Scraping Property Portals by Region: Best Real Estate Platforms That Actually Matter

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. If you are a portal, agency, investor, bank, or research team, you cannot just “scrape some real estate data” — you need a structured, regional playbook of which portals to watch and how to collect their data safely at scale.

This guide maps out the real estate portals that ScrapeIt most often scrapes for clients across North America, Europe, LATAM, MENA, Africa, and Asia–Pacific, and explains what each site is good for, who typically uses it, and what to watch out for when collecting data. It is designed to sit alongside the core real estate scraping services and the catalog of real estate sites that we scrape, and to help decision-makers choose the right mix of portals per region rather than focusing on one “global top 5.”

Why a Regional Playbook Beats a “Global Top 5” List

In the companion article “7 Real Estate Websites to Scrape in 2026: Plus 2 Hidden Gems”, ScrapeIt looks at cross-market leaders like Zillow, Rightmove, Realtor.com, ImmoScout24, and Hemnet. This piece takes a different angle: it assumes you already know the big names and now need to build a regional, portfolio-level scraping strategy.

Across the priority segments we see the same questions repeat:

• Portals & marketplaces — “Which competitor portals should we monitor in each country, and how do we normalize pricing, features, and statuses across them?”
• Agencies & broker networks — “Where do our target sellers and landlords actually list first, and how do we get those leads in the first hour rather than the first week?”
• Investors & banks — “Which portals give us the cleanest view of sold prices, distressed assets, yields, and time-on-market by neighborhood?”
• Research & data teams — “How do we sync dozens of property sites into one schema that works for modeling, dashboards, and AI training?”

“Once we had daily snapshots from the right mix of U.S., EU, and LATAM portals, portfolio reviews stopped being a ‘we think’ conversation and became a dashboard question.”

— Data lead, cross-border residential fund 

Below, you will find the portals ScrapeIt most commonly works with for real estate clients, grouped by region and use case, with links to the corresponding managed scrapers and real-world case studies.

How to Use This Guide

Think of this article as a design document for your data pipeline:

• Use the regional sections to shortlist portals for each target geography.
• Follow the links to per-site scrapers when you want a deep-dive into fields, export formats, and pricing for a given portal.
• Use the case study links to understand what “done” looks like in production: volumes, refresh frequency, and normalization challenges.
• When you are ready, hand this map to your team and simply say: “We want a job like this” — ScrapeIt’s role is to deliver ready-to-use datasets, not tools you have to maintain.

If you need a more general overview of how web scraping fits into real estate strategy (agent competitiveness, market analytics, etc.), you can pair this with the explainer article “How to Use Real Estate Web Scraping to Gain Valuable Insights.”

North America: U.S. & Canada

The U.S. and Canada are dominated by a small set of consumer brands, but each platform plays a different role in the data stack. For most clients, the “core” U.S. bundle is some combination of Zillow, Realtor.com, Redfin, Trulia, and Apartments.com, plus a niche source for sold or off-market inventory, and a dedicated feed for Canada.

Zillow (United States)

Zillow is usually the first name on any U.S. data roadmap: massive coverage, rich historical pricing, and consumer engagement metrics that help investors and portals see which properties and neighborhoods are “hot.”

Typical use cases:

• Market-level dashboards for pricing, inventory, and days-on-market.
• Cross-checking MLS data for completeness and anomalies.
• Lead generation and portfolio scouting for single-family investors.

Scraping & data nuances:

• Search and filters are API-backed and highly parameterized; your scraping partner should re-use internal API responses rather than pixel-level HTML parsing.
• Listings often appear under multiple experiences (rent vs buy vs “Zillow Owned”); you need de-duplication logic by property ID and geo.
• Historical price and “Zestimate” history tend to live in separate payloads from the main listing card; make sure your extraction includes these if you care about pricing models.

For a concrete example, see ScrapeIt’s case study on daily Zillow scraping for Massachusetts listings, where the team captures all sale and rental properties in a single state-level dataset refreshed every morning.

Realtor.com (United States)

Realtor.com is tightly linked to MLS feeds and is especially strong for status accuracy — “under contract,” “pending,” or “sold” markers tend to be more consistent than on some other portals.

Best for:

• Teams that care more about data quality than raw volume.
• Banks and funds monitoring risk by county, ZIP, or specific asset class.
• Portals that need a “golden source” to reconcile statuses across competitors.

Scraping & data nuances:

• Many key fields — status, date of last update, school ratings — are nested inside structured JSON; robust parsing is required.
• MLS-connected data tends to be richer but also more sensitive: you may need explicit scoping to what is publicly visible in each market.

Redfin (United States)

Redfin is a favorite among data teams because its listings often expose granular price histories, open house data, and indicators of listing “heat” (views, favorites).

Best for:

• Building price-trend models and liquidity indicators at neighborhood level.
• Tracking the performance of Redfin’s own brokerage vs. independents.
• Feeding valuation and agent-productivity models with richer behavioral data.

Scraping & data nuances:

• Redfin uses dynamic, component-based pages; scraping hidden JSON or internal APIs is usually safer than trying to reverse-engineer DOM changes.
• Paginated search plus “load more” behaviors mean you need resilient pagination logic and careful monitoring of missed pages.

Trulia (United States)

Trulia focuses heavily on neighborhood context — crime maps, school ratings, commute times, and user reviews — which makes it an excellent complement to Zillow and Realtor.com when you want to understand “how people live,” not just where properties are listed.

Best for:

• Investors evaluating neighborhoods, not just individual assets.
• Portals enriching their own listings with extra “lifestyle” signals.
• AI & research teams training models on neighborhood quality, risk, or gentrification patterns.

Scraping & data nuances:

• Many contextual fields live outside “core listing” structures (e.g., map overlays, review widgets) and need separate extraction and joining.
• Because Trulia is part of Zillow Group, you must pay attention to overlaps and deduplication when combining with Zillow feeds.

Apartments.com (United States)

Apartments.com is often the backbone for multi-family and rental-only analysis in the U.S. — especially for operators tracking rents across portfolios and markets.

Best for:

• Rental-only dashboards (asking rent, concessions, occupancy proxies).
• Monitoring multi-family competition around specific assets.
• Feeding underwriting models for new acquisitions or developments.

Scraping & data nuances:

• Listings frequently represent buildings or communities rather than single units; “unit mix” and floor-plan parsing is essential.
• Availability calendars and “call for details” pricing require defensive parsing — empty or masked values should be expected.

Zolo.ca and Canadian Focus

For Canada, clients often mix REALTOR.ca and niche players like Zolo. ScrapeIt’s case study on Zolo real estate data scraping shows how a client receives a daily dataset of “sold” listings, including full property and agent details, to power analytics around closed deals.

Key nuance: some Canadian portals restrict “sold” sections to logged-in users; your scraping solution must manage authenticated sessions and respect local compliance rules around personal data.

“Having yesterday’s sold deals in my inbox every morning changed how I talk to our sales team. We stopped guessing and started coaching.”

— Head of sales, brokerage network in Canada

Europe: Local Champions and Cross-Border Coverage

Unlike the U.S., Europe is a patchwork of national champions. ScrapeIt’s Real Estate Sites That We Scrape list and the EU-focused case studies show how most serious projects involve several portals per country plus a strong normalization layer.

Rightmove (United Kingdom)

Rightmove is the primary residential portal in the UK and a must-have for anyone doing pricing, inventory, or agent-performance analysis in England, Scotland, or Wales.

Best for:

• National-scale price index and supply dashboards.
• Tracking new development launches and off-plan inventory.
• Analyzing agent share by town, postcode, or micro-market.

Scraping & data nuances:

• Search results rely on internal APIs; to avoid brittle HTML logic, focus on JSON responses where possible.
• UK-specific fields (leasehold vs freehold, council tax bands, EPC ratings) must be mapped carefully if you plan to join with non-UK datasets.

ImmoScout24 (Germany & DACH)

ImmoScout24 is a key data source across Germany and, depending on your configuration, other DACH markets. It offers strong coverage of both residential and commercial assets.

Best for:

• Rent and price benchmarking in major German cities.
• Tracking new commercial listings and logistics/industrial stock.
• Feeding DACH-focused AVMs and credit models.

Scraping & data nuances:

• Many filters (cold vs warm rent, “Provision frei,” energy labels) are specific to local regulation and must be preserved during extraction.
• For cross-border projects, currency normalization (EUR vs CHF) and language differences require attention.

Funda & neighbors (Netherlands & Belgium)

Funda dominates the Dutch residential market and frequently appears in ScrapeIt’s EU projects. In the case study on 230,000 daily rows across five EU property sites, Funda leads a bundle that also includes portals like Pararius, Rentola, and Zimmo for Belgium.

Best for:

• High-quality, NL-focused analytics (pricing, stock, time-on-market).
• Portals that want to mirror or compete with Funda’s coverage.
• Cross-border Benelux projects alongside Immoweb in Belgium.

Scraping & data nuances:

• Funda exposes rich property details (floor plans, energy labels, lot characteristics) that can significantly increase payload size; plan for heavier datasets.
• When scraping several EU portals together, standardizing property types and status fields is often more work than the scraping itself; ScrapeIt typically delivers a normalized schema on top.

Hemnet, SeLoger & other national leaders

Some portals are small on a global scale but absolutely central locally. Among those ScrapeIt frequently supports are:

• Hemnet (Sweden) — the reference point for Swedish residential pricing and time-on-market. Website: https://www.hemnet.se/ 
• SeLoger (France) — extensive coverage of both sale and rental markets in France, often paired with SeLoger’s specialized verticals. Website: https://www.seloger.com/ 
• Immoweb (Belgium) — a go-to source for Belgian property data, often used in cross-border projects with NL, FR, or DE. Website: https://www.immoweb.be/en 

Scraping & data nuances: EU sites are often “easier” technically than some U.S. portals (lighter anti-bot), but cross-language, cross-currency, and cross-regulation differences mean normalization and translation are critical parts of the job.

“We didn’t realize how much work was hidden in just ‘making five EU sites look like one dataset’ until we saw the 230k-row daily export.”

— Product owner, European housing portal

Latin America: Fast-Moving Portals in Fragmented Markets

In LATAM, activity is split across several strong local portals rather than one dominant player. ScrapeIt maintains dedicated scrapers for multiple Spanish- and Portuguese-language platforms that together give robust coverage of Mexico, Brazil, Colombia, Peru, and neighboring markets.

Inmuebles24 (Mexico)

Inmuebles24 is a key portal for Mexico, widely used by both agencies and private sellers.

Best for:

• City-level pricing and rental benchmarks in CDMX, Monterrey, Guadalajara.
• Tracking new development launches and pre-sale projects.
• Investor analytics focused on residential yields in major metros.

Scraping & data nuances: You need solid address normalization (colonias, barrios, neighborhood nicknames) and currency handling for MXN vs. USD in coastal or luxury submarkets.

VivaReal (Brazil)

VivaReal (now part of Grupo ZAP) is a cornerstone of Brazilian online real estate.

Best for:

• Monitoring price and inventory dynamics in large Brazilian cities.
• Supporting local proptech products and AVMs.
• Portfolio monitoring for BR-exposed funds.

Scraping & data nuances: Expect Portuguese-language fields and rich amenity descriptions; careful text normalization is needed if you are aggregating cross-country datasets.

AdondeVivir & Metrocuadrado (Peru & Colombia)

AdondeVivir is a leading portal in Peru, while Metrocuadrado plays a similar role in Colombia.

Typical LATAM patterns ScrapeIt handles:

• Listings that appear simultaneously on aggregator portals and social-adjacent channels.
• Variable address quality (from precise to “near park X”); geocoding and enrichment are part of the pipeline.
• Complex ownership and financing structures (pre-sales, developer financing) reflected in listing text rather than structured fields.

MENA, Turkey & Africa: From Luxury Hubs to Emerging Markets

For the MENA region and parts of Africa, ScrapeIt commonly works with a cluster of portals that together cover the Gulf, Turkey, and key African markets.

Bayut & Dubizzle (UAE & GCC)

Bayut and Dubizzle together provide deep coverage of residential and commercial listings across the UAE and, through associated brands, neighboring GCC markets.

Best for:

• Luxury and high-end residential analysis (Dubai, Abu Dhabi prime areas).
• Tracking yield dynamics between districts and asset classes.
• Monitoring off-plan and developer-driven inventory.

Scraping & data nuances:

• Multiple languages and currencies (AED as standard, but often USD/GBP equivalents in text).
• Frequent relisting and broker duplication; robust deduplication by phone, coordinates, and property attributes is critical.

Sahibinden (Turkey)

Sahibinden is one of the most frequently requested Turkish sites across several ScrapeIt projects, covering not just property but also vehicles and classifieds.

Best for:

• Turkish residential and commercial inventory monitoring.
• Cross-asset analysis (property vs vehicle markets for macro research).

Scraping & data nuances: Heavy use of Turkish-language abbreviations and mixed-script listings means you need good text standardization to avoid duplicate or mis-classified assets.

Property24 & African markets

In Africa, Property24 is a critical portal, particularly in South Africa and several neighboring markets.

Best for:

• Market monitoring in South Africa’s major metros.
• Cross-border exposure analysis for funds/investors with African assets.

Scraping & data nuances: Address structures can be inconsistent; ScrapeIt typically includes address cleaning and geo-enrichment to make the data usable for modeling and mapping.

Asia–Pacific & India: High-Density Markets, High-Density Data

Asia–Pacific is structurally different: very dense markets, high online activity, and portals that blend classical listings with heavy filter logic, map views, and promotional placements. ScrapeIt maintains several India- and APAC-focused scrapers.

Housing.com, 99acres & Magicbricks (India)

India is usually covered with a three-portal core: Housing.com, 99acres, and Magicbricks.

Best for:

• City-level monitoring in Mumbai, Delhi NCR, Bengaluru, Pune, Chennai, etc.
• Tracking new project launches and under-construction supply.
• Building lead funnels for agencies and developers.

Scraping & data nuances:

• Listings mix English and local languages; normalization and Unicode handling matter.
• Map- and filter-heavy UX means many critical fields live in JSON payloads rather than visible HTML.
• ScrapeIt often includes additional layers like tags (“Ready to Move,” “New Launch”) and map hints in exports; see the detailed description on the Housing.com scraper page.

iProperty & Lamudi (Southeast Asia)

For Southeast Asia, ScrapeIt often works with: iProperty (Malaysia and surrounding markets) and Lamudi (active in several emerging Asian and LATAM markets).

Scraping & data nuances:

• Strong cross-border investor interest (Singapore, Hong Kong, global funds) means currency and tax-regime normalization becomes part of the dataset design.
• Some markets still rely heavily on developer-driven listings; project-level features (builder name, phase, handover year) matter as much as individual unit data.

Niche & Owner-First Portals: FSBO, Distress, and Off-Market Signals

Beyond big portals, many of the highest-ROI scraping projects in real estate focus on niche or “hidden gem” sites: FSBO boards, auction sites, local classifieds. ScrapeIt keeps a set of scrapers specifically for these use cases.

FSBO & owner-listing hubs

FSBO.com (For Sale By Owner) is a classic example of an owner-listing hub that agencies, investors, and iBuyers monitor aggressively.

Best for:

• Early access to motivated sellers before they list with an agent.
• Lead-generation automations for agencies and broker networks.

Scraping & data nuances: Volume per day is smaller than on big portals, but lead value per row is higher. Filtering out non-serious listings and duplicates is important to avoid wasting sales time.

Separating private vs agency listings at scale

In many regions, the challenge is not just scraping the portal, but classifying who is really behind each listing. The ScrapeIt case study on data scraping for the real estate industry shows how a client receives 85,000+ rows per day filtered down to “true private” sellers to fuel an agency’s outbound funnel.

“If our call is not in the first three calls a private seller receives, the lead is already gone. Automated scraping is the only way to consistently be in that top three.”

— Sales manager, national agency network

Putting It All Together: Designing Your Regional Scraping Strategy

The concrete portals you choose will depend on your role and region, but most successful ScrapeIt projects follow a similar structure:

1. Start with 2–4 main portals per region.
   For example, a U.S.–EU investor might combine Zillow, Realtor.com, Rightmove, and ImmoScout24 for core coverage.
2. Add niche or owner-first sources for edge.
   Layer in portals like FSBO, NoBroker, or regional sites like Inmuebles24 or Property24 to capture deals that never reach the biggest portals.
3. Standardize once — reuse everywhere.
   Use ScrapeIt’s managed service model to get a single, normalized schema across all portals (property types, statuses, location hierarchy), so your BI, pricing, and AI teams can work with one logical dataset instead of dozens of one-off exports.
4. Scale frequency to the business problem.
   Daily full refresh might be required for lead-response and underwriting use cases, while weekly or monthly snapshots could be sufficient for high-level market research.

Where to Go Next

If you are planning a regional real estate data pipeline, three next steps are usually enough to get moving:

1. Review the full catalog of Real Estate Sites That We Scrape and mark the portals that map to your current and target markets.
2. Skim the real-estate-focused case studies — for example Massachusetts Zillow daily monitoring, 230K daily rows across five EU sites, and filtering private listings at 85K rows per day — to calibrate volumes and timelines.
3. Use the form on the Real Estate Scraping Services page to describe your exact regions, portals, and refresh needs — ScrapeIt’s team will propose a configuration and pricing that match your specific workflow.

Done well, regional real estate web scraping does not just give you more data. It gives you a synchronized, multi-country view of pricing, supply, and demand — the kind of view that lets portals grow faster, agencies win more mandates, and investors move before the market catches up.

FAQ

1. Why is a regional approach to real estate web scraping more effective than focusing on a "Global Top 5" list?
A regional approach is necessary because the key property portals, demand drivers, and data nuances vary significantly by geography (e.g., Boston vs. Berlin). A successful strategy requires a structured, regional playbook that identifies the local champions and normalizes country-specific fields (like statuses, pricing, and regulations) across multiple sites, which a simple "Global Top 5" list cannot achieve.

2. What are the core property portals recommended for the North American market (U.S. & Canada)?
The core U.S. bundle typically includes Zillow, Realtor.com, Redfin, Trulia, and Apartments.com. For Canada, key sources like Zolo.ca and REALTOR.ca are often used, with a focus on capturing "sold" listing data where available.

3. How does the European real estate data landscape differ from the U.S. market?
Europe is characterized by a patchwork of strong national champions (e.g., Rightmove in the UK, ImmoScout24 in Germany, Funda in the Netherlands), whereas the U.S. is dominated by a few major consumer brands. European scraping projects require aggregating data from several portals per country, demanding a strong layer of normalization for cross-language, cross-currency, and local regulation-specific fields.

4. Besides the large, general portals, what are some high-value niche sites that real estate teams should monitor?
High-value niche sources often include "For Sale By Owner" (FSBO) hubs like FSBO.com, or portals focused on owner-listed properties like NoBroker (India). These sites provide earlier access to motivated sellers, off-market inventory, or distressed assets, offering a crucial edge for lead generation and iBuyers.

5. What is the final deliverable and key benefit of using a managed regional scraping service like ScrapeIt?
The key benefit is receiving a single, synchronized, multi-country view of pricing, supply, and demand delivered as a ready-to-use dataset, not just raw scraping tools. The deliverable is a standardized, normalized schema across all chosen portals (e.g., Zillow + Rightmove + ImmoScout24 are mapped to one logical format), allowing BI, pricing, and AI teams to work with one clean data source.