Britain’s ‘hottest properties’ are in London and the SE – claim
Outra’s Pre-Mover product tracks the 50,000 households most likely to put their property on the market within the next six months.
The top 50,000 households most likely to put their property on the market are heavily concentrated within the South East with 17 of the top 20 in London and its surrounding regions, latest data from Outra reveals.
The findings are in stark contrast to October last year when its Pre-Mover product data revealed more than half of Britain’s property hotspots were located outside the South East. At the time these areas included postcodes in Devon, Leicestershire, South Wales, the North East and North West.
POST CODES
But now post codes in Croydon, Bromley, Harrow, Wandsworth and Buckinghamshire dominate the top 10 while Leicester and Coventry are the only areas outside London and the South East to make an appearance in the top 20.
Outra, a predictive science business, is owned by Hometrack founder Giles Mackay who sold that business to Zoopla in 2017.
Dominic Grace, former Head of London Residential Development at Savills and now Senior Adviser at Outra, says: “Whilst values may be growing more slowly than elsewhere in the UK, this data demonstrates that market activity remains very strong in the South East and London, but to leverage this effectively and win instructions, agents need to ‘get smart’.
“This means harnessing predictive analytics to anticipate where demand is set to grow and accurately targeting the individual properties that are most likely to trade.
This will be key in a tougher market for agents.”
“This will be increasingly key in a tougher market for agents.”
Outra’s Pre Mover product uses AI to predict the likelihood of property owners putting their houses on the market in the next six months and models the health of the market tracking those most empowered to move rather than households in distress.
200 VARIABLES
It draws on more than 200 variables and can identify the top 50,000 households with an accuracy rate of more than one in three.
Variables include property specific data such as property type, estimated value, bedroom count and the time elapsed since the last sale.
It also includes local market characteristics such as recent house sales in each postcode district, the change in an individual house price over last 24 months and the overall liquidity within a given area.
The model’s accuracy is determined by validating predictions against real listings data over an 18-month period.
