Harnessing AI and Machine Learning for Common Commercial Real Estate Tasks in SaaS Applications

The world of commercial real estate (CRE) has witnessed significant advancements in technology, reshaping the way businesses operate within the industry. At Fischer Solutions, we are leading the way for CRE tech advancements since the release of our software in the 80s.

CRE Tech refers to the use of technology, software, and innovative solutions to enhance aspects of the commercial real estate industry. It encompasses technologies and platforms that aid in property management, data analysis, marketing, leasing, and overall operational efficiency within CRE.

Among the most transformative innovations, artificial intelligence (AI) and supervised machine learning (ML) have emerged as game-changers. One particular area where AI and ML are revolutionizing CRE tech is in the processing and abstraction of data from lease documents. Large language models have proven to be invaluable assets, facilitating efficient and accurate extraction of vital information from lease agreements. In this article, we explore the role of AI and ML capabilities in streamlining lease document analysis.

The Challenges of Lease Document Processing

Traditionally, lease document processing has been a cumbersome and time-consuming task. Commercial leases can be intricate legal documents, spanning multiple pages and containing numerous clauses, terms, and conditions.

Manually reviewing and extracting essential data from these documents is labor-intensive and prone to human errors. For CRE professionals and stakeholders this translates into significant inefficiencies, delayed decision-making, and potential compliance risks.

Some of the key challenges include:

• Complexity and Length of Documents: Reviewing and comprehending lease documents can be challenging and may require legal expertise.
• Manual Data Entry: Converting information from paper-based lease documents into digital formats may involve manual data entry.
• Version Control: Keeping track of changes and ensuring that the latest version of a document is used is challenging.
• Lack of Standardization: A lack of standardization in lease formats may present unique clauses and provisions.
• Data Security and Privacy: Ensuring data security and privacy during the processing and storage of lease documents is crucial.
• Time Sensitivity: Strict deadlines and delays in processing can lead to missed opportunities or contractual violations.
• Compliance and Regulations: Commercial lease agreements must comply with various local, regional, and national laws and regulations.
• Legacy Systems: Many organizations still rely on legacy systems that may not be optimized for lease document processing.
• Document Collaboration and Sharing: Collaboration among multiple stakeholders can be cumbersome when dealing with physical documents or disjointed digital systems.
• Document Retrieval and Searchability: Quickly accessing specific information within lease documents can be difficult, especially if the documents are not digitized or organized effectively.

Addressing these challenges often involves leveraging tech solutions, such as ManagePath and Visual Manager. By incorporating AI and Machine learning, you unlock a new way of processing data to cut down on human error.

Data Extraction with AI and ML: The CRE Tech Solution

Artificial intelligence, combined with machine learning models, presents a transformative solution to the challenges faced in lease document processing. Large language models, such as OpenAI’s GPT-3.5, have demonstrated remarkable capabilities in natural language understanding and processing.

Leveraging these language models, CRE tech companies have developed sophisticated real-time data extraction tools. These tools efficiently sift through vast amounts of lease documents, extracting critical information with astonishing accuracy.

The use of large language models that have been made available in the last calendar year has drastically improved the feasibility of using AI technology to automate lease abstraction.

Previous purpose-built solutions, such as Leverton, were a step in the right direction. However, they required so much initial configuration that they only made sense for companies with very consistent lease structures. Those previous generation tools were also cost-prohibitive when compared to the emerging LLM technologies.

Natural Language Processing (NLP) in CRE Tech

At the core of AI-driven lease document data extraction lies Natural Language Processing (NLP). NLP enables machines to understand and interpret human language and specific tasks in a manner similar to how humans do. Large language models are trained on massive datasets, allowing them to:

• comprehend complex sentence structures
• identify relevant entities
• infer context and intent from text

Consequently, these models can rapidly analyze lease documents, highlighting essential information for further processing. Once you extract text from leases, you can feed that text into an NLP.

Entity Recognition and Abstraction

One of the key capabilities of AI and ML is entity recognition. This is a process by which the technology identifies specific entities within a document. In the context of lease documents, this could include crucial details such as:

• lease duration
• rental rates
• renewal clauses
• security deposits

Large language models excel at entity recognition. Thus enabling CRE professionals to quickly extract relevant data and populate databases or software systems with accurate information.

AI-powered lease document data extraction is not limited to mere information retrieval. Machine learning algorithms can cross-reference extracted data with existing databases and validate the accuracy of the extracted information. This functionality enhances data integrity, mitigating the risks associated with errors and discrepancies.

Benefits of AI and ML in Lease Document Data Extraction

The adoption of AI and ML for lease document data extraction offers numerous benefits for commercial real estate stakeholders, including:

a. Time Efficiency:

AI-driven data extraction drastically reduces the time required to review and process lease documents. Manual extraction that might have taken days or weeks can now be accomplished in a matter of minutes. Effectively empowering professionals to make timely and informed decisions.

b. Accuracy and Consistency:

Large language models exhibit remarkable precision in data extraction. Achieving the goal of minimizing the likelihood of errors and inconsistencies that are prevalent in manual processes. Improved accuracy translates into more reliable analytics and decision-making.

c. Scalability:

AI and ML technologies are highly scalable, making them suitable for processing large volumes of lease documents simultaneously. This capability proves invaluable for CRE firms dealing with extensive portfolios and numerous transactions.

d. Enhanced Compliance:

The automated nature of AI-driven data extraction reduces the risk of compliance breaches arising from human errors. Lease terms and conditions are effectively monitored, ensuring regulatory compliance and adherence to company policies.

e. Cost Savings:

The efficiency of AI-powered data extraction translates into cost savings for CRE businesses. By reducing the need for manual labor and expediting processes, companies can allocate resources more effectively and achieve better operational efficiency.

Overcoming Challenges and Ensuring Data Security

The potential benefits of AI and ML in lease document data extraction are immense. However, it is crucial to address certain challenges and concerns associated with these technologies.

Data Privacy and Security:

Lease documents often contain sensitive and confidential information. CRE technology companies must prioritize robust data security measures to safeguard against breaches and unauthorized access. Encrypting data, employing multi-factor authentication, and adhering to industry standards are essential steps in ensuring data privacy.

Regulatory Compliance:

The use of AI and ML in CRE technology must align with existing regulations and legal frameworks. Companies should be mindful of data protection laws, such as the General Data Protection Regulation (GDPR). You must ensure compliance at all stages of data processing.

Bias and Fairness:

Large language models are trained on vast data points that may inadvertently contain biases. This presents a need for supervised learning algorithms. When using AI in lease document data extraction, it is vital to continuously monitor and address any potential biases.

AI and machine learning have emerged as transformative forces in the CRE industry, specifically revolutionizing lease document data extraction. Large language models equipped with natural language processing capabilities enable CRE professionals to streamline the analysis of lease agreements. This enables the effortless extraction of critical data with unprecedented efficiency and accuracy.

The benefits of AI-driven data extraction are manifold. Ranging from time savings and increased accuracy to enhanced compliance and cost savings.

Conclusion

As with any technological innovation, it’s crucial to address potential challenges such as data security, regulatory compliance, and fairness. By leveraging AI and ML responsibly, CRE businesses can unlock the full potential of these technologies, ushering in a new era of efficiency and productivity within the industry.

At Fischer Solutions, we invest time and resources into making sure that ManagePath and Visual Manager stay at the forefront of CRE tech. By safely and responsibly bringing commercially viable AI and machine learning features to our customers, seamlessly integrated into our existing applications. Reach out to request a FREE demo, today!