The Housing and Urban Development department calculates fair market rents at the county level to support their rent assistance programs (e.g. [0]), although that starts to get at part of the complexity: for your metric do you care about the economic cost or the amount families actually have to pay after assistance?
On the NGO side, groups like the NBER (https://www.nber.org/) disseminate more exotic socioeconomic studies, and there are others.
And of course you can find a mountain of data at data.gov, the federal portal for such things.
I think the harder part (and the part the policy community specializes in) is grappling with the nuances of those kinds of numbers. What do such high-level observations actually mean in something as complex as a continent-wide collection of 10^9 people, and how much human messiness polluted their measurement?
Take California, for example: asking rents have increased by 26% over the last two years. But this figure reflects only asking rents, not what people actually pay—especially in areas with rent control.
If you’re looking to upgrade to a bigger place (e.g., moving in with a partner or preparing for a baby), it might feel like the economy is in shambles. On the other hand, if you’re staying put in a rent-controlled apartment, things might seem manageable.
The data is there—it’s the context and perspective that shape the story.
Home Rent Affordability: Analyzing affordability by profession and location to reflect real-world dynamics.
Home Price Affordability: Assessing how accessible homeownership is across regions.
Education Costs: Understanding the burden of education expenses on families.
School Quality: Measuring the actual quality of education available.
Healthcare Cost Affordability: Evaluating access to essential healthcare without financial strain.
Drug crisis.
Crime.
Vacations and similar things.
Developing such metrics would provide a more accurate picture of economic well-being and social equity.