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realestate-price-tracker — full-stack market dashboard

Three layers most demo stacks fake one of: filtered + paginated REST, indexed aggregate queries, and a map/charts frontend that stays responsive under every filter combination.

PythonFastAPISQLAlchemy async · asyncpgPostgreSQL 16Next.js 16Recharts · React-LeafletDocker Compose
Dataset
800 listings · 6 neighborhoods
Endpoints
list · geo · stats · CSV · JSON
Aggregates
indexed, sub-millisecond
License
MIT

The problem

A market dashboard needs three layers wired correctly: filtered + paginated REST, indexed aggregate queries, and an interactive frontend with map + charts that stays responsive under filter combinations — and most demo stacks pick one and fake the rest.

The solution

SQLAlchemy async + asyncpg for non-blocking PostgreSQL access. Indexed columns on (neighborhood, price, posted_date) keep aggregates under a few ms. A seed script generates Gaussian-distributed prices around per-neighborhood medians. A single `docker compose up` brings PostgreSQL + API + dashboard online; CSV/JSON export endpoints respect the same filters as the live UI.

Overview

A full-stack dashboard tracking real-estate market data. FastAPI serves REST endpoints with filterable pagination, aggregate stats, and CSV/JSON export. A Next.js dashboard renders interactive Recharts line/bar charts and a React-Leaflet map with color-coded markers. PostgreSQL 16 with indexed queries, 800 synthetic listings across 6 neighborhoods, stitched together via Docker Compose.