But are those natives neutral about where they live? Or do they prefer to live in mostly native neighborhoods? In the latter case, the resulting outcome may be native‐​born citizens’ flight and a degree of segregation that does not reflect immigrant choice. As in the major migration of African‐​Americans from the South to the North in the first half of the 20th century, the preferences of previous settlers are central to determining neighborhood segregation outcomes and social capital consequences—including those arising from labor market networks, native language proficiency, and educational achievement (Jargowsky 2016, Kneebone 2016).

Immigration and the Neighborhood Revisited

In our American Economic Review article titled “Immigration and Neighborhood,” Albert Saiz and I (Saiz and Wachter 2011) provide evidence on the segregation of immigrants and native‐​born populations, and the consequent neighborhood sorting by income and ethnicity. As we wrote, housing markets reveal natives’ preferences and likely segregation outcomes:

We establish an empirical methodology to test for immigrant concentration and native and white flight. We find that neighborhoods with a growing immigrant presence do experience an exit of native‐​born residents and lower housing values after adjusting for changes in housing quality and accounting for other neighborhood characteristics. This dynamic, of the native born exiting neighborhoods with increased numbers of immigrant residents, is consistent with the results of a historical study by Shertzer and Walsh (2016), who find that white departure in response to the arrival of African‐​Americans in the first half of the 20th century led to increases in urban segregation.

These negative impacts of immigrants at the neighborhood level on the native‐​born population and house values may seem contrary to the literature that shows that immigrants are a contributing factor to the growth of cities and the rise of house prices and rents over time. For example, Saiz (2007) finds that an immigration inflow of 1 percent of a city’s population is associated with an increase in average rents and housing values of about 1 percent. However, these findings are in fact consistent, because the positive effects on house prices and rents are found at the metropolitan level, while the negative effects are found at the neighborhood level. As the native born leave immigrant‐​dense neighborhoods, they move to other parts of the metropolitan area; overall, house prices in the metropolitan area increase as a result.

There are alternatives to this “native flight” hypothesis, which suggest that the native born find immigrant neighborhoods relatively less attractive and that this attitude is responsible for the association between immigrants and relatively lower neighborhood house prices. Other potential explanations include

• Changes in housing quality,

• Reverse causality (that is, immigrants moving to neighborhoods with falling prices), and

• Omitted variables.

A reverse causation explanation for relatively lower housing values in immigrant‐​dense neighborhoods is highly plausible, since immigrants may be looking for more affordable housing. That possibility means that in some areas home values will grow more slowly, since prices reflect future rent and price growth.

It is possible to test for native‐​born preferences as a separate hypothesis, by focusing on quality‐​adjusted housing prices and constructing an instrumental variable in lieu of the infeasible random assignment of immigration shocks to selected neighborhoods. In Saiz and Wachter (2011), my co‐​author and I use data from the American Housing Survey to control for differences in housing quality attributes between native‐​owned and immigrant‐​owned houses (including age, number of detached housing units, number of rooms, presence of kitchen facilities, plumbing, and other criteria), to identify the quality‐​adjusted housing price impacts only.

We then devise an instrumental variable approach to deal with reverse causality and omitted variables. We use a geographic diffusion model to produce forecasts about future immigrant settlement patterns, which are related to proximity to neighborhoods that are already immigrant‐​dense. Then we assess the interactions of these predictions with other variables as instruments for the actual changes in immigrant density in a neighborhood.

The evidence supports a causal link between growing immigrant density, native flight, and a slowdown in housing value appreciation. The impact of immigration is found to be stronger in high‐​income areas with a high initial density of white residents.

In order to carry out this study, we use decennial data from those metropolitan areas of the United States at the census tract (4,000 inhabitants or more) level in the 1990 and 2000 censuses for which the change in number of foreign‐​born residents amounted to 5 percent or more of the metropolitan statistical area’s population in the previous census (using 1980 as the baseline). We include the neighborhoods’ socioeconomic characteristics in our model as controls. Among these are income, population, employment, education, age structure, ethnic composition, number of foreign‐​born individuals, distributions of marital and family status, housing price data, ownership rates, vacancy rates, latitude and longitude, state, metropolitan area, county, minor civil division, and school district identifiers.

We also directly measure the impact of immigration shocks on white and native flight. We find that these shocks are associated with absolute decreases in the level of native‐​born and white population, except in the top 5 percent of tracts (where the population more than doubled between 1990 and 2000).

We further attempt to determine why immigrant enclaves are perceived as less desirable places to live by natives. Possible reasons include expected (negative) changes in the quality of local public goods (such as education) and changes in ethnic composition (as opposed to the number of foreign‐​born residents). To do this, we examine the effects of ethnicity and socioeconomic status of immigrants using census microdata from 1990 and 2000. We calculate the average share of high‐​school dropouts, as well as demographic characteristics by immigrant national group, and then use these variables as a proxy to measure immigrant‐​driven shocks based on population shares by nationality. We use four different ethnic group designations (non‐​Hispanic white, black, Asian, Hispanic) and characteristics of an immigrant‐​driven supply shock to the local share of individuals who are high school dropouts.¹ It turns out that ethnicity and education both affect housing prices. These effects are most pronounced in neighborhoods where new immigrants are less educated and tend to be minorities. The effects no longer hold in the absence of these characteristics. Therefore, the results of our research indicate that the negative association between prices and immigrant density may be driven by the fact that immigrants tend to be of lower socioeconomic status and belong to minority groups, rather than by their “foreignness” per se. This segregating outcome of socioeconomic status concentrations is consistent with the broader societal sorting by income and ethnicity observed over time (Acolin and Wachter 2016). Furthermore, this sorting outcome appears to be driven by housing affordability factors (in addition to native preferences) that affect immigrant populations as well as affordability‐​constrained households more generally.²

Looking to the Future

As noted, these results are obtained using data from the 1990 and 2000 censuses. There are three factors that might change these relationships if we were to study more recent and future data, so that higher immigrant population is no longer causally associated with declining (relative) housing prices. These factors are socioeconomic convergence between natives and immigrants, changes in attitudes among natives, and changes in preference for living in suburbs or center cities by immigrants and natives.

First, as immigrants’ socioeconomic status converges with that of the native‐​born, it is expected that immigrants’ location preference and ability to become homeowners will become more like that of natives, as found by Painter, Gabriel, and Myers (2001) for Hispanic and Asian immigrants. This could potentially reduce the segregation and concentration of immigrant populations.

Second, it is possible that the attitude of natives toward immigrants might change. For instance, Card, Mas, and Rothstein (2008) find higher “tipping points” in certain geographical areas and evidence of changing “tipping points” over time—meaning that after some critical point the change in attitudes is significant.

Finally, changes in the perceived value of living in central locations as opposed to suburban ones on the part of young, educated native households (Couture and Handbury 2017), as well as the increased dispersion of immigrants (Painter and Yu 2008), might alter the local relationship between immigrants and housing costs, since suburbs would no longer represent as good a substitute for natives desiring to move away from immigrants. The consequence may be higher housing prices in immigrant‐​dense neighborhoods that are centrally located.

There is evidence that all three of these factors are occurring. Hence, the classical price impact studies that show either a rise in neighborhood prices with greater in‐​migration of minorities due to discrimination (Kain and Quigley 1975; Cutler, Glaeser, and Vigdor 1999) or a lower price due to majority flight (Schelling 1972) may soon be upended, with a rise in housing prices driven by the in‐​migration of minorities being associated with increasingly desirable urban neighborhoods.

References

Acolin, A., and Wachter, S. M. (2016) “Opportunity, Spatial Inequality, and Housing Access.” Working Paper, Penn Institute for Urban Research and Federal Reserve Bank of Philadelphia.

Borjas, G. (1992) “Ethnic Capital and Intergenerational Mobility.” Quarterly Journal of Economics 107 (1): 123–50.

Card, D.; Mas, A.; and Rothstein, J. (2008) “Tipping and the Dynamics of Segregation.” Quarterly Journal of Economics 123 (1): 177–218.

Couture, V., and Handbury, J. (2017) “Urban Revival in America, 2000 to 2010.” Working Paper, Becker Friedman Institute for Research in Economics, University of Chicago (May). Available at http://bfi.uchicago.edu/research/working-paper/urban-revival-america-2000–2010.

Cutler, D. M.; Glaeser, E. L.; and Vigdor, J. L. (1999) “The Rise and Decline of the American Ghetto.” Journal of Political Economy 107 (3): 455–506.

Jargowsky, P. A. (2016) “Neighborhoods and Segregation.” In S. M. Wachter and L. Ding (eds.) Shared Prosperity in America’s Communities, 20–40. Philadelphia: University of Pennsylvania Press.

Kain, J. F., and Quigley, J. M. (1975) Housing Markets and Racial Discrimination: A Microeconomic Analysis. Cambridge, Mass.: NBER Books.

Kneebone, E. (2016) “The Changing Geography of Disadvantage.” In S. M. Wachter and L. Ding (eds.) Shared Prosperity in America’s Communities, 41–56. Philadelphia: University of Pennsylvania Press.

Painter, G.; Gabriel, S.; and Myers, D. (2001) “Race, Immigrant Status, and Housing Tenure Choice.” Journal of Urban Economics 49 (1): 150–67.

Painter, G., and Yu, Z. (2008) “Leaving Gateway Metropolitan Areas in the United States: Immigrants and the Housing Market.” Urban Studies 45 (5/6): 1163–91.

Saiz, A. (2007) “Immigration and Housing Rents in American Cities.” Journal of Urban Economics 61 (2): 345–71.

Saiz, A., and Wachter, S. (2011) “Immigration and the Neighborhood.” American Economic Journal: Economic Policy 3 (2): 169–88.

Schelling, T. C. (1972) “A Process of Residential Segregation: Neighborhood Tipping.” In A. Pascal (ed.) Racial Discrimination in Economic Life, 157–84. Lexington, Mass.: Lexington Books.

Shertzer, A., and Walsh, R. P. (2016) “Racial Sorting and the Emergence of Segregation in American Cities.” NBER Working Paper No. 22077.

Notes

¹ These variables are used because other socioeconomic attributes (e.g., income) were found to be collinear to and therefore well explained by these two main factors across national groups.

² This is consistent with the higher coefficient found on regression results using ordinary least squares that include reverse causation and do not adjust for quality.