Find out how real estate professionals can leverage data analytics to assess opportunities and make smarter investment decisions.
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Real estate data analytics empowers real estate professionals to make data-driven decisions about the sale, purchase, rental, or management of a physical property. The process involves gathering all the relevant information from various sources and analyzing it to yield actionable insights. Brokers, investors, developers, owners, and other realty professionals rely on real estate data analytics to forecast an investment’s profitability, determine the best time to buy or sell, find suitable tenants, conduct successful negotiations, and allocate marketing efforts.
Real estate data analytics companies provide industry professionals with the datasets they need, as well as software and other tools that use data science and machine learning to analyze the information and glean actionable insights.
In the past, real estate data analytics focused on conventional metrics like a property’s occupancy rate, the rental fees paid by current tenants, and local market trends. But in recent years, there has been a growing appreciation of the power of non-traditional variables – like the dispersion of local coffee shops, online reviews of area businesses, and even the frequency of a building’s elevator movements – to impact market value and inform decision-making.
Today, with the availability of much more granular and precise location analytics, this data-driven approach has been taken to the next level. Real estate professionals can account for an increasingly broad range of inputs, including things like the number of people who actually visit a property each day and where they like to shop. For commercial properties in particular – think stores and shopping centers – these kinds of variables can be mission-critical, allowing industry experts to properly assess the value of their properties and optimize their management.
Indeed, to remain competitive in 2023, commercial real estate (CRE) professionals need to quickly gather and process vast quantities of data points, including things like:
Incorporating data analytics into real estate decision-making can facilitate choices and drive better outcomes. The two examples below can help illustrate the importance of data analytics for real estate management.
Let’s say you are a commercial real estate broker tasked with helping a shopping center owner find the right tenants. You know how much the previous tenant paid, how much rents have increased over the past ten years, and the average rental rate per square foot. You also have data on the center’s historical occupancy rates and on what kinds of businesses have traditionally fared well in the area. Ten or fifteen years ago, these data points – coupled with a healthy dose of good old intuition – would be used to find stores that might be a good fit and calculate things like the anticipated net operating income. But today, advanced data analysis for a shopping center retail space should account for things like:
A thorough analysis of all this information can allow you to effectively target the retailers most likely to succeed in your venue, optimize your pricing strategy, and make a data-driven case for why they are a good fit. When it comes time to negotiate the lease – or to contend with rent reduction requests – it gives you the knowledge you need to evaluate your tenants’ demands and to shore up your bargaining position.
Real estate data analytics is also a crucial tool for finding and evaluating CRE acquisition opportunities, marketing existing assets, and making development decisions. Imagine you manage a real estate investment trust (REIT) and need to quickly decide between several properties on offer. You should be able to quickly gather and analyze information to shed light on:
When it comes to real estate data analytics, not all datasets and systems are created equal. So what makes a platform good for conducting useful analysis? At a minimum, the data should be:
Placer.ai aggregates anonymized location data from millions of mobile devices – stripped of identifiers to ensure privacy – and analyzes it using advanced tools in AI, data science, and machine learning to yield highly accurate, up-to-date location intelligence. Its easy-to-use platform integrates foot traffic data with a broad range of other public and private datasets, allowing users to quickly and efficiently conduct a thorough analysis of any physical property. Real estate data analysis that in the past would have taken months to conduct, can now be accomplished with the click of a button.
Placer.ai, hailed by industry leaders as a go-to platform, boasts a number of features that take property data analysis to the next level. Its void analysis feature allows shopping center owners to find the perfect tenant in seconds, synthesizing the relevant data and ranking prospects according to factors like demographic fit, monthly foot traffic, and cannibalization rate. Placer.ai’s True Trade Area capability can determine a venue’s precise customer base, which can then be analyzed with dozens of demographic, psychographic and other data sets. The platform’s foot traffic data uncovers visitation rates over time, customer journeys, and more – and its data can be integrated into internal corporate analyses. Placer also places a high priority on privacy.
In today’s real estate market, property analytics are key to success. They allow professionals to effectively predict real estate values, find and evaluate opportunities, assess risks, and plan development. For property owners seeking tenants, managers hoping to optimize day-to-day operations, and investors looking for the next big deal, tools that provide quick and easy access to all the relevant data are more crucial than ever before.