Making the most of data analytics in real estate and property
A few weeks ago, Reuben Barry (our Director of Data Analytics) and I spent a day and a half attending a new event for us – the Alternative Real Estate and Property Technology Summit in Hertfordshire. This unique event attracts CIOs, CTOs, IT Directors and technology professionals ranging from architects, developers, real estate investment companies, landlords, estate managers, and property consultants to name a few!
As an innovation leader within the professional services space, Reuben was invited to introduce perspectives from beyond the real estate and property sector. In his presentation, he was asked speciﬁcally how he’s dealt with a key problem facing the property industry – making the most of data analytics. Reuben presented a range of case studies which demonstrated the variety of applications of data analytics and the solutions they provide from a diversity of sectors.
The presentations piqued the audience members’ interest and from listening to other talks and speaking with many of the attendees, data analytics is clearly a ‘hot topic’ right now in the industry. It’s fair to say, that whilst data analytics has been embraced and is fully embedded within other industries, such as retail, aerospace or manufacturing, its adoption within this professional services space has been much slower. There seems to be a common challenge. Generally, everyone appreciates that there is lot of data available, and whilst they understand it has the potential of adding value, often they are struggling to know where to start. This has prompted me to write a blog which explores how data analytics may be harnessed within the real estate and property space.
Where to start:
The first step in building advanced analytics into a portfolio, is for companies to consider their most critical strategic problem or question they want to answer or action they want to take. This will determine the insights they need to gleam and ultimately, what data will be of most value and needs cleansing.
The next step is to employ an effective multi-disciplined data analytics team made up of experts who understand business goals, can write code and algorithms for analysis, develop decision-making tools, and clearly translate these elements into advice for business leaders – helping to bridge the gap between technical expertise and strategic business goals or actions.
Limitations of traditional data:
Until recently, real estate firms have relied on a mix of traditional respective data and intuition as a basis for their decision-making. The limited data sources and conventional analytical methods made it difficult to glean insights and to form clear hypotheses.
This limitation has had a significant commercial impact on a variety of roles within the real estate business. Developers and investors seek to understand where to acquire property and when to trigger development. Portfolio holders need to optimise their holdings and regularly assess conditions that lead them to divest or capture value. In both cases, by the time these real estate analysts sift through millions of data points, find hidden patterns and process them into action, they are likely to have missed out on opportunities.
Route to quick, actionable insights:
Today, the availability of a range of relevant new and unconventional variables, the ability to automate the analysis of huge amounts of data from multiple information sources and our access to advanced analytics using machine learning algorithms and AI is enabling real estate firms to gain quick, actionable insights.
An example of this is where predicting price appreciation has become more granular and location-specific. By combining multiple non-conventional data points, such as;
- building energy consumptions
- in-office mobility (based on elevator movement)
- resident surveys
- mobile phone signal patterns
- Trip Advisor reviews of restaurants and local businesses
or looking at macroeconomic and demographic data, such as, crime rates and median age, these variables can all inform long-term market forecasts.
Using machine learning algorithms, the separate sources of macro and hyperlocal data are combined, patterns extracted, and forecasts made. Using these predictions to create strategic plans (rather than the raw data itself) is where the value of data analytics lies.
Opportunities for a data-driven approach:
There are many opportunities to apply data analytics in real estate. Real-time reporting enables commercial real estate companies to accurately and quickly estimate prices. It also provides their clients with personalised, customer-centric property solutions, which better meet their needs and result in higher satisfaction levels. Social media analysis allows marketers to target customers at precisely the right time when they are looking to buy or sell property. Big data collected in ‘Smart’ buildings feed decision-making systems which use IoT and machine learning, can automate the building maintenance process – leading to the emergence of ‘self-healing’ buildings.
Big data technology benefits aren’t just limited to real estate practitioners. They can be used by home-owners choosing potential properties, by providing insights such as, the estimated rush hour travel times, or number of schools and amenities in the vicinity, or crime rates for example.
The Future is bright:
As technology evolves, the ability of advanced analytics to cut through the noise and identify what matters most, challenge conventional gut feeling, and inform new hypotheses will continue to reshape the real estate industry and business models of real estate service providers.
The world of big data, AI or machine learning may seem daunting, but here at Ecovis Wingrave Yeats, we see it as an opportunity for growth. By harnessing the power of data analysis, real estate can reap the rewards from quick, actionable insights and prepare for the future. Our team of data analysts, scientists and business advisors are set to support companies ready to take the first step.View Article