Summary of Monopoly: Learning to Price Public Facilities For Revaluing Private Properties with Large-scale Urban Data, by Miao Fan et al.
MONOPOLY: Learning to Price Public Facilities for Revaluing Private Properties with Large-Scale Urban Data
by Miao Fan, Jizhou Huang, An Zhuo, Ying Li, Ping Li, Haifeng Wang
First submitted to arxiv on: 27 Nov 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Social and Information Networks (cs.SI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper proposes a distributed approach called “Monopoly” to revalue private properties by learning to price public facilities using large-scale urban data. The method organizes points of interest into an undirected weighted graph and estimates housing prices based on surrounding public facility values, which are updated iteratively until convergence. The approach outperforms mainstream methods with significant margins in experiments conducted on large-scale urban data from several Chinese metropolises. This project has implications for business intelligence and urban computing, potentially benefiting millions of users for investments and governments for urban planning and taxation. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A team of researchers developed a new way to figure out how much houses are worth. They created an approach called “Monopoly” that uses big data to price public facilities like hospitals. This helps estimate the value of private properties, which is important for people making investments or governments setting taxes. The method works by looking at points of interest and updating values until it gets a good estimate. The team tested this with real data from several Chinese cities and found it worked better than other methods. |
