Sahibinden Land Listings: 226K Plots for Sale, Plus 86K from HepsiEmlak
Land for sale across Turkey from Sahibinden and HepsiEmlak for a land investor: 226,000 plots in the first file and 86,200 HepsiEmlak rows.
HouseSigma Scraper for Listings, Estimates and Market Stats
HouseSigma prints a SigmaEstimate, estimated rent and rental yield beside the MLS facts of a listing. Our HouseSigma scraper collects estimates and facts for Ontario, BC and Alberta.
Plans from €169 a month · Free project assessment · Reply within 1 business day
CSV · Excel · JSON · XML · FTP · SFTP · Amazon S3 · Google Cloud
housesigma.comwhat you get
Typical fields for property portals
addresspriceproperty_typebedroomsarea_m2agentlisted_at
Delivery
API, JSON feed or CSV, XLSX and JSON files
ScheduleOne-time, monthly, weekly, daily or 3 times a day
Scrape HouseSigma as a Managed Service
- You send
HouseSigma pages, filters, fields and a schedule
- We run the scrapers
Built, monitored and repaired by our team
- You receive the data
API, JSON feed or CSV, XLSX and JSON files
You tell us which provinces, cities or communities matter, which listing states you need - active, sold, de-listed, for lease - and how often to look. We build the HouseSigma crawlers, run them on your schedule and repair them whenever the single-page app changes its layout. Page rendering, request pacing, proxy rotation and CAPTCHA handling are our side of the job.
Some HouseSigma data opens only after a free sign-in: sold prices, earlier prices in Listing History, the date a home was first listed, most Market Trends charts and the full details of listings marked Login Required. When a project needs them, it runs on an account registered through HouseSigma's own sign-up form or on your own account, and we say up front which columns depend on that. Field documentation comes with every delivery, and a sample can be ordered before the first scheduled run.
HouseSigma Data: Fields We Collect
Every HouseSigma listing lives at an address built from the province code (/on/, /bc/ or /ab/), the city, the street address and a 16-character home id. That id, together with the MLS number the page prints as Listing #, keys each row, so one home can be followed from run to run and joined to your own MLS records.
- Identityhome id, MLS number, listing URL, the Data Source field naming the MLS system that supplied the record, and the Listing Brokerage.
- Status and timingFor Sale, For Lease, Sold, Inactive or Expired, Days on Site, Status Change, Added to HouseSigma and Updated on.
- Pricelist price, the Price Changes table with the date, price and percentage of every change, the Coop Commission line where the board supplies it, and the Listing History of the same address, whose earlier rows show their event to every visitor and their price and listing ID after sign-in.
- Propertyproperty type (Detached, Condo Apartment, Freehold Townhouse, Condo Townhouse, Duplex and more), style, year built or building age, size in square feet, lot size with frontage and depth, zoning, basement, parking and garage, and the property tax with its year.
- Roomsbedrooms in HouseSigma's 3+2 notation for above and below grade, bathrooms by piece count, kitchens, and a room table with dimensions in metres and the level of each room.
- Home ValueSigmaEstimate with its estimated date, Estimated Rent, Rental Yield and Rental DOM, where the listing shows them.
- Demand signalsthe Market Demand gauge (Buyer's Market, Balanced, Seller's Market), the Listing Popularity score on its Cold-Medium-Hot scale and the area Popularity Score.
- Descriptionthe original listing remarks and, on many listings, HouseSigma's AI summary.
Search and city pages return the card version of the same record - status, list price, address with community, property type, beds, baths, garage and size range, plus the MLS number and brokerage on open cards, while other cards hide the address and price behind a sign-in prompt. A crawl can start from a city, a community or a property type filter and open full pages only where needed.
HouseSigma Sold Prices, Price History and Market Stats
Beyond the single listing, HouseSigma publishes a page for each city and community with filters for active, sold and de-listed homes, property type, price and lot size. Every city page carries a written monthly summary: median sold price with month-over-month, year-over-year and five-year change, average days on market, counts of sold, active and new listings, the median rental price and the split of sold homes by property type. Captured each month, those paragraphs become a time series per city.
Sold listings are the layer to plan around. Without a sign-in, a sold card in a city such as Kitchener shows the address, how long ago it sold and sometimes an Estimated Sold Price; in Toronto even the address is masked, and the sold price, its date and the full details open after logging in. Market Trends works the same way: median price, new listings and the one-, five- and ten-year value change are public, while absorption rate, rent ratio and the sold price distribution need an account. Projects that need sold prices run that part of the crawl on an account opened through HouseSigma's own sign-up form, or on the client's account.
Repeated runs fill the gap from the other side. A home recorded as For Sale on Monday and gone on Friday keeps every price change in between, and the Listing History rows show whether the same address was listed and expired before.
About HouseSigma Listings in Ontario, BC and Alberta
- Source
- housesigma.com
- Category
- property portals
- Delivery
- API, JSON feed, CSV, XLSX
- Plans
- From €169 a month
HouseSigma is a Toronto company founded in 2018 that runs a listings portal, a home-value tool and a real estate brokerage under one roof; its listing pages state that HouseSigma Inc. is a registered brokerage in Ontario. It began with Toronto and the GTA, spread across Ontario to Ottawa, London and Hamilton, and now covers British Columbia and Alberta as well. Each province has its own branch of the site with its own city and community pages.
The listings come from regional real estate boards, and each page names the MLS source in its Data Source field. HouseSigma adds its own layer on top: the SigmaEstimate and rent estimate, the Market Demand gauge, an area Popularity Score built from online activity such as listing views, a Listing Popularity score for each home and AI summaries of listing remarks. Under a listing sit three sets of comparables - Sold Comparables, Similar for Sale and Rent Comparables - plus catchment schools and community statistics.
An anonymous visitor sees a lot but not everything. Many active listings open in full, with Key Facts, room tables and community figures; others carry a Login Required notice that masks the address, price and Key Facts, and sold prices and most trend charts are for logged-in users. A dataset built from HouseSigma follows that split, and our field list marks which columns depend on an account.
25Developers
500+Customers
1.5BPages extracted
15,000+Hours saved for our clients
HouseSigma scraping plans and pricing
Delivery: CSV, JSON or XLSX files, our API or a JSON feed. MCP server on request.
Full plan detailsWhat HouseSigma Data Is Used For
HouseSigma was built to show Canadian buyers more about a price than the listing alone does. Collected in bulk, the same fields answer questions for the businesses around those buyers.
- Pricing a listing or an offerList price against SigmaEstimate, the community median price and the Similar for Sale comparables shows whether a home is priced ahead of its neighbourhood.
- Rental investment screensEstimated Rent, Rental Yield and Rental DOM per listing, set against the city's median rental price and the rental listings nearby, give a first cut of yield across a whole city.
- Market timingDays on Site, the dated Price Changes table and the Listing Popularity score separate a stale listing from one that buyers are watching.
- Brokerage intelligenceThe Listing Brokerage on each record shows which offices list what, where and in which price band.
- Developer and lender researchCommunity statistics - median price, new listings, median days on market - by property type and month track supply in the neighbourhoods a project depends on, from Brampton and Burlington condos to detached homes in Ottawa.
Related case studies
Apartments.com Data: Unit Prices for 3,000 Buildings Every Day
A daily apartments.com feed for a US apartment locator: about 3,000 buildings from the client's own list, down to each unit's rent and floor plan.
Housing.com Data: 315K Project Pages with Photos and Floor Plans
A one-time Housing.com collection for a real-estate company in India: project cards plus every photo and floor plan, uploaded straight to its cloud drive.
From our blog
Learn how to use web scraping to solve data problems for your organization
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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.
7 Real Estate Websites to Scrape in 2026: Plus 2 Hidden Gems
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.”
Benefits of Web Scraping for Real Estate: 6 Use Cases From Our Projects
Six jobs that scraped property data does, each backed by a ScrapeIt project: seller leads, listing monitoring, image datasets, weekly market supply, sold records and unit-level rentals. For every use case - the fields it needs and how often to refresh them.
About ScrapeIt
ScrapeIt builds and runs crawlers for property portals, including analytics-heavy sites like HouseSigma where much of the value sits in the estimates layered over each listing. We keep the board's identifiers intact, so a HouseSigma row joins your MLS or CRM records, and we handle personal data under GDPR: brokerages appear by name, private individuals do not. You choose the provinces and the cadence; we deliver CSV, Excel, JSON or a load into your database.
FAQ
Still unsure? Write to info@scrapeit.io
Do you offer a HouseSigma API for listing data?
Yes. The ScrapeIt HouseSigma API returns the home id, MLS number, status, list price, Listing History events and SigmaEstimate for each home as JSON from an endpoint we host, updated on the schedule you choose. The same records can also arrive as CSV or Excel files, through a JSON feed or loaded into your database.
Is it legal to scrape HouseSigma?
Yes. Many active listings with their Key Facts and room tables, the community statistics and the monthly city summaries load for every visitor, signed in or not. We collect only publicly available data - what any visitor can open on HouseSigma - and we collect it legally.
Which HouseSigma data is not available to the public without signing in?
Sold prices and sold dates, earlier prices in Listing History, the date a home was first listed, most Market Trends charts and the address, price and Key Facts of listings marked Login Required sit behind HouseSigma's sign-in. When a project needs them, it runs on an account registered through the site's own sign-up form or on the client's account. Everything else described on this page is visible to an anonymous visitor.
Can you collect HouseSigma price history and old listings?
Yes, from two directions. Each listing page shows a dated Price Changes table and the Listing History of the same address, whose earlier rows, such as Expired listings, show their prices only after sign-in, so that part runs on an account registered through HouseSigma's own form or on yours. Our repeated runs then record every status change and price cut from the day we start, giving a price history per home id that outlasts the listing itself.
Which provinces and cities can a HouseSigma dataset cover?
Ontario, British Columbia and Alberta - the three provinces HouseSigma publishes. Within them you can scope by city or community, for example Toronto, Ottawa, Hamilton, Brampton or Burlington in Ontario, Vancouver or Surrey in BC, Calgary or Edmonton in Alberta, and narrow further by property type or listing status.
How it works
- 1
Make a request
You share your needs, expectations, and desired timeframe. We'll suggest the best solution based on your request and budget.
- 2
We configure the crawlers
Our specialists configure the crawlers and extract a sample dataset for your review before proceeding with the full-scale extraction.
- 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.
- 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.
Request a quote
Tell us which sites, which fields and how often. A couple of lines is enough to start.
- We reply within 1 business day
- A sample dataset before the full run
- We sign an NDA on request
- Delivery by API, JSON feed or files