Property Market Data | Observatory Data Suite (Sell & Rent) | Real Estate Demand Market Data | 53 KPIs | Italy | 9 Years Historic Coverage
# | id |
ads:views |
ads:leads |
ads:leads-100-views |
ads:leads-avg |
ads:views-avg |
res:qt-raw |
res:specificity |
res:pc-area-sup |
res:pc-stock |
res:qt-1typology |
res:pc-1typology |
res:qt-status |
res:pc-status |
res:qt-minrooms |
res:pc-minrooms |
res:qt-1floor |
res:pc-1floor |
res:qt-garden |
res:pc-garden |
res:qt-terrace |
res:pc-terrace |
res:qt-garage |
res:pc-garage |
min-surface:qt-clean |
min-surface:qt-raw |
min-surface:pc_raw |
min-surface:avg |
min-surface:min |
min-surface:25pc |
min-surface:50pc |
min-surface:75pc |
min-surface:max |
min-surface:quality-idx |
max-price:qt-clean |
max-price:qt-raw |
max-price:pc_raw |
max-price:avg |
max-price:min |
max-price:25pc |
max-price:50pc |
max-price:75pc |
max-price:max |
max-price:quality-idx |
price-sqm:qt-clean |
price-sqm:qt-raw |
price-sqm:pc_raw |
price-sqm:avg |
price-sqm:min |
price-sqm:25pc |
price-sqm:50pc |
price-sqm:75pc |
price-sqm:max |
price-sqm:quality-idx |
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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 | Xxxxx | xxxxxx | xxxxxxxxxx | Xxxxx | xxxxxxxxxx |
2 | 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 | xxxxxxxx | xxxxx | Xxxxxx | xxxxxxxxxx | xxxxxxxxx | xxxxx | xxxxx | xxxxxxxx | xxxxxx | Xxxxxxxxxx |
3 | 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 | Xxxxxxx | Xxxxx | Xxxxxx | Xxxxx | Xxxxxxxxx | xxxxxx | xxxxxxxx | Xxxxxxxxx | Xxxxxx | Xxxxxxxxxx | Xxxxxx | Xxxxx | Xxxxxxx | xxxxxxxxx | Xxxxx |
4 | 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 | Xxxxx | Xxxxxxx | Xxxxxxxx | xxxxxxxxx | xxxxxxxx | xxxxx | Xxxxxxxxxx | Xxxxxxx | xxxxxxxxx | xxxxxxx | xxxxxxxxxx | xxxxxx | xxxxx | Xxxxxxxxxx | Xxxxxxxxx | xxxxxxx | Xxxxxx | Xxxxx | Xxxxxxxx | xxxxxxxxx |
5 | 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 | Xxxxxxxxxx | xxxxxxxxxx | xxxxxxxxxx | Xxxxxxx | Xxxxx | Xxxxx | Xxxxx | Xxxxxxx | xxxxx | xxxxxxxxx | xxxxxxx | Xxxxxxx | xxxxxx | xxxxxxxxxx | xxxxxxxxxx | Xxxxxxx | xxxxxxxxx | Xxxxx |
6 | xxxxxxx | Xxxxxx | Xxxxx | xxxxxxxxxx | xxxxxxxxx | Xxxxxxxxxx | Xxxxxxxxx | Xxxxxxxx | xxxxxxxxx | Xxxxxxx | Xxxxxxx | Xxxxx | xxxxxxxxxx | Xxxxxxxxx | Xxxxxxx | Xxxxxxx | xxxxxxxx | xxxxx | Xxxxx | Xxxxxxxx | xxxxxxxx | Xxxxxxxxx | xxxxxxxxxx | xxxxxxxxxx | 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 |
7 | Xxxxxxxxx | xxxxxxxxx | xxxxxxxx | xxxxxxxxx | xxxxx | Xxxxx | Xxxxxxxx | xxxxxxxxxx | Xxxxxx | Xxxxxxxxx | Xxxxxxxxxx | xxxxxxx | 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 |
8 | 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 |
9 | 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 | xxxxxx | xxxxxx | Xxxxxx | xxxxxx | xxxxx | Xxxxxxxxx | Xxxxxxxxx | Xxxxxx | Xxxxx | Xxxxxxxxx | Xxxxxx | Xxxxxx | xxxxxx | xxxxx | xxxxxxx | xxxxxxxxxx | xxxxxx | Xxxxxxx | Xxxxx | xxxxxxxxxx | Xxxxx | xxxxxxx | xxxxxxx | Xxxxxxxxxx |
10 | 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 | xxxxxxxx |
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Data Dictionary
Attribute | Type | Example | Mapping |
---|---|---|---|
id
|
Integer | 83073 | |
ads:views
|
Integer | 1227 | |
ads:leads
|
Integer | 0 | |
ads:leads-100-views
|
Float | 0.0 | |
ads:leads-avg
|
Float | 0.0 | |
ads:views-avg
|
Integer | 118 | |
res:qt-raw
|
Integer | 6 | |
res:specificity
|
Float | 41.67 | |
res:pc-area-sup
|
Float | 0.2315 | |
res:pc-stock
|
Float | 0.79 | |
res:qt-1typology
|
Integer | 3 | |
res:pc-1typology
|
Float | 50.0 | |
res:qt-status
|
Integer | 0 | |
res:pc-status
|
Float | 0.0 | |
res:qt-minrooms
|
Integer | 0 | |
res:pc-minrooms
|
Float | 0.0 | |
res:qt-1floor
|
Integer | 6 | |
res:pc-1floor
|
Float | 100.0 | |
res:qt-garden
|
Integer | 1 | |
res:pc-garden
|
Float | 16.67 | |
res:qt-terrace
|
Integer | 0 | |
res:pc-terrace
|
Float | 0.0 | |
res:qt-garage
|
Integer | 0 | |
res:pc-garage
|
Float | 0.0 | |
min-surface:qt-clean
|
Integer | 0 | |
min-surface:qt-raw
|
Integer | 0 | |
min-surface:pc_raw
|
Float | 0.0 | |
min-surface:avg
|
Integer | 0 | |
min-surface:min
|
Integer | 0 | |
min-surface:25pc
|
Integer | 0 | |
min-surface:50pc
|
Integer | 0 | |
min-surface:75pc
|
Integer | 0 | |
min-surface:max
|
Integer | 0 | |
min-surface:quality-idx
|
Integer | 0 | |
max-price:qt-clean
|
Integer | 2 | |
max-price:qt-raw
|
Integer | 2 | |
max-price:pc_raw
|
Float | 33.33 | |
max-price:avg
|
Integer | 107500 | |
max-price:min
|
Integer | 70000 | |
max-price:25pc
|
Integer | 70000 | |
max-price:50pc
|
Integer | 70000 | |
max-price:75pc
|
Integer | 107500 | |
max-price:max
|
Integer | 220000 | |
max-price:quality-idx
|
Integer | 0 | |
price-sqm:qt-clean
|
Integer | 0 | |
price-sqm:qt-raw
|
Integer | 0 | |
price-sqm:pc_raw
|
Float | 0.0 | |
price-sqm:avg
|
Float | 0.0 | |
price-sqm:min
|
Float | 0.0 | |
price-sqm:25pc
|
Float | 0.0 | |
price-sqm:50pc
|
Float | 0.0 | |
price-sqm:75pc
|
Float | 0.0 | |
price-sqm:max
|
Float | 0.0 | |
price-sqm:quality-idx
|
Integer | 0 |
Description
Country Coverage
History
Volume
53 | KPIs |
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 Property Market Data Observatory Data Suite (Sell & Rent) Real Estate Demand Market Data 53 KPIs Italy 9 Years Historic Coverage?
Unlock comprehensive property market data from Italy with Immobiliare.it Insights’ Market Observatory. Access detailed documentation of demand data on listings: views, leads, saved searches, propensity of spending, Web Search Data, and Web Activity Data to make informed business decisions.
What is Property Market Data Observatory Data Suite (Sell & Rent) Real Estate Demand Market Data 53 KPIs Italy 9 Years Historic Coverage used for?
This product has 4 key use cases. Immobiliare.it Insights recommends using the data for Real Estate Analytics, Real Estate Listings Monitoring, Real Estate leads, and Real Estate demand data. Global businesses and organizations buy Property Market Data from Immobiliare.it Insights to fuel their analytics and enrichment.
Who can use Property Market Data Observatory Data Suite (Sell & Rent) Real Estate Demand Market Data 53 KPIs Italy 9 Years Historic Coverage?
This product is best suited if you’re a Small Business, Medium-sized Business, or Enterprise looking for Property Market Data. Get in touch with Immobiliare.it Insights to see what their data can do for your business and find out which integrations they provide.
How far back does the data in Property Market Data Observatory Data Suite (Sell & Rent) Real Estate Demand Market Data 53 KPIs Italy 9 Years Historic Coverage go?
This product has 9 years of historical coverage. It can be delivered on a monthly, quarterly, yearly, real-time, and on-demand basis.
Which countries does Property Market Data Observatory Data Suite (Sell & Rent) Real Estate Demand Market Data 53 KPIs Italy 9 Years Historic Coverage cover?
This product includes data covering 1 country like Italy. Immobiliare.it Insights is headquartered in Italy.
How much does Property Market Data Observatory Data Suite (Sell & Rent) Real Estate Demand Market Data 53 KPIs Italy 9 Years Historic Coverage cost?
Pricing information for Property Market Data Observatory Data Suite (Sell & Rent) Real Estate Demand Market Data 53 KPIs Italy 9 Years Historic Coverage is available by getting in contact with Immobiliare.it Insights. Connect with Immobiliare.it Insights to get a quote and arrange custom pricing models based on your data requirements.
How can I get Property Market Data Observatory Data Suite (Sell & Rent) Real Estate Demand Market Data 53 KPIs Italy 9 Years Historic Coverage?
Businesses can buy Property Market Data from Immobiliare.it Insights and get the data via SFTP and REST API. Depending on your data requirements and subscription budget, Immobiliare.it Insights can deliver this product in .json, .csv, and .xls format.
What is the data quality of Property Market Data Observatory Data Suite (Sell & Rent) Real Estate Demand Market Data 53 KPIs Italy 9 Years Historic Coverage?
Immobiliare.it Insights has reported that this product has the following quality and accuracy assurances: 100% territorial coverage. You can compare and assess the data quality of Immobiliare.it Insights using Datarade’s data marketplace.
What are similar products to Property Market Data Observatory Data Suite (Sell & Rent) Real Estate Demand Market Data 53 KPIs Italy 9 Years Historic Coverage?
This product has 3 related products. These alternatives include CrawlBee Realtor.com Dataset Property Listings MLS Data Real Estate Data Residential Data Realtime Real Estate Market Data, Real Estate Market Data Consumer Behaviour Data Real-Time Real Estate Data 53 KPIs Italy 9 Years Historic Coverage, and Zillow Real Estate Data Extraction Real-time Real Estate Market Data No Infra Cost Pre-built AI & Automation 50% Cost Saving Free Sample. You can compare the best Property Market Data providers and products via Datarade’s data marketplace and get the right data for your use case.