Light-Vehicle Sales: Population-Adjusted Sales and Auto Finance Rates

In order to bring some additional clarity to the light-vehicle sales numbers posted by the BEA every month, I decided to adjust the numbers to present the number of units sold for every thousand people. Additionally, new auto finance rates are presented below, with data coming from the FRB's G19 report. I believe this presentation does a better job of presenting how depressed auto sales became during the latest recession. I've previously said that I expect a strong medium-term bounce-back in auto sales as demand builds up.

SAAR of sales per 1000 people

Auto finance rates

I consider tailwinds for sales to include:

• Reduced sales over the last two years - I expect people to eventually replace vehicles they kept for longer because of economic conditions. Sales are still very low, even assuming a twelve year replacement period.
• Historically low finance rates - Lower rates mean more purchasing capacity for the same monthly payment or a lower payment for the same principal. I add that a friend that works in a Toyota dealership has commented to me more than once that demand is not an issue for them as much as access to financing is. If that is true, a pickup in willingness to finance would fuel an increasei n sales

Headwinds include:

• High rate of unemployment - No Job, no loan
• Increasing quality means vehicles last longer - 10yr 100,000 mile warranties ring a bell?

An additional scenario that could put downward pressure on sales is if reduced access to credit and a reduced willingness to consume translate to a lower standard of living where the number of automobiles per capita significantly decreases. I think that scenario is overly pessimistic and I would sooner expect a pick-up in sales of cheaper models as consumers trade down from luxury to economy vehicles before forgoing automobile ownership altogether.

Vehicle Sales 1976-2010: A look at the future

This post is part of the special series Vehicle Sales 1976-2010.

To tie this all together, I think that we can expect a strong medium-term recovery in domestic automotive demand. I define demand as the demand for replacement vehicles plus the new demand from additional drivers. Using the Census projections we can expect about 268,722,000 individuals aged 16 and over in 2020. Using the previously observed 88% rate of licensed drivers, this would mean 236,475,360 drivers, or an increase of 24,404,194 drivers over the next 10 years. Keeping the number of vehicles per driver constant at ~1.2 this would mean an additional 29,285,033 vehicles in the national fleet. Additional to that would be the demand for new vehicles from retired vehicles. Like I mentioned before, It's hard to precisely estimate the lifespan of a vehicle, but using a hypothetical ten year lifespan and approximately 240 million vehicles (excluding buses and motorcycles) in 2009, that would translate to roughly a full turn around in the fleet, giving us a total domestic demand for automobiles of ~270 million cars and trucks over the next decade. Considering yearly sales averaged ~17.3M vehicles for the six years prior to the recession, this would be quite a strong recovery.

Vehicle Sales 1976-2010: Sales and Demand

This post is part of the special series Vehicle Sales 1976-2010.

The previous post in the series looked at the market size for vehicles and the size of the country's vehicle fleet. This post will concentrate on how growth in the market combined with other factors affect sales. The number of sales on any given year is driven by two factors, the growth in the total fleet and the number of units purchased as replacements for retired or obsolete units.

As you can see, new-additions to the fleet, while significant, are more volatile as a percentage of sales than replacements for retired units. You can see in the graph that after large dips, retired units seem to bounce back quickly. This suggests that the growth of the fleet is the most sensitive to economic conditions and that a large component of sales is purported replacements for retired units. This makes sense because while touch economic conditions can make one keep a vehicle a little longer, there is limits to how long the life of a vehicle can be extended before a replacement becomes a viable alternative.

Vehicle Sales 1976-2010: Market Size and Demographics

This post is part of the special series Vehicle Sales 1976-2010.

The main factor affecting domestic vehicle sales is the market size. The market for automobiles consists of the personal market and the corporate and public-sector markets. I decided that the best way to measure the market size was to look at the number of licensed drivers and the number of potential drivers. As previously mentioned in the introduction to the series, potential drivers are people age 16 and older. The number of licensed drivers was taken from numbers published by the FHWA. In order for the domestic vehicle fleet to increase we must have an increase in the number of drivers, an increase in automobile ownership, or a combination of the two.

Number of residents over age 16 and % of residents age 15 or under

The chart clearly shows a relatively smooth increase in the number of residents age sixteen or over, which are candidates to be drivers in blue. In red is the percentage of residents age 15 and under. As can be seen, this number has been on a decline since 1962. From this data we can reasonably deduce that we can expect no major demographic reason for an abnormal increase in the potential drivers in the near future. In fact, the number of potential drivers as a percent of the population may be close to reaching it's upper limit.

Drivers as a % of population and vehicles per driver

In this chart we can see that for the last 40 years the number of drivers as percentage of the potential drivers (residents age 16+) has essentially plateaued. This means that the growth in drivers (potential purchasers of motor vehicles) should now generally follow the growth in population. Not every person sixteen or older will be a driver and it looks like the stable number of licensed drivers is 87-88% of the 16+ population. We've also seen the number of registered vehicles rise from .85 vehicles per licensed individual to 1.2 vehicles per licensed individual. Driver-less vehicles may be a possibility in the future but, in the present, a vehicle requires a driver. This means that at any point in time no more than one vehicle per licensed driver should be in use. Taking this into consideration, it seems reasonable to expect a natural ceiling in the number of vehicles per licensed driver. Once that range is reached, the growth in the total number of registered vehicles should be similar to the growth in the number of drivers.

Vehicle Sales 1976-2010: Introduction

This post is part of the special series Vehicle Sales 1976-2010.

Every month Calculated Risk posts the graph of monthly light-vehicle sales. The sales are usually presented as the seasonally adjusted annual rate (SAAR) reported by the BEA. The reason for the adjustment is a strong seasonality in sales which make it difficult to see underlying trends in the short-term. These numbers are suitable for comparison from month to month or even year-over-year, but I found them unsuitable for looking at longer-term trends. To remedy this, I decided to find some different ways of looking at the data to try to get a better look at long term trends in light vehicle sales and hopefully gain some insight as to plausible future outcomes.

Introduction
To begin, let's consider some of the factors that affect light-vehicle sales:

• The number of drivers and potential drivers
• The level of vehicle-ownership
• The lifespan of a a vehicle

The first factor is not dependent on the current economic cycle, although it is affected by larger demographic trends which may or may not be the result of past economic cycles. Because sixteen is the age at which most teenagers can drive in this country, potential drivers will be defined as residents aged sixteen or older for the purposes of this post. To find the estimated number of residents aged 16 and older, I used the estimates published by the Census Bureau. The number of drivers is the number of licensed drivers reported by the FHWA.

Vehicle-ownership is harder to define. The Federal Highway Administration publishes a series of statistics every two years that includes the number of registered vehicles and the number of licensed drivers. The series is slightly out of date right now, but I believe it should be updated in 2011. The series is limited in various ways; for example, numbers are rounded to the millions and vehicles registered are not broken down into separate categories by use (a motorcycle, a garbage truck, a school bus and my mom's Matrix are all counted as a motor vehicle). Additionally, a vehicle does not necessarily belong to a licensed owner, or even an individual, and not every licensed individual drives. Despite this, I believe that the numbers will be useful in identifying long-term trends, even if they lack precision.

It is hard to determine the current lifespan of an automobile. We can find an approximation by looking at the mean age of automobiles, which gives us an idea of the lifespan of past models, but this isn't very reliable. New models may have longer or shorter life than past models, and things like wage levels affect the potential lifespan of vehicles--if labor is expensive, fixing and maintaining older automobiles becomes less economically attractive. Additionally, economic conditions affect how often people look for new cars.

I will hopefully have time in the future to extract more precise figures from archived reports, but the task is time-consuming and I am skeptical about it's added value (e.g. buses and motorcycles only accounted for 0.41% of the total fleet in 2008). Many the series were split-up recently and many of the numbers are only available from PDF reports from which the data must be re-entered into a spreadsheet to be usable.

Housing Affordability 1971-2009: Chart Roundup (UPDATED)

This post is part of the series Housing Affordability 1971-2009

I have updated some of the charts from the previous posts and put them in one post for easier access. For explanations on the data and commentary, please follow the links to the source posts. Click "read more" to see all updates.

From Interest Rates and Borrowing Capacity:

Housing Affordability 1971-2009: Chart Roundup

This post is part of the series Housing Affordability 1971-2009

I have collected all of the charts from the previous posts in the series and put them in one post for easier access. For explanations on the data and commentary, please follow the links to the source posts.

Housing Affordability 1971-2009: Data Sources and Collection Methods

This post is part of the series Housing Affordability 1971-2009

This is the last post of the series on housing affordability. At the bottom you will find a link to the spreadsheet so you can download it and play with the data if you want. Before that, I'll just add a couple of comments about the data and where it came from.

Owner's equivalent rent, rent of primary residence and CPI less shelter
These came from The BLS CPI website, and you can find the unique ID of the series used at the top of the "CPI-U data" worksheet. The two re-based versions just adjust the numbers to make 1971 and 1984 equal 100. This is done by taking the original value and dividing it by the 1971 or 1984 value respectively.

Monthly and yearly new home prices
These prices come from the Census. (monthly, yearly)

Monthly and yearly mortgage rates
These are posted on the Freddie Mac website. No adjustment was made for points.

Median household income
I cheated a little with this one. The series I found for median household income was limited, so instead I used the third quintile of the entire data set provided by the census.. I compared the overlapping years and the differences were so small that I decided it was OK to do this. There is no reason for the third quintile to be any less accurate than the absolute median.

Median fair-market rents
This is the rent data I decided not to use, it came from a HUD survey. Apart from being noisy and the data collection methods (phone survey with random dialing) being of poor quality, the series only started at 1979. I don't know anyone that participates in phone surveys, and didn't trust the sample to be representative.

I am making the spreadsheet available to save others the pain of having to copy values from PDF files into a spreadsheet. I do not mind if you create derivative works with any of the charts, but I do ask that you give me a mention. A simple "Credit to Morally Bankrupt for data in spreadsheet format" with a link to this post so others can download the source spreadsheet would be sufficient.

Download the spreadsheet in Microsoft Excel format.

Housing Affordability 1971-2009: Payments, Prices and Capacity

This post is part of the series Housing Affordability 1971-2009

In the last post I talked about the growth in prices in percentage terms. Today's post includes the same data, but using a nominal scale. While I think the percent change charts are great for looking at long-term, the nominal charts do a better job of communicating the differences in dollars and cents.

Here we can see the relationship between the median-price for new homes and the purchasing power of a payment equal to 30% of the median-household income. Judging by the gap, my estimate of 30% is close, but not perfect. I discussed my reasons for using this figure in Two Ways of Looking at It.  Once I post the source spreadsheet you will be able to fill-in any values you want to see plotted for the %-of-income and down-payment variables. Please note these are not in log-scale because the actual figures became a harder to read. You can find the log-scale versions at the bottom of this post.

Housing Affordability 1971-2009: Long-Term Trends

This post is part of the series Housing Affordability 1971-2009

In the last post I discussed the comparison I used for this analysis and why I chose certain data series over others. In this post we will look at long-term trends in income and prices and how lower interest rates have allowed prices to rise faster than income. Rents and the CPI less shelter figure are also included to illustrate the divergence of the trend home prices from the trend in consumer goods.

I am excluding shelter from the CPI figure because I want to display how the trend in housing differed from everything else and comparing housing prices to an unadjusted CPI would understate the growth in prices.

For rents, I decided to use the "rent of primary residence" series in the CPI. The BLS does not publish rents in their average price survey, and the only nominal figure I found came from the HUD, and after looking at collection methods, I was not impressed with the quality or coverage of the survey. Since the Census Bureau does not offer a national figure, I am still looking for better rents data¹.

Housing Affordability 1971-2009: Two Ways of Looking at It

This post is part of the series Housing Affordability 1971-2009

In the previous post, I discussed how borrowing capacity changes with respect to interest rates, finishing up with an example of the buying power of a $500 monthly mortgage payment from 1971-2009.  The example is obviously a gross oversimplification; income and price levels can and have changed since then.  To try to make some sense of this all, I decided to look at the data from two sides:

• The change over time in the cost of a median-price new home and the monthly mortgage payment necessary to buy it, a function of the price level and interest rate. 
• The change over time in the median-household income and the borrowing capacity based on it, a function of the income level and interest rate.

I chose to define borrowing capacity by calculating the amortized loan principal that would require a payment equal to 30% of a median-income household's earnings.

Housing Affordability 1971-2009: Interest Rates and Borrowing Capacity

This post is part of the series Housing Affordability 1971-2009

We'll begin the series by talking about interest rates and borrowing capacity. If you are already familiar with the subject, this may not be of interest to you as the discussion will be a bit basic. There will be more interesting things in the future, I promise.

For purchases that are as large and have as little equity as most home purchases, the effect of interest rates is very large. For example, a $100 monthly payment at the current rates of 4.4% could buy a $22,188 home assuming a 10% down-payment. The same monthly payment at 18.45%, last seen in October 1981, could only buy a $7,197 home assuming the same 10% down-payment; that's about a third of the purchasing capacity. While I picked the most extreme points in the data-set, the example serves its purpose. For this same reason, it is useless to talk about home prices without also talking about interest rates, as affordability is measured in the monthly payment, not total cost, for most people. With mortgage rates at historic lows, the buying capacity of a monthly payment is the most it has ever been. Furthermore, if deflationary pressures and extremely loose monetary policy don't cease, we could see that capacity increase even more, since purchasing capacity increases at an increasing rate as interest rates drop, as you can see below (click for larger image).

Housing Affordability 1971-2009: Introduction

This post is part of the series Housing Affordability 1971-2009

The purchase of a first home by people I know in their mid-late 20s--a purchase many believed they had been permanently priced out of a few years ago--has been a common theme lately--and for good reason, homes are more affordable now than they have been since at least the 60s. From some of my work friends, to some of my old high-school friends, not a week went by over the last six months where I didn't hear or see (primarily on facebook) a reference towards buying or shopping for a new home, and it makes sense. With mortgage rates at historic lows, lower prices in many areas and a little help from the government, there hasn't been a moment in the last 39 years where housing has been so affordable when compared to buying capacity at current median incomes.