dealnado

Our Mission

Deal analysis that works for everyone

Dealnado exists because running the numbers on a rental property shouldn't require a spreadsheet degree, hours of research, or a team of analysts. It should take minutes.

Why we built this

Every real estate investor — from the first-timer standing at an open house to the seasoned landlord vetting deal number fifty — deserves fast, accurate analysis. But traditional tools make you do all the legwork: look up property taxes, call around for insurance estimates, Google local utility averages, and manually enter every line item. Then you do the math.

We think that's backwards. You should spend your energy evaluating the deal, not assembling the inputs. So we built a tool that does the research for you — pulling expenses, market rents, and comparable cap rates automatically — so you can focus on making smart decisions.

Simple as that.

What we believe

Always improving

Dealnado is a living product. We ship regularly, listen to feedback, and keep pushing accuracy and features forward. What you see today is just the start.

Feedback is fuel

We genuinely want to hear from you — bugs, ideas, complaints, wish lists. Every piece of feedback makes the product better. Don't hold back.

Honest about AI

AI-generated data is powerful but not perfect. Estimates can be off. We're transparent about that, and we're constantly working to improve accuracy and broaden our data sources.

Serious work, fun vibes

Real estate deals are real money. We take that seriously. But there's no reason the tools you use can't be fast, beautiful, and even a little fun.

A note on accuracy

Dealnado uses AI and third-party data to estimate property expenses, market rents, and cap rates. These figures are a starting point, not ground truth. Local market conditions, property specifics, and data coverage vary. Always verify critical numbers with your agent, property manager, or local sources before making any investment decision.

We're continuously expanding our data sources and improving our models. If you see something that looks off, tell us — that feedback directly shapes how we improve.

Ready to see it in action?