How AI And Machine-Learning Modelling Can Help Drive Instructions For Agents
Blog By Katy Billany, Executive Director Of TwentyEA
The impact of AI on the UK economy cannot be understated. It is estimated that AI could grow the UK by 10.3% by 2030*, making it one of the largest commercial trends in this fast-paced economy. (*Source: Cybercrew, January 2022).
According to the Global AI Adoption Index 2021, researched and published by IBM, the UK has an AI adoption rate of more than 20%. The UK banks have been some of the earliest adopters of AI, using it to automate systems and manage record-level high-speed data to discover valuable insights. Moreover, features such as digital payments, AI bots, and biometric fraud detection systems have lead to high-quality, customer services for a broader customer base.
We have seen AI increasingly adopted by the property industry for data analysis, algorithmic trading, natural language processing, expert systems, vision, speech, planning, virtual assistants and chatbots. AI helps agents to engage with customers without human intervention and many agents are finding it helpful to install chatbots and virtual assistants on their websites.
While many agents are familiar with AI and are using it successfully, adoption of machine learning is relatively new, mainly due to the fact that it is still in its infancy. However, leading-edge brands like Spotify, Amazon and Netflix are already using machine learning. For example, Spotify offers a great personalized weekly playlist called ‘Discover Weekly’, one of its flagship features. Every Monday, each user receives a latest playlist of new recommended songs, made to their personalized choice based on their listening history and the songs they are interested in.
The combination of AI and machine learning can provide powerful insights for agents, particularly in the area of instructions, by predicting which off-market properties in their area will list in the next four months. The beauty of machine learning is that it literally learns from historic data to forecast consumer behaviour.
This is highly valuable for agents in a tight and highy competetive market. At the start of this year there were 350,980 properties for sale in the UK, 36% fewer than at the start of 2020, and the lowest amount since we began collecting and analysing data in 2008.
For many estate agents, targeted ‘off-market’ prospecting is a key way of not only finding potential vendors, but also helping clients to find their next home. A double win in terms of getting hold of more stock.
A wide criteria has been built into our Forecast Tool, such as how long the owner has lived in the property; how old they are; whether the property has been withdrawn from the market in the past; or whether a sale has fallen through, but the property has not gone back up for sale. This data is then combined with hundreds of other data points that might increase their likelihood to move – are they a young couple with a baby living in a small flat, or someone who is recently divorced living alone in a large house with lots of equity?
Even a year ago, targeting the top 30% of properties to reach 80% of new instructions was impressive, but machine learning has improved this even further, so that agents only need to communicate with 10% of the properties in their patch, to reach 62% of the vendors who will instruct. This is a remarkable five times better than random targeting. In fact, I know that some agents are generating over 60% of their valuations from Forecast data.
As the market continues to evolve, being equipped with the right proptech solution couldn’t be more important. Right now, a tool which can provide access to the properties most likely to come to the market, enabling agents to win those all-important new instructions through targeted, automated marketing campaigns is not a ‘nice to have’, it’s a must-have.”
For further information please visit https://news.twentyea.co.uk or email enquiries@twentyea.co.uk
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