Oral Defense of Doctoral Dissertation – Computational Social Science – The Washington, D.C. Housing Affordability Simulator – Shawn Joseph Bucholtz

Notice and Invitation
Oral Defense of Doctoral Dissertation
Doctor of Philosophy in Computational Social Science
Department of Computational and Data Sciences
College of Science
George Mason University

Shawn Joseph Bucholtz
Bachelor of Science, Michigan State University, 2000
Master of Science, University of Maryland, 2003


Thursday, December 7, 9:30 a.m.
Exploratory Hall, Room 162

All are invited to attend.

Dr. Robert Axtell, Dissertation Director
Dr. Andrew Crooks
Dr. William Kennedy
Dr. Katrin Anacker


This dissertation presents the Washington, D.C. Housing Affordability Simulator, or DCHAS. DCHAS is an empirical agent-based model of urban housing supply and demand, with a special emphasis on housing affordability and affordable housing production. DCHAS agents include households, landlords, developers and the local government. Past agent-based and microsimulation modeling efforts have demonstrated the importance of including agent heterogeneity and land markets in models of urban housing supply and demand. DCHAS builds upon this foundation and extends prior efforts by including six additional features important to on housing affordability and affordable housing production: agent variation appropriate to low-income households, explicit representation of Federal housing subsidies, explicit representation of affordable housing supply, rental control, zoning and regeneration of properties, and filtering and rehabilitation of housing units.

DCHAS is calibrated to the population and housing stock as it existed in 2010. The behaviors of DCHAS’s agent are parameterized with data from 2011 to 2015. Combining a 2010-based population and housing stock with agent behavior parameterized with data from 2011 to 2015, it is demonstrated that DCHAS reliably reproduces housing supply and demand outcomes observed in 2015. Then, DCHAS is used to simulate four housing supply and demand scenarios over the next ten years (2016 -2025). The principle contributions of this dissertation are to: (1) identify and explore concepts critical to housing affordability in an urban environment; (2) demonstrate how to empirically represent these concepts through the use of administrative data sources, and (3) demonstrate how to build an empirically-based ABM that can be used to simulate housing affordability under different market conditions or housing policy scenarios.

A copy of Shawn’s dissertation is available for examination from Karen Underwood, Department of Computational and Data Sciences, 373 Research Hall. The dissertation is available to read only within the Department and cannot be taken out of the Department or copied.