Exploring the Decentraland Economy: Multifaceted Parcel Attributes, Key Insights, and Benchmarking

TL;DR

We address fragmentation in Decentraland parcel data by constructing IITP-VDLand, a comprehensive dataset with 92,598 parcels, 81 attributes, and four data fragments, including a novel Rarity score derived via $Rarity.tools$. We integrate data from Decentraland, OpenSea, Etherscan, Google BigQuery, Discord, Telegram, and Reddit, spanning 2018–2023 to support analyses of characteristics, trading history, Ethereum on-chain activity, and social media signals. More than 20 state-of-the-art pricing models are benchmarked, with ensemble methods achieving the top performance (e.g., $R^2$ up to $0.8251$ and accuracy up to $74.23\%$ for resale classification), while location, proximity, and rarity emerge as strong predictors. The dataset enables robust, multimodal analyses of virtual land markets, with potential applications in price forecasting, market liquidity assessment, sentiment-driven dynamics, and network-structure studies within the Decentraland ecosystem.

Abstract

This paper presents a comprehensive Decentraland parcels dataset, called IITP-VDLand, sourced from diverse platforms such as Decentraland, OpenSea, Etherscan, Google BigQuery, and various Social Media Platforms. Unlike existing datasets which have limited attributes and records, IITP-VDLand offers a rich array of attributes, encompassing parcel characteristics, trading history, past activities, transactions, and social media interactions. Alongside, we introduce a key attribute in the dataset, namely Rarity score, which measures the uniqueness of each parcel within the virtual world. Addressing the significant challenge posed by the dispersed nature of this data across various sources, we employ a systematic approach, utilizing both available APIs and custom scripts, to gather it. Subsequently, we meticulously curate and organize the information into four distinct fragments: (1) Characteristics, (2) OpenSea Trading History, (3) Ethereum Activity Transactions, and (4) Social Media. We envisage that this dataset would serve as a robust resource for training machine- and deep-learning models specifically designed to address real-world challenges within the domain of Decentraland parcels. The performance benchmarking of more than 20 state-of-the-art price prediction models on our dataset yields promising results, achieving a maximum R2 score of 0.8251 and an accuracy of 74.23% in case of Extra Trees Regressor and Classifier. The key findings reveal that the ensemble models perform better than both deep learning and linear models for our dataset. We observe a significant impact of coordinates, geographical proximity, rarity score, and few other economic indicators on the prediction of parcel prices.

Figures (7)

• Figure 1: Decentraland Map
• Figure 2: Overview of Data collection process
• Figure 3: Highlevel view of different data-fragments and relations
• Figure 4: Comparison of number of parcels and percentage of parcels sold with respective distances to geographical proximity
• Figure 5: Sale Price History of Decentraland parcels