Citation: Korbel, J.J.; Siddiq, U.H.;
Zarnekow, R. Towards Virtual 3D
Asset Price Prediction Based on
Machine Learning. J. Theor. Appl.
Electron. Commer. Res. 2022,17,
924–948. https://doi.org/10.3390/
jtaer17030048
Academic Editor:
Eduardo Álvarez-Miranda
Received: 24 May 2022
Accepted: 4 July 2022
Published: 7 July 2022
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Article
Towards Virtual 3D Asset Price Prediction Based on
Machine Learning
Jakob J. Korbel * , Umar H. Siddiq and Rüdiger Zarnekow
Information and Communication Management, Department of Economics and Management, Technische
ruediger[email protected] (R.Z.)
*Correspondence: [email protected]; Tel.: +49-03-314-28571
Abstract:
Although 3D models are today indispensable in various industries, the adequate pricing of
3D models traded on online platforms, i.e., virtual 3D assets, remains vague. This study identifies
relevant price determinants of virtual 3D assets through the analysis of a dataset containing the
characteristics of 135.384 3D models. Machine learning algorithms were applied to derive a virtual
3D asset price prediction tool based on the analysis results. The evaluation revealed that the random
forest regression model is the most promising model to predict virtual 3D asset prices. Furthermore,
the findings imply that the geometry and number of material files, as well as the quality of textures,
are the most relevant price determinants, whereas animations and file formats play a minor role.
However, the analysis also showed that the pricing behavior is still substantially influenced by the
subjective assessment of virtual 3D asset creators.
Keywords:
3D model; virtual asset; virtual product; virtual good; pricing; machine learning; feature
scoring; e-commerce; metaverse
1. Introduction
Digital 3D models are today indispensable in various industries. Manufacturers rely
on 3D models to develop and simulate their products [
1
], retailers allow customers to
configure product characteristics based on 3D visualizations [
2
], and game developers
require 3D models not only to build their virtual worlds, but to gain profits through their
purchase within these environments [
3
]. Technological trends such as augmented (AR) and
virtual reality (VR) and ambitions from firms such as Meta to create a virtual metaverse [
4
,
5
]
foster the importance of 3D models as vital building components, assets, and objects of
trade. Consequently, marketplaces have emerged which focus on the trade of virtual 3D
assets, i.e., 3D models that are not included in a virtual environment, and thus can be
adapted for various fields of application [
6
]. Examples for virtual 3D asset platforms are the
Unity Asset Store [
7
], which focuses on the trade of virtual 3D assets for the development
of games and AR/VR environments; Thingiverse [
8
], which provides access to millions
of 3D models to manufacture products based on 3D printing; and marketplaces such as
CGTrader [
9
], Turbosquid [
10
], or Sketchfab [
11
], which offer virtual 3D assets for a variety
of domains, e.g., e-commerce, architecture, or cultural heritage. However, whereas the
pricing, value, and consumption of 3D models in virtual environments, i.e., virtual goods,
has been extensively researched, studies considering the pricing determinants and value
of virtual 3D assets are sparse, as are pricing recommendations in practice. Although
some marketplaces provide basic pricing guidelines for 3D model creators, the information
compromises general suggestions on 3D model characteristics to be considered rather than
their specific relevance. In turn, creators and sellers of virtual 3D assets must set prices for
their virtual 3D assets based on their subjective assessments.
Hence, the objective of this study was to identify relevant price determinants for
virtual 3D assets and develop an IT artefact that considers these price determinants for
J. Theor. Appl. Electron. Commer. Res. 2022,17, 924–948. https://doi.org/10.3390/jtaer17030048 https://www.mdpi.com/journal/jtaer
J. Theor. Appl. Electron. Commer. Res. 2022,17 925
virtual 3D asset price predictions. Therefore, this study relied on the design science research
(DSR) methodology, a dataset containing the meta-characteristics of 135.384 3D models from
the Sketchfab marketplace (the largest platform for virtual 3D assets [
11
]), and a machine
learning (ML) approach for the processing and analysis of 3D model characteristics.
To meet the research objectives, the paper is structured as follows. In the theoretical
background section (Section 2), the characteristics of 3D models and their respective types
are described, as well as the applied analysis approach, i.e., data mining and ML. Studies
on virtual 3D assets are sparse; therefore, the related research section (Section 3) contains
a summary of current approaches to identify and analyze price determinants based on
ML in other disciplines, e.g., accommodation, cryptocurrencies, or the stock market, to
derive an appropriate analysis framework for the study. The methodological approach is
illustrated in Section 4, whereas the implementation of the artefact is described in Section 5.
Finally, the results are evaluated and deployed in Section 6and summarized in Section 7,
concluding with future research avenues.
2. Theoretical Background
2.1. 3D Models and Virtual 3D Assets
The basis of 3D models is their geometry, produced out of meshes or bodies, repre-
senting the shape of an object which is often complemented with textures, materials, and,
depending on the purpose, animations. The geometry represents the shape of a 3D model
(Figure 1a); however, textures and materials are commonly used to create and modify the
appearance of 3D models, to make the model more appealing or real, or to assign a meaning
to an object (e.g., a 3D model in the shape of a sphere can become a volleyball by assigning
the respective texture). Texture files (Figure 1b) are 2D images that “encompass” the 3D
shapes, commonly based on UV mapping [
12
]. Materials, however, are more complex
and include one or multiple texture files. In comparison with simple textures, materials
allow the creator of a 3D model to adjust settings such as reflection, opacity, or bumps
and wrinkles (Figure 1c) [
13
]. Hence, whereas textures represent the basic “skin” of a 3D
model and require a material to be attached to the model, materials allow these skins to
become more realistic. Today, the most advanced materials are physically based rendering
(PBR) materials, because they facilitate the simulation of multiple material characteristics
based on a complex mapping synthesis [
14
]. Apart from static characteristics, 3D models
can be animated. Animations of simple objects can be realized by translating or rotating
the object; however, more complex and 3D character animations, especially, require rig-
ging [
15
]. Rigging refers to the process of including a “skeleton” in 3D objects that enables
the animation of different parts within one 3D model (Figure 1d) [
16
]. Whether 3D models
contain this information beyond their shape depends on the respective 3D file format.
The STL format, for example, is the proprietary file format for 3D printing, and thus does
not include texture, material, or animation information because those are not of use in a
3D printing process [
17
]. In contrast, all of this information can be stored in the FBX file
format [
18
], a common format for media and game development. Hence, the characteristics
of 3D models correlate with their anticipated usage and type.
The most common concepts that describe specific types of 3D models in industry are
virtual products and virtual goods. Virtual products are 3D objects that represent actual
physical goods in form (and function), and are especially useful in the manufacturing
and retail domains. With the virtual representation of the actual product, different de-
sign variants of the product can be tested, virtual prototypes created, and real products
simulated, at a fraction of the costs of physical processes [
1
,
19
,
20
]. In addition, virtual
products have gained importance in the retail industry because 3D models allow for greater
informativeness, enjoyment, or willingness to purchase if the 3D models are either used
to allow the customers to configure their products based on their specific needs or to
visualize the products in the real environment via AR to prevent, amongst others, incorrect
purchase decisions [
21
,
22
]. In contrast, virtual goods represent usable and tradeable 3D
models within virtual worlds and game environments [
23
,
24
]. Many game developers
J. Theor. Appl. Electron. Commer. Res. 2022,17 926
today rely on the free-to-play business model; therefore, these virtual goods became one
of the main revenue sources in the gaming industry [
3
], with an estimated turnover of
about USD 190 billion in 2025 [
25
]. Due to their economic relevance, the value, pricing, and
consumption mechanisms of virtual goods has been extensively researched. The results
show that the value of virtual goods, and thus, their consumption, mainly derives from
their characteristics within a closed virtual environment. Due to the closed environment,
virtual goods can exhibit scarcity (albeit artificially created), the possession of multiple
copies of the same virtual good can increase consumer utility, interactions with virtual
goods can lead to higher purchase intentions, and the characteristics of the goods can
create social distinctions [
23
,
24
,
26
–
32
]. Thus, the objective of virtual goods is trade and the
generation of revenue through their purchase, while virtual products are a mean to an end
to facilitate the creation and distribution of physical products.
JTAER 2022, 17, FOR PEER REVIEW 3
products in the real environment via AR to prevent, amongst others, incorrect purchase
decisions [21,22]. In contrast, virtual goods represent usable and tradeable 3D models
within virtual worlds and game environments [23,24]. Many game developers today rely
on the free-to-play business model; therefore, these virtual goods became one of the main
revenue sources in the gaming industry [3], with an estimated turnover of about USD 190
billion in 2025 [25]. Due to their economic relevance, the value, pricing, and consumption
mechanisms of virtual goods has been extensively researched. The results show that the
value of virtual goods, and thus, their consumption, mainly derives from their character-
istics within a closed virtual environment. Due to the closed environment, virtual goods
can exhibit scarcity (albeit artificially created), the possession of multiple copies of the
same virtual good can increase consumer utility, interactions with virtual goods can lead
to higher purchase intentions, and the characteristics of the goods can create social dis-
tinctions [23,24,26–32]. Thus, the objective of virtual goods is trade and the generation of
revenue through their purchase, while virtual products are a mean to an end to facilitate
the creation and distribution of physical products.
Figure 1. Three-dimensional model characteristics: (a) mesh/body; (b) body with texture; (c) body
with texture and material settings (rendered); (d) body with rigged geometry for animation.
Virtual 3D assets, however, are both the antecedent of virtual products and goods
and a necessity to create the environments in which virtual products and goods are used.
In comparison with both concepts, virtual 3D assets neither have a predefined purpose
nor are they already included in a virtual environment [6]. Hence, the value and pricing
mechanisms of virtual goods cannot be transferred to virtual 3D assets because the virtual
goods’ value depends on its specific virtual environment. Virtual 3D assets are not traded
in virtual environments, but on (real) online marketplaces, such as Sketchfab [11], to allow
a wide range of customers, e.g., game developers, retailers, or architects, to build their
virtual environments or use the virtual 3D asset as virtual goods, for example, by trans-
ferring and binding the 3D models to a specific environment. In contrast to communities
that provide virtual 3D assets for free, e.g., Thingiverse (3D printing) [8], the offerings of
virtual 3D asset marketplaces vary from simple, untextured low-poly models (for free), to
complex 3D environments, such as city and building models for more than USD 5.000. In
general, everyone can become a seller on the virtual 3D asset platforms by creating an
account, considering the three largest marketplaces: CGTrader, Sketchfab, and Turbos-
quid. The marketplace providers receive a share of the selling price if virtual 3D assets are
purchased. The pricing of the virtual 3D assets thereby remains with the seller. Although
some marketplaces provide analytic tools to gather market insights (e.g., CGTrader [33]),
the guidelines for sellers to set appropriate prices for their assets remain vague.
The guidelines (Table 1) suggest that the sellers should neither price their 3D models
too high nor too low compared with similar 3D models so as to not undermine the store
economy or imply a low quality of the virtual 3D asset. If higher prices are set, the de-
scription should allow the buyer to understand why the price is higher than for similar
models. Furthermore, the number of file formats should play a role in the pricing process,
as well as the quality of textures, or the optimization for games or AR/VR. According to
Figure 1.
Three-dimensional model characteristics: (
a
) mesh/body; (
b
) body with texture; (
c
) body
with texture and material settings (rendered); (d) body with rigged geometry for animation.
Virtual 3D assets, however, are both the antecedent of virtual products and goods
and a necessity to create the environments in which virtual products and goods are used.
In comparison with both concepts, virtual 3D assets neither have a predefined purpose
nor are they already included in a virtual environment [
6
]. Hence, the value and pricing
mechanisms of virtual goods cannot be transferred to virtual 3D assets because the virtual
goods’ value depends on its specific virtual environment. Virtual 3D assets are not traded
in virtual environments, but on (real) online marketplaces, such as Sketchfab [
11
], to allow a
wide range of customers, e.g., game developers, retailers, or architects, to build their virtual
environments or use the virtual 3D asset as virtual goods, for example, by transferring and
binding the 3D models to a specific environment. In contrast to communities that provide
virtual 3D assets for free, e.g., Thingiverse (3D printing) [
8
], the offerings of virtual 3D
asset marketplaces vary from simple, untextured low-poly models (for free), to complex
3D environments, such as city and building models for more than USD 5.000. In general,
everyone can become a seller on the virtual 3D asset platforms by creating an account,
considering the three largest marketplaces: CGTrader, Sketchfab, and Turbosquid. The
marketplace providers receive a share of the selling price if virtual 3D assets are purchased.
The pricing of the virtual 3D assets thereby remains with the seller. Although some
marketplaces provide analytic tools to gather market insights (e.g., CGTrader [
33
]), the
guidelines for sellers to set appropriate prices for their assets remain vague.
The guidelines (Table 1) suggest that the sellers should neither price their 3D models
too high nor too low compared with similar 3D models so as to not undermine the store
economy or imply a low quality of the virtual 3D asset. If higher prices are set, the
description should allow the buyer to understand why the price is higher than for similar
models. Furthermore, the number of file formats should play a role in the pricing process,
as well as the quality of textures, or the optimization for games or AR/VR. According to the
platform provider guidelines, textures should be in png format; the inclusion of editable
file formats is advantageous, as are high-quality materials in form of PBR materials. In
terms of animation, the seller should include as much animations as possible since models
J. Theor. Appl. Electron. Commer. Res. 2022,17 927
with more animations sell better while the success heavily depend on the rigging of the
model. However, the platform providers do not state which of the criteria are most relevant
for potential customers and whether sellers actually consider these guidelines for their
pricing decisions. Hence, an ML-based data mining approach was applied in this study to
identify price determinants and allow the prediction virtual 3D asset prices.
Table 1. Pricing guidelines of the three dominant virtual 3D asset marketplaces.
Marketplace Criteria Pricing Guidelines
CGTrader
[34]
Value for Buyers “Consider the value your work brings to the buyer”.
Price
Range
“Make sure you don’t underprice your model. Buyers might see it as a sign
of poor quality”.
Compatibility
and Quality
“[ . . . ] make sure you provide a detailed description and preview
images that showcase what distinguishes your model from the rest. That
could be a large selection of file formats, high quality textures, optimization for
games or VR/AR, etc.”.
Sketchfab
[35]
Value for Buyers “Consider the value your work offers a potential customer”.
Price
Range
“You can browse the store for similar models to guide your pricing decision”.
“Be careful not to radically undercut the price of similar models on the store.
This ultimately hurts all sellers by undermining the store economy”.
“Low pricing can be interpreted by buyers as a sign of poor quality”.
“Similarly, be aware that asking for significantly more than similar models
from other contributors can lead to reduced sales”.
Compatibility
and Quality
“If you set a higher price than similar models from other sellers, use the
model description to explain what distinguishes your model and adds to its
value. For example, the inclusion of higher resolution textures or multiple
file formats would be an added benefit”.
Compatibility
“The more file formats you include, the more successful you will be”.
“The ideal texture format for textures is PNG. Buyers will also appreciate
the inclusion of Photoshop, Gimp, or similar editable layered files”.
Quality “A complete set of PBR textures (Albedo, Metallic, Roughness) and
normal maps are desirable to buyers for contemporary game engines”.
Animation
“Models with more animation states sell better”.
“The success of animated models is often very rig dependent. Be sure to use
our additional files feature to include rigged versions in popular
software formats”.
Turbosquid
[36]
Price
Range
“Setting your prices extremely low will not necessarily lead to better sales”.
“Make sure you are pricing your models to achieve maximum sales and
royalties. Look at comparable 3D models on the site to check their prices”.
Compatibility, Quality
and Animation
“Realism”
“File formats offered”
“Texture/material/rigging settings”
Complexity “Complexity”
“Poly count”
2.2. Data Mining and Machine Learning
Data mining describes the process of identifying correlations and patterns in data
to create insights that add to existing knowledge [
37
]. It is a well-established method in
e-commerce research to derive analysis results regarding, for example, product pricing, by
acquiring and examining datasets from online marketplaces and platforms; therefore, this
is an eligible approach for the explorative objectives of this study. To allow the extraction
of knowledge from a given set of raw data, data mining comprises (1) data preparation,
(2), data pre-processing, (3) data analysis, and (4) data interpretation [37,38].
J. Theor. Appl. Electron. Commer. Res. 2022,17 928
The (1) data preparation phase focus on the identification of the most appropriate data
sample for the data analysis process [
39
]. In this process, the nature of data is analyzed,
and the appropriate data sample is thereafter extracted from the context of examination,
e.g., online websites [40].
(2) Data pre-processing refers to all processes related to the cleaning, transformation,
and selection of eligible data for the data mining task [
38
,
41
]. First, data cleaning is a
vital step in data pre-processing because real world data can be unstructured and of a
heterogeneous and noisy nature. Incomplete or inaccurate data due to missing values
or outliers, for example, significantly impact the results of any subsequent steps in the
data analysis [
42
]. Hence, inaccurate data must be treated by filling missing data with
global constants or erasing outliers and gross errors [
38
,
39
]. Second, data transformation
is required because variables in the dataset can contain different data types and ranges
that affect the analysis process. In the data transformation step, data can be normalized
to adjust the data to a common data range or smoothed by discarding data based on
min/max ranges [
38
]. Third, a dataset can include redundant variables or variables which
are irrelevant for the prediction of the target variable. These variables can impact the
analysis process negatively because they do not provide additional useful information,
adding bias to the data, increase the dimensionality, or significantly decrease the prediction
performance of the analysis models for “unseen” data, i.e., overfitting [
43
–
45
]. Therefore,
irrelevant variables must be detected and excluded from the data sample.
Finally, the dataset is (3) analyzed and (4) interpreted. ML has emerged as a promising
alternative to common data analysis methods, especially when working with large data
samples. ML-based data mining can be divided into (a) the data preparation phase, i.e.,
data transformation, exploration, and feature engineering, (b) the modelling phase, i.e.,
training and test cycles, (c) the evaluation phase, i.e., performance measuring and model
selection for deployment, and (d) the deployment phase, i.e., the usage of the trained ML
model [
46
]. The (a) data preparation phase corresponds with the previously described
generic data pre-processing phases in data mining. The (b) modelling phase comprises
both the selection of appropriate ML algorithms as well as their training and performance
optimization [
39
]. The applied ML models range from supervised to unsupervised and
reinforcement learning [
47
]. It is difficult to predict the best performing ML algorithm
for a given dataset beforehand; therefore, it is common practice to apply and evaluate
different ML algorithms with varying parameters to identify an eligible model for the ML
process [
46
]. To (c) evaluate the model performance, several methods have been developed
and applied to ML models. Amongst others, a common approach for the evaluation process
is k-fold cross-validation, in combination with error metrics such as mean absolute error
(MAE), the mean squared error (MSE), root-mean squared error (RMSE), coefficient of
determination (R2), or adjusted coefficient of determination (aR2). K-fold cross-validation
is used to validate the performance of ML models in terms of their ability to predict new,
unseen data by splitting the training dataset into k subsets and applying the model k times
while selecting a new validation set in every iteration [
48
]. After every iteration, error
metrics or performance measures are calculated, which are used to evaluate the prediction
performance of a model. Finally, the ML process is completed by its (d) deployment through
the selection and exploitation of the best performing ML model [
46
], and conclusions for
the specific field of application can be drawn by interpreting the resulting data.
3. Related Studies
The literature on pricing and the identification of price determinants for virtual 3D
assets is sparse. However, previous work emphasizes approaches to examine price deter-
minants based on ML for other fields of application, ranging from accommodation [
49
–
51
]
and the stock market [
52
,
53
], to e-commerce [
54
,
55
], cryptocurrencies [
56
–
58
], and energy
prices [
59
]. Apart from dynamic pricing approaches based on reinforcement learning [
60
,
61
],
most publications have focused on the application of supervised learning algorithms to
identify price determinants. The procedures for the data analysis vary between the super-
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