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The commercial real estate industry has warmed to using machine learning technology as a tool that can handle various administrative tasks such as creating contracts and automating marketing campaigns, according to real estate data company ATTOM.

CREs leveraging of tech looks like its only going to increase going forward. A little more than $13.1 billion was invested in property technology (proptech) companies during the first two quarters of 2022, according to the Center for Real Estate Technology & Innovation (CRETI). The total was a 5.6 percent year-over-year increase over 2021.

Real estate investment trusts (REITs) are using artificial intelligence (AI) and machine learning, and leading the AI revolution, according to a Deloitte survey. Today, 41 percent of REIT executives are fully on board with algorithms and machine learning models.

Some of the other admin tasks REITs are using AI for include sending scheduled marketing emails and automated rent reminders, according to ATTOM.

There are machine learning investment models that can analyze REIT-related data and develop investment strategies. This financial modeling is based on accurately estimating the current value based on the future source of cash revenue that a property would generate.

The models are based on historical data as well as unknown variables that can shift the markets and economic climate in the future. Machine learning can help influence investment decisions and serve as screening tools to help analysts improve on picking stocks and investment opportunities. Additionally, machine learning solutions can help property investors make quicker and more informed decisions as they can generate potential returns just from knowing a propertys address.

Property investors are using AI and machine learning to create financial documents for CRE buildings, according to ATTOM. Data scraping technology can pull close to real-time revenues from market data services or community websites. The tech solutions estimate expenses by using existing portfolios or digital data providers information. Then, machine learning algorithms can find competitive property sets and decide if a CRE asset is doing better or worse than the competitive sets in real time.

Publicly traded REITs are often more closely correlated with equities rather than real estate assets, the ATTOM team said. Analysts can apply data and machine learning to track the value of the underlying assets relative to the stock price and inform the analyst whether a public REIT is a buy or a sell.

The combination of big data, machine learning, macro-level analysis and human expertise can help make the best business decisions possible, according to ATTOM. That kind of well-rounded analysis helps investors sidestep assumptions. The analysis also factors in data that impacts real estate investments more accurately, such as competitor activity and climate.

Additionally, automation can improve how investors, clients and other stakeholders communicate. It might not be the most personable approach, but apps and chatbots can answer questions 24/7 and give all parties involved real-time updates on a projects progress. With mobile apps, investors can manage their portfolios regardless of their location. This allows for more successful strategic operations and better asset management.

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Machine learning helps drive REIT investments - Connected Real Estate Magazine