Personalized Listings for the Fastest-Growing Commercial Real Estate Marketplace

Crexi faced the challenge of efficiently connecting buyers, sellers, brokers, and tenants within the often fragmented and complex commercial real estate market. As the volume of listings and user activity grew, ensuring relevant property discovery and maintaining a seamless transaction experience became increasingly difficult. This created friction in the decision-making process and risked reduced user satisfaction across the platform. By implementing personalized property recommendations, optimized email campaigns, and advanced search capabilities, Recombee enhanced multiple aspects of Crexi’s platform, significantly improving user engagement, streamlining property discovery, and increasing overall customer satisfaction.

Recombee implemented a sophisticated ensemble of incrementally-trained recommendation models to meet the needs of both buyers and sellers on Crexi. This included collaborative filtering, content-based models, advanced deep learning, and reinforcement learning through contextual bandits. To align with Crexi’s product vision, custom ReQL geographical functions were developed; supporting points, polygons, radii, and containment logic for precise location-based search behavior. The system was further optimized to drive multiple key commercial real estate actions such as contacting brokers, submitting offers, downloading offering memoranda, and requesting due diligence materials, significantly enhancing user experience and platform performance.

Real estate brokers, listing agents, sales and marketing teams, and property investors/buyers (who receive the recommendations)

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-Collaborative Filtering Recommendation Models (Deep Variational Autoencoders, Matrix Factorization, Linear Models, …) -Content-Based Recommendation Models (Convolutional Neural Networks, LLMs, visual similarity) -Reinforcement Learning Recommendation Models (Multi-Armed Bandit Models, Deep Reinforcement Learning) -beeFormer Transformer Architecture

Head of functional/operational unit

Joint implementation with the client

Recombee’s internal team maintains the service provided.

Not applicable, as we are a system as service.

There are no technical requirements, the payment for SaaS is on a monthly basis.

Recombee’s personalized recommendation system strengthened user engagement on Crexi by making property discovery more relevant and efficient. Similar property suggestions encouraged deeper exploration, while search result personalization helped surface listings aligned with each user’s intent. Email personalization became a powerful channel for re-engagement, driving higher interest and directly influencing both leasing and purchase-related actions.

Thanks to Recombee, the personalized property recommendations led to a 40% increase in buy actions, while search personalization contributed a further 10% lift. In email performance, click-to-open rates surged by 178%, and lease actions rose by 14% among users receiving personalized messages, highlighting the value of tailored content across all touchpoints.

“Our collaboration with Recombee has supported our platform’s capabilities through its intelligent personalization algorithms and sophisticated search results. Our customers now receive highly relevant property recommendations that cater to their specific needs, with one notable email campaign seeing a 178% uplift in CTOR. Their commitment to excellence is evident in the 40% increase in listing engagements on our platform, contributing to our growth in the competitive real estate market. The team at Recombee is responsive, professional, and puts in the effort to ensure that our unique business model is supported.” – Larkin Magner, Director of Product Management at Crexi

Some key factors for success: -Client’s technical readiness: The integration of recommendations itself is not technically complex; however, it is necessary to have the basic catalog data in good order and to identify users. -Flexibility and customization options of Recombee: Clients appreciated that they could fine-tune the behavior of recommendations according to specific use cases. -Teamwork and support from Recombee: Fast and responsive support, active involvement in finding solutions for unique requirements, and a professional approach helped achieve the desired results quickly.

The biggest challenge is ensuring data consistency and measurement. It is important to guarantee that the same data is available both on the client’s side and in Recombee in order to correctly measure and optimize KPIs. The Recombee team, in cooperation with the client, ensures this (through checks, report exchanges, etc.) before the start of an A/B test.

Some of our recommendations: -Choose the right metric you want to optimize: Define your main KPI – e.g., click-through rate, conversion rate, time spent, etc. – that you want to maximize. -Select an appropriate first use case: Suitable are typically placements that are seen by many users (allowing models to be optimized quickly and significant A/B test results to be obtained fast) and are not complex to implement. Most often, these are placements on the homepage or on article/video/product detail pages. -Invest in data preparation: Identify users and maintain a consistent catalog. -Leverage the system’s flexibility: Don’t be afraid to design specific personalization use cases with the help of a combination of Logics, filters, boosters, and other tools.