Key takeaways:

* As a whole, U.S. cities maintained a constant pace of outward expansion into rural territory since the 1950s, but behind the facade two groups of thriving cities are behaving very differently.

* The first group of cities substantially reduced the pace of outward expansion beginning in the 1970s, channeling its economic strength into higher property values. This group includes San Francisco, Boston, New York, Los Angeles, Seattle, San Diego, Washington, Philadelphia, Portland and Miami.

* In contrast, the second group of cities accelerated its outward expansion, channeling economic strength into greater population growth. This group includes cities like Atlanta, Austin, Charlotte, Houston and Phoenix, as well as many others.

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Since World War II, America’s cities provided housing for many millions of newcomers largely by expanding into the surrounding countryside. Recently, academics, The Economist and even the White House have spewed fire at land use policies that choke off the supply of new housing. They blame the shortage of housing in certain key cities for hurting the economy because it prices people out of the most productive places, and they also tie it to other ills like reduced social mobility and damage to the environment. The historical role of cities’ outward growth in providing housing raises the question: has the expansion of American cities slowed down?

The cities that matter in this context are not the legal entities we call cities, but metropolitan areas – the broader clusters of human settlement tied together by residents’ daily routines. Although the standard definitions of U.S. metro areas are meant to capture these clusters, they are drawn along county lines and as a result include a lot of land that is, in fact, rural. Drawing an alternative boundary to distinguish rural land from developed areas is tricky because the transition between the two tends to be gradual.

In this study, I use the age of existing residential structures, drawn from the American Community Survey, to infer the decade that areas were first developed. Areas are classified as “developed” when they first pass a density of 200 currently existing homes per square mile, which roughly marks the earliest stages of suburbanization.1 A full account of the methods used is provided in the methodology section.

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American cities are still expanding

Like it or not, American cities taken as a whole are expanding just as fast as they used to. Summing up the developed area of all American cities, large and small, reveals that each decade from 1950s to the 2000s they expanded by about 10,000 square miles – an area roughly the size of Massachusetts.2 Expansion peaked in the ‘70s, but as the following chart shows, American cities maintained the same rapid clip of expansion even in the ‘90s and the 2000s.3

Yet even though American cities as a whole are expanding just as fast as they used to, ending the inquiry here would be a pity. It is the differences between American cities’ growth patterns that make the story interesting.

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Atlanta versus San Francisco

Take for example Atlanta and San Francisco – by which I mean the broadest definitions of Greater Atlanta and the San Francisco Bay Area.4 Atlanta’s developed footprint expanded considerably every decade since the 1950s – even in the 2000s, which lost several years’ of growth to the housing bust. San Francisco expanded much more than Atlanta in the ‘50s, but in contrast to Atlanta – and despite having an economy at least as strong as Atlanta’s throughout the years – San Francisco’s expansion began slowing down as early as the 1960s, and by the 2000s it had almost ground to a halt. A recent proposal to annex farmland to a suburb on San Francisco’s southern edge was described by analysts as “reminiscent of a bygone era.”

View expansion maps for top 40 cities.

Download additional expansion data and maps for all U.S. cities.

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Expensive cities and expansive cities

The contrast between Atlanta and San Francisco is stark, but it is not unique. It illustrates a growing divergence between two groups of cities that began emerging broadly in the 1970s.

The Bay Area belongs to a group of cities in which a strong economy coincides with a constrained supply of housing. Economists refer to the latter condition as an inelastic supply of housing, which means that developers respond to rising property values by building only a disproportionately small number of new homes. The failure of rising prices to spur new construction stems from a combination of natural geography and land use policy. Water bodies and unwieldy slopes hem in the city, leaving fewer suitable parcels for development, and an historical accumulation of land use policies impedes both densification within the city’s existing footprint and its outward expansion.

These cities are expensive because their economies generate strong demand for housing, which meets with restricted supply. In other words, they create jobs and opportunities that attract many people, but when these people compete with each other over a limited housing stock the highest bidders prevail, raising home values and rents. A key implication of housing cost escalation is that it sorts people into and out of these cities based on their financial ability, churning out a population that is increasingly well off. Because affluent residents tend to ratchet up land use regulation more than others, the process results in an even more constrained housing supply that raises property values further in a vicious cycle.

Atlanta belongs to another group of cities with strong economies that, unlike the previous group, has produced ample new housing, largely by expanding outward. In contrast to the group of expensive cities to which the Bay Area belongs, I call this group the expansive cities, with an a. The expansive cities’ housing supply is elastic, meaning that developers respond even to minor increases in property values by building a large number of new homes. Expansive cities are often located on plains or rolling hills that do not encumber development, and compared to the expensive cities – with an e – their land use policies tend to be less restrictive and to offer fewer opportunities for opponents to quash development.

The economies of expansive cities generate strong demand for housing as well, but the unencumbered nature of their housing supply keeps home prices pegged to the cost of construction, and instead channels economic strength into greater population growth. Newcomers to expansive cities are often the very same people who were priced out of expensive ones.

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A classification of American cities

The following chart plots housing price growth against outward expansion from 1980 to 2010 for the 40 largest U.S. cities, and helps classify them as expensive or expansive.5

How does one read the chart? In most cases, the farther a city is from the origin the more its demand for housing has grown. Cities along the tail on the lower right are expansive, whereas cities situated toward the upper left are expensive. Those situated along the stump jutting to the lower left belong to a third category, which I call legacy cities.

Legacy cities are cities whose economies are in decline and whose demand for housing has therefore not grown. As a result, they experienced neither much housing price growth nor much expansion.

The size of the circle around a city corresponds to its population growth (in percentage terms), so it is no coincidence that the circles tend to be larger around expansive cities than expensive ones. Expensive cities gained population as well, but they did so despite the constraints on housing supply, and in the absence of such constraints their population would have grown much more. Legacy cities’ populations grew only slightly or even decreased, as indicated by the absence of a circle.

The chart shows that, with the exception of legacy cities, housing price growth is inversely related to cities’ outward expansion. At least three things are driving the relationship:

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Massive amounts of housing were built on rural land in expansive cities and helped keep housing prices there in check, whereas the restricted outward expansion of expensive cities limited their supply of housing and contributing to housing price growth. Even though correlation alone does not imply causation, there should be no doubt that cities’ degree of outward expansion affected their housing prices directly. Land use policy impeding densification – as opposed to expansion – is likely to be stricter in the same cities whose outward growth is curbed, and such impediments to densification contributed to housing price growth as well. Recall that housing price growth sets in motion a sorting process that yields a more affluent population, which is prone to tightening land use regulation. This process means that housing price growth can indirectly cause cities to expand more modestly, which once again contributes to the relationship in the chart.

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Geography and land use policy

Albert Saiz of the MIT Center for Real Estate has conducted the most comprehensive research to date on land use constraints imposed by geography and regulation. He finds that both factors play an important role in restricting the housing supply, but that geography ultimately takes the front seat. Moreover, the importance of geography increases as cities grow larger, because in the process they exhaust the best tracts of land first. The importance of land use policy also increases as cities grow larger, and although Saiz does not go this far, a possible explanation is that in larger cities the cycle of restricted housing supply raising housing prices and in turn generating further land use regulation has had more time and scope to operate.

San Francisco’s extreme position at the expensive end of the chart stems from the combination of Silicon Valley’s economic might with the city’s confining natural geography on one hand, and its residents’ environmental zeal on the other. The latter shows up in both local and state-level land use regulation, as well as in residents’ propensity to take advantage of that regulation to impede development. The California Environmental Quality Act (CEQA), for example, is a well-intentioned law that is notorious for its abuse by opponents of development and others. Another example of land use policy that overtly targets urban expansion is California’s Williamson Act, which offers tax benefits to rural landowners who agree not to develop their land for ten years.

Los Angeles and Seattle are also surrounded by geographic obstacles to expansion, like San Francisco, and so is Miami which is trapped between the Atlantic Ocean and the Everglades. Although Los Angeles and Miami are not known for sharing San Francisco’s environmental sentiment, Seattle is, and Los Angeles shares the same state law as San Francisco.

The role of geography is less prominent in other expensive cities like Boston, New York, Philadelphia and Washington, aside from their proximity to the ocean. As a result, it easier to shift the blame for restricted housing supply in these cities a step further towards land use policy. In these cities and elsewhere, such policy shows up in the form of numerous mundane, local rules, like zoning for single family homes and minimum parking requirements. It also shows up in the form of stricter qualifications for the approval of new projects, e.g. placing the fate of projects in the hands of hyper-local authorities which are less attuned – to put it mildly – to cities’ broad regional housing needs.

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Construction costs, neighborhoods and oil booms

The expansive cities grew dramatically over the thirty year period in the chart, with many of them doubling and some even tripling their developed footprint. The expansive cities also experienced real housing price growth over the period – just not as much as the expensive ones. With the exception of Houston, Dallas and San Antonio – more on those cities in a moment – the expansive cities’ real housing price growth ranged from zero in places like Las Vegas to forty and even fifty percent in places like Denver and Salt Lake City.

The rule of thumb is that when housing supply is unrestricted then housing prices will remain near construction cost, yet real construction costs increased just over ten percent nationally during the period.6 There can be several reasons why housing price growth exceeded the construction cost growth in some of the expansive cities. The obvious one is that even among the expansive cities, housing supply is not perfectly unrestricted across the board. Another possibility is that increasing home values reflect an increase in the quality of homes for which housing price indices do not account (many facets of home quality are unobservable in the data that underpin housing price indices).

Perhaps the most interesting explanation, though, is that the distinction between expensive and expansive applies on an intra-metropolitan scale, too. Neighborhoods with more affluent residents tend to restrict the local housing supply more – by preventing densification – thereby raising property values. If the emergence of such neighborhoods modestly raises a whole city’s housing price level, it could help explain the modest housing price growth seen in some expansive cities.

Finally, the chart is not immune to temporary events like the Texas oil boom of the late 1970s and early ‘80s, which caused housing prices in Houston, Dallas and San Antonio to peak just after 1980. These cities’ slightly negative housing price growth in the chart is a figment of their unusually elevated housing prices circa 1980.

Among the top 40 cities, Minneapolis can pride itself in having the closest expansion and housing price growth numbers to urban America as a whole. Chicago, less expansive than Minneapolis, is situated at the intersection of expensive and legacy cities. The city’s location in the chart evokes the statement that it is “better understood in thirds – one-third San Francisco, two-thirds Detroit.” Note that Detroit has a tiny ring around it, which means that despite the implosion at its heart the city’s population did, in fact, grow slightly over the period. Such growth almost surely reflects the state of affairs in the suburbs.

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View expansion maps for top 40 cities.

Download additional expansion data and maps for all U.S. cities.

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The heart of the story

The table above shows the decade by decade expansion of the 40 largest U.S. cities and ranks them by their expansion in the 2000s.

The reason for choosing Atlanta as the poster child for expansive cities should now be clear – it was by far the greatest expander of the 2000s, the ‘90s and even the ‘80s. Indeed, four of the top five greatest expanders in the 2000s are expansive cities: Atlanta, Dallas, Houston and Phoenix. But the fifth largest expander is New York and in fact, depending on whether Chicago is considered an expensive city or not, either four or five of the top ten largest expanders in the 2000s were expensive cities. Clearly the expensive cities – with an e – are still expanding, too, and because they are generally larger, their growth is more prominent when it’s stated in square miles than in percentage terms, but this brings us back to the heart of the story.

The example of San Francisco and Atlanta is borne out nationally by the distinct trends of expensive and expansive groups of cities: since the 1970s, outward expansion has sped up in expansive cities, whereas in expensive cities it has slowed down.

The expansive cities are emerging players in the national arena. They may have incorporated long ago, but the bulk of their material presence is much more recent, and their economic importance is rising. The mass of people and companies priced out of expensive cities end up fueling the growth of expansive ones.

A likely scenario is that as expansive cities continue to grow larger and wealthier, they will gradually accrete their own set of restrictive land use policies. As this happens, the circumstances in expensive cities today may become more commonplace throughout the country.

Another possibility, though less likely, is that expensive cities will find ways of producing sufficient amounts of new housing to keep property values in check, as espoused by academics, the Economist and the White House. Such growth could occur through densification, renewed outward expansion, or a mixture of both. Even if land use regulation were reformed in favor of densification, densification involves real challenges that render it more costly than expansion, so it would be less effective at curbing housing price growth. At the same time, there are good reasons to resist a renewed drive for expansion.

Yet another potential scenario is that over the coming decades, self-driving vehicles will dramatically change land use as we know it. As I wrote several years ago, self-driving vehicles are likely to make development feasible at much greater distances from the city center than today, but they will also uncouple buildings from parking, freeing up valuable land for densification.

What scenario will come to bear? Only time will tell.

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View expansion maps for top 40 cities.

Download additional expansion data and maps for all U.S. cities.



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Notes:

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To get a sense of what this density means on the ground, recall that a square mile contains 640 acres, so that 200 homes per square mile amount to 3.2 acres per home on average. Of course, the acreage includes land outside of residential lots, such as land used for transportation, parks and open areas, so average lot size in at this density is substantially less than 3.2 acres. Such areas may still feel rural, but the feeling is an illusion. At this density most residents work in nearby urban areas, which means that it marks an early stage of suburbanization. Moreover, a housing density of 200 homes per square mile roughly corresponds to a population density of 500 residents per square mile, which is currently used by the Census as a baseline for identifying the outer fringe of urban areas, before adding areas with even lower population density based on subjective judgment. Note that the Census definition of urban areas’ boundaries does not lend itself to a consistent comparison of cities’ land area over time because it has varied substantially over the decades. The sum includes the areas of all metro areas defined by the White House Office of Management and Budget (OMB), including both metropolitan and micropolitan statistical areas. In percentage terms, American cities’ recent expansion is much slower than it was in the post-war period, but converting the numbers into percentage terms does not lessen the square mileage of rural land that was developed in the more recent decades. Moreover, expansion in percentage terms during the post-war period was high largely because of the compact nature of most pre-war development. More accurately, “Atlanta” refers to the Atlanta–Athens-Clarke County–Sandy Springs, GA CSA, and “San Francisco” refers to the San Jose-San Francisco-Oakland, CA CSA. 1980 was selected as the baseline year for chart because it is the earliest decade-cutoff year in which Metropolitan housing price indices are available (the expansion of residential development is observed only at decade cutoffs). It is also a convenient baseline, because large disparities in housing price trends across metropolitan areas only emerged in the 1970s and ‘80s (see Figure 1 and the description on page 2, here). To obtain a long-run view of housing prices that is not overly driven by transitory factors, e.g. the extent of fluctuation during the 2000s boom and bust, housing prices growth is taken as the percent change in the ten year average of housing price levels. See methodology section for additional details. This figure comes from comparing the growth of the RS Means national construction cost index to the the national rate of inflation (net of shelter) from 1980 to 2010.

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Methodology:

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Definition of cities: cities in the study are defined using current White House Office of Management and Budget (OMB) definitions for Combined Statistical Areas (CSAs) and Core-Based Statistical Areas (CBSAs). CBSAs are defined along county lines and each CBSA consists of one or more counties. CSAs are clusters of contiguous CBSAs, so every CSAs consists of multiple constituent CBSAs, e.g. the San Francisco-Oakland-Hayward, CA CBSA and the San Jose-Sunnyvale-Santa Clara, CA CBSA jointly comprise the San Jose-San Francisco-Oakland, CA CSA. However, some CBSAs do not fall within a CSA, e.g. the San Diego-Carlsbad, CA CBSA. The cities in this study consist of all CSAs and, in addition, all CBSAs that do not fall within a CSA (the latter include both metropolitan and micropolitan statistical areas). Determination of an area’s vintage: the decade in which an area was first developed, referred to as its vintage, is determined at the Census block-group level. Data on the estimated number of currently existing housing units in each block group, broken down by decade built, is obtained from the 2010-2014 5-year American Community Survey (ACS) summary files. Data on the land area of each block group is obtained from the 2014 Census TIGER shapefiles. The cumulative number of existing housing units built in a block group until a given decade is divided by the block group’s land area to obtain an estimate of its housing density as of that decade. Finally, the decade in which the density of currently existing housing units first exceeds 200 units per square mile is taken to be the block-group’s vintage. The estimate is biased downward for three reasons. First, developed areas’ whose use is predominantly non-residential may fail to exceed the density threshold when they are first developed, e.g. using this method airports often fail to show up as developed altogether. Second, inasmuch as housing units built in the past have been demolished and not rebuilt within the same decade, the housing density indicated by currently existing housing units for a past decade may fall short of the unobserved housing density indicated by the number housing units existing at the time. Third, the estimate may be biased downward in block groups whose current housing density is low. The reason is that less densely populated block groups tend to be larger, which means that areas whose current housing density is low are likely to be carved up into less granular plots of land, and therefore more likely to include some rural territory that lowers their calculated density. Inasmuch as such a granularity bias is present, it will tend to shift areas’ vintage estimates to be later than they would be otherwise, however we suspect that the implications of the potential granularity bias are minor. All in all, the three sources of downward bias may cause areas to show up as having been built later than they actually were, but not earlier, which means than any observation of a city’s expansion slowing down is not because of the flaw but despite it. Estimation of cities’ land area by vintage: a city’s land area as of a given decade is determined by summing the area of its constituent Census blocks – not block groups – when they satisfy two conditions. First, their vintage must be equal to or older than the given decade. Second, the blocks must be defined by the Census as part of an urban area, as per the Census’ current definition of urban areas. A brief description of the current definition is available here, and comprehensive details are available here. As a result, in block groups containing a mixture of urban and rural blocks, only the land area of the urban blocks counts towards the city’s area. The definition of urban areas involves subjective judgment and has varied substantially over time. As a result, an alternative estimate of cities’ areas obtained as the sum of (one or more) whole constituent urban areas would be subject to inconsistent definitions across time periods, whose effect would be difficult to distinguish from actual changes in area. Mapping: mapping is performed at the Census block level, using 2014 Census TIGER shapefiles. In block groups containing a mixture of urban and rural blocks, only the land area of the urban blocks is mapped as developed as of the decade corresponding to the block group’s vintage. Population: each city’s population as of a given decade is taken as the sum of its constituent counties’ populations. Thus, the population and changes thereof include people living in the rural portion of the counties comprising each city. County population estimates for 1940 through 1990 were obtained from a National Bureau of Economic Research (NBER) compilation, available here, and for 2000 and 2010 from the Census’ American FactFinder. Housing price growth: housing price growth is derived from quarterly, non-seasonally adjusted Federal Housing Finance Agency (FHFA) housing price indices for all transactions, available via the St. Louis Federal Reserve’s FRED portal. The indices were adjusted for inflation using the consumer price index for all urban consumers and for all items less shelter, also obtained from the portal. The indices are available from 1975 onwards. To obtain a long-run view of housing prices that is not overly driven by transitory factors, e.g. the extent of fluctuation during the 2000s boom and bust, housing price growth is taken as the percent change in the ten year average of the inflation-adjusted indices during the decade from 2005 to 2014 and similarly during the decade from 1975 to 1984. The FHFA indices are available for CBSAs, but not for CSAs. For each CSA, the study uses the CBSA-level index for the “main” CBSA, as indicated by the informal name used to refer to the CSA in the study. For example, the housing price index used for San Francisco, i.e. the San Jose-San Francisco-Oakland, CA CSA, is the housing price index for the San Francisco-Oakland-Hayward, CA CBSA, as indicated by the informal reference to the CSA as San Francisco, rather than San Jose. The substitution of a CBSA-level index for a CSA-level one is an approximation which we believe is innocuous.

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Expansion maps of top 40 U.S. cities:

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Individual decade-by-decade expansion maps for top 100 most populated U.S. cities (by 2010 population):

(For all U.S. cities’ maps, see below)

1. New York-Newark, NY-NJ-CT-PA CSA – 23.08 million

2. Los Angeles-Long Beach, CA CSA – 17.88 million

3. Chicago-Naperville, IL-IN-WI CSA – 9.82 million

4. Washington-Baltimore-Arlington, DC-MD-VA-WV-PA CSA – 9.02 million

5. San Jose-San Francisco-Oakland, CA CSA – 8.15 million

6. Boston-Worcester-Providence, MA-RI-NH-CT CSA – 7.89 million

7. Philadelphia-Reading-Camden, PA-NJ-DE-MD CSA – 7.07 million

8. Dallas-Fort Worth, TX-OK CSA – 6.81 million

9. Miami-Fort Lauderdale-Port St. Lucie, FL CSA – 6.17 million

10. Houston-The Woodlands, TX CSA – 6.11 million

11. Atlanta–Athens-Clarke County–Sandy Springs, GA CSA – 5.9 million

12. Detroit-Warren-Ann Arbor, MI CSA – 5.32 million

13. Seattle-Tacoma, WA CSA – 4.27 million

14. Phoenix-Mesa-Scottsdale, AZ Metro Area – 4.19 million

15. Minneapolis-St. Paul, MN-WI CSA – 3.67 million

16. Cleveland-Akron-Canton, OH CSA – 3.52 million

17. San Diego-Carlsbad, CA Metro Area – 3.1 million

18. Denver-Aurora, CO CSA – 3.04 million

19. Portland-Vancouver-Salem, OR-WA CSA – 2.91 million

20. St. Louis-St. Charles-Farmington, MO-IL CSA – 2.89 million

21. Orlando-Deltona-Daytona Beach, FL CSA – 2.82 million

22. Tampa-St. Petersburg-Clearwater, FL Metro Area – 2.78 million

23. Pittsburgh-New Castle-Weirton, PA-OH-WV CSA – 2.66 million

24. Sacramento-Roseville, CA CSA – 2.41 million

25. Charlotte-Concord, NC-SC CSA – 2.38 million

26. Kansas City-Overland Park-Kansas City, MO-KS CSA – 2.32 million

27. Columbus-Marion-Zanesville, OH CSA – 2.31 million

28. Salt Lake City-Provo-Orem, UT CSA – 2.27 million

29. Indianapolis-Carmel-Muncie, IN CSA – 2.25 million

30. Las Vegas-Henderson, NV-AZ CSA – 2.2 million

31. Cincinnati-Wilmington-Maysville, OH-KY-IN CSA – 2.13 million

32. San Antonio-New Braunfels, TX Metro Area – 2.12 million

33. Milwaukee-Racine-Waukesha, WI CSA – 2.03 million

34. Raleigh-Durham-Chapel Hill, NC CSA – 1.91 million

35. Nashville-Davidson–Murfreesboro, TN CSA – 1.76 million

36. Virginia Beach-Norfolk, VA-NC CSA – 1.74 million

37. Austin-Round Rock, TX Metro Area – 1.72 million

38. Greensboro–Winston-Salem–High Point, NC CSA – 1.59 million

39. Hartford-West Hartford, CT CSA – 1.49 million

40. Jacksonville-St. Marys-Palatka, FL-GA CSA – 1.47 million

41. Louisville/Jefferson County–Elizabethtown–Madison, KY-IN CSA – 1.43 million

42. New Orleans-Metairie-Hammond, LA-MS CSA – 1.41 million

43. Grand Rapids-Wyoming-Muskegon, MI CSA – 1.38 million

44. Greenville-Spartanburg-Anderson, SC CSA – 1.36 million

45. Memphis-Forrest City, TN-MS-AR CSA – 1.34 million

46. Oklahoma City-Shawnee, OK CSA – 1.32 million

47. Birmingham-Hoover-Talladega, AL CSA – 1.29 million

48. Harrisburg-York-Lebanon, PA CSA – 1.22 million

49. Buffalo-Cheektowaga, NY CSA – 1.22 million

50. Rochester-Batavia-Seneca Falls, NY CSA – 1.18 million

51. Albany-Schenectady, NY CSA – 1.17 million

52. Richmond, VA Metro Area – 1.16 million

53. Albuquerque-Santa Fe-Las Vegas, NM CSA – 1.15 million

54. Tulsa-Muskogee-Bartlesville, OK CSA – 1.11 million

55. Fresno-Madera, CA CSA – 1.08 million

56. Dayton-Springfield-Sidney, OH CSA – 1.08 million

57. Knoxville-Morristown-Sevierville, TN CSA – 1.04 million

58. Tucson-Nogales, AZ CSA – 1.03 million

59. El Paso-Las Cruces, TX-NM CSA – 1.01 million

60. Urban Honolulu, HI Metro Area – .95 million

61. Cape Coral-Fort Myers-Naples, FL CSA – .94 million

62. Chattanooga-Cleveland-Dalton, TN-GA-AL CSA – .91 million

63. Omaha-Council Bluffs-Fremont, NE-IA CSA – .9 million

64. North Port-Sarasota, FL CSA – .9 million

65. Columbia-Orangeburg-Newberry, SC CSA – .88 million

66. Little Rock-North Little Rock, AR CSA – .84 million

67. Bakersfield, CA Metro Area – .84 million

68. McAllen-Edinburg, TX CSA – .84 million

69. Madison-Janesville-Beloit, WI CSA – .83 million

70. Modesto-Merced, CA CSA – .77 million

71. Baton Rouge, LA Metro Area – .76 million

72. Syracuse-Auburn, NY CSA – .74 million

73. South Bend-Elkhart-Mishawaka, IN-MI CSA – .72 million

74. Des Moines-Ames-West Des Moines, IA CSA – .71 million

75. Springfield-Greenfield Town, MA CSA – .69 million

76. Boise City-Mountain Home-Ontario, ID-OR CSA – .69 million

77. Charleston-Huntington-Ashland, WV-OH-KY CSA – .68 million

78. Youngstown-Warren, OH-PA CSA – .67 million

79. Lexington-Fayette–Richmond–Frankfort, KY CSA – .67 million

80. Wichita-Arkansas City-Winfield, KS CSA – .67 million

81. Spokane-Spokane Valley-Coeur d’Alene, WA-ID CSA – .67 million

82. Charleston-North Charleston, SC Metro Area – .66 million

83. Huntsville-Decatur-Albertville, AL CSA – .66 million

84. Toledo-Port Clinton, OH CSA – .65 million

85. Jackson-Vicksburg-Brookhaven, MS CSA – .65 million

86. Colorado Springs, CO Metro Area – .65 million

87. Portland-Lewiston-South Portland, ME CSA – .62 million

88. Fort Wayne-Huntington-Auburn, IN CSA – .61 million

89. Lafayette-Opelousas-Morgan City, LA CSA – .6 million

90. Lakeland-Winter Haven, FL Metro Area – .6 million

91. Mobile-Daphne-Fairhope, AL CSA – .6 million

92. Visalia-Porterville-Hanford, CA CSA – .6 million

93. Reno-Carson City-Fernley, NV CSA – .58 million

94. Scranton–Wilkes-Barre–Hazleton, PA Metro Area – .56 million

95. Augusta-Richmond County, GA-SC Metro Area – .56 million

96. Palm Bay-Melbourne-Titusville, FL Metro Area – .54 million

97. Fayetteville-Lumberton-Laurinburg, NC CSA – .54 million

98. Lansing-East Lansing-Owosso, MI CSA – .53 million

99. Kalamazoo-Battle Creek-Portage, MI CSA – .52 million

100. Springfield-Branson, MO CSA – .52 million

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Individual decade-by-decade expansion maps for all U.S. cities (alphabetical):

1. Aberdeen, SD Micro Area

2. Aberdeen, WA Micro Area

3. Abilene, TX Metro Area

4. Ada, OK Micro Area

5. Alamogordo, NM Micro Area

6. Albany, GA Metro Area

7. Albany-Schenectady, NY CSA

8. Albert Lea, MN Micro Area

9. Albuquerque-Santa Fe-Las Vegas, NM CSA

10. Alexandria, LA Metro Area

11. Alexandria, MN Micro Area

12. Alpena, MI Micro Area

13. Altoona, PA Metro Area

14. Altus, OK Micro Area

15. Amarillo-Borger, TX CSA

16. Americus, GA Micro Area

17. Anchorage, AK Metro Area

18. Andrews, TX Micro Area

19. Anniston-Oxford-Jacksonville, AL Metro Area

20. Appleton-Oshkosh-Neenah, WI CSA

21. Ardmore, OK Micro Area

22. Arkadelphia, AR Micro Area

23. Asheville-Brevard, NC CSA

24. Astoria, OR Micro Area

25. Athens, OH Micro Area

26. Atlanta–Athens-Clarke County–Sandy Springs, GA CSA

27. Augusta-Richmond County, GA-SC Metro Area

28. Augusta-Waterville, ME Micro Area

29. Austin-Round Rock, TX Metro Area

30. Bakersfield, CA Metro Area

31. Bangor, ME Metro Area

32. Barre, VT Micro Area

33. Batesville, AR Micro Area

34. Baton Rouge, LA Metro Area

35. Beaumont-Port Arthur, TX Metro Area

36. Beckley, WV Metro Area

37. Beeville, TX Micro Area

38. Bellingham, WA Metro Area

39. Bemidji, MN Micro Area

40. Bend-Redmond-Prineville, OR CSA

41. Bennettsville, SC Micro Area

42. Bennington, VT Micro Area

43. Berlin, NH-VT Micro Area

44. Big Spring, TX Micro Area

45. Big Stone Gap, VA Micro Area

46. Billings, MT Metro Area

47. Binghamton, NY Metro Area

48. Birmingham-Hoover-Talladega, AL CSA

49. Bismarck, ND Metro Area

50. Blacksburg-Christiansburg-Radford, VA Metro Area

51. Bloomington-Bedford, IN CSA

52. Bloomington-Pontiac, IL CSA

53. Bloomsburg-Berwick-Sunbury, PA CSA

54. Bluefield, WV-VA Micro Area

55. Blytheville, AR Micro Area

56. Boise City-Mountain Home-Ontario, ID-OR CSA

57. Boone, NC Micro Area

58. Boston-Worcester-Providence, MA-RI-NH-CT CSA

59. Bowling Green-Glasgow, KY CSA

60. Bozeman, MT Micro Area

61. Bradford, PA Micro Area

62. Brainerd, MN Micro Area

63. Breckenridge, CO Micro Area

64. Brookings, OR Micro Area

65. Brookings, SD Micro Area

66. Brownsville-Harlingen-Raymondville, TX CSA

67. Brownwood, TX Micro Area

68. Brunswick, GA Metro Area

69. Buffalo-Cheektowaga, NY CSA

70. Burley, ID Micro Area

71. Burlington, IA-IL Micro Area

72. Burlington-South Burlington, VT Metro Area

73. Butte-Silver Bow, MT Micro Area

74. Cadillac, MI Micro Area

75. Camden, AR Micro Area

76. Campbellsville, KY Micro Area

77. Cape Coral-Fort Myers-Naples, FL CSA

78. Cape Girardeau-Sikeston, MO-IL CSA

79. Carbondale-Marion, IL Metro Area

80. Carlsbad-Artesia, NM Micro Area

81. Casper, WY Metro Area

82. Cedar City, UT Micro Area

83. Cedar Rapids-Iowa City, IA CSA

84. Champaign-Urbana, IL Metro Area

85. Charleston-Huntington-Ashland, WV-OH-KY CSA

86. Charleston-Mattoon, IL Micro Area

87. Charleston-North Charleston, SC Metro Area

88. Charlotte-Concord, NC-SC CSA

89. Charlottesville, VA Metro Area

90. Chattanooga-Cleveland-Dalton, TN-GA-AL CSA

91. Cheyenne, WY Metro Area

92. Chicago-Naperville, IL-IN-WI CSA

93. Chico, CA Metro Area

94. Cincinnati-Wilmington-Maysville, OH-KY-IN CSA

95. Claremont-Lebanon, NH-VT Micro Area

96. Clarksburg, WV Micro Area

97. Clarksdale, MS Micro Area

98. Clarksville, TN-KY Metro Area

99. Clearlake, CA Micro Area

100. Cleveland-Akron-Canton, OH CSA

101. Cleveland-Indianola, MS CSA

102. Clewiston, FL Micro Area

103. Clovis-Portales, NM CSA

104. Coffeyville, KS Micro Area

105. Coldwater, MI Micro Area

106. College Station-Bryan, TX Metro Area

107. Colorado Springs, CO Metro Area

108. Columbia-Moberly-Mexico, MO CSA

109. Columbia-Orangeburg-Newberry, SC CSA

110. Columbus, MS Micro Area

111. Columbus, NE Micro Area

112. Columbus-Auburn-Opelika, GA-AL CSA

113. Columbus-Marion-Zanesville, OH CSA

114. Cookeville, TN Micro Area

115. Coos Bay, OR Micro Area

116. Cordele, GA Micro Area

117. Corinth, MS Micro Area

118. Cornelia, GA Micro Area

119. Corpus Christi-Kingsville-Alice, TX CSA

120. Coshocton, OH Micro Area

121. Crescent City, CA Micro Area

122. Crestview-Fort Walton Beach-Destin, FL Metro Area

123. Crossville, TN Micro Area

124. Cullowhee, NC Micro Area

125. Cumberland, MD-WV Metro Area

126. Dallas-Fort Worth, TX-OK CSA

127. Danville, IL Metro Area

128. Danville, KY Micro Area

129. Danville, VA Micro Area

130. Davenport-Moline, IA-IL CSA

131. Dayton-Springfield-Sidney, OH CSA

132. DeRidder-Fort Polk South, LA CSA

133. Decatur, IL Metro Area

134. Defiance, OH Micro Area

135. Del Rio, TX Micro Area

136. Deming, NM Micro Area

137. Denver-Aurora, CO CSA

138. Des Moines-Ames-West Des Moines, IA CSA

139. Detroit-Warren-Ann Arbor, MI CSA

140. Dickinson, ND Micro Area

141. Dixon-Sterling, IL CSA

142. Dodge City, KS Micro Area

143. Dothan-Enterprise-Ozark, AL CSA

144. Douglas, GA Micro Area

145. Dublin, GA Micro Area

146. Dubuque, IA Metro Area

147. Duluth, MN-WI Metro Area

148. Dumas, TX Micro Area

149. Duncan, OK Micro Area

150. Durango, CO Micro Area

151. Dyersburg, TN Micro Area

152. Eagle Pass, TX Micro Area

153. Eau Claire-Menomonie, WI CSA

154. Edwards-Glenwood Springs, CO CSA

155. Effingham, IL Micro Area

156. El Centro, CA Metro Area

157. El Dorado, AR Micro Area

158. El Paso-Las Cruces, TX-NM CSA

159. Elk City, OK Micro Area

160. Elkins, WV Micro Area

161. Elko, NV Micro Area

162. Ellensburg, WA Micro Area

163. Elmira-Corning, NY CSA

164. Emporia, KS Micro Area

165. Enid, OK Micro Area

166. Erie-Meadville, PA CSA

167. Escanaba, MI Micro Area

168. Eugene, OR Metro Area

169. Eureka-Arcata-Fortuna, CA Micro Area

170. Evanston, WY Micro Area

171. Evansville, IN-KY Metro Area

172. Fairbanks, AK Metro Area

173. Fairfield, IA Micro Area

174. Fallon, NV Micro Area

175. Fargo-Wahpeton, ND-MN CSA

176. Farmington, NM Metro Area

177. Fayetteville-Lumberton-Laurinburg, NC CSA

178. Fayetteville-Springdale-Rogers, AR-MO Metro Area

179. Fergus Falls, MN Micro Area

180. Findlay-Tiffin, OH CSA

181. Fitzgerald, GA Micro Area

182. Flagstaff, AZ Metro Area

183. Florence, SC Metro Area

184. Florence-Muscle Shoals, AL Metro Area

185. Fond du Lac, WI Metro Area

186. Forest City, NC Micro Area

187. Fort Collins, CO Metro Area

188. Fort Dodge, IA Micro Area

189. Fort Leonard Wood, MO Micro Area

190. Fort Madison-Keokuk, IA-IL-MO Micro Area

191. Fort Morgan, CO Micro Area

192. Fort Smith, AR-OK Metro Area

193. Fort Wayne-Huntington-Auburn, IN CSA

194. Fredericksburg, TX Micro Area

195. Fremont, OH Micro Area

196. Fresno-Madera, CA CSA

197. Gadsden, AL Metro Area

198. Gainesville-Lake City, FL CSA

199. Galesburg, IL Micro Area

200. Gallup, NM Micro Area

201. Garden City, KS Micro Area

202. Gillette, WY Micro Area

203. Goldsboro, NC Metro Area

204. Grand Forks, ND-MN Metro Area

205. Grand Island, NE Metro Area

206. Grand Junction, CO Metro Area

207. Grand Rapids-Wyoming-Muskegon, MI CSA

208. Great Bend, KS Micro Area

209. Great Falls, MT Metro Area

210. Green Bay-Shawano, WI CSA

211. Greeneville, TN Micro Area

212. Greensboro–Winston-Salem–High Point, NC CSA

213. Greenville, MS Micro Area

214. Greenville-Spartanburg-Anderson, SC CSA

215. Greenville-Washington, NC CSA

216. Greenwood, MS Micro Area

217. Grenada, MS Micro Area

218. Gulfport-Biloxi-Pascagoula, MS Metro Area

219. Guymon, OK Micro Area

220. Hailey, ID Micro Area

221. Harrisburg-York-Lebanon, PA CSA

222. Harrison, AR Micro Area

223. Harrisonburg-Staunton-Waynesboro, VA CSA

224. Hartford-West Hartford, CT CSA

225. Hastings, NE Micro Area

226. Hattiesburg, MS Metro Area

227. Hays, KS Micro Area

228. Helena, MT Micro Area

229. Helena-West Helena, AR Micro Area

230. Hereford, TX Micro Area

231. Hermiston-Pendleton, OR Micro Area

232. Hickory-Lenoir, NC CSA

233. Hillsdale, MI Micro Area

234. Hilo, HI Micro Area

235. Hilton Head Island-Bluffton-Beaufort, SC Metro Area

236. Hobbs, NM Micro Area

237. Homosassa Springs, FL Metro Area

238. Hood River, OR Micro Area

239. Hot Springs-Malvern, AR CSA

240. Houghton, MI Micro Area

241. Houma-Thibodaux, LA Metro Area

242. Houston-The Woodlands, TX CSA

243. Huntingdon, PA Micro Area

244. Huntsville-Decatur-Albertville, AL CSA

245. Huron, SD Micro Area

246. Hutchinson, KS Micro Area

247. Idaho Falls-Rexburg-Blackfoot, ID CSA

248. Indianapolis-Carmel-Muncie, IN CSA

249. Iron Mountain, MI-WI Micro Area

250. Ithaca-Cortland, NY CSA

251. Jackson, MI Metro Area

252. Jackson, OH Micro Area

253. Jackson, TN Metro Area

254. Jackson, WY-ID Micro Area

255. Jackson-Vicksburg-Brookhaven, MS CSA

256. Jacksonville, NC Metro Area

257. Jacksonville-St. Marys-Palatka, FL-GA CSA

258. Jamestown, ND Micro Area

259. Jamestown-Dunkirk-Fredonia, NY Micro Area

260. Jasper, IN Micro Area

261. Jefferson City, MO Metro Area

262. Jesup, GA Micro Area

263. Johnson City-Kingsport-Bristol, TN-VA CSA

264. Johnstown-Somerset, PA CSA

265. Jonesboro-Paragould, AR CSA

266. Joplin-Miami, MO-OK CSA

267. Juneau, AK Micro Area

268. Kahului-Wailuku-Lahaina, HI Metro Area

269. Kalamazoo-Battle Creek-Portage, MI CSA

270. Kalispell, MT Micro Area

271. Kansas City-Overland Park-Kansas City, MO-KS CSA

272. Kapaa, HI Micro Area

273. Kearney, NE Micro Area

274. Keene, NH Micro Area

275. Kennett, MO Micro Area

276. Kennewick-Richland, WA Metro Area

277. Kerrville, TX Micro Area

278. Ketchikan, AK Micro Area

279. Key West, FL Micro Area

280. Killeen-Temple, TX Metro Area

281. Kinston, NC Micro Area

282. Kirksville, MO Micro Area

283. Klamath Falls, OR Micro Area

284. Knoxville-Morristown-Sevierville, TN CSA

285. Kokomo-Peru, IN CSA

286. La Crosse-Onalaska, WI-MN Metro Area

287. La Grande, OR Micro Area

288. Lafayette-Opelousas-Morgan City, LA CSA

289. Lafayette-West Lafayette-Frankfort, IN CSA

290. Lake Charles, LA Metro Area

291. Lakeland-Winter Haven, FL Metro Area

292. Lamesa, TX Micro Area

293. Lancaster, PA Metro Area

294. Lansing-East Lansing-Owosso, MI CSA

295. Laramie, WY Micro Area

296. Laredo, TX Metro Area

297. Las Vegas-Henderson, NV-AZ CSA

298. Laurel, MS Micro Area

299. Lawton, OK Metro Area

300. Lebanon, MO Micro Area

301. Lewiston, ID-WA Metro Area

302. Lewistown, PA Micro Area

303. Lexington, NE Micro Area

304. Lexington-Fayette–Richmond–Frankfort, KY CSA

305. Liberal, KS Micro Area

306. Lima-Van Wert-Celina, OH CSA

307. Lincoln-Beatrice, NE CSA

308. Little Rock-North Little Rock, AR CSA

309. Logan, UT-ID Metro Area

310. Logansport, IN Micro Area

311. London, KY Micro Area

312. Longview-Marshall, TX CSA

313. Los Angeles-Long Beach, CA CSA

314. Louisville/Jefferson County–Elizabethtown–Madison, KY-IN CSA

315. Lubbock-Levelland, TX CSA

316. Ludington, MI Micro Area

317. Lufkin, TX Micro Area

318. Lynchburg, VA Metro Area

319. Macomb, IL Micro Area

320. Macon-Warner Robins, GA CSA

321. Madison-Janesville-Beloit, WI CSA

322. Madisonville, KY Micro Area

323. Magnolia, AR Micro Area

324. Malone, NY Micro Area

325. Manhattan-Junction City, KS CSA

326. Manitowoc, WI Micro Area

327. Mankato-New Ulm-North Mankato, MN CSA

328. Mansfield-Ashland-Bucyrus, OH CSA

329. Marinette, WI-MI Micro Area

330. Marion, IN Micro Area

331. Marquette, MI Micro Area

332. Marshall, MN Micro Area

333. Marshall, MO Micro Area

334. Marshalltown, IA Micro Area

335. Martin-Union City, TN-KY CSA

336. Martinsville, VA Micro Area

337. Maryville, MO Micro Area

338. Mason City, IA Micro Area

339. McAlester, OK Micro Area

340. McAllen-Edinburg, TX CSA

341. McComb, MS Micro Area

342. McMinnville, TN Micro Area

343. McPherson, KS Micro Area

344. Medford-Grants Pass, OR CSA

345. Memphis-Forrest City, TN-MS-AR CSA

346. Meridian, MS Micro Area

347. Miami-Fort Lauderdale-Port St. Lucie, FL CSA

348. Middlesborough, KY Micro Area

349. Midland-Odessa, TX CSA

350. Milledgeville, GA Micro Area

351. Milwaukee-Racine-Waukesha, WI CSA

352. Minneapolis-St. Paul, MN-WI CSA

353. Minot, ND Micro Area

354. Missoula, MT Metro Area

355. Mitchell, SD Micro Area

356. Mobile-Daphne-Fairhope, AL CSA

357. Modesto-Merced, CA CSA

358. Monroe-Ruston-Bastrop, LA CSA

359. Montgomery, AL Metro Area

360. Montrose, CO Micro Area

361. Morgantown-Fairmont, WV CSA

362. Moses Lake-Othello, WA CSA

363. Moultrie, GA Micro Area

364. Mount Pleasant, TX Micro Area

365. Mount Pleasant-Alma, MI CSA

366. Mount Vernon, IL Micro Area

367. Mountain Home, AR Micro Area

368. Murray, KY Micro Area

369. Myrtle Beach-Conway, SC-NC CSA

370. Nacogdoches, TX Micro Area

371. Nashville-Davidson–Murfreesboro, TN CSA

372. Natchez, MS-LA Micro Area

373. Natchitoches, LA Micro Area

374. New Bern-Morehead City, NC CSA

375. New Orleans-Metairie-Hammond, LA-MS CSA

376. New York-Newark, NY-NJ-CT-PA CSA

377. Newport, OR Micro Area

378. Norfolk, NE Micro Area

379. North Platte, NE Micro Area

380. North Port-Sarasota, FL CSA

381. North Wilkesboro, NC Micro Area

382. Ocala, FL Metro Area

383. Ogdensburg-Massena, NY Micro Area

384. Oil City, PA Micro Area

385. Oklahoma City-Shawnee, OK CSA

386. Omaha-Council Bluffs-Fremont, NE-IA CSA

387. Oneonta, NY Micro Area

388. Orlando-Deltona-Daytona Beach, FL CSA

389. Oskaloosa, IA Micro Area

390. Ottumwa, IA Micro Area

391. Owatonna, MN Micro Area

392. Owensboro, KY Metro Area

393. Oxford, MS Micro Area

394. Paducah-Mayfield, KY-IL CSA

395. Palestine, TX Micro Area

396. Palm Bay-Melbourne-Titusville, FL Metro Area

397. Pampa, TX Micro Area

398. Panama City, FL Metro Area

399. Paris, TN Micro Area

400. Paris, TX Micro Area

401. Parkersburg-Marietta-Vienna, WV-OH CSA

402. Parsons, KS Micro Area

403. Payson, AZ Micro Area

404. Pecos, TX Micro Area

405. Pensacola-Ferry Pass-Brent, FL Metro Area

406. Peoria-Canton, IL CSA

407. Philadelphia-Reading-Camden, PA-NJ-DE-MD CSA

408. Phoenix-Mesa-Scottsdale, AZ Metro Area

409. Pierre, SD Micro Area

410. Pinehurst-Southern Pines, NC Micro Area

411. Pittsburg, KS Micro Area

412. Pittsburgh-New Castle-Weirton, PA-OH-WV CSA

413. Pittsfield, MA Metro Area

414. Plainview, TX Micro Area

415. Platteville, WI Micro Area

416. Plattsburgh, NY Micro Area

417. Pocatello, ID Metro Area

418. Point Pleasant, WV-OH Micro Area

419. Ponca City, OK Micro Area

420. Poplar Bluff, MO Micro Area

421. Port Angeles, WA Micro Area

422. Portland-Lewiston-South Portland, ME CSA

423. Portland-Vancouver-Salem, OR-WA CSA

424. Pottsville, PA Micro Area

425. Prescott, AZ Metro Area

426. Price, UT Micro Area

427. Pueblo-Canon City, CO CSA

428. Pullman-Moscow, WA-ID CSA

429. Quincy-Hannibal, IL-MO CSA

430. Raleigh-Durham-Chapel Hill, NC CSA

431. Rapid City-Spearfish, SD CSA

432. Redding-Red Bluff, CA CSA

433. Reno-Carson City-Fernley, NV CSA

434. Richmond, VA Metro Area

435. Richmond-Connersville, IN CSA

436. Riverton, WY Micro Area

437. Roanoke, VA Metro Area

438. Rochester-Austin, MN CSA

439. Rochester-Batavia-Seneca Falls, NY CSA

440. Rock Springs, WY Micro Area

441. Rockford-Freeport-Rochelle, IL CSA

442. Rockingham, NC Micro Area

443. Rocky Mount-Wilson-Roanoke Rapids, NC CSA

444. Rolla, MO Micro Area

445. Rome-Summerville, GA CSA

446. Roseburg, OR Micro Area

447. Roswell, NM Micro Area

448. Russellville, AR Micro Area

449. Rutland, VT Micro Area

450. Sacramento-Roseville, CA CSA

451. Safford, AZ Micro Area

452. Saginaw-Midland-Bay City, MI CSA

453. Salina, KS Micro Area

454. Salinas, CA Metro Area

455. Salisbury, MD-DE Metro Area

456. Salt Lake City-Provo-Orem, UT CSA

457. San Angelo, TX Metro Area

458. San Antonio-New Braunfels, TX Metro Area

459. San Diego-Carlsbad, CA Metro Area

460. San Jose-San Francisco-Oakland, CA CSA

461. San Luis Obispo-Paso Robles-Arroyo Grande, CA Metro Area

462. Sandpoint, ID Micro Area

463. Santa Maria-Santa Barbara, CA Metro Area

464. Sault Ste. Marie, MI Micro Area

465. Savannah-Hinesville-Statesboro, GA CSA

466. Sayre, PA Micro Area

467. Scottsbluff, NE Micro Area

468. Scranton–Wilkes-Barre–Hazleton, PA Metro Area

469. Seattle-Tacoma, WA CSA

470. Sebring, FL Metro Area

471. Sedalia, MO Micro Area

472. Selma, AL Micro Area

473. Sheboygan, WI Metro Area

474. Sheridan, WY Micro Area

475. Show Low, AZ Micro Area

476. Shreveport-Bossier City, LA Metro Area

477. Sierra Vista-Douglas, AZ Metro Area

478. Silver City, NM Micro Area

479. Sioux City-Vermillion, IA-SD-NE CSA

480. Sioux Falls, SD Metro Area

481. Snyder, TX Micro Area

482. Somerset, KY Micro Area

483. Sonora, CA Micro Area

484. South Bend-Elkhart-Mishawaka, IN-MI CSA

485. Spencer, IA Micro Area

486. Spirit Lake, IA Micro Area

487. Spokane-Spokane Valley-Coeur d’Alene, WA-ID CSA

488. Springfield-Branson, MO CSA

489. Springfield-Greenfield Town, MA CSA

490. Springfield-Jacksonville-Lincoln, IL CSA

491. St. George, UT Metro Area

492. St. Louis-St. Charles-Farmington, MO-IL CSA

493. Starkville, MS Micro Area

494. State College-DuBois, PA CSA

495. Steamboat Springs-Craig, CO CSA

496. Stephenville, TX Micro Area

497. Sterling, CO Micro Area

498. Stillwater, OK Micro Area

499. Storm Lake, IA Micro Area

500. Sumter, SC Metro Area

501. Susanville, CA Micro Area

502. Sweetwater, TX Micro Area

503. Syracuse-Auburn, NY CSA

504. Tallahassee-Bainbridge, FL-GA CSA

505. Tampa-St. Petersburg-Clearwater, FL Metro Area

506. Taos, NM Micro Area

507. Terre Haute, IN Metro Area

508. Texarkana, TX-AR Metro Area

509. The Dalles, OR Micro Area

510. Thomasville, GA Micro Area

511. Tifton, GA Micro Area

512. Toccoa, GA Micro Area

513. Toledo-Port Clinton, OH CSA

514. Topeka, KS Metro Area

515. Traverse City, MI Micro Area

516. Troy, AL Micro Area

517. Tucson-Nogales, AZ CSA

518. Tullahoma-Manchester, TN Micro Area

519. Tulsa-Muskogee-Bartlesville, OK CSA

520. Tupelo, MS Micro Area

521. Tuscaloosa, AL Metro Area

522. Twin Falls, ID Micro Area

523. Tyler-Jacksonville, TX CSA

524. Ukiah, CA Micro Area

525. Urban Honolulu, HI Metro Area

526. Utica-Rome, NY Metro Area

527. Uvalde, TX Micro Area

528. Valdosta, GA Metro Area

529. Vernal, UT Micro Area

530. Vernon, TX Micro Area

531. Victoria-Port Lavaca, TX CSA

532. Vidalia, GA Micro Area

533. Vincennes, IN Micro Area

534. Vineyard Haven, MA Micro Area

535. Virginia Beach-Norfolk, VA-NC CSA

536. Visalia-Porterville-Hanford, CA CSA

537. Wabash, IN Micro Area

538. Waco, TX Metro Area

539. Walla Walla, WA Metro Area

540. Warren, PA Micro Area

541. Warsaw, IN Micro Area

542. Washington, IN Micro Area

543. Washington-Baltimore-Arlington, DC-MD-VA-WV-PA CSA

544. Waterloo-Cedar Falls, IA Metro Area

545. Watertown, SD Micro Area

546. Watertown-Fort Drum, NY Metro Area

547. Wauchula, FL Micro Area

548. Wausau-Stevens Point-Wisconsin Rapids, WI CSA

549. Waycross, GA Micro Area

550. Weatherford, OK Micro Area

551. Wenatchee, WA Metro Area

552. West Plains, MO Micro Area

553. Wheeling, WV-OH Metro Area

554. Wichita Falls, TX Metro Area

555. Wichita-Arkansas City-Winfield, KS CSA

556. Williamsport-Lock Haven, PA CSA

557. Williston, ND Micro Area

558. Willmar, MN Micro Area

559. Wilmington, NC Metro Area

560. Winnemucca, NV Micro Area

561. Winona, MN Micro Area

562. Woodward, OK Micro Area

563. Wooster, OH Micro Area

564. Worthington, MN Micro Area

565. Yakima, WA Metro Area

566. Yankton, SD Micro Area

567. Youngstown-Warren, OH-PA CSA

568. Yuma, AZ Metro Area

569. Zapata, TX Micro Area