IDEAS FOR CAMDEN
PROPERTY COVENANT FOR HIGHLINE
alt.economy
What is it?
An agreement that shares the value increase to private property, resulting from Camden Highline, with the local community.
Why is it useful?
The communal value that public infrastructure creates—green spaces, mobility networks, urban regeneration—currently goes to landowners, who see their property prices rise without doing anything. If this value was shared we could use it to maintain public infrastructure and reinvest in the community.
A smart property covenant could be developed and written into land ownership contracts, that committed private landowners to sharing the value uplift resulting from the Camden Highline. This value could be automatically redistributed back to the local council, to be spent on future infrastructure projects, or to retrospectively fund projects.
A smart property covenant could be developed and written into land ownership contracts, that committed private landowners to sharing the value uplift resulting from the Camden Highline. This value could be automatically redistributed back to the local council, to be spent on future infrastructure projects, or to retrospectively fund projects.
Who would need to be involved?
- Camden Council
- Camden landowners
- Data modelling experts
- Smart contract technology experts
- Camden residents
Where do we start?
- Map existing value uplift models and identify requirements for a Camden version.
- Partner with data collection experts, property lawyers, infrastructure investment experts and Camden Council to draft proposal.
- Co-develop a paper prototype for the dataset and modelling required to analyse uplift attributed to public infrastructure investment.
- Design and prototype a digital smart covenant that automatically shares any uplift between private and public parties.
- Work with Camden council and residents to assess how proposal could be implemented and trialled.
Some thoughts…
- What would be the voting mechanism for whether or not to implement the covenant?
- How do you avoid bias in the collection and modelling of the data?
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