Zoomprop: AI-Powered Real Estate Intelligence & Investment Decision Platform
A real estate intelligence platform that aggregates property data, public records, and twelve national location sensors into investment-ready analytics.
The Problem
Real estate investment decisions were bottlenecked by fragmented, inconsistent data spread across listings, public records, and location intelligence sources. Analysts had to manually assemble context across dozens of variables—crime, schools, infrastructure, pricing trends—before evaluating a single opportunity. This created multi-week research cycles, limited deal coverage, and prevented timely identification of high-potential or distressed assets.
The deeper problem was structural. Property information, tax records, market trends, and location data live in hundreds of different systems with incompatible formats. Evaluating a single property properly meant reconciling all of them by hand, which capped how many opportunities a team could realistically assess and left portfolio monitoring as a periodic, manual exercise rather than a live one.
What We Built
We built an AI-powered real estate intelligence system that transforms fragmented property data into continuously updated investment insights. The platform ingests and normalizes multi-source data, resolves entity inconsistencies, and enriches each property with geospatial and market intelligence layers. On top of this foundation, we developed machine learning-driven valuation models, opportunity scoring systems, and automated intelligence pipelines that continuously analyze market signals. This enables real-time discovery, ranking, and evaluation of investment opportunities through a unified decision layer.
Rather than a reporting tool bolted onto a database, the system is built as a continuously running intelligence pipeline. Orchestrated jobs keep property records, public filings, and market signals current, while a spatial data layer makes location context queryable alongside the property itself.
Unified property data platform
Aggregates listings, public records, and market data into a single queryable store, normalizing formats and resolving entity inconsistencies so every property carries a complete, comparable profile.
Twelve national intelligence sensors
Layers location context onto each property—crime and safety, school ratings, broadband and transit infrastructure, energy providers, walkability, and weather risk—so neighborhood quality is a queryable attribute.
Orchestrated ingestion pipelines
Dagster-managed pipelines handle public records processing, market intelligence scoring, and property valuation refreshes on a schedule, with dependency tracking and retry handling built in.
Geospatial analysis layer
PostgreSQL with PostGIS powers radius-based search, geographic filtering, and comparable-property analysis, making "what else is nearby and what is it worth" a single query.
How It Works in Practice
An investor searching for distressed properties in a target county—work that previously meant weeks of manual research across separate systems.
- 1
Search and aggregate
The investor filters by county and criteria. For every matching property, the platform pulls the full sensor set—nearby crime incidents, school ratings within range, broadband availability, walkability, and weather risk.
- 2
Generate intelligence
Background jobs analyze tax delinquency status from public records, price trends relative to the local market, foreclosure filings, and comparable sales that indicate upside.
- 3
Score and rank
Each property receives investment potential and risk scores alongside market timing context, so the ranked list reflects opportunity quality rather than just filter matches.
- 4
Monitor continuously
Properties can be added to a monitored portfolio in one step, after which the same pipelines keep valuations and alerts current as market conditions move.
Engineering Approach
Pipelines as the system of record
Rather than treating ingestion as a periodic import, orchestrated pipelines own property records, public filings, valuations, and entity monitoring as continuously refreshed assets with explicit dependencies and retry behavior.
Spatial data as a first-class citizen
Location context lives in the same database as the property itself via a spatial extension, so radius search, geographic filtering, and comparable analysis are ordinary queries rather than a separate service call.
A stable API over a moving dataset
A REST layer exposes property search, collection management, portfolio analytics, and sensor data behind a consistent contract, keeping consumers insulated from changes in the underlying ingestion logic.
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