In construction, architects were one of the first to use technology to help with building design, and similarly, they have been one of the early adopters of AI. Apps can now virtually construct properties that fully comply with council rules while getting the best financial return for the developer.

For example, if you’re only allowed to construct 3 bedroom apartments, then the app will provide designs that do this while making the most of dead space such as passageways and taking into account the best natural light for the property. It could take architects hours to do just one of these designs, but AI can churn out hundreds of designs in minutes. 

Rather than simply ‘take over’, machine learning is showing its value by enabling humans to deliver optimal outcomes. In essence, humans and machines are forming a partnership of sorts in the guise of a modern centaur. 

The importance of data

While AI is becoming a more familiar term, it’s still a hazy subject for many people. But we can see practical examples of AI every day. From Netflix recommending what you should watch according to your previous viewing history, to Siri answering your questions based on information from previous users, all of these tools leverage machine learning to make our lives easier. 

And the reason these machines can do this so effectively is that they have been fed huge amounts of quality data. The more data the machine has at its disposal, the more intelligent it becomes – and this delivers more accurate results. 

Automated appraisals

We can apply this same concept to property valuations. Much like Netflix and Siri, Automated Valuation Models work by conducting an analysis based on existing information, and delivering a valuation based on attributes it has learned are important. 

This delivers significant benefits. At CoreLogic, we’ve seen how our AVM can help appraisers understand the local market faster, so they can easily select comparable properties to improve value predictions. By enhancing their own knowledge, valuers can then better advise the lender. This reduces the lender's risk profile and increases transparency around decision-making. 

AI reduces the chance of human error and maintains consistent and accurate outcomes through leveraging hard data.

This can be important in the lending environment because it reduces opportunities for financial loss at all stages of the buying and selling process.

For example, it can use data on the local market to indicate whether the bank should hold back on lending for a particular property – not because the customer can’t afford it, but because the property may be located in a risky area, where too many apartments are being constructed.  

AI can also help banks to monitor their property portfolios and influence their future behaviours based on a number of property indicators. The data it provides could trigger lenders to questions such as, should we change the way we lend on certain criteria? Or, are we overexposed in a certain segment of the market?

Additionally, these types of AI reduce the amount of manual and physical legwork required in the workforce. This can free people up to focus on other tasks that perhaps require more human input. As banks, architects, property appraisers and developers seek to minimise risk and increase returns, AI is fast becoming an invaluable tool to optimise efficiency and support best practice in operations.  

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