The Microfoundations of Housing Market Dynamics

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The goal of this dissertation is to provide a coherent and computationally feasible basis for the analysis of the dynamics of both housing supply and demand from a microeconomics perspective. The dissertation includes two papers which incorporate unique micro data with new methodological approaches to examine housing market dynamics. The first paper models the development decisions of land owners as a dynamic discrete choice problem to recover the primitives of housing supply. The second paper develops a new methodology for dynamically estimating the demand for durable goods, such as housing, when the choice set is large.

In the first paper, using the new data set discussed above, I develop and estimate the first dynamic microeconometric model of supply. Parcel owners maximize the discounted sum of expected per-period profits by choosing the optimal time and nature of construction. In addition to current profits, the owners of land also take into account their expectations about future returns to development, balancing expected future prices against expected future costs. This forward looking behavior is crucial in explaining observed aggregate patterns of construction. Finally, the outcomes generated by the parcel owners' profit maximizing behavior, in addition to observable sales prices, allow me to identify the parameters of the per-period profit function at a fine level of geography.

By modeling the optimal behavior of land owners directly, I can capture important aspects of profits that explain both market volatility and geographic differences in construction rates. In particular, the model captures both the role of expectations and of more abstract costs (such as regulation) in determining the timing and volatility of supply in way that would not be possible using aggregate data. The model returns estimates of the various components of profits: prices, variable costs, and the fixed costs of building, which incorporate both physical and regulatory costs.

Estimates of the model suggest that changes in the value of the right-to-build are the primary cause of house price appreciation, that the demographic characteristics of existing residents are determinants of the cost environment, and that physical and regulatory costs are pro-cyclical. Finally, using estimates of the profit function, I explain the role of dynamics in determining the timing of supply by distinguishing the effects of expected future cost changes from the effects of expected future price changes. A counterfactual simulation suggest that pro-cyclical costs, combined with forward looking behavior, significantly dampen construction volatility. These results sheds light on one of the empirical puzzles of the housing market - what determines the volatility of housing construction?

In the second paper, I outline a tractable model of neighborhood choice in a dynamic setting along with a computationally straightforward estimation approach. The approach allows the observed and unobserved features of each neighborhood to evolve in a completely flexible way and uses information on neighborhood choice and the timing of moves to recover semi-parametrically: (i) preferences for housing and neighborhood attributes, (ii) preferences for the performance of the house as a financial asset, and (iii) moving costs. In order to accommodate a number of important features of housing market, this approach extends methods developed in the recent literature on the dynamic demand for durable goods in a number of key ways. The model and estimation approach are applicable to the study of a wide set of dynamic phenomena in housing markets and cities. These include, for example, the analysis of the microdynamics of residential segregation and gentrification within metropolitan areas. More generally, the model and estimation approach can be easily extended to study the dynamics of housing and labor markets in a system of cities.






Murphy, Alvin Denis (2008). The Microfoundations of Housing Market Dynamics. Dissertation, Duke University. Retrieved from


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