
Real Estate Data Sources & Analytics
# | scrape_date |
available_from |
type |
no_of_rooms |
floor |
surface_living_(m2) |
year_built |
listing_id |
object_ref |
photo_1 |
photo_2 |
photo_3 |
feature_1 |
feature_2 |
feature_3 |
feature_4 |
feature_5 |
feature_6 |
feature_7 |
feature_8 |
land_area_(m2) |
last_refurbishment |
room_height_(m) |
number_of_toilets |
number_of_floors |
volume_(m) |
floor_space_(m2) |
feature_9 |
feature_10 |
feature_11 |
number_of_apartments |
hall_height_(m) |
object_ref_(m2) |
rent |
|||||||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
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 | xxxxxxxx | xxxxxx | Xxxxx | Xxxxxxx | xxxxxx | Xxxxxxxx | Xxxxxxx |
2 | 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 | Xxxxx | xxxxxxxxxx | Xxxxxx | xxxxxx | Xxxxxxxxx | xxxxx | Xxxxxxxxxx | xxxxxx | xxxxx | xxxxxxxx | Xxxxxx | Xxxxxxxxxx | xxxxxxxxx | Xxxxxxxxxx |
3 | 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 | xxxxxxxxx | xxxxxxx | Xxxxxx | xxxxxxxxx | xxxxx | Xxxxxxx | xxxxxxxxx | Xxxxxxxx | xxxxxxxx | Xxxxxxxx | Xxxxxxxx | xxxxxxxx | xxxxxxxxx | Xxxxxxx | Xxxxxxxxx | xxxxxxxx | xxxxx | Xxxxxxxxxx | xxxxxxxxxx | xxxxxx | Xxxxx |
4 | Xxxxxxx | Xxxxx | Xxxxxx | Xxxxx | Xxxxxxxxx | xxxxxx | xxxxxxxx | Xxxxxxxxx | Xxxxxx | Xxxxxxxxxx | Xxxxxx | Xxxxx | Xxxxxxx | xxxxxxxxx | Xxxxx | xxxxx | Xxxxxx | xxxxxxxxx | xxxxxxx | xxxxxxxxx | Xxxxxxxxxx | 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 |
5 | Xxxxx | Xxxxxxx | Xxxxxxxx | xxxxxxxxx | xxxxxxxx | xxxxx | Xxxxxxxxxx | Xxxxxxx | xxxxxxxxx | xxxxxxx | xxxxxxxxxx | xxxxxx | xxxxx | Xxxxxxxxxx | 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 |
6 | Xxxxxxx | Xxxxxx | Xxxxxxxxx | Xxxxxxxx | Xxxxxxxxxx | Xxxxxxx | Xxxxxx | 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 |
7 | 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 | Xxxxxxxxxx | Xxxxxxxxx | Xxxxxx | xxxxxxxx | xxxxxxxxxx | xxxxxxxx | Xxxxx |
8 | 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 | xxxxx | xxxxxxx | xxxxxxxxx | xxxxxxxxx | xxxxxx | xxxxxx | Xxxxxxxxxx | xxxxxxxxxx | xxxxxxxxx | Xxxxxx | xxxxxxxxxx | Xxxxxxx | xxxxxxxxx | xxxxxxx |
9 | 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 |
10 | Xxxxxxx | Xxxxxxxxx | Xxxxx | Xxxxxxxx | xxxxxxxxx | xxxxxxxxxx | xxxxx | xxxxxxxxxx | xxxxxxx | xxxxxxxx | Xxxxx | xxxxx | xxxxxx | Xxxxxx | xxxxxxxxxx | xxxxxxxxxx | Xxxxxxxxx | xxxxxxx | Xxxxxxx | Xxxxxxxxxx | Xxxxxxxx | xxxxxx | xxxxxx | Xxxxxx | xxxxxx | xxxxx | Xxxxxxxxx | Xxxxxxxxx | Xxxxxx | Xxxxx | Xxxxxxxxx | Xxxxxx | Xxxxxx | xxxxxx | xxxxx | xxxxxxx | xxxxxxxxxx | xxxxxx | Xxxxxxx | Xxxxx | xxxxxxxxxx | Xxxxx | xxxxxxx | xxxxxxx | Xxxxxxxxxx | Xxxxx | xxxxxxx | Xxxxxxxx | Xxxxxx |
... | xxxxxxxxxx | Xxxxx | Xxxxxxx | xxxxx | xxxxxx | xxxxxx | Xxxxxxxx | xxxxxxx | Xxxxxx | xxxxxxxxxx | Xxxxxxx | xxxxxxxxx | Xxxxxx | xxxxxxxxxx | xxxxxxxx | xxxxxxxxxx | Xxxxx | Xxxxxx | Xxxxxx | xxxxxx | Xxxxxxxx | Xxxxx | xxxxx | Xxxxxxxxxx | xxxxxx | xxxxx | xxxxxxx | xxxxx | Xxxxxxx | xxxxxxxxxx | Xxxxxxx | xxxxxxxxxx | xxxxxxxxxx | xxxxxxx | xxxxxxx | xxxxxxxxxx | Xxxxxxxxxx | Xxxxxx | xxxxxx | Xxxxxxxxxx | Xxxxxx | Xxxxxx | Xxxxxxx | Xxxxxx | xxxxxxxx | xxxxx | Xxxxxxxxxx | Xxxxx | xxxxxxx |
Data Dictionary
Attribute | Type | Example | Mapping |
---|---|---|---|
scrape_date
|
DateTime | 2022-10-11T16:07:22+00:00 | |
String | https://www.homegate.ch/rent/3002122393 | Website | |
String | Modernes Wohnen im Grünen | Product Name | |
String | ObjektbeschreibungPer 01.12.2022 vermieten wir diese schö... | Product Description | |
available_from
|
String | 01.12.2022 | |
Integer | 1610 | Product Price | |
Integer | 180 | Product Price | |
Integer | 1790 | Product Price | |
type
|
String | Apartment | |
no_of_rooms
|
Float | 3.5 | |
floor
|
Integer | 2 | |
surface_living_(m2)
|
Integer | 84 | |
year_built
|
Integer | 2019 | |
listing_id
|
Integer | 3002122393 | |
object_ref
|
String | 3221.1137254.ca40ac9a-3f45-11ed-842e-005056bdbc06 | |
String | Address | ||
Integer | 5222 | ZIP Code | |
String | Umiken | City Name | |
Float | 47.4843331 | Latitude-Longitude | |
Float | 8.194757267058954 | Latitude-Longitude | |
String | Weber + Schweizer Immobilien-Treuhand AG | Brand Name | |
String | Zürcherstrasse 14 8401 Winterthur | Company Address | |
Text | Apple Inc. is an American multinational technology compan... | Company Description | |
String | Company Phone Number | ||
photo_1
|
String | https://media2.homegate.ch/f_auto/t_web_dp_small/listings... | |
photo_2
|
String | https://media2.homegate.ch/f_auto/t_web_dp_small/listings... | |
photo_3
|
String | https://media2.homegate.ch/f_auto/t_web_dp_small/listings... | |
feature_1
|
String | Pets allowed | |
feature_2
|
String | Balcony / Terrace | |
feature_3
|
String | Cable TV | |
feature_4
|
String | Wheelchair access | |
feature_5
|
String | Child-friendly | |
feature_6
|
String | Parking space | |
feature_7
|
String | Garage | |
feature_8
|
String | Elevator | |
land_area_(m2)
|
|||
last_refurbishment
|
|||
room_height_(m)
|
|||
number_of_toilets
|
|||
number_of_floors
|
|||
volume_(m)
|
|||
floor_space_(m2)
|
|||
feature_9
|
|||
feature_10
|
|||
feature_11
|
|||
number_of_apartments
|
|||
hall_height_(m)
|
|||
object_ref_(m2)
|
|||
rent
|
Attribute | Type | Example | Mapping |
---|---|---|---|
String | Address | ||
Float | 49.02 | Latitude | |
Float | 51.03 | Longitude | |
Integer | 12000 | Product Price |
Description
Geography
History
Volume
20,000 | records |
Pricing
License | Starts at |
---|---|
One-off purchase |
€500 / purchase |
Monthly License |
€500 / month |
Yearly License | Not available |
Usage-based | Not available |
Suitable Company Sizes
Quality
Delivery
Use Cases
Categories
Related Searches
Related Products
Frequently asked questions
What is Real Estate Data Sources & Analytics?
Providing a comprehensive suite of real estate data sources and analytics to businesses looking to gain valuable insights into the property market. Our data sources include a vast array of information about properties such as property listings, market trends, and demographics.
What is Real Estate Data Sources & Analytics used for?
This product has 5 key use cases. Forloop.ai recommends using the data for Real Estate, Real Estate Analytics, Real Estate Investment, Real Estate Agencies, and Real Estate Insights. Global businesses and organizations buy Real Estate Market Data from Forloop.ai to fuel their analytics and enrichment.
Who can use Real Estate Data Sources & Analytics?
This product is best suited if you’re a Small Business, Medium-sized Business, or Enterprise looking for Real Estate Market Data. Get in touch with Forloop.ai to see what their data can do for your business and find out which integrations they provide.
How far back does the data in Real Estate Data Sources & Analytics go?
This Data Platform has 12 months of historical coverage. It can be delivered on a weekly, monthly, quarterly, yearly, real-time, and on-demand basis.
Which countries does Real Estate Data Sources & Analytics cover?
This product includes data covering 249 countries like USA, China, Japan, Germany, and India. Forloop.ai is headquartered in Sweden.
How much does Real Estate Data Sources & Analytics cost?
Pricing for Real Estate Data Sources & Analytics starts at EUR500 per purchase. Connect with Forloop.ai to get a quote and arrange custom pricing models based on your data requirements.
How can I get Real Estate Data Sources & Analytics?
Businesses can buy Real Estate Market Data from Forloop.ai and get the data via Email and REST API. Depending on your data requirements and subscription budget, Forloop.ai can deliver this product in .json, .csv, and .xls format.
What is the data quality of Real Estate Data Sources & Analytics?
Forloop.ai has reported that this product has the following quality and accuracy assurances: 99% match rate. You can compare and assess the data quality of Forloop.ai using Datarade’s data marketplace.
What are similar products to Real Estate Data Sources & Analytics?
This Data Platform has 3 related products. These alternatives include DATAANT Real Estate Data Dataset, API Rentals and Sale listings FSBO EU, USA, India and Canada 20+ data sources, Real Estate ownership data - commercial & non-commercial, and Real Estate Data - Real Estate Agents, Brokers, Investors 900K+ Verified Real Estate Records. You can compare the best Real Estate Market Data providers and products via Datarade’s data marketplace and get the right data for your use case.