Unfortunately, the website is no longer hosted as I have had to move my servers around my garage to make room for the Landy, but I hope to get it back up and running soon. Currently, while the House Stats original site is down, I am developing House Stats 2.0. This will involve a full re-write of the backend to a port-adapter architecture, which will allow full end-to-end testing and enable future data to be easily incorporated such as EPC certificates, local planning permissions, and land polygons from the INSPIRE dataset. Aiming for an end of February release.

A website for viewing the current state of the housing market in England in Wales with aggreations executed on the fly. You can read a more detailed post about House Stats on my website here.

• A Golang + Python pipeline downloads the latest Price Paid Data from the Land Registry every month.
• It validates, parses, and uploads millions of transactions to a PostgreSQL database using a Kafka message queue and asynchronous workers.
• Updates are applied automatically (insert, amend, delete) to keep the dataset current with minimal downtime.
• Parses postcodes into their constituent parts for grouping and searching

• Data is normalised into the third normal form to eliminate duplication and improve query efficiency.
• PostgreSQL handles relational integrity while Redis provides an in-memory cache for frequent queries.
• Full-text indexes and partial postcode searching enable sub-second lookups across millions of records.
• This is all served using a Python API written in Flask, which communicates with the data-processors using RabbitMQ

• Analytical workloads (standard deviation, outlier removal, and percentage change analysis) are executed using PolaRS a Rust drop-in for Pandas
• The system automatically scales to use multiple workers, allowing for batch processing of many areas simultaneously
• Results are cached in a MongoDB instance to allow for instant fetching of large areas that can take a few minutes to process

• A Flask API serves data to a Svelte + Chart.js front-end for highly interactive visualisations.
• Users can search by town, postcode, or county; view price trends over time; and compare regions side-by-side.
• Uses write-ahead search capability to help users find the area they are looking for
• Change time period interval to look at 1mo, 3mo, 6mo, 1yr