Case Study

Real Estate Investment Analysis Platform

Full-stack development for Brickbro — a commercial real-estate analysis platform built from scratch, turning fragmented property data into clear investment insights.

ProductionFull-Stack Development + Data
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  • NestJS
  • TypeScript
  • Next.js
  • Python
  • PostgreSQL
  • MongoDB
  • Google Maps API
  • Docker

Summary

Problem

Commercial real estate data was fragmented across sources, making investment analysis manual and inconsistent.

Solution

Built backend data services, evaluation logic, data workflows, and a reporting dashboard for investment insights.

Impact

Turned raw property data into actionable investment insights with consistent analytical reporting.

Role

As one of the full-stack developers, I contributed to the build — backend data services, evaluation logic, and frontend reporting interfaces.

Project Story

From raw data to investment intelligence

How fragmented property data became structured insights through data processing and reporting.

01 / 04

01

Data Fragmentation

Property metrics, market indicators, and location data were scattered across many sources with no consistent reporting layer.

  1. Properties
  2. Markets
  3. Location
  4. Scattered
02

Aggregation & Processing

Built backend services and contributed to data workflows that aggregate, process, and prepare property data for analysis.

  1. Ingest
  2. Process
  3. Store
03

Evaluation Logic

Evaluation logic processes aggregated data into consistent, comparable investment metrics for decision support.

  1. Data
  2. Evaluate
  3. Metrics
  4. Insights
04

Reporting Dashboard

A reporting dashboard surfaces evaluation results through clean, consistent views for investors.

  1. Data
  2. Dashboard
  3. Insights

Contributions

What I built

A high-level view of the areas I worked on.

Backend Data Services

Services that aggregate property metrics and market indicators from multiple sources behind clean boundaries.

NestJSData

Data Processing

Contributed to the workflows that ingest and prepare property and market data for analysis.

PythonData

Evaluation Logic

Turns aggregated data into consistent, comparable investment metrics for decision support.

AnalysisMetrics

Reliable Data Layer

Keeps aggregated property and market data dependable across the systems that store and serve it.

PostgreSQLMongoDB

Location Intelligence

Map and location features enrich property records with geographic context for analysis.

Google MapsGeo

Reporting Dashboard

Frontend reporting views that present investment insights clearly and consistently.

Next.jsReact

Engineering

Technical highlights

Stack

Technologies used

  • NestJS
  • TypeScript
  • Next.js
  • Python
  • PostgreSQL
  • MongoDB
  • Google Maps API
  • Docker

Takeaways

What this project demonstrates

This case study demonstrates experience with production-grade SaaS systems, complex backend workflows, third-party integrations, async processing, payment infrastructure, and scalable business logic.

Production backend engineering
Integration-heavy system design
Multi-tenant SaaS thinking
Payment and commerce workflows
Async worker architecture
Business logic modeling

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Services behind this work

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