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Exploring variations in local land use regulations in the U.S.: What matters and at what level?

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Abstract

While the geography of land use control is shaped by various forces operating at multiple levels, how land use is regulated in the U.S. has been examined with a narrow focus on either intraregional variation (within a single region) or aggregate differences between regions. This article presents an investigation of the geographical distribution of land use control with explicit consideration of both local and regional factors. Using data from two nation-wide surveys and other sources of information, it shows that substantial variation exists not only between regions but also within regions, calling for more attention to what matters and at what level. It is also found that the multilevel determinants of land use regulations are not uniform across regulation types. While low-density zoning is largely determined by local factors with limited interregional variation, a higher level of heterogeneity between regions is detected for impact fees and affordable housing requirements.

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Notes

  1. There are other notable studies shedding light on the rise of local land use regulations by extending Tiebout’s (1956) work, such as Hamilton (1975) and Henderson (1980). See also Fennell (2006) for how local land use regulations can be better understood in light of Tiebout’s perspective. As the author noted, “[t]he idea that citizens shop for governmental bundles of services and amenities” is essential for understanding the nature of land use control, especially exclusionary measures (p. 163, Fennell 2006).

  2. Been et al. (2014), for instance, juxtaposed these two perspectives and analyzed the patterns of rezoning projects in New York City (and other lots considered by the city planning commission for rezoning between 2002 and 2009) in light of the competing views. The authors reported “a surprising level of empirical support for the homevoter-based theory, even though New York City is probably the last place in the U.S. that one would expect to see zoning policy catering to the interests of homeowners, rather than the growth machine.” (p. 229, Been et al. 2014). Focusing on another large city, Los Angeles, Gabbe (2018) came up with a similar finding that upzoning was less likely to take place in neighborhoods with a larger share of homeowners. In other words, the homevoter hypothesis gained much ground, although these studies provided empirical support for the hypothesis not through comparison of localities but through comparison of neighborhoods.

  3. Some recent studies in the policy diffusion literature have suggested that “the classic view of policy diffusion as geographic clustering is often overly limiting, sometimes misleading (or even wrong), and increasingly outdated.” (p. 789, Shipan and Volden 2012). However, a considerable number of empirical studies have provided evidence that policy diffusion or “policy band-wagoning” tends to take place in a spatially explicit manner (see, e.g., Rincke 2007; Mitchell 2018).

  4. The NLLUS provides rich information about land use planning practices of local governments in the top 50 most populous metropolitan regions in the U.S. Among three versions of the NLLUS provided in 1994, 2003, and 2019, this study utilizes the 2019 dataset which contains the survey responses from 3142 jurisdictions. The WRLURI measures the varying degrees of regulatory restrictiveness through 11 sub-indices that represent different dimensions of the local regulatory environment (Gyourko et al. 2019). Its latest version provides data for a total of 2844 communities across the nation.

  5. As discussed in Sect. 2, the literature suggests that local land use regulations can be influenced by a range of internal and external forces, and the explanatory variables were chosen to capture these potential determinants. More specifically, as done in previous studies, such as Meltzer and Schuetz (2010), Christafore and Leguizamon (2015), and Pendall et al. (2018), this study included local demographic composition variables, along with the percentage of owner-occupied housing units that reflects Fischel’s (2001) homevoter hypothesis. Consideration was also given to the potential influence of the creative class given recent studies suggesting that they are likely to value and promote sustainability through more compact development (see, e.g., Natekal 2018). Some other variables used in previous studies, such as median income (logged), percentage of Hispanic population, and poverty rates, were not included, as they showed a strong correlation with one or more of the six variables used. For instance, in the two samples used in this study, poverty rates were highly negatively correlated with the percentage of white (− 0.68 in Sample #1 and − 0.62 in Sample #2) and the percentage of owner-occupied housing units (− 0.69 in Sample #1 and − 0.70 in Sample #2). See “Appendix A” for more details.

  6. The frequency differs in part because the two samples have different compositions of municipalities as shown in Table 3. The frequency gap is also attributable to the different survey instruments used.

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Acknowledgements

Earlier versions of this paper were presented at the 61st annual conference of the Association of Collegiate Schools of Planning and at the 61st annual meeting of the Western Regional Science Association.

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Authors and Affiliations

  1. Department of Urban Planning and Public Policy, University of California, Irvine, 206E Social Ecology I, Irvine, CA, 92697, USA

    Jae Hong Kim

  2. Department of Urban Design and Planning, Hongik University, Seoul, South Korea

    Jongho Won

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Correspondence to Jae Hong Kim.

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Appendix A: Correlation Tables

Appendix A: Correlation Tables

 

Percent

Percent

Percent

Percent

Percent

Local

Percent

Poverty

Percent.Highly

Median

White

Black

Owner

Creative

Under18

Pop.Size

Hispanic

Rate

Educated

Income

     

(logged)

  

(logged)

Sample #1 (N = 476)

Percent.White

1

         

Percent.Black

− 0.57

1

        

Percent.Owner

0.53

− 0.30

1

       

Percent.Creative

0.16

− 0.02

0.07

1

      

Percent.Under18

− 0.11

− 0.04

0.32

− 0.42

1

     

Local.Pop.Size (logged)

− 0.44

0.17

− 0.36

0.09

− 0.04

1

    

Percent.Hispanic

− 0.75

0.01

− 0.39

− 0.30

0.30

0.33

1

   

Poverty.Rate

− 0.68

0.42

− 0.69

− 0.25

0.07

0.34

0.60

1

  

Percent.Highly.Educated

0.28

− 0.19

0.22

0.79

− 0.32

0.05

− 0.37

− 0.50

1

 

Median.Income (logged)

0.37

− 0.36

0.61

0.46

0.04

− 0.12

− 0.32

− 0.75

0.78

1

 

Percent

Percent

Percent

Percent

Percent

Local

Percent

Poverty

Percent.Highly

Median

White

Black

Owner

Creative

Under18

Pop.Size

Hispanic

Rate

Educated

Income

     

(logged)

  

(logged)

Sample #2 (N = 494)

Percent.White

1

         

Percent.Black

− 0.55

1

        

Percent.Owner

0.47

− 0.25

1

       

Percent.Creative

0.21

− 0.01

0.23

1

      

Percent.Under18

− 0.18

− 0.03

0.25

− 0.29

1

     

Local.Pop.Size (logged)

− 0.44

0.07

− 0.28

− 0.01

0.04

1

    

Percent.Hispanic

− 0.77

− 0.02

− 0.39

− 0.32

0.32

0.39

1

   

Poverty.Rate

− 0.62

0.36

− 0.70

− 0.30

0.06

0.26

0.55

1

  

Percent.Highly.Educated

0.31

− 0.18

0.39

0.79

− 0.18

0.00

− 0.36

− 0.55

1

 

Median.Income (logged)

0.32

− 0.28

0.70

0.46

0.15

− 0.08

− 0.28

− 0.76

0.79

1

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Kim, J.H., Won, J. Exploring variations in local land use regulations in the U.S.: What matters and at what level?. Ann Reg Sci (2024). https://doi.org/10.1007/s00168-024-01270-5

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