Go big, go broad, join the dots.
There's no shame in copying when it comes to getting a competitive edge.

Go big, go broad, join the dots.

When it comes to leveraging data, the real estate industry can learn much from other industries.


1. Hedge Funds.

The famously secretive industry is constantly seeking new ways to generate "Alpha", ie beat the market. As the industry as a whole has moved significantly towards Quant (ie Automated) trading, its appetite for broader and more obscure datasets has increased exponentially. Most major Quant funds now have teams dedicated to the sourcing and interpretation of alternative datasets.

In a report commission by the Alternative Investment Management Association (AIMA) in collaboration with fund service group SS&C, it was found that 53% of the 100 hedge funds polled, who run a combined $720bn, were now turning to non-traditional data sets for new ideas.

The three main types of alternative datasets used were were consumer spending and lifestyle data, extracting information from websites, and leveraging bespoke research from unconventional sources.

Alternative data is a newer concept in the real estate world, but the growing utility of the data and breadth of available sources is creating significant opportunities for savvy operators.


2. Marketing

The marketing world has been data driven from the outset, but it is in the last 10 years or so that its appetite for Big Data has truly taken off. In a world where all information about us as individuals is relevant, the marketing industry has been quick to find ways to ingest and analyse this at a vast scale.

The companies who have done this best have reaped huge rewards. Alphabet (the parent company of Google) made $40bn in Net Profit in 2020 by, at the risk of somewhat oversimplifying their business model, monetising human preference and activity data for more than it cost them to acquire it.

To gain the benefits of this scale of data, companies must bring in a whole new type of skillset. An ongoing challenge for the real estate industry will be how to attract and retain data science specialists at a time when their skills are in such high demand.


3. Retail

We have mentioned the breadth of data used by hedge funds and the depth employed by top marketeers, but both are significantly less effective without the ability to connect and correlate different datasets.

This is something that the best retailers have learned to do very well. Whether it be through the data rich customer loyalty programs leveraged to great effect by companies like Tesco, or the complex work undertaken by the automotive industry to connect our online and offline purchasing behaviours, there is great value in bringing data together.

Take the example of a commercial building. The environmental conditions in the building (air quality, temp etc), the wellbeing and satisfaction of the employees housed within, the occupancy of the building, its location and the type of tenants are all related. The players who can effectively connect the dots will be best equipped to beat the market.


In short, getting comfortable with a more diverse range of data sources, larger datasets and the ability to analyse correlations and trends will help to determine the future winners in industries like Real Estate. Mastering these areas is, of course, easier said than done, but already the gaps between the truly data-driven organisations and those putting off the inevitable are starting to show.

Whether it is via our alternative-data-on-demand or our data-platform-as-a-service, we are very well positioned to support companies in all industries in taking the next, or even the first, step in their data journey.

Contact us on [email protected] for more info.



As our CEO Alex Storey outlines the use of alternate datasets for decision making.. REOMNIFY is keen to announce its expansion of its unique data technology platform across UK and NZ along with SG and AU. Anyone interested to up their game with differential data in these countries do write to us [email protected]. #alternativedata #dataanalytics #decisionscience

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