Inspiration
Houston's Real Estate market has been booming recently. A key metric that any real estate mogul is interested in is the amount of tax they might have to pay for a particular property they are interested in. Levyrage can quickly show you if you are in a high-paying part of town based on zip code.
How it works
Zip codes are rendered as geojson on top of a map of the Houston area. Zip codes are then colored darker if their average property tax paid is high compared to other zip codes. The user can mouse over each zip code for some basic details.
Challenges I ran into
HCAD data does not have a schema that relates their tables and makes it difficult to reverse engineer their flat files.
Accomplishments that I'm proud of
The team worked very well together. Each member was willing to help carve out tasks that could be developed in parallel. We mitigated risk by keeping our goals realistic with the given time frame. We mixed new technologies for a learning aspect with prior technology experience in order to keep our velocity high.
What I learned
If you have a lot of data in flat files, skip trying to import it into a database to calculate averages, etc. If possible, just parse the data with a script and do your calculations in memory.
What's next for Levyrage
During development, we discussed the potential of creating a map that steps through multiple years of tax information and shows how property tax might have shifted over the past ten years. A different slice of the data could be used to build a prediction engine about where property taxes are trending based on historical data and then highlight zip codes as potential hot spots for investors.
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