Final Peer Review ell65

[ell65]

This group aims to analyze building permit applications in San Francisco and see if the model they develop on this dataset is also applicable to a similar dataset on permit applications in NYC. I am initially intrigued by this project proposal because of the vast differences in the two cities including geography and climate, which would seem to affect building permits significantly. I have worked for a contractor before and have dealt with these municipal entities firsthand and am therefore very interested in the possibility that such a model could allow builders to analyze the feasibility of a project without waiting long amounts of time and spending money on applications only to be denied.

Some things I liked:

• This type of model could be useful to improve efficiency in businesses as well as evaluate whether a government is responsibly issuing permits.
• The group noticed some significant limitations to their data such as the issue of recently filed permits that are experiencing longer approval times not showing up in the data due to still being in the process. However, they also noticed that simply removing every entry where a permit has not been issued would be inherently problematic as well. I thought this section was an astute analysis of the trends in their data.
• The group tried many models on their data to find what worked best with their dataset. I found the analysis of the error metrics to be very well-explained and the group got some fantastic error rates in my opinion!

Suggestions:

• I found the addition of the fire department, business, and restaurant datasets interesting, but would have appreciated a bit more insight about why the group thinks that fire department calls, businesses, and restaurant inspections would predict the number of permits in a zipcode. I am wondering if these things would almost have more of an effect on the days to issuance than the number of permits in a zip code.
• For the zip code analysis, it might be worth exploring the differences in those zip codes to see if there is anything about certain areas that would alter the results.
• While there were certainly a handful of graphics in the report, I imagine there may be a visual representation other than a histogram or scatter plot that could provide insight about this problem

Overall fantastic project!