Predictive Analytics

The use of historical data and machine learning to forecast future trends or outcomes.

Putting Predictive Analytics in Context:

CRE investors use predictive analytics to identify high-potential markets for multifamily developments by analyzing rental demand trends and population growth.


Frequently Asked Questions about Predictive Analytics in Commercial Real Estate

Predictive analytics in commercial real estate (CRE) refers to the use of historical data and machine learning techniques to forecast future trends or outcomes.

CRE investors use predictive analytics to identify high-potential markets for developments by analyzing factors such as rental demand trends and population growth.

Predictive analytics relies on historical data—such as rental performance, demographic trends, and market activity—to model and forecast future performance.

One example provided is CRE investors using predictive analytics to identify markets with strong rental demand and population growth to support multifamily development decisions.

Machine learning algorithms analyze large datasets to detect patterns and generate forecasts, forming the core of predictive analytics methods in CRE.



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