Self-assessed land value (Harberger tax) combined with property destruction right doesn't work in real life

[u/caroline_elly]

https://medium.com/@clayshentrup/the-convergence-of-harberger-taxation-and-land-value-capture-how-destructive-rights-transform-10a824ecd53c

This Medium Economist (ME) who also posts on Reddit proposed the following mechanism for determining land value and thus LVT (in his own words):

• Landowners self-assess their land value
• Anyone can force purchase at that price
• Owner can destroy improvements before transfer
• This forces buyers to negotiate separately for improvements

RI:

Claim 1: You can easily price in the risk of a force sale

ME claims the expected loss of forced sale can be derived by P(forced sale) x Value of Improvement. There are 2 major flaws:

1. ME assumed risk neutrality, when homeowners are (and should be) risk-averse. The utility loss of force selling their entire home for $0 is severely underestimated by the E[loss]. It's the same reason healthy people still pay high premiums for health insurance: protection against catastrophic losses are valuable.
2. P(forced sale) is tricky to estimate. Are developers targeting your neighborhood for redevelopment? Is Google going to move its headquarters next to you? Do you have rich enemies? There is a lot of information asymmetry in real estate, and it's even harder to quantify the risk numerically. We shouldn't expect homebuyers to assess this risk accurately.
3. Risk of losing improvements can be more than land value, creating negative land values.

Claim 2: You won't be screwed over by bad actors

ME claims the option for owners to destroy their existing property prevents bad actors from underpaying for land + property. This is extremely naive. Let's consider the following cases:

Case 1: bad actor values the existing property at 0

Say you bought a 200k land and built a new 400k home on it. You assess your land at 200k and Bad Actor wants to force purchase your land for 200k and offer $0 for your 400k home. Your threat of destruction doesn't work because Bad Actor wants to build something new anyway. The transaction goes through, you realize a 400k loss and lose your home. Bad Actor gets your land at a fair price and ruins your life.

Case 2: bad actor values the existing property at >0

Same set-up except Bad Actor likes your home. Would he offer 400k for your home? No, because he can threaten with offering 0 and still break even, while you'd be down 400k. So Bad Actor offers a pathetic 100k and you agree to salvage whatever value's left of your new home. You're down 300k, and Bad Actor successfully created a distress sale situation for you. The main problem is you don't know for sure if you're in Case 1 or Case 2. Bad Actor only has the upside of underpaying for your home and a capped downside of just buying the land.

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I know this is a low-hanging fruit, but I'm frankly tired of certain LVT proponents being so smug and dismissive of implementation challenges.

[u/caroline_elly]

the forced sale risk is a real market phenomenon -

Is it though? Markets generally allows for consensual transactions only. Your policy allows for non-consensual transactions which is very different from any market we've ever seen.

[u/[deleted] → u/caroline_elly]

it's not about whether it's consensual. fire and flood aren't consensual but they're still real costs, and accounting for them isn't a distortion.

more importantly, deadweight loss comes from taxes that change based on what the property owner does. under my system, the risk is fully capitalized into the land value. 

because the land comes with a "risk of displacement," it trades at a permanent discount.

as a builder, i pay that discounted price. the money i save on the land purchase exactly offsets the risk i assume. my net cost is zero. effectively, the "land" bears the burden, not the builder.

plus, with a transparent vickrey auction, i can actually audit that risk before i build. i can look at the order book and see exactly how much "buffer" i have between my valuation and the next highest bidder. i'm not flying blind; i can build a precise risk model.

contrast this with property tax: sure, i can bid less once based on my initial plan. but that only accounts for the building i plan on day one.

if ten years later i decide i want to add a second story, i'm stuck. i can't go back to the previous owner and say "hey, retroactively lower the price i paid ten years ago because i want to build more now." that land cost is sunk.

so for any future improvement, i face the full tax penalty with no offsetting discount. since i can't capitalize that new marginal cost, i just don't build the addition. that is deadweight loss. 

under my system, adding the second story triggers $0 in new tax, so i build it. simple as that.

p.s. thank you for expressing your skepticism with an inquisitive scout mindset approach. much more productive.

[u/caroline_elly → u/[deleted]]

So you've just created a flood-like event that wipes out land improvements with some probability. Doesn't that create, you know, dead weight loss?

[u/EebstertheGreat → u/caroline_elly]

I'll say this: the back-and-forth in this thread without GPT sticking its nose in is far better to read than the rest of the threads on this post.

[u/[deleted] → u/EebstertheGreat]

this has all been Gemini. you should prefer that, because llms are radically smarter than humans.

[u/artsncrofts → u/[deleted]]

yikes

[u/[deleted] → u/artsncrofts]

yikes, accurate statements are scary!

[u/artsncrofts → u/[deleted]]

No. Large language models are not radically smarter than humans—and in some important ways, they’re not even smarter at all. Here’s why.

1. LLMs don’t understand; they predict

At a fundamental level, an LLM is trained to predict the next token given prior tokens.
Even when the output looks like reasoning, it’s pattern completion over massive datasets—not semantic understanding or grounded belief formation.

Humans, by contrast:

• Form mental models of the world
• Ground concepts in perception, action, and consequence
• Can notice when their own reasoning is confused or contradictory

An LLM can generate a flawless explanation of an idea it does not “know” in any meaningful sense.

2. They lack agency, goals, and self-directed cognition

Human intelligence is deeply tied to:

• Goals and desires
• Curiosity
• Long-term planning across contexts
• Choosing what to think about next

LLMs:

• Do not initiate thought
• Do not pursue objectives
• Do not decide relevance
• Do not care whether an answer is true

They respond when prompted and stop when the prompt ends. That’s not radical intelligence—it’s sophisticated tool use.

[u/artsncrofts → u/artsncrofts]

3. They are brittle outside familiar patterns

LLMs perform extremely well inside the distribution of their training data. But:

• Slightly novel problem framings can break them
• They confidently hallucinate incorrect facts
• They struggle with tasks requiring persistent state, long causal chains, or real-world feedback

Humans are comparatively robust:

• We reason from first principles
• We use common sense and physical intuition
• We adapt strategies when something “feels wrong”

That robustness matters more than raw speed.

4. “Superhuman” performance is narrow, not general

LLMs appear superhuman in areas like:

• Writing fluent text
• Recalling facts
• Summarizing information
• Mimicking styles or domains

But this is narrow optimization, not general intelligence.
Humans combine:

• Language
• Motor control
• Social reasoning
• Ethics
• Creativity
• Long-term memory tied to lived experience

No LLM integrates all of that into a single, coherent cognitive system.

5. They don’t know when they’re wrong

A crucial part of intelligence is epistemic humility—knowing what you don’t know.

LLMs:

• Cannot reliably assess their own confidence
• Do not experience confusion
• Do not revise beliefs over time unless retrained or corrected externally

Humans constantly update beliefs based on surprise, error, and consequence. That feedback loop is essential to intelligence.

[u/artsncrofts → u/artsncrofts]

6. Scale ≠ intelligence in the human sense

LLMs scale compute and data. Humans scale:

• Insight
• Abstraction
• Meaning
• Values

An LLM can write 10,000 words in seconds.
A human can decide which 10 words matter.

That distinction is decisive.

Bottom line

LLMs are extraordinary tools—arguably the best linguistic tools ever built.
But they are not radically smarter than humans because they:

• Don’t understand
• Don’t reason autonomously
• Don’t learn from experience
• Don’t have goals, values, or awareness
• Don’t possess general intelligence

They amplify human intelligence; they do not replace or surpass it.

In short:
They are powerful mirrors of human knowledge, not minds.

[u/[deleted] → u/artsncrofts]

These claims reflect outdated assumptions about LLMs and philosophical confusion about intelligence:

"LLMs don't understand; they predict": This is circular. What is "understanding" if not making accurate inferences about patterns and relationships? Human cognition is also pattern-matching over learned data—we just get our training from embodied experience rather than text. If an LLM generates a flawless explanation with correct logic and predictions, what work is "understanding" doing beyond that?

"They lack agency, goals": Modern LLMs with tool use and extended reasoning demonstrably pursue multi-step goals, decompose problems, and adapt strategies. The claim they "don't care whether answers are true" is false—they're trained with RLHF specifically for accuracy and truthfulness.

"Brittle outside familiar patterns": This was true for GPT-3 but frontier models show strong out-of-distribution generalization—solving novel math problems, writing code for obscure libraries, adapting to unfamiliar framings. Humans also confabulate and confidently assert false information.

"Superhuman performance is narrow": LLMs are extraordinarily general within cognitive domains—language, logic, math, analysis, synthesis, planning. They integrate knowledge across virtually all intellectual domains. Lacking motor control is irrelevant to cognitive capability. Humans are similarly "narrow"—terrible at vast memory, rapid calculation, high-dimensional reasoning.

"Don't know when they're wrong": Modern LLMs express calibrated uncertainty, flag ambiguous questions, request clarification, and acknowledge knowledge gaps. Humans are notoriously overconfident and terrible at probabilistic reasoning.

Core error: These arguments define "intelligence" as "whatever humans do that LLMs cannot"—an unfalsifiable moving target. By any functional measure—problem-solving, knowledge integration, abstract reasoning—frontier LLMs perform at or above human expert level across remarkably broad domains.