We extract property listings, daily pricing matrices, availability calendars, and building amenities from Sonder. 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 Property Listings objects from sonder.com. All fields typed and schema-versioned.
"property_id": "SND-LND-1024", "title": "The Kensington Classic", "city": "London", "neighbourhood": "Kensington", "property_type": "Apartment", "max_guests": 4, "bedrooms": 2, "bathrooms": 1.5, "square_meters": 85
| # | property_id | title | city | neighbourhood | latitude | longitude |
|---|---|---|---|---|---|---|
| 1 | ||||||
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Complete list of extractable fields for Pricing & Availability objects from sonder.com. All fields typed and schema-versioned.
"property_id": "SND-LND-1024", "check_in": "2026-06-15", "check_out": "2026-06-20", "base_price": 245.0, "total_price": 1350.0, "taxes": 75.0, "cleaning_fee": 50.0, "available": true, "currency": "GBP"
| # | property_id | check_in | check_out | base_price | total_price | taxes |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
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Complete list of extractable fields for Room Amenities objects from sonder.com. All fields typed and schema-versioned.
"property_id": "SND-LND-1024", "wifi_speed_mbps": 400, "kitchen_appliances": "['Oven', 'Microwave', 'Dishwasher', 'Nespresso']", "laundry_type": "In-unit washer/dryer", "parking_available": false, "workspace_setup": "Desk with ergonomic chair", "air_conditioning": true
| # | property_id | wifi_speed_mbps | kitchen_appliances | laundry_type | parking_available | gym_access |
|---|---|---|---|---|---|---|
| 1 | ||||||
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Complete list of extractable fields for Building Details objects from sonder.com. All fields typed and schema-versioned.
"building_id": "BLD-LND-045", "name": "Kensington Gardens Sonder", "address": "14 Queen's Gate, London", "unit_count": 24, "elevator": true, "luggage_storage": "24/7 smart lockers", "transit_proximity": "3 min walk to Gloucester Road Station"
| # | building_id | name | address | unit_count | elevator | front_desk_hours |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
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Complete list of extractable fields for Reviews & Ratings objects from sonder.com. All fields typed and schema-versioned.
"property_id": "SND-LND-1024", "rating_overall": 4.8, "cleanliness_score": 4.9, "location_score": 4.8, "value_score": 4.6, "review_count": 112, "source_platform": "Sonder Internal"
| # | property_id | rating_overall | cleanliness_score | location_score | value_score | review_count |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Our Sonder scraper handles complex calendar grids, React hydration states, and dynamic pricing APIs to deliver clean, normalised accommodation data.
Extract unit details, floor plans, bed configurations, and high-resolution gallery URLs across all global Sonder markets.
Scrape 365-day forward-looking availability grids to track occupancy rates and booking velocity.
Capture base rates, cleaning fees, taxes, and length-of-stay discounts. Track daily price fluctuations.
Normalise unstructured amenity lists into boolean flags and categorical arrays for kitchen, laundry, and tech setups.
Extract curated local guides, transit scores, and proximity to landmarks provided on building pages.
Aggregate data on entire Sonder-managed buildings, including shared facilities, security protocols, and total unit counts.
Concurrent extraction across London, Dubai, New York, Paris, and 30+ other markets using geo-targeted proxies.
Bypass web rendering where possible by targeting Sonder's backend GraphQL and REST endpoints directly for lower latency.
Maintain hash indexes to only emit records when pricing or availability states change, reducing warehouse ingest costs.
Brief in. Clean data out.
Provide target cities, building IDs, or required date ranges. We design the extraction schema together.
We configure Scrapy / Playwright crawlers, proxy rotation, session management, and CAPTCHA handling for sonder.com.
Schema validation, null-rate checks, price-outlier detection, and calendar verification before full launch.
JSON / CSV / Parquet pushed to your S3 bucket, BigQuery dataset, or Snowflake stage on agreed cadence.
Modern hospitality platforms use complex frontend frameworks and aggressive rate limiting. Here is how we maintain stable extraction.
Sonder relies heavily on React and Next.js. Instead of scraping the rendered DOM, our pipeline intercepts the `__NEXT_DATA__` JSON payloads injected into the page source, extracting clean, structured data before the browser even renders it.
Pricing and availability are loaded dynamically based on user search parameters. We automate API requests mimicking calendar interactions, iterating through date ranges to build a complete 365-day forward-looking matrix.
Sonder alters pricing and availability based on the searcher's IP to handle regional taxes and compliance. We route requests through residential proxies matching the target market to ensure accurate local pricing data.
High-frequency calendar queries quickly trigger WAF blocks. We implement token bucket algorithms, distributed request timing, and session rotation to stay beneath Sonder's rate-limit thresholds while maintaining high throughput.
When Sonder updates their internal API structures, our pipeline detects missing fields immediately. We use strict JSON schema validation on every run, alerting our ops team to patch selectors before bad data reaches your warehouse.
Boutique hotels and short-term rental operators benchmark their daily rates against Sonder's dynamic pricing algorithms.
Revenue managers analyse Sonder's length-of-stay discounts and booking curves to optimise their own inventory pricing.
PropTech funds track Sonder's building acquisitions and unit counts to map institutional investment in the alternative accommodation sector.
Municipalities monitor short-term rental density and zoning compliance by tracking active Sonder units in specific neighbourhoods.
Hospitality developers evaluate amenity baselines and supply gaps in target cities before breaking ground on new projects.
Hedge funds scrape forward-looking availability to forecast Sonder's quarterly occupancy rates and revenue performance.
"Sonder represents the institutionalisation of short-term rentals. Tracking their pricing and inventory provides the cleanest signal for urban hospitality demand."
Extracting data from modern Next.js applications requires more than basic HTTP requests. You need API interception, state hydration parsing, and sophisticated calendar traversal logic. DataFlirt manages this complexity, delivering clean pricing matrices so your analysts can focus on yield optimisation rather than maintaining fragile web scrapers.
Everything supported by our sonder.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 crawl orchestration, deduplication, and retry logic. Playwright handles JavaScript rendering, cookie sessions, and interaction flows. Combined via scrapy-playwright middleware.
We maintain pools of residential ISP proxies across global markets. Rotation happens per-request with sticky sessions where required. IP score monitoring prevents blacklisted pool contamination.
Pipelines run on AWS Lambda (burst) and ECS (sustained). Airflow handles scheduling, dependency management, and SLA alerting. All state stored in managed Postgres.
Data delivered to where your team already works — no new tooling required.
About sonder.com scraping, legality, and pipeline operations.
Ask us directly →Scraping publicly available information from Sonder is generally permissible under applicable law. DataFlirt targets only public, non-authenticated property listings, pricing, and availability data. We do not extract personal guest data, circumvent authentication walls, or violate GDPR.
We automate API requests to Sonder's backend, iterating through date ranges to construct a complete forward-looking availability and pricing matrix for each property, capturing base rates, fees, and taxes.
Yes. Pipelines can be scoped to specific neighbourhoods, cities, or entire countries based on your requirements.
Availability and pricing matrices can be refreshed daily. For specific high-priority properties, we can configure intraday streaming pipelines to capture rapid price adjustments.
Yes. We extract and normalise unstructured amenity lists into structured categorical arrays and boolean flags (e.g., wifi_speed_mbps, in_unit_laundry, gym_access) for easy comparative analysis.
Our smallest packages start at a defined city list with weekly delivery. For global catalogue tracking or daily calendar updates, we price based on volume and compute requirements.
Absolutely. We provide a sample run of up to 100 properties in a target market as part of the pre-engagement scoping process to validate schema fit and data quality.
20-minute scoping call. Pilot dataset within the week. Production within two. Whether you need a one-off property catalogue dump or continuous daily pricing matrices across 30 cities — we scope, build, and operate the pipeline. Tell us what you need.
