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
Property data is a set of information about a physical place or location in the real world. The most fundamental pieces of data about a property are its location and place in time. These take the form of an address (as a proxy for location) and the time of its creation. From there all else can be considered metadata about that property.
Data about an individual property is often combined into datasets containing multiple properties made navigable by geospatial navigation systems, such as postcodes, coordinates or area boundaries.
How to approach a data-driven definition
As with all data It’s useful to segment and split by high-level properties such as residential vs non-residential.
At Doorda we consider all property should conform the following schema as a minimum:
- Residential Property Data
- Address
- Property type
- Property characteristics (EPC)
- Sale price
- Tenure
- Commercial owner
- Company registrations
- Commercial Property Data
- Rateable value
- Usage category
- Internals: GIA, NIA
- Commercial Occupant
- Company name
- Company Number (allows join to Company data)
- Link to other properties
- Hygiene, Defra, Food Standards, EPC, CQC, Gambling
- Commercial Ownership
Following this method it makes possible all manner of use cases and data linkages to other data sets.
Endless Usecases of Property Data
There are so many different reasons why an individual, business or government might want to know data about a property or group of properties, it’s important to define property data in as wide and deep a context as possible.
For example:
If a company is applying for business property insurance, the insurer needs to know where the property is, how old the property is, what its value is, and estimate if the value increasing or decreasing over time.
The insurer can also do a better risk assessment of the client If they know about other properties in the area. Eg. is a property next to a petrol station, or does it score highly in Hygiene inspections? It’s important to know.
Beyond insurance it’s amazing how property information flows through so many different use cases:
- Marketing companies wanting to know addresses where new businesses have opened.
- City planners wanting to visualize a map of property types and their distance to amenities.
- Social workers trying to identify areas of children at high risk of poverty.
- A flower delivery company needing to use a GPS system to locate an address to deliver a birthday bouquet.
- Investors analyzing property markets and trends.
- Data journalists trying to uncover inequality in markets.
- Demographics analysts creating area models.
- 3rd Party apps helping citizens access government services.
- Police HQ deciding where to spend their budget fighting problem areas
- Charities deciding where to deploy anti-poverty aid efforts
- Retailers deciding where to open their next store
The list goes on.
At the beginning of this article, when we asked what property data was, it actually turned out to have quite broad consequences and invites an even better question…
What is the most useful data about a property?
Our approach is to collect, link and make available all the primary data we can on every property of every type, with the broadest set of data points about each and then we link that to even more data in order to make it even more useful.
- We link Properties to demographic data
- We link it to company and associated director data
- We link it to public procurement and regulatory data
- local area information (Schools, GPs, Transport, House prices, flood risk, crime, companies, etc.)
- local population information (health, wealth, education, claimants, etc.)
- owners (if commercial or public-sector)
- companies registered or operating there
In this way, we can enable as many use cases as possible by making publicly available data accessible as a federated API or hosted database without needing to predict the use cases.
It’s up to the user how they use it.
Wrap Up.
So to wrap up the question of what is property data is… the simple obtuse answer is data about a property… but it’s so much more.
In some ways you could say property data is a big part of who we are, it tells us a lot about people, places and things, and it’s making many different parts of our society, and economy work.
It’s the glue that links so many systems together from government to business and individuals. It’s a rich and complex set of data with a virtually unlimited set of uses good and bad, and it’s growing every day as property data becomes more available and more tightly linked to other data sources.
If your business uses, or needs, property data, then you should consider Doorda.com as a trusted, easy to use supplier.
