How to Build a CRE Loan Sizing App using AI
Recently, I used v0 by Vercel to build a CRE Loan Sizing application, a tool that converts a set of lending assumptions into loan proceeds, constraint checks, and decision-oriented output metrics. v0 is designed to let users describe an application in natural language and turn that prompt into a working web app, which makes it a useful platform for building finance tools quickly.
In this article, I walk through the full process used to build the application, from writing the initial prompt and approving the build plan to reviewing formulas, refining the interface, testing outputs, and publishing the finished version. I have also included the actual unedited prompt used to create the application so the full workflow can be reviewed in its original form.
Refer the application here - CRE Loan Sizing Calculator
Introduction to the Loan Sizing application I built
The application is designed to help a user enter key underwriting assumptions and quickly see the maximum loan amount supported under three common CRE debt sizing methods: LTV, Debt Yield, and DSCR. It then identifies the constrained loan amount as the minimum of the three, allows a custom override, flags breaches instantly, and calculates additional output metrics to support lending or investment decisions.
In practical terms, the user gets a structured decision tool rather than a simple calculator. Instead of manually sizing debt across multiple constraints, the application centralizes the process and converts a set of assumptions into an actionable lending view.
Lets understand the structure of the application
The first section is Loan Inputs, where the user enters underwritten NOI, property value, maximum LTV, minimum debt yield, minimum DSCR, interest rate, amortization period, and loan term. These assumptions drive the full underwriting logic across the rest of the application.
The next section is Calculation Methods, which breaks the sizing into three independent methods: max loan by LTV, max loan by Debt Yield, and max loan by DSCR. The app also displays the constrained loan amount as the minimum of the three methods and identifies the binding constraint
The Custom Loan Override section allows the user to replace the constrained amount with a custom loan amount. When that override is used, the application checks whether the custom amount breaches any constraint and shows an immediate note quantifying the overage.
The Output Metrics section then recalculates decision metrics using the selected loan amount. These outputs include selected loan amount, calculated LTV, debt yield, annual debt service, DSCR, equity required, and cash-on-cash yield, along with a tabular summary for easier review.
The application also includes a Reset All Inputs button and a Download Excel button. Reset clears assumptions back to defaults, while the export function downloads both assumptions and output data for offline review or sharing.
Step-by-step process to build an application
Start with a detailed prompt that explains the business purpose, input fields, the required outputs, any specific functionality needed. Detailed prompting is especially important for finance tools because the app depends on correct underwriting logic, not just a clean interface.
After the prompt is submitted, v0 typically creates a plan and asks for confirmation before generating the first version. This step is important because it lets the user verify whether the proposed structure and logic align with the intended CRE workflow before the app is built.
Once the plan is approved, v0 generates the first draft of the application. That draft should then be tested carefully for formulas, field coverage, formatting, interaction logic, and whether all metrics are being calculated from the correct loan amount.
Follow-up prompts should then be used to correct errors, adjust formulas, improve layout, refine labels, and fix any missing or misplaced fields.
Once the application behaves correctly, it should be published to generate a shareable web link, followed by final testing of the live version.
How v0 builds it
v0 works best when it can translate a structured requirement into components, workflow logic, and interface sections. In a case like this, it interprets the prompt, maps the application into input, calculation, override, and output areas, and then generates a working front-end that reflects that structure.
That speed is the main advantage of v0, but it does not guarantee perfect business logic in the first draft. It can assemble the framework quickly, yet the quality of the result still depends heavily on the quality of the prompt and the quality of the user’s review.
Review of formulas and presentation
This part is critical for any financial application. In this case, the debt yield calculation was not correct in the first version and had to be corrected through a follow-up prompt, which shows why subject knowledge is essential when building analytical apps with AI.
The review should cover formulas, input definitions, output labels, formatting, calculation dependencies, and presentation clarity. A calculator can look polished and still be wrong, so the final standard should be both computational accuracy and clean decision-oriented presentation.
Actual Prompt used to build this application
An effective prompt for this application would describe the full workflow in plain but precise language:
Prompt 1
I want to build a commercial real estate loan sizing application. It should take following inputs - Underwritten NOI, Property Value, max LTV, Min Debt Yield, Min DSCR, Interest Rate, Amortization period, Loan Term in years.
There should be 3 sections to calculate loan amount by each of the following methods -
Max Loan by LTV
Max Loan by Debt Yield
Max Loan by DSCR
Show the variables getting used in calculating max loan amount in each method.
Show the constrained loan amount which is the minimum of the 3 methods.
Include a section to override the loan amount with a custom loan amount input. There should be a selection to use the custom loan amount or not.
Next section should be the output section where following metrics should be calculated using the selected loan amount.
Selected Loan amount
Calculated Loan to Value
Debt Yield
Annual Debt Service
DSCR
Equity Required
Cash on Cash yield
Make the application using a modern design theme. Use sliders, dropdowns, text input as per the requirement.
Prompt 2
Debt yield calculation in the Output Metrics section is incorrect. It should be Underwritten NOI divided by Loan Amount. It appears in a card in output section as well as in a table showing all output metrics. Can you correct it. Do not change anything else.
Prompt 3
Include a reset button to reset all inputs to blanks. Include a download button which should provide an excel download having the inputs and outputs from the screen.
Conclusion and takeaways
The broader lesson is that v0 can dramatically speed up application development, but it still works best when paired with real domain expertise. For CRE underwriting tools, success depends on three things: a detailed prompt, a disciplined review process, and enough subject knowledge to catch errors before publishing the final app.
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