We extract UK sold house prices, active listings, property valuations, and local area records from Nethouseprices. 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 Sold House Prices objects from nethouseprices.com. All fields typed and schema-versioned.
"transaction_id": "1849204A-B192", "postcode": "SW1A 1AA", "full_address": "Flat 1, Buckingham Palace, London", "price_paid": 450000, "date_of_sale": "2023-08-14", "property_type": "Flat", "tenure": "Leasehold", "new_build": false
| # | transaction_id | postcode | full_address | price_paid | date_of_sale | property_type |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Active Listings objects from nethouseprices.com. All fields typed and schema-versioned.
"listing_id": "NHP-847291", "title": "3 bedroom semi-detached house for sale", "asking_price": 325000, "price_qualifier": "Offers in region of", "property_type": "Semi-Detached", "bedrooms": 3, "agent_name": "Connells", "added_on": "2023-11-02T10:15:00Z"
| # | listing_id | title | asking_price | price_qualifier | property_type | bedrooms |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Valuations objects from nethouseprices.com. All fields typed and schema-versioned.
"postcode": "M1 1AE", "estimated_value": 245000, "value_range_low": 230000, "value_range_high": 260000, "last_sold_price": 195000, "last_sold_date": "2018-05-22", "property_type": "Terraced", "confidence_score": 0.85
| # | postcode | estimated_value | value_range_low | value_range_high | confidence_score | last_sold_price |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Estate Agents objects from nethouseprices.com. All fields typed and schema-versioned.
"agent_id": "AGT-9921", "branch_name": "Foxtons Islington", "company_name": "Foxtons", "postcode": "N1 2XR", "phone_number": "020 7123 4567", "active_sales_count": 42, "active_lettings_count": 115, "average_listing_price": 650000
| # | agent_id | branch_name | company_name | address | postcode | phone_number |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Rental Market objects from nethouseprices.com. All fields typed and schema-versioned.
"listing_id": "RNT-55412", "title": "2 bedroom flat to rent", "monthly_rent": 1800, "weekly_rent": 415, "property_type": "Flat", "bedrooms": 2, "furnished_status": "Furnished", "available_from": "2024-01-15"
| # | listing_id | title | monthly_rent | weekly_rent | deposit_amount | property_type |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Our Nethouseprices scraper navigates postcode search grids, paginated Land Registry records, and dynamic map interfaces to extract clean, structured property data across the UK.
Extract official UK sold house prices, including transaction date, property type, new build status, and tenure.
Capture asking prices, property descriptions, floor plans, image URLs, and agent details for currently listed properties.
Search and extract records systematically across all UK postcode districts, sectors, and units without missing data.
Scrape agent branch details, contact information, and active portfolio counts to build comprehensive B2B lead lists.
Capture automated property valuations, price ranges, and historical price trends for specific addresses.
Bypass dynamic map loading limitations. We intercept API calls and render spatial data to capture properties outside standard list views.
Extract decades of transaction history for single properties to track capital appreciation and market cycles.
Run daily or weekly pipelines that only extract newly added sold records or new listings, reducing compute overhead.
Addresses are parsed into consistent fields like flat number, street, town, county, and postcode for easy joining with external datasets.
Bypass rate limits and IP bans using UK-based residential proxies and human-like request delays.
Brief in. Clean data out.
Provide target postcodes, regions, or property types. We design the extraction schema together.
We configure Scrapy crawlers, UK proxy rotation, session management, and pagination logic for nethouseprices.com.
Schema validation, null-rate checks, and address parsing verification before full launch.
JSON / CSV / Parquet pushed to your S3 bucket, BigQuery dataset, or Snowflake stage on agreed cadence.
Property portals deploy aggressive rate limiting. Here is how we maintain steady extraction yields and ensure complete postcode coverage.
Property portals block data centre IPs and non-UK traffic aggressively. We route all requests through a pool of UK residential connections, ensuring our crawlers appear as legitimate local users searching for properties.
Extracting national data requires precise grid traversal. We use complete ONS postcode directories to seed our searches, managing radius overlaps to ensure zero missed properties and deduplicating results at the pipeline edge.
Nethouseprices limits visible results per search query. When a postcode returns over 1,000 records, our crawler automatically subdivides the query by property type, tenure, or date range to extract the full underlying dataset.
Certain property boundaries and valuation data load dynamically via client-side JavaScript. We deploy headless Playwright sessions to intercept background API requests and extract raw JSON before it renders to the DOM.
Raw property addresses are often messy strings. Our pipeline parses and normalises these into distinct fields like building number, street, locality, town, and postcode, allowing precise joins with your existing property databases.
AVM providers ingest historical sold prices and active listing data to train machine learning models for accurate property valuations.
Investors track yield potentials by comparing local asking prices against historical transaction data and rental market rates.
Agencies monitor competitor market share, time-on-market metrics, and price reduction frequencies across specific postcodes.
Lenders analyse local market liquidity, property type distributions, and historical price volatility to inform lending criteria.
Researchers and local authorities analyse transaction volumes and new build premiums to understand housing market dynamics.
Home improvement businesses target recent property buyers or active sellers based on recent transaction dates and listing statuses.
"Property transaction data is the foundation of the UK real estate market, but extracting it systematically requires navigating rate limits, pagination caps, and messy address strings."
Most teams underestimate the complexity of scraping property portals. Building a reliable Nethouseprices pipeline requires UK residential proxies, systematic postcode grid traversal, and robust address normalisation. DataFlirt manages this infrastructure entirely, delivering clean, joinable property records directly to your warehouse so your analysts can focus on market trends.
Everything supported by our nethouseprices.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 postcode queue orchestration and deduplication. Playwright intercepts dynamic API calls and handles complex map-based interactions.
We maintain dedicated pools of UK residential ISP proxies. Rotation happens per-request to ensure crawlers appear as legitimate local users searching property.
Pipelines run on AWS Lambda and ECS. 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 nethouseprices.com scraping, legality, and pipeline operations.
Ask us directly →Scraping publicly available property data is generally permissible. Nethouseprices aggregates Land Registry data, which is public record. We extract only public, non-authenticated listings and sold prices. We do not extract personal user data or bypass authentication walls. Clients should review terms of service and consult legal counsel.
When a postcode search returns more than the maximum visible results, our crawler automatically subdivides the query. We filter by property type, tenure, or specific date ranges until all underlying records are exposed and extracted.
Yes. We use complete ONS postcode directories to run systematic grid searches. We manage radius overlaps and deduplicate records at the pipeline edge to ensure comprehensive national coverage without redundant data.
Yes. Raw property addresses on portals can be inconsistent. Our pipeline parses these strings into structured fields like building number, street, locality, town, county, and postcode. This ensures you can join the data accurately with your internal databases.
For active sales and lettings, we can configure daily or even intra-day pipelines for specific target regions. Full national refreshes typically run on a weekly cadence to balance compute costs with data freshness.
Yes. We can extract the complete available transaction history for any property, dating back to 1995 as recorded by the HM Land Registry and surfaced on the portal.
Yes. We provide a sample run covering a specific postcode district or town during the scoping phase. This allows you to validate schema fit, address parsing quality, and field completeness before committing to a production pipeline.
20-minute scoping call. Pilot dataset within the week. Production within two. Whether you need a national dump of historical sold prices or continuous monitoring of active listings in London, we scope, build, and operate the pipeline. Tell us what you need.
