Introduction To Real Estate Data Analytics

What is Real Estate Data Analytics?

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. 

Making Data-Driven Real Estate Decisions

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 store or shopping center each day and where else they like to shop. For commercial properties, 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:

• Local property values and rental rates. 
• Area development plans, traffic flows, and crime statistics.
• Foot traffic analytics, the shopping and dining habits of area consumers, the competitive landscape, and more.
• Migration data and population trends.
• The commuting habits of neighborhood residents and the number and frequency of visits to nearby office spaces. 
• Demographic and psychographic data.  

Key Uses for Real Estate Data Analytics

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. 

Finding the Right Tenants for a Shopping Center

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: 

• Center-wide visitation trends: How many people visited the shopping center each day over the past several months? How long did they spend there? How does current foot traffic compare to last year or to 2019 – and which days are the most crowded? How does the mall’s performance compare to that of leading competitors?
• Foot traffic analytics for specific venues: Zooming in on the stores currently located at the center – which ones get the most foot traffic, and which ones get the least? Which ones are on an upward trajectory, and which are declining? 
• Demographic insights: What is the center’s True Trade Area – meaning the specific areas from which it attracts the majority of its customer base? And what are the shopping preferences, the median household income (HHI) and the age distribution of the census block groups (CBGs) that comprise it? 
• Cross-shopping data: What other kinds of stores do customers of the center’s leading shops tend to frequent? Are there any out-of-the-way venues that are particularly popular among the center’s clientele? 
• Frequent co-tenancy: Which chains and stores tend to be co-located with the center’s existing businesses, and have a proven track record of succeeding as co-tenants?
• An in-depth analysis of potential competition: What kinds of businesses are likely to face robust demand in the relevant trade area, and which may be cannibalized by existing venues? Which local retail markets are oversaturated and which have room to grow? 

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.

Finding and Assessing Real Estate Acquisition Opportunities

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:

• Venue health: Foot traffic data like that described above can be used to analyze the performance of the various venues you are considering. Which ones are doing well and which ones have the most potential? 
• Trade area size and characteristics: The data can also be harnessed to conduct a thorough trade area analysis of each of the venues under consideration. Which CBGs do the people that frequent the property come from and how much money do they have to spend? What are their consumer preferences? 
• Local factors: Which local retail corridors are the most popular? Are there any major annual events that draw big crowds? Where are there high-traffic dining or entertainment options that could boost visits to the target property? Are there any local factors, like crime rates, that present a potential investment risk?‍
• Broader migration and demographic patterns: You should also gain a deep understanding of each property’s wider location. Are the cities growing or shrinking? Are young people moving in or out? 

What Makes a Good Real Estate Analytics Tool?

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:

• Accurate: The algorithms used to collect the data must be optimized to ensure that they correctly reflect reality on the ground. Like in polling, sample size and methodology are everything. Biases need to be modeled and accounted for, and information on points of interest (POIs) has to be accurate. 
• Granular: Location analytics should provide detailed insight into the specific attributes of actual places down to the property level.  
• Up-to-Date: This means different things for different datasets. Visitation data, for example, should be days, not months, old. Demographic data should include the most recent census and other datasets available. 
• Comprehensive: The system should integrate as much relevant data as possible, allowing users to account for all the factors that may affect the analysis.  
• Actionable: The platform should be accessible and user-friendly, providing actionable insights that help guide users to the right decisions. Data overload that overwhelms users with too much information – without giving them the tools to analyze it – can hinder, rather than support, decision-making.
• Privacy-Respecting: The solution should be constructed with privacy by design. Data should be stripped of personal identifiers like names and MAIDs and presented only in aggregate, based on statistical extrapolation – so that individuals’ movements cannot be tracked. 

Real Estate Data Analytics Software - The Placer.ai Solution

Placer.ai uses advanced tools in data science and machine learning to analyze mobile device location data – stripped of personal identifiers like names and MAIDs before it reaches the company to ensure privacy – and to generate aggregated, statistical information about physical locations. Placer's easy-to-use platform integrates highly accurate foot traffic data with a broad range of other datasets, allowing users to quickly and efficiently conduct a thorough analysis of any commercial 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 utilizes a privacy-by-design approach.

The Importance of Data Analysis in Real Estate

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.