Berkeley Program on Housing and Urban Policy

The perennial debate over the causes of housing segregation between whites and blacks has intensified in recent years, with a greater diversity of opinions than ever before. While suggestive evidence on these causes proliferates, direct evidence connecting competing hypotheses to observed levels of housing segregation is rare due to the unavailability of data. This study provides direct evidence on the causes of housing segregation using new data from the Multi-City Study of Urban Inequality. The central finding is that blacks’ preferences for black neighborhoods and whites’ preferences for white neighborhoods are major causes of housing segregation.

In this study, we present an analysis of the impacts of high tech economic growth on the incidence of critical housing problems among all households and among moderateincome working families in major metropolitan areas. We rely on data from the 1999 American Housing Survey, supplemented with data from the State of the Cities 2000, Landis and Elmer (2001), and Burby et al. (2000). Overall, we found that the level of high tech activity impacts, positively and significantly, the incidence of critical housing problems for all households and for moderate-income working households, regardless of tenure. Consistent with anecdotal information about the problems of working families, we found stronger impacts on moderate-income working households than on all households. We conclude that housing policy should be broadened to address the problems of working families as well as those of the poor, especially when dealing with problems arising from rapid economic growth.

This paper uses employment and output in high-tech industries, venture capital funding, and the number of dot-com firms per 1000 private workers, at the metropolitan level, to identify their contribution to differences in housing market outcomes. Housing prices in New Economy markets are found to be higher, peakier and more volatile than in old economy markets. Homeownership rates are found to be lower in new economy metro areas while crowding is found to be higher. Although the distribution of housing values, cost, and rents was more equal in New Economy markets, the cause would seem to be differences in metro area income levels, with poorer MSA's having greater inequalities. Regression analysis is used to identify the contribution of traditional supply and demand factors such as job growth, income, residential construction, as well as New Economy indicators, to housing market outcomes. Rather than being fundamentally different, New Economy housing markets are found to be faster and more extreme versions of traditional housing markets.

There are indications that the current generation of models used to simulate the geography of housing choice has reached the limits of its usefulness under existing specifications. The relative stasis in residential choice modeling--and urban simulation in general--contrasts with simulation efforts in other disciplines, where techniques, theories, and ideas drawn from computation and complexity studies are revitalizing the ways in which we conceptualize, understand, and model real-world phenomena. Many of these concepts and methodologies are applicable to housing choice simulation. Indeed, in many cases, ideas from computation and complexity studies--often clustered under the collective term of geocomputation, as they apply to geography--are ideally suited to the simulation of residential location dynamics. However, there exist several obstructions to their successful use for these puropses, particularly as regards the capacity of these methodologies to handle top-down dynamics in urban systems. This paper presents a framework for developing a hybrid model for urban geographic simulation generally and discusses some of the imposing barriers against innovation in this field. The framework infuses approaches derived from geocomputation and complexity with standard techniques that have been tried and tested in operational land-use and transport simulation. As a proof-of-concept exercise, a micro-model of residential location has been developed with a view to hybridization. The model mixes cellular automata and multi-agent approaches and is formulated so as to interface with meso-models at a higher scale.