RICS: 9 steps for embracing data
As the property industry adapts to a world of Artificial Intelligence, automation, Internet of Things and more, there’s one key area that cannot be ignored – data. RICS’ report, written with Remit Consulting, titled ‘The use and value of commercial property data’, looks at the benefits and challenges of the increasing amount of data being used and available within the property industry.
Here are 9 recommendations the RICS makes for the built environment to successfully integrate with the digital world.
1. The profession should attract graduates from science data courses.
2. There is a need to develop data standards for the property industry that are globally applicable and open-sourced. This would ensure the next generation of surveyors are competent in using data.
3. Data can play an important role in business plans and company strategy.
a) Valuation – organisations should estimate the timing of each stage of better data becoming available and the value that can be gained from that by increasing client bases, selling more services or increasing fees to match the increased value.
b) Property management – property managers need to gather, clean and analyse data from the whole property life cycle. They will need to factor in the data storage costs, the analysis skills for their teams and the fees that will need to be charged for a complete service.
c) Investment and lettings agency – an agent’s value is in their ability to analyse the data rather than the knowledge itself. New analysis tools will enable agents to analyse more data more quickly and free up time to make better, deeper relationships with client companies. The business strategy may be to invest in data analysts and also in ‘front of house’ client relationship exercises.
4. It is unlikely that any property organisation will have direct access to all the data it needs so many businesses will need to buy data from several sources. This can be expensive and data sources are not comprehensive. The advantage of developing a strategy is that businesses will be able to value the data to specific uses and judge whether it is worth buying a particular dataset, such as footfall in shopping centres or photographic evidence of flood areas.
5. Staying on top of data resources and managing/collecting data sources is perhaps not a core skill of surveyors at present. It may be that a full-time resource might help the business to stay on top of this more formalised data structure.
6. If data cannot be bought, it may have to be created in the future. Each property firm will have access to client data through the course of its daily business which, when collected might provide useful benchmarking. Firms will need to consider adding a contract clause to cover the use of clients’ data in aggregated form and most clients will at present allow this as long as they receive some benefits. This is also a good time to consider what resources might be needed for this work and whether the workforce has the skills and bandwidth to undertake this effectively.
7. The creation of specific datasets that help the industry will provide invaluable experience in dealing with data when it becomes available to buy on the open market. At that point, workforces will not only understand the value of the data, they will have the skills to analyse the data for clients – all companies will have lost is the time taken to collect their own data, which should represent a cost saving.
8. There is no doubt that owning a dataset of local transactions gives advantages over national firms whose own data will not be as comprehensive. If it is structured well, up-to date and accurate, this should continue to add value to the business until comprehensive property data becomes available from other sources. That might take two, five or 15 years.
9. Despite the advantages of open data, firms should not give away their competitive advantage by sharing their data with others. Nevertheless, there is a lot of data available that could augment these local datasets – this should add considerable richness to the dataset and make it more valuable.