This study explores the nature of the relationship between the number of state-regulated mobile homes and per capita income, so as to determine whether higher-income parts of Illinois have more mobile homes than would be predicted by a recent BBC News article. It does so by identifying a simple way to determine the direction and strength of any relationship between mobile homes and per capita income, which that article assumes to be negative, if only at the county level in Illinois. The study, specifically, collects and analyzes mobile home data from Illinois and per capita income data from the U.S. Census. After combining these data, then using correlation coefficients, it finds a positive relationship—albeit with different intensities, based on whether the proxy for mobile homes in each county is the number of mobile home spaces or the number of mobile home parks in 2017.
Do lower-income parts of the U.S. have more mobile homes than higher-income ones? One might think so, based on a 2013 report from BBC News. This report found that “comparing the top 10 mobile home states with the 10 most deprived states suggests a loose correlation,” although it does not provide a way of substantiating such a claim at the sub-state level. As a result, more research is needed to find out if and how mobile homes correlate with per capita income: especially as affordable housing has become an even more pressing issue in the wake of COVID-19.
My study carries out some of this follow-up work by creating, and later analyzing, a new State of Illinois dataset. It does so by drawing upon information about the 853 licensed mobile home parks and 53,291 licensed mobile home spaces that were hosted by 94 of Illinois’s 102 counties. This study, then, matches mobile home data from Fiscal Year 2017 with relevant U.S. Census information. It concludes by determining whether and how the number of mobile homes in each county correlates with the per capita incomes in that area.
Such a study may yield valuable insights into where mobile homes are located at the county-level and, potentially, into why Illinois residents choose this type of affordable housing over other competing alternatives. The study could prove to be even more useful if it yields valid and reproducible results. A simple way to achieve such results may be to use correlation coefficients.
My study, therefore, is justified in using correlation coefficients. By definition, correlation coefficients are a simplified statistical approach that determines the direction and strength of the relationship between variables. The simplified approach works by showing whether and how the number of licensed mobile homes and per capita income are related at the county level in Illinois: at least for 94 of the 102 Illinois counties during Fiscal Year 2017.
Within this context, the study proceeds in four additional parts (Parts I-III). Part I contains this study’s positive analysis. Part II describes its methodological approach. Part III contains this study’s normative analysis, which builds upon a finding that mobile homes may be positively and relatively weakly correlated with per capita income in Illinois counties. Part V is this study’s conclusion, which summarizes its key findings and recommendations.
Mobile homes, which are a historically overlooked form of U.S. housing tenure, have become an increasingly popular way for governments to provide affordable housing. In recent years, mobile homes have been used by federal, state, and local governments to offset the unintended consequences of economic development. Non-governmental actors, including legal scholars and some newspaper reporters, nonetheless assume mobile homes have a negative relationship to economic development measures. This commonly-held assumption, despite not being put to the test in detailed case studies, has been accepted as true by many U.S. statutory, administrative, and decisional laws.
For example, the Illinois General Assembly enacted its original Mobile Home Park Act, the state’s primary means of conveying indirect affordable housing subsidies to certain low-income areas. Other sources of law, such as Section 860.400 of the Manufactured Home Community Code, were added later to provide additional benefits and protections. When viewed as a whole, this body of law is intended to create and protect this frequently-overlooked form of affordable housing, mostly by limiting informational failures through mandatory legal disclosures, especially in less economically-dynamic parts of Illinois.
Unfortunately, few scholars and reporters bother to examine whether this state-level regulation actually lives up to its promise. Instead, one category of scholarship looks at the lived experience of mobile home residents. Other works examine the total cost of acquiring and maintaining a mobile home. A third category of scholarship describes the function of mobile homes in the U.S.
As a result, the existing literature on mobile homes still does not answer a basic question: How does the number of mobile homes correlate with traditional measures of economic development such as per capita income? This question is important because it avoids the difficult issue of causation, especially if it is narrowly-framed in terms of a single measure, while yielding insight into the nature of any relationship between the number of mobile homes in an area and local economic dynamism. My study thus asks and answers this open research question, at least for 94 of the 102 Illinois counties during Fiscal Year 2017.
This study does so by creating and using a new Illinois dataset, which makes three contributions to the mobile home literature. First, it identifies every licensed mobile home in Illinois and matches each with the per capita income of its host jurisdiction (i.e., county area). Second, this study uses these data to determine which Illinois county areas had the most licensed mobile homes and highest per capita incomes. Lastly, it analyzes these data using correlation coefficients, to find out if Illinois county areas with higher per capita incomes also had larger numbers of licensed mobile homes in Fiscal Year 2017.
This study creates and analyzes a new Illinois dataset, in order to determine how the number of state-licensed mobile homes correlate with per capita incomes at the county level. All mobile home data were collected by the State of Illinois, and sub-state entities such as Cook County and the City of Chicago, whereas the per capita income data comes from the U.S. Census’s American Community Survey. Both datasets were later subjected to validity checks.
These two data sources, subsequently, were combined and used to compute group-level averages by county location. These group-level averages are used later to create baselines for the full population of Illinois counties. These baselines, in turn, are used to determine whether each of the sample has been drawn from the same population and may fall along a normal distribution.
This study later uses these data to make a series of findings based upon its interpretation of correlation coefficients. Correlation coefficients are used for at least three practical reasons. First, this approach provides a way to examine the relationship between the number of licensed mobile homes and per capita incomes, assuming that these two variables are useful proxies. Second, the use of correlation coefficients allows for meaningful comparisons to be made between counties of different sizes. Lastly, it complements previous studies by finding out whether and how mobile homes correlate with per capita income in a single U.S. state (Illinois), using standard units (counties) and in a recent year (2017).
I note, however, that this study’s findings will not be accurate if it fails to account for selection effects, omitted variables and other methodological issues. This study directly addresses each issue. It accounts for selection effects by testing only those Illinois counties with a state-regulated mobile home parking space, as these geographic areas are a part of the same Illinois economy and experience similar economic conditions over time. The study also deals with omitted variables by testing these counties, individually and in groups, in order to determine if there are real differences between similarly-situated areas. Other issues, like reverse causation, are limited by using correlation coefficients to carry out this very modest study of how mobile homes correlate with per capita income in 94 of the 102 counties in Illinois.
These methodological safeguards, if properly employed in this study, help to overcome a range of theoretical and practical issues. The study, thus, asks and answers a single research question: how does the number of licensed mobile homes correlate with per capita income? I answer this question by finding out, at least during Fiscal Year 2017, whether the Illinois counties with the highest per capita incomes also had the highest numbers of licensed mobile homes.
To summarize, this study draws on mobile home data and per capita income information from the most recent American Community Survey. These sources of information are then combined and analyzed to determine the nature of any relationship between the number of licensed mobile homes and per capita income. The expectation, at least based on the findings from a 2013 BBC News article, is that mobile homes are negatively correlated with per capita income.
The use of correlation coefficients requires this study to rank each county by number of licensed mobile home spaces and per capita income. Higher numbers are equated with higher rankings, whereas lower numbers are equated with lower rankings. In cases where the starting point is zero (0) licensed mobile home spaces, then that county will be excluded from the population.
As a result, this study is able to explain the relationship between the number of licensed mobile home spaces and per capita incomes. Generally speaking, a zero (0) result implies that there is no relationship between two variables, 0.01 to 0.29 (or -0.01 to -0.29) is a weak relationship, 0.30 to 0.49 (-0.30 to -0.49) is a weak-to-moderate relationship, 0.50 to 0.69 (or -0.50 to -0.69) is a moderate-to-strong relationship, 0.70 to 0.99 (or -0.70 to -0.99) is a strong relationship and 1 (or -1) is a perfectly-linear relationship. This scale may be used to find out if and how mobile home spaces correlate with per capita income, at least in 94 of 102 Illinois counties during the study period (i.e., during Fiscal Year 2017), which is important because the lack of viable affordable housing options across state space has become an even more pressing issue in the wake of COVID-19.
The initial results of this analysis are as follows, at least in Fiscal Year 2017. There is a positive and weak-to-moderate relationship between the number of licensed mobile home spaces and per capita income (.35). Since this result is likely to be statistically significant, due to its probability values, it may support a claim that these variables could move together in Fiscal Year 2017. This study thus concludes that recent BBC News findings about how mobile homes correlate with per capita income may not be true (i.e., these variables are not negatively related, as is also assumed by many non-governmental actors).
This result does not mean that the number of licensed mobile home spaces are always positively and weakly-to-moderately correlated with per capita incomes. Rather, it merely indicates that scholars need to do additional work. One approach may consist of finding out if the number of licensed mobile homes correlate with per capital income when using different proxies. A second option involves determining if these two variables are correlated in other geographic areas. A third approach involves finding out if any other empirical approaches yield similar results. In any event, more research is needed to determine the direction and strength of any relationship between these variables in Illinois.
In employing the first option, this study seeks to further interrogate its sole research question. Specifically, the goal is to further establish whether and how mobile homes may correlate with per capita income by substituting the “licensed mobile homes” proxy variable with the “licensed mobile home parks” proxy variable. The study period (Fiscal Year 2017) and unit of analysis (94 counties with a mobile home and/or mobile home park) are the same for the initial and the secondary analysis (i.e., for mobile homes and for mobile home parks).
Within this context, my follow-up research yields a weaker but similarly-positive result (.21). As this result is likely to be statistically significant, due to its probability values, it also supports the claim that these variables may move in the same direction (albeit with different levels of intensity based upon the proxy used). Thus, the study concludes that recent BBC News findings may not be true for either proxy during the study period (i.e., during Fiscal Year 2017).
These initial and secondary results indicate that higher-income counties may have higher numbers of mobile homes, and higher numbers of mobile home parks, than would be predicted. If these findings prove to be true, then contemporary views of where mobile homes are located may not hold up under scrutiny. Thus, more research will be needed on Illinois and other U.S. states.
Other normative implications of such a finding are equally straightforward. For example, Illinois should determine why some residents of higher-income counties choose to live in mobile homes instead of other types of affordable housing such as limited equity cooperatives. Illinois should also try to identify the key reasons why higher-income counties have a higher number of mobile homes than would have been predicted. Lastly, Illinois should find out whether non-economic considerations created a substitution effect with respect to affordable housing and if it has a disparate impact by race or ethnicity. Among the state-level policies that could explain such impacts are the salience of mobile homes as an affordable housing option, the preferential tax treatment that mobile homes historically received and the current ban on local rent control.
This study looks at the nature of the relationship between the number of state-regulated mobile homes and per capita income, so as to determine whether higher-income parts of Illinois have more mobile homes than would be predicted by a recent BBC News article. It does so by identifying a simple way to determine the direction and strength of the relationship between mobile homes and per capita income, correlation coefficients, which my study uses to show that this relationship is positive and relatively weak in 2017. However, more research is needed to find out precisely how mobile homes correlate with per capita income.
#_ftnref1 . See Tom Geoghegan, Why Do So Many Americans Live In Mobile Homes?, BBC News, West Virginia (Sep. 24, 2013), https://www.bbc.com/news/magazine-24135022 [https://perma.cc/A8MB-MDPK].
#_ftnref2 . Geoghegan, supra note 1.
#_ftnref3 . See infra Appendix at Tables 4 and 5; See generally Jill Watts, As Coronavirus Magnifies America’s Housing Crisis, FDR’s New Deal Could Offer a Roadmap Forward, Time Magazine (Apr. 24, 2020), https://time.com/5826392/coronavirus-housing-history/?utm_source=newsletter&utm_medium=email&utm_campaign=history&utm_content=20200501&xid=newsletter-history (The national emergency and consequent economic crisis triggered by COVID-19 has exposed one of America’s greatest needs: adequate and safe housing.”).
#_ftnref4 . Counties are an appropriate unit of analysis because this local level of government is present in every U.S. state. See, e.g., David Kenney & Barbara L. Brown, Basic Illinois Government: A Systematic Explanation 143-45 (3rd ed. 1993). As such, counties are especially useful for fully examining the relationship between mobile homes and per capita income within and among U.S. states.
#_ftnref5 . See State of Illinois, Department of Public Health, Response To Freedom Of Information Act Request (Jan. 10, 2019) (“This email is in response to your recent Freedom of Information Act request for location information and number of spaces available for every licensed manufacturing home community.”); See County of Cook, Department of Public Health, Response To Freedom Of Information Act Request (Feb. 27, 2019) (“Please see attached letter in response to your FOIA request.”); See David Lee Matthews, Plan Would Almost Triple Units In City’s Only Mobile-Home Park, Crain’s Chicago Business, (Apr. 9, 2014), https://www.chicagobusiness.com/article/20140409/CRED03/140409750/marc-realty-wants-to-triple-units-at-chicago-s-only-mobile-home-park [https://perma.cc/V8HF-KRXR] (“Marc Realty LLC seeks a zoning change that would allow it to nearly triple the number of units at the City’s only mobile home-park . . . to 747 units, from its current 190, according to its zoning application with the city.”); See United States Census Bureau, B19013 Median Household Income In The Past 12 Months (In 2017 Inflation-Adjusted Dollars) [https://perma.cc/5DE4-ENY4]; See 2017 American Community Survey 5-Year Estimates, American Community Survey (Dec. 6, 2018) [https://perma.cc/7UFJ-YWW5]; See United States Census Bureau, 2017 Data Release: New And Notable, https://www.census.gov/programs-surveys/acs/news/data-releases/2017/release.html.
#_ftnref6 . Cf. Randall K. Johnson, Uniform Enforcement or Personalized Law? A Preliminary Analysis of Parking Ticket Appeals in Chicago, 93 Ind. L.J. Supp. 34, 43 (2018) (“Although this Article does not try to establish if any observed differences are statistically-significant, which is a valid way of determining how much confidence may be placed in a given research finding, it could serve as a point of departure for future work that does so using regression.”).
#_ftnref7 . Id.
#_ftnref8 . Some of my past work with simplified approaches has inspired follow-up research, which uses regression analysis to look at the residential property tax appeals process in the City of Chicago and Cook County as a whole. Compare Randall K. Johnson, Who Wins Residential Property Tax Appeals?, 6 Colum. J. Of Tax L. 209 (2015) (applying percentage analysis to determine who wins residential property tax appeals in Cook County) with Robert Ross, The Impact Of Property Tax Appeals On Vertical Equity In Cook County, IL, U. Chi. Harris Pub. Pol’y (Unpublished Manuscript, 2017), http://apps.chicagotribune.com/news/watchdog/cook-countyproperty-tax-divide/data/harris-study.pdf [https://perma.cc/U8W6-RH7Y] (applying regression analysis to determine who wins residential property tax appeals in Cook County). This follow-up work has garnered attention from local, state, national, and international publications, and substantiated my preliminary research findings about residential property tax appeals in Cook County. See, e.g., Jason Grotto & Sandhya Kambhampati, The Tax Divide: Commercial Breakdown, Chicago Tribune, Dec. 7, 2017, http://apps.chicagotribune.com/news/watchdog/cook-county-property-tax-divide/index.html [https://perma.cc/UL8M-FW2W] (“Owners of residential properties, as a group, also ended up paying more in property taxes than they would have if the assessor’s office had done its work properly. The total amount of property taxes levied in a given year is fixed, so if one group of property owners doesn’t pay its fair share, others have to make up the difference.”). My past work on parking tickets, and parking ticket appeals, has also inspired a host of follow-up research in Chicago that adopts my methodological approach. Compare Johnson, supranote 6 (using percentage analysis to find out how parking tickets and other related matters are distributed by zip code in Chicago) with Woodstock Institute, The Debt Spiral: How Chicago’s Vehicle Ticketing Practices Unfairly Burden Low-Income and Minority Communities (Unpublished Manuscript, Jun. 21, 2018), http://woodstockinst.org/wp-content/uploads/2018/06/The-Debt-Spiral-How-Chicagos-Vehicle-Ticketing-PracticesUnfairly-Burden-Low-Income-and-Minority-Communities-June-2018.pdf [https://perma.cc/28WW-RVPR] (using percentage analysis to examine parking tickets, and other related matters are distributed in Chicago). This other follow-up work has garnered attention from local, state, national, and international publications, and substantiated my preliminary research findings about parking tickets, parking ticket appeals, and win rates on appeal in Chicago. See, e.g., Melissa Sanchez & Sandhya Kambhampati, How Chicago Ticket Debt Sends Black Motorists into Bankruptcy, ProPublica Illinois (Feb. 27, 2018), https://features.propublica.org/driven-into-debt/Chicago-ticket-debt-bankruptcy/ [https://perma.cc/G8A8-TJ8N] (describing how excessive ticketing has especially severe consequences for disadvantaged groups in the State of Illinois).
#_ftnref9 . Correlation coefficients are “a statistical method of quantifying the association . . . between two variables.” Marcin Kozak, Wojtek Krzanowski & Malgorzata Tartanus, Use of Correlation Coefficient In Agricultural Sciences: Problems, Pitfalls And How To Deal With Them, 84 Annals Brazilian Acad. Sci. 1147 (2012). This study uses Microsoft Excel’s correlation function (CORREL) to create correlation coefficients that help to identify the nature of any relationship between mobile homes and per capita income in Illinois during Fiscal Year 2017. See, e.g., CORREL Function, Microsoft (last visited May 2, 2020) https://support.office.com/en-us/article/correl-function-995dcef7-0c0a-4bed-a3fb-239d7b68ca92 [https://perma.cc/2ZQE-YJ3N].
#_ftnref10 . Correlation coefficients also are used for three additional reasons. First, this simplified statistical approach is widely viewed as a valid way to establish the direction and strength of a relationship. Second, correlations are also easy to verify using Microsoft Excel. Lastly, this simplified statistical approach may lay a solid foundation for future research that looks at mobile homes and per capita income. Cf. Randall K. Johnson, How Tax Increment Financing (TIF) Districts Correlate with Taxable Properties, 34 N. Ill. U. L. Rev. 39, 41 n.19(2013).
#_ftnref11 . See Jeff Andrews, Can Manufactured Housing Ease America’s Affordable Housing Crisis?, Curbed (Mar. 2, 2018), https://www.curbed.com/2018/3/2/17058882/mobile-manufactured-homes-affordable-housing-crisis [https://perma.cc/3XXT-4GS3] (“[Fannie Mae] . . . will purchase around 30,000 manufactured housing mortgage loans over [the three years between December 2016 and 2019, it] . . . will also develop a pilot program for buying chattel loans and for supporting the financing of manufactured housing communities, whether owned by governments, nonprofits, or residents.”).
#_ftnref12 . See, e.g., Will Ferguson, Los Angeles County Supervisors OK “County Mobile Home Program” To Put Affordable Homes Within Reach Of The Homeless, ManufacturedHomes.com (May 17, 2019), https://www.manufacturedhomes.com/blog/los-angeles-county-mobile-home-program/ [https://perma.cc/9RRD-F8EW] (“The Los Angeles County Board of Supervisors has recently unanimously approved a motion to pursue a plan, calling it the County Mobile Home Program, that would utilize manufactured homes to address the affordable housing crisis.”).
#_ftnref13 . See, e.g., Geoghegan, supra note 1.
#_ftnref14 . See generally Manufactured & Modular Homes/Mobile Structures, Illinois Department of Public Health (IDPH) (2001), http://dph.illinois.gov/topics-services/environmental-health-protection/manufactured-modular-homes-mobile-structures [https://perma.cc/832J-QRPZ].
#_ftnref15 . See generally 210 ILCS 115; Public Act 77-1472.
#_ftnref16 . See generally 77 Ill. Admin. Code 860.400, Required Documents (requiring owners of mobile home communities to give new owners, and new renters, the following information: “a copy of the manufactured home community rules,” “a copy of the . . . publication ‘Living in a Manufactured Home Community,’” “[access to] . . . a copy of the Mobile Home Park Act and the Manufactured Home Community Code” and “the name address, and telephone number of the manufactured home community manager whom residents are to notify of a problem within the manufactured home community.”).
#_ftnref17 . See IDPH, supra note 14 (“To ensure quality living conditions for people who reside in manufactured home communities, IDPH licenses all parks with 5 or more sites (except those located in home rule units). Staff inspects each park annually for license renewal, at which time they check the water supply sewage disposal system, electrical system, lighting, road conditions, spacing of homes and garbage disposal.”).
#_ftnref18 . See, e.g., David Ray Papke, Keeping The Underclass In Its Place: Zoning, The Poor, And Residential Segregation, 41 Urb. Law. 787 (2009).
#_ftnref19 . See, e.g., Esther Sullivan, Dignity Takings and “Trailer Trash”: The Case of Mobile Home Park Mass Evictions, 92 Chi-Kent. L. Rev. 937 (2017).
#_ftnref20 . See, e.g., Anika Singh Lemar, The Role of States in Liberalizing Land Use Regulations, 97 N. C. L. Rev. 293 (2017).
#_ftnref21 . See, e.g., Soham Dhesi, Protecting Mobile Homes as Affordable Housing, UCLA. L. Rev. Seminar (2018), https://www.uclalawreview.org/protecting-mobile-homes-as-affordable-housing [https://perma.cc/CAW6-K4M7].
#_ftnref22 . See infra Appendix at Tables 4 and 5.
#_ftnref23 . For example, in keeping with a passage from a popular statistics textbook, “[i]f the two variables are associated, we will reduce our errors when our predictions about one of the variables are based on the knowledge of the other.” Joseph F. Healey, Statistics: A Tool For Social Research 341 (6th ed. 2002).
#_ftnref24 . See infra Appendix at Table 1.
#_ftnref25 . See infra Appendix at Tables 4 and 5.
#_ftnref26 . Id.
#_ftnref27 . Id.
#_ftnref28 . See infra Appendix at Tables 4 and 5.
#_ftnref29 . See State of Illinois, supra note 5; See County of Cook, supra note 5; See Matthews, supra note 5.
#_ftnref30 . See U.S. Census, supra note 5.
#_ftnref31 . This validity testing included “spot testing” for each data source.
#_ftnref32 . See infra Appendix at Table 3.
#_ftnref33 . Id.
#_ftnref34 . See James Chen, Normal Distribution, Investopedia (May 7, 2019) (“Normal distribution, also known as the Gaussian distribution, is a probability distribution that is symmetric about the mean, showing that data near the mean are more frequent in occurrence than data far from the mean.”).
#_ftnref35 . Cf. Johnson, supra note 10.
#_ftnref36 . Kenney, supra note 4.
#_ftnref37 . See Geoghegan, supra note 1.
#_ftnref38 . See, e.g., John Antonakis, Samuel Bendahan, Philippe Jacquart & Rafael Lalive, On Making Causal Claims: A Review and Recommendations, 21 Leadership Q. 1086 (2010).
#_ftnref39 . Cf. Johnson, supra note 10.
#_ftnref40 . See infra Appendix at Tables 2 and 3.
#_ftnref41 . Id.
#_ftnref42 . Cf. Johnson, supra note 10.
#_ftnref43 . Id.
#_ftnref44 . See infra Appendix at Tables 4 and 5.
#_ftnref45 . Id.
#_ftnref46 . See State of Illinois, supra note 5; See County of Cook, supra note 5; See Matthews, supra note 5; See U.S. Census, supra note 5.
#_ftnref47 . See infra Appendix at Tables 4 and 5.
#_ftnref48 . See infra Appendix at Table 4.
#_ftnref49 . Cf. Johnson, supra note 10.
#_ftnref50 . Id.
#_ftnref51 . See infra Appendix at Table 4.
#_ftnref52 . See generally Deborah J. Rumsey, How to Interpret a Correlation Coefficient r, Statistics For Dummies, 2nd Edition (June 2016), https://www.dummies.com/education/math/statistics/how-to-interpret-a-correlation-coefficient-r/ [https://perma.cc/6V83-NLHH]; See U.S. Census Bureau, supra note 5. Fiscal Year 2017 is our focus because it is the last year that per capita income data is provided by the U.S. Census; See generally Watts, supra note 3.
#_ftnref53 . See infra Appendix at Table 4.
#_ftnref54 . Statistical significance may be determined through the use of probability values, which are computed at the 0.05 level and at the 0.10 level. See, e.g., Daniel Soper, Statistics Calculators, https://www.danielsoper.com/statcalc/related.aspx?id=44 [https://perma.cc/2YSD-2H4E] (last visited May 2, 2020). Probability values cannot exceed 0.05 for one-tailed probability values or 0.10 for two-tailed probability values, in order to be considered statistically significant. The initial result had probability values of 0.000272 (one-tailed test) and 0.000544 (two-tailed test), so it can be considered statistically significant.
#_ftnref55 . Parameters for probability value computations draw on correlation values (+0.35) and sample sizes (94).
#_ftnref56 . Cf. Geoghegan, supra note 1.
#_ftnref57 . Id.
#_ftnref58 . Id.
#_ftnref59 . Id.
#_ftnref60 . Id.
#_ftnref61 . See infra Appendix at Table 5.
#_ftnref62 . The initial result had probability values of 0.021102 (one-tailed test) and 0.042205 (two-tailed test). Thus, only modest conclusions may be drawn about the relationship between these variables. These conclusions are that there is some evidence that the null hypothesis in this study cannot be accepted (i.e., that there is some relationship between the number of mobile homes and per capita income).
#_ftnref63 . Parameters for probability value computations draw on correlation values (+0.21) and sample sizes (94).
#_ftnref64 . Compare infra Appendix at Tables 4 and 5 with Geoghegan, supra note 1.
#_ftnref65 . See generally Julie D. Lawton, Limited Equity Cooperatives: The Non-Economic Value of Homeownership, 43 Wash. U. J. L. & Pol’y 187, 207 (2014) (“A limited equity cooperative restricts the amount of equity appreciation, or the resale price above the owner’s purchase price, that the cooperative owners may obtain upon resale of the cooperative share . . . The over-arching intended benefit of an LEC is to preserve the property’s affordability by ensuring the cooperative share price does not increase to a level unaffordable to future low- and moderate-income buyers [that want to buy in].”).
#_ftnref66 . See generally Alana Semuels, The Case for Trailer Parks, The Atlantic, (Oct. 24, 2014), https://www.theatlantic.com/business/archive/2014/10/the-case-for-trailer-parks/381808 [https://perma.cc/9JSL-44VH].
#_ftnref67 . Cf. Frank Mulholland, Property Tax Code on Mobile and Manufactured Homes Changed, Shelbyville Daily Union, (Jan. 12, 2011), https://www.shelbyvilledailyunion.com/news/property-tax-code-on-mobile-and-manufactured-homes-changed/article_a9ddc740-af68-531c-aa1b-10f1a14a9aae.html [https://perma.cc/9KM2-KH2Q] (“Public Act 96-1477 . . . went into effect on January 1, 2011 and changes the tax code on mobile and manufactured homes . . . [because] . . . people in [conventional residential properties and mobile homes] . . . use the same services and yet . . . mobile home and manufactured homes paid much less taxes.”).
#_ftnref68 . See Wendy Plotkin, Rent Control in Chicago After World War II: Politics, People & Controversy (1998), http://wbhsi.net/~wendyplotkin/Prologue.pdf [https://perma.cc/S3FE-2396].
Recommended Citation: Randall K. Johnson, How Mobile Homes Correlate With Per Capita Income, 11 Calif. L. Rev. Online 91 (May 2020), https://www.californialawreview.org/mobile-homes-per-capita-income.