Property Data: Examples, Providers & Datasets to Buy
- Overview
- Datasets
- Providers
- Use Cases
- Attributes
- Guide
- FAQ
- Overview
- Datasets
- Providers
- Use Cases
- Attributes
- Guide
- FAQ
What is Property Data?
Property data captures key information about residential, commercial, and industrial properties. It includes ownership records, structural characteristics, and zoning regulations.
This data is used in real estate intelligence, property investment, and urban planning. Many property datasets integrate with geospatial mapping tools, allowing businesses to analyze trends, verify addresses, and assess property values with greater accuracy.
What Are Examples of Property Data?
Property data includes a wide range of attributes. It is used in real estate data analysis, investment decision-making, and regulatory compliance. Some key examples include:
- Property Ownership: Information on current and past owners, helping verify legal claims and track market trends. This is essential for homeownership data.
- Sales History: Records of past transactions, including sale dates and prices, helping investors and analysts in real estate market data.
- Addresses: Official mailing and parcel addresses.
- Property Characteristics: Key details such as size, number of bedrooms, bathrooms, and amenities, all of which play a role in residential real estate data and property valuation.
- Geocodes: Geographic coordinates for mapping and analysis.
- Zoning Uses: Land classification for residential, commercial, agricultural, or industrial purposes, critical in urban planning data and development strategies.
- Building Permits: Records of approved construction, renovations, and structural modifications, widely used with construction data.
- Assessor Tax Data: Tax assessments, valuations, and payment history, which are essential for government planning and real estate valuation data.
Access the Best Property Databases & Datasets
The best property datasets provide comprehensive insights into ownership, valuations, and real estate trends. Datarade offers a curated selection of property datasets, chosen for quality, accuracy, and trusted providers where you can buy property data.
Commercial Property Data | 52M+ POI | SafeGraph Property Dataset
Zillow.com Data | Property Listings Data | Real Estate Transactions | Property Valuation Data
US National Property Data | 157M+ Records | 35+ Property Characteristics | Ownership Information | Property Assessments
TagX Property Data | Property Ownership data | 1 Million+ records | 8+ attributes | Colorado USA
Real Estate Data | Property Listing, Sold Properties, Rankings, Agent Datasets | Global Coverage | For Competitive Property Pricing and Investment
Factori US Home Ownership Mortgage Data | Property Data | Real-Estate Data - 340+ Million US Homeowners
Realtor Property Data, Realtor Data, Realtor API, Property Owner Data, Scrape All Publicly Available Property Listings & Data - Easy to Integrate.
Property Data, B2C Consumer Address - Consumer Marketing & Contact Data - USA, CCPA Compliant
Property Data | USA Coverage | Real Estate
Mortgage Data, Property Data, Title Data, Ownership Data | Over 150 MM Records and 200 Attributes
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Top Property Data Providers & Companies
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Immobiliare.it Insights
Immobiliare.it Insights is a proptech dedicated to the analysis and processing of Italian and foreign real estate market data. We offer highly detailed real estate analysis with demand, supply and transaction data, energy performance indicators, location intelligence and much more.+1BILDemand data+2MReal Transactions+25MEnergy Datapoints -
BIGDBM
CoverageUSABIGDBM is a leading provider of US consumer and B2B data with unrivaled quality, coverage, and affordable pricing. Additionally, BIGDBM is a highly sought-after data engineering and software development outsourcing firm for many data suppliers worldwide.765 Mil+Emails600 Mil+Phones155 Mil+Properties -
TagX
CoverageUSAUK+248VWe requested data in the form of various forms and documents associated with Tax and Bookkeeping that we plan to use to developing a digital document processing solution based on artificial intelligence.
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Datasys
Datasys compiles one of the largest consumer and business data sets in the world. We use our expansive data and technology platforms to help companies more accurately target their ideal customers and provide intelligent marketing solutions.Scalelarge quantitiesAccurateconstantly updated & verified250M +Full consumer profiles -
BatchService
CoverageUSADeepen the insights your platform or application offers by harnessing unparalleled property data and accurate owner contact information from a single API.74%Right Party Contact Rate600+Data Points900%Efficiency Improvement
Popular Use Cases for Property Data
Property data is essential for real estate professionals, investors, financial institutions, and policymakers. Common use cases include:
- Property Valuation: Assessing the market value of properties based on historical sales, tax assessments, and comparable listings.
- Property Investment: Identifying profitable real estate opportunities by analyzing property characteristics, neighborhood trends, and historical pricing.
- Real Estate Intelligence: Using data-driven insights to understand housing market trends and consumer demand.
- Real Estate Insights: Gaining a competitive edge by analyzing property data for pricing strategies and investment decisions.
- Real Estate Analytics: Building intelligent solutions for address verification, property reports, and risk analysis.
- Government Planning: Assessing zoning codes and land use policies to ensure proper urban development.
- Insurance: Evaluating property risks for homeowners’ insurance and commercial property policies.
- Mortgage Lending: Assessing loan risk by analyzing a property’s value, ownership history, and previous transactions.
- Home Repair & Improvement: Identifying properties that require maintenance or renovation based on property age, condition, and past permits.
Main Attributes of Property Data
Property data includes several key attributes that provide insights into ownership, market trends, and valuation.
- Ownership Records: Details of current and past property owners.
- Property Characteristics: Number of rooms, amenities, and structural details.
- Property Size: Square footage of buildings and lot size.
- Rental Data: Occupancy rates, lease agreements, and rental income.
- Tax History: Assessed values, tax rates, and payment history.
- Zoning Information: Land use classifications and permitted activities.
| Attribute | Type | Description | Action |
|---|---|---|---|
| String | The address of a company or contact (street name, number, zip code, city, county, country). | View 159 datasets | |
| String | The type of land, e.g. agricultural, commercial, recreational, residential, or transport. | View 121 datasets | |
| String | The name of a neighborhood. | View 119 datasets | |
| String | The name of a city district. | View 111 datasets | |
| String | The land zoning type. | View 109 datasets | |
| Float | The latitude of a point on earth's surface. Commonly abbreviated as "lat". | View 80 datasets |
Why is Property Data Important?
Property data is crucial for understanding real estate trends, assessing market conditions, and making informed financial decisions. Access to accurate property data enhances decision-making.
For example, real estate professionals use property data to determine competitive pricing strategies, while insurers assess risk levels based on building attributes and historical claims. Mortgage lenders rely on ownership records and valuation data to mitigate lending risks.
What is Property Data Analytics?
Property data analytics involves collecting and analyzing real estate information to help brokers, investors, developers, and property managers evaluate market trends, optimize pricing, and assess risk. Professionals can predict investment profitability, track property performance, and improve tenant management strategies.
In the past, real estate decisions relied on intuition and limited historical data. However, modern analytics tools integrate diverse data points - such as foot traffic patterns, local business activity, and migration trends -, to provide a more comprehensive view of the market.
Data Analytics for Real Estate
Using property data and real estate data analytics provides multiple advantages for professionals in this industry. Here are 7 key benefits:
1. More Accurate Property Valuation
Advanced data models analyze historical sales, neighborhood trends, and market conditions to determine precise property values. Automated valuation models (AVMs) use machine learning to identify pricing patterns and improve the accuracy of real estate appraisals.
2. Optimized Marketing Strategies
Brokers and real estate firms can segment their target audience based on property preferences, demographic insights, and online behavior. This enables highly personalized marketing campaigns, improving lead generation and conversion rates.
3. Better Understanding of Market Trends
Property demand, and pricing fluctuations, investors and brokers can anticipate market movements by tracking real estate cycles. This allows them to enter or exit investments at the right time and adjust strategies based on economic conditions.
4. Improved Investment Decision-Making
Data-driven insights help real estate investors assess potential returns and risks associated with different properties. Foot traffic analytics, and local business activity, investors can identify high-performing assets by analyzing trade area demographics.
5. Tenant Management
Predictive analytics tools help property owners screen tenants more efficiently and anticipate lease renewals or turnovers. Real estate firms can use data to improve occupancy rates and reduce tenant-related risks.
6. Market Analysis
Real estate professionals can monitor competitor activity, track listings, and compare performance metrics across different agencies. This allows them to refine pricing strategies, assess market positioning, and adjust business tactics accordingly.
7. Climate and Risk Management
Real estate investors can evaluate climate-related risks, such as flood zones or wildfire-prone areas, before making purchasing decisions. Predictive analytics can also help insurers and lenders assess risk exposure in various property markets.
More on Property Data
Explore related property data categories:
- Property Market Data: Real estate trends, valuations, and market insights.
- Property Transaction Data: Sales records, ownership history, and mortgage insights.
- Property Listings Data: Active and historical property listings, pricing trends, and market comparisons.
- Vacation Rental Data: Short-term rental pricing, occupancy rates, and guest trends.
Frequently Asked Questions
How is the Quality of Property Data Maintained?
The quality of Property Data is ensured through rigorous validation processes, such as cross-referencing with reliable sources, monitoring accuracy rates, and filtering out inconsistencies. High-quality datasets often report match rates, regular updates, and adherence to industry standards.
How Frequently is Property Data Updated?
The update frequency for Property Data varies by provider and dataset. Some datasets are refreshed daily or weekly, while others update less frequently. When evaluating options, ensure you select a dataset with a frequency that suits your specific use case.
Is Property Data Secure?
The security of Property Data is prioritized through compliance with industry standards, including encryption, anonymization, and secure delivery methods like SFTP and APIs. At Datarade, we enforce strict policies, requiring all our providers to adhere to regulations such as GDPR, CCPA, and other relevant data protection standards.
How is Property Data Delivered?
Property Data can be delivered in formats such as CSV, JSON, XML, or via APIs, enabling seamless integration into your systems. Delivery frequencies range from real-time updates to scheduled intervals (daily, weekly, monthly, or on-demand). Choose datasets that align with your preferred delivery method and system compatibility for Property Data.
How Much Does Property Data Cost?
The cost of Property Data depends on factors like the datasets size, scope, update frequency, and customization level. Pricing models may include one-off purchases, monthly or yearly subscriptions, or usage-based fees. Many providers offer free samples, allowing you to evaluate the suitability of Property Data for your needs.
What Are Similar Data Types to Property Data?
Property Data is similar to other data types, such as Mortgage Data, Home Ownership Data, Real Estate Market Data, Commercial Real Estate Data, and Residential Real Estate Data. These related categories are often used together for applications like Property Valuation and Property Investment.
