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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Developers
Customers worldwide
Pages extracted
Hours saved for our clients
€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.
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Real estate teams work in a fragmented data landscape. The sites that drive demand in Boston look nothing like the ones that matter in Berlin, São Paulo, Dubai, or Mumbai.
Real estate teams are operating in a data environment that is bigger, faster, and more fragmented than ever. Listings go live and disappear in hours, price cuts happen quietly, and the portals that matter most in each country are rarely the same global “top 5.”
Real estate web scraping: a powerful tool for data collection and analysis. Learn how to choose the right data collection method and benefit from real estate web scraping
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.
Step 1 - Make a Request
You share your needs, expectations, and desired timeframe. We’ll suggest the best solution based on your request and budget.
Step 2 - Configuring Custom Web Crawlers
Our specialists configure the crawlers and extract a sample dataset for your review before proceeding with the full-scale extraction.
Step 3 - Collect and Deliver
Once you approve the sample, we launch the project and start full data collection. We gather, filter, and structure the data for easy use, delivering it on time in your preferred format.
Step 4 - Maintain and Support
Our team manages ongoing processes, monitors website changes, and supports all data extraction cycles. We can also help integrate data into your systems or create dashboards to simplify analysis.
Scrapeit Sp. z o.o.
10/208 Legionowa str., 15-099, Bialystok, Poland
NIP: 5423457175
REGON: 523384582
