Data is all the buzz around real estate, but how do we categorise it, what data do we need, what are the concerns and why should I care about the content of the local water supply before I consider whether to purchase a real estate asset?
Data is simply anything that can be measured and used for reference and analysis. For the sake of an ongoing example, let’s call “data” the usual variables of size, location, amenities and market condition upon which we would base an offer for a single residential property on any given day.
Alternative data is any data which is being used for anything than its primary collection purpose, and so sits outside of the realm of traditional data. So if the crime rates in an area on any day are being used to decide the price a person may offer for our same example property, this makes the local crime statistics an alternative data set.
Big data is traditionally defined through the three V’s: information which is produced with high velocity, variety and volume. However, all three of these require benchmarking to know exactly what constitutes their scales of magnitude, with no clear guidelines as to how. Within real estate, big data can be thought of as that which is being produced in near real time, too large for traditional regression and spreadsheet models to interpret. Within our example this could represent the social media activity and demographic profiles of those who have been convicted of crime in the local neighbourhood of our example property.
The reason for the recent ‘data buzz’ is due to the rapidly increasing ability of Machine Learning: a set of self-refining computer algorithms, able to find correlation in disparate data sets. This increased ability has been brought about by the exponential development of more efficient microchip processors in the computer hardware industry. This breakthrough has suddenly made the analysis of alternative big data sets possible to the world of real estate: a revolution that is fuelling the rise of increasingly intelligent PropTech organisations.
