As a real estate investment manager navigating the ever-evolving landscape of technology and artificial intelligence, I can’t help but draw parallels between my firm’s adoption of AI and the way in which our society unabashedly trusts movie recommendations from streaming services like Netflix. It’s a strange but fitting analogy. After all, it is one thing for real estate professionals to use artificial intelligence, yet quite another, to let it influence your decision-making and to embrace it as a trusted advisor.
The commercial real estate sector is currently abuzz with talks of tech as more professionals come to accept that generative AI, machine learning, and data analytics represent the future. They understand that the judicious adoption of this technology will separate the winners from the losers in our industry. But what remains elusive is the “how.” How do we take the plunge and adopt AI, and, maybe more importantly, how do we integrate it into our existing workflows? The barriers to full adoption are substantial, and overcoming them is paramount to unlocking the real-time benefits AI can bring to investment companies.
Additionally, there’s the big issue of getting real estate teams to actually use and trust AI insights. It’s all about how much data your company can track and organize and how accurate and standardized the data that’s being fed into AI models is. In real estate, where historical data is either not publicly available or not organized in a way to derive insights, this could be a tough nut to crack, and yes, it might also cost a pretty penny.
Where to start
The first step is to stop devaluing and underutilizing your proprietary data. Realize that there are solutions already on the market that can help your team compile, organize, and standardize existing data. These solutions can extract crucial information from emails, update your network of contacts, monitor deal offers with key property details, and compile data on asset performance, comparables, pricing, rental rate growth, and more. Most importantly, these systems can yield actionable insights from your data, enhancing decision-making by eliminating human error and mitigating unforeseen risks.
Four years ago, our firm embarked on an ambitious project to capitalize on proprietary data and employ machine learning for the standardization and analysis of complex datasets. This forward-thinking approach to technology has helped my firm, Faropoint, in our strategy of acquiring last-mile infill industrial properties across the United States. Growing a real estate company is all about solving for the complexities that come with scale; we assess over 3,000 potential acquisitions annually, equating to nearly 60 deals each week. But once you solve for those complexities, it comes with a lot of advantages, namely data. We gather data from our acquisition efforts and our portfolio of more than 400 already acquired properties. This extensive collection of proprietary data is the backbone of our in-house AI system, providing unparalleled insights that are a game-changer for our firm.
Taking a cue from Netflix
The shift from just investing in AI to actually letting it guide our decision-making is all about trust. Think of it like how we’ve come to depend on Netflix movie and TV series recommendations. Think about it: when was the last time you didn’t just click on one of the movie options served on Netflix’s home screen? We trust these suggestions because they’re tailored to our viewing history, thanks to Netflix’s smart algorithm. The more we use Netflix, the better it gets at predicting what we’ll enjoy next. This principle is exactly what we need to apply to AI insights from proptech software.
To make these insights a real game-changer in real estate investment, companies must do more than just receive them; they need to actively engage with AI platforms every day. It requires moving beyond seeing AI simply as an idea generator to acknowledging its role in shaping our strategies and operations. The real hurdle is getting over that initial doubt about the reliability of AI-driven data insights. Building this kind of trust usually requires firsthand evidence of their accuracy and relevance. And importantly, this level of trust won’t develop unless there’s a robust mechanism for providing feedback to the AI, allowing it to evolve and improve its accuracy over time.
Breaking down the silos between tech and investment teams has been critical for us; it creates a consistent feedback loop and has aided in the adoption and successful implementation of AI into our daily workflows. At Faropoint, we’ve achieved this through a unique collaboration between our nine offices of local acquisition teams and our Research & Development (R&D) team in Israel. This partnership enables us to drive informed decision-making and maintain consistent quality results. Our R&D team, made up of data engineers, product managers, and data science experts, leverages the deal data collected by our investment teams to enhance the insights provided by the platform. They work in tandem with our acquisition and asset management pros to ensure data accuracy before integrating it into the property algorithm.
Elevating your strategy with a dedicated in-house R&D team, coupled with expert acquisition professionals who truly understand their local markets, is crucial. This approach not only helps in deciphering the outcomes of AI but also enables the fine-tuning of its algorithms. This combination has been the key to maintaining our competitive edge in the rapidly evolving AI landscape.
The future of real estate lies in AI adoption and integration, and learning to trust AI insights is the key to unlocking its full potential. Just as we trust Netflix to recommend the next movie or series we’ll enjoy, we can learn to trust AI to guide our investment decisions. By investing in technology, implementing it into day-to-day workflows, breaking down departmental silos, and fostering collaboration, your real estate company can confidently embrace AI and secure its place among the industry’s winners.
It’s time to take that leap of faith and start considering AI as an integral part of your analysis and operation.