The Washington, D.C. Housing Affordability Simulator
Date
2017
Authors
Bucholtz, Shawn Joseph
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Abstract
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, rent 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 three housing supply and demand scenarios over the next ten years (2016 -2025). The principle contributions of this dissertation are to: (1) identify and explore six 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.
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Keywords
Economics, Statistics, Affordability, Agent-based, Economics, Housing, Simulation, Spatial