How to Build a Personalized Real Estate Feed: Location, History & Smart Fallbacks

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!

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).

Imagine two users searching for properties in Mumbai:

A generic feed showing random listings would frustrate both. Too broad, and users scroll endlessly. Too restrictive, and they miss hidden gems.

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)

Backend: NestJS 

Database: PostgreSQL 

SearchPrisma: Raw SQL (Haversine formula)

Geolocation: Browser GPS + ipinfo.io API (fallback)

When Priya (our first-time buyer) signs up:

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

This gives us an approximate location to start with.

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.

We use a two-phase geo-filter approach:

Before calculating exact distances, the query first applies a bounding box to quickly eliminate distant listings:

After the bounding box filter, the query applies an exact distance check:

The query ranks listings based on how well they match search criteria:

Final score calculation is based on four to five key matching criteria, with additional optional factors that can be incorporated for more granular personalization.

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)

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.