There’s an evolution of our business model that’s occurring”, Brian McDade, chief financial officer, Simon Property Group, told Deloitte. “We, like other leaders in their respective industries, are going to become a data company”.
In 2024, the real estate market presents a paradox of compelling opportunities amidst challenging economic headwinds.
With continued economic uncertainty, climbing interest rates, and rising vacancy rates, particularly in office and retail spaces, real estate firms must find ways to do more with their money, stand out from competitors, and foster trust among investors.
Leveraging data analytics is one of the most powerful levers for accomplishing these goals.
Keep reading to dive into the transformative power of data analytics in real estate investment strategies, its eye-opening use cases, and challenges that must be overcome to ensure its success.
Let’s get started.
How to harness data analytics for better real estate investments
Despite the myriad ways data analytics can be used to make better real estate investments, only 13% of real estate companies have access to real-time business intelligence and analytics, according to real estate services firm Jones Lang LaSalle.
Although the industry is in its nascent phase of adoption, the consensus – at least among the respondents of NAIOP Research Foundation’s report, The Role of Data Analytics in Commercial Real Estate – is that we’ll see increasing adoption of predictive-analytical tools that guide investment decisions and capital management.
Here are some of the use cases in which data analytics can provide real estate leaders an edge over the competition with their investment strategy:
Valuations
Importance of data analytics for valuations
In the context of market volatility, asset value fluctuations, and changing property sector dynamics, traditional methods for valuing a potential real estate investment – such as net operating income, internal rate of return (IRR) and profit/loss – may not suffice.
With such challenges, accurate valuations are especially crucial for real estate firms to make informed investment decisions, manage risks, and maintain competitive advantage.
Data analytics can provide real-time insights into these market shifts, allowing for more accurate property valuations.
Actionable guidance
Analytics can be used in the due diligence process to compare cash flow projections of different properties.
Analytics can also be used to determine the rent premiums tenants are willing to pay for specific amenities. This analysis is significant in the current market where tenant preferences are rapidly changing, and firms seek to enhance and project asset value through amenities.
Environmental, Social, Governmental (ESG)
Importance of data analytics for ESG-savvy investments
Understanding both the ESG value and the associated risks of property is becoming an imperative for CRE investment strategies. Let’s unpack this further:
Cash flow and valuation projections
ESG credentials carry real weight on an asset’s revenue-generating potential.
As per Deloitte, In the United States, sustainability credentials within real estate assets are now synonymous with premier Grade A office space commanding 31% higher annual rents, but when adjusted for age and location, the rent premium is 3% to 4%.
It comes as no surprise that 34% of real estate leaders say they’re undertaking or evaluating capital projects in which decarbonization is a key element of the investment strategy.
This indicates a shift from location-centric investment decisions to a more design-centric outlook. Buildings designed with sustainability in mind can bring the most long-term value,
and therefore real estate firms must use data analysis to accurately measure the sustainability metrics of a building.
Risk mitigation
Equally pivotal in relation to ESG is using data to evaluate environmental risks of a property, such as flooding, wildfires, earthquakes, and other climate risks.
Further risk that must be mitigated against is that of “stranded assets”. Sustainability-related regulations are gathering tailwinds globally, with Europe leading the way. In fact, some 76% of the continent’s office supply could face obsolescence by 2030 if it’s not upgraded to comply with regulations.
However, nearly 60% of firms surveyed in Deloitte’s 2024 report cited a lack of necessary data processes and internal controls to meet ESG compliance standards??. With this in mind, firms who prioritize data analytics for ESG purposes can gain a competitive advantage.
Actionable guidance
In terms of evaluating a potential investment’s ESG credentials, ensure you’re fully updated on ESG trends and benchmarks through reports published by organizations like GRESB or World Green Building Council.
Request ESG certificates and, if possible, analytics from ESG tracking softwares used by the property’s existing owners. Also consider hiring an independent ESG consultant to conduct due diligence and review all the data provided.
Identifying investment opportunities
Importance of data analytics for identifying opportunities
Tech savvy commercial real estate firms are using data analytics tools to analyze supply and demand trends, local market dynamics, and consumer behaviors, in order to find high performing areas and asset classes to invest in.
Actionable guidance
Let’s take a look at specific ways data analytics can be used for identifying investment opportunities.
Site selection
One industrial real estate firm, which participated in NAIOP Research Foundation’s report, The Role of Data Analytics in Commercial Real Estate, built in-house analytics tools to assist with the site-selection process (e.g., acquisitions and development deals).
One field of data it crunches to assess an area’s growth potential is population and labor employment data from the U.S. Census Bureau and Bureau of Labor Statistics.
Adaptive reuse
Algorithms can also be used to inform asset conversion strategy. One company, for example, built an algorithm using data on 3.2 million square feet of office space in Alberta.
The data focused on the design features of the office spaces, from natural light to building shape, and then valued these features by how much each one impacts the financial pro forma.
This helped the firm make a score for each office’s potential to be converted into a multifamily building.
This is a handy use case of data analytics being used for “adaptive reuse”, identifying and evaluating opportunities for converting underperforming assets.
Challenges of data analytics for real estate firms
Real estate firms integrating data analytics into their business typically do this in two ways: by hiring data scientists to build in-house technology, or by using an external data analytics vendor.
These processes both come with their own challenges:
Using a third-party tech platform
Firms who outsource their analytics to a third-party vendor don’t need to deal with hiring a team of data engineers and building the required technology in-house, meaning they can save a considerable amount of time and money.
However, getting to grips with the third-party data vendor’s technology and system can still pose a steep learning curve and be cumbersome to manage, especially if your real estate firm lacks tech expertise.
Although a team of data engineers is not required to build algorithms and predictive models if outsourcing data analysis to an external firm, a tech and data savvy employee is still needed to actually analyze the large data sets and apply the findings.
Many firms lack this expertise and may need to hire at least one employee proficient in a programming language like R or Python to make effective use of the large amount of data being collected.
When choosing a third-party real estate data vendor, make sure their system is intuitive, user friendly, and that they have excellent customer support on hand.
Hiring tech talent
The alternative to harnessing external data vendors is building your own technology internally. This gives you full control, ownership, and customization, and for firms with the available budget and runway, it can be a great advantage and tactic to differentiate your firm.
The first major drawback is the time it takes to find the right data engineers. Hiring and retaining qualified staff is often a significant challenge when building in-house machine learning capabilities.
The next challenge is the cost of hiring at least two or three specialists (you’re competing with top tech companies on a small pool of talent – be prepared to pay salaries starting at $150,000 to $250,000 per year).
Then you have to factor in the time it takes the engineers to actually build the data systems, which will take several months at a minimum.
Ensuring synergy across teams
When you have a system for collecting data in place, whether through external or internal tech, you then need to make sure the data is being applied effectively and harmoniously throughout your organization.
This is easier said than done.
Several companies who participated in NAIOP’s The Role of Data Analytics in Commercial Real Estate report confirmed that they do not have standardized policies in place that foster cross-unit discussions regarding their data analytics.
This marks a continuation of a trend highlighted in EY’s 2020 report on tech adoption in commercial real estate, in which 43% of technology providers noting the challenge of getting widespread adoption of their tools across a client’s business, and 35% seeing a lack of wider process evolution as a result of their application being adopted.
This can lead to inefficiencies and incoherent strategies across your firm.
To combat this, consider dedicating a central team, led by a CTO, whose mission is to oversee the successful implementation of data analytics across the firm. Also encourage cross-department collaboration and knowledge-sharing to get everyone on the same page and instill a data-centric culture across the organization.
Embracing a Data-Driven Culture in Real Estate
Amidst volatile markets and industry shifts, integrating data analytics tools into your real estate investment strategy will serve as the foundation for efficient and sustainable business in 2024 and beyond.
Applied correctly, data analytics helps make better real estate investment decisions – from more accurate valuations, to assessing environmental risks and unearthing investment opportunities.
Zooming out, it’s evident that analytics tools are just one piece of the proptech puzzle.
Real estate firms should prioritize integrating a holistic suite of technological tools that ensure efficiency and sustainable growth is bred from every corner of the business.
Agora is the leading all-in-one real estate investment management platform, offering tools for fundraising, investor reporting, document management, ACH payments, financial administration, tax services, and much more.
