New 'Bumble for Real Estate' Casa Blanca Raises $2.6M

Casa Blanca, a recently launched mobile application that uses its users' input to find them their perfect home, has raised $2.6m in seed funding. The funding, which was lead by serial entrepreneur and investor Samuel Ben-Avraham, will reportedly go towards launching the product outside its current New York and Denver markets with the app to be customised for all new cities to give a more local feel.

The New York-based startup was founded by ex-realtor Hannah Bomze in October 2020 and works in the same way as many popular dating apps. Casa Blanca's users are presented with houses on the screen to which they swipe left or right, their choices feed an AI engine which then refines the search further until a perfect match is found. TechCrunch is reporting that the app has done $100m in home sales since its launch and that it is predicted to reach $280m this year.

Speaking to TechCrunch, Bomze said of her company's app: “People love that there is one place to book, manage feedback, schedule and communicate with a branded agent for one cohesive experience. We have a breadth of users from first-time buyers to people using our platform for $15 million listings.”

The Casa Blanca app asks users questions pertaining to their lifestyle and asks them to rate interior design images before letting them search through inventory. Once a user is ready to transact they will be put in touch with a Casa Blanca agent partner (the app currently has some 38 signed up) and they will be able to communicate and facilitate every step through the app.

The firm's business model is unlike some competitors in the US in that it is a straight to consumer model, cutting out middlemen buying agents and offering consumers 1% cashback on the price of their final purchase.

Casa Blanca is far from the first company to look to dating app functionality for inspiration; UK-based MoveStreets is also 'swipe-based' and in the world of jobs classifieds, German Truffls and India's Spotle both do something similar.