Chicago-area sprawl revisited: The role of demographics, prosperity and homeownership with

a note on sustainability

Siim Sööt and Brendan Dodge-Hayakawa

Metropolitan Transportation Support Initiative (METSI) Urban Transportation Center

University of Illinois at Chicago

METSI Working Paper 2013

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BACKGROUND - RECENT CHANGES IN THE ECONOMY ...................... 4

National GDP trend, 2000 to 2012 ......................................................... 4

National unemployment trends ............................................................. 5

Income trends and variations in the Chicago area .............................. 6

FACTORS CONTRIBUTING TO HIGH RATES OF SPRAWL .................... 8

Homeownership rates ............................................................................ 9

National homeownership trends ........................................................... 9

Metropolitan-area homeownership rates ............................................................ 10

Chicago homeownership rates and peer metropolitan areas ........................... 11

Household size ..................................................................................... 12

Women in the labor force .................................................................... 14

CONSTRAINTS TO URBAN SPRAWL ..................................................... 17

Physiography and Topography ........................................................... 17

Public policy ......................................................................................... 18

BUILDING PERMITS (HOUSING STARTS) AND URBAN SPRAWL ....... 19

Trend in building permits for new housing ........................................ 19

Housing building permits by county................................................... 20

Value of new starts building permits ................................................................... 20

Number of building permits by county ................................................................ 22

Percent of the region’s single-family housing permits by county .................... 23

Multi-unit structures: building permits issued ................................................... 24

Population change and urban sprawl ................................................. 25

THE ROLE OF TRAVEL TIME, MODALSPLIT AND CONGESTION ........ 29

Travel time ............................................................................................ 29

Modal split ............................................................................................ 32

Congestion ........................................................................................... 34

SUMMARY AND IMPLICATIONS ............................................................. 36

REFERENCES ......................................................................................... 38

APPENDIX ............................................................................................... 39

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INTRODUCTION Population growth in the Chicago area was rather modest from1970 to 1990 but the increase in the amount of land used for urban purposes was considerable, approximately four percent versus thirty-five percent respectively. This and a variety of other concerns contributed to public discourse on urban sprawl, future growth patterns, and the livability of the region. In subsequent decades, both economic and demographic conditions have changed. Thus, it is time to revisit the circumstances that contribute to sprawl by tracking the recent rates of sprawl and its implication for transportation demand. Our previous work (Sen et al., 1998 and Sööt et al., 2001) focused on two vital points. First, there have been a series of sociodemographic and economic trends that have contributed to large increases in land consumption that now have diminishing effects. Today, population growth and land consumption increases are largely equivalent. For approximately a century, declining household sizes created a disproportionate number of households in contrast to overall population growth driving the demand for housing units. Now household sizes have effectively stopped declining, thereby slowing the demand for housing. Growing homeownership rates have also been closely related to urban sprawl, but even before the economic downturn, homeownership rates began to stabilize and decline. Second, there is a relationship between urban sprawl and prosperity. During periods of economic expansion, rising incomes contribute to urban sprawl. We experienced that in the 1990 and the early 2000s. Since the middle of the first decade in this millennium, the economy has weakened. We conjecture that declining homeownership rates, brought upon by the recession, have had a substantial effect regarding urban sprawl rates, slowing the outward movement of the population. We are now in a period (post 2005), that contrasts with the later part of the 20th Century and the beginning of the 21st Century. This calls for another examination of the factors relating to urban sprawl in the Chicago area. The purpose of this report is to present information and stimulate discussion by focusing on the period since 2000 to assess where growth is occurring in the Chicago area, as well as the scale of the changing dynamics in urban growth. Specifically, we will re-examine our proposition that while sprawl is facilitated by the transportation system, the level of prosperity is a major factor in the degree and pace of sprawl. At the same time we will note how the data we are examining suggests that the Chicago area is becoming more sustainable with high densities and growth in core areas. This is done through an examination of aggregate data and does not include a study of specific growth patterns within communities. The report focuses on the traditional six-county Chicago region, but in several cases we consider Kendal and or Grundy Counties as appropriate. The Chicago area is also

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frequently compared with other urban areas. The vast majority of the data were derived from the latest available U.S. Census Bureau tabulations. It will not attempt to cover the vast literature on the subject as that is beyond the scope of this study. BACKGROUND - RECENT CHANGES IN THE ECONOMY Homeownership has changed dramatically in this millennium, initially increasing and then decreasing. Importantly, the decrease in homeownership rates began many years before the well-recognized downturn in the overall economy. This was true for both national data as well as for the Chicago area. However, we will first focus on measures that identify the critical point in recent years that signaled changes in the economy. We posit that the change in homeownership rates and urban sprawl are largely based on economic conditions. Therefore it is necessary to discuss initially the economic setting in which our study is based. National GDP trend, 2000 to 2012 The economic conditions at the beginning of the millennium may be assessed in several ways. Figure 1 shows the national GDP levels since 2000. Figure 1 shows that the overall economic grew steadily through the first seven years of the millennium, started to decline in mid-2008, and bottomed out in early 2009. Subsequently it has grown steadily, but so too has the population. It does not, however, well depict the scale of the shrinkage in the economy or the distribution of the growth among population (income) subgroups.

Figure 1. Gross Domestic Product, by Quarter, 2000 to 2012 Data in billions of constant dollars (chained to 2005 dollars)

Source: http://www.bea.gov/national/ accessed August 29, 2012

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Figure 2 better illustrates the dramatic change in the national economy. Figure 2 shows that there was a significant shrinkage in the general economy during the most recent economic downturn. While the growth rates in recent years have been positive and not substantially different than the middle of the previous decade, the overall economy has yet to return to full health.

Figure 2. Quarterly Changes in the National GDP

Source: http://www.bea.gov/national/ accessed August 29, 2012 National unemployment trends Unemployment data reflects the plight of those that are jobless. It shows the dramatic rise in the latter part of the previous decade, and the steady decline in the rate during the current decade as the economy is in recovery mode (Figure 3). Figure 3 best shows the persistent nature of the weak economy as it continues to negatively affect a large number of households. The economic data in this section portray a difficult period for many households, though it touches each household in different ways. We will see that these data do not very closely correspond with changing home ownership rates, but may nevertheless affect other factors that contribute to rates of sprawl such as household size.

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Figure 3. Unemployment Rates, 2000-2013 (note scale begins with 3%)

Source: http://data.bls.gov/timeseries/LNS14000000 The three measures above show that economic decline and low-points occurred from the end of 2008 to the beginning of 2010. The highest unemployment rates in early 2010 appear to have occurred after the GDP numbers began to recover in the first half of 2009. The largest drop in the GDP occurred in the last quarter of 2008. Income trends and variations in the Chicago area Household incomes are of interest here for two reasons. First, if sprawl is a function of prosperity and income, one might expect that relatively high or growing prosperity is associated with growing (sprawling) counties. As household incomes rise, these households may opt for large homes in suburban counties. Second, increases in income tend to facilitate household formation. In affluent societies, fewer generations live together—the young move out earlier and the elderly frequently live independently in their own households. Moreover, they have greater latitude in how the household finances are allocated to cover transportation and housing costs. Two sets of counties are of interest in the first ‘reason.’ Both DuPage and Lake Counties are effectively the highest income counties in the Chicago area. While the two income lines in Figure 4 appear to move in tandem, Lake County seems to pull ahead in the last several years, though the difference is modest and is not consequential. Population data show that DuPage County has experienced only modest growth in the last decade (Table 6) while Lake County continues to steadily grow. It is then not surprising that there is some evidence that Lake County’s income is beginning to inch

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ahead of DuPage County. Still, more recent data shows a decline in Lake County’s population. The other set of counties of interest are the three fastest growing counties in the region, Kendall, McHenry and Will. These counties better illustrate the connection between prosperity and urban sprawl. Will County has had the largest increase in personal income, both in percent (19%) as well as absolute (current) dollars, an increase of approximately $6400. Will County has surpassed McHenry County not only in income but has also replaced McHenry County as the fastest growing county in the state (in absolute terms). Kendall County has the second highest percentage gain in personal income and is approaching Kane County’s income level. Not surprisingly, on a percentage basis Kendall County is the fastest growing county in the state.

Figure 4.Personal Income for the Seven-County Area, 2005-2010

Source: Data Source: U.S. Department of Commerce, Bureau of Economic Analysis, 2012. http://www.bea.gov

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FACTORS CONTRIBUTING TO HIGH RATES OF SPRAWL The increase in the amount of land consumed by urban areas tends to be based on four direct factors: (1) population size and growth, (2) homeownership rates, (3) household size and (4) lot size. Since the effect of population is relatively obvious, we will concentrate on the other three factors. As households transition from rental units to single-family homes, they contribute to land consumption. Furthermore, as household sizes decrease, there are more households for a fixed population thus also contributing to land consumption. Lastly, as households move to peripheral areas, they are likely to move to properties that are larger. Using actual and estimated data, Table 1 illustrates the compounding effects of the three factors cited above. Starting with the approximate conditions in 1950, the example shows how these three factors contributed to a 75 percent increase in the amount of land utilized without an increase in the population. Table 1. Two Hypothetical Cases Reflecting Changing Urban Trends

Year Case A: ca. 1950 Case B: ca. 2000 Population 100 100 Mean household size 3.33 2.65 Households 30.0 37.74 Homeownership rate* 50% 65% Home Owners* 15.0 24.53 Renters 15.0 13.21 Mean Lot Size* (own) 8000 ft2/lot* 9000 ft2 /lot* Land use by owners 120,000 square feet 220,755 square feet Land use per renter 1000 ft2 land/unit* 1200 ft2 land/unit* Land use by renters 15,000 square feet 15,849 square feet Total Land Area # 135,000 square feet 236,604 square feet Percent increase in land - 75.3%

* Estimates by the authors for illustrative purposes only; data are not accessible. Source: Siim Sööt, et al, (2001) Travel behavior and employment decentralization, Executive Summary, p. 8, 44pp. In recent decades, however, there has been a dampening effect on the rate of land consumption, specifically households moving from single-family homes to high-rise and mid-rise condominiums. This factor does not affect homeownership rates, but is likely to contribute to a declining amount of land consumed per household. More importantly, neither homeownership rates nor household sizes are now contributors to excessive land consumption as we will see below.

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Homeownership rates Homeownership has contributed substantially to territorial growth of urban areas. As the young leave home earlier and acquire homes of their own, they increase the demand for single-family and condominium units. Therefore, the proportion of the households that own their own homes is a factor that needs to be examined further in order to fully understand rates of sprawl. National homeownership trends As a whole, national homeownership rates increased for decades contributing to the territorial expansion of urban areas. In recent years, however, there has been a reversal of this trend, specifically national homeownership. National homeownership rates peaked in the second quarter of 2004 and reached the same level (69.2 percent) in the fourth quarter of 2004. Since then, the rate has decreased to 65.4 percent by first quarter of 2012 (Figure 5). The second and third quarters in 2012 were 65.5 percent, suggesting that the eight-year decline may have stopped. It is evident, however, from Figure 5 that there have been numerous other instances in which homeownership rates have increased over a three-quarter period. Thus, at least one more quarter of data would be useful to ascertain whether the decline has ceased (During final revisions in this report fourth quarter 2012 were released showing drop to 65.4, the same level as in the first quarter and a subsequent drop to 65.0 by the first quarter 2013. Interestingly, the Midwest, one of four Census regions, was the only region to show a gain in recent quarters and is now at 70.0 percent).

Figure 5. National Homeownership Rates, by Quarter, 2000 to 2012

Source: http://www.census.gov/econ/currentdata/dbsearch?program=HV&startYear=1956&endYear=2013&categories=RATE&dataType=HOR&geoLevel=US&notAdjusted=1&submit=GET+DATA

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While the decline started in 2004, one could also describe 2005 and early 2006 as a volatile period in which ownership rates gyrated substantially. In this regard, the steadier decline began in the third quarter of 2006. Regardless of the precise beginning, it is evident that declining homeownership rates were a harbinger of economic conditions to come.

Metropolitan-area homeownership rates Homeownership rates are generally lowest in the largest metropolitan areas. Table 2 shows the homeownership rates for the 15 largest metropolitan areas (note that metropolitan area boundaries change over time and may contain large counties that may only partially be urbanized). All but one of the 2011 rates is in the 50 to 70 percent range, with the Detroit area recording a high figure of 73.5%. Philadelphia had the second highest rate (69.7%), and Chicago came in third (67.7%).These three areas, along with Washington D.C. (67.6%), are among the minority of places in Table 2 that have rates higher than the nation level, 64.6%.

Table 2. Homeownership rates among 15 largest metropolitan areas 2005 2006 2007 2008 2009 2010 2011

Inside Metro Areas 67.4 67.4 66.8 66.4 65.9 65.4 64.6 Entire Nation 68.9 68.8 68.1 67.8 67.4 66.9 66.1

1 New York 54.6 53.6 53.8 52.6 51.7 51.6 50.9 2 Los Angeles 54.6 54.4 52.3 52.1 50.4 49.7 50.1 3 Chicago 70.0 69.6 69.0 68.4 69.2 68.2 67.7 4 Dallas-Ft. Worth 62.3 60.7 60.9 60.9 61.6 63.8 62.6 5 Houston 61.7 63.5 64.5 64.8 63.6 61.4 61.3 6 Philadelphia 73.5 73.1 73.1 71.8 69.7 70.7 69.7 7 Washington D.C. 68.4 68.9 69.2 68.1 67.2 67.3 67.6 8 Miami-Fort Lauderdale 69.2 67.4 66.6 66.0 67.1 63.8 64.2 9 Atlanta 66.4 67.9 66.4 67.5 67.7 67.2 65.8

10 Boston 63.0 64.7 64.8 66.2 65.5 66.0 65.5 11 San Francisco-Oakland 57.8 59.4 58.0 56.4 57.3 58.0 56.1 12 Riverside-San Bernardino 68.5 68.3 66.6 65.8 65.9 63.9 59.2 13 Detroit 75.1 75.8 76.1 75.5 73.9 73.6 73.5 14 Phoenix 71.2 72.5 70.8 70.2 69.8 66.5 63.3 15 Seattle-Tacoma 64.5 63.7 62.8 61.3 61.2 60.9 60.7

Source: http://www.census.gov/housing/hvs/data/ann11ind.html

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Chicago homeownership rates and peer metropolitan areas A stated earlier, homeownership rates are typically lowest among the largest metropolitan areas and increase with smaller and smaller populations. The highest rates are in the small to medium-sized metropolitan areas e.g., 76% in Grand Rapids MI and 73% in Birmingham AL but 51% in Los Angeles and 52% in New York (Appendix Table A.1). The Chicago area with a 67% rate is closer to Birmingham than to Los Angeles (2012, third quarter,). Regarding peer places, Figure 6 shows that as the two largest metropolitan areas, New York and Los Angeles have much lower homeownership rates than the fourth and fifth largest metropolitan areas, Houston and Dallas-Fort Worth. Chicago is a clear exception and there are many more to be found. At the same time, Chicago is an exception, in part because it can decentralize and in part because typical wages are adequate for high homeownership rates. Like New York and Los Angeles, Chicago exhibits a decline in rates though the rankings have stayed the same among the five largest metropolitan areas (Figure 6). Houston and Dallas are quite different in that there is no clear long-term increase nor decrease and neither of the two has consistently had a higher homeownership rate. Houston did, however, appears to challenge Chicago in the beginning of 2008 but the rate dropped off and by the end of 2012 was approximately five points below Chicago.

Figure 6. Homeownership Rates for Largest Metropolitan Areas, by Quarter, 2005-12

Source: http://www.census.gov/housing/hvs/data/ann12ind.html

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Lastly, the five metropolitan areas show substantial fluctuations during the twelve-year period and they each had higher rates at the beginning of the period than at the end. It should also be noted that many of the fluctuations in the lines in Figure 2 are due to sample size and therefore the relatively high margins of error. Figure 7. Homeownership Among12 largest Metropolitan Areas, 2012

Figure 7 shows recently released data for a larger set of peer metropolitan areas. It effectively illustrates how the Chicago area compares with other places in its size class. Note in particular how well Chicago compares with the sun-belt metropolitan areas.

Source: http://www.census.gov/housing/hvs/data/ann12ind.html Household size For over a century, declining household size has been the result of increasing prosperity. As household incomes increase and the young are able to establish their own households, the number of households increase at a rate higher than population growth. Recent evidence, however, shows that this trend has decreased and mean household size has either stabilized or begun to increase. While we are not concluding that this is a result of stagnating incomes, it is widely known that middle-class incomes have not risen for several decades. Figure 7 shows how in the forty year period, 1970 to 2010, there has been a decline in household size that resembles a negative exponential relationship with time, i.e., there is a constant percent decline that has less and less effect as the numbers decrease in magnitude. Alternatively, one may describe the declines as initially being linear with an uptick in recent years. Indeed, the smallest average was in 2007 and 2008 at 2.56 and the most recent year, 2010, is at 2.59, the same level as the beginning of the millennium.

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Figure 8. Average household size, national data

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http://www.census.gov/compendia/statab/cats/population/households_families_group_quarters.html

Further, during the forty-year period, there was a short-term increase in the early 1990s. Again, without establishing a direct causal relationship, it is worth noting that there was a dislocation in the economy during this period as well. The same temporal association existed in the end of the forty-year period. At the same time, in many communities increasing household size has been attributed to the increase in immigrant families. As stated earlier, there is negative association between household size and the number of households. For a constant population, if the household size decreases then the number of household will increase. While there are many other factors, namely population change—(population change is not constant over time), we explore the basic association in Figure 8. The evidence here is not overwhelming but for approximately twenty years, (early 1980s to early 2000s) household sizes declined and the number of households increased. Since 2002, when there was an increase of approximately 2.2 million households over the number in 2001, there has been continued growth but the annual (year-to-year) increase in number of households has been declining. This decline is likely related to decreases in population growth rates, though it was not assisted by smaller and smaller households as in the past. In 2002 the average household size was 2.58 versus 2.57 in 2009 and 2.59 in 2010 respectively.

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Figure 9. Average households size (left axis) and increase in the number of households (in thousands—right axis, three-year moving average) for the nation

Source: http://www.census.gov/compendia/statab/cats/population/households_families_group_quarters.html

Note that since the early, middle 2000s there was a sharp drop in the increase of households. Other than a slight uptick in household size, there were likely other factors. The declining prosperity decreased the rate of household formation (the young starting their own households). At the same time, population growth in the nation through lower immigration numbers slowed overall growth. Clearly, the rate of household formation and its effect on land consumption has many facets but mean household size is an important factor. Women in the labor force While not part of the three items highlighted above, the proportion of the women in the labor force has also been one of many critical factors regarding urban sprawl. Labor force participation is also important in understanding the demand for transportation services especially during commuting (peak) hours. Homeownership rates, as a driver of urban sprawl, are dependent upon household income. In the 1970s and early 1980s the proportion of the women in the labor force increased dramatically and in many cases, substantially increased household income. It was likely a major factor in the solid increase in homeownership during that time. Figure 9, however, shows that the increasing participation of women in the labor force in recent years has ceased and indeed has declined for approximately a dozen years. At

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the same time the proportion of men in the labor force has also been declining, though their decline has occurred throughout the study period.

Figure 10. Labor Force Participation Rates, Population Sixteen and Older

Source: http://www.bls.gov/cps/wlf-databook-2011.pdf Table 2 shows that the highest percent for male participation was at the beginning of the graph and for women it was in 1999. For the entire working population, the highest percentage, 67.1, occurred over a several-year period from 1997 to 2000.

Table 3. Percent of the Population 16 Years and Older in the Labor Force

Women Men Total Lowest % 43.3 71.2 60.2

Lowest year 1970 2010 1971 Highest % 60.0 79.7 67.1

Highest year 1999 1970 1997-2000 2010 58.6 71.2 64.7

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During the 40-year study period, the greatest change in the overall labor force occurred in the 2001-2003 and the 2009-2010 periods. In the former period, the labor force shrank by 0.9 points and 1.3 in the latter period. The recent drop is known to all but the 0.9 point drop at the beginning of the millennium is less well known. It appears to have been a precursor to the subsequent drop in homeownership rates. What is also important about Table 3 is that the highest participation rates occurred in the very late 1990s. From 1970 to 1999 the female participation rate increased by a remarkable 16.7 points. Since the male rate declined, the overall increase was approximately 7 points. In effect for a constant population, the number of workers and therefore the number of commuters increased by the increase in the participation rate. This contributed to congestion but also to greater prosperity and urban sprawl. Since the late 1990s the participation rates have declined for both men and women. The greatest gender change began in the mid-1970s. From 1974 to 1976 (inclusive) a major shift occurred in the proportion of the men in the labor force. While male participation declined by 1.3 points, the increase among women of 2.6 points (Table 4) resulted in an overall increase of 0.8 points. The five-year period from 1978 to 1982 (inclusive) showed even a greater increase in female participation at a time when the male participation dropped once more. More recently, from 2005 to 2010, both men and women had a drop in labor force participation rates.

Table 4. Major change in the labor force participation rates 1974-1976 1978-1982 2005-2010 Women +2.6 +4.2 -0.8 Men -1.3 -1.1 -2.1 Total +0.8 +1.7 -1.3

Source: Computed from http://www.bls.gov/cps/wlf-databook-2012.pdf Ultimately, an important question is what level of income makes homeownership feasible. While a declining labor force participation rate is not seen as a positive, it may, however, reflect the increased efficiencies in our economy. Homeownership rates increased for half a decade while participation rates were declining (1999 to 2005). If high homeownership rate are achieved by fewer people working, then that may be considered to be a positive development. For the time being, however, it is associated with declining ownership rates. From a transportation perspective, congestion has increased in many metropolitan areas. However, with a decline in the labor force participation rate, the increase in congestion would certainly have been higher had the rate stabilized or continued to grow. If the participation rates increased, it may have also contributed to additional daily traffic for a multitude of purposes.

Commented [O1]: In female participation?

Commented [O2]: Move up to “Since the male rate declined…”

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CONSTRAINTS TO URBAN SPRAWL There are at least two principal reasons why many urbanized areas do not sprawl: physiography (topography, water and marsh land) and public policy. Physiography and Topography The lack of extensive flat land for urban expansion is evident in Table 5 (urbanized areas are contiguous areas with at least 1000 inhabitants per square mile and therefore are very different than metropolitan areas that may include farm land and vast uninhabited areas such as deserts). Most of the Californian urbanized areas are constrained by topography and are prominent in Table 5. Californian places account for

Table 5. Population densities of the 25 most densely populated urbanized areas among the 75 largest urbanized areas, 2010 (land area and density based on square miles) Urbanized area (abbreviated name) Population Land area Density 1 Los Angeles, CA 12,150,996 1736 6999 2 San Francisco-Oakland, CA 3,281,212 524 6266 3 San Jose, CA 1,664,496 286 5820 4 New York-Newark, NY-NJ-CT 18,351,295 3450 5319 5 Honolulu, HI 802,459 170 4716 6 Las Vegas, NV 1,886,011 417 4525 7 Miami, FL 5,502,379 1239 4442 8 San Diego, CA 2,956,746 732 4037 9 Mission Viejo, CA 583,681 151 3878 10 Fresno, CA 654,628 171 3822 11 Salt Lake City, UT 1,021,243 278 3675 12 Sacramento, CA 1,723,634 471 3660 13 New Orleans, LA 899,703 251 3579 14 Denver, CO 2,374,203 668 3554 15 Riverside-San Bernardino, CA 1,932,666 545 3546 16 Portland, OR-WA 1,849,898 524 3528 17 Chicago, IL-IN 8,608,208 2443 3524 18 Washington, DC-VA-MD 4,586,770 1322 3470 19 El Paso, TX-NM 803,086 251 3205 20 Phoenix, AZ 3,629,114 1147 3165 21 Baltimore, MD 2,203,663 717 3073 22 Seattle, WA 3,059,393 1010 3028 23 Concord, CA 615,968 204 3023 24 Houston, TX 4,944,332 1660 2979 25 Colorado Springs, CO 559,409 188 2978

Source: http://www.census.gov/geo/www/ua/ua2k.txt and http://en.wikipedia.org/wiki/List_of_United_States_urban_areas

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seven of the twelve most densely populated urbanized areas. Miami and New Orleans, high on the list, are constrained by wetlands, while Portland is constrained by public policy. We have seen that most of the large urbanized areas are characterized by low homeownership rates. Again we note the unusual status of the Chicago area. There is not a major urban place that has both a higher density and higher homeownership rate than Chicago. Detroit and Philadelphia are shown to have higher homeownership rates than Chicago, as show in Table 2, yet both have substantially lower population densities. In effect, the Chicago area uses space more efficiently while achieving high homeownership rates. We can see the importance of this by examining places in California. They account for eight of the twelve lowest ownership rates (Appendix Table A.1). As we will see, this distinction also applies to density; as previously illustrated, locations in California have exceptionally high densities. While we have a mismatch in data units (urbanized areas versus metropolitan areas), in comparing homeownership and density, California is known for scarcity of ample urban land which drives up densities and home (land) prices, thus resulting in low homeownership rates. At this gross level, it appears that the Chicago area achieved a moderately high average density while maintaining a high homeownership rate. This points to a balance between sustainability and high homeownership rates. To the extent that homeownership is an element of sustainability, in aggregate the Chicago area has achieved this aspect of sustainability. Public policy An effective means of curtailing geographic expansion of an urbanized area is the development of an urban growth boundary as was adopted in Oregon by 1980. It demarcated an area for urban purposes to separate it from rural areas. The Portland area has been successful in limiting the territorial expansion of the region and resulting in a higher density than the Seattle area (Table 5). However, Portland still has much lower densities than many of the large Californian places. It effectively has the same density as the Chicago area, approximately 3500 residents per square mile—twice the density of the Atlanta area. Furthermore, since 1990 Portland’s density has increased from approximately 3000 per square mile and while Chicago’s density decreased from 4000 per square mile. During this time, the homeownership rate in the Portland area has remained steady while the rate in the Chicago area has increased. Since 1986 when the Census Bureau began reporting homeownership rates on an annual basis (for the 75 largest metropolitan areas), the rate in Portland has held at 65.2%, higher than the 1986 national figure of 63.8%. By the third quarter of 2012 the national rate had risen to 65.5%. Meanwhile, Portland’s rate was again at 65.2%, below the national level (In comparison, Seattle’s rate dropped by 4.5% during the same time, consistently below

19

the national level). Conversely, the Chicago rate has climbed a remarkable 12.3% to 67.0%, well above the national level. Affordable land at the fringe of the Chicago area is a factor in the increasing rate. While there are a series of factors that have led to effectively no net change in over a dozen years in Portland, the urban growth boundary is likely a contributing factor. If that is the case, then it is important to focus on growth strategies that yield higher urban densities and sustainability while not dampening homeownership rates. One additional note. There are numerous ways in which local or state government may shape urban growth. Land may be set aside for a variety of purposes such as recreation, forest, and wildlife preservation which may cause the urban land uses to be spread over a larger territory. The National Wildlife Refuge in New Orleans is approximately 38 square miles, about the size of the Miami city limits. In Phoenix, South Mountain Preserve and North Mountain Preserve each consume 25 square miles, while Chicago’s Lincoln Park is nearly 2 square miles (And is 97th largest city park in the nation). In many cases these municipal lands are included in gross density calculations and may distort density data. BUILDING PERMITS (HOUSING STARTS) AND URBAN SPRAWL The issuance of building permits is a direct indicator of anticipated growth. Both permits issued for single-family and multi-units buildings are discussed below by county in the Chicago area. We begin with the number of building permits and then shift to the percent of the metropolitan area building permits issued in each county. We close out this section with a discussion of the population change that should convey the same information as the implied housing starts. Trend in building permits for new housing Building permits may be classified into several size categories. We have chosen to examine (1) single-family units and (2) all others units into multi-family structures. In the latter case, the percentage of all units that are multi-unit has varied substantially in the last several decades (Figure 11—there appears some large fluctuations due largely to the timing of the permits issued to particularly large projects). In the early 1980s, over half of the new starts were multi-unit structures. The percentage declined steadily until it reached 18 percent in 1993. Since 1993 it appears that multi-unit permits began to return to earlier periods (dashed line in Figure 11) in which multifamily units have accounted for approximately 40 percent of all new starts and even exceeded 50 percent in 2007 and 2008. This points to higher densities and more sustainable communities. Still, some of the high percentages in the last several years were achieved when relatively few units were built.

20

Figure 11. New starts and percent multi-unit structures

http://censtats.census.gov/bldg/bldgprmt.shtml Housing building permits by county In this section we examine the building permits by county. This is an alternative view of regional growth in contrast to population data.

Value of new starts building permits It is clear from Figure 12 that Cook County has dominated the statistics on the value of building permits. Only in 1988 did DuPage County come close to the level in Cook County. In recent years Cook County has exceed the second leading county by more than 50 percent. It is also evident that DuPage lost its second place status to Will County in the late 1990s. It was during this time that Lake County’s numbers were only slightly less than DuPage County. By 2000, however, Kane County also surpassed the values form DuPage County. Will County was in second place for nearly half dozen years in the first decade of this millennium.

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By 2007 the home building industry was distressed and far fewer units were built everywhere. However from 2009 to 2010 the levels in Cook and DuPage Counties increased by more than 35%, while effectively there was no change in Kane and McHenry counties.

Figure 12. Total values of building permits by county (in $000s)

Source http://censtats.census.gov/bldg/bldgprmt.shtml

Figure 13. Cook County percent of all housing permits

Figure 13 shows the dominance of Cook County in NE Illinois by both the number housing permits (units) and the value of the starts. More importantly the resurgence of Cook County is evident. The focus on Cook County so evident in the early 1980s has reappeared showing more evidence of the growing sustainability of the region.

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Number of building permits by county Figure 14 also clearly shows how the growth in Cook County, largely dominated by suburban Cook in the 1980s. Since the county reached as far west as DuPage County, e.g., Elgin, it included vast open spaces. Cook County’s dominance in single-family building permits, however, ended in the mid-1990s. During its reign, DuPage County was number two in number of housing starts followed by Lake County. In the early 1980s there was little difference between Cook and DuPage Counties and by the early 1990s DuPage and Lake Counties had similar numbers of new starts.

Figure 14. New Building Permits for Single Family Houses, 1980-2010

Source: http://censtats.census.gov/bldg/bldgprmt.shtml By the end of the 1990s, Will County began to exert its dominance. This lasted for nearly ten years. In the first half dozen years of the new millennium Will County accounted for over forty percent of the population growth in the entire state. Indeed the growth had pushed past not only southern Cook County into Will County, but also west past DuPage County into Kane County. At the same time there was also growth in McHenry County, which was impressive as a percent increase, yet lagged behind Kane in absolute growth. It did, however, rival the growth in much larger counties, DuPage and Lake. DuPage is worth noting for its slow growth since the mid-1990s. With its population approaching a million residents, the county was filling in and there was less and less land affordable for middle class households. While DuPage is still the second largest

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county in the state, its status may well be challenged by Will County in the near future. It is also worth noting that the growth of DuPage County in the 1970s resembled the growth rates in Will County in recent decades. Figure 14 also depicts when the decrease in growth occurred in each of the counties. McHenry County was the first, in 2002, to experience the beginning of the decrease in new starts. Will County had its peak the following year. These two are the most distant from downtown Chicago. Cook and DuPage together with Kane were the last to experience the beginning of the decline. All three counties have long established urban areas. Kane is the most distant from Chicago but the Fox River valley communities from Elgin to Aurora have historically been major population centers. This suggests an early decline in sprawl, with the more peripherally situated areas first experiencing a decline in new starts. Figure 14 is of particular interest because it shows that the decrease in expansion on the fringe of the region started approximately a decade ago, well before the downturn in the economy. The decline began even before the start of the decline in national homeownership rates in 2004.

Percent of the region’s single-family housing permits by county The effect of the economic downturn may also be portrayed by illustrating the ‘market share’ of new housing starts over the last several decades. Figure 15 shows the proportion of all single-family units that have been started by county—each year sums to 100%. Until about the early 1990s, the greatest growth was in Cook and DuPage Counties. The two counties extended sufficiently far from the Chicago CBD to include farm land and low density peripheries. These two counties now have far less open land prime for residential development. Beginning in 1980, Will County started capturing an ever increasing share of the new construction. This growth did not abate until 2004, and corresponds to the national peak in homeownership rates (Figure 5). Since then there has been a steady decline in the county’s share suggesting the rate of decentralization has responded to the declining economy. Both Kane and McHenry Counties also exhibited a declining share though less dramatically than Will County. While the new starts in Will are greater than in DuPage, this may be attributable to the larger land area of the county and greater amount of affordable land. Since 2004, however, there has been a steady increase in the shares for both Cook and DuPage Counties, and to a lesser extent in Lake County. Cook County’s share has recently been twice that of DuPage County, however, Cook County is also five times larger in population. Still the two counties together form a compact geographic region in the core of the metropolitan area, and have been the benefactors of shifting new housing starts in the region. This figure again illustrates that the rate of sprawl common in previous years shows signs of reversing.

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Figure 15. Single-family Housing Units as a Percent of the Six County Area, 1980-2010

Source: http://censtats.census.gov/bldg/bldgprmt.shtml

Multi-unit structures: building permits issued Examining only multi-unit structures shows a similar pattern. While Figure 13 included all housing units and Figures 14 and 15 graphed only single-family units, Figure 16 portrays only multi-unit starts. There are two points that merit discussion. First, DuPage County had a large number of multi-unit permits issued in the 1980s. It ranked second during this time and had two to four times the units recorded by the third largest county. When this subsided, Lake County started to show some activity in multi-unit housing starts. Second, the dominance of Cook County is particularly striking in recent years. This dominance started in 1996, and shows only a modest sign of diminishing in recent years. Retirees seeking residences in walkable neighborhoods in proximity to public transportation contributed to construction activity in Cook County, evident in Figure 16.

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http://censtats.census.gov/bldg/bldgprmt.shtml Population change and urban sprawl Decennial population data are readily available (Table 6), however, they do not convey the dramatic changes in the middle of the decade, nor do they adequately describe the events of the last six years. They do show, however, that the outlying counties had the greatest growth (2000 to 2010). Kendall, Lake and McHenry also noteworthy increases, while Will and Kane Counties were the only counties with a population increase in excess of 100,000.

Table 6. Population by County, 2000 and 2010 County 2000 Pop 2010 Pop Change Change % Kendall 54,544 114,736 60,192 110.4% Will 502,266 677,560 175,294 34.9% Grundy 37,535 50,063 12,528 33.4% Kane 404,119 515,269 111,150 27.5% McHenry 260,077 308,760 48,683 18.7% Lake 644,356 703,462 59,106 9.2% DuPage 904,161 916,924 12,763 1.4% Cook 5,376,741 5,194,675 -182,066 -3.4% Total 8,183,799 8,481,449 297,650 3.6%

Source: U.S. Bureau of the Census, 2000 and 2010 http://factfinder2.census.gov. Accessed, November 2012

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Cook County, however, exhibited a decline, seemingly contradicting the data on new housing permits. The difference is largely attributed to the fact that population change is a net figure that includes increases and decreases in the number of people, while data on housing permits only indicates growth. Further permits are not a direct statement of population increase since the number of unoccupied units and the number of residents per unit is uncertain. Indeed in Chicago, there has been rather a mixed picture with increases in many trendy neighborhoods and large-scale population shifts from other neighborhoods. This has resulted in a decrease of almost a quarter of a million residents in the city of Chicago outside the downtown area. The rest of Cook County has grown, but not enough to offset the decreases in city neighborhoods. We return to this point in later when we discuss the map shown in Figure 17 below. Table 7 displays more detail and also more recent data. Importantly, the decline of 65,000 residents, experienced in the last four years of the previous decade (2006-2010), has largely been regained in the last two years. Moreover, approximately three-quarters of the 61,000 increase is accounted for by Cook and DuPage Counties. There have been signs of continued sprawl with Kendall, Kane and Will Counties all exhibiting population increases in the last two columns of Table 7.

Table 7. Populations and population changes by county, 2006-2012 County 2006 2012 2006-12 2006-10 2010-12 Cook 5,288,655 5,231,351 -57,304 -93,980 36,676 DuPage 932,670 927,987 -4,683 -15,746 11,063 Kane 493,735 522,487 28,752 21,534 7,218 Kendall 88,158 118,105 29,947 26,578 3,369 Lake 713,076 702,120 -10,956 -9,614 -1,342 McHenry 312,373 308,145 -4,228 -3,613 -615 Will 668,217 682,518 14,301 9,343 4,958 Total 8,496,884 8,492,713 -4,171 -65,498 61,327

Source: U.S. Census Bureau, 2000 and 2010

Maps frequently well convey some of the population change variations in the region. To that end we have chosen to use PUMAs (census microdata areas with at least 100,000 residents). Counties are too large and census tracts are too small and show too much detail. The PUMAs on Figure 17 show that population continues to grow in the far fringes during the five-year period from 2006 to 2011. While the map shows percentages and the PUMAs are relatively similar in population, the vast territory on the fringe may convey the impression that the entire fringe is growing. In reality the intense growth is in select areas of housing development. In the city of Chicago, where the PUMAs cover less territory, the areas of high growth are more precisely mapped. Specifically, the downtown area has the greatest increase with two other adjacent PUMAs experiencing increases in excess of five percent.

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Figure 17. Population changes in the six-county area by PUMAs, 2006-2011 (there are no PUMAs in the -15% to -11% category)

Legend: Chicago 3500s, suburban Cook 3400s, Lake 3300s, DuPage 3200s, Will and Grundy 3100s and McHenry, Kane and Kendall 3000s. Source: U.S. Census Bureau, ACS PUMS Of the six PUMAs with the greatest increase in population, half are in Chicago. PUMA #3006 had the greatest increase followed by #3510. The three fastest growing suburban PUMAs accounted for an increase of approximately 75,000 versus 50,000 for the Chicago three. Since only one-third of the area’s residents live in Chicago this suggests a disproportionate share of the large growth occurred in Chicago.

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Perhaps most surprising is that that three suburban PUMAs also lost population. One is in the far north along Lake Michigan surrounding Waukegan and the other two are in southern Cook County. The Waukegan area lost nearly 10,000 residents. There was relatively little change throughout the rest of the region. At the same time, there are two city PUMAs that had population drops of more than 15 percent (#3507 and #3516). There were also three other city PUMAs that had losses of over five percent, one in the relatively low density northwest corner of the city near O’Hare Airport (#3505). A census tract level map (not shown here) reveals that most of the tracts that constitute PUMAS #3515, #3516 and #3519 lost population. Collectively, these three account for a decline of 57,000 residents. Table 8 shows that the five PUMAs that had the greatest population losses accounted for approximately 85,000 (note that these data have an unspecified margin of error for each number). These five account for three-quarters of the city’s loss of 111,000 residents. Table 8 Population changes: five highest positive and negative changes, 2006-2011

PUMA Population 2006

Population 2011

Difference 2006-2011

Percent Difference

3516 163,159 129,244 -33,915 -20.8% 3507 110,526 92,329 -18,197 -16.5% 3515 164,024 151,125 -12,899 -7.9% 3505 139,316 128,143 -11,173 -8.0% 3519 108,042 97,949 -10,093 -9.3% Subtotal 685,067 598,790 -86,277

3503 136,590 140,796 4,206 3.1% 3509 226,276 232,639 6,363 2.8% 3511 110,901 118,238 7,337 6.6% 3502 151,222 165,577 14,355 9.5% 3510 150,053 180,010 29,957 20.0% Subtotal 775,042 837,260 62,218

Source: Compiled by the authors from U.S. Census Bureau PUMS data http://factfinder2.census.gov. Accessed, February, 2013. At the same time the five most rapidly growing PUMAs added approximately 60,000 residents. These PUMAs are in middle and upper income neighborhoods and are likely to offset the loss of purchasing power in neighborhoods with declining populations. Table 8 then illustrates the changing dynamics of the city of Chicago. While there is a net decrease in the population (200,000 from 2000 to 2010), it shows substantial growth in middle and upper income neighborhoods. In previous decades these new residents may have located to suburban Chicago. This further suggests that the forces that contribute to sprawl are changing.

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THE ROLE OF TRAVEL TIME, MODALSPLIT AND CONGESTION One of the factors that contribute to how and why an urban area sprawls is the overall travel conditions. To better understand the role of travel we will consider three characteristics (1) travel time, (2) modal split and (3) congestion. Each of these measures needs to be considered in the context of their derivation. Travel times are self reported and are commonly rounded to the nearest five or ten minutes. Mode split data may vary based on whether it is reported as most common mode, all modes used, or the mode used on the survey day. Congestion times are perhaps the hardest to ascertain and tend to be indirect information so we are less definitive on that measure. Travel time to work Travel times to work are reported by the ACS but caution needs to be exercised in interpreting these numbers. First, many travelers report times in multiples of five, ten or fifteen minutes with 30 minutes being a common response. Second, travel times vary and the data are provided as reported as their typical commute time. Third, each mean figure for a county (place) has a margin of error associated with it and the numbers in Table 9(next page) should be interpreted with knowledge of the magnitude of the margin of error. These errors commonly range from about 0.5 to 1.5 minutes, and tend to by inversely related to the number of respondents (county size). Since the counties in Table 9 are ranked by size, the margin of error tends to increase reading down the table. The error is also related to the standard deviation of the respondents (e.g., if everyone reports close to twenty minutes the error would be small).Despite these caveats, the travel time data are useful and reveal or confirm some anticipated and unanticipated results. In the early part of this millennium we witnessed a strong relationship between population growth and distance from Chicago as well as an association between increase in population and increase in travel time (Figure 18). The latter seems quite logical. At least in the short run, as places grow rapidly, the population growth exceeds job growth which leads to long commutes. Likewise, in Cook County, where there was a small drop in population, there was a corresponding drop in mean travel time to work. The graph part of the figure visually shows the strength of the relationship while the map illustrates the increasing time with increasing distance from the core areas. Specifically, the primary core would be the Chicago downtown, though the early development to the north along Lake Michigan may also be considered as part of an extended core area. It is also apparent from the Figure 18 that Cook County is the only county in which the change in travel time was greater than the change in population. This suggests that other factors are at play in declining travel times than just population change. The travel may have become more efficient or the distances may have decreased. In either circumstance it a positive development for the core area of Chicago metropolitan area.

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Figure 18. Percent Change in Population (map, top number) and Percent Change in Mean Travel Time to Work (map, bottom number), 2000-2005

Source: U.S. Bureau of the Census, 2000 and 2005 ACS

The strong relationship found in the 2000 to 2005 period is not replicated in the next six-year period (Table 9) perhaps as an aftermath of the economic turbulence. We see that the greatest population growth was in Kane and Will Counties, yet they had opposite changes in travel times. Part of the explanation of the lack of a relationship may stem from the data caveats expressed above and the standard error in the data. Specifically, the previous period started in 2000 when the change was based on 2000 Census data which have a rather small standard error. For the 2005 to 2011 period, not only were there high standard errors in the one-year ACS sample data, but it covers a period with dramatic economic changes.

Table 9. Change in population and travel times to work, 2005 to 2011 Population Travel time Kane 9.4% 2.1% Will 7.3% -4.1% Lake 2.5% -2.9% McHenry 1.9% 2.3% DuPage 1.2% -2.7% Cook 0.1% 0.0%

Source: U.S. Bureau of the Census, 2005-2011 ACS http://factfinder2.census.gov. Accessed, March, 2013.

We can see the year to year variability in travel time data shown in Table 10. Many of these variations reflect actual changes not just sampling error. Specifically, Table 10 shows the growing travel times until 2008 as well as the drop in 2009, which reflects the

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economic downtown and decline in the number of jobs—the last line shows the unweighted average.

Table 10. Mean Travel Times by County (and Chicago) (Averages are unweighted)

County* 2000 2005 2006 2007 2008 2009 2010 2011 Average Chicago 34.0 34.3 33.4 34.4 34.6 33.2 32.9 33.5 33.8 Cook 32.6 31.9 31.7 31.9 32.4 31.5 31.4 31.9 31.9 DuPage 29.0 29.3 28.6 29.0 29.3 28.8 29.2 28.5 29.0 Lake 30.1 30.7 30.6 30.7 31.5 30.7 29.4 29.8 30.4 Will 32.0 34.3 33.0 34.0 34.6 32.6 33.5 32.9 33.4 Kane 27.3 28.6 28.3 29.0 29.2 28.5 29.5 29.2 28.7 McHenry 32.2 34.3 32.5 33.9 33.6 34.1 33.6 35.1 33.7 Average# 30.5 31.5 30.8 31.4 31.8 31.0 31.1 31.2

*Chicago and counties in order of population size # Unweighted average for the six counties (Chicago row not included)

Source: adapted from: U.S. Census Bureau (ACS) http:factfinder2.census.gov. Accessed March, 2013.

There are other points worth noting. First, travel times are highest in Chicago and two peripheral counties, Will and McHenry. Chicago’s times are high due to the longer commutes by public transportation. While this also contributes to the high times in the peripheral counties (Metra trips are frequently long), the typical distance to work is the more common contributor. Second, the lowest travel times are reported by Kane and DuPage Counties. Kane County has traditionally been characterized by well-established communities along the Fox River from Elgin to Aurora. There has been local employment in the small and large towns along the river. DuPage County is rather different having been transformed from a series of bedroom communities in the 1960s and 1970s to a series of employment centers and corridors, to a point where now more commuters travel to the county than from the county. Thus, it is a net importer of workers. More important to the theme of this report, we see some evidence of the positive travel-time effects of the downturn in the economy. During the three years preceding the dip in 2009 (2006-2008), travel times were climbing only to drop noticeably from 2008 to 2009. Table 10 shows drops in all of the counties save McHenry (Though the increase of 0.5 minutes is well within the margin of error--1.0 for both 2008 and 2009, there is not a statistically significant change in travel time). From 2009 to 2011, travel times have increased for all except in Lake and DuPage Counties. Not surprising, both counties are net importers of workers. Conversely, the largest increases are in Kane and Will Counties, both rapidly growing counties where the increase in jobs apparently has not kept pace with population growth.

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Modal split Both the city of Chicago and the metropolitan area rank high on the proportion of the workers that commute by public transportation. In most years, Chicago shares the second ranking with Washington D.C. behind New York. Most of this ridership is in Chicago but is also located in suburban Cook County as well as select neighborhoods in other suburban counties. Figure 19 shows that the (1) number of workers and (2) public transit commuters to work have grown and decreased since 2005, with the peak year occurring in 2008. It is not unexpected for these two figures to track together (note that both numbers are from the same source, the ACS). What is noteworthy, however, is that two figures tracked together at the beginning of the graph, but diverged in the latter years. This indicates that commuting non-transit share declined. Indeed the transit share rose from 12 percent to 13 percent for the six-county area. The one-point increase may not seem dramatic, but it is clearly a positive sign. Figure 19. Number of workers (left axis) and number of daily public transportation (transit) commuters (right axis)

U.S. Bureau of the Census, 2005 to 2011 One-Year ACS http://factfinder2.census.gov

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Figure 20 extends the span of time by also including the 2000 data (see also these data in Appendix Table A.2). It shows that expectedly much of this increase in transit commuting was in the city of Chicago. Indeed there was more than a two-point increase in the city from 2005 – 2011, but less than that from 2000 - 20011. There has also been an increase in suburban Cook County but it is less than one point, measured from either 2000 or 2005. The other counties do not generally show an increase in the proportion commuting by public transportation. In DuPage County there has been an increase since 2005, but it has not been sufficient to offset the early drop which occurred from 2000 to 2005. Conversely in Lake County there have been large swings in the use of public transportation, but the 2011 share is not where it was at the beginning nor at the levels in the middle of the previous decade. The other counties show relatively little change.

Figure 20. Percent of work trips by public transportation (scale for only Chicago and Cook County are on the right axis)

Source: U.S. Census Bureau 2000 and One-Year ACS data, 2005-2011 http://factfinder2.census.gov Lastly, one more data set provides some clues about public transportation use. Table 11 offers a crude comparison of Census commuting ACS data with RTA ridership information. The ACS data are in hundreds of thousands daily commuters and RTA (annual) are in millions. If in 2005 the 454,000 ACS reported public transportation users

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each made a round trip five days a week, for fifty weeks of the year. This translates into 227 million trips annually, compared to the 605 million rides tallied by the RTA system. While the 227 million figure may be high or misreported, it approximates the commuter share of all RTA riders. Regardless of how accurate it may be, it is useful to examine the “ratio” between the RTA and ACS data called the index in Table 11. The index in Table 11 is an artificial number created by dividing the two numbers above it in each column. It provides a clue as to how fluctuations in commuter traffic affect RTA ridership. It shows that the highest index was in the middle of the study period. In essence, commuters (workers) contributed most to RTA ridership with the low points occurring in 2005 and again in 2010. The variations in the index however, are not sufficiently regular to draw definite conclusions about its longitudinal trend, though they are at least consistent with much of this report that shows 2007 and 2008 to be the peak years for most statistics.

Table11

Comparison between ACS commuter data and RTA ridership (daily ACS data in 000s, annual RTA unlinked passenger trips in 000,000s)

Source: U.S. Census ACS and http://www.rtams.org/rtams/systemRidership.jsp http://factfinder2.census.gov Nevertheless, it may be worth noting that when the index is low, non-commuter riders account for the remainder of the riders. In that context, between 2008 and 2011 RTA annual ridership data are essentially the same, there is at least weak evidence that public transportation is increasing for non-commuting trips. Though not over whelming as a statistic, this is what one would expect in sustainable communities. Congestion It is well known that reoccurring congestion is highly related to the economy. During improving economies congestion tends to increase. As the economy encounters job losses, it decreases. It is not surprising that recent measures of congestion show that congestion has declined, though as with the national economy, there are quarterly fluctuations.

2005 2006 2007 2008 2009 2010 2011

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454 478 498 509 494 469 496

Annual RTA riders

605 612 622 653 639 633 652

Index 0.751 0.780 0.800 0.780 0.773 0.742 0.760

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The Texas Transportation Institute computes an annual congestion index for the largest metropolitan areas. Figure 21 also shows that while the population in the region has grown, the congestion levels have, as expected, also increased in recent years. Figure 21. Chicago area congestion index and population in millions

Interestingly, with some exceptions, congestion grew from 1982 to 2004/2005 when it hit a peak and then declined until 2007/2008 when it leveled and then began to increase again. The peak congestion index coincides with the peak homeownership rates in the Chicago area. However, like the upswing in the congestion index in recent years, the homeownership rates have continued to decline along with the national rate until 2012.

Source: http://mobility.tamu.edu/ums/congestion-data/ and U.S. Census Bureau Since congestion is closely tied to employment we also graph the association between the congestion index and employment, though for a shorter time period. Figure 22 Figure 22. Chicago area congestion index and employment in millions, 2000 to 2011

shows the shorter-term association between employment and congestion. They tack relatively closely with about a three-year unexpected lag. Perhaps it is a coincidence since it is not immediately clear why a drop or a rise in congestion would cause employment to follow the same trend.

Source: http://mobility.tamu.edu/ums/congestion-data/ and http://www.bls.gov/schedule/archives/metro_nr.htm

0.8

0.9

1.0

1.1

1.2

1.3

1.4

7.0

7.2

7.4

7.6

7.8

8.0

8.2

8.4

8.6

8.8

9.0

1982

1984

1986

1988

1990

1992

1994

1996

1998

2000

2002

2004

2006

2008

2010

Population

Congestion

1.11

1.13

1.15

1.17

1.19

1.21

1.23

1.25

3.95

4.00

4.05

4.10

4.15

4.20

4.25

4.30

Employment

Congestion

36

At the national level, INREX (2013) reports that Chicago is not on the list of the ten most congested metropolitan areas in the nation. It does, however, indicate that the national level of congestion increased from 2011 to 2012, yet decreased in the first quarter of 2013. The congestion study by the Texas A&M Transportation Institute (2012) however, ranks Chicago as tied with Atlanta as the seventh most congested metropolitan. Washington, D.C, Los Angeles, San Francisco, New York and Boston are the metropolitan areas with the most congestion. SUMMARY AND IMPLICATIONS This report is presented to stimulate discourse on the effects of the economic swings experienced in the Chicago area during the beginning of this millennium. It provides evidence that there remains a relationship between sprawl and prosperity, while at the same time, we see growing evidence of sustainability. This is against a backdrop of continued suburban sprawl into some but not all peripheral counties. Items that signify movement toward sustainable communities

• New building permits show more recent activity in core counties than in the periphery of the region.

• Since 2004 both Cook and DuPage Counties have seen an increase in their share of single family building permits over the six-county area.

• The share of single family building permits has therefore declined in outer collar counties, most notably in Will County.

• Since 1992 an increasing proportion of the building permits in the six-county area have been issued to units in multi-unit structures.

• While population is increasing in peripheral areas, there is now substantial growth of over 50,000 residents in the area around downtown Chicago and close-in neighborhoods.

• From 2010 to 2012 Cook County accounted for over half of the population growth in the six-county area. McHenry and Lake Counties lost population continuing a trend that started in the middle of the last decade.

• The already high proportion of work trips by transit is growing, in both Chicago and suburban Cook County.

• From 2005 to 2011 three of the five collar counties experienced a drop in travel time to work. Will County experienced the largest drop even with a seven percent population growth. Cook County’s travel time dropped in the beginning of the last decade, but has remained steady since then.

• Since the end of the previous millennium, the proportion of the population that is working has declined (even during periods of expanding economy). This suggests that economic growth can occur with proportionally fewer workers, thereby contributing less to peak-period congestion levels.

• These trends have occurred during a time when Chicago area homeownership rates (2005 to 2011), exceeded the level both nationally and inside metropolitan

37

areas. Also, since 2005 homeownership rates have declined in the Chicago metropolitan area, though it is less than the nation as a whole and less than in all metropolitan areas collectively.

• In 2005 the Chicago metropolitan area homeownership rate was 1.1 points above the national level increasing to 1.6 points higher in 2011.

There are then both long-term and short-term trends that point toward more sustainable communities in the Chicago six-county area. Perhaps the most positive element is the consistently high levels of homeownership, something that is generally not found in high-density metropolitan areas. Chicago’s homeownership rates are typically more than 10 to15 points higher than in the eight most densely populated urbanized areas in the nation. This includes by order of highest density, Los Angeles, San Francisco, San Jose, New York, Honolulu, Las Vegas, Miami and San Diego. The lone exception in the group is Miami, where the 2012 homeownership level is only five points less than that in the Chicago area. Regarding sprawl and prosperity, the Chicago area experienced large-scale population decentralization in the 1920s when the city of Chicago grew by nearly a million residents, while the core areas (within two miles of the downtown area) declined in population. The growth occurred in what was then the urban periphery though within the Chicago city limits. This outward expansion has continued at various rates since then and has clearly changed in the last dozen years. The decline in the rate of decentralization appears to have begun in 2002 (for McHenry County), and has not shown strong signs of returning. Since the forces that drove the high rates of decentralization have predictably changed, some was aspects were anticipated. Population levels have declined though they are nearly back to peak levels a few years ago. Homeownership rates are rebounding but have not returned to earlier levels. Household size that declined for over a century have begun to inch upward. Lastly, women entered the labor force in large numbers several decades ago but their participation rates have declined. The overall decline in the labor force participation rates may also have affect household incomes and homeownership rates. The result is that the far fringes of the metropolitan areas are still growing as they did in earlier periods, albeit at a slower rate. While Will, Kane, Kendal and Grundy have all been growing, in the last two years the core counties, Cook and DuPage have accounted for the vast majority of the growth (see also Metsi Report 2013-1). Perhaps the most noteworthy point is that both Chicago and suburban Chicago have areas characterized with both population growth and population loss. While it is well documented that Chicago has lost population, so too has Lake and McHenry Counties. These counties have lost population since 2006. Concurrently, both the city and suburban counties have areas of solid growth. How long this reinvestment in existing city and close-in suburban neighborhoods continues will last is unpredictable. The move toward sustainability is well established.

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While the decentralization is likely to continue, perhaps it will assume a more environmentally friendly form than those that characterized many previous peripheral developments. REFERENCES TRB Sprawl Revisited – Ewings Gordon, P. INREX (2013), Key Findings: 2012-2013 INREX Traffic Scorecard Annual Reporthttp://www.inrix.com/scorecard/summary.asp (accessed April 2013).

Sen, A et al. (1998): Highways and Decentralization, Urban Transportation Center, University of Illinois at Chicago, 33 pp, http://www.utc.uic.edu/research/reports/hwy_urb_decentrztn.pdf Sööt, S. et al., (2001) Travel behavior and employment decentralization, Executive Summary, Urban Transportation Center, University of Illinois at Chicago, 41 pp, http://www.utc.uic.edu/research/reports/decentralization.pdf Sööt S. and B. Dodge-Hayakawa (2013), Housing transportation trade-off , Research Report, Urban Transportation Center, University of Illinois at Chicago, xx pp. forthcoming. Texas A&M Transportation Institute (2012), Urban Mobility Report, 65 pp. http://d2dtl5nnlpfr0r.cloudfront.net/tti.tamu.edu/documents/mobility-report-2012.pdf accessed May 2013.

The Trust for Public Land, 2011 City Park Facts, 24 pp. , accessed March 2013. http://www.tpl.org/publications/books-reports/ccpe-publications/city-park-facts-report-2011.html U.S. Bureau of Labor Statistics, Consumer Expenditure Surveys, Metropolitan Statistical Area (MSA) Tables, http://www.bls.gov/cex/csxmsa.htm#top accessed January 2013 U.S Bureau of Labor Statistics, Women in the Labor Force, A Data book, February 2013. Washington D.C., 104 pp. U.S. Census Bureau, American Community Survey, 2005, 2006, 2007, 2008, 2009, 2010, and 2011 1 Year County, http://factfinder2.census.gov, accessed February, 2012. U.S. Census Bureau (2012) Building permit data, accessed November 2012.

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http://censtats.census.gov/bldg/bldgprmt.shtml U.S. Census Bureau (2012) , Homeownership rates for the 75 largest metropolitan statistical areas, http://www.census.gov/housing/hvs/data/rates.html, accessed February 2013. U.S. Census Bureau (2013), Homeownership data for Q1 2013, accessed May 2013 http://www.census.gov/housing/hvs/files/qtr113/hown113.png U.S. Department of Commerce, Bureau of Economic Analysis. “Regional Economic Accounts,” 2012.http://www.bea.gov/regional/ U.S. Department of Labor, (2011) Women in the Labor Force. A Databook, Report 1034, http://www.bls.gov/cps/wlf-databook-2011.pdf, accessed April 2013. U.S. Department of Transportation, ( ) Liveability in Transportation Guidebook, http://www.fhwa.dot.gov/livability/case_studies/guidebook/livabilitygb10.pdf APPENDIX Table A.1 Seventy –five largest metropolitan areas ranked by 2012 homeownership rates

Rank Metropolitan Statistical Area First quarter 2012

Second quarter 2012

Third quarter 2012

3Q Average

1 Grand Rapids-Wyoming, MI 79.1 75.1 76.3 76.8 2 Allentown-Bethlehem-Easton, PA-NJ 72.9 74.9 80.5 76.1 3 Detroit-Warren-Livonia, MI 72.8 73.5 74.0 73.4 4 Birmingham-Hoover, AL 73.1 70.1 73.1 72.1 5 Omaha-Council Bluffs, NE-IA 72.0 70.7 72.5 71.7 6 Albany-Schenectady-Troy, NY 74.6 68.1 72.2 71.6 7 St. Louis, MO-IL 71.9 71.6 70.9 71.5 8 Poughkeepsie-Newburgh-Middletown, NJ. 71.5 74.8 68.0 71.4 9 Bridgeport-Stamford-Norwalk, CT. 71.0 73.0 69.8 71.3

10 Baton Rouge, LA 70.2 74.5 67.9 70.9 11 Akron, OH 71.6 70.7 68.6 70.3 12 Minneapolis-St. Paul-Bloomington, MN-WI 68.0 70.6 72.2 70.3 13 Hartford-West Hartford-East Hartford, CT 65.5 71.6 73.6 70.2 14 Springfield, MA 70.4 71.0 65.9 69.1 15 Philadelphia-Camden-Wilmington, PA 68.9 68.4 69.8 69.0 16 San Antonio, TX 70.4 71.0 65.6 69.0 17 Raleigh-Cary, NC 65.5 70.6 68.4 68.2 18 El Paso, TX 69.9 66.7 66.7 67.8 19 Rochester, NY 67.5 67.3 68.3 67.7 20 Pittsburgh, PA 67.4 67.0 68.4 67.6 21 Tampa-St. Petersburg-Clearwater, FL 67.7 68.1 66.6 67.5 22 Tulsa, OK 69.0 70.2 63.0 67.4

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23 Richmond, VA 66.9 66.4 68.3 67.2 24 Oxnard-Thousand Oaks-Ventura, CA 66.4 70.0 64.7 67.0 25 Chicago-Naperville-Joliet, IL 66.9 67.1 66.9 67.0 26 Indianapolis, IN 68.0 65.6 67.1 66.9 27 Orlando, FL 68.9 68.0 63.8 66.9 28 Oklahoma City, OK 66.7 65.2 68.6 66.8 29 Dayton, OH 65.6 64.7 69.7 66.7 30 Washington-Arlington-Alexandria, DC-VA-MD-WV 67.0 65.8 66.6 66.5 31 Boston-Cambridge-Quincy, MA-NH 65.2 68.3 65.7 66.4 32 Salt Lake City,UT 64.8 66.6 67.8 66.4 33 Baltimore-Towson, MD 67.0 65.8 66.3 66.4 34 Nashville-Davidson-Murfreesboro, TN 65.6 66.4 66.6 66.2 35 Jacksonville, FL. 66.5 63.1 68.9 66.2 36 Columbia, SC 64.1 67.6 65.9 65.9 37 Greensboro-High Point, NC 65.5 65.5 66.2 65.7 38 Tucson, AZ 61.3 64.4 70.0 65.2 39 Portland-Vancouver-Beaverton, OR-WA 63.8 65.5 66.3 65.2 40 Kansas City, MO-KS 67.1 63.7 64.0 64.9 41 Cleveland-Elyria-Mentor, OH 66.3 61.2 63.5 63.7 42 Cincinnati-Middletown, OH-KY-IN 63.2 61.9 65.4 63.5 43 Louisville, KY-IN 59.3 65.9 65.0 63.4 44 Phoenix-Mesa-Scottsdale, AZ 64.2 62.9 62.3 63.1 45 Houston-Baytown-Sugar Land, TX 63.3 64.5 60.6 62.8 46 New Haven-Milford, CT 65.2 62.0 61.2 62.8 47 Miami-Fort Lauderdale-Miami Beach, FL 63.1 63.5 61.8 62.8 48 Albuquerque, NM 64.6 62.2 61.0 62.6 49 New Orleans-Metairie-Kenner, LA. 59.6 66.3 61.7 62.5 50 Buffalo-Cheektowaga-Tonawanda, NY 61.7 62.8 63.0 62.5 51 Worcester, MA 66.3 62.0 57.9 62.1 52 Virginia Beach-Norfolk-Newport News, VA 62.0 62.6 61.3 62.0 53 Dallas-Ft. Worth-Arlington, TX 62.6 62.1 61.1 61.9 54 Denver-Aurora, CO 61.9 62.0 61.8 61.9 55 Atlanta-Sandy Springs-Marietta, GA 63.2 61.2 60.8 61.7 56 Milwaukee-Waukesha-West Allis, WI 63.3 60.8 61.1 61.7 57 Toledo, OH 56.1 65.5 63.5 61.7 58 Providence-New Bedford-Fall River RI-MA 61.9 61.3 61.4 61.5 59 Columbus, OH 62.9 62.1 58.3 61.1 60 Memphis, TN-AR-MS 57.9 63.1 60.1 60.4 61 Austin-Round Rock, TX 58.8 60.3 61.2 60.1 62 Seattle-Tacoma-Bellevue, WA 59.9 59.0 59.5 59.5 63 Charlotte-Gastonia-Concord, NC-SC 56.5 60.0 61.4 59.3 64 Riverside-San Bernardino-Ontario, CA 59.1 59.4 58.7 59.1 65 Sacramento-Arden-Arcade-Roseville, CA 57.9 59.0 59.5 58.8 66 San Jose-Sunnyvale-Santa Clara, CA 59.1 58.5 58.6 58.7 67 Syracuse, NY 53.3 56.3 60.3 56.6 68 San Diego-Carlsbad-San Marcos, CA 55.7 55.9 54.2 55.3 69 Honolulu, HI 54.9 54.8 55.8 55.2 70 San Francisco-Oakland-Fremont, CA 53.8 54.3 51.7 53.3 71 Las Vegas-Paradise, NV 54.3 52.1 51.1 52.5 72 Bakersfield, CA 50.6 51.0 53.3 51.6 73 New York-Northern New Jersey--Long Island, NY 49.8 52.7 52.1 51.5 74 Fresno, CA 50.2 42.3 60.7 51.1 75 Los Angeles-Long Beach-Santa Ana, CA 49.7 49.9 50.8 50.1

Source: http://www.census.gov/housing/hvs/data/rates.html

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Table A.2. Percent of the commuters that use public transportation

County* 2000 2005 2009 2011 Change

2000– 2005

2005-2009

2009-2011

Chicago 26.08% 25.30% 26.50% 27.60% -0.8 1.2 1.1 Suburban Cook 8.32% 8.09% 8.06% 8.78% -0.2 0.0 0.7 Cook 17.25% 16.70% 17.90% 18.50% -0.6 1.2 0.6 DuPage 6.73% 6.00% 6.20% 6.50% -0.7 0.2 0.3 Kane 2.72% 2.40% 2.10% 2.60% -0.3 -0.3 0.5 Lake 4.59% 4.00% 4.60% 3.80% -0.6 0.6 -0.8 McHenry 3.15% 3.90% 2.90% 3.20% 0.8 -1.0 0.3 Will 4.06% 4.50% 4.20% 4.00% 0.4 -0.3 -0.2

Source: adapted from: U.S. Census Bureau, http://factfinder2.census.gov Figure A.1 Increase in population and number of households, 1972 to 2009

0

500

1,000

1,500

2,000

2,500

3,000

3,500

4,000

4,500

1972

1974

1976

1978

1980

1982

1984

1986

1988

1990

1992

1994

1996

1998

2000

2002

2004

2006

2008

HouseholdsPopulation

42

Figure A.2 Per Capita Personal Income by County, 2000-2010

Data Source: U.S. Department of Commerce, Bureau of Economic Analysis, 2012 http://www.bea.gov

$30,000

$35,000

$40,000

$45,000

$50,000

$55,000

$60,000

Cook DuPageKane KendallLake McHenryWill