Increase number of generated leads with real estate personalization
With diverse customers’ tastes and flourishing offerings in real estate, it may be challenging to find the right fit for each customer. This is why Recombee concentrates on individualization of each journey. Personalization increases customer satisfaction, and makes your services more efficient leading to higher amounts of broker contact and tour requests, successful viewings and properties sold.
Our deep learning recommendations show the most relevant listings to each of your visitors. Recombee doesn’t only analyze the visitor’s behavior and choices but also their geo-location preferences to be the leading recommendation engine for real estate. Our AI also analyzes property features such as year built, images or property descriptions.
Recombee’s robust recommendation engine analyzes item properties such as geolocation, number of rooms, floor number and amenities, and interactions like viewing the vacancies or likes. Optionally, additional property information can be translated through natural language processing (NLP) as well as image processing.
Adapting to your data
A robust system that can utilize all data available to generate great recommendations for your users, including collaborative filtering and content-based models.
Dynamically Retrained Models
Real-time content personalization to fit every home-seeker's tastes and adapt to constantly changing content.
Specific Functionalities for Real Estate
Filtering based on polygons in a map incorporating the user's geolocation and earth distance ReQL functions to operate in a set radius.
AI-powered A/B Testing
In-house AutoML AI applied to keep maximal KPIs and advance the deep learning algorithm functions.
Advanced Business Rules
Boosters or filters to push forward desired listings and easy to manipulate, adjustable rules for additional optimization of your content.
Real AI Inside
Using Deep Learning to understand similarity of properties from images and attributes. Ensembles of real-estate tailored recommendation algorithms based on historical on-site behavior.