For reasons of confidentiality, I would prefer not to do so. Furthermore, implementing something like this is not yet a high priority.
There are some information services that offer global zip data, sometimes even as open source. Of course, we could combine this ourselves with other data sources on demographics, income or purchasing power. However, if something like this were available from a single source, it would naturally be easier to implement.
If you want to start a business with something like this, I would reckon that it might be hard to get started, because of the already existing competition, but it might become easier the more datasets you were able to integrate. -- Perhaps you should go deep first instead of broad. Something like starting with a single state and integrating all sort of data sources and then expand from there.
Working on revamping our calculator page on Levels.fyi to make it more useful to see refreshers and stock growth over time. Check it out at https://levels.fyi/calculator/
This is interesting since zip codes came up in consideration for how we built out our pay choropleth map in the US: https://levels.fyi/heatmap
Though ultimately it was far too granular (for example the Bay Area would be so many different zip codes). Instead we went with Nielsen's DMA (Designated Market Area) mappings within the US to abstract aggregated data a bit better. And of course this DMA dataset also had a different original use case. It was used for TV / media market surveys so it has some weird vestiges. Some regions are grouped very far and wide (you'll notice there's a bit of Denver within Nevada and its just a remnant of how it used to be categorized), but it still provides a bit of a broader level grouping than something acute like zip code.
We've also been considering using Combined Statistical Areas using population instead. This is something that is under way, and in the interim we've considered charting styles that don't necessarily need borders (for example this bubble map: https://www.levels.fyi/bubble-plot/europe/). The benefit with DMAs is that it offers full border coverage of the entire US whereas some hubs can still be missing from CSAs if relying on a population threshold. But the plan is to create some of our own regional definitions and borders using our own submissions combined with population. Will be an interesting project.
How do you think it should be treated? I think at the individual granular data point level adding a tag or note about the equity not being immediately liquid is a good start. But I don't think it'd be a good idea to weigh the stock differently since that can depend on so many things. For example SpaceX and some other private companies do offer regular liquidity and I would consider their equity close to liquid.
Appreciate the feedback though, and definitely agree we can work on how we display the data and make it more clear.
1. Salary (straightforward, on regular schedule, and you'll get it)
2. Bonuses and RSUs (various vesting rules, and ways you can never see it)
3. Startup stock and (worse) stock options (probably worthless, vesting rules, and you might need an advisor to make sure you don't exercise and come out with a big negative)
For zip code, you can view the coverage we have via our explorer page here: https://borderly.dev/explorer
Curious about the customer, can you send some more info to me at zuhaz3@gmail.com
reply