The problem with treating data like property

COMMENT: We all know that data is important – indeed, it comes up in conversations enough – but for a real estate business to thrive, it cannot apply the traditional ways of property thinking to data as it is fundamentally different, writes Dan Hughes, chief executive of Liquid REI. Real estate is one of the most important sectors in the world, from driving productivity to keeping us happy at home, enabling us to get things at the click of a button through to being an attractive investment. All of this is at least in part due to the physical attributes of property; you can touch it and you can see it. This is what makes property attractive, and all of the disciplines, the culture, the skills and the processes in this sector have grown up around these physical attributes. But with data now the buzzword in property, I thought it would be helpful to provide a few tips on how real estate practitioners need to treat property data differently to property. Data is easy to replicate. A property in a specific location is unique, but two copies of the same data set can exist. Why does this matter? Because as an industry our business models are based around leveraging the fact that every property is unique. We cannot automatically use the same principles where data is part of our proposition going forward. Of course, it is possible to create a competitive advantage from data, for example by protecting the IP through licensing, but it is no longer a given. While the value of property does not necessarily directly relate to the cost of it, there is a strong correlation. A small building tends to cost less to build and has less value than a large one. The value of a data set, however, cannot in any way be assumed from the cost to create it. For example, data sets are often created as a by-product of something else, so there is no real cost in creating them. However, they can be very valuable. Real estate needs to look at the value of data in a very different way from the value of a property. When we assess the quality of a property, we need to take into account the use for which it has been designed; for example an office building is designed for people to work in. Assessing the quality of data is no different. However, unlike property, which typically has one use, data can be used in unlimited ways. Therefore we need to change our traditional approach to understanding quality when it comes to data. For one purpose it may be good quality and for another it may be poor. Companies that work in the real estate sector use business models that are based around the way that property behaves; the uniqueness of a property, the cost-to-value relationship and a consistent understanding of building quality. As data becomes more important to the way that we run our businesses, we need to change our way of thinking. Data is not necessarily unique, does not necessarily cost anything to create and can be simultaneously good and bad quality, depending on what you are using it for. Successful real estate businesses of the future will understand the difference between buildings and data and treat them accordingly.