Estimating property prices in the U.K. with Gaussian Processes

This repository contains code that goes along with an upcoming blog post on my personal website. Together, they act as a systematic look at a statistical model called the Gaussian Process, and use it to estimate future property prices in the U.K. The blog post covers the underlying theory, and the code in this repository encompasses a (Django) web application that helps load historical transaction information and a Gaussian Process model (in TensorFlow) that predicts future prices. The web application uses OpenStreetMap to visualise historical pricing data and future predictions.

Setup

Fetch the source code and install requirements

TODO: pip requirements
TODO: npm requirements

Fetch initial data from different sources

TODO: Historical property transaction data
- https://www.gov.uk/government/statistical-data-sets/price-paid-data-downloads
- http://prod.publicdata.landregistry.gov.uk.s3-website-eu-west-1.amazonaws.com/pp-complete.csv
TODO: Postcode geocodes (National Statistics Postcode Lookup)
- http://geoportal.statistics.gov.uk
- https://ons.maps.arcgis.com/home/item.html?id=0c31adaeb0444d119b336ca00cb54efe (and click Download)
TODO: London borough information
- https://data.london.gov.uk/dataset/london-borough-profiles/resource/c1693b82-68b1-44ee-beb2-3decf17dc1f8
TODO: Postcode geoshapes
- Wards (December 2014) Generalised Clipped Boundaries in Great Britain
- http://geoportal.statistics.gov.uk/datasets/73f28a6716d747caa32f52d9aa5e92a3_2
- http://geoportal.statistics.gov.uk/datasets/73f28a6716d747caa32f52d9aa5e92a3_2.geojson

Usage

TODO: Here is how you run the webapp, and here is an example of it running in the wild.

Copyright and license

https://www.gnu.org/licenses/agpl-3.0.en.html

Get in touch with me if you wish to do anything proprietary with it (without sharing your improvements back to the community).