The world of commercial real estate (CRE) is complex. One of the most critical tools available to navigate these rocky waters is CRE data.
Real estate is notably slower than many industries when it comes to adopting new technologies, but the interest in data is increasing and is being integrated as brokers, investors, and managers within the industry see its potential.
But what is and how can it help you? Let’s dive into it.
What is CRE analytics?
Commercial real estate (CRE) analytics uses data to give real estate investors actionable insights to find investment opportunities and improve a portfolio.
While some may see data science or analytics as just a buzzword, it’s so much more. CRE analytics can answer many questions including:
- Is right now a good time to invest?
- How well are my investments performing right now?
- If my investments are down, when can I expect them to recover?
Data science in the real estate industry also combines machine learning and data visualization. Analyzing data is essential for real estate investors and managers to efficiently manage, advertise, and grow their commercial space.
When is data analytics used in commercial real estate?
Data analytics in real estate is used when brokers, agents, investors, and operators are looking for current, relevant, and accurate information to make informed decisions.
Analyzing data is often completed regularly to:
- Provide up-to-date demographic, geospatial, and footfall information
- Report on the success of marketing campaigns
- Complete a heatmap analysis
- Make predictions and projections on revenue
- Find the right tenant
- Analyze the performance of a shopping mall
What are the challenges of data in commercial real estate?
The real estate industry has previously fallen into the trap of messy data due to various reasons including:
- Silos: Data is often siloed in different departments or systems. This can make it difficult to access and analyze data effectively.
- Time: Agents, operators, and investors may need to integrate multiple data sources to include financial and demographic data, which can be time-consuming.
- Security: Sensitive, private information is often handled in the real estate industry and so the systems in place must have taken steps to protect it from unauthorized access and use.
- Machine learning: Artificial intelligence (AI) can find patterns in data that humans often can’t, but few processes are in place within the industry to use these models, so it can be easy to miss opportunities.
At pass_by, our data uses machine learning to process over 6 billion daily data points and make predictions with over 5 years of historical data to back it up.
Find out more about our data and how you can use it to improve your CRE.
How data analytics drives better real estate investing
Data analytics drives better commercial real estate investing by helping investors and estate managers avoid costly decisions.
Real estate is the largest asset class in the world – worth more than all stocks and bonds combined – yet it is one of the last to adopt technology.
Louisa Xu, Partner at IVP
Using data, particularly as competitors fall behind in the adoption of technology, can give investors an edge.
An MSCI report found that real estate companies which implement big data saw a performance boost of 3-6%. It is a valuable tool that, when implemented, can quickly improve performance.
Identify market trends
Data analytics can be used to identify trends in the commercial real estate market, such as changes in foot traffic, prices, rental rates, and demand.
This information can help investors make more informed decisions about where and when to invest, how investments are performing, and how a portfolio could be improved.
For example, if data shows that a particular neighborhood is experiencing rapid price appreciation, an investor may want to consider investing in that area.
Data can reveal further details on the best sites for a retail store expansion or show which ones are underperforming according to industry benchmarks and your competitors.
Data for site planning can include transport information, such as local public transport links. Look for the usual methods of transportation of consumers within a specified area, along with the bus routes and any nearby train stations to estimate any passing trade.
Enriched understanding of consumers
Psychographic data reveals a detailed overview of the local population, including details of their regular visits to sites and the scale of their wealth. This data is collected into groups for easy categorization–allowing further research in the real estate sector while respecting privacy considerations.
An enriched understanding of consumers can help you understand what drives them and how to draw them into your commercial space.
How to use data analytics for real estate?
How you use data analytics for real estate depends on the space and if you are an investor or manager. Let’s break down each type and how best to make use of the mountains of data at your disposal.
1. Real estate developers
Developers use data to analyze and monitor the development process of a commercial real estate asset. This starts with identifying a suitable market, site planning, designing the space, marketing, and advertising.
Developers can use data to make data-driven decisions for creating a profitable asset.
2. Property investors and owners
Data can help investors and owners make informed decisions on tenant vacancies, analyze a property’s performance, and minimize risks. Whether you’re deciding to buy, sell, expand, or adjust, data acts as a foundation to build the next steps.
3. Property agents
Property agents, managers, and consultants can provide higher quality services when data is there to back up their experienced insights.
An agent may use data to make informed suggestions on whether leasing or selling a property is the preferred option in a complex market.
4. Marketers in real estate
CRE data spans the entirety of each stage, which includes marketing a retail space for potential investors, partnerships, buyers, and tenants.
Marketing campaigns have many measurements of success–one of which can be increasing footfall to a commercial property.
What are the types of data in commercial real estate?
Data has many forms in commercial real estate. How important each one is and which ones you use will depend on what you are trying to achieve. In general, the three main types are property, tenant, and market data.
Commercial property data
Property data is the foundation of real estate analysis. It includes a wide range of attributes, like:
- Property location
- Number of units
- Type of asset
Commercial tenant data
This is information on the current tenants, properties, lease terms, a ranking of the best potential tenants according to the data.
Commercial tenant data is widely used by investors, operators, and leasing brokers.
Comparing real estate properties, analyzing the market, and investment opportunities are all completed using market data. This includes details of historical returns and competitor analysis.
Where does commercial real estate data come from?
As demand increases for data, the number of sources has increased. Where you get data depends on what you need it for. For purchasing property, a broker and market research report may be all you need.
For developing a commercial property and analyzing its success, a database will be needed.
CRE data can come from databases, brokers, and public records.
Commercial real estate databases
Data intelligence services provide comprehensive data on commercial properties, including property, tenant, footfall, and market data.
Real estate agents and brokers can provide specific details of sales, tenancy, leasing, and average prices within their specialty and area.
Public records are a valuable source of commercial real estate data, such as property ownership records, tax records, and zoning records.
These records can be accessed through county assessor websites, city and county GIS websites, and state revenue/treasury office websites.
Data analytics vs real estate management
Data analytics is the process of collecting, cleaning, and analyzing data to extract meaningful insights. It can be used to improve a variety of business processes, including real estate management.
Real estate management is the process of overseeing the day-to-day operations of a property or group of properties. This includes tasks such as collecting rent, maintaining the property, and finding and screening tenants.
Analyzing data is an important part of real estate management and many tools are available to agents and brokers to support efficient property and portfolio management.
Can data analytics predict the future of commercial real estate?
Commercial real estate has seen troubled times in recent years. The pandemic caused a rise in work-from-home (WFH), which disrupted many offices and retail companies.
Investing in data analytics, systems, and infrastructure can give CRE companies an edge as predictions can be made with high accuracy in many cases such as:
- The potential success rate of a tenant in a mall vacancy
- Completing void analysis
- The future foot traffic expected in a retail space
- Whether an asset is performing as expected or below an industry benchmark
To see how data can support your operations in real estate and retail, speak to our team today.