We extract room listings, pricing dynamics, geographic coordinates, amenities, and lister verification data from Badi. 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 Room Listings objects from badi.com. All fields typed and schema-versioned.
"listing_id": "bd_98421x", "title": "Bright double room in Gracia", "price_monthly": 650.0, "currency": "EUR", "room_type": "private", "available_from": "2026-09-01", "minimum_stay": 3
| # | listing_id | title | description | price_monthly | currency | deposit |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Location Data objects from badi.com. All fields typed and schema-versioned.
"listing_id": "bd_98421x", "city": "Barcelona", "neighbourhood": "Gracia", "latitude": 41.4036, "longitude": 2.1534, "distance_to_center": 2.4
| # | listing_id | city | neighbourhood | street | latitude | longitude |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
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Complete list of extractable fields for Amenities & Rules objects from badi.com. All fields typed and schema-versioned.
"listing_id": "bd_98421x", "wifi": true, "heating": true, "smoking_allowed": false, "pets_allowed": false, "couples_allowed": false
| # | listing_id | wifi | heating | air_conditioning | washing_machine | elevator |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Lister Profiles objects from badi.com. All fields typed and schema-versioned.
"user_id": "usr_44912p", "first_name": "Laura", "age": 28, "occupation": "Architect", "verification_status": "verified", "response_rate": 95
| # | user_id | first_name | age | gender | occupation | languages |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Tenant Preferences objects from badi.com. All fields typed and schema-versioned.
"listing_id": "bd_98421x", "preferred_gender": "any", "preferred_age_min": 22, "preferred_age_max": 35, "student_friendly": true, "capacity": 1, "lgbtq_friendly": true
| # | listing_id | preferred_gender | preferred_age_min | preferred_age_max | preferred_occupation | student_friendly |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Our Badi scraper bypasses map-based pagination limits and extracts full listing details, lister profiles, and pricing dynamics with residential proxies and JavaScript hydration.
Title, description, price, availability dates, minimum stay, and included bills parsed directly from the listing payload.
Precise latitude and longitude, neighbourhood mapping, and city normalisation for spatial analysis.
Extract age, gender, occupation, languages spoken, and verification status of the current flatmates.
Structured extraction of property features like WiFi, heating, and rules regarding pets, smoking, or couples.
Monitor monthly rent fluctuations, deposit requirements, and hidden fees across thousands of listings.
Capture the target demographic for each listing, including age ranges, occupation preferences, and student status.
Grid-based coordinate iteration ensures complete extraction of urban areas, bypassing standard API limits.
Extract inventory across London, Barcelona, Madrid, Berlin, and other major European hubs.
Configure daily diffs for active inventory tracking or weekly full syncs for historical market analysis.
Brief in. Clean data out.
Provide target cities, bounding boxes, or specific lister IDs. We map the extraction schema.
We configure grid traversal algorithms, proxy rotation, and Playwright rendering for badi.com.
Coordinate validation, price-outlier checks, and schema verification before full deployment.
JSON, CSV, or Parquet pushed to your S3 bucket or BigQuery dataset on an agreed cadence.
Extracting real estate data requires navigating dynamic map grids and aggressive rate limiting. Here is how our infrastructure maintains stability.
Badi limits search results in dense urban areas. We divide target cities into micro-bounding boxes and iterate programmatically, ensuring 100% coverage of available inventory without hitting truncation limits.
Frequent map API requests trigger IP bans. Our crawlers route traffic through EU-based residential ISP proxies with realistic request timing, preventing blacklisting and ensuring continuous data flow.
Badi relies heavily on client-side rendering. We use full Playwright browser sessions to execute JavaScript and hydrate listing details that simple HTTP clients cannot access.
We maintain a hash index of active listings. Subsequent pipeline runs only extract and deliver new listings, price changes, or status updates, reducing downstream processing load.
Front-end changes can break extraction. We implement fallback selector chains targeting nested JSON payloads and structured data, maintaining pipeline health even when the UI updates.
Co-living operators track neighbourhood pricing dynamics and amenity benchmarks to optimise their own rental yields.
Investors identify high-demand rental zones and calculate gross rental yields using precise coordinate data.
Property managers benchmark deposit requirements, minimum stays, and bill-inclusion trends against local averages.
Urban planners and housing analysts monitor demographic shifts, student housing demand, and affordability metrics.
Machine learning teams use historical listing data to train price prediction models and automated valuation algorithms.
Agencies identify unverified listers or high-turnover properties for targeted property management outreach.
"Badi holds the most granular data on urban room rentals and flatmate preferences, but map-based pagination makes it notoriously difficult to extract at scale."
Extracting data from Badi requires traversing dynamic map grids, parsing complex JSON payloads, and rotating EU residential proxies to avoid rate limits. DataFlirt manages this entire infrastructure layer, delivering clean, deduplicated rental records directly to your warehouse so your data engineering team can focus on downstream analytics.
Everything supported by our badi.com scraper — rendered SPA elements, auth walls, rate-limit evasion and beyond.
Open-source tooling on proven cloud infra — no vendor lock-in, full observability.
Scrapy handles grid traversal and deduplication. Playwright manages JavaScript rendering and API payload interception for dynamic listings.
We maintain pools of EU residential ISP proxies. Rotation happens per-request to prevent IP bans during intensive map scraping.
Pipelines run on AWS Lambda and ECS. Airflow handles scheduling and dependency management. Geospatial data stored in PostGIS.
Data delivered to where your team already works — no new tooling required.
About badi.com scraping, legality, and pipeline operations.
Ask us directly →Scraping publicly available real estate listings is generally permissible. DataFlirt extracts only public, non-authenticated room data and public lister profiles. We do not extract private messages, payment data, or circumvent authentication walls.
Badi limits the number of results returned per map view. We programmatically divide target cities into micro-bounding boxes, extracting data grid by grid to ensure 100% coverage without hitting truncation limits.
We support extraction across all markets where Badi operates, including Barcelona, Madrid, London, Berlin, and Paris. You define the bounding boxes or city names, and we configure the pipeline.
We typically configure daily syncs for active inventory, capturing new listings and price changes within 24 hours. Higher frequency runs can be configured for specific high-demand neighbourhoods.
Yes. Every pipeline run produces a timestamped snapshot. We maintain a time-series record for each listing, tracking price adjustments and availability status over time.
We extract only the data publicly visible on a lister's profile, such as first name, age, occupation, and verification status. Direct contact details and private messaging are gated and not extracted.
Our minimum engagement starts at a defined list of target cities with weekly delivery. For high-frequency extraction across all European markets, we price based on compute volume and proxy bandwidth.
20-minute scoping call. Pilot dataset within the week. Production within two. Whether you need a one-off city export or a continuous price-monitoring feed across Europe - we scope, build, and operate the pipeline. Tell us what you need.
