In the current study, the details of a real estate recommender system developed for Zingat.com are discussed. The system developed is a hybrid of collaborative and content filtering approaches. Scalable methods in both the model building phase and in the recommendation list generation phase were used to work on the data set of the project (as of 2018, 300k listings and 6 million monthly sessions). This study also explained the challenges faced in developing and implementing the system, the recommendation techniques used to overcome these challenges, and the final product used for recommendation. Based on these, future recommendations are discussed.