Problem Statement

Traditional real estate websites rely heavily on structured data, such as price, bedrooms, bathrooms, and square footage, limiting users' ability to search by personal preferences that aren't explicitly structured. Attributes like natural light, architectural style, or proximity to grocery stores are often buried in unstructured agent descriptions. Users had to manually sift through property details and images, making the home-searching experience tedious, time-consuming, and often frustrating.

User Research

User Interviews
We interviewed prospective homebuyers and current customers to understand which attributes mattered most but were missing from typical search filters. Participants frequently cited lifestyle-related factors like proximity to grocery stores, ambiance of a neighborhood, or the “feel” of natural light in a home.

MLS Data Analysis
We then conducted an AI-powered analysis of over 10,000 property listings, focusing on unstructured data fields (agent remarks). This helped us map out how often key attributes appeared, so we could design our questionnaire around fields that were both high-value to users and frequently mentioned in listings.

Preference Questionnaire
Based on these findings, we created a questionnaire flow that captured users’ unique preferences. We included high-coverage attributes (e.g., “close to schools,” “modern style”) and some lower-coverage but high-value ones (e.g., “lots of natural light,” “nearby restaurants”) to account for various user needs.

Hypothesis

We believed that systematically capturing user preferences and converting unstructured listing descriptions into structured data would lead to:

1. Higher Engagement – Users would be more engaged when searching because relevant results surfaced faster.

2. Increased Tour Requests – With a tailored set of listings, users would be more inclined to schedule tours.

3. Better Lead Generation – An interactive and personalized quiz would draw in potential buyers, improving the conversion rate from site visitor to lead.

Approach

Questionnaire Flow

• A simple, intuitive series of questions about lifestyle preferences and home features.

• Progress indicators and optional “skip” buttons to reduce drop-off.

• Contextual prompts that educated buyers on why we asked each question (e.g., “Proximity to groceries can save you time and boost convenience”).

AI-Processed Listings

• We utilized ChatGPT + ScaleAI to parse agent remarks for hidden attributes.

• A dedicated back-end service tagged each listing with structured fields like “lots of natural light” or “big backyard.”

Match Score Algorithm

• Each user’s questionnaire responses were translated into a preference profile.

• Our system calculated a personalized “match score” for every listed property, sorting results to highlight top matches without entirely excluding lower-scoring ones, mitigating the “fear of missing out.”

Iterative Refinements

• Multiple rounds of design reviews and user testing informed adjustments: simplifying question wording, adding relevant attributes, and refining how results were displayed to avoid overwhelming users.

Testing

Usability Testing

We tested the questionnaire with a pilot group of homebuyers to gauge clarity and flow. Feedback led to the creation of short tooltips explaining certain real estate terms (e.g., “What does ‘open concept’ mean?”).

A/B Testing

We compared the AI-Based Search flow against the existing search experience. Key performance metrics included completion rates, user satisfaction scores, and percentage of users who requested an in-person tour.

Refining the Algorithm

Adjusted weighting in the match score based on user feedback—some discovered “nice-to-have” features shouldn’t overshadow fundamental preferences like location or price range.

Impact

Higher Questionnaire Completion

83% of existing users completed the new preference questionnaire, indicating strong interest in more personalized search results.

Improved Tour Rate

A 46% bump in tour rate (the percentage of users requesting an in-person tour after browsing) showed that more relevant results translated into tangible action.

Boosted Lead Generation

Using this flow with Google Ads nearly doubled our sign-up conversion rate, from 7.14% in the old funnel to 13.88% in the new AI-based quiz flow.

Enhanced User Satisfaction

Qualitative feedback highlighted how users appreciated the deeper insight into listings, especially for less obvious features like property layout or nearby amenities.