Platform · Trade Me Property

Trade Me Property Data Scraping

Property inside a general classifieds marketplace rather than a dedicated portal. That changes who lists, and what the listing actually says.

Trade Me Property data scraping collects New Zealand residential listings, asking prices and attributes. What makes it structurally distinct from every other property portal covered here: it sits inside a horizontal marketplace rather than being a dedicated property portal. That lowers the barrier to listing, which changes the mix of who lists and the consistency of what a listing contains.

Every other portal in this set was built for property. This one has property in it, which sounds like the same thing and produces a different dataset.

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trademe.jsonl LIVE FEED

{"listing_id":"tm-44120","region":"Example region", "district":"Example district", "lister_type":"agency", "sale_method":"auction", "price":"null","price_stated":false, "rating_valuation":845000, "rating_valuation_date":"2024-07-01", "auction_datetime":"2026-09-11T13:00+12:00", "caution":"845000 is a STATUTORY assessment. it does not fill the empty price field"} {"listing_id":"tm-88120","lister_type":"unstated", "attribute_completeness_rate":0.71, "floor_area":"null","land_area":612, "note":"horizontal marketplace. a non-professional filled this in"} {"region":"Example small region", "region_listing_count":11, "suppressed":false, "note":"eleven listings reported as eleven. whether that supports your analysis is your call"}

3 of 84,220 listing rows · New Zealand, nationalsale_method primary · rating valuation never a price · schema v1.0

Independence and trademarks. Actowiz Solutions is not affiliated with, endorsed by or connected to Trade Me Property or its owners. Trade Me Property and related marks belong to their respective owners, used here only to name the publicly accessible source this service collects from.

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Trade Me Property at a glance

How we handle Trade Me Property specifically

Platform-specific handling, not a generic retail template pointed at a different domain.

Portal: Trade Me Property — New Zealand
The structural difference: A horizontal marketplace, not a dedicated property portal
Consequence: Lower barrier to listing, so a broader mix of listers
Data effect: Attribute completeness varies more than on a dedicated portal
Sale method: Auction, tender, deadline sale and by negotiation all common
Price: Frequently absent, as in Australia. Method of sale is the primary field
Rating valuation: Council valuations are referenced. Captured, never used as a price
Refresh: Daily, and sub-daily around auction days

Platform specifics

What a horizontal marketplace changes

These are the reasons a Trade Me Property dataset needs its own handling rather than a shared retail schema.

A lower barrier to listing changes the population

A dedicated property portal is used by agencies, with agency data-entry standards behind it. A horizontal marketplace accepts listings from agencies and from private sellers on the same footing.

Private listings sit alongside agency ones, with different completeness and different photography.
Attribute fields are filled inconsistently, because a non-professional is filling them.
Listing quality varies more than on a portal built for the category.

That is the same effect our NoBroker page describes on the owner-direct side, with the difference that here both populations share one dataset rather than sitting on separate platforms.

So lister_type is recorded where the listing distinguishes it, and flagged unstated where it does not. An analysis that pools agency and private listings without the flag is blending two populations with different pricing behaviour and different data quality.

And a completeness rate, reported

attribute_completeness_rate ships per batch across the core fields. On a dedicated portal that number would be uninteresting. Here it is worth knowing before you build anything on the attributes.

Sale method, and a price field that is frequently empty

New Zealand shares Australia's pattern: a large share of listings carry no price, because the method of sale is auction, tender or deadline sale rather than a fixed asking price.

Auction — price discovered on the day, as our Domain page sets out.
Tender and deadline sale — offers by a date, with no published guide in many cases.
By negotiation — no price stated, deliberately.
Fixed asking price — the minority in several markets.

sale_method is the primary field rather than price, and price_stated is a boolean so an empty price is visible as a deliberate absence rather than a missing value.

Council rating valuations

New Zealand listings frequently reference a council rating valuation. That is a rating assessment for local authority purposes, on a periodic revaluation cycle — it is not a market valuation and it is not a price.

We capture rating_valuation and rating_valuation_date where published, and never present it as a price or use it to fill an empty price field. A dataset that does either is substituting a statutory assessment for a market number.

Small market, and what that means for a panel

New Zealand is a small market by listing volume, which has two practical consequences.

National coverage is achievable at a cost that would only buy a city elsewhere.
Regional cells get thin quickly. A regional analysis can hit sample sizes too small to say anything, and the thinness is real rather than a collection gap.

We ship region_listing_count per batch so a thin cell is visible as thin. We do not suppress or smooth thin regions — a region with eleven listings is reported with eleven, and whether that supports your analysis is your call rather than ours.

What we do not produce

Transaction prices. Asking only, and frequently not even that.
Time to sell. A listing disappearing can mean sold, withdrawn or relisted.
Lister personal data. Agency name is a commercial entity; private listers are individuals and nothing about them is collected.

That last point matters more on a horizontal marketplace than on an agency portal, because private listings frequently carry more personal context than an agency listing would.

Scope

What we collect on Trade Me Property, and what we do not

The right column matters more than the left. Anyone can list fields; the limits are what tell you whether the dataset will hold up.

✅ What we collect

lister_type where distinguished, flagged unstated where not
attribute_completeness_rate reported per batch
sale_method as the primary field, not price
price_stated as a boolean, so an empty price is a deliberate absence
Rating valuation captured with its date, never used as a price
region_listing_count per batch, so thin cells are visible
Thin regions reported as thin, never suppressed or smoothed
Auction date and outcome where published
Agency name as a commercial entity

❌ What we do not, and why

Agency and private listings pooled without the lister flag
A rating valuation presented as a price or used to fill an empty one
A thin regional cell smoothed or suppressed
A listing disappearance recorded as a sale
Any personal data about private listers

Core Trade Me Property fields

The full dictionary is agreed during scoping. These are the fields specific to this platform.

Field	What it is on this platform
listing_id / region / district / suburb	The listing and where it is
lister_type	agency, private or unstated
attribute_completeness_rate	Per batch, across the core fields
sale_method	auction, tender, deadline_sale, negotiation or fixed_price
price / price_stated / price_type	Frequently absent. The boolean makes that visible
rating_valuation / rating_valuation_date	A statutory assessment. Never a price
auction_datetime / auction_outcome	Where published
bedrooms / bathrooms / floor_area / land_area	As published, completeness varies
agency_name	A commercial entity. No private lister data
region_listing_count	Per batch, so thin cells are visible
first_seen / last_seen / disappearance_reason	Lifecycle, reason unknown by default

Use cases

What teams do with Trade Me Property data

New Zealand market coverage at national scale

A small market by volume means national coverage is achievable at a cost that would buy one city elsewhere, with region counts shipped so thin cells are visible rather than assumed adequate.

Agency versus private listing behaviour

lister_type recorded where distinguished, so two populations with different pricing behaviour and data quality are analysed separately rather than blended.

Sale-method mix analysis

Auction, tender, deadline sale and negotiation as distinct states, which on a market where most listings carry no price is the more informative signal.

Data-quality-aware attribute analysis

Completeness rate reported per batch, since a horizontal marketplace produces more variable attribute filling than a dedicated portal.

The 24-hour sample — run on your sources, not ours

Send us a Trade Me Property item or category list. We run real collection against it and return the output within 24 hours, with the platform-specific fields populated so you can check them yourself rather than take our word for it.

Real extraction from your actual sources
Returned within 24 hours
Coverage and QA note included
You keep the data either way
No card, no trial clock
Named engineer on the call

Get my free sample Book a 20-min scoping call Reply within one business day. Reference calls available under NDA.

How we engage

Three ways to engage us

Same collection pipeline and QA underneath. The difference is who holds the schedule and how the data reaches you.

Managed service (most common)

We own the collection, the QA and the delivery. You receive clean data on a schedule and never touch a scraper.

Dedicated engineer assigned to your account
Site changes fixed by us, not reported to you
Scheduled delivery to your warehouse or S3
Named contact on Slack or email

Best fit: Teams who need the data, not the infrastructure.

API access

The same collection pipeline exposed as an authenticated REST endpoint your systems query directly.

On-demand and scheduled endpoints
Rate limits agreed to your load profile
Sandbox keys for integration testing
Versioned schema with deprecation notice

Best fit: Product and engineering teams building on live data.

One-time or project extraction

A defined pull for a specific question — market sizing, diligence, a pitch, a one-off audit.

Fixed scope agreed in writing upfront
Single delivery with full QA report
Methodology documented for your records
Converts to managed if you want continuity

Best fit: Research, strategy and diligence work with a deadline.

Pricing

Every engagement is quoted individually, because the honest answer depends on your scope: how many sources, how many records, how often, and how the data reaches you. We scope it with you, run a free pilot on your own sources, and then quote a fixed monthly figure — no per-request metering and no overage billing when volumes move. Request a quote and you will have a number after one call.

Trade Me Property is usually collected alongside its competitors

Almost nobody buys a single platform in isolation. Trade Me Property data becomes useful when it sits next to the competitor set on one schema, refreshed on one schedule, so a price index or availability comparison is genuinely like-for-like.

That is what real estate data covers, and a Trade Me Property-only engagement can be expanded into it without rebuilding. If you already know you need several platforms, start there instead — it is the same pipeline and usually the better scoping conversation.

FAQ

Trade Me Property data scraping: frequently asked questions

Platform-specific questions, including what cannot be collected here.

Because the barrier to listing is lower. Agency listings sit alongside private ones on the same footing, so attribute fields are filled inconsistently and listing quality varies more than on a portal built for the category.

Both populations share one dataset here, rather than sitting on separate platforms as they do in markets with a dedicated owner-direct portal.

Where the listing distinguishes it, yes. Where it does not, lister_type is flagged unstated rather than guessed.

Pooling them without the flag blends two populations with different pricing behaviour and different data quality.

Because the method of sale is frequently auction, tender, deadline sale or by negotiation rather than a fixed asking price. New Zealand shares Australia's pattern here.

sale_method is the primary field, and price_stated is a boolean so an empty price reads as a deliberate absence rather than a missing value.

No, and we never do. A council rating valuation is a statutory assessment for local authority purposes on a periodic revaluation cycle — not a market valuation.

We capture it with its date and never use it to fill an empty price field. A dataset that does is substituting a statutory assessment for a market number.

Depends on the region, and we make that visible rather than deciding for you. region_listing_count ships per batch.

A region with eleven listings is reported with eleven. We do not suppress or smooth thin cells — whether that supports your analysis is your call.

We quote individually, and this is among the cheaper property engagements because national coverage is achievable at a cost that would buy a single city in a larger market.

One scoping call, a free pilot within 24 hours including the attribute completeness rate, then a fixed monthly quote. Request a quote.

See real Trade Me Property data before you commit to anything

Send us an item or category list. We return the output within 24 hours with the platform-specific fields populated.

Get a free sample Talk to an engineer

Free pilot, no card, no obligation. If we cannot collect a field you need on this platform, the sample shows you that too.

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