.
• Key on the listing_id UUID. It’s the only stable identity across re-crawls and the basis for diffing prices over time.
• Keep floors as an array. Maisonettes and multi-level units list several floors; flattening to a single number loses the layout.
• Persist multi_listing and agent. Together they let you collapse the same property advertised by multiple agencies — a real source of double-counting in Greek listings data.
• Keep the full city/area/neighborhood hierarchy. Neighborhood-level granularity is what makes €/m² heatmaps useful.

Typical use cases

• Market research — €/m² heatmaps by neighborhood, listing density, and time-on-market trends across Athens, Thessaloniki and the islands.
• Competitive analytics — cluster listings by agent branding/logo to track which agencies dominate which areas.
• Investment screening — compute rental yield and rent-to-sale ratios per area to surface opportunities.
• Lead generation — discover active agencies in a target area and segment them for B2B outreach.
• Price tracking — re-run crawls and diff on listing_id to watch price cuts and new inventory.
• De-duplication research — use the multi-listing flag to identify the same physical property listed by several agents.

The value is the combination of neighborhood-level €/m² granularity and freshness — a diff-able feed turns xe.gr into a live Greek property-market dataset.

Cost math for the managed approach

No browser, no proxy, no anti-bot bandwidth — xe.gr is among the cheapest property portals to scrape. Cost is essentially compute, so a daily multi-area price-tracking crawl lands in low single-digit dollars per month. Against a JS-heavy portal that demands a browser and residential proxies (hundreds per month in bandwidth alone), the Next.js-SSR approach is dramatically cheaper. The main thing you’re buying with a managed actor is the resilience of the dual extraction path so a framework update doesn’t silently zero out your feed.

Common pitfalls

• Relying only on the DOM — scraping rendered cards is fragile and incomplete. Prefer the embedded Next.js JSON; keep Cheerio as the fallback, not the primary.
• Ignoring multi-listing dedup — the same flat advertised by three agencies inflates your inventory count and skews €/m² averages. Collapse on the multi-listing signal.
• Flattening floors — loses maisonette layout; keep the array.
• Trusting derived flags blindly — renovation/furnishing flags inferred from image alt text are heuristic. Treat them as hints, not ground truth.
• Not diffing over time — a single crawl is a snapshot. Price-cut tracking and time-on-market need scheduled re-runs diffed on the listing UUID.

Wrapping up

xe.gr is a best-case real-estate scrape: a Next.js SSR site whose data lives in an embedded JSON blob means fast, browser-free, proxy-free extraction with rich fields. The work is preferring the JSON path with a Cheerio fallback, normalizing floors and €/m², and de-duplicating multi-agent listings. For a one-off neighborhood pull, a script does it. For an ongoing Greek-property feed with €/m² heatmaps and price tracking, let a managed actor own the dual-path extraction and normalization.

▶ Open the xe.gr property scraper on Apify — Greek listings by area, price and size with €/m², floors, year, location and agent. Direct HTML, no browser. Schedule it and diff prices over time.