Searching for a home is often overwhelming: multiple real estate portals, duplicate listings, and scattered information make it stressful and inefficient. Recomendia was designed to simplify this process by aggregating listings, personalizing results based on each user’s profile, and enriching them with contextual market data to support smarter decisions.
Problem
During initial research, several pain points emerged:
Fragmented experience: too many listings across different portals (Idealista, Fotocasa, Habitaclia, Pisos.com), often duplicated or outdated.
Lack of insights: users couldn’t easily access key data such as local price trends, neighborhood averages, renovation costs, or taxes.
No personalization: most platforms offered a generic experience that ignored users’ unique lifestyles, priorities, and constraints.
Approach
To address these issues, we focused on user-centered design and intelligent data aggregation.
We conducted surveys and interviews to identify what homebuyers truly value: their trade-offs, context, and priorities.
Solution
We built Recomendia, a smarter home-search platform that:
Aggregates listings from multiple portals and automatically removes duplicates.
Ranks and personalizes results through a custom recommendation algorithm.
Presents listings with clear, informative cards linked to original sources.
Key features include:
Buyer profile questionnaire: captures lifestyle, preferences, and weighting of priorities.
Contextual data overlays: price per m², historical trends, and comparisons with neighborhood averages.
Smart alerts: personalized notifications to avoid information overload.
Mobile-first UI: a smooth, intuitive experience across devices.
Outcome
Recomendia transformed a fragmented and stressful process into a cohesive, data-informed, and personalized experience. Users could now make confident, faster decisions, seeing the listings that truly matched their needs and being the first to visit their dream home.