We extract vacation rental listings, host profiles, dynamic pricing, availability calendars, and reviews from Niumba. 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 niumba.com. All fields typed and schema-versioned.
"property_id": "1029482", "title": "Villa con piscina en Mallorca", "property_type": "Villa", "location_string": "Palma de Mallorca, Balearic Islands", "max_guests": 8, "bedrooms": 4, "bathrooms": 3, "check_in_time": "16:00"
| # | property_id | title | property_type | location_string | latitude | longitude |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Pricing & Availability objects from niumba.com. All fields typed and schema-versioned.
"property_id": "1029482", "check_in_date": "2026-07-10", "check_out_date": "2026-07-17", "price_per_night": 250.0, "total_price": 1750.0, "currency": "EUR", "is_available": true, "minimum_stay": 7
| # | property_id | check_in_date | check_out_date | price_per_night | total_price | currency |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Reviews & Ratings objects from niumba.com. All fields typed and schema-versioned.
"review_id": "R849201", "property_id": "1029482", "reviewer_name": "Carlos M.", "review_date": "2025-08-12", "rating_overall": 5.0, "rating_cleanliness": 5.0, "review_text": "Perfect stay for our family holiday.", "stay_date": "August 2025"
| # | review_id | property_id | reviewer_name | review_date | stay_date | rating_overall |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Host Profiles objects from niumba.com. All fields typed and schema-versioned.
"host_id": "H9281", "host_name": "Maria G.", "joined_date": "2018-04", "response_rate": 98.5, "response_time": "within an hour", "total_reviews": 142, "properties_listed": 3, "superhost_status": true
| # | host_id | host_name | host_url | joined_date | response_rate | response_time |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Amenities & Features objects from niumba.com. All fields typed and schema-versioned.
"property_id": "1029482", "has_wifi": true, "has_pool": true, "has_ac": true, "has_parking": true, "pet_friendly": false, "outdoor_features": "['BBQ grill', 'Garden', 'Patio']"
| # | property_id | has_wifi | has_pool | has_ac | has_parking | kitchen_appliances |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Our Niumba scraper handles every layer of the platform: property listings, dynamic date-based pricing, availability calendars, host profiles, and reviews - with JavaScript rendering and session management built in.
Title, description, location, bedrooms, bathrooms, and house rules scraped at the property level.
Capture dynamic rates for specific date ranges, including seasonal spikes and last-minute discounts.
Track booked versus available dates across thousands of properties to calculate occupancy rates.
Extract historical guest reviews, sub-ratings for cleanliness and location, and host responses.
Track host portfolios, response rates, response times, and total review counts across their listings.
Structure unstructured amenity lists into boolean flags for pools, AC, parking, and pet policies.
Extract latitude and longitude coordinates for precise mapping and spatial analysis.
Isolate base rates, cleaning fees, service fees, and local taxes for accurate total-cost modelling.
Monitor organic position for specific regions and dates to track platform visibility.
Brief in. Clean data out.
Provide target regions, property URLs, or date ranges. We design the extraction schema together.
We configure Scrapy / Playwright crawlers, proxy rotation, and calendar interaction logic for niumba.com.
Schema validation, null-rate checks, and price-outlier detection before full launch.
JSON / CSV / Parquet pushed to your S3 bucket, BigQuery dataset, or Snowflake stage on agreed cadence.
Niumba relies on complex calendar widgets and strict bot protection. Here is how we stay resilient.
Niumba operates under the TripAdvisor network perimeter. We use residential ISP proxies with realistic browser fingerprints and full cookie session management to bypass bot detection.
Availability calendars and dynamic price widgets require full Playwright browser sessions with JavaScript execution to trigger network requests for pricing data.
Scraping availability requires iterating over hundreds of date permutations per property. Our pipeline manages this matrix efficiently to prevent excessive request loads.
DOM structures for vacation rentals change frequently. We use fallback chains including CSS selectors, XPath, and JSON-LD extraction to maintain pipeline integrity.
We maintain a hash index of last-seen values for prices and availability. Subsequent runs only push diffs, reducing downstream processing load.
Revenue managers track competitor rates and seasonal spikes to optimise their own property pricing.
Investors calculate potential rental yields by analysing historical occupancy and daily rates in specific postcodes.
Tourism boards monitor short-term rental density and host professionalisation across Spanish regions.
Property managers benchmark their reviews, response times, and amenity offerings against top-performing local hosts.
Hedge funds track booking velocity and forward-looking availability to model regional tourism demand.
OTAs enrich their own metasearch platforms with verified property metadata and baseline pricing signals.
"Niumba holds the definitive dataset for Spanish vacation rentals, but extracting historical pricing and availability requires traversing millions of date combinations."
Most teams underestimate the compute required to track dynamic pricing across short-term rental platforms. Reliable Niumba scraping requires residential proxies, full JavaScript rendering for calendar widgets, and complex date-iteration logic. DataFlirt absorbs that complexity so your engineers can focus on the analysis - not the infrastructure.
Everything supported by our niumba.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 and deduplication. Playwright handles JavaScript rendering, cookie sessions, and calendar interactions.
We maintain pools of residential ISP proxies across targeted regions. Rotation happens per-request with sticky sessions where required.
Pipelines run on AWS Lambda and ECS. Airflow handles scheduling, dependency management, and SLA alerting.
Data delivered to where your team already works — no new tooling required.
About niumba.com scraping, legality, and pipeline operations.
Ask us directly →Scraping publicly available information from Niumba is generally permissible under applicable law. DataFlirt targets only public, non-authenticated property, pricing, and review data. We do not extract personal guest data or circumvent authentication walls.
We use residential ISP proxies, full Playwright browser sessions with realistic fingerprints, and request timing modelled on human behaviour to navigate the TripAdvisor network perimeter.
Yes. You define the check-in and check-out parameters, and our pipeline queries the platform for those exact dates to extract accurate daily rates and availability.
Pipelines can be configured to run daily or sub-daily depending on your requirements. Change detection ensures you only process updates when a property is booked or unbooked.
Yes. We extract host names, join dates, response rates, and map multiple properties to individual host IDs to track portfolio sizes.
Our packages start at a defined geographic region or property list with weekly delivery. For continuous national tracking, we price based on volume and delivery frequency.
Absolutely. We provide a sample run of up to 200 properties as part of the pre-engagement scoping process so you can 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 dump or continuous pricing feeds across Spain's rental market - we scope, build, and operate the pipeline. Tell us what you need.
