7 Best Real Estate Scrapers (Comparison)

7 Best Real Estate Scrapers

Real estate data scraping has become the fastest way to build repeatable pipelines for pricing, comps, and lead generation, without manually opening dozens of tabs. Whether you need structured real estate data for analytics or want to monitor the real estate market daily, the right tool makes the difference between a stable dataset and a constant game of whack-a-mole.

In this guide, I compare 7 options built for collecting large-scale property datasets from major portals and real estate listing sites. You’ll learn which platforms are easiest to set up, which are enterprise-grade, and which are best for teams that need all the data without building a full scraping stack in-house.

Quick Summary of the Best Real Estate Data API Tools

Here’s the shortlist if you want to pick a best tool for real estate web scraping quickly:

Pick based on your scale, budget, and how quickly you need valuable insights for your business.

1. ScrapingBee

If you’re collecting property datasets across multiple markets, our solution is often the easiest “start today” option, especially when target sites rely heavily on JavaScript. You send a URL, and it renders and returns HTML so you can parse it like any normal response. That’s huge for modern real estate web pages where the listing page content loads dynamically.

It also helps reduce failures from bot protection by handling browser-like behavior and scaling request throughput without you building your own rotation stack. In practice, it works well for Zillow data, Rightmove, and other global portals where repeated requests can trigger rate limiting.

For teams building a repeatable pipeline, the real estate data API approach is straightforward: pass input parameters (like geo location or query filters), fetch search results, and parse the property details you care about, like address fields and agent metadata. Many data teams export scraped data to a JSON file for downstream apps, or into a CSV file/Google sheet for quick sharing.

The setup can take a few minutes, and if you get stuck, the support team is responsive.

Pricing starts at $49/month on the Freelance plan.

2. Bright Data

Bright Data is commonly chosen by large organizations that need global scale, strict control, and deep targeting. The headline advantage is the proxy infrastructure: a large network plus advanced configuration for routing traffic through specific locations and networks, which can be useful when scraping popular sites that localize content.

For real estate scraping at enterprise volume, teams often value reliability and consistent throughput over simplicity. The tradeoff is that setup can be more complex, especially if you’re trying to keep collection stable while avoiding blocks and capturing clean raw data for long-running projects.

Bright Data can also be a good fit if your workflow depends on tracking price changes and keeping accurate information up to date across many pages and markets. The tradeoff for power is complexity: setup, targeting rules, and cost controls can take time to get right. It can also be overkill if you’re running a small project and don’t need enterprise-grade configuration.

Pricing varies significantly based on product and usage (proxies vs scraping APIs), and it’s typically positioned at a higher cost tier with sales-assisted plans.

3. Apify

Apify is a flexible option if you like the idea of a scraper “app store.” You can start with ready-made actors for common targets, or build your own custom actor to extract data from new sources. That makes it friendly for teams who want simple steps to get results, plus the option to customize later.

For example, you might run an actor that collects listings by property type, then store output per page so your pipeline can retry failures and keep a clean crawl log. If you’re technical, you can create a custom actor that normalizes fields and returns only the details you need.

It’s also a strong choice for non-engineers because automation and scheduling are built in. The main caveat is scale: some actors require tuning (timeouts, concurrency, retries) when you expand to new markets or larger volumes.

Apify pricing is usage-based (compute/proxy consumption) and depends on how much you run.

4. Oxylabs

Oxylabs is positioned as a premium provider with strong infrastructure and a focus on data quality. If you need high success rates across challenging targets, especially at scale, this is one of the most reliable options in the market.

For real estate use cases, that reliability matters when you’re collecting price and inventory signals over time (like property prices, square footage, and rental rates) and turning them into repeatable dashboards. When you can keep collection stable, you can track market trends, compare property values, and build scoring models that surface more detailed information without constant scraper maintenance.

Pricing for its Web Scraper API starts around $49/month, scaling up for higher volumes and enterprise needs. It’s premium-priced compared to simpler tools, so it may not be the best fit for hobby projects or early experimentation. And like most scraper APIs, you’ll still own the “last mile” of parsing and data modeling for your exact schema.

5. Zyte

Zyte (formerly Scrapinghub) is often chosen by teams that care about compliance and want a mature tooling ecosystem. Its strengths are smart proxy management, automatic blocking detection, and documentation that’s built for long-term maintainability, useful in the real estate industry, where targets change frequently.

If you’re building a pipeline that needs consistent market insights, Zyte’s approach can reduce the operational overhead: fewer surprise bans, less manual tuning, and more predictable collection. It’s also practical when your target is a broker or realtor portal that may require careful request behavior and escalation paths (including a formal contact process).

Zyte API pricing is tiered and usage-based per successful request, with different rates for HTTP vs browser-rendered requests, great for teams optimizing cost over time, but it can feel slower to grasp for beginners.

6. ScraperAPI

ScraperAPI is a popular entry point when you want a simple “send URL, get HTML” workflow with proxy rotation included. Integration is generally straightforward, making it a good fit for smaller projects, prototypes, or lightweight monitoring.

It’s often used by teams that need exports in csv format with a timestamp date column for tracking, or by customers building internal tools that don’t require advanced controls. The tradeoff is that power users may want more knobs (fine-grained targeting, session control, or custom browser behaviors) when scaling to complex targets.

Pricing commonly starts around $49/month, with higher tiers unlocking more volume and concurrency.

7. DataHen

DataHen is a strong choice if you want a more hands-off experience. Instead of operating the scraping stack yourself, you can lean on their team and platform to deliver cleaned datasets—especially helpful if you don’t have engineering bandwidth to maintain scrapers.

This approach shines when you have specific investment criteria (regions, property segments, update frequency) and you want consistent delivery without worrying about bot defenses, parser updates, or infrastructure. DataHen can also support custom pipelines and integrations so the output lands where you need it.

Pricing depends on the engagement, but their self-service “Professional” plan starts from $149/month, and fully managed services are typically quote-based.

How Real Estate Scrapers Actually Work

At a high level, a scraper visits a target webpage, downloads HTML (or renders it in a browser), and then you extract the fields you need into a consistent structure. That could be listing metadata, agent info, or price history, depending on your project.

The tricky part is that many real estate sites are dynamic and protected. Scrapers often need JavaScript rendering, retries, and IP rotation to keep access stable. If you’re new to the space, our guide on real estate web scraping walks through the basics and common pitfalls.

API vs Custom Scraping for Real Estate Data

If you need results fast, an API-based approach usually wins: fewer moving parts, fewer bans, and less maintenance. You focus on parsing and data quality rather than infrastructure. If you need maximum control (custom flows, complex navigation, unusual targets), custom scraping can be worth it, at the cost of more engineering time.

A good rule: start with an API to validate the dataset and use cases, then move to custom only when you’ve proven it pays off. This breakdown on API vs web scraping lays out the tradeoffs in practical terms.

Start Scraping Real Estate Data Faster

If you’re trying to go from idea to dataset quickly, don’t overbuild on day one. Pick a tool that matches your scale, confirm the fields you need, and ship a first version that delivers consistent outputs.

For many teams, ScrapingBee is a reliable option that removes much of the scraping complexity (rendering, anti-bot handling, and predictable integration), so you can spend your time on analysis, not firefighting blocks.

Frequently Asked Questions (FAQs)

What is the best real estate scraper overall?

For most teams, the best option is the one that stays stable on JavaScript-heavy listing sites with minimal setup. If you want quick implementation, strong rendering, and fewer blocking issues, an API-first tool is usually the most practical choice.

Can real estate websites block scrapers?

Yes. Many sites use bot detection, rate limits, and fingerprinting. Blocks can be triggered by too many requests, predictable traffic patterns, or repeated access from the same IP range. This is why rotation, retries, and browser rendering are common requirements.

Do I need proxies for scraping property data?

Often, yes, especially at scale. Proxies help distribute traffic and reduce the chance of getting rate-limited or banned. If you’re scraping small volumes, you might get by without them, but proxies become important once you expand to multiple markets or frequent updates.

How often should real estate listings be scraped?

It depends on your use case. For tracking inventory and pricing, daily or multiple times per day can be useful in fast-moving markets. For analytics and research, weekly snapshots may be enough. The key is matching frequency to volatility and your data freshness needs.