Navigating CRE Underwriting: Best Practices & Tools

Underwriting is the foundation of the commercial real estate (CRE) lending process, enabling lenders to make smart decisions about the risk and viability of commercial property investments. 

Underwriting involves a thorough examination of many factors, including the borrower’s credit, the property’s net operating income (NOI), market conditions, and financials. This analysis helps lenders determine the loan-to-value ratio (LTV), debt service coverage ratio (DSCR), and whether the loan amount requested makes sense.

Underwriting commercial real estate is complicated and involves considering property type, cash flow, expenses, and potential risks like vacancy rates and natural disasters. Accurate underwriting is important for the lender’s profitability and for the borrower to achieve a sustainable and profitable commercial real estate investment.

In this post, we will examine best practices and discuss how Blooma can improve the accuracy, speed, and reliability of the CRE underwriting process. 

By using technology and proven methods, lenders can simplify their workflows and mitigate risk.

Understanding CRE Underwriting

Commercial real estate underwriting is the process of evaluating and quantifying the risk associated with property investments. The goal is to determine whether a loan request is a good investment for the lender, whether the borrower can pay the debt, and whether the property is a good investment.

Some of the key components include: 

• Financial Analysis: This involves a deep dive into the borrower’s financials, including credit score, credit history, and overall financial health. The underwriter reviews the borrower’s cash flow, operating expenses, and net operating income (NOI) to see if they can service the debt.
• Market Analysis: Underwriters examine market conditions to understand the property’s risk and reward. This includes cap rates, market trends, vacancy rates, and rental income projections to ensure the property is in a good market.
• Property Analysis: A deep dive into the commercial real estate property itself to determine its value and potential for appreciation. This includes what type of property (office building, multifamily), its condition (new building or an existing building), and any zoning restrictions that could impact its use. Property taxes, insurance, and natural disaster risks are also considered.
• Commercial Real Estate Loan Metrics: The loan-to-value ratio (LTV) and debt service coverage ratio (DSCR) quantify the risk of the loan. LTV is the loan amount to the property’s value, and DSCR is the property’s net operating income to debt.
• Due Diligence: Thorough due diligence is done to verify all information provided by the borrower and to find any potential issues that could impact the loan. This includes reviewing the borrower’s credit, occupancy rates, and the property’s historical performance and future projections.

By thoroughly examining these components, lenders can make informed decisions that balance potential returns with associated risks

Challenges in Traditional CRE Underwriting

The path of traditional commercial real estate underwriting is full of issues that can negatively impact both the speed and the accuracy of the final assessment. Delayed decisions often introduce greater risk exposure and financial losses. 

Here are a few of the more common problems CRE professionals encounter.

Inconsistent Data

Data inconsistency is a major problem in traditional loan underwriting. Property, market, and borrower financial data are typically housed in multiple locations and formats. This lack of uniformity can create inaccuracies and gaps that make it hard to see a clear, complete picture of the investment opportunity. 

For instance, if there are multiple ways to calculate key metrics like net operating income (NOI) or loan-to-value (LTV) ratios, underwriters may come to different conclusions.

Slow Process

Traditional underwriting is typically a slow process. Physically gathering, analyzing, and verifying data can take months to evaluate and underwrite a loan, delaying decision-making. In a market where speed is essential and timely decisions matter, slow underwriting processes can be a big disadvantage to investment success. Slow processes also tend to frustrate borrowers and create missed opportunities.

Judgmental Decisions

Traditional underwriting also tends to be highly dependent on underwriters’ judgment. Experience and gut feeling are important but can also create bias and inconsistency. Judgmental decisions can cause different outcomes for similar loan requests, creating unfair or less-than-ideal lending choices. A lack of uniformity in decision-making can question the overall validity of the underwriting.

Require Easier, More Accurate, and Faster Practices

Today’s fast-paced and highly competitive CRE market demands easier, more accurate, and faster underwriting practices. Faster turnaround times, consistent data analyses, and more judgmental decision-making are all needed for lenders and investors to stay highly competitive and accurately minimize risk.

Blooma: Revolutionizing CRE Underwriting

Blooma, our CRE underwriting tool, utilizes advanced technology to solve traditional underwriting challenges and offers lenders unprecedented speed, accuracy, and efficiency in commercial real estate transactions.

Streamlining Underwriting

Designed to streamline and improve CRE underwriting, Blooma’s platform, capabilities, and interface work together and alone to ease complex financial calculations, automate data gathering, and maintain underwriting consistency for any and all deals. With that holistic functionality, lenders are empowered to make better, faster decisions with their current underwriting resources.

AI and Machine Learning

A major part of the tool-changing CRE underwriting is Blooma’s artificial intelligence (AI) and machine learning (ML) capabilities. They:

• Increase Accuracy: AI utilizes algorithms that analyze enormous data sets with unparalleled precision, reducing human bias and error to ensure that fundamental values like net operating income (NOI), loan-to-value (LTV) ratios, and debt service coverage ratios (DSCR) are calculated uniformly and accurately.
• Minimize Time Spent Analyzing Data: By modeling data analysis for machine learning, the process is automated to conserve precious time evaluating a property’s performance and risk. Deals that once took weeks to model now take a fraction of the time, empowering lenders to be faster responders to market demands.
• Deliver Real-Time Analysis: Blooma’s artificial intelligence capabilities deliver real-time analysis of market activity, borrower, and property performance. This evolving picture of asset value and risk gives lenders unprecedented ability to be proactive, data-driven decision makers who optimize returns and minimize risk.
• Enhance Decision Quality: Blooma’s AI and ML capabilities improve the objectivity and consistency of underwriting decisions by utilizing automated workflows that ensure all pertinent data is analyzed and reducing subjective judgment reliance on individual underwriters by providing a uniform method of loan application evaluation.

A Turnkey Platform for 21st Century CRE Lenders

More than automation, Blooma’s platform provides a turnkey solution for 21st-century CRE lenders. Covering every step of the underwriting process, from property appraisal and financial analysis to market study and risk assessment, Blooma is a one-stop shop for lenders looking to deploy capital efficiently and effectively. 

Its integration capabilities also allow it to interface with current systems, ensuring a natural transition that maximizes advanced technology.

Advanced Features of Blooma for Underwriting Excellence

Blooma’s platform provides several advanced features that are tailored to the CRE underwriting process; designed to streamline workflows, enhance precision, and offer real-time intelligence so that lenders can make better, data-based decisions.

Data Aggregation

Data aggregation automation is among Blooma’s most effective features. Typically, aggregating property and market data from disparate data sources and consolidating it into a single platform is a laborious and manual process that leaves plenty of room for error. 

With it, you can benefit from: 

• Automated Data Retrieval: Blooma plugs into various data sources such as financial statements, market data, and property records to automatically retrieve all the necessary data.
• Uniform and Accurate Data: Blooma eliminates the potential for manual entry mistakes during the aggregation process to ensure that underwriters use consistent and accurate data.
• Time Savings: The automated data aggregation process saves considerable time during underwriting assignments, so underwriters can spend less time gathering data and more time evaluating and deciding on loans.

Instant Risk Monitoring

Blooma’s instant risk monitoring tools give underwriters real-time insights into the health and riskiness of CRE investments. Some of the features include:

• Interactive Risk Metrics: Blooma’s platform constantly recalculates and presents risk metrics like LTV ratios, DSCR, and NOI instantly based on the latest financial data and market trends.
• Real-Time Alerts: Blooma’s platform can detect significant changes in risk metrics instantly, allowing underwriters to act in real time should any negative trends emerge during the underwriting process.
• Complete and Uniform Risk Profiles: Blooma’s comprehensive platform integrates all data points to provide complete and uniform risk profiles for each individual property and borrower so that lenders can make better decisions.

Advanced Predictive Modeling

Another one of Blooma’s effective features that improves decision quality is predictive modeling, which forecasts future trends and results. Some of the features include:

• Market Trends: Blooma’s platform can detect and predict market trends using machine learning algorithms so that lenders can prepare for incoming changes in property appreciation, occupancy levels, cap rates, and more.
• Borrower Activity: Predictive modeling can analyze borrower activity, such as credit scores and repayment history, to forecast future trends and predict defaults.
• Financial Performance: Blooma’s platform can analyze past performance and current market trends to predict the potential investment outcomes of loans, allowing lenders to pursue higher-yielding and safer deals in the future.

Data-Based Decisions

Overall, Blooma’s advanced underwriting features support a data-based approach to CRE underwriting. From delivering uniform and accurate information in real-time to forecasting future trends, Blooma’s platform enables lenders to:

• Support Due Diligence: With more complete and accurate data, lenders can perform due diligence more effectively to mitigate the risks of overlooked and ignored risk factors.
• Enhance Precision: Automation, interactive risk metrics, and predictive modeling can streamline the underwriting process so that lenders can act faster and more efficiently to make better investment decisions. 

Streamlining the Underwriting Process with Technology

By using automation and digital tools, you can improve both the speed and quality of your underwriting. Here are the benefits you can unlock: 

• Reduced Processing Time: Automation reduces processing time by eliminating manual data entry and consolidation. You can process more applications in less time
• Faster Approvals: With streamlined workflows, the lender and borrower can go from initial application to final approval quicker.
• Less Human Error: Automated data aggregation and analysis ensure that the information used in underwriting is accurate and consistent and reduces errors from manual processes.
• Standardized Evaluations: Digital tools standardize the underwriting criteria and processes, so evaluations are consistent across all applications.
• Single Source of Truth: Technology platforms, like Blooma, pull data from multiple sources, such as financial statements, property records, and market analysis, into one single system. 
• Live Updates: Continuous data integration means underwriters always have the latest information, enabling them to make more accurate and timely decisions.
• Proactive Risk Management: Technology allows for proactive risk management by identifying issues early in the underwriting process. So lenders can mitigate risk and make better lending decisions.

Adopting Blooma: A Step-by-Step Guide

This step-by-step guide is designed to help brokers, lenders, and debt-equity players get up and running with Blooma, optimize the features, and train their teams for maximum impact.

Step 1: Initial Assessment and Planning

• Identify Pain Points: Review your current underwriting workflows to identify the bottlenecks and areas that can be automated and digitized.
• Set Goals: Define the goals for using Blooma, e.g., reducing processing times, improving data accuracy, or enhancing risk assessment.
• Involve the Right People: Get all relevant stakeholders involved, including underwriters, IT teams, and compliance officers.
• Create a Plan: Develop an implementation plan with key milestones, timelines, and responsibilities.

Step 2: System Integration

• Single Source of Truth: Integrate Blooma with your existing data sources, financial databases, property records, and market analysis tools so that all the information is in one place.
• Real-Time Data: Enable real-time data so your underwriting team has up to date information always.
• Set Up: Work with Blooma’s technical support team to set up the platform to your specifications.
• API Integration: If required, integrate Blooma’s API with your existing systems to automate the data and process flow.

Step 3: Training and Onboarding

• User Training: Run training sessions for your underwriting team to get them up to speed with Blooma.
• Ongoing Support: Provide ongoing support and resources, such as user manuals and video tutorials, to help your team improve.
• Customized Programs: Develop role-based training programs that focus on the specific needs and tasks of different team members, underwriters, analysts, and compliance officers.

Step 4: Optimise Blooma’s Features

• Predictive Modelling: Use Blooma’s AI-driven predictive modeling to forecast market trends and risks so you can make better decisions.
• Risk Assessment: Use real-time risk assessment tools to calculate loan-to-value (LTV), debt service coverage (DSCR), and net operating income (NOI).
• Data Aggregation: Automate the gathering and aggregation of data from multiple sources to speed up the underwriting process.
• Alerts: Set up alerts to notify your team of critical changes or risks in real-time.

Step 5: Continuous Improvement

• Performance: Review your underwriting process and Blooma’s impact.
• Feedback: Get feedback from your team and make changes.

Transform Your CRE Underwriting with Blooma

Adopting best practices in underwriting has many advantages. By working with Blooma, you’ll eliminate guesswork, reduce friction and operating costs, and make data-based decisions for your business. 

With full training and integration with your existing systems, Blooma can facilitate a hassle-free adoption of better underwriting techniques and tools.

Are you ready to transform your CRE underwriting experience?

Get in touch with us today to book a demo and learn how Blooma can put you on the road to underwriting success.