High Peak Financial
IMG_4035.jpg

Blog

This blog is written by Austin Conner and covers a mix of business topics that interest me.

I am a contract CFO/financial consultant and I work with with a number of business models and industries: recurring revenue (SaaS - B2B and B2C, ecommerce subscription, membership, consumer products), paid apps (edtech, fitness), internet advertising and traditional ecommerce/retail.


Using a Choropleth Map to better understand CFPB mortgage product complaint data

There are so many ways to look at a large data set like the CFPB complaint database. I was curious how mortgage complaints varied by state. Since we had three digit zip codes for each complaint, we could also highlight what large city in each state had the highest number of complaints by merging the city names found in another dataset. The Choropleth map below is shaded based on the number of mortgage complaints in that state. As you scroll over the map, you will see a pop-up with the top issue for the mortgage complaints in that state, the issue as a percent of total number of complaints in that state, and the location of the highest number of complaints in that state (the 3 digit zip code is the last number).

“Trouble during payment process” is the most common issue.


Do the city areas with the highest number of mortgage complaints also have the highest concentration of consumers “struggling to pay mortgage?”

In the following chart, Rank (x-axis) is based on the number of total mortgage product complaints. Bubble size is scaled based on the total number of complaints so larger bubbles mean more complaints. The y-axis is the percent of mortgage complaints that highlight the issue “struggling to pay mortgage.’

bubble.jpg

In this scatter representation, we see that vertical bubbles lower on the y-axis may be areas with residents of higher socioeconomic brackets.


You can go to the Jupyter Notebook for the analysis by clicking on the button: