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It seems like a simple enough of a question, but sometimes it’s worth revisiting things we think we know, to re-examine them and to dive a bit deeper. The things we think are simple often reveal hidden depths… And that’s certainly true of property data.
First things first; to define what property data is we need to define what ‘property’ is.
Derived from the Middle English: from an Anglo-Norman French variant of Old French propriete, from Latin proprietas, from proprius ‘one’s own, particular’.
When you think about it, that covers a lot of ground. By definition, a property can be almost anything you can imagine — from any item on the supermarket shelf, to a car, a house, or even just a theory.
In this article, we’re referring to the concept of a building or buildings and the land belonging to it or them. Schema.org covers some of these types of definitions such as Place or perhaps House but their definition is not suited for the majority of use cases, as they are simply trying to define a thing, but property is more than just a thing.
How we define property data
Data pertaining to individual properties is frequently aggregated into datasets containing multiple properties, which are then organized and made accessible through geospatial navigation systems. These systems utilize features like postcodes, coordinates, or area boundaries to facilitate navigation and exploration of the data.
How to approach a data-driven definition
When dealing with data, it’s beneficial to categorize and separate it based on overarching properties, such as residential versus non-residential.
A comprehensive property schema typically includes the following elements as a minimum:
Residential Property Data:
- Address
- Property type
- Property characteristics (e.g., EPC)
- Sale price
- Tenure
- Commercial owner
- Company registrations
Commercial Property Data:
- Rateable value
- Usage category
- Internals (e.g., GIA, NIA)
- Commercial occupant
- Company name
- Company Number (facilitating linkage to Company data)
- Links to other properties
- Hygiene, Defra, Food Standards, EPC, CQC, Gambling
- Commercial ownership
Employing this method enables a wide range of use cases and facilitates data linkages to other datasets.
Endless Usecases of Property Data
There exists a multitude of reasons why individuals, businesses, or governmental entities might seek information about a property or collection of properties. Therefore, it’s crucial to define property data within a comprehensive and expansive context, encompassing both breadth and depth.
For example:
When a company seeks business property insurance, insurers require comprehensive information about the property, including its location, age, value, and projected value fluctuations over time. Additionally, insurers conduct more accurate risk assessments by considering factors such as proximity to potential hazards like petrol stations or high scores in hygiene inspections.
The utility of property information extends far beyond insurance, spanning various use cases:
- Marketing companies leverage property data to identify locations of newly opened businesses.
- City planners use property data to visualize property types and their proximity to amenities.
- Social workers utilize property data to pinpoint areas with children at high risk of poverty.
- Service providers like flower delivery companies rely on property data for GPS navigation to deliver to specific addresses.
- Investors analyze property markets and trends.
- Data journalists investigate market inequalities.
- Demographics analysts develop area models.
- Third-party apps facilitate citizen access to government services.
- Law enforcement allocates resources to address problem areas.
- Charities allocate anti-poverty aid efforts strategically.
- Retailers make informed decisions about store locations.
In essence, the concept of property data encompasses a wide array of applications, underscoring its broad impact and significance.
What is the most useful data about a property?
Our approach involves aggregating, linking, and providing access to an extensive range of primary data on every type of property, enriched with a diverse array of data points. This data is then further enhanced by linking it to additional datasets to maximize its utility.
We establish connections between properties and various datasets, including:
- Demographic data
- Company and director information
- Public procurement and regulatory data
- Local area information (such as schools, healthcare providers, transportation options, house prices, flood risk, crime statistics, and local businesses)
- Local population data (covering aspects like health, wealth, education, and welfare claimants)
- Ownership details (particularly for commercial or public-sector properties)
- Registered or operational companies associated with the properties
By adopting this approach, we aim to facilitate a wide range of potential applications by providing accessible, federated API or hosted database access to publicly available data. Users have the flexibility to utilize the data according to their specific needs and objectives.
Wrap Up
In essence, property data is more than just information about a physical location—it encompasses a vast array of insights into people, places, and things, serving as a cornerstone of our society and economy. It acts as the connective tissue binding together various systems, spanning government, business, and individual domains.
Property data is intricately woven into the fabric of our lives, providing a wealth of information with virtually limitless potential applications, both positive and negative. With each passing day, as property data becomes increasingly accessible and interconnected with other data sources, its depth and breadth continue to expand, enriching our understanding of the world around us.
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