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Platform · Domain

Domain Data Scraping

A large share of Australian listings carry no asking price, on purpose. The auction is the price discovery, and the guide is not a number.

Domain data scraping collects property listings, price guides, auction schedules and results across Australian markets. What makes this market structurally different: a large share of listings carry no asking price at all, because the property is going to auction and the published guide is deliberately a range — or absent. A schema expecting a price field will be empty on the most active part of the market.

Every other property portal we cover has an asking price as the primary field. Here the primary field is frequently a method of sale.

Free pilot on your own Domain list, returned in 24 hours. No card, no trial clock — and you keep the sample data either way.

domain.jsonl LIVE FEED
{"listing_id":"dm-44120","suburb":"Example Suburb","state":"VIC", "sale_method":"auction", "price_guide_low":980000,"price_guide_high":1060000, "price_guide_text":"as published", "guide_revisions":2, "auction_datetime":"2026-09-06T11:00+10:00", "note":"we do NOT publish a midpoint. nobody quoted 1020000"} {"listing_id":"dm-44120", "auction_outcome":"passed_in", "highest_bid":955000, "note":"the most informative outcome. sold-or-not loses it entirely"} {"sold_price":"null","price_published":false, "sold_price_visible_share":0.58, "clearance_rate":"not_computed", "caution":"published sold prices are a SELF-SELECTING subset. clearance basis is yours to state"}
3 of 604,220 listing rows · Australiasale_method is the primary field · guides never collapsed · schema v1.0

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

Our Data Powers
B2C Marketplace
amazon
D2C + Marketplace
NYKAA
D2C + Marketplace
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udaan
Food Delivery
Uber Eats
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blinkit
Taxi Aggregator
Uber
E-Commerce
Tmall
Domain at a glance

How we handle Domain specifically

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

Portal
Domain — Australia
The structural difference
Auction is a primary method of sale
Consequence
Many listings carry no price, by design
Price guide
A range or absent. Not an asking price
What is published
Auction date, time and venue — and afterwards, the result
Results
Sold, passed in, or withdrawn. Three distinct outcomes
Clearance rate
A market-level figure we do not compute. Reported by others on varying bases
Refresh
Daily, and sub-daily around auction days
Platform specifics

An auction market needs a different schema

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

The method of sale is the primary field, not the price

Australian residential property is frequently sold at auction, particularly in the major metropolitan markets. For those listings:

  • There may be no price at all. "Contact agent" or "auction" is the whole price field.
  • Where a guide exists it is a range, and the range is a marketing decision rather than a valuation.
  • The guide can move during the campaign, which is itself a signal.
  • The final price is only known after the auction, and only if it sold.

sale_method is the primary field — auction, private treaty, expression of interest, tender. We record price_guide_low, price_guide_high and price_guide_text separately, and we never collapse a guide range into a single figure, because the midpoint of a marketing range is not a price anybody quoted.

Guide movement is a real signal

Where a guide is revised during a campaign, we record each revision with its timestamp. A guide rising or falling before auction day says something about the campaign that the final result alone does not.

Three auction outcomes, and why two of them are frequently conflated

After an auction a property is sold, passed in, or withdrawn. Those are different events.

  • Sold — it met the reserve and transacted at auction.
  • Passed in — bidding did not meet the reserve. The property is usually still for sale, frequently by negotiation, and may sell days later at a different price.
  • Withdrawn — the auction did not proceed.

A dataset that records only sold-or-not loses the middle case, which is the most informative one about where bidding actually stopped.

We record auction_outcome with all three states, plus highest_bid where published on a pass-in, and subsequent_sale_price where a later sale is subsequently published against the same listing.

What we do not compute

Clearance rate. It is widely reported and it is computed on varying bases — which auctions are counted, how late results are collected, whether pass-ins that sell later are reclassified. Two published clearance rates for the same weekend frequently differ.

We deliver the underlying outcomes with timestamps so you can compute it on a basis you can state. We do not publish a number whose definition we would have had to choose for you.

What is published after the fact, and what is not

Sold prices

Where a sold price is published against a listing we capture it, with price_published flagged. Not all sales publish a price, and the share that does varies by state and by agency practice.

sold_price_visible_share ships per batch, because a market analysis built on published sold prices is built on a self-selecting subset — and the selection is not random.

What we do not collect

  • Vendor or buyer identity. Not collected in any market.
  • Individual agent details. Agency name is a commercial entity; the agent is a person.
  • Official transfer records. Those come from state land registries and are a separate source with different coverage and lag.

The last one is worth being clear about: a portal sold price is what was published by an agency. A land registry transfer is the transaction record. They are different sources and they do not always agree.

Scope

What we collect on Domain, 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

  • sale_method as the primary field — auction, private treaty, EOI, tender
  • Price guide low, high and raw text, never collapsed to a midpoint
  • Guide revisions recorded with timestamps during a campaign
  • auction_outcome across sold, passed in and withdrawn
  • Highest bid on a pass-in where published
  • Sold price where published, with price_published flagged
  • sold_price_visible_share per batch, since the subset is self-selecting
  • Auction date, time and venue as scheduled, and reschedules
  • Property attributes, suburb and state as published

❌ What we do not, and why

  • A guide range collapsed into a single price figure
  • A clearance rate computed on a basis we chose
  • Sold-or-not recorded where the outcome was passed in
  • A portal sold price treated as a land registry transaction record
  • Vendor, buyer or individual agent details

Core Domain 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 / suburb / state The listing and where it is
sale_method The primary field. Auction, private treaty, EOI, tender
price_guide_low / price_guide_high / price_guide_text Range and raw text, never collapsed
guide_revisions Each revision with its timestamp
auction_datetime / auction_venue / auction_rescheduled As scheduled, and changes
auction_outcome sold, passed_in or withdrawn
highest_bid On a pass-in, where published
sold_price / price_published / sold_price_visible_share Where published, flagged, and the batch share
subsequent_sale_price Where a later sale publishes against the same listing
agency_name A commercial entity. No individual agent data
observed_at Timestamp
Use cases

What teams do with Domain data

Auction campaign analysis

Guide revisions with timestamps through a campaign alongside the outcome, showing how guides move before auction day — a signal the final result alone does not contain.

Outcome analysis that keeps the middle case

Sold, passed in and withdrawn as three states with highest bid on pass-ins, which is the most informative outcome about where bidding actually stopped.

Clearance computed on your own basis

Underlying outcomes with timestamps, so a clearance rate can be computed on a definition you can state rather than inheriting one you cannot.

Guide-to-result spread

Published guide against sold price where both exist, with the visible share stated so nobody treats a self-selecting subset as the whole market.

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

Send us a Domain 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.

Domain is usually collected alongside its competitors

Almost nobody buys a single platform in isolation. Domain 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 Domain-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

Domain data scraping: frequently asked questions

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

Because the property is going to auction and the price is discovered there. 'Contact agent' or 'auction' is frequently the whole price field, by design.

A schema expecting an asking price will be empty on the most active part of this market, which is why sale_method is the primary field here rather than price.

No, and we would not. A guide is a range and the range is a marketing decision, not a valuation. The midpoint of a marketing range is a number nobody quoted.

We deliver low, high and the raw text separately, plus each revision during the campaign with its timestamp — guide movement is itself a signal.

Because it is the most informative outcome. A pass-in means bidding did not meet the reserve; the property is usually still for sale and may transact days later at a different price.

A dataset recording only sold-or-not loses exactly the case that tells you where bidding stopped. We record all three states, plus highest bid where published.

No. Clearance rates are computed on varying bases — which auctions are counted, how late results are collected, whether pass-ins that later sell get reclassified. Two published rates for the same weekend frequently differ.

We deliver the underlying outcomes with timestamps so you can compute it on a basis you can state, rather than a number whose definition we chose for you.

No. A portal sold price is what an agency published; a land registry transfer is the transaction record. They are different sources, with different coverage and lag, and they do not always agree.

We also report sold_price_visible_share, because not all sales publish a price and the subset that does is self-selecting rather than random.

We quote individually on listing volume, suburbs in scope and refresh. Auction weekends need sub-daily collection to catch outcomes before listings update, which is the main cost variable.

One scoping call, a free pilot within 24 hours, then a fixed monthly quote. Request a quote.

See real Domain 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.

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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