Turning Housing Complexity Into Clarity

AI-powered property insights for smarter, more sustainable home decisions.

EcoHome AI is a sustainability-focused property discovery platform that helps buyers compare and evaluate homes with confidence.

It uses AI to simplify dense data — especially sustainability terminology and ratings — so users can better understand listings and focus on what matters most.

EcoHome AI’s mission is to turn dense housing data into clear, actionable guidance—supporting informed decisions from search to shortlist.

Onboarding captures each buyer’s sustainability knowledge level to tailor explanations and reduce jargon-driven overwhelm.

Project Overview

EcoHome AI is a sustainability-focused property discovery platform designed to simplify how buyers evaluate homes.

The goal of this project was to design an AI-powered experience that translates complex property and sustainability data into clear, actionable insights. Rather than overwhelming users with technical ratings and jargon, EcoHome reframes property evaluation around transparency, comparison, and long-term impact.

This project focused on:

• Defining the MVP for eco-conscious buyers.

• Designing responsive experiences across mobile, tablet, and desktop.

• Establishing a scalable sustainability scoring system

• Creating a clear visual hierarchy for complex data.

The result is a streamlined product experience that helps buyers move from search to confident shortlist—without confusion.

First-time and unseasoned buyers are expected to make one of the largest financial decisions of their lives. Yet, the information provided to them is often overwhelming, inconsistent, and difficult to interpret.

Property listings often include dense specifications, unclear sustainability ratings, and limited contextual insights into long-term costs or neighborhood impacts. Without accessible explanations, buyers struggle to confidently compare options before visiting in person.

There is a clear opportunity to bridge the gap between data availability and true understanding.

Process

To design EcoHome AI, I combined buyer persona insights with a review of current real estate platforms to understand where decision-making breaks down. This work surfaced four key findings:

• Sustainability ratings and terminology are difficult to interpret without plain-language context.

• Buyers need faster ways to compare trade-offs without having to scan full listings.

• Early personalization reduces overwhelm and irrelevant browsing.

• Trust increases when sustainability, cost-to-operate, and essential property details are grouped consistently.

Using these insights, I mapped core user flows and built low-fidelity wireframes to define structure and information hierarchy. Through iterative testing and refinement, I evolved the experience into a clarity-first comparison flow—translating dense property and sustainability data into understandable, actionable guidance that supports confident evaluation.

Primary Personas

Research revealed that the core friction wasn’t a lack of interest in sustainability—it was a lack of confidence in interpreting the information.

Buyers wanted to make responsible decisions, but struggled to translate technical ratings into meaningful comparisons.

This insight informed the development of a primary persona who represents the intersection of ambition, values, and uncertainty in sustainable home buying.

Key Design Decisions

Making Sustainability Data Scannable
Since sustainability information is complex, it was organized into simple categories with visual indicators, enabling users to compare properties quickly.

Supporting Side-by-Side Comparison
With many factors to consider when purchasing a home, comparison tools were designed to help users evaluate multiple homes at once without having to navigate between separate pages.

Designing for Multiple Devices
Because users are likely to search for properties across devices, the interface was designed responsively for mobile, tablet, and desktop breakpoints.