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How to Leverage AI for Predictive Analytics in Commercial Real Estate
From investors to developers and asset managers to lenders, predictive analytics and AI help make better data-driven decisions, forecast trends more accurately and quickly, and streamline operations in less time. Here’s why real estate teams should incorporate predictive analytics into their workflow.
What is Predictive Analytics?
Predictive analytics uses data, algorithms, and machine learning to make predictions – ranging from future trends to events to behaviors –by analyzing patterns. In commercial real estate, for example, predictive analytics can be used to forecast market growth, population growth, property valuation, rental demand, rental prices, tenant behavior, and more.
How does AI play a role in predictive analytics? In commercial real estate, real estate teams can leverage AI to enhance predictive analytics by processing large data sets much faster and more accurately than a human could. AI algorithms learn from historical data, identify patterns, and improve predictions over time. By combining AI with predictive analytics, real estate teams can process information faster, make better-informed decisions, anticipate market changes, and identify investment opportunities more quickly.
Why Real Estate Teams Should Use Predictive Analytics
Real estate teams should leverage predictive analytics for several reasons, including:
1. Better Decision-Making
With greater data insights, predictive analytics helps real estate teams make better decisions. AI can analyze large data sets, offering a more holistic picture of investment opportunities or bottlenecks, and suggest the best path forward. For example, AI can forecast future property values based on market trends, which can inform investors about when to buy and sell assets based on market fluctuations.
2. More Precise Market Forecasting
Real estate teams can build financial models in Excel and conduct data analysis. However, this process can be time consuming, even with templates, and is prone to human error. With predictive analytics, AI can review and process historical datasets by analyzing factors such as population growth, interest rates, GDP, and employment trends to make probability-based predictions about how a specific real estate market may perform in the future.
AI’s ability to process large amounts of data in real time makes it an invaluable tool for market forecasting. Predictive models can analyze factors like economic indicators, population growth, employment trends, and interest rates to predict how the real estate market will behave in the future. Armed with this knowledge, real estate investors and developers can anticipate supply and demand at the neighborhood level, mitigate any necessary risks, and optimize their portfolios accordingly.
3. Better Risk Management
Real estate teams that use predictive analytics can achieve better overall risk management. Historical data is essential to identify patterns, which enables AI to predict potential risks such as a market downturn, tenant default, or a decline in property values. With predictive analytics, real estate teams can be more responsive earlier and take proactive measures to diversify their portfolio, modify investment strategies, or hedge their risk.
4. More Accurate Property Valuation
AI and predictive analytics can enhance both the accuracy and efficiency of conducting property valuations. The traditional methods for valuation may focus on historical sales. However, the data may be incomplete or focused on a geography that is either too narrow or wide. Finding comparable properties also may be challenging if the underlying asset is unique. AI can analyze more inputs to arrive at a more accurate and objective valuation. Factors such as neighborhood location, property features, and neighborhood trends can be analyzed with predictive analytics, which help real estate teams make better decisions about buying, selling, and financing properties.
Focus On High Quality Data
An AI model is only as good as the quality of the underlying data. Real estate teams therefore need access to comprehensive datasets regarding property transactions, market trends, economic factors, and tenant behavior, among other inputs. Practically, most data is highly fragmented. For example, property records, financial information, and market data need to be accessed from different sources. Real estate teams need a comprehensive platform that can aggregate and centralize data into one location where it can be analyzed more easily. That said, real estate teams need to invest not only in platforms but also in the expertise of data scientists who can fully implement predictive analytics and AI models.
Conclusion
AI and predictive analytics are transforming commercial real estate by offering better decision-making, market forecasting, risk management, and property valuation. By centralizing and analyzing high-quality data more quickly and accurately, leveraging machine learning, and predicting tenant behavior, real estate teams can stay ahead of market trends and make data-driven decisions that maximize returns.