Utilizing Market Data to Make Strategic CRE Investments

Published: 04-11-24    Category: Insight

Specializes in providing actionable insights into the commercial real estate space for investors, brokers, lessors, and lessees. He covers quarterly market data reports, investment strategies, how-to guides, and top-down perspectives on market movements.

Market data on a screen.

In today's unpredictable world of commercial real estate (CRE), data analysis has become an essential tool for CRE investors looking for ways to reduce risk when reviewing their next move.

By leveraging market data and emerging technologies, CRE professionals can gain valuable insights into property valuations, tenant demand, and future trends.

Here, we will explore the importance of data analysis tools in the current CRE market, together with a description of the tasks performed by different tools. We'll also check out the story of a CRE investor whose skills with data analysis paid off handsomely.

First, we'll start with data that helps investors finalize the sort of purchase everyone loves: a bargain.

Identifying Undervalued Properties

One of the primary benefits of using market data in CRE is the ability to identify undervalued properties. By analyzing historical sales data, rental rates, and occupancy levels, investors can uncover hidden gems.

This approach allows CRE professionals to make informed decisions about which properties to acquire and at what price point, potentially leading to significant returns on investment (ROI).

An effective strategy for identifying undervalued CRE properties is to research the demographic data and market trends of the area where you want to invest. Look for signs of growth, demand, and development. These may include population growth, income levels, employment rates, infrastructure projects, and business activities.

Another method especially effective for smaller CRE purchases is to research data for distressed properties. While not a new strategy, it's still effective. Search for bank-owned properties, pre-foreclosure listings and foreclosure auctions.

Another dataset to check out: future tenant demand.

Strategies for Predicting Future Tenant Demand

Data analysis also plays a crucial role in understanding tenant demand, identifying types of CRE with future growth, and predicting future trends within the CRE market.

A patient CRE investor will work with others to examine demographic data, employment statistics and predictions, and industry growth patterns.

This data can provide valuable insights into which sectors and locations are likely to experience increased demand for commercial space.

Networking within certain industries may provide intel regarding an established business' expansion plans that aren't yet public knowledge. Careful due diligence may put some investors into prime positions to welcome this growth ahead of others.

Investors who carefully complete their data analysis homework can feel more confident when making decisions about property acquisitions, development projects, and tenant mix.

No matter what type of potential purchase a CRE investor may be considering, it's important to review the latest and greatest data resources. Let's do that now.

Emerging Data Resources

The business world is welcoming dozens of new data tools, including those that have been created specifically for the real estate market.

These provide CRE investors with additional ways to analyze and interpret market information as part of initial due diligence.

For example, one provider has created a set of tools that provides search capabilities that include contextualizing CRE property data and market conditions at several levels. Investors may also conduct research within existing properties and sub-markets, and compile data for market comparisons.

Other data tools incorporate machine learning algorithms. A typical example of this algorithm is a set of instructions and techniques that enable a computer system to learn and improve its performance on a specific task without being explicitly programmed.

Instead of following predefined rules, the algorithm "learns" from patterns in data to make predictions or decisions.

Key aspects of machine learning algorithms include:

  • Data: The algorithm is fed a dataset, which can be labeled (supervised learning) or unlabeled (unsupervised learning).
  • Model: The algorithm builds a mathematical model based on the input data, which can be used to make predictions or decisions on new, unseen data.
  • Training: The algorithm adjusts its internal parameters to minimize errors between predictions and outcomes, improving its performance.
  • Evaluation: The model's performance is assessed using a separate dataset to ensure it generalizes well to new data.

Another method for analyzing and visualizing real estate data in a spatial context incorporates a real estate GIS (Geographic Information System) tool.

These software applications integrate property datasets with geographic information to help real estate professionals, investors, and other stakeholders analyze and visualize real estate data in a spatial context.

Datasets often used by GIS tools include property boundaries, zoning information, demographic data, and market trends.

Investors use GIS tools to visualize future property trends, using research data for over 150 million CRE buildings. Some GIS tools provide a self-service interface and enable users to custom-draw any area to acquire its CRE data.

Key features and benefits of real estate GIS tools include:

  • Property mapping: Visualizing property locations, boundaries, and characteristics on interactive maps.
  • Spatial analysis: Analyzing relationships between properties, amenities, and other spatial factors to identify trends, opportunities, and risks.
  • Site selection: Identifying optimal locations for real estate development projects based on criteria like accessibility, zoning, and market demand.

Most GIS tools also provide market analysis and data integration, and create charts and reports.

Perhaps the most exciting method for gathering CRE data is provided by artificial intelligence (AI) tools and chatbots. These enable investors to confer directly with an AI chatbot.

AI improves forecasting accuracy by integrating real-time external data and existing intelligence. This enables investors to quickly adapt a development plan in response to new market trends.

Recently, new AI models that concentrate on providing data, together with providing complex data graphs and charts, have entered the market.

Wondering if there are real-life examples of CRE investors who used market data to make a profitable decision? Here's one.

Cadre Cashes In

In 2015, real estate investment firm Cadre, co-founded by entrepreneur Ryan Williams, used data analytics to identify an attractive investment opportunity in Atlanta, Georgia.

By analyzing market trends, demographic data, and property-specific information, Cadre determined that the Peachtree Corners submarket in Atlanta was poised for growth due to its proximity to major transportation hubs, educated workforce, and a favorable supply-demand balance.

Cadre acquired a 160,000-square-foot office building in Peachtree Corners for $27.5 million.

As a result of these data-informed decisions and proactive management, Cadre increased the property's occupancy rate from 87% to 99% and grew the net operating income by 27% over a three-year holding period.

In 2019, Cadre sold the property for $39.2 million, realizing a significant ROI for its investors.

This story demonstrates the power of using data and analytics to identify attractive investment opportunities, make informed decisions, and drive profitable outcomes.

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