How Is Data Analytics Used in Commercial Real Estate?

Data analytics empowers everyone in the commercial real estate (CRE) industry. It peels back the layers of the CRE market, resulting in more informed decision-making, lower risks, and smarter investments. 

Unfortunately, many CRE databases are incomplete and rife with inaccuracies. This is due to data scraping with no additional verification, poor updating management, and a slew of other issues. Canyon Data has solved these problems to ensure you have access to data you can trust. 

If you’ve used other CRE databases you quickly learn how inadequate they are, which is why our mission is to transform the industry with updated, accurate data. Let’s talk more about why CRE analytics are so important and how you can use good data to your advantage. 

Why Data Is Important in Real Estate

Diving into the CRE market without studying relevant data is never a good approach. Data science should get to the heart of matters and offer the most truthful perspective for any aspect of the market—but currently, this isn’t how most CRE databases operate. This is why people need Canyon Data—we offer real-time, verified data so you can make informed and confident decisions. 

Even if you’re a seasoned real estate player, access to the right data can reveal new insights that you did not have access to before. While intuition and experience are helpful to anyone in the real estate business, quality data sparks some of the most valuable power moves.

What Is Commercial Real Estate Analysis?

Data analytics in commercial real estate involves examining the big picture and smaller details of multiple factors in the market. There are tons of data points to consider when making CRE decisions. For example, the Canyon Data Qualitative Ratings (a.k.a. QR) can provide a starting place for your evaluations of a property, or you can use them outright. Our proprietary ratings utilize some of the techniques often employed by CRE Appraisers. The rating hierarchy used in this analysis is Excellent, Very Good, Good, Above Average, Average, Below Average, and Fair. 

Take a look at some of the key points that go into our QR: 

• Location – This will reveal real estate prices in the area, demographics, population numbers, geographic features, the state of the local market, transportation infrastructure, and a slew of other details. 
• Access – This will consider how easy or difficult it is to get to a piece of property, like whether you can drive to the property or enter buildings on the property.
• Exposure – It’s always a good idea to factor in the fronting street type. Being next to a major road might benefit some businesses (like a shopping center), but others may suffer from the loudness of a busy road (like an apartment complex). 
• Quality – This looks at the materials used to construct buildings and other structures on a piece of property. 
• Condition – Determining the condition of a property requires looking at a wide range of factors like the way the property has been maintained, the year built, renovations, and more. 
• Appeal – You’ve probably heard the term “curb appeal” before. This looks at the overall attractiveness of a property. 
• Utility – It’s helpful to understand the purpose behind a property or building design. Evaluating utility considers the property design purpose and how well it measures up to that purpose. 

Real Estate Analytics Use Cases

Collecting all of that data is only the beginning—it’s how you use it that really matters. Since commercial real estate has multiple players, let’s examine some hypothetical data analysis use cases from multiple perspectives. 

• A property manager notes that rental rates in their surrounding area are increasing at a higher rate than their own rate increases. They decide to use this information to their advantage and advertise that they have the most competitive rates in the area. If that doesn’t work as anticipated, they may instead simply bump their rates up to match the market. 

• A broker is looking for an existing building for a client who wants to open their first retail storefront. The client has primarily sold their clothing through e-commerce, but they’re ready to try brick-and-mortar. They find a building they love with gorgeous curb appeal, but the broker examines the location and sees that a large number of retail shops in the area have failed to last even one year. Based on these findings, the broker convinces the client to go with a slightly less attractive building located in a more successful retail location.

• A lending institution sees that the demographic of their region is transitioning from baby boomers to younger generations, like millennials and Gen Z. The lender uses this data to redirect their marketing strategy by joining TikTok and delivering short-form videos that appeal to this younger audience.

• An investor wants to expand their portfolio to include commercial real estate in a different city nearby. They research multiple data points to understand the market and growth rates of surrounding areas to pinpoint where they will have the highest return on investment. A nearby town catches their eye, as its CRE has really taken off in the past year—but it has no history of significant growth prior to that. The investor weighs the pros and cons to determine if they can handle the risk. In the end, they decide the risk is too much and instead choose a different investment property in another city with a stronger history of CRE development. 

• A developer that specializes in constructing office buildings has been tasked to find or build a new space. However, this client emphasized that they want to minimize external noise as much as possible, yet also be close to a major airport. Undeterred, the developer examines the quality ratings of existing buildings to see if they will meet those specifications. They get lucky and find a commercial space with mass-loaded vinyl sound barriers built into the existing walls. With a few upgrades to the space, the developer is able to deliver a quiet working environment just minutes away from the airport. 

As you can imagine, the number of possible use cases are endless. Where there are needs, desires, questions, or concerns—robust, exhaustive data will help you find the answer. 

How Do You Gather Real Estate Data? Use Canyon Data

One of the trickiest parts of data analysis is gathering good data. It’s easy to discover real estate databases with a quick Google search, but many are not updated as regularly as they should be. In fact, most only update their databases once every year to every five years. Plus, the majority of databases only cover a portion of all the available data points out there, which forces you to hop from place to place to string together even the simplest analysis. And to top things off—much of the existing data is simply scraped with no additional verification of accuracy. 

These are the main reasons we created Canyon Data. CRE industry participants deserve accurate and reliable data to reduce risks and make the most informed decisions possible. Here’s how we’re changing the world of CRE data analysis for the better:

• Consistent and Regular Updates – We update our data every 30 days, so you can rest assured you’re not basing decisions on stale information. 
• Rigorous Validation – Each of our properties must go through a five-step validation process, so every data point is high-quality with verified accuracy. 
• An All-Encompassing Dataset – No more pulling data from 20 places to figure out where things stand (or paying for all those subscription fees). Our real estate data is unparalleled because it covers every nook and cranny of the CRE market, amounting to over 150 data points. 
• Local Research Teams – We believe local researchers understand their area better than anyone else, which is why we employ local CRE appraisers who are well-versed in various CRE industries. 
• The Power of A.I. – By harnessing the power of A.I. and robotics, we’ve been able to take our dataset to new heights and offer the widest range of insights and information. 

Canyon Data is currently rocking the Boise, Idaho CRE market with plans to expand to Portland, Seattle, and Salt Lake City in 2022 and 2023. If you’d like to put data to work for your CRE plans, contact us! We would love to share our passion for better data analysis with you.