PropTech Recommendation Engine

The Problem

Traditional property search still relies heavily on explicit filters: bedrooms, bathrooms, price range, and zip code. But real housing decisions are rarely that simple.

People often care about preferences that are hard to express in a search box: neighborhood feel, commute tradeoffs, building style, lifestyle fit, perceived safety, long-term flexibility, and even emotional attachment. This project explores how recommendation systems can help surface homes that better match those deeper, often implicit preferences.

Approach

This project applies collaborative filtering and content-based recommendation methods to improve property matching and ranking.

1. User Preference Modeling

The system learns latent preferences from browsing behavior, saved listings, stated preferences, and other interaction signals.

2. Property Embedding

Each property is represented in a high-dimensional feature space that captures both physical attributes and neighborhood characteristics.

3. Hybrid Recommendation

The model combines collaborative signals with content-based property features to generate more personalized rankings.

4. Satisfaction Prediction

Beyond predicting clicks or saves, the system estimates longer-term housing satisfaction using post-purchase or post-move survey data.

Technical Details

The recommendation system uses a two-tower neural network architecture, with one encoder representing user preferences and another representing property features.

The model is trained on historical interaction data and validated against downstream outcomes such as saved listings, inquiries, tours, purchase decisions, and satisfaction signals.

Impact

Early results suggest a 40% improvement in engagement metrics compared with traditional filter-based search.

This indicates that the model can capture meaningful preference signals that standard search tools often miss, helping users discover properties that better align with how they actually make housing decisions.