Explore Italy’s property landscape with precision — ScrapeIt extracts detailed listings from Wikicasa, delivering ready-to-use housing data tailored for CSV, JSON, or analytics platforms.
Wikicasa aggregates properties across all of Italy — from Milan’s urban condos to countryside villas in Tuscany. ScrapeIt builds extraction flows that match your targeting: filter by region, price, energy class, square meters, or listing type (vendita vs. affitto). Our system processes Italian-language content, parses location data accurately (including CAP and province), and standardizes fields for export. No scraping tools or browser tricks needed — just a structured stream of Italian real estate data, ready for analysis or integration.
From high-rise penthouses in Rome to restored villas in the Umbrian hills — our Wikicasa scraper captures listings rich in structured details and brings them into focus. ScrapeIt extracts pricing in euros, precise geographic data (including CAP codes and province), and listing types across both residential and commercial segments.
You’ll get technical specs like livable area (in mq), floor number, energy class (from A4 to G), and the building’s condition or renovation status. We also parse fields unique to Italian listings — such as heating type (centralizzata, autonoma), property exposure (doppia, angolare), and visibility tags like “Nuovo” or “Esclusiva.” Where available, we include listing descriptions in Italian, agent contact blocks, media galleries, and embedded map data — all organized for direct export into your workflow.
Wikicasa listings often contain hidden value in the form of visual cues, badges, and embedded metadata. Scrape Wikicasa data with ScrapeIt to uncover deeper layers — like “In Evidenza” (featured), “Prezzo ribassato” (price reduced), or “Nuovo annuncio” — all of which signal listing freshness, seller urgency, or paid promotion.
We also extract filters related to accessibility, mortgage assistance, investment properties, and green building status (such as Classe Energetica A1+). Listings may include map placement, visibility level, or even mentions of virtual tours or certified appraisals. These elements help platforms and analysts go beyond simple housing records — offering behavioral context, urgency indicators, and commercial positioning within the Italian property market.
Italy’s real estate scene gets a digital upgrade with wikicasa.it, a platform built around agency-verified listings and advanced search tools. As part of the RE/MAX and Casa.it network, it hosts a wide selection of residential, commercial, and upscale properties — all listed by licensed professionals.
Wikicasa serves urban markets like Rome, Milan, and Naples, as well as provincial areas and popular seaside towns. Features like energy certificate filters, map-based browsing, mortgage calculators, and alerts make it ideal for both everyday users and investors. The site is fully in Italian, with a mobile-optimized interface and an app for on-the-go property tracking.
More than just listings, Wikicasa delivers insights: property price trends, new-build spotlights, and neighborhood-level reports give users the context they need to buy, rent, or evaluate the market intelligently.
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Pages extracted
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Customized plans that grow with your data needs.
€199 / one-time
setup fee — included
€169 / mo
setup fee €499
€229 / mo
setup fee €499
€349 / mo
setup fee €499
€549 / mo
setup fee €799
Wikicasa offers clean listings and rich filters — but it wasn’t designed for scale or integration. Using a Wikicasa scraper unlocks granular real estate data from across Italy, ideal for tracking regional pricing trends, inventory fluctuations, or property availability by CAP code, energy class, or property type.
Whether you're powering a national aggregator, enriching CRM data, or building dashboards for market intelligence, ScrapeIt’s Wikicasa scraper gives you the structure Wikicasa doesn’t. You can monitor new construction rollouts, analyze investment-labeled units, or benchmark urban vs. coastal housing — all without relying on limited front-end filters or manual copy-paste.
Learn how to use web scraping to solve data problems for your organization
If you work in travel tech, an OTA, a hotel chain, or at an airport, you are in a price-and-availability arms race. Fares change by the hour, room inventory disappears in minutes, and competitors test new bundles and ancillaries constantly.
If you sell online, run a marketplace, or advise e-commerce clients, you already know why eBay matters: it’s one of the few places where big retailers compete side by side with thousands of small merchants and private sellers.
E-commerce teams do not just need “some” competitor data anymore. They need a continuous stream of real prices, discounts, stock levels, reviews, and seller behavior from the platforms that actually shape their markets.
Instead of handing you a one-size-fits-all scraper, ScrapeIt builds a pipeline tailored to the way you need Italian property data. With our service to scrape Wikicasa data, we extract only the listings that matter — filtered by location, energy class, agency type, or listing status.
Need daily updates on new rentals in Bologna? Historical snapshots of luxury listings in Milan? Structured data on price drops in coastal Liguria? We make it happen. You choose what to track — we handle the parsing, formatting, and delivery. Fast, accurate, and built for real estate workflows.
We support real estate marketplaces, CRM providers, proptech startups, and analytics firms focused on the Italian housing market.
Yes — we can organize data by location granularity, including comune, CAP, or even custom-defined zones.
Definitely. We extract promotional labels, status flags, and visibility signals that help categorize listings beyond price and location.
Of course — we can extract only listings with Class A or newly built homes, for example, depending on your needs.
Data is available in CSV, JSON, or XLSX — all cleaned, structured, and ready to integrate into your analysis or application.
1. Make a request
You tell us which website(s) to scrape, what data to capture, how often to repeat etc.
2. Analysis
An expert analyzes the specs and proposes a lowest cost solution that fits your budget.
3. Work in progress
We configure, deploy and maintain jobs in our cloud to extract data with highest quality. Then we sample the data and send it to you for review.
4. You check the sample
If you are satisfied with the quality of the dataset sample, we finish the data collection and send you the final result.
Scrapeit Sp. z o.o.
10/208 Legionowa str., 15-099, Bialystok, Poland
NIP: 5423457175
REGON: 523384582
