Matias Recchia is Co-Founder and CEO of Keyway, the AI- powered real estate investment manager.
If you’re not using AI to power your multifamily real estate investments and operations in 2025, it’s not too late to start. From market opportunity identification to tenant sourcing and retention to optimizing rental pricing, I’ve found that AI can provide a competitive advantage for real estate owners and operators through improved decision-making and meaningful investment returns.
AI is not only streamlining operations but also fundamentally changing the way real estate stakeholders acquire, manage and scale real estate portfolios. Based on my own experiences as a real estate investment manager, here are three ways you can use AI to empower your team:
1. Identifying Market Opportunities
AI can provide you with access to real-time, actionable data. For example, you can use it to identify a neighborhood with high growth potential early—based on income trends, job prospects, school quality and supply-demand characteristics—providing investors with an early look at where to allocate capital.
AI can also analyze large datasets regarding demographic trends, economic indicators and competitor developments to provide a holistic view to investors. For example, you can use AI tools to ascertain in which neighborhoods rental demand is growing, which amenities will win more tenants in a particular city, and which neighborhoods are undervalued relative to those in comparable rental assets.
2. Optimizing Rental Pricing
Optimizing rental pricing is about maximizing occupancy and profitability for landlords in any market environment while simultaneously providing fairness and transparency to tenants. AI and machine learning can play an integral role in improving comparable property data, or comps, for commercial real estate.
Traditionally, real estate teams have relied on manual processes to manage comps based on old data. But by leveraging AI and machine learning, you can reduce human error, manual labor and time. Furthermore, by relying on public data rather than landlord-reported private data, you can ensure that your real estate stakeholders have access to unbiased data with full transparency.
Rent optimization also means landlords can price their units based on real-time data, reducing the likelihood of underpricing or overpricing. Real estate teams need multiple data points beyond gross rent to inform their rental pricing decisions. For example, rent trends, fee and concession trends, neighborhood dynamics, competitor pricing and economic indicators represent several components that comps platforms should analyze. You can also train your AI platform to incorporate cap rates and long-term appreciation potential to garner a more accurate picture of investment returns.
Tenant screening is another component that can optimize both rent and occupancy. Consider using AI and machine learning to review applications, employment history, credit and other factors to predict whether a tenant will be reliable and financially responsible. With more reliable tenants, landlords can reduce turnover, which translates to higher recurring cash flow and occupancy.
3. Driving Asset Performance
If you’re a property manager, consider incorporating AI-powered energy management systems and offering automated tenant communications. You can also leverage AI to drive predictive maintenance. For example, AI can monitor HVAC equipment and elevators to determine when the next maintenance or repair is required so managers can avert major delays of services. By analyzing usage, useful life and prior maintenance work, you can proactively avoid tenant inconvenience, reduce complaints and save operational costs.
Asset managers can leverage AI to deploy capital more systematically. For example, by sharing financial goals, investment returns and time horizons with your AI platform, you can better determine when to refinance assets, which assets should be prepped for sale and what’s the optimal way to reinvest proceeds. Capital deployment optimization can be done in Excel, but I’ve found that AI reduces reliance on individual decision-making and takes a more objective approach.
For example, my company uses an AI-powered program to manage our document workflow. Keeping track of leases, ensuring consistent lease terms and identifying inconsistencies in legal language are critical must-haves for any real estate team, and using AI tools instead of relying solely on your legal team can help reduce human error. When you have a clear lease management strategy, you can reduce legal costs and maintain consistency across your portfolio.
Final Thoughts
I believe the future of multifamily real estate will be marked by dynamic pricing, predictive maintenance and AI-powered energy optimization. AI can give property owners and asset managers a real-time full view of their portfolio, which is important in a real estate market that is unpredictable and subject to economic and market fluctuations.
By reducing manual errors, removing bias and creating uniformity in decision-making, real estate teams can have greater control over underwriting, operational roadblocks and tenant satisfaction, leading to an operationally sound multifamily market powered by technology, efficiency and data.
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