An Agent-Based Model of the Housing Market Bubble in Metropolitan Washington D.C.

Abstract

Several independent datasets concerning household behavior in the Washington, D.C. metropolitan area are combined to create an agent-based model of the recent housing market bubble and its aftermath. Comprehensive data on housing stock attributes, primarily from local government sources, are used as input to the model, as are administratively-complete data on household characteristics. Data covering all real estate transactions over the period 1997-2009 are used as targets for model output, including inventory levels and average days-on-market in addition to price statistics. Finally, a very large sample of mortgage service data (80-90 percent coverage of metro D.C.) serve as both input to the model (e.g., mortgage types, interest rates obtained) as well as target output (e.g., refinancing rates, foreclosure levels). The model consists of a large number of heterogeneous households who make rent or buy decisions, are matched to homes and commonly seek mortgages with which to purchase homes. These households have homogeneous rules of behavior but heterogeneous realized behavior since decisions depend on local household characteristics (e.g., size, composition, financials). This is a so-called agent-based computational model since each household and the banks originating mortgages are software agents while each home and each mortgage are soft- ware objects. The model is capable of running at full-scale with the metropolitan D.C. housing market, over 2 million households. Overall, we find that certain empirically- grounded household decision rules are capable of generating a home price bubble much like what was observed during this time period. The model does not get the absolute bubble level and the timing of its bursting exactly right but does a good job on certain market ’internals’ such as real estate sales, inventories and market tightness. Not in the model at present are fine-grained aspect of household decision-making (e.g., moving in advance of schools starting) and thus the model lacks certain well-known temporal phenomena like seasonality. Also, while the home purchase market is deeply represented in the model, few details of the rental market are present, a further weakness. These limitations and parameter sensitivities are described. Despite these flaws, we use the calibrated model to perform a few policy experiments. Our preliminary findings are that tighter interest rate policies would have done little to attenuate the price bubble, while limiting household leverage would have had a larger effect.

Keywords: Housing markets, agent-based modeling, financial bubbles

JEL Classification: G1, G5