As a real estate investment manager, navigating the ever-evolving landscape of technology and artificial intelligence, I’m excited about my company’s adoption of AI and our society’s unabashed recommendation of movies from streaming services like Netflix. I can’t help but draw parallels between the way we trust without trust. It’s a strange but apt analogy. After all, for real estate professionals, it’s one thing to use artificial intelligence, it’s quite another to let it influence decision-making and accept it as a trusted advisor.
The commercial real estate sector is currently buzzing with technology buzz, as more professionals accept that generative AI, machine learning, and data analytics represent the future. They understand that adopting this technology wisely can mean the difference between winners and losers in the industry. However, what is still difficult to understand is the “method”. How can you take the plunge and adopt AI? More importantly, how can you integrate AI into your existing workflows? There are significant barriers to full adoption, and those must be overcome to fully leverage the real-time benefits that AI brings to investment firms. The most important thing to do is to.
Then there’s the big challenge of getting real estate teams to actually use and trust AI insights. It all depends on how much data companies can track and organize, and how accurate and standardized the data fed to AI models is. In the case of real estate, this can be a difficult problem to solve, and certainly can be quite costly, as historical data is not publicly available or organized in a way that derives insights. There is also gender.
where to start
The first step is to stop devaluing or underutilizing your proprietary data. Recognize that there are already solutions on the market that help teams edit, organize, and standardize existing data. These solutions extract critical information from emails, update your contact network, monitor deal offers including key property details, property performance, comparable properties, pricing, rental growth, etc. You can edit data about. Most importantly, these systems can deliver actionable insights from data and enhance decision-making by eliminating human error and mitigating unforeseen risks.
Four years ago, we embarked on an ambitious project to leverage our unique data and employ machine learning to standardize and analyze complex datasets. This forward-thinking approach to technology informs my company, FaroPoint’s strategy to acquire last-mile infill industrial properties across the United States. To grow a real estate company, it’s important to solve the complexities that come with scale. We evaluate more than 3,000 potential acquisitions annually, which equates to nearly 60 deals each week. But once these complexities are resolved, the benefits of data are many. We collect data from our acquisition activity and portfolio of over 400 properties we have already acquired. This extensive collection of proprietary data is the backbone of our internal AI systems and provides unparalleled insights that drive innovation for our company.
Take a cue from Netflix
The transition from simply investing in AI to actually letting AI make decisions is all about trust. Think of it like how we’ve come to rely on Netflix for movie and TV series recommendations. please think about it. When was the last time you didn’t click on one of the movie options on your Netflix home screen? Thanks to Netflix’s smart algorithms, these suggestions are tailored to your viewing history. We trust these suggestions. The more you use Netflix, the better you become at predicting what you’ll enjoy next. This principle is exactly what needs to be applied to the AI insights gained from proptech software.
For these insights to be true game-changers in real estate investing, companies need to do more than just receive them. They need to be actively engaged with the AI platform every day. We need to move beyond viewing AI simply as an idea generator and recognize its role in shaping strategy and operations. The real hurdle is overcoming initial doubts about the reliability of AI-powered data insights. Building this kind of trust typically requires direct evidence of its accuracy and relevance. And importantly, this level of trust cannot be fostered without robust mechanisms to provide feedback to the AI and allow it to evolve and improve accuracy over time.
Breaking down the silos between technology and investment teams is very important to us. It created a consistent feedback loop that helped us successfully implement and implement AI into our daily workflows. At Faropoint, we achieved this through a unique collaboration between our local sourcing team’s nine offices and our Israeli research and development (R&D) team. This partnership will help drive informed decision-making and maintain consistent quality results. Our R&D team, comprised of data engineers, product managers, and data science experts, leverages the trading data collected by our investment teams to enhance the insights provided by our platform. They work with our acquisition and asset management experts to ensure data accuracy before integrating it into real estate algorithms.
It’s important to strengthen your strategy with a dedicated in-house R&D team and acquisition experts who truly understand the local market. This approach not only helps decipher the results of the AI, but also allows fine-tuning of the algorithm. This combination has been key to maintaining our competitive edge in a rapidly evolving AI environment.
The future of real estate lies in the adoption and integration of AI, and being able to trust AI insights is key to unlocking its full potential. Just like we trust Netflix to recommend the next movie or series we can enjoy, we can learn to trust AI to guide our investment decisions. By investing in technology and implementing it into daily workflows, breaking down silos between departments and fostering collaboration, real estate companies can confidently deploy his AI and position themselves as industry winners. can be secured.
It’s time to take the plunge and start considering AI as an integral part of your analytics and operations.
