“Reffie is an AI-powered workflow platform for the real estate industry to manage prospective renters' past, present, and future. Reffie positions itself upstream of other software platforms in the real estate sector to serve as a unifier for renter data from many sources.”
The residential housing market in the United States is characterized by high demand, with renting constituting nearly ⅔ of housing options for ~50M households. However, the industry's current state is riddled with inefficiencies, exacerbating the already urgent need for housing. For renters, navigating the market feels like a maze of unanswered emails, delayed responses, and fragmented information. Property managers, meanwhile, drown in paperwork, struggle to track leads across platforms and grapple with unsustainable manual workflows.
Prospective renters face substantial hurdles in finding a place to live. The typical communication process between prospective renters is handled by entry-level employees who are managing multiple and, in some cases, dozens of lead platforms, leading to issues like no response or delayed communication. Prospective renters lack any grasp on the pulse of their outreach for apartments and are often stuck hoping that listings appear as they refresh listing sites. For property managers, the manual nature of their work hinders their ability to unify communications across platforms, track prospect information, and respond in a timely manner, making it a non-scalable approach - especially when managing 100-200 rental units, which is often the norm. Reffie is tackling these problems head-on, solving inefficiencies for renters and property managers. Reffie is an AI-powered workflow platform for the real estate industry to manage prospective renters' past, present, and future. Reffie allows for multi-family operators to manage prospective tenant sourcing and communications in one place, decreasing lead time by 50%. Reffie integrates with all major tenant sourcing tools (e.g., Zillow and Apartments.com), ingests tenant data into a structured format, and provides one communication nexus. While there are myriad ancillary prop-tech software tackling tenant screening, rent collection, marketing, etc, the vast majority of this software has no self-reinforcing advantage, making it easy to remove and replace with a competitor. In contrast, Reffie’s advantage of collecting renter records at the point of application will accumulate into the leading tenant database. The platform with the most renter data will fulfill all downstream tasks (filling rentals, screening, payments). As renter records grow, Reffie creates a flywheel of collecting data from the first interaction through the leasing lifecycle, allowing real estate operators to leverage the data to retain and source the best tenants.
When evaluating for founder-market fit, the Reffie team checks the box on exceptional. CEO and Co-founder Connie Lee has experienced the problem Reffie is solving head-on while managing her small real estate portfolio in the Los Angeles area. While constantly being bombarded with leads for renters from various sources, Connie struggled to manage the inbound and fill her vacancies quickly and efficiently. Connie grew the enterprise sales team at Zip Recruiter from 5 to 120 sales reps, reaching $112M ARR in under 2 years, before transitioning to work in more operations-based roles at Boulevard (Index Ventures) and Mosaic (a16z). On the technical side, Daniel has spent the majority of his professional career building and shipping security and AI products to millions of users. Between 15 patents in cyber security and a master's degree, Daniel is no stranger to the nuances of building secure and data-driven solutions. Connie and Daniel have known each other for over six years, initially connecting through their mutual passion for literature and technology. Their shared interests led them to co-found a book club, and they've since embarked on co-founding Reffie together. We at Redbud VC are excited about Connie and Daniel’s expertise as operators intersecting with a variety of complex problems for multi-family operators and tenants.