With the rise in American incomes in recent decades, consumers are increasingly demanding housing in high-amenity areas.1 Location amenities can improve the quality of life, whether they are natural resources (such as a coastline, mountains, or climate), durable institutions (such as universities), cultural opportunities (like museums), or consumption amenities that arise from a variety of choices (as found in many densely populated areas).

How have housing location decisions been influenced by higher costs in cities and increasing consumer preferences for area amenities? This question is addressed in the paper “The Push of Big City Prices and the Pull of Small Town Amenities,” by Heidi Artigue of the University of Pennsylvania and Jeffrey Brinkman and Svyatoslav Karnasevych of the Philadelphia Fed. 

As background, the authors explain that rents in many big U.S. cities have soared over the last four decades as compared to rents in less-populated areas. Given the limited supply of land for development within cities, a housing supply crunch developed, which, they note, has important implications for the affordability of cities and the population growth rates in more affordable locations.

The authors created a simple model that illustrates that the increasing demand for amenities affects house prices and population levels differently in big cities than in smaller towns and rural areas. In their model, housing demand increases with location amenities and wages and decreases with higher rents, while housing supply increases with higher rents.2 They test the predictions of their model using U.S. county-level data from 1980 to 2019 and, for comparison purposes, classify counties based on four population sizes: large, midsize, small, and rural.3 Further, to measure location amenities across counties, they used a standard statistical method from the quality-of-life literature.4

They found that most places with high amenities experienced increases in both house prices and population over their sample period. In places that are more densely populated and have high amenities, they found that price changes were larger, which is intuitive given the housing supply constraints in desirable cities. (In fact, the largest and most expensive cities experienced a net population loss to other, less-expensive places, although these largest cities continued to grow slightly on net because of natural increase and international migration.) In contrast, they found that, in less-densely populated areas with high amenities, in-migration occurred without a significant impact on prices in relative terms, and population changes were larger because these areas could provide more housing. 

The authors also show that natural features — specifically, mountains or a nearby coastline — had important amenity value in rural areas and small towns. (They did not find a similar positive correlation with natural features in large metro areas.) They also show that the presence of universities had greater amenity value in small towns and rural areas than in large metros, noting that “universities may provide some spillovers in terms of quality of life beyond jobs and peer effects.”

Additionally, the authors show that population density was a disamenity for small towns but a positive amenity for big cities. The authors speculate that “very large cities may provide increased variety with increased density, while in small towns, additional density may have a pure congestion effect through traffic or losses in open space.”

By decomposing the population changes over the past four decades, they found that population growth was the highest in midsize metropolitan areas, with larger cities and rural areas having slow population growth. Digging deeper, however, they found important differences within these size categories. For example, although the average population growth rate in rural areas was 25 percent (1980–2019), compared to 45 percent across the entire U.S., 16 percent of rural counties had a population growth rate higher than the national average, and 5 percent of these rural areas had more than doubled their population. These findings have important implications for small metro areas with different amenity levels, the authors explain, as high-amenity counties in small metro areas increased their population significantly through domestic migration between 2000 and 2019, while low-amenity counties in this size category experienced significant depopulation.

In summary, Artigue, Brinkman, and Karnasevych show that the largest and most expensive cities experienced a housing supply crunch, leading to higher rents and a net out-migration as people moved to more affordable locations, particularly to high-amenity nonurban locations. The authors suggest that “rural areas and small towns may provide a release valve for housing demand pressure in the form of out-migration from housing-constrained large cities.” However, given that large cities provide significant production advantages over smaller localities, the out-migration from cities, they note, may cause significant welfare losses and thus should remain a public policy concern. In light of the changing patterns of work and location choices in the wake of the COVID-19 pandemic, the authors’ results may also help guide predictions of future population outcomes.

  1. The views expressed here are solely those of the authors and do not necessarily reflect the views of the Federal Reserve Bank of Philadelphia or the Federal Reserve System.
  2. For more on this topic, see, for example, Gerald Carlino and Albert Saiz, “Beautiful City: Leisure Amenities and Urban Growth,” Journal of Regional Science, 59:3 (2019), pp. 369–408. 
  3. Nevertheless, the price elasticity of housing supply — that is, the change in housing supply resulting from a change in housing prices — is not constant. Instead, the authors found that the price elasticity of housing supply decreased with city size, which can be explained by the fact that there is less available land for development as cities grow.
  4. The authors used U.S. census data on housing costs and population from 1980 to 2019. For certain years, they also used data from the American Community Survey and the Decennial Census of Population and Housing.
  5. For more details, see Jennifer Roback, “Wages, Rents, and the Quality of Life,” Journal of Political Economy, 90:6 (1982), pp 1257–1278.