The bulk of these data derives from public sources. Cohesive aggregation is exceeding difficult and generally available only on proprietary platforms. In context, my firm and former employers have generated large revenues from such aggregation. In a related, but different example, I presently have the average FICO scores for loan and lease customers of nearly all US OEM directly originated assets. All that information is, technically, public. Aggregation and comparison is exceedingly valuable for competitors.
As with all information similar in kind to this, it is useless unless users can effect policy changes because of this information.
Just as a specific example there is this:
( I present here assertions based on this type of data, but will not dusclose all the sources:
TSLA originated leases in the US have overall credit quality matching and/or exceeding the highest in the industry, with only originations from Mercedes Benz and BMW roughly equal. All include SUV and car originations but not commercial vehicles. On an even more important metric, collateral substitution rate, Tesla is the lowest.
The significance of the previous information is that, despite prejudice against Tesla paper it is growing in popularity precisely because the assets are very low risk, lower than the ratings imply. Further, as Model 3 and more recently Model Y have dominated originations the demonstrated risk levels have NOT declined. Moving downscale historically increases risk, but there is no evidence of such for Tesla.
Thus far the 2022 originations from all major issuers show declines in credit quality and increases in collateral substitution (this metric is an intrinsic measure of fundamental asset quality that is not otherwise reflected in core portfolio data.
So, how is that information useful? After all it does resemble speaking in Greek to an English speaker?
What it tells us is that all the price cuts and promotions have not increased fundamental risk for Tesla.
An implication otherwise might be that losses on lease termination and repossessions should be rising rapidly but that is not supported by the data. However…
When combining this with aggregate auction results (also accessible, but expensive and difficult to aggregate) we begin to understand that the former hyperactive Tesla secondary market has become more like ‘normal’ vehicles.
Suddenly all this yields important forecast perspective. Tesla has had gains on secondary market activity for recent years, accentuated during The Pandemic. The froth is gone! This conclusion can be supported in Tesla financial statements, but without details about components.
With all that we understand just how important non-traditional revenue has become. Such as subscriptions, Megapack/Powerpach, VPP, Supercharger revenue. FSD, Premium Connectivity and everything TE derived services, even collision, Insurance etc.
So, the fundamental problem with knowing the use of aggregated public data is that it usually appears to be generic and obvious.
As
@AudubonB implied it is time consuming and not obvious to use this kind of information. FWIW, most industry CEO’s turn off attention when faced with data. Hint: if they need a PoerPoint to understand they will never understand!
Elon Musk and senior Tesla people all devour data and struggle to learn more every hour. That obsessiveness means they are on this kind of data every day and that financial decision-making is rapidly iterating to incorporate events before most competitors know what is happening.
FWIW, such data as this yields decisions that seem spur-of-the-moment such as transfer of FSD and referral revisions. Those actually end improving credit quality, reducing acquisition costs and having high buyer value at modest incremental costs. The recent Supercharging promotion fits the identical logic category.
Sorry for long response. Making this concise without reams of data is difficult, and reams of data always tend to seem boring and irrelevant.
FWIW, companies like SRI, Battelle, and many others are specialists in this arcane part of decision support.
