Vibe Coding
An informal term for the practice of building software applications through natural language conversation with an AI model, without writing traditional code directly. Rather than specifying syntax, the user describes what they want to build and the AI generates the working code. Vibe coding platforms such as v0, Lovable, and Bolt have made it possible for CRE professionals without technical backgrounds to build custom tools, dashboards, and automation workflows tailored to their specific underwriting or asset management needs.
Putting Vibe Coding in Context
An asset manager who wants a custom dashboard to track lease expirations and NOI variance across a 12-property portfolio describes the tool in plain language to a vibe coding platform. Within a session, the platform generates a working web application with the relevant data inputs, display logic, and formatting. The asset manager iterates by describing changes conversationally, arriving at a functional internal tool without involving a developer or waiting on an IT backlog.
Frequently Asked Questions about Vibe Coding
What kinds of CRE tools can realistically be built through vibe coding?
Vibe coding is well suited for building lightweight internal tools that would otherwise require developer time to produce. In CRE, practical examples include deal screening calculators, portfolio summary dashboards, lease expiration trackers, investor reporting templates with dynamic inputs, and simple data entry interfaces that feed a spreadsheet or database. Tools with clearly defined inputs, outputs, and display logic are the strongest candidates. Highly complex financial models with intricate interdependencies or tools requiring deep integration with enterprise systems are better suited to purpose-built software or a developer-assisted approach.
How does vibe coding differ from using a workflow builder or writing prompts in ChatGPT?
A workflow builder connects existing applications through pre-built logic, and prompting a general AI model produces text or analysis outputs. Vibe coding produces an actual software application with its own interface, logic, and data handling that can be shared, deployed, and used repeatedly as a standalone tool. The output is code, not content, which means the resulting tool can be iterated on, hosted, and embedded in a firm’s existing operations in a way that a chat response or workflow template cannot.
What are the main limitations of vibe coding for CRE professionals to understand?
The primary limitation is that the generated code may contain errors or structural weaknesses that are not visible to a non-technical user until the tool fails in use. Because the user cannot read or audit the underlying code, bugs can go undetected, particularly in calculation logic where a formula error in an underwriting tool could produce plausible but incorrect results. Vibe coded tools also tend to become difficult to maintain or extend as complexity increases, since each iterative change adds code that may conflict with earlier logic. For tools that will be used in a live deal process or shared across a team, some level of technical review is advisable before relying on the output.
Which vibe coding platforms are most relevant for CRE professionals today?
The platforms most commonly used for vibe coding as of 2025 are v0, developed by Vercel and focused on user interface generation, Lovable, which emphasizes full application building from a conversational prompt, and Bolt, which targets rapid prototyping with deployment capability. Replit also offers an AI-assisted coding environment that falls within the broader vibe coding category. Each platform has different strengths in terms of design quality, deployment options, and the complexity of applications it handles well. CRE professionals building internal tools typically benefit most from platforms that offer easy data input handling and clean tabular or dashboard-style output.
Does vibe coding require any technical knowledge to use effectively?
A non-technical user can produce functional tools through vibe coding, but a basic understanding of how software applications are structured significantly improves the quality of what gets built. Knowing the difference between front-end display logic and back-end data handling, understanding what an API connection does, or being able to describe data relationships clearly all help the AI generate more accurate and usable code on the first pass. The skill gap that matters most is not coding syntax but the ability to specify requirements precisely, which is the same prompt engineering discipline that improves outputs across all AI tools.
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