How data science is changing the property landscape

This is the age of big data – and in proptech, the possibilities created by big data will be endless.

Big data and data science are already changing the property landscape, and they will continue to do so well into the future.

The ability of property managers to capture, manipulate, and innovate with data in ways not possible in the past, allows for property businesses to operate in entirely new ways and opens up brand new opportunities.

 

What is big data?

 

The amount of data generated each day on this planet is mind boggling – and exponential growth of big data in recent years will only escalate.

Consider these facts. There are expected to be 10 billion mobile devices in use by 2020.

There are 294 billion emails sent every day.

There are over 1 billion Google searches every day.

More than 230 million Tweets are generated every day.

There are trillions of sensors and monitors around the globe connecting with the Internet of Things with real-time data.

The scale of big data these days is hard to accurately comprehend.

 

What is data science?

 

Some university professors coined the term ‘data science’ in the 1980s to describe statistics subjects.

The term has come to mean any way that data is used to find answers to questions we are investigating.

Data science can involve algorithms, modelling, proving or disproving theories, uncovering trends, or using data to generate insights to help us make better decisions.

 

Data science in Proptech

 

Property management companies must embrace big data today or it’s possible they will lose their competitive advantage in the future.

So just how can property managers harness big data to the best effect?

 

To create greater transparency

Capturing data, displaying it, and making it accessible to both clients and inside the business is a powerful tool.

Inside the business, improved transparency will give businesses greater clarity about both strengths and weaknesses, allowing you to see where strengths lie and to remedy problems as they arise.

Having more data will also help you make better-informed decisions.

For clients, access to transparent data gives them a sense of control over their property, and helps them to manage it more effectively.

 

To automate decision making

 

It will no longer be feasible for businesses to make decisions based on intuition.

Data-driven decision making will help property managers make better decisions.

According to a Harvard study, businesses that use data-driven decision are on average 6 per cent more profitable than companies that don’t.

The build-to-rent or multifamily industry already uses automated pricing models to help manage daily operations.

These models are data driven, and as such as removed from human emotion or bias.

 

To break operations down into smaller sub-sets

 

Data can be broken down to reflect different market segments, enabling property managers to facilitate different strategies for different segments.

Property managers can also drill down into different investment types, such as asset class or location, to refine operations or to test different variabilities.

 

To deliver new products and innovations

 

Data science can help property managers improve the effectiveness and efficiency of what it is already doing, for example by lowering costs, but it can also help to grow revenue with the opportunity to develop entirely new opportunities.

Data is one of the most powerful tools available to us in the 21st century, and those property organisations that harness it in new and useful ways will not only be able to transform their businesses, they will transform the property landscape.

Author: Beyond Strata

Beyond Strata is a professional owners corporation management firm that was founded on overarching principles of professionalism, honesty, integrity, openness and transparency. We strive to continually improve our people, culture, technology and processes improving our firm’s ability to provide the highest level of service possible to our clients.

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