We run the crawler and you receive files. Trade Me data arrives as CSV, JSON or XLSX, or through a REST endpoint your systems call. Delivery goes to S3, Google Cloud Storage, SFTP, a database you nominate, or email for small sets.
Schedules match the pace the data moves. Rentals and open homes reward a daily pass. A regional for-sale snapshot is usually weekly. A single extract for a one-off market study is fine too.
Every run is checked before it ships: row counts against the previous run, null rates per column, and an alert when a field stops populating because a template changed. When the site changes, we fix the crawler. That is the part you are actually buying.
Scope is agreed per project. A property build usually starts from the listing page and the search result rows behind it, and lands as one row per listing per collection date.
Motors, jobs and marketplace rows carry their own columns: make, model, year, odometer and body type for vehicles; job title, employer, location and any listed pay for jobs; category, auction closing time and Buy Now price for general goods. We keep the source column names and the cleaned ones side by side so a disputed value can be traced back to the page it came from.
Trade Me does not serve its pages to naive clients. A straight request to a property search path, /a/property/residential/sale/wellington/wellington, returned HTTP 406 Not Acceptable when we checked it on 31 August 2026. That is a refusal, not a page of listings, and it is the first thing that breaks a script written against the site over a weekend.
robots.txt is the other constraint, and we read it before scoping anything. It names a sitemap index at /a/sitemap_index.xml. It disallows the parameterised search surface - paths carrying search?, refine, page=, sort_order=, region and district - while explicitly allowing long lists of canonical paths, including property by region and suburb and jobs by region, category and employment type. The file carries a maintenance note dated 15/09/25, so it is edited rather than forgotten. We keep collection inside what it allows, run at a modest rate, and re-read it, because those allow lists move.
Addresses are predictable, which helps. A residential sale listing sits at /a/property/residential/sale/listing/ followed by a numeric listing id. That id is what makes a longitudinal dataset possible: it lets you match the same property across a price revision or a relisting instead of counting it twice.
Trade Me is the dominant online marketplace in New Zealand. It started as a general auction site and grew large vertical sections around that core: Trade Me Property, Trade Me Motors, Trade Me Jobs, Trade Me Services, and the general marketplace where goods still sell by auction with a closing time or by Buy Now.
Property leads this page. It splits into residential for sale, residential to rent, lifestyle, rural, commercial for sale and for lease, retirement villages and new homes. Listings come from licensed agencies and from private sellers - Trade Me publishes its own guide for owners listing without an agent - so agency attribution is not a safe assumption on every record.
The vocabulary is local, and it decides how the numbers should be read. A listing carries a sale method rather than always a figure: asking price, enquiries over, auction, tender, price by negotiation (no price shown, offers invited), or deadline sale (offers close at a stated date and time). Sellers give floor and land area in square metres, and either capital value or rateable value, the council valuation a property is rated on rather than a market appraisal. Rent is quoted per week. The geography runs region, then district, then suburb, from Tāmaki Makaurau Auckland or Whangārei down to a single suburb such as Karori or Fendalton.
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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
Trade Me publishes a real developer API, and we say so before selling anything. It is documented, it uses OAuth, and property search is part of it. If your need sits inside its rules, use it and keep your money.
The rules decide most cases. Trade Me states that the API exists for in-trade sellers to manage their own listings, and it names buyer-side tools, price monitoring, price comparison and data aggregation among the uses it does not support. An agency loading its own stock through the API is on solid ground. A research team building a picture of a regional market is outside what the API is offered for, and no amount of polling changes that.
What remains is narrower and public. We read pages served to any visitor, on a schedule, and we keep the history. Be precise about what that adds. The single listing response does document StartDate, EndDate and AsAt, so the age of a live listing is answerable in one call, and we are not going to pretend otherwise. Price history is the real gap. What a listing asked last month, a switch from deadline sale to asking price, the week it vanished and the week it returned under a new id - those exist only if somebody was recording them all along.
Monitoring of real estate listings on funda.nl, pararius.com, rentberry.com, rentola.com, and zimmo.be to support the growth of a European property portal.
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Daily detection of new private property listings in Switzerland on Homegate.ch and ImmoScout24.ch, giving the agency first access to high-value leads.
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Scraping residential listings from Immobilienscout24.de in Germany and Mallorca, including complete data and resized images.
Learn MoreLearn how to use web scraping to solve data problems for your organization
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
ScrapeIt is a managed web scraping service. We build the crawler, run the Trade Me scraper on your schedule, watch it, and repair it when the site changes. You do not maintain code and you do not run proxies.
Start by telling us the regions, the property types and the fields you need. We come back with a sample extract from a small slice, so you can check the columns against a real listing before any schedule is agreed.
Yes, and for some buyers it is the right answer. Trade Me publishes a documented developer API at developer.trademe.co.nz. You register an application, authenticate with OAuth, and production applications are reviewed by Trade Me staff. Property search is included: residential and rental endpoints at api.trademe.co.nz return JSON or XML, alongside commercial, lifestyle, rural and retirement village searches, plus separate used motors and jobs searches. The documented rate limit is 1,000 calls per hour per application per user, with HTTP 429 once you pass it and catalogue methods exempt. Page size is capped at 25 rows unauthenticated and 500 authenticated. The deciding limit is not technical, though. Trade Me states the API exists for in-trade sellers to manage their own listings, and lists buyer-side tools, price monitoring, price comparison and data aggregation among the uses it does not support. If you are an in-trade seller or agency managing your own listings, use the API. If your use falls outside that, it will not be approved for it, and you should take your own advice on what your intended use requires.
Yes, and the per week part matters. New Zealand quotes residential rent weekly, not monthly. Trade Me's own rental search takes a minimum and maximum price in NZD per week and returns a rent per week value. A team that assumes a monthly figure and multiplies by twelve will be wrong on every row, because a year holds 52 weeks rather than 48. We deliver the number as published and label the column per week. If your model needs a monthly figure we add a derived column, rent times 52 divided by 12, and keep the original beside it. We do not convert silently.
The documented residential search response carries the structured basics: listing id, title, address, suburb and district, price display, bedrooms, bathrooms, floor area, land area, property type, geographic location, agency details, photo URLs and open homes. Two things buyers ask for regularly are not in that list. Rateable value or capital value is entered by the seller when the listing is created, and chattels - the fixtures, fittings and appliances included in the sale - are normally written into the description rather than exposed as their own field, so they need parsing out of free text. There is a PropertyAttributes collection on the listing detail response, but what it holds depends on what the seller entered, so it is not something to plan a column on. The third absence is history. The listing detail response does carry StartDate, EndDate, AsAt and ViewCount, so the age of a live listing is answerable directly. What it asked last month is not. Price reductions, a change of sale method, and withdrawal followed by a relist exist only in a record taken repeatedly.
Treat them as temporary. Listings run to a closing date and leave the active search once they expire, sell or are withdrawn. That is the strongest argument for continuous collection over a single large pull: whatever you did not record before a listing left is gone from your side of the fence. We put a first seen date, a last seen date and a collection timestamp on every row, so a disappearance becomes a data point rather than a gap. When a listing id reappears at a different price, you can read it as a relist instead of counting a new property. Auction outcomes and the spread between an initial asking figure and a final result are built the same way, from the sequence of observations rather than from one call.
By default we deliver listing and commercial data: the property, the price, the sale method, the location, the agency. Agency name and branch are business attribution and we keep them. Individual agent names, direct phone numbers and email addresses can be excluded from your delivery on request, and we will exclude them by default if you say so at scoping. Private sellers do appear in property and in the general marketplace, so this is not a theoretical point. New Zealand's Privacy Act 2020 governs the collection and use of personal information about identifiable individuals. We are not your legal advisers and you should settle your intended use with your own counsel. We collect only pages that are public to any visitor, and nothing behind a login.
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.
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