On today’s episode of the podcast series, we’re joined by Property Economist for FNB, John Loos who believes that data analytics in the property industry is of vital importance. Having been in the industry for 20 years, he has seen firsthand how limited data back then led to billions of Rands worth of losses due to banks not fully understanding the housing boom. There was only house price data, no housing market data, which meant it was a speculative market with very little information on what was actually happening. All these things lead to a very high-risk environment for banks. If another housing boom took place today, banks would be inside the mind of the market and the dynamics much better than they were back then.

John feels offensive analytics is not used as much as it should be on the banking side of things. When it comes to housing bubbles, such as in the US at the moment, there’s plenty of data but not enough buyer or seller education, meaning buyer panic still sets in. Data has taken us so far, but he believes it needs to be used more and be used to educate.

More and more diverse data sets are being collected regarding properties and valuations and different metrics; we ask if this as a property economist has made it easier or more difficult to find the signal in all the data noise. The answer is definitely easier. The insights available now compared with 20 years ago are phenomenal. One example was managing to identify that the Namibian market was overheated using the methodologies that had been built up for South Africa.

Do you think AI will become more prominent in the property industry, specifically in banking and asset management, or do you think there will always need to be a human touch? Where there have been mistakes in the past, and there will be big mistakes again, is that machines or models must give you 100% of the answer. What was always drummed into John from early on is that machines/models are there to help you make the decision. If you allow them to actually make the decision, you’re going to get yourself into trouble; as a rule of thumb there should always be humans behind the scenes applying their minds.

On today&rsquo;s episode of the podcast series, we&rsquo;re joined by Property Economist for FNB, John Loos who believes that data analytics in the property industry is of vital importance. Having been in the industry for 20 years, he has seen firsthand how limited data back then led to billions of Rands worth of losses due to banks not fully understanding the housing boom. There was only house price data, no housing market data, which meant it was a speculative market with very little information on what was actually happening. All these things lead to a very high-risk environment for banks. If another housing boom took place today, banks would be inside the mind of the market and the dynamics much better than they were back then.
John feels offensive analytics is not used as much as it should be on the banking side of things. When it comes to housing bubbles, such as in the US at the moment, there&rsquo;s plenty of data but not enough buyer or seller education, meaning buyer panic still sets in. Data has taken us so far, but he believes it needs to be used more and be used to educate.
More and more diverse data sets are being collected regarding properties and valuations and different metrics; we ask if this as a property economist has made it easier or more difficult to find the signal in all the data noise. The answer is definitely easier. The insights available now compared with 20 years ago are phenomenal. One example was managing to identify that the Namibian market was overheated using the methodologies that had been built up for South Africa.
Do you think AI will become more prominent in the property industry, specifically in banking and asset management, or do you think there will always need to be a human touch? Where there have been mistakes in the past, and there will be big mistakes again, is that machines or models must give you 100% of the answer. What was always drummed into John from early on is that machines/models are there to help you make the decision. If you allow them to actually make the decision, you&rsquo;re going to get yourself into trouble; as a rule of thumb there should always be humans behind the scenes applying their minds.

Find out how decision makers are using data analytics to turn insights into action.

RMB’s Head of Data Analytics, Matthew Bernath, talks to business leaders across sectors to explore how data analytics is being used to take the guess work out of decision making.

Find out how decision makers are using data analytics to turn insights into action.
RMB&rsquo;s Head of Data Analytics, Matthew Bernath, talks to business leaders across sectors to explore how data analytics is being used to take the guess work out of decision making.

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