Quick Answer – GoProxies supports real estate data collection with the help of a big ethically sourced IP network, accurate geographical targeting, and auto-handling of JavaScript rendering, CAPTCHA solving, and IP rotation.
One thing about real estate research that never makes it to the slide presentation is that someone is checking whether the figures presented are actually valid. Has this listing been updated recently, or are you looking at outdated information? Is there a duplicate of this listing from the same apartment on other websites? Are changes made to prices unnoticed? Why do some sources differ in square footage?
None of this sounds very impressive. But it is important.
As property companies venture into automation and artificial intelligence in 2026, the quality and accuracy of the data become even more important. This issue is elaborated upon in the GoProxies reviewers’ article about the real estate scraper.
Imagine you’re monitoring 20,000 properties.
One listing goes missing.
Was it sold?
Was it rented?
Did the owner simply remove the advertisement?
Or did the website change something?
If you’re checking a handful of properties, you can investigate them manually.
With thousands, that’s another story.
This is why automation in collecting property data can come in handy. GoProxies says its real estate data API returns structured information such as addresses, prices, property specifications, agent details, geographic coordinates, and listing status.
Having information in a consistent format makes it much easier to compare records over time.
And that’s the key word here: over time.
A single listing tells you what exists.
Repeated collection can tell you what’s changing.
Welcome to duplicate listings.
You may come across one apartment that is listed by an agency, only to discover the exact same apartment on another portal.
The address is slightly different.
The description has been rewritten.
The price difference between the two apartments is €5,000.
Suddenly, your database thinks you’ve found three apartments.
You haven’t.
You’ve discovered an apartment with a very busy internet life.
A web scraping real estate workflow can help gather the underlying information consistently, but businesses still need rules for matching records and resolving conflicts.
In fact, that’s actually a good thing.
Automation should handle repetitive collection.
People should still decide what counts as a trustworthy match.
Sometimes, yes.
Picture two databases.
Database A contains five million old listings.
Database B contains 500,000 listings that are refreshed regularly.
Which one would you rather use to understand today’s rental market?
I’d take the second one before the coffee gets cold.
GoProxies claims that their infrastructure was designed to collect on a mass scale and includes 80M+ ethically sourced IPs from 200+ locations and is targeted by country, state, city, ISP, and ASN.
The important part isn’t simply having a giant pool.
It’s being able to collect from the places that matter and keep the information flowing.
This is where generic property research becomes misleading.
A city average might show prices rising.
But maybe three neighborhoods are rising sharply while another is flat.
Perhaps rental inventory is falling near a university but increasing elsewhere.
The more detailed the information, the more questions can be answered.
GoProxies says its residential network supports city- and postcode-level targeting across more than 200 countries, allowing property data to be collected from specific geographic perspectives.
That can be useful for investors, aggregators, and proptech teams that don’t want a broad national picture when their actual question is much smaller.
I’d say it could make the analyst more valuable.
That’s completely different.
AI can process huge amounts of information quickly. It can categorize properties, identify patterns, and help summarize changes.
But someone will have to check if the pattern is reasonable or not.
A model might notice that average asking prices have increased.
An experienced analyst might ask, “Has the mix of properties changed?”
That’s a totally different question.
Maybe more luxury apartments entered the dataset. Suddenly, the average increased, even though ordinary homes didn’t become more expensive.
The numbers weren’t wrong.
The interpretation was.
That’s why better data collection and human judgment still belong together.
Property websites can be complicated.
Many of them rely heavily on JavaScript. Automated requests can encounter CAPTCHAs. Pages change. Access patterns can trigger restrictions.
GoProxies claims that their real estate API can handle all of this stuff on its own: render JavaScript, rotate IP addresses, bypass CAPTCHA issues, and return JSON.
That can remove a lot of repetitive infrastructure work from a team’s plate.
And honestly, if you’ve ever spent an afternoon trying to figure out why your perfectly good script suddenly receives an empty page, you’ll understand why that’s appealing.
Not at all.
A small investment group could monitor one city.
A property startup could test a new valuation product.
A local agency could watch competing listings.
A research team could track rental inventory in a handful of neighborhoods.
GoProxies offers pay-as-you-go and flexible pricing and says account creation doesn’t require a credit card. It also advertises 24/7 support through Slack, Telegram, and email.
That makes it possible to test an idea before committing to a massive data operation.
Which is probably sensible.
Nobody needs a 10-million-record database before they’ve figured out what they’re actually trying to learn.
The property sector is becoming more and more automated in its analysis, whereas the real estate market is, by nature, very local and dynamic.
Recent German figures show the point nicely: in Q2 2026, residential property prices continued rising while commercial property prices fell year over year.
One market.
Two very different stories.
That’s why a single headline rarely tells you enough.
The interesting information often sits underneath it.
It’s what happens afterward.
A real estate scraper api can help collect the raw material, but the real advantage comes when businesses turn that information into something useful.
A better market report.
A smarter valuation model.
A more accurate property search.
A warning that inventory is suddenly falling.
Or just an analyst who isn’t spending all of Tuesday copying figures from one browser tab to another.
GoProxies uses a combination of its real estate API, a large ethically sourced IP network, precise geographic targeting, and infrastructure designed for high-volume collection.
And perhaps that’s the question worth asking.
Not, “How much data can we collect?”
But, “How much unnecessary work can we remove while still knowing what’s actually happening?”
That’s a much more interesting problem to solve.
What would you automate first if you were responsible for monitoring a property market every day?
The GoProxies real estate API returns structured data such as the address, price, property specifications, agent’s information, geographical location, and listing status. Structured data is more convenient because you can easily compare it between different dates.
GoProxies is responsible for collecting structured data, but it is up to businesses to establish guidelines for comparing records and conflict resolution. The automated system is responsible for data collection; human intervention is required for record matching.
According to GoProxies, its network infrastructure hosts more than 80 million ethically obtained IPs with 200+ data centers. Targeting can be performed by country, state, city, ISP, and ASN.
Yes, GoProxies offers pay-per-use service, flexible pricing plans, and does not ask for a credit card when opening an account. This makes GoProxies an option for startups.
