Successful real estate investing requires smarter use of advanced tech

As economic headwinds continue to blow, there has never been a more critical time for real estate firms to leverage technologies that enable improved investment decision-making and risk management.

Real estate firms have made major strides in the adoption of more advanced technologies. A comprehensive study we released at Altus Group this year showed that nearly 60% of UK firms are already investing in data science capabilities such as AI and machine learning. At the same time, 71% of UK firms report having chief data officers in place, while 39% of companies have dedicated in-house data science teams undertaking tasks such as writing bespoke algorithms to drive predictive modelling. This data suggests that we have reached a tipping point where technology is simply a cost of participation in the market. Instead, the real differentiator in commercial real estate will be how effectively firms leverage and deploy AI, ML and other technologies to their advantage.

For firms looking to better leverage their technology, the most important consideration is understanding that the output of a technological application is only as good as the data input. Improving the quality of data that goes into an algorithm will enhance the range of insights that any model provides.

When thinking about data quality, there are a few components to consider. First, what unique data does a company have access to and how can that data be harnessed to gain useful insights? Second, what is the universe of external data that might be relevant to inform decision making? Governments and industry bodies produce an enormous amount of data on industry trends and demographics that can be incredibly valuable to firms, for example.

Finally, and most importantly, how can in-house data and third-party data be woven together to provide new, multi-dimensional insights? We need to move beyond an analytics approach that seeks to derive trends from linear datasets, and instead look at how we can combine data layers to find new levels of insight.

That said, a savvy approach to data isn’t just about scale. A vital skill for real estate data scientists going forward will be understanding how to selectively choose and combine data to best meet commercial objectives. AI operators need to be able to narrow the focus for the technology by grounding it in solid principles of economic and market theory. Making sense of the scale of big data will be an incredibly important part of how the market matures in its use of technology.

With that in mind, another key part of maximising the potential of advanced technologies is not losing sight of the importance of human beings. The skillset of an exceptional portfolio manager is not the same as that of a leading AI engineer. How successfully the industry is able to upskill and pull in talent and expertise from other sectors will determine how quickly firms are able to unlock greater value from their technologies.

As the deployment of AI and machine learning models moves from being an investment in experimentation to a critical part of the R&D process, business leaders will need to continually stay abreast of new technologies in order to drive further commercial success. The good news is that, for those who are prepared to commit to long-term innovation, the potential of these technologies to drive competitive advantage – whether in the form of profitability, revenue share or market leadership – is limitless.