Inflation in the Car Market (7-8/8)

A launch pad for further exploration in Callisto Notebooks

Welcome back Data Science Adventurers!

In the last part of the Car Prices Project, we got everything set up to make it easy to acquire and plot FRED data. Before continuing, make sure you have loaded the `auto_search` DataFrame and tested the `chart(id, DataFrame)` function. If you have followed along with the cell managing article as well, you should have 4-5 cells that are commented with what they will plot.

For each of those cells, you will have to add a chart() call. Here are how the cells should be. Each comment and chart() call in a separate cell.

# 1. Auto Sales - Demand
auto_sales = chart("TOTALSA", auto_search)
# 2. Auto inventory — Supply
auto_inventory = chart("AUINSA", auto_search)
# 3. Auto Production and Manufacturing - Supply
auto_inventory = chart("IPG3361T3SQ", auto_search)
# 4. Producer Price Index for making New Vehicles — see what the bottleneck is
auto_manufacturing = chart("PCU336110336110", auto_search)

Once you run these, you’ll see 4 different charts for each cell.

Examining Trends between Charts

It’s easy to see from each of the graphs what is happening. Car sales have declined rapidly during lockdowns, and so did the auto parts supply (plot #3). Soon, the lockdowns lifted, stimulus checks were received, and people’s mindset quickly shifted away from the “the world is ending”. The demand for cars skyrocketed (plot #1).

Consumer demand quickly increases with a stimulus, however, the global supply chain is not a quick fix. Car parts and material supply lagged behind the demand which increased the prices the manufacturer had to pay for their supplies. The Producer Price Index (PPI) shows this rapid increase.

If we delve further into the supply issues, we will see the cascading effect on the prices. Let’s look at the producer price index for auto parts, stamped auto parts 1 A common way of making metal auto parts , and iron ore.

Run each of the following in a separate cell

# Vehicle Parts PPI
auto_parts = chart("PCU33633363", auto_search)
# Metal Stamped Parts PPI

metal_search ="car metal")
auto_parts_stamping = chart("PCU3363703363700", metal_search)
# 5. Reasons behind the bottleneck - Raw Materials
iron_search ="iron")
producer_price_index = chart("WPU101", iron_search)

All these mark a tremendous increase in the PPI. So far we have established a simple cascading price increase. The price increase of iron ore, stamped metal parts, and all vehicle parts result in an increase in PPI for making the whole new car. This constrains the inventory, which, combined with demand snapping back (fueled by stimulus checks and other federal programs), causes substantial inflation in the used car market.

For this series, this level of analysis is already way too much than you might have cared to know. However, the story is not done yet.

In the second half of 2022, some prices are slightly coming back down to reality. As production has started to go back to ‘normal’ levels. The loan-to-value ratio (how much it was financed divided by the actual price of the product) in 2021-2022 for auto sales also increased. This is going to cause a lot of depreciation in the next year or two for people who vastly overpaid for cars during the prior years. The good news is that prices are decreasing. As of Nov 2022, the Used Car Price Index is at 196, down from the peak of 213. However, the car manufacturing industry and the global supply chain have a bit more work to do because the used car index was at 139 in Nov 2019.

Concluding Thoughts

If you’re new to this kind of data science and programming, congratulations! You’ve just done data science project! As you can see it has been complicated, there is a lot of trial and error that goes on when doing any kind of data science – even for seasoned professionals! The result has been rewarding.

We have created our personal economic data tracker! You can customize this to be your dashboard of data you care about – FRED is filled with all kinds of economic data. You’ll find anything from food price indexes to mortgage rates. You’ll be able to find what you’re looking for using this notebook as a template.

I do use the FRED data as a good litmus test for seeing if car prices are abnormally high. No matter how much negotiation you are willing to do with a car salesman, when demand is high and supply is low, the economic forces are on their side. In the near future, the tide might be turning!

Callisto has been created to make data exploration easy. So, I don’t expect you to take my word for any of the analyses I’ve given. I encourage you to dive into the data and see for yourself. This is all ripe for further exploration. Callisto is designed with you, the one behind the behind, in mind. The data explorer will let you have a point-and-click UI to access your data and export it. Shareable Graphs and Cell Outputs will integrate well with any medium you choose for publishing your results. The drag-and-drop cell management will make organizing intuitive. More helpful features like these are added to Callisto with every update. The tools provided here will put you well on your data science journey! Happy exploring!