5 Steps to Increase Commercial Real Estate (CRE) Revenue with Data Analytics

The Commercial Real Estate (CRE) market is intricate and operates on a lot of data. This makes data-driven solutions a must as this means less time spent collecting certain data about properties according to specific characteristics, and more time for making the right decisions before the market changes.

The means of communication between sellers and buyers has changed enormously with companies being more tech-enabled, but this is expected to revolutionize even more CRE companies and how they can reduce costs and drive more profits. We’ve seen how with the help of data, the process of matching and buying is facilitated but is this all data science has to offer?

Experts believe there’s an untapped potential in the use of analytics to invest money more wisely and make better decisions. This also includes the seamless exchange of accurate data about the CRE market, properties, and their qualities in order to drive beneficial decisions and predictions.

Data Science and Commercial Real Estate (CRE)

When speaking of purchasing or selling properties in the CRE market, numbers play a major decisive role. Sellers use numbers to point out and persuade buyers about the value of a specific property. However, not all companies are convinced about the use of analytics in this market, and this translates to more opportunities for those who do.

This happens because not everyone understands the power that stands behind the term “data science”, and how it can impact the CRE market. All we see are numbers and statistics, but behind them lies an architecture of algorithms, machine learning, data mining, deep learning, and predictive and descriptive analysis.

These numbers help in building effective marketing campaigns and sales strategies that target ideal customers. From 2017 to 2021, the number of companies that made use of data science increased by 56%. Implementing data science in the real estate market provides CRE experts with much more data than normal, including risk level, area description, expected CAP rate, available funds, etc.

Moreover, it also answers some of the most important issues in the industry, regarding the ideal time to invest, ROI predictions, and estimating the worth of properties and portfolios.

The Uses of Data Analytics (DA) in CRE

Brokers can make use of data analytics to build a CRE strategy that takes into account emerging market trends based on the information about demographics, location, and segmentation. Therefore, they’ll make more calculated decisions with minimal mistakes. For example, data analytics can be applied in:

• Portraying benefits for tenants
• Traffic and customer information (for retail centers)
• Detailed revenue charts
• Heatmap analysis
• Calculators for property renting

Not only is this information for making decisions in the present but it also helps brokers and agents for predicting the future outcome of a property and its possible revenue. Different types of CRE Analytics are applied to reveal certain aspects.

• Descriptive Analytics. Brings data from past transactions and actions (LTV, DCR, ROI) for a certain timespan.
• Diagnostic Analytics. Collects and presents data from the past, indicating the causes of certain events, and serves as a basis for prescriptive and predictive analytics.
• Predictive Analytics. As the term suggests, it focuses on forecasting the potential outcome and possible risks, helping brokers outline the proper strategies to manage the results.

Collection of CRE Data

When working with data, most of the work includes gathering the information, before drawing useable statistics that can help with decision-making. This data can be found in different sources such as APIs, social media, reviews, etc. Data Analysts have to take this data through five main stages:

• 1st step. CRE data gathering from different sources.
• 2nd step. Cleaning of data from incomplete or unusable data and transformation for further use.
• 3rd step. The visualization of data and conversion into statistics, and variables used to build effective models.
• 4th step. Use of machine learning and deep learning to determine what algorithms and models fit the business needs.
• 5th step. The presentation of results and their practical implementation to stakeholders.

Final Thoughts

As the real estate market gets more and more competitive, companies should consider a deeper implementation of new technologies and especially data science practices in order to thrive. However, implementing advanced analytics and interpreting their results is challenging and requires expertise in this field.

At Blue Orange Digital, as an NYC-based data science company, we’ve helped businesses from real estate and healthcare to the financial and commerce sectors build solutions based on machine learning, data science, data transformation, and data visualization. Schedule a short 15-minute meeting here to see if we can help your business too.