Using data for housing justice

Housing decisions in the UK are shaped by information that's technically public, but practically out of reach. Information about who owns a building, who's behind a property company, how a planning application changes between approval and completion, and more all exists on paper somewhere. But finding it usually means paying a fee, navigating a clunky council portal, or knowing exactly which register to search and how to read it once you get there.

For housing organisers, that gap isn't just inconvenient. It's a barrier to the kind of research that makes campaigns work: knowing who you're up against, how many properties they hold, and where the leverage actually is.

The Data for Housing Justice project ran a series of workshops to help close that gap, supporting housing organisers to build practical skills in identifying property and land ownership, reading company registration data, and working with public datasets. Here's what we learned about how those skills translate into real organising work.

Identifying property and land ownership

Property and land ownership data for England and Wales is managed by HM Land Registry. When a property or land is sold, inherited, leased or mortgaged, it must be registered. However, roughly 12% of property and land is not registered, because ownership has not changed hands since registration was made compulsory. Anyone can search the Land Registry to view who owns a property, title plans and restrictions. However to access detailed information, you must pay a fee of £7.

We supported organisers to learn how to identify property and land owners, and how to map ownership patterns across places, using the LandExplorer tool. 

By upskilling in this, organisers were able to:

• Access identifying information about individual or corporate landlords that they have otherwise struggled to identify, to better target organising.
• Map unlicensed landlords, to complement data on licensed landlords that can be free to access from local councils, to create more complete datasets on landlords in an area.
• Identify other properties owned by a negligent or unethical landlord, to help build collective organising power amongst tenants.

Exploring company registration data

In the last 25 years, landlord company registrations have increased by more than 1700%. While some landlords choose to operate as private individuals, today, there are more than 440,000 buy-to-let companies registered via Companies House. Companies House is a government register that holds registered records of companies operating in the UK. Anyone can search Companies House to find information about company status, registered addresses, named officers and financial accounts.

We supported organisers to learn how to use Companies House and other databases to investigate ownership information and structures. By upskilling in this, organisers were able to:

• Uncover companies that are subsidiary or connected to the landlord company they are organising against, to identify additional targets for their campaigns.
• Find out more information about individuals responsible for landlord companies or housing associations, including registered addresses, to better target campaigns.
• Use financial reporting to discern levels of profit, or where profit is being strategically shifted to a connected company, to help campaign against disrepair or rent increases.

Using publicly available datasets

Across England and Wales, lots of housing-related data is published in the public domain and technically available for anyone to access and use. However, in reality, this data is scattered across different places, in different formats, and often requires some expertise to make sense of. This means there are significant barriers to access information and data, and we suspect these attempts to obscure information and data are intentional.

We supported organisers to become more familiar with publicly available datasets and strategies for accessing datasets held by public authorities. By upskilling in this, organisers were able to:

• Gain visibility of datasets that were previously unknown to them, to provide them with new sources of information that could be useful to their organising.
• Understand when Freedom of Information requests could be made, and how to make them effectively, to gain access to information held by public authorities such as local councils.
• Understand how big datasets and FOI strategies could be used to uncover patterns of rent extraction.

Putting into practice: Case study 1

An organiser was able to use publicly available data on private rental properties in their city, and compare that to the licensing data they had accessed via local council websites and Freedom of Information requests. By comparing these two datasets, they could identify that there were significantly less licenses than private rental properties, indicating a good number of unlicensed landlords might be operating in the area. They used the LandExplorer tool to identify the owners of private rental properties that didn't appear in the landlord licensing register, to build a more complete dataset of landlords. They were able to visually map this data, to make it easier to use the data to inform door knocking activities.

Putting into practice: Case study 2

An organiser used LandExplorer to identify where a negligent landlord owned other properties. They were able to use this information to engage other tenants experiencing negligence by the same landlord, to build collective organising power. After using Companies House to investigate the landlord, who was registered under a landlord company, they were able to identify several landlord companies connected to the same individual. This helped them to identify even more properties owned by this landlord, to enable them to engage more tenants, and build a much stronger and more targeted campaign.

If you’re a housing organiser or interested in using data for housing justice please sign up to our newsletter to hear about future workshops. You can also start using LandExplorer for free here.