A lack of affordable housing is a major problem in US cities from the Bay Area to Boston. Austin is no exception. In 2015, the Austin-Round Rock metropolitan area was named one of the most economically segregated areas in the country. With more people moving to the region – roughly 3,000 people per month – the disparity will worsen. This research project will develop a values-driven AI system that evaluates historical housing development and helps policymakers shape equitable, inclusive and sustainable plans and regulations. Using deep learning technology and an open data repository with demographic, development, transportation, and energy consumption data, this system will link and study 50 years of Austin housing development figures in an effort to help tackle the future of local housing. More specifically, we ask:

  • How has housing development shifted over the past 50 years in relation to changes in policies, plans, property values and transportation and energy costs?
  • Are there significant spatial and demographic relationships between housing affordability, mobility and energy use over the past 50 years?
  • What are the future likelihoods of these patterns to persist? Can we predict how development might change given different regulatory variables?

This project will consist of four phases. First, researchers will design and develop a web-based housing development data repository. Second, the team will run a historical data analysis based on the data repository. Third, researchers will build a predictive AI system trained and tested on the data repository. Finally, the group will construct a broad government and community outreach system for testing the model and disseminating results. Our research aims to evaluate and inform urban residential development policies in Austin. The techniques and findings developed from this study can also be applied to many similar cities nationally and internationally. Additionally, the data repository created here will be used to study how environmental and socioeconomic factors have affected the spread of COVID-19 in the city of Austin.

 

Project team: Junfeng Jiao (School of Architecture), Weijia Xu (Texas Advanced Computing Center), Jake Wegmann (School of Architecture), Katie Pierce Meyer (University of Texas Libraries), Hao Zhu (Department of Electrical and Computer Engineering), Chen Feng (School of Architecture), Josh Conrad (School of Architecture), Cara Bertron (City of Austin Planning and Zoning Department), Matt Dugan (City of Austin Planning and Zoning Department), John Clary (City of Austin Transportation Department), Molly Emerick (Austin Energy), Matt Hollon (City of Austin Watershed Protection Department) and Jacqui Hrncir (City of Austin Communications & Technology Management Department)