We extract property listings, suburb reviews, auction results, agent profiles, and street ratings from Homely. Delivered as clean JSON, CSV, or Parquet to S3, BigQuery, or Snowflake on your cadence.
Structured, schema-consistent data across all major object types — delivered clean, typed, and ready to query.
Complete list of extractable fields for Property Listings objects from homely.com.au. All fields typed and schema-versioned.
"property_id": "9a8b7c6d", "status": "For Sale", "property_type": "House", "address": "42 Wallaby Way", "suburb": "Sydney", "state": "NSW", "postcode": "2000", "price_guide": "Contact Agent", "bedrooms": 4, "bathrooms": 2, "car_spaces": 2, "land_size_sqm": 450
| # | property_id | url | status | property_type | address | suburb |
|---|---|---|---|---|---|---|
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Complete list of extractable fields for Suburb & Street Reviews objects from homely.com.au. All fields typed and schema-versioned.
"review_id": "r_102938", "location_type": "Suburb", "location_name": "Richmond", "state": "VIC", "postcode": "3121", "overall_rating": 4.5, "review_title": "Great vibe, terrible parking", "author_type": "Resident", "date_posted": "2026-02-14"
| # | review_id | location_type | location_name | state | postcode | overall_rating |
|---|---|---|---|---|---|---|
| 1 | ||||||
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Complete list of extractable fields for Sold History objects from homely.com.au. All fields typed and schema-versioned.
"property_id": "5x4y3z2w", "address": "15 Smith Street", "suburb": "Collingwood", "state": "VIC", "sold_price": 1250000, "sold_date": "2025-11-20", "sale_method": "Auction", "bedrooms": 3, "days_on_market": 28
| # | property_id | address | suburb | state | postcode | property_type |
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Complete list of extractable fields for Agent Profiles objects from homely.com.au. All fields typed and schema-versioned.
"agent_id": "ag_998877", "agent_name": "Jane Doe", "agency_name": "Ray White", "total_sales": 42, "average_sale_price": 950000, "active_listings_count": 5, "agent_reviews_rating": 4.9, "agent_reviews_count": 34
| # | agent_id | agent_name | agency_name | agency_id | role | phone_number |
|---|---|---|---|---|---|---|
| 1 | ||||||
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Complete list of extractable fields for Local Q&A objects from homely.com.au. All fields typed and schema-versioned.
"question_id": "q_554433", "suburb": "Newtown", "state": "NSW", "question_title": "Is the flight path noise bad?", "answer_count": 12, "date_asked": "2026-01-10", "tags": "['noise', 'lifestyle', 'families']"
| # | question_id | suburb | state | question_title | question_body | author_name |
|---|---|---|---|---|---|---|
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Our Homely scraper captures standard listing data alongside Homely's unique community content. We navigate map interfaces, handle dynamic pagination, and normalise location taxonomies automatically.
Capture beds, baths, parking, land size, price guides, and high-resolution image URLs for all Buy and Rent properties.
Extract Homely's unique community reviews. Capture star ratings across peace, transport, and cost of living metrics.
Track historical transaction data, sale methods, and days on market to build accurate automated valuation models.
Monitor agent active listings, historical sales volume, average sale prices, and client review scores.
Scrape user-generated questions and answers about specific suburbs to power sentiment analysis pipelines.
All addresses are parsed and normalised into standard state, suburb, and postcode fields for easy database joining.
Extract structured datetime objects for upcoming open homes and scheduled auctions.
Run daily diffs to detect new listings, price changes, status updates, and newly posted suburb reviews.
We handle Homely's rate limits and bot challenges using Australian residential proxies and realistic browser fingerprinting.
Brief in. Clean data out.
Provide target postcodes, states, or specific data types like suburb reviews. We configure the extraction schema.
We deploy Playwright crawlers, configure Australian proxy rotation, and map Homely's dynamic JSON responses.
Automated checks ensure address completeness, price normalisation, and review text integrity before production launch.
Clean structured records pushed to your S3 bucket, Snowflake stage, or Postgres database via automated pipelines.
Real estate portals actively block automated collection. We maintain the infrastructure so you receive clean data without managing proxies or broken selectors.
Homely uses map-based bounding boxes and complex URL structures for search queries. We translate standard postcodes and suburb names into the exact API parameters required to guarantee 100% coverage of the target area.
Homely is built as a single-page application. We intercept the underlying next_data JSON payloads and execute JavaScript where necessary to capture complete listing details and paginated review content without missing lazy-loaded elements.
Aggressive scraping triggers IP bans. We distribute requests across a pool of rotating Australian residential IP addresses, masking our crawlers as standard local user traffic to maintain continuous extraction.
Price guides like 'Offers over $800k' or 'Contact Agent' break analytical models. We apply regex-based normalisation to extract numeric bounds and standardise property types, beds, baths, and parking fields before delivery.
We hash property records to detect changes. If a property price drops or changes status from 'For Sale' to 'Under Offer', our pipeline emits an update event immediately.
PropTech firms ingest historical sold prices, land sizes, and property features to train machine learning models for accurate property valuations.
Analysts use Homely's unique street and suburb reviews to quantify lifestyle factors, noise complaints, and community sentiment for investment scoring.
Agencies track days on market, price guide adjustments, and auction clearance rates to identify motivated vendors and negotiate better deals.
Real estate franchises monitor competitor agent performance, sales volumes, and client review scores to identify top talent for recruitment.
Investors correlate active rental prices with recent sold data to map high-yield suburbs across Australian capital cities.
Councils and developers analyse local Q&A forums and street reviews to understand resident pain points regarding transport, parking, and amenities.
"Homely contains the most granular qualitative data on Australian suburbs. Extracting it cleanly requires navigating complex SPA architecture and aggressive rate limits."
Building a reliable pipeline for Homely means handling dynamic JavaScript rendering, intercepting undocumented API endpoints, and maintaining a pool of Australian residential proxies. DataFlirt manages this entire infrastructure layer. You define the target postcodes; we deliver the normalised data.
Everything supported by our homely.com.au scraper — rendered SPA elements, auth walls, rate-limit evasion and beyond.
Open-source tooling on proven cloud infra — no vendor lock-in, full observability.
We bypass heavy DOM parsing by intercepting Homely's internal API requests and Next.js state objects, resulting in faster extraction and lower failure rates.
Australian real estate portals block offshore data centre IPs. We route all requests through AU-based residential nodes to maintain high success rates.
Raw data is processed through our Python 3.12 normalisation pipelines to clean text fields, parse dates, and standardise geospatial coordinates before delivery.
Data delivered to where your team already works — no new tooling required.
About homely.com.au scraping, legality, and pipeline operations.
Ask us directly →Scraping publicly available property listings, reviews, and agent profiles is generally permissible under Australian law, provided it does not breach copyright or extract personally identifiable information beyond public professional details. DataFlirt strictly targets public data and ignores authenticated or private user areas.
We utilise Australian residential proxies, realistic browser fingerprinting via Playwright, and request throttling. This mimics standard user behaviour and prevents IP bans or CAPTCHA loops.
Yes. We can configure the pipeline to iterate through every suburb in a state, extracting all paginated street and suburb reviews, including star ratings and text bodies.
Pipelines can be scheduled daily, weekly, or monthly. For high-priority monitoring, we can configure intraday runs to detect price adjustments or new listings in specific postcodes.
Yes. Homely price guides are often unstructured text. Our pipeline includes regex-based normalisation to extract the minimum and maximum numeric values into dedicated integer fields.
We extract all historical sold records currently visible on the Homely platform for your target suburbs, including sold dates, prices, and sale methods.
We extract the high-resolution image URLs and floorplan URLs. If you require the physical image files, we can configure a secondary pipeline to download and store them in your S3 bucket.
Once extracted and validated, data is pushed automatically to your preferred destination. We support AWS S3, Snowflake, BigQuery, or direct Webhook integration for real-time updates.
20-minute scoping call. Pilot dataset within the week. Production within two. From national property listing feeds to targeted suburb sentiment analysis. We build and maintain the extraction infrastructure. Tell us your requirements.
