There is clearly problem with the immediate nature of such market, that is being forced to move out quickly out of a property if one undervalues it.

Then there is clearly the problem with current market set up, which basically allows people to trade with infinite length property rights, especially on land. This gives incentive to buy and never sell, because you know that new generations will have to live somewhere and you will be the one able to provide this service. Esentially leading back to pure feudalism.

A middle ground solution could be to make the land market with land tax not immediate, ie. sell whenever someone else outbids you, but as auction for the property every, let's say, 7 years.

This would give you enough time to consider investing into the property withou losing it right away due to trivial underpricing. It would also solve the optimal property allocation, just on longer term. Another advantage for agricultural land would be that a piece of land couldn't enter the market unless it underwent the seventh sabbatical year of resting, which you could actually enforce under this system.

* > The solution: users would choose an AI to generate real time valuations.*

Sadly this isn't a solution, it just adds another layer of complexity to the decision making process. AI isn't magically unbiased, there's a multitude of ways for human bias/ particular interests to enter the model, from feature selection to model layout up to interpretation of the results.

So now people have to be both experts at assessing housing and neighborhood values and AI to make an informed decision.

That's the big problem of market-based social designs: There's always the fundamental asymmetry between a professional, better-equipped actor (a corporation specialized in that particular market, or a rich person like the bezos example, who can outsource that to employees) and a normal person.

Regulation, society's solution to that problem, tries to level the field: People knowledgable in the problem domain think about possible negative externalities and risks to citizens, and implemenent barriers for abuse.

Normally I would agree with you completely, indeed AI is (very) far from perfect. However, I think we're hung up on the home property example. As Vitalik suggests, this proposal works a lot better in situations where the underlying property is more fungible, and the participants in the market are more or less equal. The property example I like from the article is radio spectrum licenses.

I'd also add that using an AI also doesn't necessarily mean something opaque. A hand-made decision tree would be preferable to a neural net, for example. The decision tree could prompt you for your human decision if it encounters an outlying, uncertain instance.

I actually find the housing problem quite apt, because it so easily illustrates the problem of information asymmetry of the market participants. If you can't guarantee that, you don't have an efficient market. How can you prevent that, if not with the help of protection through law (i.e. regulation)?

> this idea could work very well in other types of property markets

Constantly offering to the market is market making. It's a difficult, risky and specialized domain in any asset classes. Most markets cannot support real-time market making for the simple reason that price discovery is expensive and intrinsically tied to liquidity.