Land value tax explorer

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  Color coding the tax difference after applying land value tax

Inspiration

As we walk through San Francisco, we see many unused lots, vacant office buildings and commercial spaces, and empty apartments. At the same time, people live in tents on the street due to the unaffordability of property in the city. Clearly, the incentives of landowners are not aligned with those of the residents of SF.

What it does

We built a web dashboard which allows citizens and legislators to visualize the effects of a proposed land value tax change in San Francisco. This change decreases the existing property tax assessments while charging a flat rate per square footage of each lot. The map highlights areas with increased taxes in light blue, whereas areas with decreased taxes are shown in a darker blue. This reveals that a majority of properties would receive reduced tax bills, whereas speculators holding land that should be used for more productive purposes get charged more.

How we built it

We found data from DataSF for property tax and boundary data. We used GPT4 to identify promising targets for case studies. We created a Vercel app using Mapbox to display lots color-coded by tax impact.

Challenges we ran into

Lots of the data from DataSF was either missing or inaccurate. It was difficult to work around these values to produce a compelling visualization.

Accomplishments that we're proud of

We pulled together an impressive team based on a vision alone. Some members of the team weren't aware of the concept of a land value tax before starting, but we were all on board after some short discussion.

What we learned

Map data visualisation, all about land tax and how hot having it enables speculation with peoples basic needs.

What's next for Land value tax

Ideally a separate model should be trained on the case studies to individually provide evaluation on a separate plot of land or office building, and we can test different parameters of the simulation.

Built With

• geojson
• gpt
• mapbox