Why forecast sales?
Humans have the magical ability to plan for future events, for future gain. It’s not quite a uniquely human trait. Because apparently ravens can match a 4-year-old.
An abundance of data, and some very nice R packages, make our ability to plan all the more powerful.
A couple of months ago we looked at sales from an historical perspective in Digital Marketplace. Six months later. In this post, we’ll use the sales data to March 31st to model a time-series forecast for the next two years. The techniques apply to any time series with characteristics of trend, seasonality or longer-term cycles. Continue reading “But can ravens forecast?”
Revisiting an old post
Last September I wrote a post entitled Is the Government realising its ambition for SMEs on G-Cloud? Six months on, I wanted to revisit and update this article, fold in a second Digital Marketplace framework, and share the R code here. Revisiting an old post also provides an opportunity to see if one can simplify and improve older code. Continue reading “Digital Marketplace. Six months later.”
The anatomy of SW10
Analyses of house sales often focus on the wider UK market. In this blog, we’ll take a deep dive into one of London’s more-than 100 postcode districts. We’ll draw on 10,000 property transactions to see how key events have shaped the market. The object of our focus will be SW10 which forms part of the Royal Borough of Kensington and Chelsea.
We’ll start with the anatomy of SW10 per the chart below. Over 80% of property transactions were for leasehold flats / maisonettes. In contrast, detached freehold properties are a prized scarcity: Only 40 of the circa 10k transactions, over the past 20 years, were for detached properties. Continue reading “House sales in London SW10 take a few punches”