Visual Property Search for Real Estate Platforms
For real estate platforms with 100K+ listings. Enable buyers to search by photo or description. 40% higher engagement, 25% faster time-to-lead.
Real estate portals, MLS providers, and property management platforms seeking to differentiate with superior property discovery and matching capabilities
Text-based property search fails to capture buyer preferences like "modern kitchen with island" or "backyard with mature trees". Buyers scroll through hundreds of irrelevant listings
Ready to implement?
Why Mixpeek
40% higher search engagement, 25% faster time-to-lead, and visual similarity matching that captures buyer preferences text search cannot express
Overview
Home buyers often struggle to articulate exactly what they want. This use case shows how Mixpeek enables visual property search that understands photos and descriptions like "mid-century modern with open floor plan" to surface the perfect matches.
Challenges This Solves
Text-Based Limitations
Buyers search by bedrooms and price but not visual preferences
Impact: High bounce rates, buyers miss listings that match their style
Inconsistent Descriptions
Agent descriptions vary wildly in quality and accuracy
Impact: Search misses listings with poor descriptions but great photos
Feature Discovery
Amenities visible in photos not captured in structured data
Impact: Buyers cannot search for "pool", "view", or "hardwood floors" reliably
Personalization Gap
No way to learn buyer preferences from browsing behavior
Impact: Generic recommendations, low conversion rates
Implementation Steps
Mixpeek analyzes every property photo to understand features, style, and amenities, enabling buyers to search using images of their dream home or natural language descriptions
Process Property Photos
Analyze all listing photos for searchable features
import { Mixpeek } from 'mixpeek';const client = new Mixpeek({ apiKey: process.env.MIXPEEK_API_KEY });// Process property listing photosawait client.buckets.connect({collection_id: 'property-listings',bucket_uri: 's3://listings/photos/',extractors: ['image-embedding', // Visual similarity'object-detection', // Rooms, features, amenities'scene-classification', // Interior style, condition'image-captioning' // Natural language descriptions],settings: {property_features: ['kitchen_style', 'bathroom_type', 'flooring','view', 'pool', 'fireplace', 'garage', 'backyard','architectural_style', 'condition', 'natural_light'],generate_descriptions: true}});
Enable Visual Search
Allow buyers to search using reference images
// Visual similarity searchasync function searchByImage(imageUrl: string, filters?: PropertyFilters) {const results = await client.retrieve({collection_id: 'property-listings',query: {type: 'image',url: imageUrl // Buyer uploads photo of dream kitchen},filters: {price: { $lte: filters?.maxPrice },bedrooms: { $gte: filters?.minBeds },location: { $within: filters?.geoRadius }},return_fields: ['listing_id', 'photos', 'price', 'address','detected_features', 'similarity_score'],limit: 50});return results.map(r => ({...r,matching_features: r.detected_features.filter(f => f.confidence > 0.8)}));}
Natural Language Property Search
Enable descriptive text search
// Search by natural language descriptionasync function searchByDescription(query: string, filters?: PropertyFilters) {// e.g., "modern kitchen with white cabinets and island"// e.g., "craftsman home with original details"const results = await client.retrieve({collection_id: 'property-listings',query: {type: 'text',text: query},filters: {price: { $lte: filters?.maxPrice },status: 'active'},return_fields: ['listing_id', 'photos', 'price', 'address','detected_features', 'ai_description'],limit: 50});return results;}
Personalized Recommendations
Learn from buyer behavior to improve matches
// Track buyer preferences from interactionsasync function trackBuyerPreference(buyerId: string, listingId: string, action: 'save' | 'view' | 'contact') {const listing = await client.collections.get(listingId);await client.users.updatePreferences({user_id: buyerId,positive_signals: action === 'save' ? listing.detected_features : [],view_history: [listingId],weight: action === 'save' ? 1.0 : 0.3});}// Get personalized recommendationsasync function getRecommendations(buyerId: string) {const prefs = await client.users.getPreferences(buyerId);return client.retrieve({collection_id: 'property-listings',query: {type: 'preferences',user_id: buyerId},filters: {status: 'active',listing_id: { $nin: prefs.viewed_listings }},limit: 20});}
Feature Extractors Used
Image Embedding
Generate visual embeddings for similarity search and clustering
Object Detection
Identify and locate objects within images with bounding boxes
Scene Classification
Categorize images based on scene type (indoor, outdoor, etc.)
Image Captioning
Generate descriptive captions for images automatically
Retriever Stages Used
Expected Outcomes
40% increase in search sessions per user
Search Engagement
25% faster from first search to contact agent
Time to Lead
35% more listings viewed per session
Listing Views
50% increase in saved/favorited listings
Save Rate
30% improvement in lead quality scores
Agent Satisfaction
Frequently Asked Questions
Related Resources
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