A mixed strategy for vehicle valuation
Abstract
The number of vehicles in Kenya grows at a rate of 12% annually, with the national registered fleet
standing at 4 million as of 2018. All these vehicles have to be valued regularly for a variety of
reasons not limited to insurance, resale, leasing and accounting. As such, it is important to have an
easy to use, reliable, readily available system that can determine the value of a vehicle given some
properties about the said vehicle. The variation of values obtained from different valuers for
identical vehicles exposes irregularities in the contemporary automobile valuation systems. When in
need of quick car valuation services, the lack of consistent, accurate and readily available tools to
perform the required valuation is glaring, as the primary way to get an automobile valued is through
contacting an expert from a licensed evaluation firm or an insurance agent. The existing car
valuation mechanisms rely chiefly on expert opinions and the use of the formulae to calculate a
used car’s compound annual depreciation which is subtracted from the price at 0 mileage, adjusted
for inflation over the years. There have been attempts to automate vehicle valuation by use of
machine learning, which yielded promising results. Multiple regression analysis has been employed
to identify vehicle properties that have the greatest bearing on the value of the vehicle, as well as
predict the price given values of the different parameters. This approach has also been applied
successfully in other domains for valuation of assets such as land and FMCGs. For this study, a
multi-agent systems architecture was employed to encapsulate three regression models for vehicle
value prediction, as well as a natural language processing model to extract vehicle features from
vehicle descriptions in unstructured text. The three models were built and trained to generate
predictions, each leveraging either of the SVM-based regression and Neural Networks (ANNs)
implementation in WEKA, or the Deep Learning regression provided by WekaDeeplearning4j
version 3.8.5. The best performing model provided a reliable option for vehicle valuation, with
11% relative mean error, having been trained on only 1000 rows of data, out of a possible 200,000
records, and thus was used in the design of the functional prototype. Given the temporal, budgetary
and computational resource restrictions on this study, there is great potential for improving the
performance of the prediction models given more time, data and computing power.
Publisher
University of Nairobi
Rights
Attribution-NonCommercial-NoDerivs 3.0 United StatesUsage Rights
http://creativecommons.org/licenses/by-nc-nd/3.0/us/Collections
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