Want to see someone’s eyes glaze over? Communicate the importance of data integrity. We’ve all been exposed to a long list of reasons why real estate companies should undergo a “digital transformation.” Moving from spreadsheets to integrated digital systems can prevent costly errors, speed up processes, enable collaboration, and enhance data-driven decision-making. But while that sounds great, you can’t just throw money at new technology or bring in a consulting team. Transforming a traditional real estate company into a truly digital-first organization requires building a foundation that not many people have the time or patience to tackle.
Real estate data is not the same as data in other industries. Unlike other industries that rely heavily on data, such as financial services and media, real estate data is not standardized. “When you actually have to sit down and think about real estate data, you’re faced with so many difficult definitions,” said LD Salmanson, co-founder of Shale. “You find yourself looking for answers to very strange questions like: “What is Dallas?” Or do you define real estate as a building or as a parcel? ” These definitions may seem unimportant, but they are critical in determining how the data is used.
In the age of AI, the need for organized data becomes even more important. Machine learning and artificial intelligence are powerful tools, but they are also error-prone. If the data used to train the model is not standardized, it can lead to incorrect conclusions or even “hallucinations” where the AI invents new data to support its answer.
Building a clean and organized “data lake” takes time and money. Unfortunately, this process is often overlooked as real estate companies choose to pursue more interesting technology projects. “There are a lot of new technologies that real estate companies can experiment with, so they often don’t have the budget for things like data architecture,” Salmanson says.
Training and change management are essential for successful data management transformation. Leaders must prioritize employee buy-in and engagement. Employees must have the skills to navigate new data tools and processes while developing ownership and enthusiasm for change. Leader buy-in sets the direction for organizational change.
The complex nature of real estate portfolios can make it difficult to bring all of your real estate data into one clean system. Each building often has its own partners, with different LPs and debt sources, and often uses different property management software systems. These different systems produce data in different formats, which must be unified to be useful.
Another important aspect to consider is regulatory compliance, including awareness of privacy laws, data security, and reporting requirements. Compliance is essential to effective real estate data management, as non-compliance can have legal and reputational implications. Incorporating compliance into your data management strategy means implementing security measures, conducting audits, and staying up to date with evolving regulations, especially in the ever-changing landscape of AI and machine learning. .
Obtaining data feeds directly from property management software companies also comes at a cost. Some also charge for the ability to create APIs. Additionally, software development expertise may be required to automatically organize data to match the schema used by each company. Salmanson believes that if these problems persist, property management software companies will need to change their strategies. “Some companies still charge to extract data from their platforms, which will be free in the near future, but my guess is that eventually it will all be free,” he said. said.
Everyone likes to see the results of a good data strategy, but few like to put in the effort to create systems that can power it. As profitability declines for some real estate companies, they may want to cut back on technology investments. However, such companies would be wise to consider other parts of their budgets that do not impact data reduction efforts. Like the plumbing in a building, few people want to think about the dirty details of how a data system is designed, but when a system fails, it becomes everyone’s top priority. .
