Optimizing the Loan Underwriting Process in CRE

The loan underwriting process is key in commercial real estate (CRE) transactions, it determines the viability and success of property investments. This process involves a thorough review of a borrower’s financials, credit history, and property value. 

According to the Mortgage Bankers Association, commercial and multifamily mortgage originations were up 79% in 2021 compared to 2020, and this trend is only set to grow, so underwriting efficiency in CRE is more important than ever.

In this article, we will cover the basics of loan underwriting in CRE, the challenges and best practices, and why Blooma is the best tool to optimize this critical function.

The Fundamentals of Loan Underwriting in CRE

Loan underwriting in CRE refers to a thorough review of a borrower’s creditworthiness and property value. This critical function ensures lenders minimize risk while lending and protects both the lender and the borrower.

The loan underwriting process assesses the borrower’s credibility and the value of the property being financed. Lenders rely on this process to review the borrower’s financial stability, credit card history, and repayment capacity to ensure they can service the loan. Other factors include the down payment, mortgage application details, and closing costs. 

At the same time, the property value is reviewed to determine its potential as collateral to reduce the lender’s risk exposure.

Key Stages of the Loan Underwriting Process

1. Application Review: The underwriting process starts with a thorough review of the loan application. This includes reviewing the borrower’s financial history, including the borrower’s income, savings accounts, tax returns, pay stubs, bank statements, and credit reports. The mortgage lender also reviews the borrower’s debt-to-income ratio (DTI) and loan-to-value to see if they can handle more debt/lines of credit. 
2. Credit Analysis: Lenders do a detailed credit analysis to assess the borrower’s creditworthiness. This involves reviewing the borrower’s credit score, credit history, and credit report, which provides insight into their borrowing behavior, repayment history, and overall financial responsibility. The borrower’s credit score affects the interest rate and loan terms offered.
3. Collateral Evaluation: In this stage, the lender appraises the property to determine its market value and suitability as collateral. The property’s value, location, condition, and potential to generate income are key factors in this evaluation. This ensures the property can cover the loan amount in case of default and protect the lender’s investment.
4. Loan Approval: After thorough analysis and evaluation, the lender decides on the loan application. If approved the lender outlines the loan terms, interest rates, repayment schedule, and any conditions the borrower must meet. The final approval also includes ensuring all additional documentation is complete and accurate, and the borrower meets all regulatory and policy requirements.

Common Challenges in Traditional Underwriting

Commercial real estate loan underwriting is complex, and the mortgage process has many twists and turns. Understanding these issues is key for both lenders and borrowers to navigate CRE transactions.

Data Discrepancies

One of the biggest issues in traditional underwriting is data discrepancies from manual underwriting. Borrowers provide a multitude of financial documents, such as monthly income pay stubs, monthly debt commitments (like student loans), any unpaid taxes, and bank statements, that may not always match or present a clear financial picture. 

Discrepancies or incomplete data can lead to errors in evaluating a borrower’s financial situation and creditworthiness. This is even more true for self-employed borrowers or those with multiple income streams, as their financial information is more complex and less standardized.

Long Evaluation Periods

Traditional mortgage underwriting processes are slow and manual. They require reviewing a lot of documentation and detailed financial analysis and verifying multiple pieces of information can extend the evaluation period. 

Long underwriting timelines can delay home loan approvals, and borrowers and homebuyers need access to funds for property purchases, renovations, or refinancing. 

This is even more problematic in a competitive CRE market, where speed and agility are key for mortgage payments and loan approvals. 

Subjective Decision Making

Subjective decision-making is another big challenge in traditional underwriting. Lenders rely on the expertise and judgment of individual underwriters to assess the risk of a loan. While experience and intuition are valuable, they can introduce bias and inconsistency in the decision-making process, especially with different types of loans. 

Different underwriters can interpret the same financial data differently and get different loan approvals and terms. This subjectivity can lead to unfair outcomes and borrower distrust.

Impact on Speed and Outcome of CRE Loan Approvals

Data inconsistencies, long evaluation periods, and subjective decision-making can have several negative impacts on CRE loan approvals:

• Delayed Transactions: Long underwriting processes can delay the overall timeline of CRE transactions, frustrate borrowers and potentially lose opportunities.
• Increased Costs: Long evaluation periods can increase the cost of loan processing for both lenders and borrowers. This includes higher administrative costs and lost income due to delayed property investments.
• Inconsistent Decisions: Subjective decision-making can lead to inconsistent loan approvals, with some borrowers getting better terms than others based on the underwriter’s judgment rather than standardized criteria.
• Reduced Transparency: Inconsistencies and subjectivity can reduce transparency in the underwriting process and make it difficult for borrowers to understand and trust the decision-making process.

Blooma: Streamlining Underwriting with Advanced Technology

By using advanced technology, Blooma simplifies the entire underwriting process making it faster, more accurate, and less human error prone.

Blooma uses AI and ML to automate the analysis of huge amounts of financial data. Here’s how:

• Automated Data Collection: Blooma’s platform collects and aggregates financial data from various sources, such as tax returns, cash flow, homeowners credit reports, insurance policies, bank account statements, and other documentation in the application process. No more manual data entry is required, and fewer human errors too!
• Better Data: AI algorithms analyze the aggregated data to find patterns, inconsistencies, and anomalies. So the data used in underwriting is accurate and up to date and a solid foundation for making informed lending decisions.
• Real-Time Risk Assessment: Machine learning models learn from historical data and market trends to predict potential risks on a loan. Real-time risk assessment allows lenders to evaluate borrowers better and make faster data-driven decisions.
• Predictive Analytics: Blooma uses predictive analytics to forecast future financial performance and market conditions. So lenders can anticipate challenges and opportunities and have their underwriting decisions aligned with the latest market insights.

The use of AI and ML in Blooma’s underwriting platform benefits CRE professionals:

• Faster: Blooma reduces the time it takes to underwrite by automating routine tasks and data analysis. Lenders can process more loans in less time and be more operationally efficient.
• Better: Advanced algorithms ensure the data used in underwriting is accurate and consistent, leading to fewer errors and more reliable lending decisions.
• Objective: AI-driven analysis removes subjectivity from traditional underwriting. Decisions are based on standard criteria and data-driven insights, which means fair and transparent lending.
• Better Risk Management: Real-time risk assessment and predictive analytics allow lenders to identify and mitigate risks proactively. 

Blooma’s Impact on the Underwriting Workflow

Blooma, with its technology solutions, optimizes every step of this process from application to decision. Here’s how:  

1. Initial Application Review: Blooma automates the collection and organization of application data, such as borrower financials, credit reports, and property details. This reduces time spent on manual data entry and ensures all required information is available for analysis.
2. Credit Analysis: AI in Blooma analyzes the borrower’s credit score, credit history, and debt-to-income ratio with precision. This automated analysis picks up patterns and red flags more effectively than manual methods and ensures a full creditworthiness assessment of the borrower.
3. Collateral Evaluation: Blooma’s platform uses market data and property metrics to value and condition the collateral. This includes property type, location, occupancy rates, and comparable market values to give a full picture of the asset’s value.
4. Risk Assessment: Blooma uses machine learning models to perform real-time risk assessments based on historical data, market trends, and predictive analytics. This allows lenders to identify risks early on and make more informed decisions on loan terms and conditions.
5. Decision: Blooma simplifies the decision-making process by providing a data-driven analysis of the loan application. Lenders get detailed reports and insights to support a transparent and objective underwriting decision

Advanced Risk Assessment with Blooma

Blooma takes risk assessment to the next level by giving you more insight into borrower financial stability and property values. With predictive analytics and real-time data at your fingertips, you can make better, more informed lending decisions.

Blooma benefits you in the following ways: 

• Credit Analysis: Blooma’s AI-powered platform analyzes the borrower’s credit score, credit history, and debt-to-income ratio in depth. By analyzing these metrics with precision, Blooma identifies risks and gets a full picture of the borrower’s financial health.
• Predictive Financial Stability Metrics: Blooma uses machine learning models to predict borrower behavior based on historical data and current trends. These predictive insights allow lenders to anticipate potential issues like late payments or default risk and proactively manage risk.
• Real-Time Market Data: Blooma integrates real-time market data to value and condition the property. This includes property type, location-specific factors, occupancy rates, and comparable sales. Real-time data means lenders have the most up-to-date and accurate information for property evaluations.
• Collateral Analysis: The platform analyzes the property’s net operating income (NOI), cap rate, and debt service coverage ratio (DSCR) in depth. These are key to understanding the property’s financial performance and return on investment.
• Environmental and Zoning Compliance: Blooma checks environmental risks and zoning regulations that could impact the home’s value and legal status. By complying with these factors, lenders can mitigate the legal and regulatory risks associated with the property.
• Predictive Risk Modeling: Blooma’s predictive risk modeling uses historical data, market trends, and machine learning algorithms to forecast risks. These include property value movements, market volatility, and borrower financial stability. 

Integrating Blooma into Existing Lending Operations

Here’s a step-by-step guide to help CRE professionals integrate Blooma, along with tips on training staff and aligning internal processes with Blooma’s features.

1. Needs Assessment: Review your current underwriting processes to identify the gaps where Blooma can add the most value and any other eligibility criteria for your automated underwriting processes. Define what you want to achieve by integrating Blooma, e.g. reduce processing time, improve risk assessments or data accuracy.
2. Configure Blooma to Your Workflow: Work with the Blooma support team to configure the platform to your underwriting criteria, reporting requirements, and compliance standards. API Integration: Ensure Blooma is integrated with your existing loan origination systems (LOS) and customer relationship management (CRM) tools to enable data flow and communication.

3. Data Migration and Validation: Securely move your existing borrower and property data to Blooma’s platform and ensure data integrity. Validate all data to ensure everything is imported correctly and in the right format.
4. Create Staff Training Programs: Provide initial training sessions for all relevant staff to familiarize them with Blooma’s interface, features, and functionality. Offer training for different roles within your organization, e.g., underwriters, loan officers, and risk managers, focused on the features they will use most.
5. Align Your Internal Processes: Use Blooma’s communication tools to centralize all loan-related conversations and document sharing to reduce miscommunication. Set up automated alerts for key milestones and risk indicators to ensure timely responses and actions.

Future Trends in CRE Loan Underwriting

Here’s how future trends will shape the loan underwriting process and why you need to get with the times with Blooma.

Predictive Analytics and Machine Learning

Predictive models powered by machine learning will be able to assess risk more accurately by crunching vast amounts of historical and real-time data. 

Loan underwriting will also become more dynamic with continuous adjustments based on real-time data feeds so lenders can make informed decisions quickly.

In the area of credit scoring, more and more people will look beyond traditional credit reports to alternative data sources such as social media activity, utility payments, and rental history. This will give a more complete picture of a borrower’s creditworthiness.

Blockchain

Blockchain will provide an immutable ledger for all loan transactions, so data will be secure and tamper-proof. This will reduce fraud and increase stakeholder trust. Smart contracts on the blockchain will automate and enforce underwriting agreements, reduce intermediaries, and speed up the loan approval process.

Secondly, decentralized storage will allow for secure and transparent sharing of documents between lenders, borrowers, and other parties involved in the underwriting process.

Sustainability and ESG

Underwriting will focus more on green buildings and sustainable real estate projects, and lenders will consider environmental, social, and governance (ESG) factors in their risk assessments.

Advanced tools like Blooma will include ESG metrics in their analysis so lenders can evaluate the long-term viability and ethical impact of their investments.

Transform Your Loan Underwriting with Blooma

Using Blooma for your loan underwriting means you’ll be ahead of the curve and stay competitive. 

By incorporating technology into your underwriting process, you can make faster, more accurate, and more profitable CRE loan deals. Blooma’s AI analysis, real-time data, and full risk assessment make it an essential tool for modern CRE professionals.

See Blooma in action. Book a demo.