The Road Home Project

High Level Views

Let’s get a quick overview of the dataset you gave me to see if any immediate properties stick out.

##   GenderDesc                              RaceDesc        Age       
##  Female: 30   American Indian or Alaska Native:  5   Min.   :22.00  
##  Male  :196   Black or African American       : 31   1st Qu.:39.25  
##               Multi-Racial                    :  4   Median :53.00  
##               White                           :186   Mean   :49.96  
##                                                      3rd Qu.:59.00  
##                                                      Max.   :86.00  
##                                                                     
##    EnrollDate            ExitDate           DaysEnrolled   
##  Min.   :2014-07-21   Min.   :2014-07-22   Min.   :  2.00  
##  1st Qu.:2014-11-05   1st Qu.:2015-01-07   1st Qu.: 19.50  
##  Median :2015-01-07   Median :2015-03-31   Median : 61.00  
##  Mean   :2015-01-17   Mean   :2015-03-11   Mean   : 71.12  
##  3rd Qu.:2015-03-29   3rd Qu.:2015-05-31   3rd Qu.:120.25  
##  Max.   :2015-07-17   Max.   :2015-07-31   Max.   :184.00  
##                       NA's   :30                           
##   LenthofStay        Enrolled StillEnrolled       Exited     
##  Min.   :  1.00   Min.   :1   Min.   :0.000   Min.   :0.000  
##  1st Qu.: 19.50   1st Qu.:1   1st Qu.:0.000   1st Qu.:1.000  
##  Median : 68.50   Median :1   Median :0.000   Median :1.000  
##  Mean   : 74.27   Mean   :1   Mean   :0.146   Mean   :0.854  
##  3rd Qu.:133.00   3rd Qu.:1   3rd Qu.:0.000   3rd Qu.:1.000  
##  Max.   :183.00   Max.   :1   Max.   :1.000   Max.   :1.000  
##  NA's   :30                                                  
##     ClientID    
##  Min.   : 1097  
##  1st Qu.:37856  
##  Median :54920  
##  Mean   :47384  
##  3rd Qu.:60429  
##  Max.   :69247  
##                 
##                                                     ExitReason 
##  Completed Program                                       :178  
##  Criminal activity/destruction of property/violence      :  1  
##  Death                                                   :  1  
##  Left for a housing opportunity before completing program:  4  
##  Non-Compliance with Program                             :  4  
##  Other                                                   :  4  
##  NA's                                                    : 34  
##                                                                                                                       ExitDestination
##  Rental by client, no ongoing housing subsidy                                                                                 :99    
##  Rental by client, VASH Subsidy                                                                                               :62    
##  Emergency Shelter, including hotel or motel paid for with shelter voucher                                                    :10    
##  Place not meant for habitation (e.g., a vehicle, an abandoned building, bus/train/subway station/airport or anywhere outside): 4    
##  Rental by client, other (non-VASH) ongoing housing subsidy                                                                   : 4    
##  (Other)                                                                                                                      :17    
##  NA's                                                                                                                         :30    
##   monthExited    
##  Min.   : 0.000  
##  1st Qu.: 2.000  
##  Median : 4.000  
##  Mean   : 4.845  
##  3rd Qu.: 6.000  
##  Max.   :12.000  
## 

Initial Observations:

  1. The median age of clients is ~50 years old.
  2. The average number of days your clients stay enrolled is 71 days.
  3. 14.6% of your clients are still enrolled.
  4. (obviously) 85.4% have exited the program
  5. 44% exited with rental and without any ongoing subsidy, followed by 27% that recieved a VASH subsidy, which seems to me like a success rate of at least 71%.
  6. You have 6.5 males to every female client.

The Main Question

What’s peaked my interest is to figure out the percentage of clients who exited the program in a “bad” way and see if there are any particular properties that stand out among them.

A high nonRental percentage is considered a bad thing, so we will typically be looking to find low spots in the graph because these will me high rental outcome rates (and obviously, low nonRental outcome rates).

NOTE: I’m going to consider anything that doesn’t result in renting a “bad” result (hereby known as nonRental). So the majority of this analysis will consist of attempting to understand this.

nonRental Type

It looks like the highest nonRental outcome is Emergency Shelters, and after that uninhabitable living quarters.

nonRental Age

It does appear as though there is a peak at 30 and 50-60 year olds in nonRental outcomes, but as you can see by the regression line, it isn’t anything too crazy.

This is explained by the fact that you have two main sets of age groups that you work with, namely a ~30 year old group and a 50-65 year old group.

Females

In our early conversations and in my exploratory analysis I noticed a strong correlation between lack of completion and Female clients. Let’s see if this holds true for nonRental outcomes.

##   GenderDesc   nonRentals 
##  Female:30   Min.   :0.0  
##  Male  : 0   1st Qu.:0.0  
##              Median :0.0  
##              Mean   :0.3  
##              3rd Qu.:1.0  
##              Max.   :1.0
##   GenderDesc    nonRentals    
##  Female:  0   Min.   :0.0000  
##  Male  :196   1st Qu.:0.0000  
##               Median :0.0000  
##               Mean   :0.1122  
##               3rd Qu.:0.0000  
##               Max.   :1.0000

Females have a 70% chance of renting in the program, while males have an 88.8%.

Let’s investigate this further.