Data counteracting valuation complexity
Valuing cars and vans is becoming more and more difficult thanks to factors such as a greater diversity of models, increased number of sales channels and larger macroeconomic influence, according to Glass’s.
However, despite this increasing complexity, overall levels of accuracy are improving, even if some less robust players are being driven out of the market for valuation data as a result of these pressures.
Rupert Pontin, head of valuations at Glass’s, said, ‘Being able to access accurate valuations data is essential for anyone involved in buying and selling used vehicles of any kind but the process of producing this information is, we believe, becoming ever more difficult.
‘Look back a few years and there was much less model diversity. Also, the number of disposal routes was a fraction of what can be found today and the impact of macroeconomic trends on values tended to be less. The picture was really a lot simpler.
‘Now, the situation has changed completely and, while we are able to use much improved technology in order to handle this increased complexity, the core task of valuations is getting ever more complicated.’
Rupert said that this was the reason that some smaller and less committed companies were being slowly driven out of the sector and also why some quite large disparities on valuations could be found.
‘Every year we analyse over 1.4 million trade observations and around eight million retail observations. We carry out in the region of 2,000 telephone market surveys, 500 manufacturer visits and 500 auction visits. In addition to this, our valuations team, who spend at least half of each month talking to contacts in the market; have more than 200 years of experience between them. Simply to collect the raw data needed to make valuations today means that you have to be a sizeable operation and to know how to process that information requires a very specialised skill set. However, despite this, we are actually becoming increasingly accurate.’
Rupert said that Glass’s had an overall residual value accuracy of 98.8%.