Bright Data | House Price Data | Custom Dataset of House and Real Estate Pricing, Web-Scraped - Available at scale for any use case
Verified Data Provider| # | timestamp |
rea_property_id |
property_type |
postcode |
year_built |
last_sold_date |
last_sold_agency |
bedrooms |
bathrooms |
findAgentsURI |
floor_area |
fullSuburb |
house_type |
lat |
lon |
leadGen |
photo_count |
images_urls |
rawSuburb |
street_address |
street_address_with_suburb |
suburb |
url |
propertyMarketTrends |
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 | |
| String | WA | State Name | |
|
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 | |
| Integer | 863 | Land Size | |
| String | m2 | Land Size | |
|
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":... | |
| String | 863m2 | Land Size | |
|
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
Country Coverage
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 B2B Marketing 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 B2B Marketing 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 B2B Marketing 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 5 reviews from clients. Bright Data appears on selected Datarade top lists ranking the best data providers, including Best 8 APIs for SEO Optimization, Best 6 SERP Tracking APIs for Accurate Monitoring, and Best +8 Web Scraping APIs to use in 2023.
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 product has 3 related products. These alternatives include Bright Data Real Estate Price Data Custom Dataset of House and Real Estate Pricing, Web-Scraped - Available at scale for any use case, Grepsr Real Estate Products, Property Listing, Sold Properties, Rankings, Agent Datasets Global Coverage with Custom and On-demand Datasets, and Forager.ai - Global Telemarketing Data API & Dataset 89M Mobile Numbers 95%+ Accurate Phone #s Bi-weekly Updates B2C Contact Data. You can compare the best B2B Marketing Data providers and products via Datarade’s data marketplace and get the right data for your use case.
