Pioneering in Build-to-Rent: Camden Homes and CooperZadeh Management

Camden Homes has earned a strong reputation for delivering high-quality homes throughout Texas, offering properties both for purchase and lease. This dual offering allows the company to meet the diverse needs of modern families while supporting long-term growth across various housing segments. To manage its leasing operations, Camden Homes partners with CooperZadeh Management, a firm known for its meticulous property oversight and commitment to resident satisfaction.

With a rapidly expanding footprint and an increasing number of units under management, the Camden-CooperZadeh partnership is uniquely positioned at the intersection of innovation and scalability. But with this scale comes complexity—particularly in an environment marked by rent softness and regional supply fluctuations.

The Rental Pricing Challenge

For Gayla McCown, VP of Property Management at CooperZadeh, the pain points were clear.

“Pricing rentals accurately in a dynamic market has always been challenging,” she explained. “We’ve relied heavily on manual market surveys to keep up with competitors, but the process is time-consuming, labor-intensive, and prone to error. It pulls our site teams away from what they do best—serving residents and securing renewals.”

In fact, internal estimates suggested that survey collection and analysis consumed up to 10 hours per community per week, with data often stored in outdated spreadsheets disconnected from the company’s core leasing systems. For a fast-moving portfolio with hundreds of new leads each week, this fractured system simply couldn’t scale.

“It’s death by a thousand paper cuts,” said McCown. “The more time we spend piecing together data from multiple sources, the slower and less efficient our leasing operations become.”

A Smarter, Automated Real Estate Management Solution

To solve these challenges, Camden Homes launched a 60-day pilot with Beekin’s LeaseMax AI Rent Module. The technology uses artificial intelligence to generate real-time rental pricing recommendations by analyzing a broad set of variables: local market trends, competitive rents, property characteristics, and even publicly available data sets such as transportation networks, land cover, demographic trends, and pollution levels.

LeaseMax AI-powered real estate analytics distinguishes itself with its extensive reach—its AI Market Rent Module incorporates data from 16 million apartments and single-family homes, offering a richer, more nuanced perspective than typical market surveys or limited proprietary databases.

Critically, Beekin’s solution demonstrated an impressive median absolute percentage error (MdAPE) of just 5–8%, positioning it as the most accurate commercial rent forecasting model currently available for BTR and multifamily housing.

Implementation: A 60-Day AI-Powered Real Estate Analytics Test Drive

During the 60-day pilot, Camden Homes—one of Texas’s leading build-to-rent operators—integrated LeaseMax’s AI-powered real estate analytics platform into their revenue management strategy. To ensure a thorough evaluation, Camden simultaneously benchmarked LeaseMax against two other market data providers.

Key components of the implementation included:

• Data Synchronization: Property-level and market data were seamlessly connected to LeaseMax’s platform, enabling dynamic rent pricing insights tailored to the build-to-rent model.

• Training & Integration: Camden’s internal teams were quickly onboarded through intuitive dashboards, API connectivity, and direct integration with tools like Salesforce—supporting efficient cross-functional collaboration.

• Performance Monitoring: Real-time pricing recommendations were continuously assessed against actual leasing outcomes and internal feedback from asset managers and leasing professionals, with an eye on both revenue performance and resident retention.

By the end of the pilot, LeaseMax outperformed competing platforms in pricing accuracy. More importantly, it empowered Camden to implement dynamic rent updates directly within their operational systems—streamlining revenue management processes and minimizing manual intervention across their Texas-based build-to-rent portfolio.

Real Results, Real Feedback

The outcomes were clear: better pricing recommendations, improved team productivity, and enhanced decision-making. For CooperZadeh’s CEO, Cyrus Zadeh, the results validated a broader vision.

“We’ve always believed that the future of real estate will be powered by data and automation,” Zadeh shared. “At CooperZadeh and Camden Homes, we’ve invested heavily in building a modern, scalable tech stack—and Beekin fits perfectly into that strategy. Their ability to ingest and synthesize massive volumes of data into actionable insights is truly impressive.”

The move toward AI-driven pricing not only accelerated operations but also gave leasing teams a stronger foundation for strategic decisions—especially as the company scales into new regions and markets.

What’s Next?

Following the pilot’s success, Camden Homes is moving forward with a full-scale rollout of Beekin’s LeaseMax AI Rent Module (AI-powered real estate analytics module) across all current and upcoming rental communities. This initiative will be executed over the next 18 months, underscoring Camden’s commitment to staying at the forefront of innovation in residential real estate.

For other developers and property managers considering a shift toward data-driven revenue strategies, the Camden-Beekin partnership serves as a compelling proof point.

Download the Case Study to Learn More

Want to explore the full results of this pilot program—including implementation metrics, real-world use cases, and insights from Camden’s executive team?

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About Beekin

Beekin provides AI-powered solutions for real estate professionals, helping them optimize rental pricing, tenant retention, and investment decisions. Built for multifamily, single-family, and BTR portfolios, Beekin’s platforms empower clients to harness the power of predictive analytics and real-time market intelligence.