Do inclusionary zoning policies equitably disperse affordable housing? A comparative spatial analysis

Abstract

This article examines the impact of inclusionary zoning (IZ) policies on the production and spatial distribution of low-income housing at the neighborhood level. Using an original, geo-coded property-specific database of more than 11,000 IZ units built between 1980 and 2000 in Montgomery County, Maryland, and Suffolk County, New York, this study provides the first evidence of the locational determinants of IZ unit production and spatial clustering by census tracts. Using a comparative analytic approach, the impact of institutional framework—more specifically, the difference between jurisdictions with regional versus local housing and land use authority—is examined in relation to the effectiveness of IZ programs in promoting an equitable dispersal of low-income housing units. This analysis provides evidence of spatial concentrations of IZ units built between 1980 and 2000, although the characteristics of neighborhoods in which clustering occurs differ between the two study areas.

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Notes

  1. 1.

    “Workforce” housing typically refers to housing for working individuals and families (e.g., police officers, nurses, firefighters, and teachers) whose incomes are between 80 and 120 % of area median income.

  2. 2.

    The introduction of IZ policies in New Jersey was the result of the Mount Laurel I and II rulings by the New Jersey Supreme Court in the mid-1970s and early 1980s. The court-imposed low-income housing “fair share” requirements initiated a state-wide mandate, but the local governments retained some control over how and when the requirements would be met. Despite the mandate, New Jersey continues to struggle to produce equitable housing options for low-income households (Wish and Eisdorfer 1997).

  3. 3.

    Data from the 1970 Decennial Census are included to provide certain lagging indicators in the regression models.

  4. 4.

    The original databases include all housing units built between 1971 and 2008, although this study is limited to the time period between 1980 and 2000.

  5. 5.

    For example, IZ units built in the 1970s in Montgomery County were only subject to 5-year price controls (Trombka et al. 2004).

  6. 6.

    The entropy index is given by:

    \(H_{i} = \varSigma \sum\limits_{m = 1}^{M} {Q_{im} /\ln (m)}\)

    where

    \(\begin{aligned} Q_{im} = & - \pi_{im } \ln (\pi_{im} )\quad {\text{if}}\;\pi_{im} > 0 \\ { =}\, & 0\quad {\text{otherwise}} \\ \pi_{im} { =}\, & {\text{the}}\;{\text{proportion}}\;{\text{of}}\;{\text{the}}\;{\text{population}}\;{\text{of}}\;{\text{tract}}\;i\;{\text{consisting}}\;{\text{of}}\;{\text{persons}}\;{\text{in}}\;{\text{group}}\;m \\ M{ =}\, & {\text{number}}\;{\text{of}}\;{\text{groups(four}}\;{\text{for}}\;{\text{race,five}}\;{\text{for}}\;{\text{income)}} \\ \end{aligned}\)

    Consistent with Galster et al. (2008) and HUD income guidelines, five income categories are defined: very low income (households earning less than 50 % of AMI); low-income (households earning between 51 and 80 % of AMI); moderate-income (households earning between 81 and 100 % of AMI); high moderate Inc (households earning between 101 and 120 % of AMI); and high-income (households earning more than 121 % of AMI).

  7. 7.

    Another econometric approach is to use a Tobit model and to account for the relatively large number of neighborhoods in the sample with no IZ units built during the study period. However, Sigelman and Zeng (1999) shows that Tobit models are not appropriate for data when censoring has not occurred. Since there cannot be a negative number of IZ units, while there is clustering at zero, the data are not censored at zero. In this case, they state, a two-step approach that first looks at the yes/no binary decision and then secondarily looks at estimates of the actual number of units produced. This is also the empirical approach used by Larson (2004). As a robustness check, the regression was run using a Tobit model and resulted in similar estimates to those presented in Table 6.

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Kontokosta, C.E. Do inclusionary zoning policies equitably disperse affordable housing? A comparative spatial analysis. J Hous and the Built Environ 30, 569–590 (2015). https://doi.org/10.1007/s10901-014-9430-5

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Keywords

  • Spatial analytics
  • Low-income housing
  • Affordable housing
  • Inclusionary zoning
  • Social equity