A sneak peek into the industry.
It’s no secret AI has integrated into nearly every facet of our lives. From booking a flight to unlocking our phones using “face ID”, these seemingly routine activities rely on the capabilities of AI. Beyond personal applications, AI has made significant inroads into every niche of the business landscape. Yet, when it comes to the realm of Commercial Real Estate, it begs the question: how advanced is the integration of AI in this sector? Let’s delve into the current state of AI integration in CRE.
That’s fair to say commercial real estate (CRE) could benefit from a better use of AI, especially when it comes to managing data. All stakeholders within the real estate industry, including buyers, sellers, financial institutions, and brokers, heavily rely on real estate data to make informed decisions. Many real estate firms, however, continue to depend on outdated legacy technologies.
Breaking the Spreadsheet Chains: Excelling Beyond Excel
According to research by Altus Group, firms still use spreadsheets 60% of the time for reporting, 51% for property valuations and cash flow analysis, and 45% for budgeting and forecasting. A Deloitte survey reaffirmed these findings, exposing that 61% of global real estate owners and investors still rely on legacy technology infrastructures.
The use of these outdated systems limits effective communication among different aspects of the business, leading to redundancies, inefficiencies, and hindering data flow across the organisation. The substantial cost incurred in maintaining these legacy systems, termed as “technical debt,” accounts for an estimated 10-20% of new product technology expenses.
73% of enterprise data remains unused
The bleak state of CRE AI-integration is further highlighted by the fact that only 13% of real estate companies have access to real-time business intelligence and analytics. This deficiency in real-time data utilisation leads to significant missed opportunities, and only increases the upfront cost of technology integration in the future. Industry leaders have the potential to leverage untapped data, as up to 73% of enterprise data across industries remains unused.
According to JLL’s 2023 Global Real Estate Technology Survey, AI and generative AI were identified among the top three technologies expected to have the most significant impact on real estate over the next three years by investors, developers, and corporate occupiers. This signals a growing awareness of the potential transformative power of AI within the commercial real estate sector.
Defining different types of AI in real estate
In the realm of AI development, a critical misconception stems from the use of AI as an umbrella term. The applications and utilisation of AI are expansive, not only within CRE, but across diverse industries.
Despite the seemingly confined term ‘generative AI’, the applications vary based on the stakeholder utilising them, the specific stage within the buying process, and the nature of the data used for training these models. Below are three of the main applications of artificial intelligence within CRE:
- Enhancing Data Management
As businesses expand, the accumulation of vast volumes of documents and datasets necessitates a proportional expansion in operational tools. In commercial real estate, AI serves as a valuable asset for efficiently extracting data from reports, such as offering memorandums, due diligence documentation, and comparables, at a scale previously unattainable.
AI can alert stakeholders regarding impending updates or proactively report on changes in tenant demand. It significantly aids in classifying, storing, and retrieving documents as needed, enabling stakeholders to make more informed decisions by leveraging historical data.
- Loan and Financial Modeling
AI plays a pivotal role in swiftly processing numerical data through machine learning trained on prior financial models, resulting in accurate and actionable outputs. Investors evaluating Net Operating Income (NOI) and Return on Investment (ROI) can utilise AI tools to comprehend the crucial investment factors when considering a deal.
Additionally, underwriters and lenders can better predict potential returns on specific contracts and optimise their liquidity for more secure investments. Conducting risk assessments becomes more efficient with AI algorithms swiftly analysing economic factors, market trends, and historical data, empowering stakeholders to make informed decisions about potential acquisitions.
- Streamlining Administrative Processes
Recent advancements in natural language processing, such as ChatGPT and similar AI technologies, have enabled the automation of various administrative tasks. This automation liberates brokers and CRE stakeholders to focus on the relationship and expertise aspects of their businesses.
AI-powered technologies can swiftly compile market and data reports, schedule meetings, and personalise client communications. This shift from people serving technology to technology serving people enhances the efficiency and productivity of CRE professionals.
How does AI help real estate asset managers?
There are a lot of potential applications of AI in redefining the role of asset managers; from mitigating liquidity problems and optimising decision-making to harnessing the power of data lakes, ultimately shaping a more efficient and informed real estate asset management landscape.
- Addressing the Liquidity Challenge
The real estate industry grapples with a fundamental challenge: managing liquidity in a market inherently defined by illiquidity. Real estate stands as a tangible asset class, offering stability and long-term returns, yet the inherent illiquidity makes it challenging to swiftly sell assets at desired prices, meet investor redemption requests, or capitalise on time-sensitive opportunities.
Traditional methods to mitigate liquidity risks in real estate involve maintaining significant cash reserves or establishing lines of credit. However, these methods come with their costs and limitations, potentially hindering the pursuit of profitable investment opportunities. AI emerges as a transformative tool to redefine liquidity control in asset management within the real estate sector.
Through AI, asset managers can efficiently gauge potential returns on investments, assess liquidity risks, and make informed decisions on property acquisitions and dispositions. Predictive pricing models brought about by AI combat uncertainties stemming from poor-quality data, instilling confidence in evaluating property values and potential risks. This optimization strategy enhances portfolios by adding more liquid assets, ultimately driving profits.
- Eliminating Data Silos and Harnessing Data Lakes
Access to data-driven insights is a critical factor in driving decisions for all businesses, including asset managers. Enhancing accessibility to data generated by real estate assets significantly influences portfolio performance. A key tool that asset managers integrate is the use of a data lake.
A data lake serves as a centralised repository storing both structured and unstructured data in its ‘raw’ format, compared to hierarchical data warehouses. This metaphorical lake of information enables seamless access to varied data formats across multiple regions, fostering centralised and accessible data instead of disjointed information pockets.
The flexibility of data lakes accommodates the ever-evolving landscape of AI and automation, allowing for easy integration with new technologies.
While AI has become an integral part of various industries, the CRE landscape lags behind, particularly in the effective utilization of real-time data and the adoption of modern technologies.
However, there are promising signs of a shift as Investors, developers, and corporate occupiers recognize AI’s transformative potential for the industry. As the CRE sector stands at the cusp of technological evolution, there is an opportunity for industry stakeholders to embrace AI and propel the sector into a new era of efficiency, informed decision-making, and profitability. The journey toward comprehensive AI integration in CRE is underway – now’s the time for real estate companies to jump on the AI train!
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