Being a homeowner is undoubtedly a significant milestone in a person's life. In that pursuit, a prospective buyer spends an enormous amount of time for evaluating potential homes that are available in the market. A study shows that 90% of home buyers rely on the Internet as the primary resource for home related information. However, existing online home search tools i.e. search engines, listing sites, and forums require the user to formulate appropriate search queries to discover the most desired home, which is a complicated task-specifically for the first time home buyers. Another challenge for the home buyers is to filter the search results consisting of hundreds of homes that are generally returned against a search query. With such a process, the perspective home buyer becomes a victim of the well-known 'information overload' issue. In this paper, we introduce a new home discovery tool called RAVEN. It uses interactive feedback over a collection of home feature-sets to learn a buyer's interestingness profile. Then it recommends a small list of homes that match with the buyer's interest, thus resolving the 'information Overload' problem and eventually decreasing the interval between home search initiation and purchase. Please visit https://youtu.be/e2w3nqM6mnw for a demo video of the system. RAVEN is live at http://bit.ly/2fiZnTo.