A next-generation property discovery platform that uses AI-powered natural language search, predictive pricing models, and interactive neighborhood intelligence to help buyers find their perfect home 3x faster.

Project Overview

A proptech startup recognized that traditional property search platforms still rely on rigid dropdown filters — bedrooms, price range, location — forcing buyers to translate nuanced lifestyle preferences into database queries. We built Forevra, a platform that lets users describe what they want in natural language ('quiet neighborhood near good schools, modern kitchen, under 30 minutes to downtown') and matches them with properties using semantic AI, combined with predictive pricing models and rich neighborhood analytics.

Client Background

Our client is a well-funded proptech startup backed by $4.5M in Series A funding, with a vision to reimagine how people discover and evaluate residential properties. Their founding team includes former real estate agents frustrated by the limitations of legacy listing platforms and data scientists who saw an opportunity to apply NLP and predictive analytics to property search. They needed an engineering partner to bring this vision to life within a competitive fundraising timeline.

The Challenge

Traditional property search relies on rigid filters (beds, baths, price) that can't capture lifestyle preferences like 'quiet neighborhood', 'walkable to coffee shops', or 'good natural light' — the factors that actually drive buying decisions.

Property pricing models on existing platforms use simple comparable sales analysis, ignoring hyperlocal factors like upcoming infrastructure projects, school rating trends, and neighborhood gentrification patterns.

Listing data came from 8 different MLS feeds with inconsistent formats, duplicate listings, stale data, and incomplete descriptions — requiring robust data pipeline engineering.

The platform needed to render an interactive map experience with neighborhood analytics overlays (safety scores, transit access, school ratings) without sacrificing mobile performance.

Speed to market was critical — their Series A runway created pressure to launch a compelling MVP within 14 weeks to hit user acquisition milestones for Series B positioning.

Our Approach

Data Pipeline & NLP Foundation (Weeks 1-4)

We built a robust data ingestion pipeline that normalizes listings from 8 MLS feeds, deduplicates entries, enriches missing data using GPT-4 for description enhancement, and generates semantic embeddings for each listing. Each property gets a rich vector representation capturing not just features but lifestyle signals extracted from descriptions, photos, and neighborhood data.

Semantic Search Engine (Weeks 3-7)

Using Pinecone as our vector database, we built a natural language search engine that translates user queries into multi-dimensional embeddings. The system handles complex queries like 'modern 3-bed with home office space, near parks, walkable neighborhood under $600K' by decomposing intent into weighted feature vectors and matching against the listing embeddings. We achieved an 89% relevance score in blind A/B tests against traditional filter-based search.

Predictive Pricing & Analytics (Weeks 6-10)

We developed a gradient-boosted pricing model trained on 5 years of transaction data combined with 23 hyperlocal features — school rating trajectories, transit project timelines, commercial development permits, and neighborhood demographic shifts. The model predicts 12-month price trajectories with 91% accuracy within a 5% margin, giving buyers data-driven confidence in their decisions.

Interactive Map & Mobile Optimization (Weeks 8-14)

We built an interactive map interface using Mapbox GL that overlays neighborhood intelligence layers — walkability scores, safety ratings, transit access, school quality, and noise levels. The frontend uses Next.js with aggressive code splitting and image optimization to maintain sub-2-second load times on mobile. We also built a saved search feature with real-time push notifications when matching listings appear.

Results & Impact

Users find relevant properties 3x faster with natural language search compared to traditional filter-based search in controlled A/B tests.
Platform serves 42,000+ active listings across 3 major metropolitan areas with real-time updates from 8 MLS feeds.
Natural language search achieves an 89% relevance score — users found what they were looking for in their first 5 results 89% of the time.
User engagement (time on platform, saved searches, scheduled viewings) increased by 28% compared to the client's benchmark from competitor platforms.
Predictive pricing model accuracy of 91% within a 5% margin has become a key differentiator in investor conversations, contributing to the client's Series B raise.
Mobile load times consistently under 2 seconds even with interactive map and neighborhood analytics overlay active.