Real Estate Agency

Overview

A boutique agency serving the Northeast needed better visibility into rapidly changing home prices. We delivered an automated pipeline that collects public listings, tax assessments, and demographic indicators, and turns them into pricing guidance that agents trust during client consultations.

What we implemented

1. Data sourcing framework: Scrapers and API integrations that pull MLS exports, municipal records, and census data on a rolling basis.
2. Normalization and deduping logic: Pandas workflows that reconcile property identifiers, remove stale listings, and standardize feature sets across counties.
3. Comparative market analysis engine: Python models producing suggested list prices, rent estimates, and comp sets aligned with agent criteria.
4. Interactive notebooks: Google Colab templates for ad-hoc research, allowing analysts to adjust assumptions and instantly regenerate reports.

Key Features

• Regional coverage: Automated aggregation across 40+ towns with configurable refresh cadences.
• Transparent pricing logic: Feature importance visuals that help agents explain valuation recommendations to clients.
• Lead prioritization: Alerts highlighting listings with forecasted appreciation or short time-to-sale windows.
• Seamless export: One-click delivery to branded PDF packets and CRM-ready CSV files.