Embrace AI Disruption In Commercial Real Estate Investing

Matias Recchia is Co-Founder and CEO of Keyway, the commercial real estate technology platform designed for small and medium businesses.

Artificial intelligence (AI) is poised to disrupt a number of industries and, if leveraged strategically, can lead to significant advancements in the commercial real estate sector for investors, owners, asset managers and other stakeholders.

As data availability and transparency increase, AI’s profound impact on the commercial real estate landscape will empower industry participants to embrace data-driven approaches to enhance decision-making. Stakeholders who understand this monumental shift now can have a significant advantage compared with their peers.

How AI Is Changing The Real Estate Industry

With AI, historical property prices, market trends, economic indicators and demographic information can be aggregated and analyzed for more accurate property valuations, allowing investors to make more informed decisions by predicting future market performance.

Many real estate companies are integrating AI into their company. For example, industry peers are leveraging AI as part of the user experience, powering algorithm-based buyer recommendations, automating property valuation estimates and offering AI-powered market trend analyses and insights. However, we are only at the precipice of recognizing AI’s full potential in commercial real estate.

How AI Can Be Applied To Commercial Real Estate

While the commercial real estate sector historically has been slow to adopt technology, this backdrop creates a ripe opportunity for shrewd entrepreneurs and investors to leverage technology and AI to improve outcomes. There are multiple opportunities for AI to disrupt commercial real estate, including:

Improved Data Analysis For Better Investing Potential

AI is a powerful gateway for data aggregation. By analyzing vast and fragmented data sets, AI can identify potential value-add targets within a given market. For example, this includes identifying markets with potential for outsized future rent growth or sourcing properties that are mismanaged or energy inefficient.

Streamlined Property Management

AI can be leveraged to automate routine property management tasks such as maintenance requests and tenant communication, while also performing predictive maintenance to save property managers time while improving overall property efficiency and value. Leveraging historical data, AI can identify underutilized areas within commercial buildings and recommend optimal space reconfigurations, thereby creating more efficient real estate spaces.

Improved Personalized And Tailored Experience

AI can improve overall investor personalization and provide recommendations to both tenants and investors based on their individual preferences, budgets and location requirements. Generative AI can help as well and offer a chat-based experience in which individuals can have an informed conversation and make much more confident decisions.

Better Risk Mitigation

Proactive maintenance and risk mitigation is improved by AI’s ability to predict equipment failures and identify potential environmental risks via building sensor data analysis. This enables property owners to implement proactive maintenance strategies, reducing downtime, minimizing costly repairs, and safeguarding assets from unexpected damages. Given climate-related natural disasters, AI can help protect against these environmental risks, providing proactive protection against difficult-to-expect hazards.

Leveraging AI

At the company I co-founded where I serve as CEO, we are disrupting commercial real estate by integrating AI, machine learning and data science into traditional real estate investing. How? We take an end-to-end comprehensive approach to our AI application, allowing us to source, underwrite, buy, sell and manage assets on behalf of our investors.

We’re combining both machine learning and generative AI output for an improved user experience. Our approach considers historical data on property performance, market volatility and vacancy rates, among other factors, along with predictive analytics to balance between investment returns and downside risk. We’re also improving overall transaction and workflow management efficiency through AI. For example, we leverage AI to complete tasks such as lease data extraction, legal document creation, IRR forecasting, automated comps generation and deal flow optimization.

One unique strategy that my company has spearheaded is called a transactability score. We leverage AI to provide insights on mortgage origination, debt maturities and an owner’s overall leverage to gauge how likely a property owner is to sell.

We’re not only leveraging AI to make better decisions, but also to reduce time and cost in real estate investing. I’ve found that embracing a human-AI partnership—in which human, investor expertise complements AI’s intelligent capabilities—is the best way forward for the commercial real estate sector.

Potential Challenges For Industry Leaders

Implementing AI also brings some common roadblocks that organizations often face. One major challenge is the contextual understanding. AI won’t understand or process every facet of real estate investment decision-making. Humans have experience and intuition, which can provide a unique advantage in real estate investing. Humans can also understand neighborhood dynamics of a specific property market that may not be evident in real estate data. In contrast, AI may not fully understand the impact of local market conditions, neighborhood dynamics, zoning regulations or specific property features that can significantly influence overall investment decisions.

Another roadblock is data validation issues. Real estate data interpreted solely by AI won’t tell all the answers. Humans can critically evaluate and validate the data used by AI models, ensuring its accuracy and relevance. They can identify data gaps, correct biases and assess the reliability of information, mitigating the risk of flawed decision-making based solely on AI outputs.

Lastly, there’s the challenge of a reliance on historical data. AI and machine learning use historical data to make predictions about the future. However, humans can analyze policy changes, economic changes or unexpected events to get a more precise predictor of future real estate markets.

Final Thoughts

I believe the next five years will transform the commercial real estate sector. While some observers have expressed concern that AI will lead to massive unemployment in our industry, I view AI as a complement, not a replacement, for human ingenuity. The best combination is for humans and AI to work collaboratively to increase efficiency, speed and scale, while reducing time and costs. With continual AI advancements, the next-generation winners will be those companies that not only embrace AI but also combine human expertise, machine learning and generative AI to fully unlock the market’s untapped potential.


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