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The commercial real estate (CRE) industry is undergoing significant changes, influenced by economic trends, shifting market demands, and technological advancements.

An article released by Business Opportunities stated that “the real estate industry is ripe for disruption. Despite hesitancy in the industry, innovative technology is already making headway and changing how real estate transactions work.”

This is especially important today. The US real estate market is growing. It is expected to reach $119.80 trillion by 2024, according to a Statista report.

In this changing landscape, Generative Artificial Intelligence (AI) is emerging as a key technological development. Generative AI is different from traditional AI. While traditional AI focuses on data analysis, generative AI can create new content and solutions.

This offers strong tools for CRE lending and asset management. This technology is starting to automate complex tasks. It helps with property valuation and loan underwriting. This makes these processes more efficient and accurate.

In asset management, generative AI is changing investment strategies. It helps predict market trends and improve building management with data insights. Its impact on the industry is clear.

Generative AI is not just improving current processes. It is starting a new era in how we handle commercial real estate.

Adding to this, a recent survey conducted by EY Financial Services in August 2023 sheds further light on the impact of Generative AI.

Executives and managing directors from wealth and asset management firms were surveyed. Each of these firms makes over $2 billion. They were asked to identify the top three areas where Generative AI could significantly impact their organizations. The responses indicated a wide range of impactful use cases throughout the value chain.

Data ingestion is key for creating strategies and managing investments. It has the biggest impact, followed by middle office operations, client onboarding, marketing, and client acquisition. 

It is important to recognize that the full potential of AI is still mostly unknown, even to AI researchers. Companies like Google, OpenAI, and Microsoft are exploring AI. However, they cannot fully predict how AI will affect specific jobs or industries. The capabilities and impacts of AI are, to a large extent, still being discovered.

Research into Large Language Models (LLMs) and their optimal applications is ongoing, with numerous significant studies already published. However, it's clear that we are still in the early stages of this technological transformation.

The best way to understand and leverage AI in any industry is through direct, task-specific applications. Each industry can gradually uncover how AI can be most effectively used, learning and adapting as the technology evolves.

This article will explore how generative AI is changing the face of CRE lending and asset management, providing new opportunities for innovation and efficiency.

1. Generating Real-time, In-depth Analysis
2. Enhancing Underwriting
3. Comprehensive Deal Due-Diligence for Lenders and Asset Managers
4. Streamlining Credit Memo Preparation
5. Facilitating Clear and Efficient Communication
6. Automating Legal Review Processes

To fully capture the transformative potential of generative AI in CRE lending and asset management, we have structured this blog into two parts.

Understanding Generative AI

What is Generative AI?

Generative AI is a kind of artificial intelligence. It can create new content, ideas, or data that did not exist before.

Generative AI is different from regular AI. While regular AI looks at and understands existing information, generative AI makes new things. It can create text, images, or code based on what it has learned.

In the world of AI models and applications, there are currently a few standout players to consider. ChatGPT is regarded as state-of-the-art at present.

In addition to ChatGPT, other leading Large Language Models (LLMs) include Claude 2, developed by Anthropic, and Gemini by Google. 

Key Technology Behind Generative AI:

• Machine Learning (ML): This involves algorithms that allow computers to learn from and make decisions based on data. ML is the foundation of most AI systems, including generative models.
• Neural Networks: These are a subset of machine learning inspired by the human brain. They are composed of layers of interconnected nodes or 'neurons' that process information and learn complex patterns in data.
• Deep Learning: A more advanced form of machine learning, deep learning uses large neural networks with many layers (hence 'deep') to analyze and interpret complex patterns in large datasets.
• Natural Language Processing (NLP): For generative AI dealing with text, NLP is crucial. It enables the understanding and generation of human language by machines.

Differences Between Generative AI and Traditional AI:

Purpose: Traditional AI is designed mainly for analysis, interpretation, and decision-making based on existing data. Generative AI, on the other hand, focuses on creating new data and content.

Data Handling: While traditional AI models might classify, sort, or respond to data, generative AI models use their training to produce entirely new data that are similar to but separate from the training data.

Complexity and Computational Power: Generally, generative AI models are more complex and require greater computational power than traditional AI models. This is because they need to understand and replicate patterns in data to generate new, coherent outputs.

Generative AI represents a significant leap in the capabilities of artificial intelligence, moving from understanding the world as it is to imagining and creating things that never existed before.

Beyond Basic Applications

The field of AI has great potential for business use. However, its use in the Commercial Real Estate (CRE) sector is still quite new. The question arises: how can AI be effectively deployed in CRE, and what new opportunities might it unlock?

In a report from ULI, an AI expert said, “AI will change many things quickly and on a large scale. I don’t think we fully understand what this means for real estate and society as a whole.”

However, the current applications in CRE have mostly been mundane. The executive also said, “When I think about how AI can change real estate, it still seems basic. It affects how you interact with customer service or property management.”

In the same report, a real estate investment firm developer echoed this sentiment, with numerous tech tenants, including AI companies, concurs. “There are ways we can’t even fathom that will be helpful in all businesses. But the one I’ve heard of more recently is administrative tasks. It basically serves as your superpower assistant: lunch, property tours, running in the background handling all those sorts of things.” 

The Impact of Generative AI Today

Generative AI is rapidly transforming the way businesses operate, bringing about significant improvements in task completion and efficiency. By automating complex, repetitive tasks, this advanced form of AI allows for quicker and more accurate completion of work.

According to McKinsey, generative AI has the potential to automate up to 70% of tasks across various industries. This automation goes beyond simple routine tasks, extending to more complex processes that traditionally require human intelligence.

The productivity gains from the implementation of generative AI are substantial. In data-heavy sectors such as finance and technology, generative AI is not just a tool for efficiency; it’s a catalyst for innovation.

The integration of generative AI can lead to a productivity increase equivalent to up to 5% of industry revenue. This is because generative AI doesn't just speed up processes; it also reduces errors and generates new insights, leading to higher-quality outcomes and more informed decision-making.

Furthermore, the rise of generative AI is reshaping the nature of work through human-AI collaboration. This technology is not about replacing human workers but enhancing their capabilities.

It supports a collaborative environment where AI handles the heavy lifting of data processing, and providing insights, enabling humans to focus on strategic, creative, and decision-making roles.

As detailed in an Ernst & Young article, Generative AI is an emerging force in AI applications. It offers great abilities in tasks like searching for information, retrieving it, and putting it together. This is especially true for unstructured content. It also generates impressive content in different formats, such as text, images, and code.

Generative AI is skilled at handling large amounts of information. It can create responses that are easy for people to understand.

This ability is inspiring leaders in the C-suite. They are imagining how Generative AI could change traditional business value chains. It can help companies prepare for future challenges and create significant value for everyone involved.

The influence of Generative AI extends beyond the corporate sphere, as it is poised to have a significant and enduring impact on society at large.

Dr. Andrew Ng, an AI pioneer, famously compared AI to electricity, emphasizing its transformative potential—- “Just as electricity transformed almost everything 100 years ago, today I actually have a hard time thinking of an industry that I don’t think AI will transform in the next several years.”

In CRE, for instance, this technology is not only streamlining operations but also paving the way for more strategic decision-making in investment, lending, and asset management. Let's explore the specific advantages that generative AI brings to commercial real estate industry finance.

AI and Productivity: Transforming Workflows with Smart Technology

As artificial intelligence (AI) continues to advance, compelling evidence is emerging about its substantial impact on individual productivity. Early controlled studies have shown remarkable time savings ranging from 20% to 70% across various tasks, coupled with an enhancement in output quality when AI tools are utilized.

A team of social scientists worked with Boston Consulting Group on an experiment. This study showed how AI can change professional settings. This large study looks at the future of professional work in the AI era. It includes 18 tasks that are common in top consulting firms.

The results were striking: consultants utilizing ChatGPT-4 consistently outperformed their counterparts who did not use the AI tool, excelling in every performance metric.

In this study, researchers from Harvard, UPenn, and the University of Michigan looked at consultants using ChatGPT-4. They found that these consultants finished 12% more tasks. They finished tasks 25% faster. Most importantly, the quality of their work was 40% higher than those who did not use ChatGPT-4. 

It's important to note that these findings predate the latest enhancements to GPT-4. Recent advancements were not included in the study. These include a new data analytics mode, plugin integration, and updated web search features. This suggests that the productivity gains and quality improvements observed in the study might be even more pronounced with the latest upgrades to GPT-4.

These insights show how AI, especially advanced models like GPT-4, can greatly impact productivity and efficiency at work. As AI keeps evolving, it clearly changes workflows and boosts productivity in many industries. This marks a big shift in how we approach and do work today.

Generating Real-time, In-depth Analysis

Generative AI is starting to transform the way investment analysis is conducted in the commercial real estate (CRE) sector. Traditional methods had analysts spending many hours analyzing data. Now, AI algorithms can process large amounts of information in just minutes. This technological advancement leads to faster and more accurate analyses, greatly enhancing decision-making efficiency.

A key player in this transformation is GPT technology, a type of generative AI.

As an AI system, it can collect financial and operating data from loan-related materials. This includes borrower and property financial statements, budgets, rent rolls, leasing agreements, and market reports. This significantly shortens the time needed for data intake, thereby speeding up the entire origination process

The benefits of generative AI extend to the processing of extensive documentation such as information memorandums, market overviews, or due diligence materials. AI shows a promising efficiency in this domain, with the ability to scan, process, and condense large volumes of text into accurate summaries. This capability is particularly valuable in the CRE sector, where the ability to quickly synthesize and analyze complex documents is essential.

This significant reduction in time—from weeks to just a few days—is a testament to the role of GenAI in streamlining the origination process. This initial stage, enhanced by GenAI, lays down a foundation of accuracy and efficiency vital for the subsequent stages of the CRE loan cycle.

Limitations in Advanced Data Analysis

While GenAI has transformed data analysis, it's important to recognize its limitations for effective application.

• One key constraint is that GenAI primarily processes structured data. For example, it cannot directly analyze a scanned financial statement. However, this limitation can be overcome with third-party applications that preprocess and standardize data into a format conducive to GenAI analysis.

For instance, Smart Capital Center serves this purpose, converting various vast amounts of unstructured data, including financial documents, statements, rent rolls into a standardized dataset for analysis.

It takes various forms of data – scanned financial statements, rent rolls, excel spreadsheets, text entries, or complex financial reports, and converts them into a uniform dataset ready for analysis. This standardization process not only ensures compatibility with the analytical capabilities of Generative AI but also enhances the accuracy and reliability of the insights generated.

• Another limitation lies in the accuracy of the output of Generative AI systems:

Users must review and verify the results, especially in scenarios where precision is critical. The efficacy of GenAI is optimal when exact precision is not the paramount concern or when users have a clear expectation and seek visual confirmation or clarification from generated charts. Moreover, users can modify prompts to refine results.

For example, consider generating a unit mix using a specific methodology that excludes vacant units. If a table includes a subtotal at the bottom, GenAI might initially misinterpret it as a separate unit type.

However, in Advanced Data Analytics mode, GenAI can identify such outliers, suggesting they are likely subtotals to be excluded, but still necessitating user confirmation. This synergy between the system and human analysis proves most effective.

Explicit instructions, such as directing GenAI to ignore potential subtotals, can yield accurate unit mix results. 

Hence, at Smart Capital, leveraging the power of Generative AI, we're introducing an option for users to request customized charts and analytics generated in real-time, tailored to their specific use-cases.

This dynamic feature is engineered to accommodate a vast array of specific requirements and scenarios that may be required in various situations. If an analyst needs a visual representation of a certain specific financial trend or a certain comparative analysis that is not part of the standard set of visualizations, the GenAI-powered system can generate these in real-time based on user prompt.

For instance, a user might need to analyze a unit mix while excluding vacant units or require a financial projection that accounts for variable market conditions, the Gen-AI powered platform is equipped to handle such nuances and it is designed to interpret the subtleties of user request in a much simpler, faster way, requiring much less manipulations from the user.

We've built our platform to ensure that users remain in control. After the initial generation, users can review and verify the results.

Enhancing Underwriting

Generative AI is transforming the field of underwriting for lenders and asset managers by bringing automation and better insight into many steps of the underwriting process.

In the underwriting process, generative AI systems synthesize vast amounts of financial data, interpret complex patterns, and predict future financial health, which are pivotal for making lending decisions.

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AI can help integrate and interpret data, such as data from financial statements, balance sheets, rent rolls, development budgets, and financial, and operating projections, to assess a borrower's financial stability and the profitability of a property or construction project. Advanced algorithms evaluate historical financial performance and project future cash flows, enabling underwriters to identify risks and opportunities that might not be immediately apparent. 

Automation systems using generative AI can model debt service capabilities. They do this by using real-time data feeds. This helps keep underwriting assessments current with property performance and market conditions. It can quickly adjust analyses based on fluctuating interest rates, changing property performance, market dynamics, and other factors.

This active method of financial analysis helps lenders and asset managers get the best and most accurate financial information. This information is important for making decisions about underwriting and managing portfolios.

Using generative AI in financial analysis makes underwriting faster and better at predicting market changes. This results in more informed, data-driven decisions that can better mitigate risks and capitalize on viable lending opportunities.

Document Review and Approval

With AI, reviewing and approving documents like insurance policies and appraisal reports takes less time.

AI algorithms can quickly scan through documents to identify key terms, conditions, and clauses. This ability lets us quickly check if a document meets the required standards and rules. It greatly cuts down the time needed for manual review.

AI systems can find potential risks and problems in documents. They do this by looking for patterns and inconsistencies that humans might miss. This includes finding mistakes in appraisal reports or noticing possible problems in environmental assessments. This ensures that all risks are marked for further review.

AI can automate the workflow. It can send documents to the right people for review and approval.

This is done based on set criteria. This speeds up the process and makes sure all approvals are obtained in the right order. This helps reduce delays.

Beyond just reviewing documents, AI can provide predictive insights based on the content of the documents and historical data. AI can predict how likely an insurance claim will happen. It uses details from the policy and past claims. This helps in making decisions.

To discover further applications of generative AI in CRE, head to Part 2 of this article.