AI in real estate

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AI in the UK Real Estate - 2020

What AI trends and solutions are available in the property industry nowadays?

Proptech revolution in 2020 - awaited by many, pushed by the Covid pandemic

2020 update:

Real estate is the largest asset class in the world – and considered the most exciting asset class by many. Unfortunately, it has an infamous reputability for adapting new solutions extremely slowly. You may be surprised to see that what have been pretty much a standard in other fields, like hospitality or travel, is still unheard of in the world of real estate.

Years 2019 and 2020 has brought many changes. August 2020 introduced a new property tax in the UK, and before that the industry braced the introduction of Fitness for Habitation Act, Client Money Protection and the Tenant Fees Act. But it was the real “black swan” – the Covid pandemic, that has suddenly changed the need for digitalising the real estate.

During the pandemic, words like “remote”, “deep tech”, “automated” were thrown left and right and property companies started introducing new solutions with a speed never seen before.

97% of property leaders anticipate technology to have an impact on their business, with 73% viewing it as an opportunity

* KPMG, “The Road to Opportunity” Report

The proptech revolution in the UK shown in a simple graph. (source: houzen’s report for RICS)

What is AI?

Artificial Intelligence (AI): Coined in 1956 by Dartmouth Assistant Professor John McCarthy , AI is a general term that refers to hardware or software that exhibits behaviour which appears intelligent. In the words of Professor McCarthy, it is ‘the science and engineering of making intelligent machines, especially intelligent computer programs.’

What is Machine Learning?

Machine learning (ML): Machine learning is the subfield of computer science that, according to Arthur Samuel in 1959 , gives ‘computers the ability to learn without being explicitly programmed’.

How can AI improve real estate in the UK?

Artificial intelligence is an extension of ML and DL, and mostly sits on the frontend layer of the product. A powerful ML and DL logical interaction with large data pools can be used to make the AI more intelligent and produce meaningful sales or customer service opportunities, which would in turn help grow revenue or reduce costs significantly.

The biggest benefit of these technologies is completeness in analysis. Historically, most statistical analysis was based on sampling a varied set of data points from the entire population. With ML/AI, the machine can scan the entire document or information packs and come up with a conclusion based on the entire data set in no time. This could potentially change the entire dynamic in document or audit heavy environments such as mortgages, conveyancing and valuations, and potentially relocate jobs of auditors, document checkers and analysts to something more creative and higher up the value chain to just supervise the machine.

Examples of AI usage in real estate:

Use big data and machine learning to predict hot and upcoming neighbourhoods in cities.

Machine learning and AI powered apps that show tenants the optimal places to live in.

Intelligent architectural design using artificial intelligence and machine learning.

Intelligent property management driven by machine learning and artificial intelligence.

Asset optimisation for portfolio managers through machine learning.

Real time property valuations derived from pictures and videos using computer vision and deep learning.

AI - the most awaited real estate solution in the UK

houzen’s team interviewed real estate respondents for a RICS report, asking them about their hopes and dreams for the upcoming property solutions. As a result, the 2 key points real estate professionals should be mindful of are:

1. Corporations are investing in deep technology professionals with a generalist approach and who can implement strong logic skills, to develop smart algorithms for the business. They are not unduly concerned with seeking extensive domain knowledge to find disruptive solutions.

2. Most algorithms are being developed in the machine learning space, with AI, big data and blockchain coming second. For database solutions, AI is the most exciting space.