Nov 5, 2025
How to Build a Personalized Real Estate Feed: Location, History & Smart Fallbacks
Learn how to build a personalized real estate feed using location, user history, and smart fallbacks for accurate, privacy-first property recommendations.
Author
Abhishek Krishnan RathaurSoftware Engineer - I
Subject Matter Expert
Konakanchi Venkata Suresh BabuTech Lead - II
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Table of Contents
Hey folks! Recently, I have been working on an exciting real estate application where sellers can easily list their properties, and buyers can discover homes tailored just for them. One of the key challenges we tackled was personalizing the user’s feed — ensuring that buyers see the most relevant listings based on their location, past searches, and preferences, while also keeping the experience fresh with smart fallbacks.
In this post, I will walk you through our approach to building a dynamic, user-centric feed that balances personalization with discovery. Whether you’re a developer, product manager, or just curious about recommendation systems, this breakdown will give you practical insights!
The Challenge: Making Property Discovery Smarter
The Challenge: Making Property Discovery Smarter
In today’s hyper-competitive real estate market, relevance isn’t just important — it’s the difference between keeping users engaged or losing them forever. With countless property platforms available, buyers expect instant access to listings that perfectly match their needs, while sellers demand that their properties are seen by the right audiences. A generic, one-size-fits-all approach leads to frustrated users who waste hours scrolling through irrelevant options, while valuable properties get buried in the noise. This challenge is compounded by the fact that 78% of buyers abandon a platform after just three irrelevant recommendations (2023 PropTech Survey).
Why Personalization Matters
Imagine two users searching for properties in Mumbai:
- First-time buyer Priya wants a 2BHK apartment under ₹1.5Cr near schools.
- Investor Rohan seeks commercial spaces in Bandra with high rental yields
A generic feed showing random listings would frustrate both. Too broad, and users scroll endlessly. Too restrictive, and they miss hidden gems.
The Core Problem
The Core Problem
Most platforms fail at:
1. Cold-start personalization (What to show new users?)
2. Over-reliance on manual filters (Forcing constant tweaking)
3 . Static recommendations (Ignoring evolving preferences)
Our Solution
We built an adaptive recommendation engine that:
1. Learns from explicit preferences (onboarding choices)
2. Adapts to implicit behavior (searches, clicks, dwell time)
3. Works seamlessly for logged-in and anonymous users
4. Respects privacy while maximizing relevance
Technical Foundations
Technical Foundations
Tech Stack
Backend: NestJS
Database: PostgreSQL
SearchPrisma: Raw SQL (Haversine formula)
Geolocation: Browser GPS + ipinfo.io API (fallback)
Example Scenario
When Priya (our first-time buyer) signs up:
- Onboarding: Selects “Family home”, budget range, school proximity
- First search: Filters for “2BHK, near international schools”
System response:
- Prioritizes 2BHKs in her budget
- Boosts listings near top-rated schools
- Gradually learns she prefers gated communities
Meanwhile, anonymous users get location-aware defaults based on:
1. Browser GPS (if permitted) → 500m precision
2. IP geolocation → Neighborhood-level
3. City-level trending listings → Fallback
1. Personalized Feeds Need Data — But Where Do We Get It?
To customize a user’s feed, we need search metadata — location, property type, budget range, and preferences. But this data comes from different sources depending on whether the user is logged in or not.
For Authenticated User: Leveraging Past Preferences
For authenticated users, our personalization system combines explicit onboarding preferences with implicit behavioral learning to deliver hyper-relevant property recommendations. During onboarding, users provide explicit baseline preferences — including locations (like hometown or work city), property type, budget ranges, and property category — which we store as their primary preference profile. Beyond these initial inputs, our system automatically captures and analyzes every search interaction, converting filters like location, budget ranges, or property type selections into evolving implicit preferences.
Location history (array of {lat, long} coordinates)
Property category (residential/commercial)
Property type (apartment, villa, office, etc.)
Budget range (budget_min, budget_max)
This helps us prioritize listings that match their past behavior.
For anonymous users, we prioritize immediacy while respecting privacy. If location permissions are granted, we use real-time browser GPS coordinates for precise matching. When denied, we fall back to IP-based geolocation (via services like ipinfo.io) to approximate the user’s city/region, supplemented by popular local listings. This ensures even first-time visitors receive contextually relevant options without friction, while logged-in users benefit from a feed that grows smarter with every interaction.
This gives us an approximate location to start with.
2. Building the Search Metadata
Here’s the logic we use to construct the feed criteria:
1.Privacy-first — We don’t store precise location for anonymous users.
2.Progressive personalization — The feed improves as users engage more.
3.Smart defaults — If no budget is set, we show mid-range properties.
3. Location-Based Filtering: Fast & Accurate
We use a two-phase geo-filter approach:
A. Phase 1: Bounding Box Filter (Fast Approximate Filtering)
Before calculating exact distances, the query first applies a bounding box to quickly eliminate distant listings:
B. Phase 2: Precise Distance Calculation (Haversine Formula)
After the bounding box filter, the query applies an exact distance check:
4. Dynamic Relevance Scoring
The query ranks listings based on how well they match search criteria:
A. Property Type Matching
B. Listing Type Matching
C. Area Matching
D. Car Parking Capacity Matching
E. Budget Matching
Final Score Calculation
Final score calculation is based on four to five key matching criteria, with additional optional factors that can be incorporated for more granular personalization.
5. Smart Sorting & Prioritization
Listings are sorted using a three-tier ranking system:
This ensures:
1. Relevance (User’s search preferences)
2. Business Needs (Manually prioritized listings)
3. Engagement (Trending properties)
Final Summary
Thank you for joining this exploration of how we built a smarter property recommendation system! By combining user preferences, location data, and search history, we created feeds that adapt to each person’s needs — whether they’re a first-time visitor or a returning user. For logged-in users, the system learns from past behavior, while guests still get relevant results using approximate locations. This balance ensures everyone finds what they’re looking for quickly.
To make searches lightning-fast, we optimized our database with smart indexing on key filters like price, property category, and property type. By pre-filtering results and caching popular listings, we cut query times by over 80%. These tweaks ensure smooth performance, even with millions of properties in our system.
Looking ahead, AI will take personalization even further. Imagine the system automatically suggesting filters based on your search queries or predicting preferences you haven’t even stated yet. We’re excited to explore these innovations — and we’d love to hear your ideas too! Thanks for reading, and happy house hunting!
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