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Community features and urban sprawl: the case of the Chicago metropolitan region
Land Use Policy 18 (2001) 221–232
Community features and urban sprawl: the case of the Chicago metropolitan region$ Tingwei Zhang*,1 Urban Planning a Policy Program, College of Urban Planning and Public Affairs, University of Illinois, Room 235, CUPPA Hall, 412 S. Peoria Street, Chicago, IL 60607, USA Received 29 June 2000; received in revised form 26 September 2000; accepted 14 November 2000
Abstract Both local (endogenous) and regional (exogenous) factors contribute to urban sprawl. Local features include social-economic status, transportation accessibility, housing stock and land use regulations. The geographic location of a community is a key regional factor affecting new development. By using GIS techniques to separate local factors from regional ones, the research found that social-economic and housing stock-related factors are more important than spatial-related factors to a community’s attraction to new developments. Therefore, policy makers should pay more attention to broader policy issues instead of looking to transportation related policies in fighting against sprawl. r 2001 Elsevier Science Ltd. All rights reserved. Keywords: Sprawl; Community features; Transportation policy; GIS technique
Introduction Urban sprawl and government’s role in sprawl is a controversial topic for researchers and policy makers. The debate over sprawl is fundamental: is the rapid expansion of urbanized land in American metropolitan areas a natural evolution of urbanization, or is it the consequence of faulty policy choices? Many American planners have expressed their concerns about sprawl’s social cost in the form of central city decline and the environmental cost in the loss of farm land and open space (Ewing, 1997; Freilich and Peshoff, 1997; Hylton, 1995; RERC, 1974; Young, 1995; Rusk, 1993; Katz and Bernstein, 1998). Facing the rapid expansion of $ The paper was prepared as part of the Chicago Metropolitan Case Study sponsored by the Brookings Institution and financially supported by the MacArthur Foundation. Graduate research assistant Jihong Zhu contributed to the quantitative analysis part of the research. The author is grateful to Wim Wiewel and Charles Orlebeke for their valuable comments on the paper. An earlier version of the paper was presented at the 2000 UAA annual conference at Los Angeles. *Tel.: +1-312-355-0303; fax: +1-312-413-2314. E-mail addresses: [email protected] (T. Zhang). 1 Tingwei Zhang is an assistant professor of the urban planning and policy program in the University of Illinois at Chicago. His current publications include urban sprawl issues in China such as in Cities (UK) and City Planning Review (China).
urbanized areas, planners and policy makers want especially to know one answer: what is government’s contribution to sprawl if sprawl is in fact ‘‘bad’’ development? For those who argue that the federal government is ‘‘supporting’’ directly or at least involved indirectly in urban sprawl, public spending on transportation and the personal income tax deductibility of mortgage interest are two public policies believed to contribute to urban sprawl. On the other hand, some researchers think sprawl is a normal process of urbanization, and they argue rising incomes and consumer preferences guide the growth of new urban territory into fringe areas (Gordon and Richardson, 1997). Although the sprawl debate has produced various definitions, sprawl is generally believed to result from poorly planned, large-scale new residential, commercial and industrial developments in areas previously not used for urban purposes. Leap-frog land use patterns, strip commercial development along highways, and very low density single-use developments are three characteristics of urban sprawl in America (Ewing, 1997). A number of studies suggest a relationship between highway developments and rapid urban expansion (Hylton, 1995; Parker, 1995). Government’s investment in infrastructure, especially highway construction, is therefore believed to promote sprawl.
0264-8377/01/$ - see front matter r 2001 Elsevier Science Ltd. All rights reserved. PII: S 0 2 6 4 - 8 3 7 7 ( 0 1 ) 0 0 0 1 8 - 7
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T. Zhang / Land Use Policy 18 (2001) 221–232
However, McDonald and McMillen’s research on the distribution patterns of real estate developments in the Chicago metropolitan area found that new housing developments in the 1990–1996 period exhibited very few clear spatial patterns. New residential developments were likely to have occurred near to downtown Chicago, but they were not affected by good transportation accessibility such as proximity to commuter rail stations, highway interchanges, and suburban employment centers. The scattered pattern of residential developments and the authors’ findings challenge the assumption that surface transportation accessibility is an important contributor to urban sprawl; therefore the authors called for further investigation (McDonald and McMillen, 1998). Since McDonald and McMillen considered location and transportation accessibility as independent variables in their models and found little impact of the transportation factor and some mixed impacts of the location factor on new developments, their findings may imply the existence of local factors that affect new urban development. This study examines the effects of local factors on residential development in the Chicago metropolitan area from 1970 to 1996 and discusses the possible impacts of community features on sprawl. The study was conducted originally as part of a larger case study of the Chicago region. Federal spending behavior, housing policy and housing markets were not the focus of the research, although all of them intertwine and have impacts on new developments. The study intends to answer one question: what role do local factors play in sprawl? Local factors refer first to the socio-economic situation of a community, including demographic changes, the community’s economic status, and its education quality. The housing stock of a community is the second local factor. The third set of local factors relates to land use policies such as zoning regulations that may affect local development magnitude and speed. Transportation related factors such as location and accessibility are important but could be considered either local or regional; this issue is discussed later in the article. Separating local factors from regional factors is critical in urban sprawl research. Various factors have been suggested as contributing to sprawl: federal policy on mortgage interest taxation and public spending, community location, transportation accessibility and community features. Federal taxation policy on mortgage interest applies to all American municipalities. If we compare several communities in a region and find considerable differences of new residential developments among these communities, it would be hard to argue that federal taxation policy is a cause for residential developments in certain communities but not in others. Orlebeke’s discussion of federal housing policy has correctly pointed out that the policy of tax benefit
enjoyed by home owners does not itself cause population decentralization, but it may interact with other features of the housing market and thus have an impact on sprawl (Orlebeke, 1999). These ‘other features’ are related to the characteristics of a community, and they need to be examined. Second, the location factor is believed the key consideration in configuring the housing market. ‘Location, location, location’ is a traditional maxim of the American real estate business. Location has two dimensions: geographic location and features of a place. The former refers to regional factors and the latter to local ones. Geographically, a location too far from jobs and services or too close to traffic-generating attractions may have significant impacts on new housing development. From this perspective, the geographic location as a regional factor is critical to housing construction. However, in the Chicago area, we have observed a group of communities sitting almost at the same geographic location in terms of distance to jobs and services, but exhibiting different housing development patterns. This gives us reason to suggest that the differences may come from local factors, rather than regional factors such as geographic location. In other words, if location makes difference, we would have expected similar new development patterns across communities when the geographic location factor is held fixed. In the Chicago area, although there are many sub-employment centers in the region, downtown Chicago remains the top job and service center.2 (A detailed analysis is provided by Putnam et al., 2000.) Transportation accessibility is an important influence driving the real estate market, as suggested by many researchers. Good location implies either location at a strategic geographic position or being connected conveniently to the transportation web. In this regard, transportation accessibility is both a regional and a local factor. However, places geographically located at similar positions to jobs and services do not necessarily have the same transportation accessibility, so the two factors could be, and should be, separated in analysis. If we can technically hold the geographic location factor (within certain distance range to a job/service center) fixed from the transportation accessibility factor, then the following hypothesis could be tested. New housing development will exhibit various distribution patterns 2 Tracy Cross, a real estate consultant in the Chicago region, suggested in his presentation at the 1999 UIC Great Cities Winter Forum: ‘‘The center point of (Chicago region) employment has not moved since 1988, as might have been expected, due mainly to the recession of the 1990s. Drawing a circle around the regional center of employment (which is about a mile west of O’Hare airport), one finds the city of Chicago is now closer to the center than many of the growing suburbs farther out in the region. This new location dynamic is one of the reasons the city is becoming an attractive residential choice’’.
T. Zhang / Land Use Policy 18 (2001) 221–232
according to local factors of a community, should the geographic location factor be held fixed.
Methodology Guided by these rationales, this study builds on McDonald and McMillen’s research, but also differs in several important aspects. First, the study takes a comprehensive approach in reviewing the historic growth process. Housing developments from 1970s to 1990s are analyzed so that a complete picture of urban growth over three decades could be plotted. Second, the study holds the regional factor (location) fixed so that impacts of local factors can be separated out. In addition, the research controls for community size in investigating new housing development so that impacts of the socio-economic features rather than the size of the community could be examined. ‘New housing units per 100 residents’ is thus used to normalize new housing units over population size. Similar treatment was applied to independent variables such as the education level and the ethnic and age composition of residents; all are percentage based. This study has two objectives in selecting analytical tools: holding the regional factor fixed, and examining the impacts of various local factors on new housing developments. The spatial analysis technique of GIS is employed to satisfy the first objective and regression analysis is used for the second one. To hold the regional factors fixed, we selected municipalities that are located the same distance from the Chicago downtown (CBD). Although Chicago’s CBD is no longer the only employment center and there are many sub-employment centers in the region, it remains the most important jobs and services center in the region. Furthermore, the distribution patterns of both the highway and the public transportation systems (Metra, Amtrak, Pace and CTA) in the Chicago area radiate from the CBD. This distribution pattern makes it fair to use the CBD as a center in selecting places with similar transportation access to jobs and services. In examining the distribution of new developments, we further found that most new growth occurred on a ring of about 48 km (30 miles) of the CBD, and the pattern also radiates. (See Map 1) Using GIS technique, all municipalities ‘‘hit’’ by this ring are included automatically. The regional factor (location to jobs and services) of these communities is thus being held fixed, because all municipalities selected are located with the same distance to the CBD and have similar transportation accessibility to the CBD. A total of 24 municipalities out of the 269 communities in the Chicago region were selected. While innovative, this method does have a trade-off problem. Because only those communities hit by the ring
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are selected, the size of the sample may be too small. In this case, the sample size of 24 communities is too small for a decent regression analysis. Therefore, we added a second ring of 64 km (40 miles), and 61 municipalities between 48 and 64 km rings were selected and analyzed in the second set of regression models (Map 2). The 1970–1990 demographic and housing development data are from 1980 and 1990 Census reports. Population change from 1990 to 1997 is based on the Northeastern Illinois Planning Commission’s (NIPC) estimates made in November 1997. The number of new housing developments from 1990 to 1996 is also from NIPC, defined as housing construction involving at least one acre of land, 10,000 ft2 of building, or $1 million in expenditure. The NIPC data show new housing units in each quarter section; these units were then merged based on municipalities. Additional information on the communities, such as education quality (high school graduation rate and the American college testing performance (ACT)) and transportation conditions were collected from website: www.geocities.com.3 The 1999 demographic information source is the Clarilas data company. Clarilas provides limited demographic information (such as ethnic composition) but misses most important indicators; this restricts a complete investigation of community features and their impacts on new development from 1990 to 1996. As a result, the 1990s data was used in tendency analysis but not in regression analysis. Local zoning ordinances and interviews with local government planners provided information on land use regulations. We focused especially on zoning requirements of minimum lot size for a single family house (R1).
Analysis Housing development and community features varied across the 24 municipalities from 1970 to 1996. Three sets of indicators are used to represent community characteristics: transportation conditions, demographic and economic characteristics, housing-related indicators (Table 1 transportation conditions, Table 2a demographic characteristics, Table 2b economic characteristics, and Table 3 housing-related characteristics). Also, based on their geographic locations, the 24 communities are divided into five groups (Table 4). Examining the community features of the 24 municipalities, some preliminary findings were obtained. Table 1 shows that the average distance from each community center to the CBD is 32.4 miles and the variance is small, which is the expected result of controlling the location factor. The travel time, however, varies greatly due to varying access to major 3
Website: http://www.geocities.com/Heartland/Meadows/8536/.
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Map 1.
highways. Municipalities in the north and northwest groups have lower accessibility than that of the west, southwest, and south groups. But new development pattern does not always follow the distribution pattern of highways. The north and northwest areas are actually
main growth centers in the region, although they have relatively lower accessibility to highways. Population change from 1980 to 1998, as shown in Table 2a, varies greatly across the 24 municipalities. With the mean of population growth rate of 25.8% over
T. Zhang / Land Use Policy 18 (2001) 221–232
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Map 2.
the 1980–1990 period, some municipalities experienced significant population increase (60% in South Barrington Village, 53% for Deer Park Village, and 50% in Naperville), while others lost population (Indian Creek Village lost 19%, Romeoville lost 10%, and University Park lost 10%). It is obvious that most northwest communities have gained population; the mean of population growth rate of this group is 40.3%, the highest among all five groups. Population growth trends
shifted somewhat in the 1990–1997 period. The overall mean of population growth rate increased to 28%, and the highest growth rates occurred in the seven southwest communities. The mean of population growth of this subgroup is 41.9%, with Mokena as the highest (107.0%) among all 24 municipalities. It seems that new housing development has a strong relationship with population change, since the four communities experiencing above-average housing
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Table 1 Transportation conditionsa Place name
Direction
Bartlett village Crete village Deer Park village Frankfort village
W S NW SW
Hoffman Estates village Indian Creek village Inverness village
NW N NW
Kildeer village Lake Bluff village Lake Forest city Lockport city
NW N N SW
Long Grove village
NW
Mettawa village ($) Mokena village Naperville city New Lenox village
N SW W SW
Romeoville village
SW
Sourth Barrington village Streamwood village University Park village
NW NW W
Vernon Hills village
N
Warrenville city West Chicago city Willowbrook village
W W S
a
Primary land-use feature
Distance to CBD (miles)
Travel time to CBD (min)
Highways server in the area
Public transit
Transportation mean
Residential Residential Residential Residential, commercial Residential, commercial Residential Residential, commercial Residential Residential Residential Residential, industrial Residential, commercial Residential Rural Residential Residential, rural Residential, commercial, industrial Residential, commercial Residential Residential, commercial, industrial Residential, Commercial, industrial Residential Industrial Residential, industrial
30 32 30 37
45 60 55 60
5 3 1 2
MD-W ME Metra Metra
89 87 85 87
7 7 9 7
2 2 1 3
2 3 5 3
29
55
1
Metra
92
3
2
2
30 30
80 55
1 1
MD-N Metra
96 87
2 7
0 1
2 5
30 33 31 35
55 80 44 53
2 2 2 1
Metra MD-N MD-N HC
86 76 71 94
6 13 13 2
2 4 10 4
6 8 6 1
34
55
2
Metra
85
6
5
4
35 35 30 40
45 60 45 60
2 5 5 2
Metra RI MD-W RI
78 90 85 88
7 7 10 8
9 2 3 2
6 1 3 2
32
35
1
Metra and 94 Antra
1
3
2
35
65
1
Metra
90
3
1
5
33 35
55 60
1 3
MD-W ME
94 85
3 13
1 1
2 2
40
60
1
MD-N
92
3
2
3
30 32 20
40 50 30
5 5 5
MD-W Metra Metra
90 91 86
4 2 11
1 5 1
4 1 2
Dirve (%)
Transit (%)
Others (%)
Work at home (%)
Data source: http://www.geocities.com.
increase in all the 1970–1980, 1980–1990 and 1990–1996 periods also experienced significant population growth (Bartlett, Long Grove, Kildeer, New Lenox, and Vermon Hills). This phenomenon suggests that both population change and new housing development measure the same growth trends and they are actually tautological, or highly correlated with each other. Therefore, we omitted the population growth variable in the following analysis and focused on new housing development. Property values in a community may affect housing investment, as suggested by common sense. As shown in Table 3, the housing stock of three municipalities (Lake Forest, Mettawa, and South Barrington) has a very high
per unit value (averaging more than $5,000,000), and all three experienced considerable growth. We further examined the role of land use regulation on new residential developments. It is clear that in the last three decades several communities have continued to attract more new development than others: Bartlett, Frankfort, Naperville, New Lenox, South Barrington, and Vernon Hills. By interviewing local government planners, we found that most of those ‘‘favorable communities’’ require very large lot sizes for new single family houses. Table 5 shows the minimum lot size requirement for single family residential zones of the 24 communities. Mettawa, Long Grove, and South Barrington have the largest lot size requirements (5, 3 and 2.5 acres,
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T. Zhang / Land Use Policy 18 (2001) 221–232 Table 2 (a) Demographic characteristicsa Place name Population change (%) 1980– 1990 Bartlett village 32 Crete village 20 Deer Park village 53 Frankfort village 39 Hoffman Estates village 20 Indian Creek village @19 Inverness village 38 Kildeer village 29 Lake Bluff village 20 Lake Forest city 15 Lockport city 3 Long Grove village 58 Mettawa village 5 Mokena village 25 Naperville city 50 New Lenox village 40 Romeoville village @10 Sourth Barrington village 60 Streamwood village 24 University Park village @10 Vernon Hills village 36 Warrenville city 34 West Chicago city 15 Willowbrook village 42
New housing units (per 100 persons)
Bartlett village 32 Crete village 20 Deer Park village 53 Frankfort village 39 Hoffman Estates village 20 Indian Creek village @19 Inverness village 38 Kildeer village 29 Lake Bluff village 20 Lake Forest city 15 Lockport city 3 Long Grove village 58 Mettawa village ($) 5 Mokena village 25 Naperville city 50 New Lenox village 40 Romeoville village @10 Sourth Barrington village 60 Streamwood village 24 University Park Village @10 Vernon Hills village 36 Warrenville city 34 West Chicago city 15 Willowbrook village 42 a
Age
Education
1990– 1970– 1980– 1998 1979 1990
1990– 1996
White (%)
black (%)
Others Under 19–50 (%) 18 (%) (%)
Above High College Graduate 55 (%) school (%) (%) (%)
78 19 13 41 4 14 5 31 2 7 42 35 16 107 37 54 35 39 13 4 20 19 21 6
26 7 1 27 5 0 6 9 4 4 24 17 7 7 6 18 9 8 12 0 13 5 17 2
93 94 97 99 87 97 95 98 98 96 99 96 93 99 93 99 91 93 91 19 91 95 83 89
2 4 1 0 3 0 1 0 1 1 0 1 0 0 2 0 2 1 2 79 2 1 2 1
5 2 2 1 10 3 4 2 1 3 1 3 7 1 5 1 7 6 7 2 7 4 15 10
9 19 12 18 10 14 18 16 24 22 22 15 14 12 11 14 11 10 9 9 9 10 13 23
26 11 20 22 18 11 11 17 5 7 6 15 5 16 19 17 5 18 11 21 33 22 11 35
(b) Economic characteristics Place name Population change (%) 1980– 1990
Race
12 8 17 8 9 5 16 12 7 6 3 18 15 10 20 13 1 20 12 1 16 13 4 19
33 27 31 29 30 25 27 28 27 27 26 32 23 34 32 32 33 36 31 37 30 32 31 19
57 53 57 54 60 61 55 56 49 50 52 53 63 55 57 54 56 54 60 53 60 58 56 58
37 42 23 38 34 44 21 20 14 13 57 24 9 46 19 48 67 21 48 39 27 33 59 28
56 49 58 49 58 46 62 61 57 58 38 56 64 47 62 45 31 62 48 51 61 56 36 56
7 9 19 13 8 10 18 19 28 29 5 20 27 8 19 6 2 17 4 10 12 11 5 16
New housing units
Working class
High school
1980– 1990
1970– 1979
Employed Private 1990 (%)
Government Others Median (%) (%) (89) hh income ($)
Cost per Graduate ACT student rate ($)
12 8 17 8 9 5 16 12 7 6 3 18 15 10 20 13 1 20 12 1 16 13 4 19
26 11 20 22 18 11 11 17 5 7 6 15 5 16 19 17 5 18 11 21 33 22 11 35
2329 690 369 2953 14,089 88 1000 457 2195 8486 2622 2303 359 1474 42,566 1278 4690 2384 4316 3136 6552 2309 11,205 42
9 13 5 11 7 12 6 5 10 6 13 4 7 13 8 11 10 5 7 27 9 8 12 8
5895 6938 10,469 6857 10,220 13,042 10,220 10,469 15,554 15,554 7003 10,469 9218 6857 5496 6857 6165 7545 5558 6938 10,469 5896 8578 10,957
Data source: http://www.census.gov.
87 83 87 83 90 80 86 87 80 83 84 86 85 81 87 86 85 87 91 69 86 86 85 86
4 4 8 6 3 8 8 9 10 11 4 10 8 5 5 4 5 8 2 4 5 6 3 6
51,524 46,283 97,533 60,499 49,475 46,964 113,799 105,060 82,904 94,824 35,468 107,596 90,552 42,602 60,979 43,073 42,114 122,487 48,758 34,375 48,873 49,091 37,406 50,294
85.6 90 97 91 96 86 96 97 99 98 88 97 95 91 81 91 81 97 88 90 97 92 76 95
21.5 20 24 23 23 24 23 24 25 25 23 24 24 23 23 23 22 24 21 20 24 24 23 24
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Table 3 Housing featuresa Place name
New housing units (per 100 persons)
Bartlett village Crete village Deer Park village Frankfort village Hoffman Estates village Indian Creek village Inverness village Kildeer village Lake Bluff village Lake Forest city Lockport city Long Grove village Mettawa village Mokena village Naperville city New Lenox village Romeoville village South Barrington village Streamwood village University Park Village Vernon Hills village Warrenville city West Chicago city Willowbrook village a
1970–1979
1980–1990
26 11 20 22 18 11 11 17 5 7 6 15 5 16 19 17 5 18 11 21 33 22 11 35
12 8 17 8 9 5 16 12 7 6 3 18 15 10 20 13 1 20 12 1 16 13 4 19
Median rent ($)
Owner cost ($)/month
Median unit value ($)
Zoning minimum lot size (ft2)
607 453 1001 556 693 917 1001 813 666 762 472 400 575 527 698 516 763 1001 891 523 713 735 559 743
1422 1330 2271 1541 1321 1359 2388 2402 2092 2402 1071 2402 2402 1226 1706 1145 958 2402 1186 1097 1546 1309 1168 1653
130,700 107,500 319,000 162,800 133,100 195,800 369,600 380,000 286,000 500,001 85,800 420,100 500,001 116,500 176,200 109,700 73,800 500,001 107,000 60,700 141,200 116,100 94,100 184,900
6000 9000 43,560 20,000 14,687 40,000 43,560 43,560 7500 F 9000 87,120 217,800 40,000 30,000 12,500 20,000 108,900 13,600 13,500 80,000 40,000 20,000 21,780
Data source: http://www.census.gov.
Table 4 Spatial distribution of the 24 municipalities Subgroup
Municipality name
North (5)
Lake Bluff, Lake Forest, Mettawa, Vernon Hill, Indian Creek Long Grove, Kildeer, Deer Park, Invermess, Sourth Barrington, Hoffman estates Streamwood, Bartlett, West Chicago Warrenville, Naperville, Romeoville, Lockport, New Lenox, Mokena, Frankfort University Park, Crete, Willowbrook
Northwest (6) West (3) Southwest (7) South (3)
respectively) for single family houses. This explains why property values there are so high (median value $500,001 in Mettawa and South Barrington, $420,100 in Long Grove). In the City of Chicago, an average single family house on a typical lot of 125 by 25 ft has a lot size of only 3125 ft2. The large lot size and high property values of houses in these suburban municipalities attract affluent buyers who are a prime target of the real estate market. We finally selected 17 independent variables of three groups for simple regression analysis: four transportation related, nine demographic and economic, and four property related indicators. The results are shown in Table 6.
Table 5 Minimum lot size requirements for single family house in six communitiesa Municipality name
Miniumum lot size required for R1 (ft2)
Bartlett Crete Deer Park Frankfort Hoffman Estate Indian Creek Inverness Kildeer Lake Bluff Lake Forest Lockport Long Grove Mettawa Mokena Naperville New Lenox Romeoville South Barrington Streamwood University Park Vernon Hills Warrenville West Chicago Willowbrook
6000 9000 43,560 (1 acre) 20,000 43,560 (1 acre) 40,000 43,560 (1 acre) 43,560 (1 acre) 12,000 No requirement 9000 130,680 (3 acre) 217,800 (5 acre) 40,000 30,000 12,500 20,000 108,900 (2.5 acre) 20,000 13,500 80,000 40,000 20,000 21,780 (0.5 acre)
a
Source: Zoning ordinance of various municipalities.
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T. Zhang / Land Use Policy 18 (2001) 221–232 Table 6 Regression analysis (simple-24 municipalities) Community features
Independent variable
Transportation related
% of public transit users Number of highways Travel time to CBD % of private car users
Demographic and economic related
Property related
R2
Beta
Std. error
t test
Sig. level
0.014000 0.015000 0.011000 0.015000
0.185488 0.442669 @0.057227 @0.122102
0.337208 0.773499 0.118172 0.212026
0.550070 0.572293 @0.484273 @0.575881
0.587813 0.572926 0.632978 0.570541
Percent of white % of 19–50 year old % with college degree % with graduate degree Median household income % of working in government % of working in private sector High school graduation rate Cost per high school student
@0.118000 0.087000 0.508000 0.154000 0.283000 0.386000 0.309000 0.159000 0.001000
0.127903 0.502959 0.472195 0.298145 0.000111 @0.811857 0.769246 0.380114 @0.000071
0.074381 0.347954 0.099014 0.149226 0.000038 0.206371 0.245430 0.186559 0.000429
1.719571 1.445474 4.768973 1.997941 2.945262 @3.933975 3.134275 2.037498 @0.165681
0.099550 0.162420 0.000092 0.058234 0.007484 0.000708 0.004822 0.053805 0.869921
Lot size Median rent Owner’s monthly cost Median housing unit value
@0.198000 0.096000 0.308000 0.205000
0.000056 0.010207 0.006187 0.000018
0.000025 0.006661 0.001977 0.000008
2.276141 1.523246 3.128957 2.384241
0.033428 0.139692 0.004883 0.026167
The main findings from the analysis are: 1. All four transportation related variables show very weak relationships to new housing development. None is statistically significant. 2. Economic status, including the number of people working in the private sector (relatively higher salary than those in the public sector) and household income, has positive impacts on new residential development. Both indicators are statistically significant at the 0.01 level. On the other hand, the variable ‘percentage of people working in the public sector’ (with relatively lower income) has a strong negative relationship to new housing construction, as expected. 3. Also as expected, ‘Percentage of residents with college degrees’ has a very strong positive relationship with new housing development. The same is true for ‘percentage of residents with graduate degrees’, although the relationship is slightly weaker. 4. Other economic indicators are not statistically significant to new housing development. 5. Population composition (measured by ‘percentage of white people’) does not show much significance statistically. Since in only one municipality (University Park) among the 24 communities are white people the minority, this finding has limitations and the relationship was re-examined in the 61-sample model. As expected, the result of the 61-sample model shows a strong relationship between the ethnic composition of the population and new developments. Communities with white concentration seem to attract more new developments (see Table 7).
6. The quality of local schools (measured by high school graduation rate) is statistically significant to new housing construction. However, ‘cost per high school student’ has almost no relationship to new housing construction. (Local school quality was not used in the 61-sample model due to lack of available data.) 7. Property value (measured by housing unit value and owner’s monthly cost) has a positive relationship to housing construction, and both variables are statistically significant at the 0.05 level. Median housing rent, however, does not show much significance statistically on new housing construction. This is probably because the home ownership rate in the Chicago region is high (65.8% in 1996) and renters have less impact on the housing market, especially in suburbs. 8. Minimum lot size for a single family house has a strong relationship to new housing development. This supports the suggestion that regulation requiring larger lots with higher prices may stimulate the marketplace to produce more homes. Based on the test of possible multicollinearity among independent variables, a multivariate regression model with nine independent variables was constructed (Table 8 Regression analysis: multi-24 municipalities). With a considerable statistical significant level (an R2 of 0.62), the model shows that none of any individual variables is statistically significant to new housing development. Only two housing-related variables (owner’s monthly cost and lot size) show some relationship, but not strongly. Transportation-related factors (travel time to CBD, public transit accessibility and number of
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Table 7 Regression analysis (simple-61 municipalities) Community features
Independent variable
R2
Beta
Std. error
t test
Sig. level
Transportation related
% of public transit users Number of highways in the area Travel time to work % of private car users
0.047 0.001 0.162 0.074
0.78314 @0.10144 0.51045 0.30593
5.509 5.336 5.169 5.433
1.715 @0.174 3.371 2.166
0.092 0.862 0.001 0.034
Demographic and economic related
Percent of white Percent of black Median age of residents % with college degree or higher Median household income % of working in private sector % of working in government
0.122 0.113 0.090 0.353 0.316 0.146 0.128
0.14875 @0.15962 0.47710 0.31691 0.00015 0.38421 @1.13022
5.289 5.315 5.386 4.541 4.670 5.216 5.270
2.865 @2.746 2.410 5.671 5.216 3.176 @2.947
0.006 0.008 0.019 0.0001 0.0001 0.002 0.005
Property related
Lot size Median rent % of renters % of owners Housing unit value Median age of housing stock
0.072 0.153 0.084 0.117 0.252 0.643
0.00004 0.01538 @0.28646 0.19501 0.00003 0.51130
5.590 5.195 5.403 5.305 4.881 3.375
1.894 3.266 @2.323 2.792 4.463 10.299
0.065 0.002 0.024 0.007 0.0001 0.0001
Table 8 Regression analysis (multi-24 municipalities) Independent variable
R2
Model Median household income % with graduate degree Public transit Travel time to CBD Number of highway Lot size Housing unit value Median rent Owner’s monthly cost
0.621
Std. error
Beta
t test
Sig. level
@1.13184E-05 @0.467262703 0.259713622 @0.047822122 0.920076454 5.62968 E-05 @3.96643E-05 0.006759381 0.020403622
0.000228893 0.506897269 0.50634256 0.125398036 0.847336527 5.08553E-05 4.50065E-05 0.007037524 0.014446692
@0.04944814 @0.92180947 0.51287547 @0.38136261 1.085845381 1.107000987 @0.88130125 0.960477155 1.412338652
0.961313822 0.373419243 0.616639762 0.709094346 0.297272637 0.288357032 0.394157335 0.354339916 0.181339516
highway in the community) have very weak impact, although ‘number of highways’ displays some statistical significance. These results support the hypothesis: transportation accessibility is less influential than other local factors, especially the housing stock and land use regulations, in new residential development decisions. The 61-sample models confirm the basic findings of the 24-sample models (Tables 7 and 9). Socio-economic factors such as ethnic composition (percentage of white), residents’ income and education level, together with the median age of the population body all have a strong relationship to new housing development. A community with more white residents and higher income people may attract more developments. Supporting the findings of the 24- and the 61-sample models reveal again none of the transportation-related factors to be significant statistically. The housing stock, on the other hand, represented by the median age of houses and percentage of owners in a community, is strongly related to new
housing development. The finding that communities having an older housing stock attract fewer new developments (a negative relation) explains the phenomenon of fewer new houses built in older municipalities, either due to less buildable land for new development, or because housing buyers prefer new suburban communities to old urban neighborhoods. In contrast to simple regression models, lot size shows only limited influence on new housing in the multiple regression models.
Discussion In general, findings from this investigation indicate that new housing developments in the Chicago region are strongly affected by local factors representing community features. When the regional factor (location) is held fixed, socio-economic characteristics such as household income, percentage of residents with higher
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T. Zhang / Land Use Policy 18 (2001) 221–232 Table 9 Independent variable
R2
(a) Regression analysis (multi-61 municipalities) Model 0.8752 Median age of residents Median household income % of white % with college degree or higher Travel time to work % use public transit Number of highway in the area Median age of housing stock Housing unit value Median rent Lot size % of owner (b) Regression analysis (multi-61 municipalities) Model 0.631 % with college degree or higher Median age of residents % of white Median household income
Std. Error
Beta
t test
Sig. level
@0.302 0.0001 0.080 0.205 @0.245 0.086 @0.504 0.516 0.000 @0.004 0.0001 0.157
@1.274 @0.124 2.313 2.261 @1.491 0.203 @1.714 5.957 0.0001 @0.873 @1.174 2.067
0.213 0.902 0.028 0.032 0.147 0.841 0.098 0.0001 1.000 0.390 0.250 0.048
0.2955 @0.9943 0.1547 0.0001
3.005 @4.088 3.519 2.896
0.005 0.0001 0.001 0.006
2.31667
3.513
education and relatively higher income, and ethnic composition of residents show considerable impacts on new housing construction. Local school quality is related to new construction. The housing stock represented by property values, and age of the housing stock is another factor influencing the pace of new developments. Land use regulations, especially the minimum lot size requirement for single family houses, exhibits some relationship to new housing. Transportation accessibility, on the other hand, has quite limited impacts in the Chicago case. This is supported by the fact that the number of accessible highways shows low significance statistically. Moreover, the relationship is negative, which indicates that highly accessible transportation may actually discourage rather than encourage new developments. The research found little evidence to support the assertion that highway construction, mostly funded by public money, is a direct contributor to new housing developments in fringe areas. This conclusion has received support from new research on the Chicago region conducted by the Urban Transportation Center (UTC) of the University of Illinois at Chicago. By using a sophisticated gravity model (which may serve better in a multi-centered metro area), UTC’s study shows that travel time, a measure of transportation accessibility, does not have much relationship to land use patterns. Instead, changes of population, household size, homeownership rate and residential lot size have considerable influence on the consumption of land by new development (UTC, 2000). All of these findings paint a picture of new residential developments in the Chicago region from 1970s to 1990s: the market, driven by higher demand and high
potential profits in certain suburban areas, has produced more housing in these areas. The market-favorite communities have common features: high property value, large residential lot size, fewer minorities, higher income residents and high quality schools. None of these features is new to researchers or even to average people in defining an ‘attractive community’. Most of those communities are located in the ex-urban areas because the land is available and relatively less expensive, and the so-called ‘urban diseases’ have not yet occurred there. These are the communities where there is a heavy concentration of America’s middle class. Middle class communities are not only the main targets of the market place but are the targets of elected government officials, since it is the middle class that holds the critical voting power. Here, we may suggest a new application of the urban regime theory: the coalition of real estate interests and local government promotes new development in certain areas of the metropolitan region. This is proved by land use regulations (such as minimum lot size) in these market-favorite communities that reflect local government’s desire to work together with real estate groups. There are two approaches to solve the ‘sprawl problem’ if sprawl is judged too costly for the society. One strategy is to control growth at the edges of rapid suburban developments. Policies such as Smart Growth and the establishment of a regional government to oversee land use decisions intend to stop or at least slow down sprawl in fringe areas. While much has been suggested from this perspective, our findings point in another direction. If policymakers paid more attention to improve the quality of central cities and older close-in
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suburbs, then middle-class growth rates might increase, or at least remain unchanged in such communities, thereby reducing growth pressures on fringe areas. By keeping middle class in central cities, a more balanced development pattern across the whole metropolitan region could thus be achieved. The most important lesson learned from the study is that sprawl is not a direct consequence of spatial-related factors such as transportation accessibility and location; rather, it is rooted in the features of communities. When education systems, the housing stock, and overall community quality are improved, many more communities may have the opportunity to become attractive to the market place. While the federal government’s policy of public spending on highway construction does not show much impact on sprawl (at least in the Chicago region), we should urge policymakers to pay attention to much broader policy issues in efforts to control sprawl. Only when the gap between the quality of central city communities and that of their ex-urban neighbors is reduced, will unbalanced development pattern be changed and the sprawl problem finally solved.
References Ewing, R., 1997. Counterpoint: is Los Angeles-style sprawl desirable? Journal of the American Planning Association 63 (1), 107–126.
Freilich, R., Peshoff, B., 1997. The social costs of sprawl. The Urban Lawyer 29 (2), 183–198. Gordon, P., Richardson, H., 1997. Point: are compact cities a desirable planning goal? Journal of the American Planning Association 63 (1), 95–106. Hylton, T., 1995. Save Our Land, Save Our Towns. PB Books, Harrisburg, PA. Katz, B., Bernstein, S., 1998. The new metropolitan agenda: connecting cities and suburbs. Brookings Review Fall 1998, 16(4), 4–7. McDonald, J., McMillen, D., 1998. Employment subcenters and subsequent real estate development in suburban Chicago. Paper prepared for the Brookings-UIC Sprawl Project. Orlebeke, C., 1999. Housing policy and urban sprawl in the Chicago metropolitan region. UIC Great Cities Institute Working Paper. Parker, A., 1995. Patterns of federal urban spending: central cities and their suburbs, 1983–1992. Urban Affairs Review 31 (2), 184–205. Putnam, G., Almousa, W., Persky, J., Wiewel, W., 2000. Restructuring, decentralization, and stabilizing: Chicago area employment during the boom. University of Illinois at Chicago, working paper. Real Estate Research Corporation (RERC), 1974. The costs of sprawl: environmental and economic costs of alternative residential development patterns at the urban fringe. Government Printing Office, Washington, DC. Rusk, D., 1993. Cities Without Suburbs. Woodrow Center Press, Washington, DC. Urban Transportation Center, UIC. 2000. Urban sprawl: past, present and the future: experience in the Chicago area. Conducted by Soot, Thakuriah, Metaxatos, Yang, Dirks, Yannos and Stauffer. Presented at Urban Sprawl Symposium, UIC, September 8, 2000. Young, D., 1995. Alternatives to Sprawl. Lincoln Institute of Land Policy, Cambridge, MA.
Community features and urban sprawl: the case of the Chicago metropolitan region$ Tingwei Zhang*,1 Urban Planning a Policy Program, College of Urban Planning and Public Affairs, University of Illinois, Room 235, CUPPA Hall, 412 S. Peoria Street, Chicago, IL 60607, USA Received 29 June 2000; received in revised form 26 September 2000; accepted 14 November 2000
Abstract Both local (endogenous) and regional (exogenous) factors contribute to urban sprawl. Local features include social-economic status, transportation accessibility, housing stock and land use regulations. The geographic location of a community is a key regional factor affecting new development. By using GIS techniques to separate local factors from regional ones, the research found that social-economic and housing stock-related factors are more important than spatial-related factors to a community’s attraction to new developments. Therefore, policy makers should pay more attention to broader policy issues instead of looking to transportation related policies in fighting against sprawl. r 2001 Elsevier Science Ltd. All rights reserved. Keywords: Sprawl; Community features; Transportation policy; GIS technique
Introduction Urban sprawl and government’s role in sprawl is a controversial topic for researchers and policy makers. The debate over sprawl is fundamental: is the rapid expansion of urbanized land in American metropolitan areas a natural evolution of urbanization, or is it the consequence of faulty policy choices? Many American planners have expressed their concerns about sprawl’s social cost in the form of central city decline and the environmental cost in the loss of farm land and open space (Ewing, 1997; Freilich and Peshoff, 1997; Hylton, 1995; RERC, 1974; Young, 1995; Rusk, 1993; Katz and Bernstein, 1998). Facing the rapid expansion of $ The paper was prepared as part of the Chicago Metropolitan Case Study sponsored by the Brookings Institution and financially supported by the MacArthur Foundation. Graduate research assistant Jihong Zhu contributed to the quantitative analysis part of the research. The author is grateful to Wim Wiewel and Charles Orlebeke for their valuable comments on the paper. An earlier version of the paper was presented at the 2000 UAA annual conference at Los Angeles. *Tel.: +1-312-355-0303; fax: +1-312-413-2314. E-mail addresses: [email protected] (T. Zhang). 1 Tingwei Zhang is an assistant professor of the urban planning and policy program in the University of Illinois at Chicago. His current publications include urban sprawl issues in China such as in Cities (UK) and City Planning Review (China).
urbanized areas, planners and policy makers want especially to know one answer: what is government’s contribution to sprawl if sprawl is in fact ‘‘bad’’ development? For those who argue that the federal government is ‘‘supporting’’ directly or at least involved indirectly in urban sprawl, public spending on transportation and the personal income tax deductibility of mortgage interest are two public policies believed to contribute to urban sprawl. On the other hand, some researchers think sprawl is a normal process of urbanization, and they argue rising incomes and consumer preferences guide the growth of new urban territory into fringe areas (Gordon and Richardson, 1997). Although the sprawl debate has produced various definitions, sprawl is generally believed to result from poorly planned, large-scale new residential, commercial and industrial developments in areas previously not used for urban purposes. Leap-frog land use patterns, strip commercial development along highways, and very low density single-use developments are three characteristics of urban sprawl in America (Ewing, 1997). A number of studies suggest a relationship between highway developments and rapid urban expansion (Hylton, 1995; Parker, 1995). Government’s investment in infrastructure, especially highway construction, is therefore believed to promote sprawl.
0264-8377/01/$ - see front matter r 2001 Elsevier Science Ltd. All rights reserved. PII: S 0 2 6 4 - 8 3 7 7 ( 0 1 ) 0 0 0 1 8 - 7
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However, McDonald and McMillen’s research on the distribution patterns of real estate developments in the Chicago metropolitan area found that new housing developments in the 1990–1996 period exhibited very few clear spatial patterns. New residential developments were likely to have occurred near to downtown Chicago, but they were not affected by good transportation accessibility such as proximity to commuter rail stations, highway interchanges, and suburban employment centers. The scattered pattern of residential developments and the authors’ findings challenge the assumption that surface transportation accessibility is an important contributor to urban sprawl; therefore the authors called for further investigation (McDonald and McMillen, 1998). Since McDonald and McMillen considered location and transportation accessibility as independent variables in their models and found little impact of the transportation factor and some mixed impacts of the location factor on new developments, their findings may imply the existence of local factors that affect new urban development. This study examines the effects of local factors on residential development in the Chicago metropolitan area from 1970 to 1996 and discusses the possible impacts of community features on sprawl. The study was conducted originally as part of a larger case study of the Chicago region. Federal spending behavior, housing policy and housing markets were not the focus of the research, although all of them intertwine and have impacts on new developments. The study intends to answer one question: what role do local factors play in sprawl? Local factors refer first to the socio-economic situation of a community, including demographic changes, the community’s economic status, and its education quality. The housing stock of a community is the second local factor. The third set of local factors relates to land use policies such as zoning regulations that may affect local development magnitude and speed. Transportation related factors such as location and accessibility are important but could be considered either local or regional; this issue is discussed later in the article. Separating local factors from regional factors is critical in urban sprawl research. Various factors have been suggested as contributing to sprawl: federal policy on mortgage interest taxation and public spending, community location, transportation accessibility and community features. Federal taxation policy on mortgage interest applies to all American municipalities. If we compare several communities in a region and find considerable differences of new residential developments among these communities, it would be hard to argue that federal taxation policy is a cause for residential developments in certain communities but not in others. Orlebeke’s discussion of federal housing policy has correctly pointed out that the policy of tax benefit
enjoyed by home owners does not itself cause population decentralization, but it may interact with other features of the housing market and thus have an impact on sprawl (Orlebeke, 1999). These ‘other features’ are related to the characteristics of a community, and they need to be examined. Second, the location factor is believed the key consideration in configuring the housing market. ‘Location, location, location’ is a traditional maxim of the American real estate business. Location has two dimensions: geographic location and features of a place. The former refers to regional factors and the latter to local ones. Geographically, a location too far from jobs and services or too close to traffic-generating attractions may have significant impacts on new housing development. From this perspective, the geographic location as a regional factor is critical to housing construction. However, in the Chicago area, we have observed a group of communities sitting almost at the same geographic location in terms of distance to jobs and services, but exhibiting different housing development patterns. This gives us reason to suggest that the differences may come from local factors, rather than regional factors such as geographic location. In other words, if location makes difference, we would have expected similar new development patterns across communities when the geographic location factor is held fixed. In the Chicago area, although there are many sub-employment centers in the region, downtown Chicago remains the top job and service center.2 (A detailed analysis is provided by Putnam et al., 2000.) Transportation accessibility is an important influence driving the real estate market, as suggested by many researchers. Good location implies either location at a strategic geographic position or being connected conveniently to the transportation web. In this regard, transportation accessibility is both a regional and a local factor. However, places geographically located at similar positions to jobs and services do not necessarily have the same transportation accessibility, so the two factors could be, and should be, separated in analysis. If we can technically hold the geographic location factor (within certain distance range to a job/service center) fixed from the transportation accessibility factor, then the following hypothesis could be tested. New housing development will exhibit various distribution patterns 2 Tracy Cross, a real estate consultant in the Chicago region, suggested in his presentation at the 1999 UIC Great Cities Winter Forum: ‘‘The center point of (Chicago region) employment has not moved since 1988, as might have been expected, due mainly to the recession of the 1990s. Drawing a circle around the regional center of employment (which is about a mile west of O’Hare airport), one finds the city of Chicago is now closer to the center than many of the growing suburbs farther out in the region. This new location dynamic is one of the reasons the city is becoming an attractive residential choice’’.
T. Zhang / Land Use Policy 18 (2001) 221–232
according to local factors of a community, should the geographic location factor be held fixed.
Methodology Guided by these rationales, this study builds on McDonald and McMillen’s research, but also differs in several important aspects. First, the study takes a comprehensive approach in reviewing the historic growth process. Housing developments from 1970s to 1990s are analyzed so that a complete picture of urban growth over three decades could be plotted. Second, the study holds the regional factor (location) fixed so that impacts of local factors can be separated out. In addition, the research controls for community size in investigating new housing development so that impacts of the socio-economic features rather than the size of the community could be examined. ‘New housing units per 100 residents’ is thus used to normalize new housing units over population size. Similar treatment was applied to independent variables such as the education level and the ethnic and age composition of residents; all are percentage based. This study has two objectives in selecting analytical tools: holding the regional factor fixed, and examining the impacts of various local factors on new housing developments. The spatial analysis technique of GIS is employed to satisfy the first objective and regression analysis is used for the second one. To hold the regional factors fixed, we selected municipalities that are located the same distance from the Chicago downtown (CBD). Although Chicago’s CBD is no longer the only employment center and there are many sub-employment centers in the region, it remains the most important jobs and services center in the region. Furthermore, the distribution patterns of both the highway and the public transportation systems (Metra, Amtrak, Pace and CTA) in the Chicago area radiate from the CBD. This distribution pattern makes it fair to use the CBD as a center in selecting places with similar transportation access to jobs and services. In examining the distribution of new developments, we further found that most new growth occurred on a ring of about 48 km (30 miles) of the CBD, and the pattern also radiates. (See Map 1) Using GIS technique, all municipalities ‘‘hit’’ by this ring are included automatically. The regional factor (location to jobs and services) of these communities is thus being held fixed, because all municipalities selected are located with the same distance to the CBD and have similar transportation accessibility to the CBD. A total of 24 municipalities out of the 269 communities in the Chicago region were selected. While innovative, this method does have a trade-off problem. Because only those communities hit by the ring
223
are selected, the size of the sample may be too small. In this case, the sample size of 24 communities is too small for a decent regression analysis. Therefore, we added a second ring of 64 km (40 miles), and 61 municipalities between 48 and 64 km rings were selected and analyzed in the second set of regression models (Map 2). The 1970–1990 demographic and housing development data are from 1980 and 1990 Census reports. Population change from 1990 to 1997 is based on the Northeastern Illinois Planning Commission’s (NIPC) estimates made in November 1997. The number of new housing developments from 1990 to 1996 is also from NIPC, defined as housing construction involving at least one acre of land, 10,000 ft2 of building, or $1 million in expenditure. The NIPC data show new housing units in each quarter section; these units were then merged based on municipalities. Additional information on the communities, such as education quality (high school graduation rate and the American college testing performance (ACT)) and transportation conditions were collected from website: www.geocities.com.3 The 1999 demographic information source is the Clarilas data company. Clarilas provides limited demographic information (such as ethnic composition) but misses most important indicators; this restricts a complete investigation of community features and their impacts on new development from 1990 to 1996. As a result, the 1990s data was used in tendency analysis but not in regression analysis. Local zoning ordinances and interviews with local government planners provided information on land use regulations. We focused especially on zoning requirements of minimum lot size for a single family house (R1).
Analysis Housing development and community features varied across the 24 municipalities from 1970 to 1996. Three sets of indicators are used to represent community characteristics: transportation conditions, demographic and economic characteristics, housing-related indicators (Table 1 transportation conditions, Table 2a demographic characteristics, Table 2b economic characteristics, and Table 3 housing-related characteristics). Also, based on their geographic locations, the 24 communities are divided into five groups (Table 4). Examining the community features of the 24 municipalities, some preliminary findings were obtained. Table 1 shows that the average distance from each community center to the CBD is 32.4 miles and the variance is small, which is the expected result of controlling the location factor. The travel time, however, varies greatly due to varying access to major 3
Website: http://www.geocities.com/Heartland/Meadows/8536/.
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Map 1.
highways. Municipalities in the north and northwest groups have lower accessibility than that of the west, southwest, and south groups. But new development pattern does not always follow the distribution pattern of highways. The north and northwest areas are actually
main growth centers in the region, although they have relatively lower accessibility to highways. Population change from 1980 to 1998, as shown in Table 2a, varies greatly across the 24 municipalities. With the mean of population growth rate of 25.8% over
T. Zhang / Land Use Policy 18 (2001) 221–232
225
Map 2.
the 1980–1990 period, some municipalities experienced significant population increase (60% in South Barrington Village, 53% for Deer Park Village, and 50% in Naperville), while others lost population (Indian Creek Village lost 19%, Romeoville lost 10%, and University Park lost 10%). It is obvious that most northwest communities have gained population; the mean of population growth rate of this group is 40.3%, the highest among all five groups. Population growth trends
shifted somewhat in the 1990–1997 period. The overall mean of population growth rate increased to 28%, and the highest growth rates occurred in the seven southwest communities. The mean of population growth of this subgroup is 41.9%, with Mokena as the highest (107.0%) among all 24 municipalities. It seems that new housing development has a strong relationship with population change, since the four communities experiencing above-average housing
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Table 1 Transportation conditionsa Place name
Direction
Bartlett village Crete village Deer Park village Frankfort village
W S NW SW
Hoffman Estates village Indian Creek village Inverness village
NW N NW
Kildeer village Lake Bluff village Lake Forest city Lockport city
NW N N SW
Long Grove village
NW
Mettawa village ($) Mokena village Naperville city New Lenox village
N SW W SW
Romeoville village
SW
Sourth Barrington village Streamwood village University Park village
NW NW W
Vernon Hills village
N
Warrenville city West Chicago city Willowbrook village
W W S
a
Primary land-use feature
Distance to CBD (miles)
Travel time to CBD (min)
Highways server in the area
Public transit
Transportation mean
Residential Residential Residential Residential, commercial Residential, commercial Residential Residential, commercial Residential Residential Residential Residential, industrial Residential, commercial Residential Rural Residential Residential, rural Residential, commercial, industrial Residential, commercial Residential Residential, commercial, industrial Residential, Commercial, industrial Residential Industrial Residential, industrial
30 32 30 37
45 60 55 60
5 3 1 2
MD-W ME Metra Metra
89 87 85 87
7 7 9 7
2 2 1 3
2 3 5 3
29
55
1
Metra
92
3
2
2
30 30
80 55
1 1
MD-N Metra
96 87
2 7
0 1
2 5
30 33 31 35
55 80 44 53
2 2 2 1
Metra MD-N MD-N HC
86 76 71 94
6 13 13 2
2 4 10 4
6 8 6 1
34
55
2
Metra
85
6
5
4
35 35 30 40
45 60 45 60
2 5 5 2
Metra RI MD-W RI
78 90 85 88
7 7 10 8
9 2 3 2
6 1 3 2
32
35
1
Metra and 94 Antra
1
3
2
35
65
1
Metra
90
3
1
5
33 35
55 60
1 3
MD-W ME
94 85
3 13
1 1
2 2
40
60
1
MD-N
92
3
2
3
30 32 20
40 50 30
5 5 5
MD-W Metra Metra
90 91 86
4 2 11
1 5 1
4 1 2
Dirve (%)
Transit (%)
Others (%)
Work at home (%)
Data source: http://www.geocities.com.
increase in all the 1970–1980, 1980–1990 and 1990–1996 periods also experienced significant population growth (Bartlett, Long Grove, Kildeer, New Lenox, and Vermon Hills). This phenomenon suggests that both population change and new housing development measure the same growth trends and they are actually tautological, or highly correlated with each other. Therefore, we omitted the population growth variable in the following analysis and focused on new housing development. Property values in a community may affect housing investment, as suggested by common sense. As shown in Table 3, the housing stock of three municipalities (Lake Forest, Mettawa, and South Barrington) has a very high
per unit value (averaging more than $5,000,000), and all three experienced considerable growth. We further examined the role of land use regulation on new residential developments. It is clear that in the last three decades several communities have continued to attract more new development than others: Bartlett, Frankfort, Naperville, New Lenox, South Barrington, and Vernon Hills. By interviewing local government planners, we found that most of those ‘‘favorable communities’’ require very large lot sizes for new single family houses. Table 5 shows the minimum lot size requirement for single family residential zones of the 24 communities. Mettawa, Long Grove, and South Barrington have the largest lot size requirements (5, 3 and 2.5 acres,
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T. Zhang / Land Use Policy 18 (2001) 221–232 Table 2 (a) Demographic characteristicsa Place name Population change (%) 1980– 1990 Bartlett village 32 Crete village 20 Deer Park village 53 Frankfort village 39 Hoffman Estates village 20 Indian Creek village @19 Inverness village 38 Kildeer village 29 Lake Bluff village 20 Lake Forest city 15 Lockport city 3 Long Grove village 58 Mettawa village 5 Mokena village 25 Naperville city 50 New Lenox village 40 Romeoville village @10 Sourth Barrington village 60 Streamwood village 24 University Park village @10 Vernon Hills village 36 Warrenville city 34 West Chicago city 15 Willowbrook village 42
New housing units (per 100 persons)
Bartlett village 32 Crete village 20 Deer Park village 53 Frankfort village 39 Hoffman Estates village 20 Indian Creek village @19 Inverness village 38 Kildeer village 29 Lake Bluff village 20 Lake Forest city 15 Lockport city 3 Long Grove village 58 Mettawa village ($) 5 Mokena village 25 Naperville city 50 New Lenox village 40 Romeoville village @10 Sourth Barrington village 60 Streamwood village 24 University Park Village @10 Vernon Hills village 36 Warrenville city 34 West Chicago city 15 Willowbrook village 42 a
Age
Education
1990– 1970– 1980– 1998 1979 1990
1990– 1996
White (%)
black (%)
Others Under 19–50 (%) 18 (%) (%)
Above High College Graduate 55 (%) school (%) (%) (%)
78 19 13 41 4 14 5 31 2 7 42 35 16 107 37 54 35 39 13 4 20 19 21 6
26 7 1 27 5 0 6 9 4 4 24 17 7 7 6 18 9 8 12 0 13 5 17 2
93 94 97 99 87 97 95 98 98 96 99 96 93 99 93 99 91 93 91 19 91 95 83 89
2 4 1 0 3 0 1 0 1 1 0 1 0 0 2 0 2 1 2 79 2 1 2 1
5 2 2 1 10 3 4 2 1 3 1 3 7 1 5 1 7 6 7 2 7 4 15 10
9 19 12 18 10 14 18 16 24 22 22 15 14 12 11 14 11 10 9 9 9 10 13 23
26 11 20 22 18 11 11 17 5 7 6 15 5 16 19 17 5 18 11 21 33 22 11 35
(b) Economic characteristics Place name Population change (%) 1980– 1990
Race
12 8 17 8 9 5 16 12 7 6 3 18 15 10 20 13 1 20 12 1 16 13 4 19
33 27 31 29 30 25 27 28 27 27 26 32 23 34 32 32 33 36 31 37 30 32 31 19
57 53 57 54 60 61 55 56 49 50 52 53 63 55 57 54 56 54 60 53 60 58 56 58
37 42 23 38 34 44 21 20 14 13 57 24 9 46 19 48 67 21 48 39 27 33 59 28
56 49 58 49 58 46 62 61 57 58 38 56 64 47 62 45 31 62 48 51 61 56 36 56
7 9 19 13 8 10 18 19 28 29 5 20 27 8 19 6 2 17 4 10 12 11 5 16
New housing units
Working class
High school
1980– 1990
1970– 1979
Employed Private 1990 (%)
Government Others Median (%) (%) (89) hh income ($)
Cost per Graduate ACT student rate ($)
12 8 17 8 9 5 16 12 7 6 3 18 15 10 20 13 1 20 12 1 16 13 4 19
26 11 20 22 18 11 11 17 5 7 6 15 5 16 19 17 5 18 11 21 33 22 11 35
2329 690 369 2953 14,089 88 1000 457 2195 8486 2622 2303 359 1474 42,566 1278 4690 2384 4316 3136 6552 2309 11,205 42
9 13 5 11 7 12 6 5 10 6 13 4 7 13 8 11 10 5 7 27 9 8 12 8
5895 6938 10,469 6857 10,220 13,042 10,220 10,469 15,554 15,554 7003 10,469 9218 6857 5496 6857 6165 7545 5558 6938 10,469 5896 8578 10,957
Data source: http://www.census.gov.
87 83 87 83 90 80 86 87 80 83 84 86 85 81 87 86 85 87 91 69 86 86 85 86
4 4 8 6 3 8 8 9 10 11 4 10 8 5 5 4 5 8 2 4 5 6 3 6
51,524 46,283 97,533 60,499 49,475 46,964 113,799 105,060 82,904 94,824 35,468 107,596 90,552 42,602 60,979 43,073 42,114 122,487 48,758 34,375 48,873 49,091 37,406 50,294
85.6 90 97 91 96 86 96 97 99 98 88 97 95 91 81 91 81 97 88 90 97 92 76 95
21.5 20 24 23 23 24 23 24 25 25 23 24 24 23 23 23 22 24 21 20 24 24 23 24
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Table 3 Housing featuresa Place name
New housing units (per 100 persons)
Bartlett village Crete village Deer Park village Frankfort village Hoffman Estates village Indian Creek village Inverness village Kildeer village Lake Bluff village Lake Forest city Lockport city Long Grove village Mettawa village Mokena village Naperville city New Lenox village Romeoville village South Barrington village Streamwood village University Park Village Vernon Hills village Warrenville city West Chicago city Willowbrook village a
1970–1979
1980–1990
26 11 20 22 18 11 11 17 5 7 6 15 5 16 19 17 5 18 11 21 33 22 11 35
12 8 17 8 9 5 16 12 7 6 3 18 15 10 20 13 1 20 12 1 16 13 4 19
Median rent ($)
Owner cost ($)/month
Median unit value ($)
Zoning minimum lot size (ft2)
607 453 1001 556 693 917 1001 813 666 762 472 400 575 527 698 516 763 1001 891 523 713 735 559 743
1422 1330 2271 1541 1321 1359 2388 2402 2092 2402 1071 2402 2402 1226 1706 1145 958 2402 1186 1097 1546 1309 1168 1653
130,700 107,500 319,000 162,800 133,100 195,800 369,600 380,000 286,000 500,001 85,800 420,100 500,001 116,500 176,200 109,700 73,800 500,001 107,000 60,700 141,200 116,100 94,100 184,900
6000 9000 43,560 20,000 14,687 40,000 43,560 43,560 7500 F 9000 87,120 217,800 40,000 30,000 12,500 20,000 108,900 13,600 13,500 80,000 40,000 20,000 21,780
Data source: http://www.census.gov.
Table 4 Spatial distribution of the 24 municipalities Subgroup
Municipality name
North (5)
Lake Bluff, Lake Forest, Mettawa, Vernon Hill, Indian Creek Long Grove, Kildeer, Deer Park, Invermess, Sourth Barrington, Hoffman estates Streamwood, Bartlett, West Chicago Warrenville, Naperville, Romeoville, Lockport, New Lenox, Mokena, Frankfort University Park, Crete, Willowbrook
Northwest (6) West (3) Southwest (7) South (3)
respectively) for single family houses. This explains why property values there are so high (median value $500,001 in Mettawa and South Barrington, $420,100 in Long Grove). In the City of Chicago, an average single family house on a typical lot of 125 by 25 ft has a lot size of only 3125 ft2. The large lot size and high property values of houses in these suburban municipalities attract affluent buyers who are a prime target of the real estate market. We finally selected 17 independent variables of three groups for simple regression analysis: four transportation related, nine demographic and economic, and four property related indicators. The results are shown in Table 6.
Table 5 Minimum lot size requirements for single family house in six communitiesa Municipality name
Miniumum lot size required for R1 (ft2)
Bartlett Crete Deer Park Frankfort Hoffman Estate Indian Creek Inverness Kildeer Lake Bluff Lake Forest Lockport Long Grove Mettawa Mokena Naperville New Lenox Romeoville South Barrington Streamwood University Park Vernon Hills Warrenville West Chicago Willowbrook
6000 9000 43,560 (1 acre) 20,000 43,560 (1 acre) 40,000 43,560 (1 acre) 43,560 (1 acre) 12,000 No requirement 9000 130,680 (3 acre) 217,800 (5 acre) 40,000 30,000 12,500 20,000 108,900 (2.5 acre) 20,000 13,500 80,000 40,000 20,000 21,780 (0.5 acre)
a
Source: Zoning ordinance of various municipalities.
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T. Zhang / Land Use Policy 18 (2001) 221–232 Table 6 Regression analysis (simple-24 municipalities) Community features
Independent variable
Transportation related
% of public transit users Number of highways Travel time to CBD % of private car users
Demographic and economic related
Property related
R2
Beta
Std. error
t test
Sig. level
0.014000 0.015000 0.011000 0.015000
0.185488 0.442669 @0.057227 @0.122102
0.337208 0.773499 0.118172 0.212026
0.550070 0.572293 @0.484273 @0.575881
0.587813 0.572926 0.632978 0.570541
Percent of white % of 19–50 year old % with college degree % with graduate degree Median household income % of working in government % of working in private sector High school graduation rate Cost per high school student
@0.118000 0.087000 0.508000 0.154000 0.283000 0.386000 0.309000 0.159000 0.001000
0.127903 0.502959 0.472195 0.298145 0.000111 @0.811857 0.769246 0.380114 @0.000071
0.074381 0.347954 0.099014 0.149226 0.000038 0.206371 0.245430 0.186559 0.000429
1.719571 1.445474 4.768973 1.997941 2.945262 @3.933975 3.134275 2.037498 @0.165681
0.099550 0.162420 0.000092 0.058234 0.007484 0.000708 0.004822 0.053805 0.869921
Lot size Median rent Owner’s monthly cost Median housing unit value
@0.198000 0.096000 0.308000 0.205000
0.000056 0.010207 0.006187 0.000018
0.000025 0.006661 0.001977 0.000008
2.276141 1.523246 3.128957 2.384241
0.033428 0.139692 0.004883 0.026167
The main findings from the analysis are: 1. All four transportation related variables show very weak relationships to new housing development. None is statistically significant. 2. Economic status, including the number of people working in the private sector (relatively higher salary than those in the public sector) and household income, has positive impacts on new residential development. Both indicators are statistically significant at the 0.01 level. On the other hand, the variable ‘percentage of people working in the public sector’ (with relatively lower income) has a strong negative relationship to new housing construction, as expected. 3. Also as expected, ‘Percentage of residents with college degrees’ has a very strong positive relationship with new housing development. The same is true for ‘percentage of residents with graduate degrees’, although the relationship is slightly weaker. 4. Other economic indicators are not statistically significant to new housing development. 5. Population composition (measured by ‘percentage of white people’) does not show much significance statistically. Since in only one municipality (University Park) among the 24 communities are white people the minority, this finding has limitations and the relationship was re-examined in the 61-sample model. As expected, the result of the 61-sample model shows a strong relationship between the ethnic composition of the population and new developments. Communities with white concentration seem to attract more new developments (see Table 7).
6. The quality of local schools (measured by high school graduation rate) is statistically significant to new housing construction. However, ‘cost per high school student’ has almost no relationship to new housing construction. (Local school quality was not used in the 61-sample model due to lack of available data.) 7. Property value (measured by housing unit value and owner’s monthly cost) has a positive relationship to housing construction, and both variables are statistically significant at the 0.05 level. Median housing rent, however, does not show much significance statistically on new housing construction. This is probably because the home ownership rate in the Chicago region is high (65.8% in 1996) and renters have less impact on the housing market, especially in suburbs. 8. Minimum lot size for a single family house has a strong relationship to new housing development. This supports the suggestion that regulation requiring larger lots with higher prices may stimulate the marketplace to produce more homes. Based on the test of possible multicollinearity among independent variables, a multivariate regression model with nine independent variables was constructed (Table 8 Regression analysis: multi-24 municipalities). With a considerable statistical significant level (an R2 of 0.62), the model shows that none of any individual variables is statistically significant to new housing development. Only two housing-related variables (owner’s monthly cost and lot size) show some relationship, but not strongly. Transportation-related factors (travel time to CBD, public transit accessibility and number of
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Table 7 Regression analysis (simple-61 municipalities) Community features
Independent variable
R2
Beta
Std. error
t test
Sig. level
Transportation related
% of public transit users Number of highways in the area Travel time to work % of private car users
0.047 0.001 0.162 0.074
0.78314 @0.10144 0.51045 0.30593
5.509 5.336 5.169 5.433
1.715 @0.174 3.371 2.166
0.092 0.862 0.001 0.034
Demographic and economic related
Percent of white Percent of black Median age of residents % with college degree or higher Median household income % of working in private sector % of working in government
0.122 0.113 0.090 0.353 0.316 0.146 0.128
0.14875 @0.15962 0.47710 0.31691 0.00015 0.38421 @1.13022
5.289 5.315 5.386 4.541 4.670 5.216 5.270
2.865 @2.746 2.410 5.671 5.216 3.176 @2.947
0.006 0.008 0.019 0.0001 0.0001 0.002 0.005
Property related
Lot size Median rent % of renters % of owners Housing unit value Median age of housing stock
0.072 0.153 0.084 0.117 0.252 0.643
0.00004 0.01538 @0.28646 0.19501 0.00003 0.51130
5.590 5.195 5.403 5.305 4.881 3.375
1.894 3.266 @2.323 2.792 4.463 10.299
0.065 0.002 0.024 0.007 0.0001 0.0001
Table 8 Regression analysis (multi-24 municipalities) Independent variable
R2
Model Median household income % with graduate degree Public transit Travel time to CBD Number of highway Lot size Housing unit value Median rent Owner’s monthly cost
0.621
Std. error
Beta
t test
Sig. level
@1.13184E-05 @0.467262703 0.259713622 @0.047822122 0.920076454 5.62968 E-05 @3.96643E-05 0.006759381 0.020403622
0.000228893 0.506897269 0.50634256 0.125398036 0.847336527 5.08553E-05 4.50065E-05 0.007037524 0.014446692
@0.04944814 @0.92180947 0.51287547 @0.38136261 1.085845381 1.107000987 @0.88130125 0.960477155 1.412338652
0.961313822 0.373419243 0.616639762 0.709094346 0.297272637 0.288357032 0.394157335 0.354339916 0.181339516
highway in the community) have very weak impact, although ‘number of highways’ displays some statistical significance. These results support the hypothesis: transportation accessibility is less influential than other local factors, especially the housing stock and land use regulations, in new residential development decisions. The 61-sample models confirm the basic findings of the 24-sample models (Tables 7 and 9). Socio-economic factors such as ethnic composition (percentage of white), residents’ income and education level, together with the median age of the population body all have a strong relationship to new housing development. A community with more white residents and higher income people may attract more developments. Supporting the findings of the 24- and the 61-sample models reveal again none of the transportation-related factors to be significant statistically. The housing stock, on the other hand, represented by the median age of houses and percentage of owners in a community, is strongly related to new
housing development. The finding that communities having an older housing stock attract fewer new developments (a negative relation) explains the phenomenon of fewer new houses built in older municipalities, either due to less buildable land for new development, or because housing buyers prefer new suburban communities to old urban neighborhoods. In contrast to simple regression models, lot size shows only limited influence on new housing in the multiple regression models.
Discussion In general, findings from this investigation indicate that new housing developments in the Chicago region are strongly affected by local factors representing community features. When the regional factor (location) is held fixed, socio-economic characteristics such as household income, percentage of residents with higher
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T. Zhang / Land Use Policy 18 (2001) 221–232 Table 9 Independent variable
R2
(a) Regression analysis (multi-61 municipalities) Model 0.8752 Median age of residents Median household income % of white % with college degree or higher Travel time to work % use public transit Number of highway in the area Median age of housing stock Housing unit value Median rent Lot size % of owner (b) Regression analysis (multi-61 municipalities) Model 0.631 % with college degree or higher Median age of residents % of white Median household income
Std. Error
Beta
t test
Sig. level
@0.302 0.0001 0.080 0.205 @0.245 0.086 @0.504 0.516 0.000 @0.004 0.0001 0.157
@1.274 @0.124 2.313 2.261 @1.491 0.203 @1.714 5.957 0.0001 @0.873 @1.174 2.067
0.213 0.902 0.028 0.032 0.147 0.841 0.098 0.0001 1.000 0.390 0.250 0.048
0.2955 @0.9943 0.1547 0.0001
3.005 @4.088 3.519 2.896
0.005 0.0001 0.001 0.006
2.31667
3.513
education and relatively higher income, and ethnic composition of residents show considerable impacts on new housing construction. Local school quality is related to new construction. The housing stock represented by property values, and age of the housing stock is another factor influencing the pace of new developments. Land use regulations, especially the minimum lot size requirement for single family houses, exhibits some relationship to new housing. Transportation accessibility, on the other hand, has quite limited impacts in the Chicago case. This is supported by the fact that the number of accessible highways shows low significance statistically. Moreover, the relationship is negative, which indicates that highly accessible transportation may actually discourage rather than encourage new developments. The research found little evidence to support the assertion that highway construction, mostly funded by public money, is a direct contributor to new housing developments in fringe areas. This conclusion has received support from new research on the Chicago region conducted by the Urban Transportation Center (UTC) of the University of Illinois at Chicago. By using a sophisticated gravity model (which may serve better in a multi-centered metro area), UTC’s study shows that travel time, a measure of transportation accessibility, does not have much relationship to land use patterns. Instead, changes of population, household size, homeownership rate and residential lot size have considerable influence on the consumption of land by new development (UTC, 2000). All of these findings paint a picture of new residential developments in the Chicago region from 1970s to 1990s: the market, driven by higher demand and high
potential profits in certain suburban areas, has produced more housing in these areas. The market-favorite communities have common features: high property value, large residential lot size, fewer minorities, higher income residents and high quality schools. None of these features is new to researchers or even to average people in defining an ‘attractive community’. Most of those communities are located in the ex-urban areas because the land is available and relatively less expensive, and the so-called ‘urban diseases’ have not yet occurred there. These are the communities where there is a heavy concentration of America’s middle class. Middle class communities are not only the main targets of the market place but are the targets of elected government officials, since it is the middle class that holds the critical voting power. Here, we may suggest a new application of the urban regime theory: the coalition of real estate interests and local government promotes new development in certain areas of the metropolitan region. This is proved by land use regulations (such as minimum lot size) in these market-favorite communities that reflect local government’s desire to work together with real estate groups. There are two approaches to solve the ‘sprawl problem’ if sprawl is judged too costly for the society. One strategy is to control growth at the edges of rapid suburban developments. Policies such as Smart Growth and the establishment of a regional government to oversee land use decisions intend to stop or at least slow down sprawl in fringe areas. While much has been suggested from this perspective, our findings point in another direction. If policymakers paid more attention to improve the quality of central cities and older close-in
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suburbs, then middle-class growth rates might increase, or at least remain unchanged in such communities, thereby reducing growth pressures on fringe areas. By keeping middle class in central cities, a more balanced development pattern across the whole metropolitan region could thus be achieved. The most important lesson learned from the study is that sprawl is not a direct consequence of spatial-related factors such as transportation accessibility and location; rather, it is rooted in the features of communities. When education systems, the housing stock, and overall community quality are improved, many more communities may have the opportunity to become attractive to the market place. While the federal government’s policy of public spending on highway construction does not show much impact on sprawl (at least in the Chicago region), we should urge policymakers to pay attention to much broader policy issues in efforts to control sprawl. Only when the gap between the quality of central city communities and that of their ex-urban neighbors is reduced, will unbalanced development pattern be changed and the sprawl problem finally solved.
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