In the world of commercial real estate, an invisible wall has always existed between professional investors with their teams of analysts and everyone else. The former had access to proprietary data and complex forecasting models; the latter had to make do with intuition and public statistics. The ESTEX platform is tearing down this wall with an algorithm that doesn’t just analyze data—it thinks like the most meticulous investment consultant.
Not Data, but Meaning: What the Algorithm Truly “Sees”
EstraMatch AI works not with abstract numbers, but with specific, often non-obvious parameters that shape the real long-term value of a property. Unlike traditional models that evaluate the past, the algorithm forecasts the future.
- Pedestrian Ecosystem: The algorithm analyzes more than just a “good location.” It calculates the Walk Score—pedestrian accessibility within a 500-meter radius. The presence of cafes, banks, pharmacies, parks, and subway stations within walking distance increases a residential complex’s appeal and supports higher rental rates. For logistics hubs, the key parameter becomes the Freight Score—the density of transport arteries and distance to ports.
- Rental Rate Dynamics for Coworking Spaces: In the post-pandemic era, hybrid work has become the norm. EstraMatch tracks how demand for office space changes depending on the prevalence of coworking spaces in a district. An increase in flexible workspaces can signal a shift from long-term leases by large corporations to short-term leases—which are more volatile but also offer higher margins.
- Municipal Planning as a Growth Driver: The most powerful source of excess returns is information about the future. The algorithm scans open municipal data, transport infrastructure development plans (construction of a new subway station, launch of a light rail line), and urban renewal programs. Acquiring an asset before these plans become public knowledge is the key to multiplying its value.
- Climate and ESG Risks: EstraMatch evaluates properties from a sustainability standpoint. It assesses flood risks due to rising sea levels, a building’s energy efficiency, and the use of eco-friendly materials. An asset with a high ESG rating is not only less risky but also more attractive to a growing pool of socially responsible investors.
How the Algorithm is Trained: The Data Science Team and Backtesting
EstraMatch is not a static formula frozen in code. It is a constantly learning system. The ESTEX data science team, which includes specialists with experience from NASA and leading hedge funds, acts as its “mentors.”
The training process looks like this:
- Historical Data Accumulation: The algorithm has been “fed” data on thousands of commercial real estate properties worldwide over the past 20 years—purchase prices, rental rates, occupancy, and profitability metrics.
- Backtesting: The algorithm is tasked with analyzing, say, the London market situation in 2015 and “predicting” which properties would turn out to be the most profitable by 2020. Its forecasts are then compared against actual historical data.
- Weight Adjustment: The parameters the algorithm overvalued or undervalued are adjusted. For instance, it might have initially underestimated the importance of bicycle lane density around an office building, but backtesting revealed a strong correlation with rental growth for IT companies.
Data-Driven vs. The Human Factor: A Quiet Revolution in Asset Valuation
Traditional real estate valuation methods are often based on the experience and intuition of realtors and managers. This is an art form, susceptible to cognitive biases: excessive optimism, anchoring to past experiences, and emotional influence.
EstraMatch offers a fundamentally different approach:
- Scale: A human can deeply analyze 3-5 markets at once. The algorithm tracks 127 parameters across 12,000 properties in 15 jurisdictions in real-time.
- Objectivity: The algorithm is not swayed by trends or a seller’s slick presentation. It won’t buy an ocean view if the building is in a high seismic risk zone.
- Speed: While a team of analysts prepares a report on a single property over a week, EstraMatch recalculates the profitability of the entire portfolio every 4 hours.
The error of the traditional approach is a missed opportunity or, worse, millions in losses. The algorithm’s error is a mathematical inaccuracy that can be corrected in the next learning cycle, minimizing losses.
Conclusion
EstraMatch AI is not just a tool. It is a full-fledged investment partner, transforming real estate from an art into a precise science. It doesn’t replace humans but empowers them, taking on the Herculean task of routine analysis and providing the ESTEX team and end-investors with ready-made, substantiated data for decision-making. In a world where the winner is the one with the best information, such an algorithm becomes the most valuable asset.
