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

arXiv: https://arxiv.org/abs/2404.07533 · HTML: https://mlsanity.com/paper/2404.07533/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 41 references that are in the map.
- First posted on arXiv 11 Apr 2024.

## Builds on (most cited first, 6 of 6)

- [Mapping the NFT revolution: market trends, trade networks and visual features](https://mlsanity.com/paper/2106.00647/) (2021)
- [TweetBoost: Influence of Social Media on NFT Valuation](https://mlsanity.com/paper/2201.08373/) (2022)
- [Show me your NFT and I tell you how it will perform: Multimodal representation learning for NFT selling price prediction](https://mlsanity.com/paper/2302.01676/) (2023)
- [NFT Appraisal Prediction: Utilizing Search Trends, Public Market Data, Linear Regression and Recurrent Neural Networks](https://mlsanity.com/paper/2204.12932/) (2022)
- [Understanding NFT Price Moves through Tweets Keywords Analysis](https://mlsanity.com/paper/2209.07706/) (2022)
- [Exploring the data of blockchain-based metaverses](https://mlsanity.com/paper/2308.16787/) (2023)

## Built on by (most cited first, 1 of 1)

- [Leveraging Large Language Models for Sentiment Analysis: Multi-Modal Analysis of Decentraland's MANA Token](https://mlsanity.com/paper/2605.20192/) (2026)

## Abstract (arXiv)

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.