Let’s begin by looking at B&C housing maintenance code violations to understand who the worst actors are.
When we break these up by building size, what does the distribution of the number of violations look like?
Small, medium, and large buildings: 85 percentile of number of violations
## 85%
## 82
## 85%
## 87
## 85%
## 108
Where the buildings that have the most violations are: 85% for each building group
Do these properties (85%) have arrears associated with them? Using DOF property charge data
What is the average amount of arrears associated with the worst (85%) buildings (using the most recent extract date of 9/4/21)? And which property has the most $ in arrears?
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 12 1771 10143 166031 46261 36703222
## bbl units_char n_open add sum_bal_2
## 1: 3003240021 15 or more 151 15 STRONG PLACE 36703222
For the worst buildings (85%), what is the distribution of arrears associated with them [including only buildings with less than $10,000 in debt]
For the worst buildings (85%), what is the relationship between arrears and number of b&c violations?
## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
