AvalonBay Communities

David Madrid is the Vice President of Innovation and Venture at AvalonBay Communities. He manages the Venture portfolio and chairs the innovation committee to shape future initiatives and partnerships.

David Madrid shared his expert insights for the 2025 edition of PropTech Outlook, offering valuable thoughts on the AI-led transition in the industry.

The transformation of real estate is no longer theoretical. It is happening now, and artificial intelligence is at the center of it.

AI is no longer experimental. It is reducing costs, enhancing decision-making, and accelerating productivity across industries. By enabling organizations to scale faster, operate leaner, and compete with fewer constraints, AI is already proving its leverage.

While some debate the timeline for artificial general intelligence (AGI), we do not need AGI to see seismic shifts in how value is created. Narrow AI is already outperforming humans in high-value tasks, and it is advancing quickly. The question is not whether AGI will arrive, but how we can prepare for the accelerating automation that is already reshaping industries.

What we are witnessing is not just technological; it is a restructuring of competitive advantage. This convergence could fundamentally change industries, including real estate. New businesses may rise and fall at unprecedented speed. Traditional investment models like venture capital and private equity must evolve, not just in structure but in how they assess value and deploy capital.

The shift is already underway. More capital will flow to operators that integrate AI at their core, as investors recognize the competitive advantages. These operators will scale faster, operate leaner, and unlock value in ways others cannot. As automation compresses, it operates with lower costs and amplifies output; traditional efficiency metrics will evolve. Investors will favor platforms that scale with minimal labor—those that already have AI in their operating DNA.

As VP of Innovation and Venture at AvalonBay, my role is to anticipate forces like these—the kind that reshape how we create value. It is about staying ahead of the curve to deliver better experiences for residents while outperforming the market.

The Skeptic’s Case and Why It Misses the Point

Many seasoned executives look at AI predictions and think, "I have heard this before." The dot-com bubble promised to revolutionize everything. So did blockchain. Most transformative technologies are overhyped in the short term and underestimated in the long term.

Skeptics have valid concerns. U.S. productivity growth has been anemic for decades despite heavy tech investment. Many AI applications remain flashy demos that struggle with real-world complexity.

Still, skeptics overlook something critical. AI does not need to be perfect to transform real estate. It only needs to outperform humans in specific, high-value tasks. In many areas, it already does.

GPT models can process complex legal documents faster than attorneys, accelerating initial review while lawyers focus on strategy. Computer vision systems detect maintenance issues more accurately than experienced property managers. Predictive algorithms forecast market trends with greater precision than seasoned analysts.

The question is not whether AI will transform real estate, but whether you will be ready to move when it does.

Three Ways This Changes Real Estate

1. Underwriting Gets Superhuman 

Today, deals are analyzed using spreadsheets, reports, and instinct. Tomorrow, AI will process every comparable sale, demographic shift, employment trend, and zoning change in real time. It will not just evaluate deals, but predict market dynamics over the hold period.

2. Operations Become Predictive    

Imagine your property system knowing a dishwasher will fail before the resident does. It orders the part, schedules maintenance, and sends a text—all automatically. Leading operators are already testing AI for multi-channel communication and resident services. Human empathy is still missing, but operational efficiency is undeniable.

3. Buildings Learn to Self-Optimize 

AI-enabled systems monitor energy consumption, adjust HVAC in real time, and forecast demand to cut costs. With solar, batteries, EVs, and smart grids growing, AI becomes essential to manage complexity. Properties with smart energy systems will save money and sell excess power back to the grid. Optimization will be table stakes; differentiation will come from how deeply AI is embedded in daily operations.

Why Platform Scale Matters

Being a larger operator becomes critical, not just for cost advantage but for shaping the future. Tomorrow’s dominant players will not just use AI as a tool—they will weave it into every aspect of operations as a competitive moat.

Scale creates unique advantages:

• Proprietary data sets to train better models

• Amortized AI development costs across portfolios

• Ability to attract top AI talent

• Stronger vendor terms

This is not only about buying better software. Large operators should build integrated AI capabilities, where each system strengthens the next. For example, resident communication AI improves underwriting AI, which in turn enhances asset management AI—creating a flywheel smaller operators cannot match.

But scale works only if it is fast. Without cutting red tape, even the largest firms risk being leapfrogged by nimble, AI-native competitors.

Four Strategic Paths Forward

Across the industry, I see four responses to AI adoption:

• Denial Strategy: Ignoring AI entirely. It may work short-term, but it is risky when competitors are building unmatchable capabilities.

• Vendor Strategy: Buying third-party AI solutions. Provides incremental improvement but little competitive advantage.

• Partnership Strategy: Partnering with AI companies to share data and development costs, maintaining some differentiation.

• Platform Strategy: Building AI as core infrastructure, hiring talent, and developing proprietary models.

In practice, companies that combine partnership and platform approaches achieve both speed and defensibility.

The key is this: AI is not just about improving what you already do, but about creating entirely new ways to compete.

Beyond Operations: Demand Will Shift

The opportunity is not only operational, it is structural. As AI reshapes work and income, demand may shift toward housing with greater flexibility, affordability, and community. Efficiency will matter, but livability may become the new competitive edge. Operators designing for adaptability—not just occupancy—will lead in the next era.

Where Leaders Should Start

So how should leaders act—today, not years from now?

• In the next few months, assess your data infrastructure. AI is only as strong as the data it learns from. Decide whether to buy, partner, or build.

• This year: Identify AI leaders within your organization—or bring in external talent. You need someone who understands both AI capabilities and your sector to place strategic bets.

• Within a few years: Integrate AI into high-impact processes before you are forced to play catch-up. Those who wait will face AI-native competitors with a massive learning advantage.

AI is not an add-on. It must be treated as core infrastructure for how your business runs and competes.

The Bottom Line

I cannot predict exactly what real estate will look like in five years. But I do know this: the winners will not be the ones who waited.

The time to experiment, learn, and build is now—even before the plan is perfect. In a space where the rules are being rewritten, hesitation may be the biggest risk of all.