Real Estate & Property Intelligence
Upgrade simple property filters with hybrid semantic vector scoring.
1. Business Challenge
Property listing portals rely on simple keyword grids (e.g. city names, bedroom counts). They cannot parse multi-dimensional semantic requirements—such as finding listings with specific zoning permit permissions, proximity parameters to transit grids, and cashflow yields simultaneously.
2. Solution Overview
We build hybrid search engines combining PostgreSQL pgvector indices with spatial algorithms. The solution ingests municipal records, zoning details, transport lines, and coordinates, structuring listings. Users search using conversational inputs (e.g. 'duplex with high yields within 10 minutes walk of transit'), and the system re-ranks listings based on yield calculations.
3. Architecture Approach
Semantic Query Parser
Allows conversational query parsing to extract location parameters, budget caps, and structural preferences.
Investment Yield Math
Integrates local rental tables and database metrics to calculate cashflow yield rates on listings.
Geographical Bounding Box
Combines coordinate maps with vector indexes to run rapid proximity checks relative to local assets.
4. Expected Outcomes
5x Faster Property Discovery
Investors identify target properties in hours instead of weeks of cross-checking listing portals.
50% Faster Decisions
Immediate scoring of zoning permits and yields reduces acquisition review times significantly.
Relevant Services
Case Study References
Hybrid semantic search and yield scoring engine cutting property filtering times by 80%.
Technology Stack
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