u/franky_on_steroids

I want to build a LongtermTrends rent-vs-buy tool, and maybe later add a guided decision agent around it, similar in spirit to the FIRE Agent but for housing decisions.

I do not want it to be another calculator that pretends the answer is simply "rent" or "buy."

By agent, I do not mean a bot that spits out a verdict. I mean a guided assistant that helps gather the context that matters: the financials, country-specific assumptions, risk tolerance, values, family plans, job and income stability, need for freedom, desire for roots, liquidity, concentration risk, and what each option would do to daily life.

The point would be to assist the decision, not make it for the person.

The math still matters. A good tool should compare renting plus investing against buying, not just rent payment versus mortgage payment. LongtermTrends already has housing context charts like home prices vs. median income, home prices vs. inflation, stocks vs. real estate, stocks vs. bonds, and the 60/40 portfolio. But charts alone do not answer a household-level rent-vs-buy decision.

Looking at existing tools, PWL's rent-vs-buy calculator is strong because it compares after-tax net worth and is built for a serious planning context, but it is Canada/province-aware. The New York Times calculator has a polished sensitivity feel and US-tax-law assumptions. NerdWallet's calculator decomposes costs well, but it lives in a mortgage marketplace environment.

The country issue seems important. Mortgage rules, property tax, transaction costs, stamp duties, tax deductions, capital-gains treatment, rent regulation, subsidies, notary fees, insurance, and taxation of the renter's alternative investments can all change the answer. A US-first tool, a Canada-first tool, and a Switzerland/Europe-aware tool are not the same product.

For a first version, I am leaning toward a geography-neutral model: user-supplied assumptions, no built-in country-specific tax engine, and optional tax adjustment fields defaulted to zero. The result would be "under these assumptions," not "you should rent" or "you should buy."

But the spreadsheet is not the whole decision.

Buying can support stability, family, roots, control, schools, community, and a sense of home. It can also reduce flexibility, concentrate net worth, increase fixed costs, add maintenance work, and make it harder to move for work, family, health, or freedom.

Renting can support flexibility, lower fixed obligations, geographic freedom, and keeping capital invested or liquid. It can also create uncertainty, less control, weaker roots, and exposure to rent increases or landlord decisions.

There is an old Berkshire meeting joke I like here. At the 1998 Berkshire Hathaway annual meeting, Buffett pushed Charlie Munger on when a married person needs a house. Munger's answer: "when your wife wants one." Very old-fashioned, yes, but it gets at something true: housing is never only a spreadsheet decision.

This is also where the last LongtermTrends meetup on money and happiness feels relevant. We used the PERMA-V model from positive psychology: Positive Emotion, Engagement, Relationships, Meaning, Accomplishment, and Vitality. The frame came from Ben Felix / PWL material such as Using Your Money To Be Happier, Finding and Funding a Good Life, PWL's Goal Survey Summary, and the Rational Reminder episode on a financial goals master list.

That material made one point very concrete: many financial goals are surface versions of deeper life goals. "Buy a home" might really mean stability, privacy, family rhythms, shorter commute, roots, school access, or room for hobbies. "Keep renting" might really mean freedom, optionality, career mobility, lower stress, more liquidity, or not being tied to one place.

So I am trying to think about this as three layers:

1. The calculator: under these assumptions, what happens financially?
2. The life checklist: what kind of ordinary week does each choice create?
3. The agent: what context should be gathered before someone can make a decision they can still defend later?

I would love your reaction to this direction.

Do you think this is the right way to think about rent vs. buy, or am I missing something important?

If you already bought a home: what mattered most at the time? Are you happy with the decision now, or do you regret parts of it?

If you rent: is that mostly because of money, flexibility, lifestyle, uncertainty, or something else?

If you are deciding now: what feels hardest to compare?

Have you used rent-vs-buy tools before? What helped, and what did they fail to capture?

What context is actually needed to make this decision well? Financials, country rules, taxes, maintenance, opportunity cost, risk tolerance, relationship/family plans, schools, commute, job stability, freedom, roots, stress, health, something else?

What should the tool look like? What should it calculate, compare, or stress-test? Are there other tools, calculators, agents, or workflows you have used that you liked and would want this to learn from?

What should the agent look like? What should it ask, how should it behave, and what kind of output would actually help you or would have helped you in the past?

In short: what is important to you when making the rent-vs-buy decision, and are we addressing all of it?

I want to address a common critique of the LongtermTrends page on the Buffett Indicator and add some nuance.

The page currently shows several versions:

Wilshire 5000 / GDP as the main Buffett Indicator chart, public and private corporate equities / GDP as a broader quarterly series, and then Dow / GDP and S&P 500 / GDP as narrower historical/proxy views.

The common critique is: "U.S. market cap divided by U.S. GDP is flawed because U.S.-listed companies earn revenue globally, so you are comparing a partly global numerator with a domestic U.S. denominator."

There is also a broader critique: some people argue that GDP is an increasingly incomplete measure of the economy, especially in a world of software, data, free digital services, and AI tools. It is useful and standardized, but it was designed to measure production, not welfare, distribution, sustainability, or the full value people get from digital goods that are cheap or free.

Those critiques have truth in them, but the terms matter. The latest official S&P DJI / FactSet / FactSet Revere data I found show S&P 500 companies getting about 71% of revenue from the U.S. and therefore about 29% from outside the U.S., based on fiscal 2024 foreign-sales figures.

That raises the next question: if S&P 500 companies get roughly 29% of their revenue outside the U.S., does U.S. GDP include anything comparable on the foreign side?

This is where the terms get tricky. The numerator-side number is company revenue by customer geography. The denominator is GDP, which is not company revenue. The BEA defines GDP as the value of final goods and services produced in the United States.

So foreign demand is partly included, but only in a specific way. In the expenditure formula, exports are included and imports are subtracted. If a U.S. company produces something in the U.S. and sells it abroad, that is part of U.S. exports and therefore part of U.S. GDP.

But that is not the same as saying all foreign revenue of U.S.-listed companies is included in U.S. GDP. Using BEA/FRED exports and GDP, U.S. exports were about 10.8% of GDP in 2025 and about 11.1% in Q1 2026. Exports are material, but they are not the same size as the S&P 500's non-U.S. revenue share.

Revenue from foreign subsidiaries, foreign production, or foreign supply chains does not map cleanly into U.S. domestic production. And even exports are not directly comparable to corporate revenue, because company revenue is gross sales, while GDP is value added.

There is one more wrinkle: Buffett's original version did not use GDP. In his 2001 Fortune essay, he talked about total market value relative to GNP. Jesse Felder also stresses this framing when he calls it the Buffett Yardstick.

That may matter conceptually. GDP measures production inside U.S. borders. GNP measures production by U.S. residents, wherever they are located. So GNP may sound like a better match for U.S. companies with global operations.

But I am not sure it solves the issue. GNP still is not company revenue, and for the U.S. today it is very close to GDP: FRED GNP was about $31.883T in Q1 2026, versus GDP of about $31.819T. That means GNP was only about 0.2% higher.

Looking at other Buffett Indicator pages, most either turn the ratio into a valuation signal or add a small caveat section. Current Market Valuation labels the market against trend and standard-deviation bands and discusses interest rates and international sales. Advisor Perspectives / dshort compares GDP and GNP versions and says they differ very little, while also warning that the indicator is not useful for short-term timing. buffettindicator.org even shows a globalization-adjusted version. Investopedia and Corporate Finance Institute mostly explain the formula, valuation zones, and limitations. I am trying to figure out whether LongtermTrends should add a stronger caveat, a clearer signal, or some alternative comparison.

So this is the part I am unsure about.

First: is it fair to compare the market cap of U.S.-listed companies with U.S. GDP at all? Does the fact that U.S. companies operate globally make the Buffett Indicator invalid, or just less precise? Is the foreign revenue of U.S.-listed companies reflected enough in GDP through exports, or not really? Would GNP/GNI be a better denominator, since Buffett originally used GNP, or is the difference too small to matter in practice?

Second: is GDP itself still a good denominator here? Is it a useful standardized measure of the economy, or is it too flawed in an age of AI, free digital services, data, software, and intangible value?

If you were editing the page, what would you add? Would you add a caveat, a critique, a disclaimer, a different chart, or some kind of valuation signal? What wording would help people interpret the Buffett Indicator better? And are there better measures you would compare it with?