The market dynamics of the used car market is not intuitively clear. Many different factors need to be considered and the effects could take different directions under certain conditions. In this article, we investigate the leading indicators driving used car sales in Germany. We find that certain market and survey indicators are good for predicting used car sales in Germany while macro indicators are not. Using our newly found indicators, we forecast that used car sales is likely to have recovered to normal levels by the beginning of 2021.
Used car sales in Germany
Cheap cars are often used as an example of inferior goods in economics, meaning that as incomes rise, the demand for cheap cars will go down in favour of more expensive cars. There is a possibility then that there is a trade-off between new and used cars depending on income, but there is also the possibility that this trade-off exists only between different types of cars, rather than between new and used ones.
Studying the historical development of the market in the graph below, we see that the Corona crisis created a massive dent in used cars sales in April. Interestingly, the recovery was as quick as the fall, with the fall followed by a spike in sales in July. The magnitude of the drop is indeed extreme but is actually not very surprising given that Germany experienced a lockdown as a response to the coronavirus crisis. Not only was the demand for transportation likely to be very low during the spring, but most used car dealerships might even have been closed. The following spike is likely the result of a combination of suppressed demand during the lockdown and an urge to avoid public transport.
Following the 2008 financial crisis on the other hand, the development was much less dramatic and appears to follow a downward trend that had been present since 2005. Thus, it appears that used car sales might not be overly dependent on the business cycle. Hence the impact on used car sales by economic crises is quite unclear. To shed some light on the dynamics of the used car market in Germany, we will test a range of different types of indicators and see how they can be used to predict sales in the coming six months. We will also try to account for the unusual effects of the Corona crisis.
Wide range of indicators tested
Each indicator tested will be assessed according to how much they help to improve our forecasting accuracy, allowing us to not only to discover important market drivers, but also rank them according to their relative importance.
Indicio incorporates 15 different types of multivariate models, mainly different types of vector autoregression (VAR) models. These work by revealing the linear interdependencies of the variables and are very useful in forecasting applications. VAR models have a great advantage in that they do not rely on indicators being strictly exogenous. However, this also means that we cannot analyse the explicit causal impact of each indicator. We can only determine whether, and to what extent, an indicator improves forecast accuracy. But since the platform handles these complex dynamics automatically, we only need to consider if an indicator provides useful information, not in which way it does it. Having a hypothesis about how an indicator provides this information is still important though, otherwise we cannot be certain that the predictive power of our model holds in the future.
The following indicators have been tested:
- New Car Registrations in Germany
- Motor Vehicle Fuel Prices (CPI)
Whether or not users choose between a new and a used car depending on their income, new car registrations should provide valuable information for predicting used car sales. If that is the case, then lower new car registrations should correspond to higher numbers of used car sales. And if there is no trade-off, new car registrations and used car sales should follow each other, reflecting the general consumer demand for cars. New car sales is also what determines future used car supply, thus it could make for a great indicator.
Low fuel prices are likely to increase car sales of both new and used cars. However, other effects might be present as well. Since new cars generally have better fuel economy, especially with an ongoing electric car boom, higher fuel prices might lead consumers away from older cars in favour of new ones with more efficient engines.
- Unemployment rate in Germany
- Euro Exchange Rate Index (EER42)
Wages and salaries are tested to see if we can capture the income effect directly. The unemployment rate might capture such effects as well but will also reflect the demand for cars used for commuting to work. Higher unemployment might thus mean less used car sales.
The Euro Exchange Rate Index is included because exchange rates typically affect the exports of used cars. Germany has historically been a popular country to import used cars from, and the exchange rate is likely to affect this propensity. However, the effect could be muted by the Euro since the same currency is also used by many neighbouring European countries.
- ZEW Indicator of Economic Sentiment – Germany
- Consumer confidence indicator Germany
- Manufacturing PMI and Services PMI for Germany
- IFO Expected Business Climate Germany
The ZEW sentiment indicator is constructed from a survey of expert economists and analysts about their view of the future of the economy, and the Consumer confidence indicator is constructed from a survey of regular consumers regarding expected future income and economic conditions. So basically, one is a survey of experts and the other a survey of the general population.
To capture the outlook of businesses themselves, manufacturing and services PMI are tested to give an insight into the current situation. Meanwhile, the IFO Expected Business Climate unveils the outlook on the future.
Stock market indicators
- Volkswagen Stock Price
Finally, stock prices of major German car manufacturers might have an impact. These could reflect both general demand for cars as well as the market outlook for the car industry in general, possibly incorporating aspects not considered in this article. Volkswagens stock price is included because it is the largest car manufacturer in Germany and one of the largest in the world. However, stock prices can behave erratically and thus the impact of this indicators is uncertain.
To account for the large drop and subsequent spike in used car sales during the spring, we add event effects during the spring and in July. We also model the seasonal trend and add it as an indicator.
There are of course a multitude of other indicators that could also be tested. And even though some of the indicators in this analysis will be excluded, this may be because they contain the same information as included variables. The Indicio platform will only keep the best indicators, regardless of them having a positive or negative impact on used car sales, excluding redundant and non-informative indicators.
Market and macro indicators the most relevant
The indicator analysis reveals that the trend component and both Market and survey indicators are useful for our model. While macro indicators do not appear to benefit our model.
Beginning with the market indicators, we find that new car registrations are the most important indicator for forecasting used car sales but that fuel prices also play an important role. For survey indicators, both the ZEW indicator and the manufacturing PMI are influential in our model. So is the Volkswagen stock price.
The full results from the analysis can be seen in the table below. Influence can vary between 1 and -1, with any indicator with an influence above 0 being beneficial for our model.
|Indicator||Influence||Included in forecast?|
|Trend: Used Cars – Germany||0.891||Yes|
|New Car Registrations||0.799||Yes|
|Manufacturing PMI Germany||0.570||Yes|
|Volkswagen Stock price||0.496||Yes|
|Exchange rate index||-0.658||No|
|IFO Expected Business Climate||-0.820||No|
|Services PMI Germany||-0.909||No|
These findings can also be used as leads in order to find even better indicators, or for determining which indicators to focus on when conducting a scenario analysis. In this case, it would probably be interesting to see how our forecast would change given different outcomes in one of the top influencing indicators, such a spike in fuel prices or a downturn in new car sales.
“Back to normal” in 2021?
After the indicator analysis is finished, we can move on to estimating our multivariate models using the indicators we have found. Each model is evaluated according to its historical accuracy, allowing the platform to weigh the models according to their error for each month ahead, creating a balanced forecast that is as accurate as possible.
It is also time to evaluate our selected indicators. The platform will estimate the best possible models with the indicators provided but using bad indicators will still lead to a bad forecast. To do this, the multivariate models can be benchmarked against univariate models, or by simply assessing the model fit visually by looking at how well our model predicts past values. In the graph below, observed values are in white and past forecasts starting at each month are represented by the multi-coloured lines. Studying the graph, we can see that the model generally does a good job of predicting historical values. Sometimes we are missing some smaller variations in the original data, however these are likely to be just noise and a completely perfect fit could be a sign that our model is overfitted. We can also see that we have captured the corona effects quite successfully, although not perfectly.
The final forecast is shown in the lighter area of the graph. We find a downturn in sales following the spike in July, with used car sales bottoming out in December. The drop appears quite dramatic at a first glance but is in line with the seasonal pattern of the data. To fully understand the relative development of used car sales, we need to look at the seasonally adjusted forecast.
Adjusting for the seasonal effects, the model still predicts a downturn during the latter part of the year with sales starting to recover later in the autumn. This is not surprising since it is reasonable that the boom in sales during the summer is only temporary. Nevertheless, the downturn in the autumn period is consistent with the general downturn in the economy reflected through our leading indicators and it appears likely for sales to return to normal levels at the beginning of next year.
This forecast is of course only relevant given no further unexpected events during 2020 such as a second wave of the corona virus. Such scenarios could be investigated using the scenario analysis tool. If you want an example of scenario analysis with Indicio, see https://www.indicio.com/stock-market-effects-apartment-prices-swedish-metropolitan-regions/.