Case Study: How We Revamped a Real Estate Platform’s Search & Reduced Bounce Rate by 45% with an Intelligent API

Leveraging Django REST Framework, PostgreSQL Advanced Features, and React to transform a sluggish, frustrating user experience into a competitive advantage.

Introduction

Our client operates a leading commercial real estate platform in the DACH region (Germany, Austria, Switzerland). Their website allowed users to search for office spaces, retail properties, and warehouses. While they had a vast inventory, their search functionality was primitive—relying on basic filters that often returned irrelevant results or, worse, no results at all. Users would quickly get frustrated and leave the site, increasing the bounce rate and costing the client valuable leads.

The Client’s Challenge: Drastically improve the search experience to be fast, intuitive, and intelligent, helping users find the perfect property even with vague or complex criteria.

Key Pain Points

• High Bounce Rate: A 65% bounce rate on search result pages indicated user frustration.
• Ineffective Filtering: Filters were rigid (e.g., exact square meters, specific cities) and didn’t handle partial matches or relative terms («large office near a train station»).
• Performance: Database queries were inefficient, leading to slow page load times (>3 seconds) for complex searches.
• Zero Results: Common searches often returned empty result sets, offering no alternatives or suggestions.

Our Investigation: Diagnosing the Search Breakdown

We analyzed the existing platform and identified three core issues:

• Database Bottleneck: The search was powered by simple WHERE clauses across numerous tables, causing massive joins and full table scans.
• Non-Contextual Filters: The filters didn’t «understand» the user’s intent. A search for «Berlin» would not include properties in «Potsdam,» even though it’s a nearby major suburb.
• Poor API Design: The frontend (React) was making multiple chaotic API calls for every filter change, putting immense strain on the backend.

The Solution: Architecting an Intelligent Search Ecosystem

We designed a multi-layered solution focusing on backend intelligence to empower a sleek frontend experience.

Step 1: Redesigning the Data Layer with PostgreSQL Power

We leveraged advanced PostgreSQL features to make the database work smarter, not harder.

a.) Full-Text Search with SearchVector:
We moved away from LIKE queries to PostgreSQL’s built-in full-text search. This allows for stemming (so «office» matches «offices») and ranking by relevance.

# models.py
from django.contrib.postgres.search import SearchVector, SearchQuery, SearchRank
from django.db import models

class Property(models.Model):
    title = models.CharField(max_length=200)
    description = models.TextField()
    city = models.CharField(max_length=100)
    # ... other fields

    # Method to perform full-text search
    @classmethod
    def advanced_search(cls, search_term):
        # Create a search vector from multiple fields, weighting 'title' as most important
        search_vector = SearchVector('title', weight='A') + \
                       SearchVector('description', weight='B') + \
                       SearchVector('city', weight='A')
        search_query = SearchQuery(search_term)

        return cls.objects.annotate(
            rank=SearchRank(search_vector, search_query)
        ).filter(rank__gte=0.3).order_by('-rank') # Filter by a relevance threshold

b.) Geospatial Search with PostGIS:
We installed the PostGIS extension to enable location-based searches. This was the key to «near me» functionality.

# models.py (with Django and PostGIS)
from django.contrib.gis.db import models
from django.contrib.gis.geos import Point
from django.contrib.gis.measure import D  # ``D`` is for Distance

class Property(models.Model):
    # ... other fields
    location = models.PointField(geography=True, srid=4326) # Stores longitude/latitude

    @classmethod
    def find_nearby(cls, latitude, longitude, radius_km):
        """Finds properties within a given radius of a point."""
        user_location = Point(longitude, latitude, srid=4326)
        return cls.objects.filter(
            location__distance_lte=(user_location, D(km=radius_km))
        )

Step 2: Building a Robust & Efficient Django REST Framework (DRF) API

We created a single, powerful API endpoint that could handle complex, combined queries efficiently.

# api/views.py
from rest_framework.response import Response
from rest_framework.views import APIView
from django.db.models import Q, F
from .models import Property

class PropertySearchAPIView(APIView):
    """
    A single endpoint for intelligent property search.
    Handles: full-text, location, size, price, and more.
    """

    def get(self, request):
        # Extract parameters from the request
        search_term = request.GET.get('q', '')
        min_size = request.GET.get('min_size')
        max_price = request.GET.get('max_price')
        lat = request.GET.get('lat')
        lng = request.GET.get('lng')
        radius = request.GET.get('radius')

        # Start with all objects
        queryset = Property.objects.all()

        # 1. Apply Full-Text Search if a term is provided
        if search_term:
            queryset = Property.advanced_search(search_term)

        # 2. Apply Numeric Filters (using Django's Q objects for safety)
        filters = Q()
        if min_size:
            filters &= Q(square_meters__gte=min_size)
        if max_price:
            filters &= Q(price__lte=max_price)
        queryset = queryset.filter(filters)

        # 3. Apply Location Filter if coordinates are provided
        if lat and lng and radius:
            queryset = queryset.filter(
                # This uses the PostGIS-powered method from our model
                id__in=Property.find_nearby(float(lat), float(lng), float(radius)).values('id')
            )

        # 4. Optimize the query by selecting related fields and prefetching images
        queryset = queryset.select_related('agency').prefetch_related('images')

        # 5. Paginate the results for performance
        paginator = self.pagination_class()
        page = paginator.paginate_queryset(queryset, request)
        serializer = PropertySerializer(page, many=True)

        return paginator.get_paginated_response(serializer.data)

Step 3: Enhancing the Frontend (React) Experience

With a powerful backend in place, we guided the frontend team to create a seamless UI.

• Debounced Search: The frontend waits for the user to stop typing for 300ms before sending an API request, preventing unnecessary calls.
• Dynamic Filter Updates: Changing a single filter refines the existing results with a single API call, thanks to our unified endpoint.
• Fallbacks & Suggestions: If a search returns few results, the UI suggests widening the search radius or relaxing filters.

The Measurable Results

The new intelligent search system delivered transformative outcomes.

Conclusion: Search as a Strategic Feature, Not a Basic Filter

This project demonstrates that core platform functionality like search should be treated as a primary product feature. By applying modern database capabilities and thoughtful API design, we transformed a major user pain point into a powerful competitive advantage that directly drives revenue.

Is your platform’s search functionality holding you back? Our team specializes in building high-performance, intelligent data systems that unlock user engagement and growth.

Let’s discuss how we can engineer a solution that puts your users first.