I think my thinkings on these issues has to be elaborated on since you seem to have a number of misconceptions about them, as illustrated by this remark:
Your weakness is that you go by good intentions and nice pictures inside your head, or what you see in nice places.
I am a software developer by trade who typically work in scientific related software. I am used to looking at things in a highly technical fashion.
I am also trained in machine learning and artificial intelligence. One of the insights from these fields is that a lot of problems cannot be solved with simple set of rules. One of the big failures of AI research back in the 70s came from the pursuit of expert systems. They where based on the idea that one could represent expert knowledge through an elaborate set of rules expressed in a formalized language.
Yet this approach fell flat on its face. Reality is often exceptionally messy, and humans have developed an exceptional ability to reason about messy data. You can identify the face of a friend in a split second. But asked what chain of reasoning led you to conclude this was your friend, and you would most likely fail.
My criticism of your thinking is really about you are trying to apply an expert system style approach to a rather messy reality. This is the whole problem with overly data driven style of thinking focusing on what is measurable.
The mathematician searches around the lamppost on his hands and knees. “What are you looking for?” a bystander asks.
“My keys, I dropped them as I was leaving the bar” comes the reply. The bystander looks over his shoulder, “But the bar is back that way”, he says, pointing into the distance. “Yeah, I know, but the light is much better here” the mathematician replies.
Instead of utilizing the enormous capability of the human brain to deal with messy conflicting data, one dumbs down the issue to being about a few formalized and easily measurable parameters. In effect they are replicating the failure of 70s expert system. But instead of they make it even worse. Instead of letting a fast computer perform their simplistic decision making, they waste valuable humans brains making decision of the style a computer could make.
This is my issue with your way of thinking. You approach the problem as if we where playing the Sim City computer game. If I played a city planning computer game I would apply the same cold rational as you are doing. But we are living in the real world, which contains significantly more parameters than a computer game.
How a commute happens isn’t merely a pretty picture. You are attempting to treat commute towns in widely different cities as the same thing. I am a big fan of data, but I think it must always be supplemented with human observation and analysis. If you compare two cities on commute time, one must be able to visit those cities and look at what commute actually looks like for the citizen in practice.
If the average commute in city A is a 25 minutes but upon closer inspection it turns out that most commuters spend their time on enormous 12 lane highways, then city B with 30 minutes commute where people are say walking or biking in pleasent surroundings. Say they bike through green boulevards, parks or historical districts. If you only try to evaluate by numbers alone then city A is better. However a human can look at the number and add extra data they receive by observing each city. In the neural network of their brains all this messy data then gets processed to reach the conclusion that hey the people in city B are better off.
With such human analysis one can formulate hypothesis, such as citizens in B being happier about their commute than citizens in A. You can then perform a survey to test this hypothesis. But even after these results you are still in a messy reality. You cannot multiply or add the number of this survey with the average commute times to be left with a single number telling you which city is better. There is still need for a human evaluation.
The funny thing is that you do seem to actually accept this on occasion, but you shift in your way of thinking all through your analysis. You speak of the child playing on the concrete tarmac. Well those are consideration that has to be added to the full analysis. The problem is that your overall analysis almost exclusively focused on reducing property costs doesn’t actually capture such concerns.
You speak of wonderful parks, but those would only exist due to planning, not due to a free-for-all sort of approach to city development.
I care about increased rates of crowding, a return to the trends of the Victorian era
You simplify my approach to being singularly about a pleasant bike ride. My example of a pleasant bike ride was just to illustrate that focusing on a single parameter can lead you astray. Crowding is another parameter. The point of a holistic approach is to consider multiple parameters and find a balance.
Let me grossly exaggerate to make a point: If I can create radically lower house costs by creating an industrial style concrete jungle, then I would not do it. Certainly not if one can create a far more pleasant city with only somewhat higher house costs.
A good city would have to consider things such as how well does it work for the elderly, children, teenagers, workers and business. Do we have parks and playgrounds? Does transport options exists for those who cannot easily drive?
That is why the field of urban planning even exists. There are a ton of parameters to optimize and analyze. You seem to want to dumb it down, by letting the market make all the decisions. In such a situation where there are no coordinated decision making for the common good, you easily end up with a “tragedy of the commons” situation. Yes I know you want to alleviate that situation by pricing externalities. But I have already in previous articles elaborate on why that is frequently hard or impossible to do.