Most real estate firms today find themselves dealing with massive amounts of data. Between tenant leases, property management records, historical building data, and local economic and demographic information, firms have more capability to produce actionable insights with their data than ever before. Unfortunately, most firms also find that it is difficult (if not impossible) to use the data they have because of the way it is stored. Pulling and analyzing data frequently means a painstaking process of searching multiple databases, resulting in wasted time and limited research potential.
Having the data is one thing, but being able to use it in a meaningful way is another.
The good news is that there are ways for firms to collect all of their data in one easily searchable location. By focusing on data best practices and utilizing the assistance of data professionals, firms can unlock the full potential of their real estate data and eliminate the frequent headaches that are so often associated with it.
Data shouldn’t live in spreadsheets
Spreadsheets are great for a lot of things, but acting as a data warehouse isn’t one of them. Still, it’s a common practice across the real estate industry to utilize spreadsheets as a means of data storage. A firm might have one spreadsheet for lease comps, another for rental rate and vacancy data, and a third for tracking tenant inquiries. Spreadsheets like these often begin as a temporary solution but are then adopted as a long-term solution that becomes difficult to stop.
Kevin Mattice, Chief Product Officer at Cherre, said this type of data management is a primary driver of the headaches firms experience when it comes to their data. “Real estate companies have a lot of data but don’t get to use it because it’s in silos,” Mattice said. “When data is stored across multiple spreadsheets that don’t speak to each other, even the simplest questions become a pain to answer. Some firms have existing internal platforms, but those were often built before the value of data was front and center. This is a major limiting factor that can handicap a firm’s ability to pull useful insights from its data.”
Even for firms that have their proprietary, internal data in an organized state, it’s not uncommon for the external data they purchase to continue to be siloed away from the rest. Incorporating purchased data sources often requires the use of APIs or connections to more advanced data warehouses, neither of which are supported by typical spreadsheets.
If a firm wants to get the most out of its data, the best first step is often moving data out of siloed spreadsheets and into a true data warehouse.
Condensing a firm’s entire dataset into a unified data warehouse that can scale and grow with the business can be a complicated, time-consuming process. This complication is a major contributing factor to why so many commercial real estate companies struggle with their data. With that said, there are a few steps firms can follow to help the process move in the right direction.
First and foremost, a firm needs to survey its stakeholders to define exactly what business goals they hope to achieve as a result of the effort. Many companies start a data consolidation project without a clear end goal in mind other than “better data,” which can lead to unnecessary confusion during the development process and disappointment at completion. Having even a rough framework in place to define the business goals will help steer the project in the right direction from the outset.
Tyler Christensen, Senior Solution Architect at Cherre, said defining these business goals and establishing user personas is arguably the most important part of the process. “Everything starts with understanding data purpose – who’s going to be producing it, who will be consuming it, and what the end goal of the data will be for different types of users,” Christensen said. “It’s a balancing act between taking the time to answer those questions and not getting stuck in analysis paralysis. The worst-case scenario is spending 18 months on a project that ends up not solving its original purpose.”
The second most important step is deciding whether the firm wishes to tackle the development of the project on its own or utilize the assistance of a pre-existing third-party platform. This decision will have a major impact on the resources and time a firm should expect to dedicate to this type of project.
“If a firm chooses to tackle the development of a unified data warehouse themselves, they’re typically looking at a multi-year time horizon to achieve a finished product,” Khan said. “The firm would need to hire a development team that understands the needed systems and has the capability to set up and maintain the data warehouse. The alternative would be to use a third-party solution, which can help clean the data and design the optimal system for the firm. This option typically results in a faster end product and lower costs over the long-term.”
Whichever type of team a firm decides to utilize for its project, a major focus of the early stages of development should be thoroughly cleaning, standardizing, and connecting the data prior to pushing it to the data warehouse. It often comes as a surprise just how much time is needed to clean a firm’s data to a point that it will create the most value, but it is time well spent. You want to get your house in order before you add the new fancy furniture.
“It can get really complex when you start to think about buildings with multiple addresses, whether to put periods after abbreviations and how to standardize data at both an address and geolocation level,” Christensen said. “Creating a data lake and dumping a bunch of information in isn’t going to help. Without cleaning the data and standardizing it so that it can all speak together fluidly, you’ll be right back where you started.”
In other words, be prepared to spend significant time and resources in the early stages of the project if you want positive outcomes down the road. Without doing so, your data warehouse efforts will likely be doomed from the beginning.
Managing the management
Once the difficult part of establishing a unified data warehouse is completed, and a firm’s data is easily searchable, the value of the firm’s time, money, and effort will begin to be unlocked. Instead of wasting time repeatedly cleaning data and manipulating multiple spreadsheets, a few clicks should be all that’s needed.
Khan says once all of a firm’s data is connected and a superset of data can be established, a firm should be capable of near-endless research initiatives, many of which can be done at a scale and level of detail that wouldn’t have been possible before. “After you’ve established your large data set, you gain the ability to do research at a greater scale than you ever could manually,” Khan said. “You can run analysis across an entire city, state, or country in a matter of hours compared to weeks. It really gives you the ability to shrink the scope of your projects and begin producing better faster answers.”
A good example of this that many firms can relate to is the process of prospecting. An investment firm might be interested in a certain type of building that meets specific investment criteria and needs the ability to identify properties that meet those criteria on a large scale. Rather than spending weeks manually searching for properties and piecing together unorganized information from different sources, a few searches within the superset of connected data should now be all the firm needs to do.
It’s also important to note that data strategy and architecture projects such as these should be seen as ongoing initiatives and not a stagnant resource upon completion.
“It’s not a set it and forget it type of job,” Christensen said. “That mentality is new for the real estate industry, where brokers typically move from one project to another with no ongoing support. The discipline of not only establishing a data strategy but maintaining that strategy over the long term can often require a cultural shift within real estate firms, but it’s an important shift to make.”
There are also hundreds of external data providers that can be incorporated into a data warehouse through the use of APIs, allowing firms to add new information and unlock new insights. Though making these connections can also be a complicated and time-consuming process due to the standardization efforts they require, the information they provide once the business logic has been established can be invaluable.
A unified data warehouse also allows a firm’s research team to create front-end applications, reports, and models using business intelligence tools like Tableau, creating additional value at little to no cost.
If the actionable insights provided by a firm’s data are the building, a powerful, unified data warehouse is the foundation it’s built on.