The importance of clean data in commercial real estate decision-making

Success in the commercial real estate industry ultimately hinges on making well-informed decisions. 

Accurate data is the foundation for these decisions, setting the scene for industry efficiency, accuracy, and profitability – from property development to investment, acquisition, and leasing.

The truth is, you’ll only be aware there’s an issue with your data when it lets you down. In this article, we’ll discuss the importance of clean data and how technology can help improve accuracy for profitable decision-making. 

While we understand the intricacies of data management may not be at the forefront for many professionals outside the domain of data analysis, understanding the role of high-quality data is critical for gaining insights and making informed decisions.

Clean data can be defined as free from errors, corruption, formatting inconsistencies, duplication, or missing fields. This is a foundational basis for any type of analysis, enabling users to validate the field and subsequently renounce (or support) working theories.

Even minor differences, such as variations in date formats or inconsistent labelling, can significantly impact the accuracy of the analysis. 

Imagine you have lease renewal dates recorded in two different formats in different sections of your dataset – one is “MM/DD/YYYY” (e.g., 03/15/2024), and the other is “DD/MM/YYYY” (e.g., 15/03/2024). This inconsistency in date formats could cause an analyst to overlook or misinterpret critical lease renewal dates while trying to identify upcoming renewals to forecast revenue and occupancy rates.

Quality should always trump quantity when it comes to data management. Despite the abundance of available data in the commercial real estate industry, the true power lies in its relevance, accuracy and application. Whether you’re using Excel or specialised tools such as Coyote, the efficacy of analysis hinges on the integrity of the underlying data. 

Issues such as irrelevant information, duplicate entries, or mistaken inputs can undermine the efficiency of analysis and stunt the progression of your objectives.

When the data you’re working with underpins business decisions, you need to be able to use that data. Commercial real estate companies often receive data from multiple suppliers and systems, increasing the risk of errors such as mislabelled fields, spelling mistakes, or missing dates. 

These inaccuracies, if unchecked, can persist undetected for extended periods, potentially leading to misguided assumptions and dramatically flawed conclusions.

Picture a property firm considering acquiring a prestigious office building. Based on the data they hold, they move forward with the acquisition, projecting a 15% annual return on investment.

After finalising the deal and integrating the property into their portfolio, they realise the actual returns are only around 8% annually. 

Upon closer examination, they discovered discrepancies in the data, leading to an overestimated rental income and an underestimation of expenses. As a result, the company faces a significant shortfall in expected revenue, leading to financial strain and operational challenges. 

Adopting and maintaining a proactive approach to data auditing is crucial to maximise efficiency and accuracy. Solutions like Coyote facilitate data consolidation from various sources, streamline processes and provide a centralised platform for analysis. But, it’s essential to recognise that effective data management still requires an understanding of the nuances inherent in commercial real estate.

By embracing data management and utilising appropriate tools and expertise, businesses can harness the full potential of their real estate data to gain actionable insights, maintain a competitive edge and outperform the market.