Make the expensive decision with the full maths.
Rent or buy? Lease or buy? Pay cash, or invest it? These model both choices side by side — including the opportunity cost of the money you don’t spend — and tell you what would have to change for the answer to flip.
Most online calculators answer an easier question than the one you asked. A rent-versus-buy tool compares a mortgage payment to a rent cheque; a lease calculator compares monthly payments. Both quietly ignore what the other option does with the money it did not spend, which is the entire point of the comparison.
Choose a decision
Find out which leaves you with more wealth — and how long you would have to stay for that to be true.
Compare cash, financing and leasing over the years you actually keep the car, with every dollar dated.
Compare real estate against putting the same capital in the market — leverage, vacancy and upkeep included.
Why Frugal?
Each of these runs every option month by month and prices what the money would otherwise have been doing. How depends on the question — comparing two households over thirty years, three ways to pay for one car, and two uses of the same capital are not the same exercise — so each model says which footing it uses.
- Equal footing. Both choices get the same resources. Money not spent on one option stays invested in the other, and the model refuses to report a result if the two ever diverge.
- Nothing hidden. Every assumption is visible and adjustable, and each result says how many of them are still our defaults rather than yours.
- Know when the answer changes. Every result names the tipping point for each input — the value at which the decision reverses — and which input it is most sensitive to.
- No false certainty. These are models, not predictions. When the margin is too narrow to call, the page says so instead of declaring a winner.
Three things these do that a payment calculator cannot
The arithmetic of a monthly payment is not hard, and there are hundreds of tools that will do it. What follows is the part that decides whether the answer is any use to you.
The tipping point, solved rather than guessed
Knowing that buying wins by $40,000 is worth much less than knowing what would have to be true for it to lose. So every result is run backwards: for each input in turn, the model holds everything else still and solves for the value at which the verdict flips. Not a nudge up and down — an actual search for the crossing point, reported as a number you can argue with.
That turns a vague worry into a checkable claim. “Buying wins unless appreciation comes in under 1.8% a year” is something you can hold against thirty years of local history. “Buying wins by $40,000” is not. The same pass names the input the answer is most sensitive to, which is usually not the one people spend their time arguing about.
A sensitivity grid, not a single scenario
Two assumptions dominate every one of these decisions, and nobody knows either of them: what the market returns, and what property does. Rather than make you change one, re-read, change it back and try again, each result carries a grid of verdicts — the answer computed across a full range of both at once.
What you are looking for is the shape. A grid that is one colour means the decision is robust and you can stop worrying about the assumptions. A grid split down the middle means the honest answer is that it depends, and the page says so rather than picking the corner it happens to have started in.
Monte Carlo, because the average future never happens
Running one set of assumptions tells you what happens if the next thirty years are smooth. They will not be. The rent-vs-buy model can run thousands of randomised futures, drawing returns and appreciation year by year rather than applying a tidy average, and reports how often each choice came out ahead.
The answer is a probability and a spread, which is a far more honest object than a single number. A 55% chance of coming out ahead is a coin-toss dressed up as a decision; a 90% chance is a decision. Sequence matters too — a bad first five years hurts in a way the same five years later on does not — and only running the paths shows it.
What these will not do
These are models, not predictions. The inputs that matter most — investment returns, appreciation, rent growth — are unknowable, and no amount of arithmetic makes them knowable. That is why these tools are built to show you how much your assumptions matter rather than to hand you a number and imply it is settled.
They also will not price the things that usually decide the question. Whether you want to be the person who fixes the boiler, whether the commute is survivable, whether you will still want to live there in six years: none of that is financial, and a calculator that pretended to weigh it would be lying. The numbers are one input to the decision, and frequently not the deciding one. Each calculator’s methodology page lists what it deliberately leaves out, and those omissions usually matter more than any rounding.
Questions people ask
Are these calculators free?
Yes, entirely, with no account and nothing to install. They run in your browser. The site is paid for by advertising, which is why there is no version that charges you for the answer.
Do you store the numbers I type in?
No. Scenarios are calculated and then discarded — nothing you enter is saved to an account, because there are no accounts. The privacy policy sets out exactly what is and is not collected.
Why does your answer differ from other rent-vs-buy calculators?
Almost always because of the invested difference. Most tools compare a mortgage payment against a rent cheque and stop there, which quietly assumes the renter sets fire to the money they did not spend on a deposit. These models keep both households on equal footing: whatever one option does not spend stays invested in the other, and the accounting refuses to report a result if the two ever diverge.
What is the break-even point, and why does it matter more than the verdict?
It is the year at which the two paths cross — before it, one choice is ahead; after it, the other is. It matters more than the final number because you control it: how long you stay is a decision, while appreciation and market returns are not. If the break-even lands past the point you realistically expect to move, the verdict at thirty years is irrelevant to you.
How accurate are these models?
The arithmetic is checked on every build and the checks are published on the model integrity page, including an independent reimplementation that reproduces every headline figure to the cent. But arithmetic being sound is not the same as a model being right for your decision. The assumptions you feed it matter far more than any rounding, which is why every result reports how sensitive it is to them.
Do these include tax?
The rent-vs-buy and rental-property models do, optionally: federal brackets, the standard deduction versus itemising, the SALT cap, capital gains and the primary-residence exclusion. It ships switched off so every published figure is pre-tax and comparable. When you switch it on, the page shows exactly which rules were applied.
Can I trust a calculator that shows adverts?
Judge it on whether the workings are open. Every assumption is on the page, every methodology is published, the invariant checks run in public and the models decline to call a result that is too close to call. Advertising pays for the hosting; it does not get a say in which answer comes out, and there is no affiliate link to a lender or an estate agent anywhere on this site.
Why do some results refuse to name a winner?
Because the margin is inside the noise. When the gap between two options is smaller than the effect of shifting an unknowable input by a plausible amount, declaring a winner is false precision. In that case the honest output is that the decision is a toss-up on the numbers and should be made on the things the model cannot price.