OPDA makes free data tools available across property industry through new framework

Open Property Data Association launch v3.0 of schema with new data tools

The Open Property Data Association (OPDA) has released the latest version of its framework for property data standards to make free and shareable data tools available across the property industry. The new version is expected to help accelerate the digitisation of the property market, making transactions easier and more efficient for homebuyers and property industry professionals.

The current home buying process is notoriously slow partly because less than one per cent of our property data is digital. Converting all property data sources and documents to a digital format and making it shareable through open data standards are essential if this there is to be a much-needed transition to digital property transactions.

The Property Data Trust Framework (PDTF) is a standardised set of data and governance principles. It’s the industry’s data toolkit which includes a common data dictionary, a standardised way to describe property attributes, and a methodology for sharing data with trust and provenance attached. The new version is freely available to the entire property industry and their software providers, from estate agents, through to lawyers, lenders and brokers, without any restrictive, proprietary licences.

Dale Jannels, CEO of One Mortgage System and OPDA founding member, said: “It’s great to see the property data trust framework used and available for all. OMS is fully behind the use of the new framework structure which will be of benefit to the end consumer in both speed and ease of buying a property. I’m excited to see how this evolves throughout 2024 and beyond.”

The latest version of the toolkit, or schema, comes after several months of collaborative effort from contributors across the industry. It has benefitted from lessons identified at a previous stage based on a large number of real transactions. This means that anyone adopting the standards can be confident that they will work in the real world. Importantly, the framework traces where each item of data is from so that users can verify and evidence the data sources.

The new version of the framework is fully compliant with the National Trading Standards (NTS) Material Information parts A, B and C which were introduced late last year. It also supports mapping to the various forms that the industry currently relies on including the Buying and Selling Property Information, Law Society Transaction Forms, and the Property Information Questionnaire. It allows for mapping across the Royal Institute of Chartered Surveyors Data Schema forms.

The framework has also been extended to enable the material information data to be mapped into the newly updated version of the Rightmove Automated Data Feed. This means it will become much easier to verify that property listings are compliant with the Trading Standards requirements and to share the data with Customer Relationships Management systems, portals, and to instruct the seller’s conveyancer at point of listing.

Ed Molyneux, CTO of pioneering proptech firm Moverly, and OPDA founding member, added: “Open data standards are fundamental to a successful transition to digital property transactions"

Ed continues “Now that National Trading Standards Parts B and C have been published, listing properties becomes significantly easier using digital property data.
“Most transactions will get underway digitally by default. Ensuring this data is properly provenanced so it can be safely re-used is critical, and that’s where the framework really shines. At Moverly we’re really on board with the framework and the new version of the structure.”

OPDA is aiming for open data to be adopted as widely as possible and would encourage all estate agent, mortgage, and conveyancing software providers to start implementing the toolkit in their own platforms and API services. Future extensions of the framework will include upgraded tools for use in mortgage advice, buyer’s conveyancing, and lending use cases.

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