Housing Price Disparities: A Machine Learning Approach to Factors like Housing Status, Public Transit, and Density on Single-Family Prices.

Site: Austin, Texas

Press material

The material on this website can be used freely in any publication provided that:

1. It is duly credited as a project by the UT Austin Urban Info Lab

2. A PDF copy of the publication is sent to Chenyf56@utexas.edu

Team

Yefu Chen
Junfeng Jiao
Arya Farahi

CRediT authorship contribution statement

YC led the overall research idea, oversaw data analysis, modeling, paper drafting, and revision.

JJ developed the data analysis, data modeling, paper reviewing, and revision.

AF guided data analysis and modeling and helped with paper revision.

Declaration of competing interest

The authors declare there is no conflict of interest in the whole paper development process.

Data availability

Data will be made available on request.

The cover image is sourced from Pexels, is free of copyright issues, and can be used for educational purposes. https://help.pexels.com/hc/en-us/articles/360042295174

Acknowledgment

The authors would like to acknowledge the funding supports from NSF (1952193 and 2133302), UT Good Systems, and USDOT.