How Building data is created
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A new building geometry which represents a single building footprint. This geometry consists of adjoining building parts which have been determined to be part of the same building. When contained in a Land Use Site, adjoining building parts are represented by a single feature.
Separate, non-adjoining building parts within a Land Use Site are also represented as Building features.
For the initial capture of Basement Presence attributes, OS has used data captured and supplied by Verisk. This data will be maintained by data updates from Verisk and supplemented by OS Surveyed data.
Values for this attribute are: Present, Not Present, Unknown, Null. As only buildings that contain at least one address are within scope, Null is used for buildings that are out of scope, such as garages or sheds.
Sourced from OS Address attribution.
Values for this attribute are: Present, Not Present, Unknown, Null. As only buildings that contain at least one address are within scope, Null is used for buildings that are out of scope, such as garages or sheds.
The initial data is captured by field surveyors who recorded approximately 88 000 access locations. Ongoing data capture will be conducted by OS field surveyors.
For the initial capture of Building Age Period, OS has used data captured and supplied by Verisk. This data will be maintained by data updates from Verisk and supplemented by OS Surveyed data.
The Building Age Period ranges vary for earlier periods and move to consistent decadal ranges from 1980 onward. Earlier than 1919, it difficult to identify commercial buildings, so there is a catch-all range of pre-1919, as well as more defined ranges going back to 1837 covering only residential buildings.
The Building Age Period can be Unknown where OS does not know the building age and Null if the building is out of scope, that is, the building does not have at least one address (for example, domestic outbuildings such as sheds and separate garages).
For the initial capture of Building Age Year, OS has used data captured and supplied by Verisk. This data will be maintained by data updates from Verisk and supplemented by OS Surveyed data.
If a Building Part has an address and a Building Age, and is part of a larger Building feature with other Building Parts that do not have an address, the resulting Building feature will inherit the Building Age Period of the single addressed part of the Building. This can create anomalies in the data. Where a Building has multiple Building Ages from different Building Parts, the oldest date is used.
The Building Age Year can be Unknown where OS does not know the building age and Null if the building is out of scope, that is, the building does not have at least one address (for example, domestic outbuildings such as sheds and separate garages).
Building height data is created using a combination of manual and automated methods. High-confidence height values are manually measured, while other values are derived using Ordnance Survey's height algorithm; the algorithm processes the height data and assigns confidence levels (such as High, Moderate, Low, Incomplete, or Not Assessed) based on the accuracy and completeness of the data.
For the initial capture of Construction Material, OS has used data captured and supplied by Verisk and OS. OS data identifies static caravans and mobile homes. This data will be maintained by data updates from Verisk and supplemented by OS Surveyed data.
Where multiple Building Parts make up a single Building, the Construction Material will be derived from the Building Part with the largest area.
The Construction Material can be Unknown where OS does not know the material and Null if the building is out of scope, that is, the building does not have at least one address (for example, domestic outbuildings such as sheds and separate garages).
Created using OS Address and OS Land Use Sites data.
There are three data sources for the Number of Floors data:
Ordnance Survey observed data (Field / Remote Survey, Verified Customer Feedback data, or targeted desk-based data improvement). This is the highest quality data.
Energy Performance Certificate (EPC) data (only provided for England and Wales for now, due to perceived data quality at the time of ingestion for Scotland).
Ordnance Survey modelled data (machine learning model trained on truth data and which utilises building heights and topographic data to predict the number of floors).
The following information defines the third party provenance attribute values and how they are created.
Verisk data is sourced from multiple third party data sources and / or created using algorithms. The Verisk data provenance can be identified using the following attributes:
This additional attribution will help users determine the value and suitability of this data to meet specific use cases.
This is not a named third party provenance value; however, the process used to create Building Age Period, Construction Material, and Basement Presence data is partly dependent on the process used to create Verisk UKBuildings data.
Much of the data supplied by Verisk is based on initial capture and classification of building characteristics from vertical and oblique aerial imagery. Trained observers outlined defined areas, encompassing buildings with similar age, use and physical characteristics, using a detailed residential and non-residential classification. This classification provides the basis for many of the age values, and, with age being a key determinant of Basement Presence and Construction Materials, this is a key foundation of this data.
Within the Verisk UKBuildings data, Construction Material was observed for non-residential buildings (in urban areas) and was modelled based on Verisk premise age and type for residential buildings. So, for example, all low Victorian Terrace buildings would have brick walls and slate roof materials. Modern detached houses would have brick walls and tile roof materials.
Address Analysis is an umbrella term describing construction dates derived from investigation of historic address data and modern Postal Address data (post-1996).
Changes in modern Postal Addresses data used to infer when buildings were built. For example, if an address first appears in 2008, it could be assumed that the building relating to that address was built in that year.
Historic address data from Historic Maps and Census data, can indicate that a specific building existed when the data was collected (assuming the building hasn’t been demolished and rebuilt with the same name). For example, if a building called 'The Old Mill' is present on a historic map from 1895 and present in modern address data, it can be assumed that the building Construction Date is earlier than 1895.
Age bands given in EPCs relate to changes in building regulations impacting heating and energy efficiency, and are therefore do not necessarily align to the Building Age Period attribute values.
A building may have more than one EPC or may be given more than one age in a single EPC, which can sometimes lead to inconsistencies. For example, it is common to find buildings in a single housing estate that may not be assigned the same single Building Age.
EPC is the primary source of data for this attribute and is extracted from the Walls Description field. This is often the internal walls, which can lead to a difference in what values are represented across the data. This will be a mix of internal / structural and external construction material.
The Floor Level characteristic of Domestic EPCs, provides confirmation that an address is at basement level, for single storey flats or maisonettes.
Open data sources that can contain multiple references to age, which can lead to challenges when deciding which age to extract.
The sale prices of properties in England and Wales submitted to HMLR for registration.
Verisk have used advanced analytics and data modelling to derive building information where no definitive or observed data can be identified.
Some building characteristics used in this process include:
Known age characteristics of nearby buildings.
Building size, shape, orientation, and distance from road. For example, modern buildings are smaller and parallel to roads.
Address and postcode characteristics.
Local area statistics, such as Census.
Age and other characteristics of the building – uses the concept of architypes to describe standard characteristics of properties of similar age and residential type. Such architypes provide default construction characteristics if other information is not available.
Known characteristics of nearby buildings. For example, buildings along a terrace are likely to have the same construction material.
Local area statistics (ONS).
Age and other characteristics of the building – basements are most commonly found in older buildings, and modern high-rise buildings.
Known characteristics of nearby buildings. For example, if a building along a terrace or road has a basement, it is possible that other buildings on the same terrace or road will also have a basement.
Building size, shape, orientation, distance from road and slope.
Local area statistics (ONS).
HM Land Registry.
Analysis of Local Authority planning and building control data to identify buildings with a basement. Basements were also identified from planning applications.
Valuation Office Agency (Fair Rent Register)
Non-Domestic EPC (England and Wales & Scotland)
Cabinet Office (ePIMS)
Data derived from Verisk UKBuildings data and this manual observation process.
Imagery is captured and processed using a supervised ML model and an Automated Feature Extraction (AFE) algorithm to create a polygonised vector dataset of roof segments, which is then aggregated and transformed into summary attributes, including Primary Roof Material, Solar Panel Presence, and Green Roof Presence.
The data for roof shape and aspect is derived using automated methods that involve Ordnance Survey's topographic data, imagery, and height models. An Automated Feature Extraction (AFE) algorithm delineates the roof faces and smooths their delineation using the Digital Surface Model (DSM). The DSM is overlaid to derive pitch and orientation values for each roof face. The roof faces are then aggregated at the building level, transforming the spatial dataset into summary statistics.
Two open data Valuation Office datasets (Fair Rent Register and 2018 Non-domestic rating data) were used to identify buildings with basements.
The primary material can be the internal or structural material, or the externally visible material and the can help determine this. Typically, data sourced from EPC will show the internal or structural value.