Bright Data | House Price Data | Custom Dataset of House and Real Estate Pricing, Web-Scraped - Available at scale for any use case
| # | timestamp |
rea_property_id |
property_type |
state |
postcode |
year_built |
last_sold_date |
last_sold_agency |
bedrooms |
bathrooms |
findAgentsURI |
floor_area |
fullSuburb |
house_type |
land_size_num |
land_size_unit |
lat |
lon |
leadGen |
photo_count |
images_urls |
rawSuburb |
street_address |
street_address_with_suburb |
suburb |
url |
propertyMarketTrends |
land_size |
sale_history |
floor_area_num |
offMarket |
parking |
powerListing |
powerProfile |
avm_estimate_lastUpdated |
estimated_price |
estimated_price_confidence |
estimated_value |
estimated_value_high |
estimated_value_low |
last_sold_sale_type |
last_sold_price |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 1 | xxxxxxxxxx | Xxxxxxxxx | xxxxxx | xxxxxxxxxx | Xxxxx | Xxxxxx | Xxxxxxxxxx | Xxxxxx | Xxxxxxxxx | Xxxxxxxxxx | xxxxxxxxx | Xxxxxxxxx | xxxxxxxxx | Xxxxxxx | xxxxxx | Xxxxx | xxxxxxxxxx | xxxxxx | Xxxxxxxxxx | xxxxxx | Xxxxx | Xxxxxx | xxxxx | xxxxxxxx | xxxxxxx | Xxxxx | Xxxxxxxx | xxxxxxxxxx | xxxxxx | Xxxxxxxxx | xxxxxx | Xxxxxxxxx | Xxxxxxxxx | xxxxxxxxxx | Xxxxxx | Xxxxx | xxxxxx | xxxxxxx | xxxxxxx | Xxxxx | xxxxxx | Xxxxxxxxxx |
| 2 | xxxxxxxx | xxxxxx | Xxxxx | Xxxxxxx | xxxxxx | Xxxxxxxx | Xxxxxxx | Xxxxx | xxxxxx | xxxxxxxxxx | Xxxxx | xxxxxxxxxx | xxxxxxxxx | Xxxxxxx | xxxxxxxx | xxxxxxxx | Xxxxxxxxxx | Xxxxxxxx | Xxxxxxxx | xxxxxxxxx | Xxxxxxxxxx | Xxxxxx | Xxxxxxxxx | xxxxx | xxxxxxx | xxxxxxxxx | Xxxxxx | Xxxxxxx | Xxxxxxxxx | xxxxxxxxx | xxxxxxxxx | Xxxxx | xxxxxxxx | Xxxxxxx | xxxxxxxxx | Xxxxxxx | xxxxx | Xxxxxxx | xxxxxxx | Xxxxx | xxxxxxxxxx | Xxxxxxx |
| 3 | Xxxxx | xxxxxxxxxx | Xxxxxx | xxxxxx | Xxxxxxxxx | xxxxx | Xxxxxxxxxx | xxxxxx | xxxxx | xxxxxxxx | Xxxxxx | Xxxxxxxxxx | xxxxxxxxx | Xxxxxxxxxx | xxxxxxxx | xxxxx | Xxxxxx | xxxxxxxxxx | xxxxxxxxx | xxxxx | xxxxx | xxxxxxxx | xxxxxx | Xxxxxxxxxx | xxxxxxxxxx | Xxxxx | xxxxxxx | Xxxxxxxx | Xxxxxxx | xxxxx | xxxxxxxx | xxxxxxxxxx | Xxxxxx | xxxxxxxxx | Xxxxx | xxxxx | xxxxxxxxx | xxxxxxx | Xxxxxxxxx | Xxxxxxx | xxxxxxxxxx | Xxxxx |
| 4 | xxxxxxxxx | xxxxxxx | Xxxxxx | xxxxxxxxx | xxxxx | Xxxxxxx | xxxxxxxxx | Xxxxxxxx | xxxxxxxx | Xxxxxxxx | Xxxxxxxx | xxxxxxxx | xxxxxxxxx | Xxxxxxx | Xxxxxxxxx | xxxxxxxx | xxxxx | Xxxxxxxxxx | xxxxxxxxxx | xxxxxx | Xxxxx | Xxxxxxx | Xxxxx | Xxxxxx | Xxxxx | Xxxxxxxxx | xxxxxx | xxxxxxxx | Xxxxxxxxx | Xxxxxx | Xxxxxxxxxx | Xxxxxx | Xxxxx | Xxxxxxx | xxxxxxxxx | Xxxxx | xxxxx | Xxxxxx | xxxxxxxxx | xxxxxxx | xxxxxxxxx | Xxxxxxxxxx |
| 5 | xxxxxxxxx | Xxxxx | Xxxxx | Xxxxxxxxx | xxxxxxxxxx | xxxxxx | xxxxxxxxx | xxxxxxx | Xxxxxxx | Xxxxxxxxxx | Xxxxxxxxxx | Xxxxxxxx | Xxxxxxxxx | xxxxx | Xxxxxxx | xxxxxxxxxx | Xxxxxxxxx | Xxxxxxxx | xxxxxxxxxx | xxxxxxx | Xxxxxxxx | xxxxx | Xxxxxx | xxxxxx | xxxxxxxx | xxxxxxx | Xxxxx | Xxxxxxxxx | Xxxxx | Xxxxxxx | Xxxxxxxx | xxxxxxxxx | xxxxxxxx | xxxxx | Xxxxxxxxxx | Xxxxxxx | xxxxxxxxx | xxxxxxx | xxxxxxxxxx | xxxxxx | xxxxx | Xxxxxxxxxx |
| 6 | Xxxxxxxxx | xxxxxxx | Xxxxxx | Xxxxx | Xxxxxxxx | xxxxxxxxx | xxxxxxxx | Xxxxxx | xxxxxxxxxx | xxxxxxxxx | xxxxx | Xxxxx | xxxxxxx | xxxxxxxxxx | Xxxxxx | Xxxxxxxxx | xxxxxxx | Xxxxxxxx | xxxxx | xxxxx | Xxxxxxxxxx | Xxxxxxx | Xxxxxxxx | Xxxxxxx | xxxxx | xxxxxxx | Xxxxx | xxxxxxxxxx | Xxxxxxxxxx | xxxxxxx | Xxxxx | xxxxxxxxx | xxxxxxxx | Xxxxxxxx | xxxxxxxx | Xxxxxxx | Xxxxxx | Xxxxxxxxx | Xxxxxxxx | Xxxxxxxxxx | Xxxxxxx | Xxxxxx |
| 7 | Xxxxxxxxxx | xxxxxxxxxx | xxxxxxxxxx | Xxxxxxx | Xxxxx | Xxxxx | Xxxxx | Xxxxxxx | xxxxx | xxxxxxxxx | xxxxxxx | Xxxxxxx | xxxxxx | xxxxxxxxxx | xxxxxxxxxx | Xxxxxxx | xxxxxxxxx | Xxxxx | xxxxxxx | Xxxxxx | Xxxxx | xxxxxxxxxx | xxxxxxxxx | Xxxxxxxxxx | Xxxxxxxxx | Xxxxxxxx | xxxxxxxxx | Xxxxxxx | Xxxxxxx | Xxxxx | xxxxxxxxxx | Xxxxxxxxx | Xxxxxxx | Xxxxxxx | xxxxxxxx | xxxxx | Xxxxx | Xxxxxxxx | xxxxxxxx | Xxxxxxxxx | xxxxxxxxxx | xxxxxxxxxx |
| 8 | xxxxxxxxx | xxxxxxxxx | Xxxxxxx | Xxxxxxx | Xxxxxxx | Xxxxxxx | xxxxxxx | Xxxxxxxxxx | xxxxxxxx | Xxxxx | xxxxxxxxxx | xxxxxxxxxx | xxxxxx | xxxxxxxx | Xxxxxxxx | xxxxxx | xxxxxxxx | xxxxxx | Xxxxxxxx | xxxxxxxxx | xxxxx | Xxxxxxxxxx | Xxxxxxxxx | Xxxxxxx | xxxxxxxx | Xxxxxxx | Xxxxxxxxxx | Xxxxxxxxx | xxxxxxxxxx | xxxxxxx | Xxxxxxxxx | xxxxxxxxx | xxxxxxxx | xxxxxxxxx | xxxxx | Xxxxx | Xxxxxxxx | xxxxxxxxxx | Xxxxxx | Xxxxxxxxx | Xxxxxxxxxx | xxxxxxx |
| 9 | Xxxxxxxxxx | Xxxxxxxxx | Xxxxxx | xxxxxxxx | xxxxxxxxxx | xxxxxxxx | Xxxxx | xxxxxxxx | xxxxxxxxxx | xxxxxxxx | Xxxxx | xxxxxxxx | xxxxxx | Xxxxxxxx | xxxxxxxxxx | Xxxxxxx | xxxxxxxxxx | Xxxxxxx | Xxxxxxxxx | xxxxxx | Xxxxx | Xxxxx | Xxxxxx | Xxxxxxxxx | Xxxxxxxxx | xxxxx | Xxxxx | Xxxxxxxxxx | Xxxxxxx | Xxxxxxxxxx | Xxxxxxxx | xxxxxxx | xxxxxxxx | Xxxxxx | Xxxxxxxxx | Xxxxxxxxxx | Xxxxxx | Xxxxxx | Xxxxxxx | xxxxxxxxxx | Xxxxxxx | Xxxxxxxxxx |
| 10 | xxxxx | xxxxxxx | xxxxxxxxx | xxxxxxxxx | xxxxxx | xxxxxx | Xxxxxxxxxx | xxxxxxxxxx | xxxxxxxxx | Xxxxxx | xxxxxxxxxx | Xxxxxxx | xxxxxxxxx | xxxxxxx | Xxxxxxxx | Xxxxxxxx | Xxxxxxx | xxxxxx | xxxxx | xxxxx | Xxxxxxxxx | xxxxx | Xxxxx | Xxxxxx | xxxxxxxxxx | Xxxxxxxx | Xxxxxxxx | Xxxxxxxxx | Xxxxxxxxxx | xxxxxx | Xxxxx | Xxxxxxxx | xxxxxx | Xxxxx | Xxxxxx | xxxxxx | xxxxxxxx | Xxxxxxxx | Xxxxxxxxxx | xxxxxxxxxx | Xxxxx | Xxxxxxx |
| ... | Xxxxxxx | Xxxxxx | Xxxxxx | xxxxxxxx | Xxxxxxxxx | Xxxxx | Xxxxxxx | xxxxxxxxx | Xxxxxxxxx | xxxxxxx | Xxxxxxxx | Xxxxxxxxxx | xxxxxx | xxxxxxxxxx | xxxxxx | xxxxxx | Xxxxxxxxxx | xxxxxxx | Xxxxxxxxxx | xxxxx | Xxxxxxxxx | Xxxxxxx | Xxxxxxxxx | Xxxxx | Xxxxxxxx | xxxxxxxxx | xxxxxxxxxx | xxxxx | xxxxxxxxxx | xxxxxxx | xxxxxxxx | Xxxxx | xxxxx | xxxxxx | Xxxxxx | xxxxxxxxxx | xxxxxxxxxx | Xxxxxxxxx | xxxxxxx | Xxxxxxx | Xxxxxxxxxx | Xxxxxxxx |
Data Dictionary
| Attribute | Type | Example | Mapping |
|---|---|---|---|
|
timestamp
|
DateTime | 2023-03-29T00:00:00+00:00 | |
|
rea_property_id
|
Integer | 7696552 | |
|
property_type
|
String | house | |
|
state
|
String | WA | |
|
postcode
|
Integer | 6401 | |
|
year_built
|
Integer | 1998 | |
|
last_sold_date
|
DateTime | 2022-05-03T00:00:00+00:00 | |
|
last_sold_agency
|
String | Professionals Avon Valley - NORTHAM | |
|
bedrooms
|
Integer | 4 | |
|
bathrooms
|
Integer | 2 | |
|
findAgentsURI
|
String | https://www.realestate.com.au/find-agent/northam-wa-6401 | |
|
floor_area
|
String | Unavailable | |
|
fullSuburb
|
String | Northam, WA 6401 | |
|
house_type
|
String | house | |
|
land_size_num
|
Integer | 863 | |
|
land_size_unit
|
String | m2 | |
|
lat
|
Float | -31.64303 | |
|
lon
|
Float | 116.67976 | |
|
leadGen
|
String | {"actionUrl":"https://property.value.realestate.com.au","... | |
|
photo_count
|
Integer | 63 | |
|
images_urls
|
String | https://i2.au.reastatic.net/1592x624-resize,extend,r=33,g... | |
|
rawSuburb
|
String | northam | |
|
street_address
|
String | 2 Wood Drive | |
|
street_address_with_suburb
|
String | 2 Wood Drive, Northam, WA 6401 | |
|
suburb
|
String | Northam | |
|
url
|
String | https://www.realestate.com.au/property/2-wood-dr-northam-... | |
|
propertyMarketTrends
|
String | {"propertyType":"house","bedrooms":"4","medianSoldPrice":... | |
|
land_size
|
String | 863m2 | |
|
sale_history
|
String | null | |
|
floor_area_num
|
String | null | |
|
offMarket
|
Boolean | t | |
|
parking
|
Integer | 0 | |
|
powerListing
|
String | null | |
|
powerProfile
|
String | null | |
|
avm_estimate_lastUpdated
|
String | 27 February, 2023 | |
|
estimated_price
|
String | $360,000 - $470,000 | |
|
estimated_price_confidence
|
String | high | |
|
estimated_value
|
Integer | 415000 | |
|
estimated_value_high
|
Integer | 470000 | |
|
estimated_value_low
|
Integer | 360000 | |
|
last_sold_sale_type
|
String | sold | |
|
last_sold_price
|
String | null |
Description
Geography
Pricing
| License | Starts at |
|---|---|
| One-off purchase | Available |
| Monthly License | Available |
| Yearly License | Available |
| Usage-based | Available |
Suitable Company Sizes
Quality
Delivery
Use Cases
Categories
Related Searches
Related Products
Frequently asked questions
What is Bright Data House Price Data Custom Dataset of House and Real Estate Pricing, Web-Scraped - Available at scale for any use case?
Use our real estate dataset, which covers all properties in Australia, to examine a wide range of houses and apartments, such as location, room size, past sales, and more, sourced from realestate.com.au. Depending on your needs, you may purchase the entire dataset or a customized subset.
What is Bright Data House Price Data Custom Dataset of House and Real Estate Pricing, Web-Scraped - Available at scale for any use case used for?
This product has 5 key use cases. Bright Data recommends using the data for Business Intelligence (BI), Market Intelligence, HR Intelligence, Data Intelligence, and Customer Data Intelligence. Global businesses and organizations buy Job Postings Data from Bright Data to fuel their analytics and enrichment.
Who can use Bright Data House Price Data Custom Dataset of House and Real Estate Pricing, Web-Scraped - Available at scale for any use case?
This product is best suited if you’re a Small Business, Medium-sized Business, or Enterprise looking for Job Postings Data. Get in touch with Bright Data to see what their data can do for your business and find out which integrations they provide.
Which countries does Bright Data House Price Data Custom Dataset of House and Real Estate Pricing, Web-Scraped - Available at scale for any use case cover?
This product includes data covering 245 countries like USA, China, Japan, Germany, and India. Bright Data is headquartered in United States of America.
How much does Bright Data House Price Data Custom Dataset of House and Real Estate Pricing, Web-Scraped - Available at scale for any use case cost?
Pricing information for Bright Data House Price Data Custom Dataset of House and Real Estate Pricing, Web-Scraped - Available at scale for any use case is available by getting in contact with Bright Data. Connect with Bright Data to get a quote and arrange custom pricing models based on your data requirements.
How can I get Bright Data House Price Data Custom Dataset of House and Real Estate Pricing, Web-Scraped - Available at scale for any use case?
Businesses can buy Job Postings Data from Bright Data and get the data via S3 Bucket, SFTP, Email, and Streaming API. Depending on your data requirements and subscription budget, Bright Data can deliver this product in .json and .csv format.
What is the data quality of Bright Data House Price Data Custom Dataset of House and Real Estate Pricing, Web-Scraped - Available at scale for any use case?
Bright Data has reported that this product has the following quality and accuracy assurances: 97% Success rate in real-time. You can compare and assess the data quality of Bright Data using Datarade’s data marketplace. Bright Data has received 1 review from clients.
What are similar products to Bright Data House Price Data Custom Dataset of House and Real Estate Pricing, Web-Scraped - Available at scale for any use case?
This Data Platform has 3 related products. These alternatives include Bright Data G2 software products, reviews and alternatives dataset (Public web data) g2.com), Jobs Posting Data Feeds from Job Aggregator sites: Indeed/Glassdoor/Monster/Zip recruiter Grepsr, and Coresignal From the Largest Professional Network Employee Data / Global / 636M Records / Fresh Data / 50 Months of Historical Data / Updated daily. You can compare the best Job Postings Data providers and products via Datarade’s data marketplace and get the right data for your use case.
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