<p> This dissertation presents a series of related agent-based models (ABMs) of the housing market in the Washington DC Metropolitan Statistical Area. The models investigate the causes of the housing market bubble and crash during the time period 1997-2009 and policies that could have avoided such a crisis. The work in this dissertation contributes to three research areas: understanding the underlying causes of the housing crisis, demonstrating the ability of ABMs to generate important macro phenomena, and improving ABM methodology. </p><p> Using the housing market models, I investigated counterfactual policies related to the causes of the crisis. I show that leverage and expectations are the two most prominent contributors to the bubble, but that other factors, such as interest rates, norms governing the share of income going to housing, and seller behavior all influence the bubble. I find that lending standards and refinance rules play almost no part in the bubble, contrary to some theories of the housing crisis. Towards the end of the dissertation, I pair the housing market with a model of mortgage-backed securities. I show that the increased velocity of lending made possible by securitization can increase the size of bubbles and make markets more fragile, increasing the likelihood of crashes. </p><p> The ABMs in this dissertation exploit multiple large, heterogeneous data sets and utilize behavioral rules that are more realistic than conventional neoclassical specifications to reproduce detailed housing market dynamics. Input data include loan level data, multiple listing service (MLS) records, and demographic information from a variety of sources. The ABMs exploit this data by choosing the precise areas of input distributions to use based on the context of the model. This allows the ABMs to match not only aggregate outputs, but intermediate outputs and data distributions. For example, the ABMs in this dissertation not only reproduce empirical macro phenomena, such as the shape of the house price index, but also intermediate variables (e.g., distribution of loan types, average leverage, average days on market, average ratio of sold price to original listing price) and output distributions (e.g., distribution of house prices). </p><p> Throughout the dissertation I follow several methodological principles in construction and analysis of the ABMs. First, I demonstrate the use of data to constrain the models. Next, I describe a sensitivity analysis methodology that goes beyond parametric variations, but also varies model rules in what I term a structural sensitivity analysis. I demonstrate how criticisms about ABMs with regard to their opacity, brittleness, and dependency on arbitrary modeling decisions can be resolved through such an analysis. I also describe the architectural design of the models, which makes explicit the theoretically-inspired behavioral rules, facilitating structural sensitivity analyses.</p><p>
| Identifer | oai:union.ndltd.org:PROQUEST/oai:pqdtoai.proquest.com:10275284 |
| Date | 12 September 2017 |
| Creators | Goldstein, Jonathan |
| Publisher | George Mason University |
| Source Sets | ProQuest.com |
| Language | English |
| Detected Language | English |
| Type | thesis |
Page generated in 0.0028 seconds