Real Estate Dataset: Property Listings, Rental & Pricing Data for Enterprise Intelligence

A real estate dataset is a structured collection of data extracted from property listings portals, real estate platforms, MLS aggregators, rental marketplaces, and commercial property databases. It typically covers three core data types: property listings data (property attributes, bedroom and bathroom counts, floor area, location, amenities, listing agent information, and property images), rental property data (monthly rent, rental type, availability, occupancy signals, lease terms, deposit structure, and rental yield), and property pricing data (asking price, sold price, price per square foot, days on market, price reduction signals, neighbourhood median, and historical price time-series). Enterprise teams use real estate datasets for automated property valuation models (AVMs), rental market intelligence, investment due diligence, real estate market research, proptech product development, and AI model training.

The Property Listings Dataset includes comprehensive property listing records covering: property title and full description, property type (residential, commercial, land, industrial) and sub-type (house, apartment, condo, townhouse, villa, studio, office, retail unit), listing type (for sale or for rent), bedroom count, bathroom count, total room count, internal floor area in sqft and sqm, plot or land size, year built, property age, renovation status, amenities (parking, pool, gym, garden, elevator, balcony), furnishing status (furnished, semi-furnished, unfurnished), full address, postcode, neighbourhood, city, country, validated latitude and longitude coordinates, property image URLs, virtual tour URLs where available, listing agent and agency data, listing date, platform source, and listing ID. All records are entity-resolved across portals and deduplicated to a single canonical property record.

The Rental Property Dataset provides structured rental property intelligence covering: rental type (long-term residential, short-term, vacation rental, serviced apartment), monthly rent, weekly rent, and nightly rate where applicable, currency-normalized values, minimum and maximum lease term, availability status and availability date, occupancy rate indicator (low / moderate / high / fully booked), furnished status and included utilities (water, electricity, internet, gas), pet policy, smoking policy and other tenant restrictions, deposit amount and deposit structure, rental yield percentage estimate, 12-month rental price change history, and platform source attribution. Rental property records are linked to the Property Listings Dataset via a shared property_id for cross-dataset joins.

The Property Pricing Dataset provides multi-level pricing intelligence at both property and market level including: current asking price, last sold or transacted price, price per square foot and price per square metre, currency-normalized values across 100+ currencies, price reduction flag and reduction percentage, days on market (DOM), historical asking price time-series data (up to 36 months), neighbourhood median price for comparable properties, price premium or discount versus market average percentage, gross and net rental yield estimate, price trend direction (appreciating / stable / declining), and price index change by area and property type. This multi-level pricing structure supports automated valuation models, investment analysis, and market research workflows simultaneously.

Coverage spans 50+ real estate platforms globally. In the UK and Europe: Rightmove, Zoopla, OnTheMarket, PrimeLocation, Immowelt, ImmobilienScout24, SeLoger, Idealista, and Funda. In the USA and Americas: Zillow, Realtor.com, Redfin, Trulia, LoopNet (commercial), Realtor.ca, ZAP Imóveis, and Inmuebles24. In Asia-Pacific, India, and MENA: 99acres, MagicBricks, Housing.com, Property Finder, Bayut, PropertyGuru, Domain.com.au, and REA Group. Rental platforms: Airbnb, Vrbo, SpareRoom, Apartments.com, Rentola, NoBroker, and Zillow Rentals. Also covering commercial real estate databases, new development project portals, and regional MLS networks. Custom platform additions are available for enterprise accounts on request.

Cross-portal deduplication is a core infrastructure feature of the WebDataInsights Real Estate Dataset — not an optional add-on. The same physical property listed on multiple portals (for example, Rightmove and Zoopla simultaneously) generates separate listing records on each platform. Our real estate data pipeline performs property-level entity resolution using a combination of address normalization, postcode and geolocation matching, and property attribute fingerprinting (bedrooms, bathrooms, floor area, year built) to identify and resolve duplicate records. Enterprise clients receive one canonical property record per physical property, not one record per portal listing, ensuring that inventory counts, market sizing calculations, and pricing analyses reflect actual market reality rather than portal listing duplication.

Yes. Rental yield is a first-class field in both the Rental Property Dataset and the Property Pricing Dataset. The rental_yield_pct field in the Rental Property Dataset captures the gross rental yield percentage derived from the monthly rent and asking price captured in the same collection cycle. The rental_yield_gross field in the Property Pricing Dataset provides a market-level gross yield estimate for the property based on local rental market rate comparables. These fields enable buy-to-let investors, REIT analysts, and real estate investment teams to screen for high-yield rental markets and compare rental yields across property types, neighbourhoods, and geographies without requiring additional data processing.

Refresh cadence is fully configurable based on your use case:

• Daily snapshots: For active property market monitoring, price change tracking, days-on-market updates, and listing inventory dashboards — the standard cadence for most real estate intelligence and investment teams
• Weekly batches: For market research workflows, neighbourhood trend analysis, and strategic reporting cycles
• Real-time / near-real-time: For proptech platforms requiring live property search, instant price change alerts, and rental availability monitoring
• One-time historical export: For AVM model training, investment due diligence research, and retrospective market analysis using 36-month price history archives

Real estate datasets power several specialized AI and ML applications in proptech and financial services:

• Training automated valuation models (AVMs) using property attributes, pricing, and neighbourhood-level market data
• Building property recommendation and matching engines using listing attributes, location, and user preference signals
• Developing rental demand forecasting models using rental rate history, occupancy signals, and seasonal availability patterns
• Fine-tuning LLMs on real estate domain language for property search assistants, virtual agents, and automated listing description generation
• Building property price prediction models using days-on-market, price reduction frequency, and neighbourhood median data
• Training neighbourhood classification and gentrification prediction models using property pricing trend data
• Developing mortgage risk assessment models using property valuation and market trend data

Data is delivered in your preferred format: JSON, JSONL, CSV, TSV, or Parquet for batch delivery. Real-time access is available via REST API with property type, location (postcode, city, coordinates + radius), price band, bedroom count, listing type, and rental yield filtering. Cloud delivery is supported to Amazon S3, Google Cloud Storage, and Azure Blob Storage on configurable schedules. Custom delivery pipelines including SFTP, webhook push, and direct data warehouse ingestion (Snowflake, BigQuery, Redshift) are available for enterprise accounts. All formats ship with schema documentation, field dictionaries, and sample records across multiple markets.

Yes. All real estate data is collected from publicly available property listings, portals, and databases under a defensible legal framework aligned with GDPR, CCPA, and applicable data protection regulations. No non-public transaction data, private sale records, or personally identifiable owner information (PII) is included in any delivery. Listing agent names are included only where publicly displayed on the source portal. Full source provenance documentation is available for enterprise data governance and legal review. We recommend clients conduct their own legal assessment for jurisdiction-specific use cases and downstream AI training applications.

Yes. Enterprise clients can request fully custom real estate dataset configurations including specific portal coverage, country or metropolitan market scope, property type inclusions (residential only, commercial only, or both), price band filters, bedroom count filters, rental type scope (long-term, short-term, or both), neighbourhood-level geographic boundaries, custom field mappings, and tailored delivery schemas. Representative sample datasets — matching your target portals, markets, property types, and delivery format — are provided during the evaluation process before any commercial commitment. Contact our data team to scope your requirements and receive a sample within 48 hours.