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A cartographic exploration of census data on select housing challenges among California residents

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A cartographic exploration of census data on select housing challenges among California residents

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A Cartographic Exploration of Census Data on Select Housing Challenges
Among California Residents

by

Lucresia Graham

A Thesis Presented to the
FACULTY OF THE USC DORNSIFE COLLEGE OF LETTERS, ARTS AND SCIENCES
University of Southern California
In Partial Fulfillment of the
Requirements for the Degree
MASTER OF SCIENCE
(GEOGRAPHIC INFORMATION SCIENCE AND TECHNOLOGY)

December 2021

Copyright © 2021 Lucresia Graham

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To my family, Eric, Aurora and Miranda, you fill my cup with joy (even while you deplete my
midnight oil); to my sisters Tania and Indira, I know I can do all things because you have blazed
trails before me; to my cohorts, Ari and Nathan, you have provided me with focus, perspective
and wit exactly when and how I needed them.

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Acknowledgements
I extend my heartfelt gratitude to my advisor, Professor Swift, for her constant encouragement
and guidance as I navigated this process. I am grateful to Professor Wilson for the vision and
expertise he provided in the form of direction for this project and to Professor Ruddell for the
healthy discussions and critical eye he applied to shape the final product. I would also like to
thank Professor Bernstein for being an excellent teacher, an always interesting conversationalist,
and my instructor for a full 50 percent of this SSI Master’s program. More generally, I wish to
express my appreciation to the US Census Bureau for continuing to collect critically important
data and innovating new ways of doing it and to the US Department of Housing and Urban
Development for continuing to assist those most in need of housing.

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Table of Contents
Dedication ....................................................................................................................................... ii
Acknowledgements ........................................................................................................................ iii
List of Tables ................................................................................................................................. vi
List of Figures ............................................................................................................................... vii
Abbreviations ................................................................................................................................. ix
Abstract ........................................................................................................................................... x
Chapter 1 Introduction .................................................................................................................... 1
1.1. Study Area ..........................................................................................................................1
1.2. Background .........................................................................................................................3
1.3. Motivation ...........................................................................................................................5
1.4. Research Overview and Objectives ....................................................................................6
1.5. Thesis Structure ..................................................................................................................7
Chapter 2 Related Literature ........................................................................................................... 9
2.1. The Effect of Housing Unaffordability ...............................................................................9
2.2. Defining Housing Unaffordability ....................................................................................11
2.3. Difficulties Related to Housing Affordability ..................................................................12
2.4. Spatial Analyses of Housing Difficulties ..........................................................................14
2.5. Housing Datasets ..............................................................................................................15
Chapter 3 Methods ........................................................................................................................ 18
3.1. Initial Planning and Selection of Housing Difficulties .....................................................18
3.2. Housing Data ....................................................................................................................19
3.3. Individual Housing Difficulties ........................................................................................21
3.4. Classification of the Data ..................................................................................................23
3.5. Combined Housing Difficulties ........................................................................................26

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3.6. Focus Studies ....................................................................................................................28
Chapter 4 Results .......................................................................................................................... 29
4.1. Individual Housing Difficulties ........................................................................................29
4.2. Two Housing Difficulties .................................................................................................34
4.3. Three Housing Difficulties ...............................................................................................34
4.4. All Four Housing Difficulties ...........................................................................................45
4.5. Focus Studies ....................................................................................................................45
4.5.1. Urban vs. Rural Overcrowding ................................................................................47
4.5.2. All Four Housing-Related Challenges in Relation to Low-Income Areas ..............47
4.5.3. Focus Area: A Look at Overcrowding in the City of Los Angeles .........................49
Chapter 5 Discussion and Conclusions ......................................................................................... 53
5.1. Discussion .........................................................................................................................53
5.1.1. Top Quartile Maps ...................................................................................................53
5.1.2. Combination Maps ...................................................................................................53
5.1.3. Focus Study Maps ....................................................................................................55
5.2. Challenges .........................................................................................................................59
5.3. Conclusions .......................................................................................................................60
5.4. Solutions ...........................................................................................................................61
5.5. Future Work ......................................................................................................................68
References ..................................................................................................................................... 70
Appendix A: Most Impacted Census Tracts ................................................................................. 75
Appendix B: Margin of Error (Most Impacted Census Tracts) .................................................... 79

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List of Tables
Table 1. Data Sources for the Four Housing-Related Challenges ................................................ 19
Table 2. Focus Study: Overcrowding in Urban and Rural Counties ............................................ 57
Table 3. Focus Study: Most Impacted Areas In Relation to Income By County .......................... 58
Table 4. Solutions to Housing-Related Challenges ...................................................................... 62

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List of Figures
Figure 1. State of California ............................................................................................................ 2
Figure 2. California Population Density ......................................................................................... 4
Figure 3. Standard Hierarchy of Census Geographic Entities ...................................................... 20
Figure 4. Data Distribution: Percent of Housing-Cost Burdened Households ............................. 24
Figure 5. Data Distribution: Percent of Overcrowded Households .............................................. 24
Figure 6. Data Distribution: Percent of Units with Lack of Plumbing ......................................... 25
Figure 7. Data Distribution: Percent of Workers with Long Commute ........................................ 25
Figure 8. Relationship Diagram of Maps Showing Combined Housing-Related Challenges ...... 27
Figure 9. Sample SQL Query for Top Quartiles of Housing-Related Challenges ........................ 28
Figure 10. Top Quartile of Census Tracts with Housing-Cost Burdened Households ................. 30
Figure 11. Top Quartile of Census Tracts with Overcrowded Households .................................. 31
Figure 12. Top Quartile of Census Tracts with Units with Lack of Plumbing ............................. 32
Figure 13. Top Quartile of Census Tracts with Workers with Long Commute ........................... 33
Figure 14. Areas with Most Housing-Cost Burden and Overcrowding ........................................ 35
Figure 15. Areas with Most Housing-Cost Burden and Lack of Plumbing .................................. 36
Figure 16. Areas with Most Housing-Cost Burden and Long Commute ..................................... 37
Figure 17. Areas with Most Overcrowding and Long Commute ................................................. 38
Figure 18. Areas with Most Overcrowding and Lack of Plumbing .............................................. 39
Figure 19. Areas with Most Lack of Plumbing and Long Commute ............................................ 40
Figure 20. Areas with Most Housing-Cost Burden, Overcrowding, and Long Commute ........... 41
Figure 21. Areas with Most Housing-Cost Burden, Overcrowding, and Lack of Plumbing ........ 42

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Figure 22. Areas with Most Housing-Cost Burden, Lack of Plumbing, and Long Commute ...... 43
Figure 23. Areas with Most Overcrowding, Lack of Plumbing, and Long Commute ................. 44
Figure 24. Areas Impacted by All Four Housing-Related Challenges ......................................... 46
Figure 25. Overcrowding in Urban and Rural Counties ............................................................... 48
Figure 26. Santa Barbara County Population Density .................................................................. 49
Figure 27. Census Tracts with 4 Housing Challenges and Low-Income Households .................. 50
Figure 28. Los Angeles Census Tracts with Overcrowding and Low-Income Households ......... 51

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Abbreviations
ACS American Community Survey
AHS American Housing Survey
AMI Area Median Income
ARP American Rescue Plan
CDBG Community Development Block Grant
CEQA California Environmental Quality Act
CHAS Comprehensive Housing Affordability Strategy
COPRAS Complex Proportional Assessment
GIS Geographic Information System
HA Housing Affordability
HAS Housing Affordability Stress
HEI Housing Expenditure to Income
HUD US Department of Housing and Urban Development
LIHTC Low Income Housing Tax Credit
MCDM Multi-Criteria Decision Making
MOE Margin of error
SSI Spatial Sciences Institute
US United States of America
USC University of Southern California
VMT Vehicle Miles Travelled

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Abstract
Short of becoming homeless, everyone must live somewhere, but the circumstances leading to an
individual’s choice of housing can be complex. Housing choices represent both personal factors
and outside influences and are often wrapped up in the overly simplified concept of “housing
affordability.” In California, the unaffordability of housing is particularly acute. This thesis
uniquely combined multiple datasets from the US Census Bureau and the US Department of
Housing and Urban Development to classify areas of the state according to the number of select
housing-related challenges that residents experienced as a result of their housing
accommodations. The challenges were then mapped, individually and collectively, to observe the
geographic distribution of the phenomena. This innovative method supplements the 30-percent
ratio (of housing costs to income) methodology traditionally used to denote housing affordability
and adds a visual and spatial display of housing challenges at a statewide level and in several
focus areas that have been negatively impacted by the current housing crisis. Finally, a review is
provided of existing and potential solutions to the four housing challenges investigated. The
results may be of interest to affordable housing providers, legislators, and even residents.

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Chapter 1 Introduction
Housing is the largest expense in most household budgets. The affordability of housing is often
defined by a housing cost that does not exceed 30 percent of a household’s income, but this is
only a partial description of the difficulties households can experience with finding and
maintaining a place to live (Jewkes and Delgadillo 2010). Some recent research has sought to
determine the prevalence of “housing affordability stress” (HAS) or the difficulty of paying
housing costs that are more than what a household can pay while still being able to afford the
other necessities of life (Baker, Mason, and Bentley 2015). Yet another vein of research has
focused on “sustainable housing affordability,” an attempt to combine the fiscal, social, and
environmental factors related to housing that influence a household’s residential circumstances
(Mulliner and Maliene 2015). For research related to housing and housing problems in the US,
the most comprehensive and widely available sources of data come from the US Census Bureau
and the Department of Housing and Urban Development (HUD) (US Department of Housing
and Urban Development Office of Policy Development and Research 2021). This thesis
combines that data regarding various housing-related challenges to explore the distribution of
these difficulties of life experienced by households across the State of California by Census tract
and to envision existing and possible solutions for these challenges.
1.1. Study Area
The State of California is well known around the world as the “Golden State” for its
natural beauty, agricultural bounty, technological epicenters, ecological diversity, and cinematic
history. Covering more than 163,000 square miles, California is the third largest state in the
union (see Figure 1). It is divided into 58 counties and has the highest population of any state, all
of whom must live somewhere, as housing is one of our most basic needs.

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Figure 1. State of California

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Unfortunately, California’s housing market is one of the most expensive in the US for
renters and homeowners alike. A 2019 Bloomberg article, aptly (and ambitiously) titled
“California’s Affordable Housing Crisis: Why Prices Are So High and How to Solve It,” pointed
out that the state has a median home price more than twice the national average, four of the five
most expensive housing markets and outsized populations of both persons experiencing
homelessness and residents living below the poverty line.
Not surprisingly, in such a large region, the resident population is spread unevenly
throughout the state (see Figure 2), having been shaped by the influence of topography,
hydrology, climate, and historical development patterns.
1.2. Background
There are many contributing factors to the current state of California’s unaffordable
housing market. The location of major employment centers, both currently and historically, and
the geography of physically desirable areas contribute to the uneven distribution of population
and its concomitant demand for housing (Gregory 1993). Jurisdictions throughout the state have
relative autonomy to set their own zoning and land-use regulations and often are subject to the
pressures exerted by single-family homeowners protecting their home values. Combined with a
desire to preserve community character, this results in opposition to more—and more dense—
housing, creating a patchwork of housing-development capacity (Fischel 2001).
After decades of such jurisdictional limits or outright prohibitions on the construction of
new housing, there has been inadequate new housing development. In addition, the overall
supply of residential units is insufficient and expensive, and existing housing stock is aging,
sometimes with deferred maintenance. In fact, a study of housing regulation in California found
a direct correlation between land-use restrictions and higher prices of rental and owner-occupied

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Figure 2. California Population Density (Source: US Census Bureau 2021b)

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housing (Quigley and Raphael 2005). And California’s consumers of housing are as diverse as
its housing stock: while many residents live in poverty, the state is home to a greater number of
billionaires than any nation in the world after the US and mainland China, according to Forbes
Magazine in its 2020 list of the World’s Billionaires.
On an individual level, households choose where to live based on the cost, condition, and
supply of available housing, as well as the particular household’s financial resources and
personal preference for amenities (Stone 2006). For the most part, these elements are not
captured in the 30-percent housing-expenditure-to-income (HEI) ratio that is often used to assess
housing affordability, set maximum rents in rent-restricted housing, or qualify applicants for a
mortgage. The HEI ratio also does not account for the fact that personal finances, expenses, and
employment vary widely among households and even over time. The experience of housing-
related difficulties then can occur at different income levels and in different geographic
locations. While city centers and coastal areas may have higher real estate costs per square foot
than other areas in the state, the actual affordability of housing and accessibility of employment
involves a complex interplay of different factors. When viewed together, the occurrence of
housing-related challenges can reveal the difficulties of life-impacting residents in unique and
insightful ways.
1.3. Motivation
The problems experienced by residents in California’s housing markets have far-reaching
implications. While the affordability of housing has received much attention since the US
Housing Act of 1949, the focus in recent decades has been almost exclusively on the HEI ratio of
housing costs to income. The HEI is a simple ratio that fails to consider external factors or
internal preferences affecting a household’s choice of residence. In response, some recent

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research incorporates a wider range of housing-related criteria to calculate housing affordability
or the newer concept of sustainable housing affordability (Mulliner, Smallbone, and Maliene
2013). However, the success of such methods may be hindered by their increased complexity,
requiring data and processing times that may be unavailable or cost prohibitive. Furthermore,
most of the research on housing affordability fails to include a spatial component, such as a
spatial analysis of where and possibly why housing affordability is a problem. The motivation
for the spatial analysis conducted in this thesis project was to design a process to identify and
display geographic areas in the state of California where households are impacted by a select set
of challenges.
1.4. Research Overview and Objectives
This thesis project entailed a multi-step process to identify areas in the state of California
where households are most impacted by certain housing-related challenges. In the context of this
thesis, “most impacted” is defined as being in the top quartile of a statistical dataset. The
housing-related criteria that are the focus of this research were chosen as they intersect with
several key aspects of life: household budgets, household size, physical conditions, and
transportation (US Census Bureau 2018, US Department of Housing and Urban Development
2020). Accordingly, the specific criteria chosen were Housing-Cost Burdened households, or
those paying 30 percent or more of the household’s income on housing; Overcrowded
households, or those with more than one person per room in the housing unit; housing units
Lacking Complete Plumbing or Complete Kitchen Facilities, meaning having hot and cold water,
a flush toilet and a bathtub or shower in the bathroom and having a kitchen sink with a faucet, a
stove or range, and a refrigerator; and workers aged 16 or older with a Longer-Than-Average
Commute Time, or 30 minutes or more each way. The focus criteria were not ranked in terms of

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relative importance but rather were considered as individual factors and then combined to
identify areas experiencing one, two, three, or all four housing-related challenges.
The Census and HUD data were imported into Esri’s ArcGIS Pro version 2.8.0 and
mapped to identify those areas where the top quartile of Census tracts experienced each of the
four challenges (US Census Bureau 2018, US Department of Housing and Urban Development
2020). The primary objective of this project was to conduct a spatial analysis that could answer
these key research questions:
• Are there any areas of the state plagued by all four housing difficulties?
• In what areas of the state are residents the most beleaguered by these challenges?
• Do metropolitan areas experience more housing difficulties?
1.5. Thesis Structure
The remaining chapters contain additional, detailed information regarding the motivation
for this thesis project and about how the research questions were approached and the spatial
analysis was carried out.
Chapter 2, Related Literature, summarizes the history of housing and affordable housing
in the US. Unfortunately, efforts to achieve longstanding goals of affordable and attainable
housing for all Americans have fallen short, so there is now several decades’ worth of empirical
literature related to unaffordable housing and its consequences. The definition of housing
affordability is also discussed, as the term is used with great frequency but with little consensus
across professional fields.
Chapter 3, Methods, provides an outline of the steps taken to format the Census and HUD
data and spatially analyze the housing-related challenges. The key housing difficulties were
identified, and data sources of appropriate vintages were selected. Each tabular dataset was

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filtered, formatted, and mapped individually before combining the four datasets and mapping the
results to produce both statewide maps and urban and non-urban local or focus-area maps.
Chapter 4, Results, describes the results of the spatial analysis. Maps and tables provide
detail on the housing difficulties, both statewide and in select, local focus areas determined to be
the most impacted by the phenomena.
Chapter 5, Discussion and Conclusion, includes an extensive discussion of the study’s
findings and potential implications. Challenges and flaws encountered in the study are explained
in detail to the best of the author’s knowledge, possible solutions to the housing challenges are
discussed, and suggestions for future studies are outlined.

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Chapter 2 Related Literature
The availability, condition, and affordability of housing have real implications for society in
terms of public health, economic activity, and social equity. This chapter examines the origins of
housing discussions, explains the concept of housing affordability (and unaffordability) and the
lack of consensus on definitions, presents a modern way of incorporating related affordability
criteria into a spatial analysis, and summarizes the various analytical methods available.
2.1. The Effect of Housing Unaffordability
In 1948, the United Nations declared housing to be a human right. The US Housing Act
of 1949 aspired to make decent housing available to every American. However, currently, no
state in the US has sufficient residential units to house its extremely low-income residents who
earn just 30 percent of the area median income (AMI) (Aurand et al. 2020). The State of
California in particular has a shortage of nearly one million housing units for such households.
It is not only the lowest income categories that experience the adverse effects of HAS,
which are widespread and can be manifested in ways that may not be initially or intuitively
obvious. Housing affordability is often conceptualized as a combination of two binary factors:
first, whether a household has a permanent place to live, and second, if the cost of the
household’s housing exceeds a certain (arbitrary) percentage of income. The demand for housing
is one of the most basic requirements in Maslow’s (1943) hierarchy of needs, but of course, not
all housing is comparable in quality, location or amenities. Rather than face homelessness, many
households will accept poor quality housing if higher quality housing is unattainable or in
insufficient supply. Therefore, substandard housing can actually be a byproduct of housing
unaffordability (Stone 2006). Moreover, poor quality housing can negatively affect public health,
both directly and indirectly. For example, exposure to pests and lead-based paint can degrade

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residents’ health, and paying too much for unaffordable housing leaves too little for other
essential costs like adequate healthcare (Freeman 2002).
Unfortunately, certain segments of the population may experience HAS more acutely
than other groups. Children, in particular, are at increased risk of physical and mental health
impacts, such as illness, child abuse, and developmental delays, when they and their families
experience unaffordable housing and “hypermobility,” defined as moving more than five times
during childhood (Crowley 2003). Ethnographer Matthew Desmond chronicled his studies of the
plight of evicted families in the city of Milwaukee, Wisconsin, and he pointed out that in 2011,
75 percent of those in the city’s eviction court were black, of which 75 percent were women
(Desmond 2016).
On a broader societal level, disproportionately high housing payments leave little, if
anything, for the accumulation of savings and eventual achievement of the idealized American
Dream of homeownership. This is unfortunate, as it turns out that parental housing tenure
influences the eventual outcomes of children in terms of education, wealth accumulation, and
homeownership. Parental homeownership is an important advantage for all three measures and is
a key source of intergenerational wealth accumulation (Boehm and Schlottman 2001) and
therefore a component of social equity. Clearly then, programs to promote secure housing and to
minimize HAS are key for a stable, prosperous society.
Fortunately, there are existing programs and mechanisms that aim to address some of
these housing issues. Starting with the US Housing Act of 1937, the federal government began
providing loans and grants to local housing agencies for subsidized public housing. Shortly
thereafter, and the Housing Act of 1949 expanded funding to clear slums and promote urban
redevelopment (US Department of Housing and Urban Development 2014b). Further federal

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legislation in subsequent decades provided housing for veterans and college families; authorized
rent-control provisions and relocation assistance payments; insured housing mortgages; and
outlawed housing discrimination. In recent years, state governments have exercised their power
to intervene in housing matters as well. For example, the State of California has passed several
laws with comprehensive approaches to transportation and housing problems. In 2008, Senate
Bill 375 was passed as the Sustainable Communities and Climate Protection Program in an
attempt to curtail air pollution and climate change through coordinated planning of
transportation, housing, and land use (California Air Resources Board 2021). At the local level,
counties and municipal governments have the ability to allow or require price- and rent-restricted
housing in their jurisdictions. Throughout California, jurisdictions have enacted inclusionary-
housing programs that require a certain percentage of new housing developments be offered at
prices and rents affordable to certain lower-income categories (Calavita and Grimes 1998).
Additional efforts and increased funding should be considered at all levels, which is discussed
further in Chapter 5, Discussion and Conclusions.
2.2. Defining Housing Unaffordability
Any study of the affordability of housing must begin with a clear and concise definition
of affordability, as the term is oft used but ill defined. Although the use of the term is
problematic, the phrase “housing affordability” is applied regularly in at least six different
contexts, among them: the description of household expenditures, analysis of trends,
administration of housing subsidy programs, definition of housing need, prediction of a
household’s ability to pay, and selection criteria for renters or buyers (Hulchanski 1995).
Hulchanski (1995) ultimately recommends avoiding the use of the term “housing affordability,”
although his 1995 seminal article is cited frequently by housing researchers since its publication.

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Not only is the terminology of affordability problematic, but the underlying concept of
affordable housing is also ambiguous. Market-rate housing that consumes some reasonable
portion of a household’s income should be distinguished from price-restricted, below-market
housing. A 30-percent HEI ratio is commonly used to set maximum rent amounts for price-
restricted housing and as a proverbial rule of thumb of affordability by housing professionals and
lenders. A critique of the HEI ratio method, however, is that it is arbitrary, representing either a
“normative standard” or an empirically observed value but not necessarily an appropriate cost
(Stone 2006). Nor does the HEI ratio encompass the fact that particular households may have
“diverse and incompatible definitions of affordability” for their budgets.
2.3. Difficulties Related to Housing Affordability
Given the complexity of issues related to housing affordability and the drawbacks of the
30-percent HEI ratio, there is the opportunity for new methods of evaluating the affordability of
housing by area. Housing professionals and researchers are concerned with affordability,
especially given that nearly a third of all Americans spend more than 30 percent of their income
on housing (Veal and Spader 2018). More than 40 percent of Californians are similarly Housing-
Cost Burdened (Buyahar and Cannon 2019). The incorporation of a broader range of criteria
improves the accuracy of any measure of affordability, making it more reflective of the true
conditions experienced by households.
Several recent attempts have been made to improve the assessment of affordability by
accounting for a variety of household costs. The Center for Neighborhood Technology and the
Center for Transit Oriented Development developed an alternative affordability index,
incorporating not only the direct costs of housing but also transportation, commute, and
associated opportunity costs as a share of household income, to approximate the effects of

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residential location (Jewkes and Delgadillo 2010). Interestingly, while developing their
affordability index, the authors found in Minneapolis, Minnesota, that a commute distance longer
than 12-15 miles resulted in an increase in transportation costs that usually negated the savings
on housing costs. In California, the statewide average travel time to work for workers aged 16 or
older is 30 minutes (US Census Bureau 2018).
Relatedly, a new conceptualization of affordability is sustainable housing affordability,
bringing together the concepts of housing affordability, social well-being, and sustainable
communities (Mulliner and Maliene 2015). Mulliner and Maliene (2015) proposed a set of 20
distinct criteria of sustainable housing affordability and surveyed 600 housing professionals in
the United Kingdom to determine the relative importance of the criteria. In order of importance,
the ranked criteria included: (1) housing prices in relation to income; (2) rental costs in relation
to income; (3) interest rates and mortgage availability; (4) availability of rented accommodation
(private and social); (5) quality of housing; (6) access to employment; (7) energy efficiency of
housing; (8) availability of low-cost home ownership products; (9) access to good quality
schools; (10) access to public transport; (11) access to health services; (12) availability of market
value home ownership products; (13) access to early-years child care; (14) access to shopping
facilities; (15) safety (crime); (16) low presence of environmental problems; (17) deprivation in
area; (18) access to open green public space; (19) waste management; and (20) access to leisure
facilities.
Naturally, though, a more complex index also means a more complicated one, with a
greater number of datasets, longer processing times, and more opportunity for error. This may
prove cost- (or time-) prohibitive for many housing professionals, which therefore would be a

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barrier to adoption of such a new calculation methodology. A more manageable but nevertheless
expanded subset of criteria may be warranted.
2.4. Spatial Analyses of Housing Difficulties
There are many ways to define and assess the affordability of housing. A recent study
cataloged different approaches to the measurement of housing affordability and, for each
method, described the weaknesses that should be considered prior to use in future research
(Ezennia and Hoskara 2019). One method, the Multi-Criteria Decision Making (MCDM)
approach, which combines multiple factors into one index for analysis, was found to be
beneficial and the best choice for complex decision making. MCDM allows for incorporation of
many criteria with different units of measure; however, it was also noted that applying MCDM
can be time-consuming and that different MCDM approaches can produce different results.
While MCDM methods are useful, this concerning critique of the process—that different
MCDM methods sometimes yield different results for the same analytical subject—means that
selection of the particular methodology is critically important. Therefore, Mulliner, Malys, and
Maliene (2016) did a comparative analysis of six MCDM methods: the weighted product model,
the weighted sum model, the revised analytic hierarchy process, the technique for order of
preference by similarity to ideal solution, and the complex proportional assessment method
(COPRAS). Ultimately, the recommendation was to use more than one method whenever
possible in order to make more informed decisions. Of the six approaches studied, the COPRAS
method was selected by Mulliner, Malys, and Maliene (2016) as the best combination of
consistency, transparency, low processing time, and ease of use.
An important aim of this thesis project is to implement a multifactor review of the
geographic distribution of housing-related challenges as a measure of HAS. Relatively few

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studies of housing affordability have incorporated spatial analysis. One study focused on the
concept of HAS and the relationship between housing costs and income levels within and
between major Canadian cities pointed out that “[m]ost existing research dealing with housing
affordability issues remains aspatial and does not indicate where the greatest affordability
problems can be expected to be” (Bunting, Walks and Filion 2004). This thesis proposes a
relatively simple—and therefore easily replicable—multifactor methodology for incorporating a
spatial component into discussions of housing affordability.
Observation of any unevenness in the distribution of housing challenges could add
dimension to existing data on poverty. Also, information on HAS may provide insights of value
to land-use planners, housing professionals, and residents.
2.5. Housing Datasets
Reliable data is key to accurate assessments of housing affordability. There are several
types of Census and survey data gathered by the US Census Bureau, which in turn provide high-
quality housing-related datasets.
The decennial census is a requirement of the US Constitution, and it has collected data on
the population of the nation since 1790 (US Census Bureau 2002). What began as a simple count
of population steadily grew to include a longer form with a range of questions covering
demographics, income, housing, employment, transportation, race/ethnicity, and more, until the
long form was replaced by two separate innovations: the American Community Survey (ACS) in
2005 and the Census short form in 2010. Whereas the decennial census is conducted every 10
years and provides population counts (which then determine congressional representation), the
ACS is conducted every month of every year and provides annual estimates of many more
topics. ACS data is available in 5-Year Estimates and 1-Year Estimates. The 1-Year Estimates

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are more current than the 5-Year Estimates but offer lower statistical certainty of estimates. In
addition, small cities and geographic units, such as Census tracts, are not included in the 1-Year
Estimates, so the 5-Year Estimates must be used when a count of complete estimates is needed
for areas at the Census tract level.
Overall, the Census tract is an exceedingly useful geographic entity. Census tracts are
small geographic units, containing roughly 2,500 to 8,000 residents, within a larger county area
(US Census Bureau n.d.). Census tract boundaries are set by visible, logical features, and they do
not often change, only when major changes in the demographic or economic makeup of the area
occur and only during the decennial Census. It is worth noting that the geographic area of a
Census tract is inversely proportional to its resident population size—the higher the population in
an area, the smaller the Census tract. In dense urban areas, Census tracts are relatively small and
numerous.
Every year, ACS 5-Year Estimates data is provided by HUD in the form of custom
tabulations, known as the Comprehensive Housing Affordability Strategy (CHAS) data (US
Department of Housing and Urban Development 2021a). The additional processing time required
to prepare the CHAS data for public release means that the most recent dataset is several years
behind the current ACS estimates. This data is valuable for government and housing
professionals as it provides information on existing housing problems across all income levels
but especially at Low-Income levels. The CHAS data are used by HUD to inform the distribution
of grant funds, and local governments use it to identify need and plan the expenditure of HUD
funds. HUD relies on CHAS and other Census data regarding housing-related issues to determine
need and allocate funding to jurisdictions nationwide. For example, funds under HUD’s HOME
Investment Partnerships Program (HOME Program) are distributed to those areas experiencing

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(1) insufficient housing stock, (2) substandard housing, (3) low-income households in units that
are likely to need rehabilitation, (4) local costs of producing new housing, (5) poverty rates, and
(6) relative fiscal difficulty of carrying out housing activities (US Department of Housing and
Urban Development n.d.). Similarly, the federal Community Development Block Grant Program
(CDBG Program) funds are allocated based on a jurisdiction’s population as well as the slowing
of its population growth since 1960, the number of residents living in poverty, the incidence of
overcrowded housing, and the number of housing units built before 1940 (US Department of
Housing and Urban Development 2012).
Consequently, a subset of housing challenges was selected for this project; namely,
Housing-Cost Burdened households, or those paying 30 percent or more of the household’s gross
income on housing expenses, which includes rent or mortgage and utilities; Overcrowded
households, or those with more than one person per room in the housing unit; housing units
Lacking Complete Plumbing or Complete Kitchen Facilities, meaning having hot and cold water,
a flush toilet and a bathtub or shower in the bathroom and having a kitchen sink with a faucet, a
stove or range, and a refrigerator; and workers aged 16 or older with a Longer-Than-Average
Commute Time for California, or 30 minutes or more each way. The 2013-2017 5-Year
Estimates of CHAS and ACS data were obtained as the most current dataset available.

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18
Chapter 3 Methods
As a review of the associated literature reveals, the subject of the affordability of housing has
been discussed at length. Housing affordability can be assessed in a number of different ways,
each of which has its strengths and weaknesses. Applying a geographic component to the study
of HAS for households in California provides an additional facet to a housing-affordability
analysis. For this study, a set of housing-affordability criteria were selected, which then guided
the collection of relevant data, specifically the Census ACS and HUD CHAS 2013-2017 5-Year
Estimates. The attribute tables of each variable were considered and filtered separately before
being imported into ArcGIS Pro for mapping and spatial analysis of individual and combined
housing challenges. Finally, the resulting maps were reviewed, compared with income maps and
other pertinent datasets, and analyzed for potential insights.
3.1. Initial Planning and Selection of Housing Difficulties
Data-driven information and analytical methods regarding the incidence of housing-
related challenges can provide valuable insights to housing professionals, such as the
administrators of subsidized housing programs, developers of nonprofit housing, and legislators
who fund housing grant programs. A reliable and replicable methodology could supplement the
often critiqued but widely applied HEI ratio method if a mix of housing-related criteria is chosen
so that the methodology fits the needs and budgetary constraints of the housing industry. In the
United Kingdom, Mulliner and Maliene (2015) proposed a set of 20 economic, social and
environmental criteria for assessing a concept they referred to as sustainable housing
affordability, which brings together the ideas of sustainable communities and housing
affordability. This thesis project considered a subset of criteria similar to Mulliner and
Maliene’s, namely: (1) housing costs in relation to income (Housing-Cost Burden); (2) an aspect

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of household size (Overcrowding); (3) a physical housing problem (as represented by the Lack of
Complete Plumbing or Kitchen Facilities in a unit); and (4) a Longer-Than-Average Commute
Time (longer than the statewide average of 30 minutes).
3.2. Housing Data
The datasets for each of the housing-related challenges and demographic characteristics
were acquired (see Table 1). Information on four housing-related challenges, Housing-Cost
Burdened households, Overcrowded households, housing units either Lacking Complete
Plumbing or Complete Kitchen Facilities, was obtained from HUD’s Consolidated Planning
Comprehensive Housing Affordability Strategy (CHAS), which is a further refinement of the US
Census Bureau’s American Community Survey (ACS) 5-Year Estimates data. To create the

Table 1. Data Sources for the Four Housing-Related Challenges
Information/ Criteria Source Geographic Scale Year
Housing Cost-Burdened
Households
HUD CHAS – Census
ACS 5-Year Estimates
Table 3
Census tract 2013-2017
Overcrowded Households HUD CHAS – Census
ACS 5-Year Estimates
Table 3
Census tract 2013-2017
Occupied Housing Units
Lacking Complete
Plumbing or Complete
Kitchen Facilities
HUD CHAS – Census
ACS 5-Year Estimates
Table 3
Census tract 2013-2017
Travel Time to Work Census ACS 5-Year
Estimates Table B08303
Census tract 2013-2017
Census data polygons Census TIGER/Line
shapefiles
Census-designated
place, Census tract,
water polygons
Most recent
Jurisdictional boundaries California State
Geoportal
State, County, City,
Census Designated
Place
Most recent

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CHAS data, HUD creates customized tabulations of ACS data from the US Census Bureau. As a
result of the additional processing time, the most recent, available CHAS dataset is several years
behind the most current ACS dataset. At the time of this writing, the latest available version of
the CHAS data was the 2013-2017 5-Year Estimates dataset. The 2013-2017 ACS 5-Year
Estimates of Travel Time to Work were utilized for commute times and to ensure consistency
with the CHAS data.
An important consideration in any analysis is the selection of an appropriate scale, and
Census data is available in a range of geographic units (see Figure 3). The Census block or block
group can be unavailable for specific subjects or, if available, can be too small area-wise, making
the data difficult to display or interpret, while the County level would likely be too large to the

Figure 3. Standard Hierarchy of Census Geographic Entities (Source: US Census Bureau 2017)

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21
point of obscuring any valuable insights to be gained from this spatial analysis. Therefore, the
Census tract was determined to be the ideal areal unit for spatial analysis of housing issues.
Census tracts are available for all locations and represent a fine enough resolution, meaning the
most appropriate map scale, to be informative.
3.3. Individual Housing Difficulties
Once all the relevant data for the criteria were obtained, the data tables were reformatted,
and the criteria summed, as explained in detail in the next section of this chapter. Each dataset
contained more information than was needed for this thesis, so the data fields for each criterion
were reviewed for inclusion or exclusion, e.g., travel times to work of less than 30 minutes were
excluded.
The dataset for each housing-related challenge required unique, customized summation
and calculations. For the proportions of housing units that were Housing-Cost Burdened,
Overcrowded or Lacking Complete Plumbing or Complete Kitchen Facilities, data came from
HUD CHAS – Census ACS 2013-2017 5-Year Estimates Table 3, whereas Longer-Than-
Average Commute Times were calculated from the US Census ACS 2013-2017 5-Year
Estimates Table B08303.
The US Census Bureau defines Housing-Cost Burden as paying more than 30 percent of
gross income toward housing expenses. The percentage of Housing-Cost Burdened households
was calculated using Equation 1, where R is renter-occupied, and O is owner-occupied housing
units with housing expenses greater than 30 percent of income but less than or equal to 50
percent, and those with housing expenses greater than 50 percent, and T is the total number of
occupied housing units.

22

22
!
"
!"#$"%
+ "
$"%&
+ %
!"#$"%
+ %
$"%&
&
'× 100 (1)

Overcrowding is defined by the Census Bureau as more than 1.0 person per room (US
Census Bureau 2021a). Accordingly, Overcrowded households were tallied using Equation 2,
where R is the number of renter-occupied housing units and O is the number of owner-occupied
housing units with more than 1.0 person but less than or equal to 1.5 persons per room, and those
with more than 1.5 persons per room, and T is the total number of occupied housing units.

!
"
'."#'.$
+ "
'.$&
+ %
'."#'.$
+ %
'.$&
&
'× 100 (2)

Likewise, the percentage of housing units Lacking Complete Plumbing or Complete
Kitchen Facilities was calculated using Equation 3, where R is the number of renter-occupied
housing units and O is the number of owner-occupied housing units lacking either complete
plumbing or kitchen facilities, and T is the total number of occupied housing units.

!
"
)
+ %
)
&
'× 100 (3)

Finally, a one-way commute time of 30 minutes or more was classified as a Longer-
Than-Average Commute Time for this study because the average travel time to work in
California is 30 minutes (US Census Bureau 2017). Using Equation 4, Longer-Than-Average
Commute Time was calculated, where M is the share of workers whose travel time to work was
30 to 34, 35 to 39, 40 to 44, 45 to 59, 60 to 89, and 90 or more minutes and W is the total number

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23
of workers. In this case, workers were defined as workers aged 16 and over who did not work
from home, including members of the Armed Forces and civilians who were at work the week of
the survey.

!
/
!"#!*
+ /
!$#!+
+ /
*"#**
+ /
*$#$+
+ /
,"#-+
+ /
+"&
0
'× 100 (4)

3.4. Classification of the Data
The four housing-related challenges that are the focus of this research undoubtedly share
a common theme in that they are related to and have an impact upon housing and affordability;
they differ, however, in an important way: how the data are distributed statistically. As the
frequency distribution charts (Figure 4 to Figure 7) illustrate, some of the housing-related
challenge datasets are normally distributed or symmetrical about the mean, which indicates that
data near the mean occur more frequently than data far from the mean. Other datasets are
distinctly not normally distributed.
Given the variation in data distribution among the four housing-related challenge
datasets, it was necessary to classify the data for mapping in a way that would minimize false
comparisons or “comparing apples to oranges.” Each dataset was classified into quartiles, which
divides the number of features into four classes to obtain an average, and finally counts the
quantity in each group and arranges them as close to the average as possible. In this manner, it
was possible to correlate the top quartile for each housing-related criteria with the areas most
impacted by the particular phenomenon, and map the data for visual spatial analysis.

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24

Figure 4. Data Distribution: Percent of Housing-Cost Burdened Households

Figure 5. Data Distribution: Percent of Overcrowded Households

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25

Figure 6. Data Distribution: Percent of Units with Lack of Plumbing

Figure 7. Data Distribution: Percent of Workers with Long Commute

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26
It is worth noting that while this method results in a more evenly distributed grouping of
the data, the absolute values within each class may vary considerably from the lowest value to
the highest value in the class. Nevertheless, this method of analysis makes it possible to evaluate
the classes that are impacted by the selected housing-related challenges as a comparison of
greater or lesser negative impacts and also allows for comparison among classes and between the
different criteria.
To map the data, geographic information system (GIS) feature layers for the geographic
boundaries of Census tracts and counties were added to an Esri Geodatabase then mapped in
ArcGIS Pro using a geographic projection for the State of California. The individual housing-
criteria datasets were imported into the Geodatabase as well, then joined with the Census-tract
polygon features. The symbology of the resulting feature sets was modified to classify the
percentage data into quartiles. The top quartile of each criterion was first displayed
independently on a statewide-level map before reviewing overlap among the different criteria.
3.5. Combined Housing Difficulties
The aim of this study was not only to find the regions and local areas in California most
impacted by one of the four selected housing-related challenges but to identify those areas
experiencing two, three, or all four challenges, which ostensibly compounds the difficulties
experienced by households. The top quartiles of each challenge were successively combined into
multiple individual maps, as illustrated in Figure 8.

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Figure 8. Relationship Diagram of Maps Showing Combined Housing-Related Challenges

GIS provides an optimal method for accomplishing this type of spatial analysis. After
reviewing the individual datasets for each housing challenge and joining them in ArcGIS, SQL-
syntax queries of the tables yielded those Census tracts affected by two, three, and four of the top
quartiles of each factor. The SQL query to select the Census tracts affected by all four housing
challenges is shown in Figure 9. Statewide maps showing the spatial distribution of those results

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28
were created, depicting one, two, three and four criteria combined, as well as focus-area maps to
provide a detailed view of the affected areas, particularly in dense urban areas.

Figure 9. Sample SQL Query for Top Quartiles of Housing-Related Challenges

3.6. Focus Studies
In addition to the single and combined housing-related challenges at a statewide level and
in the most impacted areas, several focus areas and topics were chosen for closer analysis based
on characteristics that are often associated with housing challenges (California Department of
Housing and Community Development 2021b). These focus areas and topics included:
• A comparison of Overcrowding in urban versus rural counties
• A review of areas most impacted by four housing challenges in relation to income
• A comparison between Housing-Cost Burden and Low-Income households
The next chapter, Chapter 4, presents the outputs of these spatial analyses. Chapter 5
follows with a discussion of the results in the context of steps that may be taken to alleviate the
impacts of the housing-related challenges investigated in this study.

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Chapter 4 Results
The application of the research methods described in Chapter 3 of this thesis resulted in 15 maps
displaying various combinations of the four housing-related challenges. Overall, it was observed
that for each map where the number of challenges depicted increased, the number of Census
tracts negatively impacted by the housing-related challenges decreased.
4.1. Individual Housing Difficulties
As outlined previously, a map was created to display each of the four individual housing
challenges, each with a symbol corresponding to the housing-related challenge or combination of
challenges shown in Figure 8. Each map reveals the areas that, in relation to the rest of the state,
are most impacted by each housing challenge.
The maps cover the range of housing-related challenges examined in this thesis and
exhibit the uneven distribution of these issues throughout the state. For Housing-Cost Burdened
households, Figure 10 shows the Census tracts where 40.5 to 96.0 percent of all occupied
housing units reported spending more than 30 percent of their gross income on housing
expenses. The areas most impacted by Overcrowding, depicted in Figure 11, are those Census
tracts with 13.1 to 65.0 percent of total occupied housing units inhabited by more than 1.0 person
per room. For Lack of Complete Plumbing or Complete Kitchen Facilities, Figure 12 displays
those Census tracts where 2.1 to 52.3 percent of renter- and owner-occupied housing units were
lacking either complete plumbing facilities or complete kitchen facilities. Finally, Figure 13
shows the Census tracts throughout the state where 52.1 to 100 percent of all workers aged 16
years of age or older reported a one-way commute of 30 minutes or more, representing a Longer-
Than-Average Commute Time.

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Figure 10. Top Quartile of Census Tracts with Housing-Cost Burdened Households

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31

Figure 11. Top Quartile of Census Tracts with Overcrowded Households

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32

Figure 12. Top Quartile of Census Tracts with Units with Lack of Plumbing

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33

Figure 13. Top Quartile of Census Tracts with Workers with Long Commute

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34
4.2. Two Housing Difficulties
The next step in the analytical process was to combine the data to reveal the areas
impacted by two housing-related difficulties. With four challenges, there are six possible ways in
which Census tracts could be affected. Figure 14 shows areas that experienced a combination of
Housing-Cost Burden and Overcrowding. Figure 15 depicts areas with both Housing-Cost
Burden and a Lack of Complete Plumbing or Kitchen Facilities, while Figure 16 displays areas
most affected by both Housing-Cost Burden and a Longer-Than-Average Commute Time. Figure
17 and Figure 18 show those Census tracts most impacted by Overcrowding and a Longer-Than-
Average Commute Time and those with the most Overcrowding and a Lack of Complete
Plumbing or Kitchen Facilities, respectively. Finally, areas experiencing the most Lack of
Complete Plumbing or Kitchen Facilities and a Longer-Than-Average Commute Time are
displayed in Figure 19.
4.3. Three Housing Difficulties
The next analytical step of reviewing the areas impacted simultaneously by three housing
difficulties resulted in four maps. Because the number of Census tracts affected by three of the
challenges was significantly lower, zoomed-in maps of certain local areas assist with a visual
investigation of those areas most negatively impacted by all three challenges. Figure 20 depicts
the combination of Housing-Cost Burden, Overcrowding, and Longer-Than-Average Commute
Time. Figure 21 demonstrates areas with Housing-Cost Burden, Overcrowding, and Lack of
Complete Plumbing or Kitchen Facilities. Figure 22 shows Housing-Cost Burden, Lack of
Complete Plumbing or Kitchen Facilities, and Longer-Than-Average Commute Time. Finally,
Figure 23 shows Overcrowding, Lack of Complete Plumbing or Kitchen Facilities, and Longer-
Than-Average Commute Time.

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35

Figure 14. Areas with Most Housing-Cost Burden and Overcrowding

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36

Figure 15. Areas with Most Housing-Cost Burden and Lack of Plumbing

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37

Figure 16. Areas with Most Housing-Cost Burden and Long Commute

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38

Figure 17. Areas with Most Overcrowding and Long Commute

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39

Figure 18. Areas with Most Overcrowding and Lack of Plumbing

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40

Figure 19. Areas with Most Lack of Plumbing and Long Commute

41
41

Figure 20. Areas with Most Housing-Cost Burden, Overcrowding, and Long Commute

42
42

Figure 21. Areas with Most Housing-Cost Burden, Overcrowding, and Lack of Plumbing

43
43

Figure 22. Areas with Most Housing-Cost Burden, Lack of Plumbing, and Long Commute

44
44

Figure 23. Areas with Most Overcrowding, Lack of Plumbing, and Long Commute

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4.4. All Four Housing Difficulties
Finally, refining the selection criteria further to those Census tracts that experienced a
combination of all four housing-related difficulties reduced the number of the most negatively
impacted areas significantly. The result was a mere 65 Census tracts—out of a total of 8,057
tracts in California—throughout three urbanized areas (see Appendix A). As illustrated in Figure
24, the vast majority of impacted Census tracts were located in the Los Angeles metropolitan
area, while two were identified in the greater San Francisco Bay Area and one in the downtown
area of the City of Stockton.
4.5. Focus Studies
In addition to viewing the individual and combined housing-related challenges, a subset
of the challenges was selected for juxtaposition with additional factors to see if further insights
could be gleaned from the combined datasets at local scales. One of the apparent differences
between impacted and non-impacted areas was urban status, so the spatial distribution of the
most Overcrowded areas was reviewed in several urban and rural counties. In addition, the
distribution of Low-Income households was assessed in the areas determined to be impacted by
all four housing challenges. Finally, the correlation between income and housing costs as a
portion of income was analyzed to determine if Low-Income groups are disproportionately
affected or if Housing-Cost Burden seems to affect all income groups.

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46

Figure 24. Areas Impacted by All Four Housing-Related Challenges

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4.5.1. Urban vs. Rural Overcrowding
California has a mix of urban and rural areas throughout the state. To compare the
occurrence of Overcrowding in different urban and rural configurations, three counties were
reviewed, as shown in Figure 25. The Counties of Los Angeles and San Francisco were selected
as the urban counties, and the County of Santa Barbara was selected as the rural county. In Los
Angeles and Santa Barbara Counties, the most Overcrowded Census tracts were found outside of
the park and national forest lands, shaded in green, and correlated to the most densely populated
areas of incorporated cities. For example, see Figure 26 for a population density map where the
most densely populated areas, depicted in red, resemble the areas with Overcrowding shown in
Santa Barbara County in Figure 25. However, in the City and County of San Francisco, which is
an urban county like Los Angeles and also infamous for its expensive housing market,
Overcrowding occurs only in a few select areas in the eastern and southeastern portions of the
city, with no obvious pattern.
4.5.2. All Four Housing-Related Challenges in Relation to Low-Income Areas
As one of the motivations for this thesis project was to review housing affordability, a
review of the results in the context of where households have the lowest incomes is also
warranted. Toward that end, Figure 27 highlights those Census tracts most negatively impacted
by the top quartile of all four of the focus housing-related challenges, overlaid onto a map
showing the percentage of Low-Income households by Census tract (US Department of Housing
and Urban Development 2020). The Low-Income category includes all households whose
incomes do not exceed 80 percent of the HUD-Adjusted Median Family Income (HAMFI).
There is a strong apparent correlation between the 65 Census tracts experiencing all four housing
challenges and the tracts where more than 75 percent of households are Low Income.

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48

Figure 25. Overcrowding in Urban and Rural Counties

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49

Figure 26. Santa Barbara County Population Density

4.5.3. Focus Area: A Look at Overcrowding in the City of Los Angeles
The housing problem of Overcrowding is thought-provoking because it may have human
impacts beyond housing and housing affordability. Public health directives to isolate or
quarantine, such as those issued during the ongoing COVID-19 pandemic, may be difficult to
follow in Overcrowded residences. If not correctly recorded or accounted for, Overcrowding
could obscure the need in a given housing market for more numerous or larger housing units
from local jurisdictions and housing providers. Households might have preferred to lower the
number of persons per bedroom by obtaining separate units if other, larger, or more affordable
housing options had been available. It is also possible that large families—either those with more
children than the 1.9 children that the average American family with children has (US Census
Bureau 2020) or those with extended-family living arrangements—might not wish for separate
housing units but rather larger units that the local housing market may not be able to provide.

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Figure 27. Census Tracts with 4 Housing Challenges and Low-Income Households

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51
For these reasons, Overcrowding in the City of Los Angeles was juxtaposed with two
different but interrelated factors: Housing-Cost Burden and (Low) Income (see Figure 28).

Figure 28. Los Angeles Census Tracts with Overcrowding and Low-Income Households

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52
While it is probable that not all Housing-Cost Burden is reserved for lower-income categories
nor that all Low-Income households experience Housing-Cost Burden, information may be
revealed by comparing these challenges, as they relate to Overcrowding. Figure 28 shows the
two most negatively affected groups: those Census tracts most impacted by Overcrowding and
Housing-Cost Burden, and those where a majority of households qualify as Low Income. These
maps and information demonstrate some of the housing problems currently experienced by
residents in California.
The next chapter further discusses these results, in the context of existing and potential
solutions, at all government bureaucratic levels, providing insights and suggestions for ways
these issues might be addressed at present and in the future.

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53
Chapter 5 Discussion and Conclusions
The intent of this project was to investigate several questions about housing affordability and
challenges in the State of California. Spatial analysis proved effective in answering those
questions and providing additional information that may be of interest to housing professionals.
5.1. Discussion
Displaying the results of the spatial analysis of the housing-related challenges using maps
clearly illustrated the extent of housing problems throughout California and that urban areas have
been most impacted by all four of the housing challenges analyzed in this study; however, not all
urban areas experienced these impacts to the same degree.
5.1.1. Top Quartile Maps
When viewed individually, the first set of maps showing the geographic distribution of
each housing-related challenge, Figure 10 through Figure 13, were all quite different from each
other, with seemingly little overlap among them. Even in the early stages of the process, it
seemed likely that areas where all of the housing problems were compounded would be
somewhat limited.
5.1.2. Combination Maps
For the maps displaying two housing-related challenges, Figure 14 through Figure 19,
there appeared to be a concentration of these issues in the metropolitan areas of Los Angeles and
San Francisco, but there were also several notable outliers. For example, Northern California had
several areas experiencing Housing-Cost Burden and Lack of Complete Plumbing or Kitchen
Facilities (see Figure 15), as well as Lack of Complete Plumbing or Kitchen Facilities and
Longer-Than-Average Commute Time (see Figure 19). The areas with Overcrowding and

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Longer-Than-Average Commute Time, as shown in Figure 17, were again mainly in the greater
Los Angeles and San Francisco areas. At the same time, Central California and a large Census
tract near the southern border with Mexico were also identified as experiencing these problems.
The map of Overcrowding and Lack of Complete Plumbing or Kitchen Facilities, illustrated in
Figure 18, exhibited the most concentration in Los Angeles, Orange, and Riverside Counties, a
few Census tracts around the San Francisco Bay, and some notable areas in the Central Valley
and near the southern border.
The next set of maps of the areas that experienced three housing-related challenges is
displayed in Figure 20 through Figure 23. The locations most impacted by Housing-Cost Burden,
Overcrowding, and Longer-Than-Average Commute Time, depicted in Figure 20, were
concentrated in the Los Angeles area and some surrounding bedroom communities such as
Chatsworth and Santa Clarita. The San Francisco Bay Area seemed, unexpectedly, to be less
impacted by these three challenges. This could be a function of a combination of factors, such as
the Bay Area having a greater proportion of higher-income jobs (e.g., in technology-based
sectors) and more efficient public transit (e.g., the Bay Area Rapid Transit system) (Jessen
2021). The map of Housing-Cost Burden, Lack of Complete Plumbing or Kitchen Facilities, and
Longer-Than-Average Commute Time shown in Figure 22 displays the greatest concentration in
the small, dense Census tracts of the Los Angeles and San Francisco areas. There were also some
apparent anomalies northeast of Los Angeles and northwest of San Francisco. Still, upon closer
inspection, those turned out to be relatively unpopulated areas of national parks, military bases,
or airports. In general, these unpopulated areas lack sufficient residents for accurate estimates,
such as Overcrowding or Housing-Cost Burden, and therefore the data can seem over- or under-
representative of true conditions.

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55
The geographic distribution of the areas most impacted by the top quartile of all four
housing-related challenges shown in Figure 24 supports the conclusion that some areas of the
state are more impacted than others and reveals that the Los Angeles area seems to be more
impacted than any other urban metropolitan areas in the state. Although the relative impacts of
the individual housing-related problems vary throughout the state, there are markedly few areas
impacted simultaneously by all four challenges, as illustrated in Figure 24. There are 65 Census
tracts in this category (see Appendix A), almost all of which are in the Los Angeles/Orange
County area, while two are in the greater San Francisco Bay Area and one in downtown
Stockton.
5.1.3. Focus Study Maps
Three focus studies further analyzed two of the housing problems in the context of
affordability. The first focus study provided a comparison of two urban counties and one rural
county, represented in Figure 25. Of the two urban counties, the first is the City and County of
San Francisco, which is an internationally known and densely populated city approximately 50
square miles in area. In comparison, the County of Los Angeles is about 4,800 square miles,
famous for its dependence on the automobile (Houston et al. 2015). The County of Santa Barbara
is a rural county, about 3,800 square miles in area and located 100 miles west of Los Angeles.
There are both similarities and differences among the three counties. Los Angeles and Santa
Barbara have large swaths of primarily uninhabited land in the Angeles and Los Padres National
Forests, whereas San Francisco has several smaller but sizable parks within its city limits,
including Golden Gate Park and Presidio of San Francisco. Nevertheless, even accounting for
uninhabited green spaces, Overcrowding is distributed uniquely in all three counties. The Census
tracts in Los Angeles County most impacted by housing-related challenges seem to be spread

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56
throughout the densely populated parts of the Los Angeles Basin and the San Fernando Valley
area to the west, with some additional tracts in the Palmdale area to the north. A more limited
distribution of impacted Census tracts occurred in San Francisco, where only 18 Census tracts
are most impacted by Overcrowding, located in the east in a rough area often referred to as The
Tenderloin (O’Mara 2018) and southeastern portions of the city. In Santa Barbara County,
Overcrowding predominantly occurs in the most densely populated areas, namely the
incorporated cities, e.g., Santa Barbara, Lompoc, Santa Maria. There is one apparent anomaly in
the northwest of the county, owing to the small rural City of Guadalupe. According to the Census
data portal (data.census.gov) has less than 8,000 residents, of which over 90 are Hispanic or
Latino and almost 18 percent live below the poverty line.
A review of these three counties and the Census tracts therein affected by Overcrowding
reveals further insights. Table 2 reveals that Los Angeles County has a far higher number of
Census tracts than the other two counties and the greatest number of Census tracts affected by
Overcrowding. Los Angeles County also has the largest average household size, with 3.01
persons per household, followed by Santa Barbara with 2.94 persons. Interestingly, while the
counties of San Francisco and Santa Barbara have lower numbers of residents and Census tracts
than Los Angeles County, they also exhibit higher proportions of their populations living below
the poverty level, as defined by the US Census Bureau. In addition, of the three counties
reviewed, Santa Barbara County has the highest percentage of total county residents living in
Overcrowded housing units. These results are noteworthy as they invite further analysis as to the
specific characteristics and underlying explanations of the differences experienced in urban areas
as opposed to rural areas.

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57
Table 2. Focus Study: Overcrowding in Urban and Rural Counties
County Name
Total
No. of
Census
tracts
No. of
Overcrowded
Census tracts
Total
Population
in County
% of Total
Population
Overcrowded
% Below
Poverty
Level
Average
Household
size
Los Angeles 2343 973 10,105,722 10.6 9.8 3.01
San Francisco 195 18 864,263 3.5 11.7 2.35
Santa Barbara 89 26 442,996 18.0 11.7 2.94

The next focus study compared the areas most impacted by the housing challenges with
Census tracts classified by their share of households with the lowest incomes. As illustrated in
Figure 26, the 65 Census tracts determined to be most impacted by the top quartile of all four of
the focus housing-related challenges were overlaid on a map showing the percentage, by Census
tract, of Low-Income households earning no more than 80 percent of the HAMFI. Upon review,
48 of the 65 Census tracts experiencing all four housing challenges also had more than 75
percent of households in the Low-Income category (see Appendix A). In comparison, the
remaining 17 Census tracts had 50 to 75 percent Low-Income households, confirming a
correlation between the housing challenges and the lower-income categories. Income categories
vary by area, however, so it can be informative to compare the relative income limits and
population counts by county, as displayed in Table 3. Of the five counties with the most-
impacted areas, Los Angeles County has more Census tracts and more residents than the other
four counties combined. The 60 Census tracts in Los Angeles County determined to be most
impacted contain 248,793 residents, whereas the remaining five tracts in the other four counties
account for 23,279 residents. This data reinforces the initial impression that the greater Los
Angeles area is impacted to a greater extent by the housing-related challenges examined in this
study.

58

58
Table 3. Focus Study: Most Impacted Areas In Relation to Income By County
County Name
Total No. of
Census tracts
No. of Census
tracts impacted
by all 4 housing
challenges
Total
population
in County
Population in
Census tracts
impacted by
all 4 housing
challenges
Low-income
limit ($) for
4-person
household
Contra Costa 207 1 1,123,678 6,513 80,400
Los Angeles 2343 60 10,105,722 248,793 72,100
Orange 582 2 3,155,816 11,823 83,450
San Francisco 195 1 864,263 3,061 105,350
San Joaquin 139 1 724,153 1,882 48,900

The final focus study centered on the City of Los Angeles, with Overcrowding combined
with both Housing-Cost Burden and (Low) Income data. It is important to note that not all
households experiencing Housing-Cost Burden are Low Income and that not all Low-Income
households experience Housing-Cost Burden. Nevertheless, they are interrelated since Housing-
Cost Burden is a function of income, so a comparison of the two factors related to Overcrowding
seemed warranted. Two groups are depicted in Figure 28: the Census tracts in both the top
quartile in terms of Overcrowding, 13.1 to 65 percent, and the top quartile in terms of Housing-
Cost Burden, 40.5 to 96 percent (represented with a thick black outline) and those Census tracts
most impacted by Overcrowding where a majority of households qualify as Low Income
(represented with orange shading). It is significant that, of all the tracts with both Overcrowding
and Housing-Cost Burden in the City of Los Angeles, almost all are in tracts where more than
half of households qualify as Low Income. Furthermore, there are more Census tracts
experiencing the top quartile of Overcrowding where a majority of households are in the Low-
Income category than there are Census tracts in both the top quartiles of Overcrowding and
Housing-Cost Burden. This finding supports the conclusion that a portion of households may

59

59
experience more Overcrowding and less Housing-Cost Burden by securing smaller (cheaper)
housing units than they would if larger (more expensive) accommodations were available.
Each of the above categories identified a selection of specific Census tracts as most
impacted which could change slightly in future studies depending on the methodology used and
the sampling error of the source data. While these concerns are discussed in the next section and
should always be considered, the overall patterns identified in this study are illustrative and
useful for identifying—in general—the geographic areas in need of the most housing resources.
5.2. Challenges
The methodology presented in this study has yielded valuable information about the
housing-related challenges in the State of California. However, as with any multi-step process,
there are weaknesses to consider and issues that warrant future investigation. This section
examines the analysis challenges encountered in this thesis project.
There are two important limitations to consider in terms of the CHAS and ACS data used
in this study. The first is the rounding scheme used by HUD in its custom tabulations, wherein
total counts are not rounded, but other estimates are: values of zero remain zero, values of one
through seven are reported as four, and all other values round to the nearest multiple of five
(Foster 2007). This rounding methodology can result in inconsistencies within the CHAS data
tables (internal calculations) and between CHAS data and other Census-generated tables
(external comparisons). Within the CHAS data tables, rounding can result in summation
calculations totaling more (or less) than 100 percent. The second limitation involves ACS data
and the margins of error (MOE) provided, which can be significant in some cases. Per the ACS
data table notes, the data are based on a sample of the population and are therefore subject to
sampling variability, which is represented by an MOE. The MOE has approximately a 90-

60

60
percent probability that the true value is within the range of the estimate plus or minus the
margin of error value (US Census Bureau 2018). In this study, for example, the Longer-Than-
Average Commute Time data contained some significant MOEs of more than 10 percent (for
example, see Appendix B). In light of these limitations, the findings of this study should be
viewed as indications of larger trends or general needs, rather than pinpointing specific problems
in an exact location.
Another limitation of this study involves the data used for the Lack of Complete
Plumbing or Complete Kitchen Facilities. At the Census tract level, the incidence rates were
found to be relatively low. While it would be ideal to have as few units as possible experiencing
these housing problems, the data may not offer a complete picture of the true extent of the
problem. As pointed out by O’Dell, Smith, and White (2004), physical housing condition
problems, such as a Lack of Complete Plumbing or Kitchen Facilities, tend to be more localized
to individual properties than other housing problems, such as Housing-Cost Burden, which tends
to be more evenly distributed across blocks or neighborhoods. Future analysis could attempt to
review the Lack of Complete Plumbing or Kitchen Facilities using other data sources or at
different scales.
5.3. Conclusions
Of particular interest to this study were the following questions: Are there any regions or
local areas of the state plagued by all four housing difficulties? In what parts or locations of the
state are residents the most beleaguered by these challenges? Do metropolitan areas experience
more housing challenges than rural areas?
Applying the methods described in this thesis study produced outputs that effectively
answered these questions. While the spatial distribution of each individual housing difficulty

61

61
seemed different from the others, there were in fact some areas of the state impacted by all four
housing difficulties. These areas were markedly concentrated in the greater Los Angeles area.
Overall, urban metropolitan areas were identified as more negatively impacted by the housing
problems than rural areas, but some of the more densely populated areas of non-urban areas were
determined to experience multiple housing problems as well.
5.4. Solutions
This thesis project was envisioned to analyze the extent of housing problems in
California as an extension to traditional calculations of housing affordability, based on the
author’s years of professional experience in land-use planning and affordable housing.
Approaches to solving housing-related problems exist at every jurisdictional level, some
instituted decades ago and some only recently passed (US Department of Housing and Urban
Development 2014b). Table 4, Solutions to Housing-Related Challenges, contains a listing of
some approaches that could be or have been implemented to alleviate problems of housing
affordability, with varying degrees of success.
Additional programs are also currently being discussed since a “housing affordability
crisis” has permeated the news media and general consciousness, as an internet search quickly
reveals (Winck 2021). Solutions to housing problems can take many forms, from the local
government or nonprofit agency level to the state and federal level.
The US federal government has a long history of attempting to address housing-related
problems through public housing, grant programs, and tax credits (US Department of Housing
and Urban Development 2014b). Beginning with the New Deal, the federal government built and
managed public housing but discontinued building new public housing projects in 1974 when

62
Table 4. Solutions to Housing-Related Challenges
Solution Status
Jurisdictional Level
Notes
Housing-
Related
Challenges*
Addressed** Local State Federal Other
Housing assistance payments
(e.g., Section 8, Veterans
Affairs Supportive Housing)
Existing

X X Housing “voucher” programs provide funds to local
housing authorities and other organizations to provide
low-income households with monthly tenant
assistance payments
H, O
LIHTC Program Existing X X X Federal tax credit program, allocated to project
applicants by individual states’ tax-credit allocation
committees to construct new affordable housing
H, P
HOME Program funding Existing X X X

Grant program to construct, acquire or rehabilitate
affordable housing; federal funding is distributed to
states & participating jurisdictions to fund housing
developers, contractors, etc.
H, O, P
CDBG funding Existing X

X X Grant program to promote community development;
federal funding provided to entitlement jurisdictions
which is not primarily for housing but can be used to
pay for rehabilitation of housing units
H, P
ARP Child Tax Credits Existing

X

Direct payments to families with children H, O
By-right allowance for ADUs Existing X State legislation (2017 & 2020) to allow creation of
additional dwelling unit(s) on residential and
commercial properties
H, O, C
Changes to CEQA
calculation of traffic impacts
Existing X Change to required environmental review of new
projects to analyze transportation impacts in terms of
generation of Vehicle Miles Travelled (instead of by
traffic delays at intersections)
C
Housing cooperatives Existing X X Jointly controlled corporations established to provide
housing for member households that own a share and
occupy a unit of housing
H
Elimination of single-family
residential zoning
Potential X

Elimination of zoning regulations that restrict
residential properties to only 1 unit
H, O, P, C
Elimination of minimum
parking requirements
Potential X

Elimination of minimum parking requirements that
utilize limited available land and increase residential-
project costs
H, C

63
Solution Status
Jurisdictional Level
Notes
Housing-
Related
Challenges*
Addressed** Local State Federal Other
By-right upzoning of
residential properties (e.g.,
2020 SB 10 Planning &
Zoning, 2018 SB 50 Planning
& Zoning)
Potential X State legislation to allow residential property owners
to add additional units regardless of current local
zoning laws
H, O, P, C
Subsidized childcare Potential X X X Universal childcare facilities, funded by state or
federal program and likely implemented by local
agencies, which would cost less and presumably
provide a range of convenient location options (e.g.,
near homes, near parents’ jobs)
H, C
*H = Housing-Cost Burden, O = Overcrowding, P = Housing deficiencies (including Lack of Complete Plumbing or Complete Kitchen), C = Long Commutes
**While solutions in this table may indirectly address all four of the housing-related challenges in this study, this table lists those most directly addressed..

64

President Nixon issued a moratorium on housing spending; eventually, public housing was
replaced by housing-voucher programs (National Low Income Housing Coalition 2019).
Housing vouchers, from such programs as Veterans Affairs Supportive Housing and
“Section 8,” are funded with federal dollars and distributed by local housing authorities or other
agencies to landlords on behalf of tenant households (US Department of Housing and Urban
Development 2021d). These vouchers effectively lower the housing costs of millions of families
nationwide. There is also a Project-Based Section 8 program that provides financial assistance to
developers of housing units that they contractually agree to retain as affordable for a certain
period of time. Similarly, the Low-Income Housing Tax Credit (LIHTC) Program offers sizable
federal tax credits to developers of affordable housing (US Department of Housing and Urban
Development 2021c). Created in 1986, the program is “the most important resource for creating
affordable housing in the United States today,” according to HUD, which is why some housing
professionals believe the program should be expanded.
The CDBG Program is another federal program, enacted in 1974, that provides annual
grants to states and local governments to promote community development (US Department of
Housing and Urban Development 2014a). The CDBG program’s regulations include housing
rehabilitation as an eligible use of funds. CDBG is one of HUD’s longest-running programs and
is quite effective as a whole. While housing rehabilitation is a relatively small share of the
program’s accomplishments, it is nevertheless a critical program funding the preservation and
improvement of the physical condition of residential housing stock nationwide. In some
jurisdictions, such as the County of Santa Barbara, CDBG funds are given to the local chapter of
Habitat for Humanity to fund small home repairs. The HOME Program, in contrast, is intended
solely for the purpose of creating and improving affordable housing by providing annual federal

65

grants to state and participating jurisdictions (US Department of Housing and Urban
Development 2021b).
The HOME Program has been a key source of affordable housing for Low-Income
households since it began in 1994, by funding new construction and rehabilitation of housing,
assisting homeowners and providing monthly tenant-based rental assistance payments (US
Department of Housing and Urban Development 2021b). Unfortunately, the program has seen
steadily decreasing budget allocations for over a decade, which is undercutting its effectiveness.
A recent federal program began providing child tax credits in the form of direct payments to
families with children as part of the American Rescue Plan (ARP) (The White House 2021).
While there are no restrictions or directives on the use of the funds, such a payment can be
expected to alleviate the burden of living expenses such as housing costs. Another creative,
potential solution that is being considered at the federal level is the provision of universal
subsidized childcare (Warren Democrats 2021). Childcare is a significant expense for working
families, so a program such as this would allow families more freedom to redirect income to
housing expenses or other uses. Also, if subsidized childcare centers were conveniently located,
it is possible that such a program would minimize daily family car trips or alleviate some traffic
congestion and therefore shorten commute times. Such innovative solutions may be needed to
solve problems as important as those related to unaffordable housing.
The State of California is also implementing creative solutions to housing-related
challenges and a shortage of housing in general. In 2017 and again in 2020, the State Legislature
enacted laws allowing for the creation of one or more accessory dwelling units (ADUs) on
certain properties, despite any local zoning laws prohibiting them (California Department of
Housing and Community Development 2021a). Although the ADUs are not required to be price

66

restricted and therefore are not necessarily affordable, they do add to the limited supply of
housing units available. Also, ADUs are restricted in size compared with regular housing units,
which may lower the rents charged for such units.
For the environmental review of new projects, the state enacted Senate Bill 743, which
made a deceptively minor change to the California Environmental Quality Act (CEQA) for
assessment of a project’s environmental impacts: beginning in July 2020, transportation impacts
would no longer be measured in terms of traffic delays caused, but rather by a measure of the
number of Vehicle Miles Travelled (VMTs) generated (California Governor’s Office of Planning
and Research 2021). This is a significant change for the field of housing because traffic impacts
have often been cited as justification for opposing new housing developments. Under the new
law, projects that facilitate shorter commutes will be considered to have a lower environmental
impact, which will likely allow for the construction of more infill housing development,
discourage urban sprawl (or the development of housing and amenities at the edges of urban
areas) and encourage the co-location of jobs and housing. Potential benefits are decreased air
pollution, decreased commute times and costs, and increased equity for residents who can more
easily access jobs, services, and high-opportunity areas. Another current program that is
available to help lower Housing-Cost Burden involves limited-equity housing cooperatives,
which independently provide permanently affordable homeownership for Low- and Moderate-
Income households (California Center for Cooperative Development 2021). Each member
household owns one share in the cooperative corporation, and the state ensures the long-term
affordability of the shares by restricting price increases to no more than 10 percent per year and
requiring that any profits from a sale of the cooperative as a whole be dedicated to charities.
Additionally, the state has made several attempts to enact legislation that would grant property

67

owners the right to build multiple residential units on their property, regardless of local zoning
restrictions, including several authored by State Senator Scott Wiener of San Francisco
(California State Senate Democratic Caucus 2021). While such bills have been unsuccessful so
far and have faced much opposition from single-family neighborhood groups in local
jurisdictions, it seems likely that some version of the bill will pass in the future. The validity of
accusations that such actions would irreparably damage community character remains to be seen.
However, such a change would remove a major barrier to the creation of housing where it is
needed. Increasing the allowable residential density in this manner could address the housing
problems of Housing-Cost Burden, Overcrowding and Longer-Than-Average Commute Time (if
more units, in convenient locations, at a range of price points are created), as well as Lack of
Complete Plumbing or Kitchen Facilities and other physical problems since new units are
inspected for minimum standards before being approved for occupancy.
At the local level, solutions to more significant housing problems have seemed more
difficult to implement, but programs designed to ameliorate their effects are common. Local
jurisdictions are primarily the pass-through agencies distributing federal and state grant funds,
such as CDBG and HOME funds, to non-governmental organizations and non-profit agencies.
As discussed previously, these programs are important sources of funds for the rehabilitation of
housing and direct rental assistance payments to households. There are critical changes that cities
and counties in California should still make to address housing problems. The elimination of
restrictive single-family zoning regulations at the municipal level, such as the landmark policy
enacted by the City of Minneapolis, Minnesota in 2019 (McCormick 2020), would allow for
more housing units at greater densities per acre, with the advantage of allowing for community
input in the zoning-code development process, in contrast to a top-down approach from the state

68

level. Unfortunately, such attempts to “upzone” or increase the allowable residential density on a
property have thus far often been met with vehement neighborhood opposition. Another creative
solution is the elimination of minimum residential parking requirements, which decrease land
available for housing units and increase total development costs (Shoup 2014). Residential
parking requirements are adopted and implemented at the local level, so such requirements can
be eliminated if decision-makers and planners muster the political will and allay neighborhood
apprehensions. In theory, less onerous parking requirements are an innovative approach that
would facilitate the development of more housing in a broader range of sizes and price points,
thereby addressing housing-related challenges, including those that were the focus of this study.
Governments, housing developers, and other organizations can and should think
creatively to solve the housing problems experienced today. While some programs have been
implemented and a modicum of progress has been made, the current circumstances demand
further action at all bureaucratic levels to produce and preserve housing for individual
households, make it more affordable, and address social disparities.
5.5. Future Work
The spatial analysis applied in this study proved useful for viewing and analyzing the
CHAS and ACS data related to housing affordability. While the results of this thesis project were
illuminating, the potential combinations that could be reviewed in future studies are seemingly
endless. It would be interesting to analyze the same data using the more acute versions of
Housing-Cost Burden and Overcrowding for comparison. Related to the criteria investigated
used in this study, the Census defines severe Housing-Cost Burden as spending more than 50
percent of gross income on housing expenses and severe Overcrowding as housing units with
more than 1.5 persons per room (US Census Bureau 2021a). The consideration of race and

69

ethnicity data into this type of study would also be beneficial. The CHAS data contains racial
and ethnic data as part of its tabulations, and given the uneven distribution of housing
affordability and related problems across demographic groups, valuable insights could be gained.
Consideration of additional datasets is also warranted; for example, for the Lack of Complete
Plumbing and Complete Kitchen Facilities, the Census Bureau’s American Housing Survey
(AHS) or other datasets should be reviewed for suitability. Finally, if analyses like this are to be
replicated, methods of automation should be developed for tallying data fields and for mapping
the data, such as writing Python scripts and using ModelBuilder in ArcGIS Pro to automate the
methodology workflow calculations in the interest of time and cost efficiencies. An Esri GIS
Story Map may also be a worthwhile exercise to better explain research findings to decision
makers and the public.
The methodology explored in this study was determined to be simple—in terms of
calculations—but effective. The outputs demonstrate that housing problems occur at different
intensities throughout the state and identify areas experiencing the most acute challenges that
warrant additional resources and program efforts. While individual agencies and developers can
work to make changes, large-scale governmental programmatic changes seem to be the most
effective way to solve the current housing problems in California.

70

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Appendix A: Most Impacted Census Tracts

% of Total

County
Census
Tract
No. of
Occupied
Housing
Units
No. of
Workers aged
16 or over
Housing Units:
Lacking
Complete
Plumbing or
Kitchen
Housing
Units:
Overcrowded
Housing Units:
Cost Burdened
Workers:
Longer Than
Average
Commute
Location/
General Area
% of
households
in Low-
Income
category
Contra
Costa
3790 1830 2455 4.4 16.4 41.5 52.4 SF Bay Area 75+
San
Joaquin
6 565 711 7.1 15 45.8 54.4 Stockton 75+
San
Francisco
264.04 720 1380 2.8 20.1 41 61.5 SF Bay Area 50-75
Orange 879.02 1240 2265 4.4 24.6 42.7 52.5 LA/SoCal 75+

878.06 1575 2267 4.8 13.3 49.8 52.6 LA/SoCal 75+
Los
Angeles
1926.1 1435 1968 3.5 28.2 45.6 61.4 LA/SoCal 75+

1927 1040 1800 4.3 26 44.7 65.3 LA/SoCal 50-75

1957.2 895 1226 3.9 13.4 41.3 56.3 LA/SoCal 50-75

1958.02 935 1316 2.7 13.9 40.6 53.6 LA/SoCal 50-75

1283.02 1455 2081 2.4 23 45.4 55.6 LA/SoCal 50-75

2134.02 1505 2036 3 35.5 40.9 63.9 LA/SoCal 75+

2324 1905 2924 2.9 16.3 45.7 62.2 LA/SoCal 50-75

2328 955 1539 3.1 26.7 46.6 61.7 LA/SoCal 75+

2393.1 1020 1582 4.8 25 42.2 63.5 LA/SoCal 75+

2216.02 920 1106 4.9 26 42.9 62.2 LA/SoCal 75+

2349.02 1390 1530 3.6 13.7 61.9 56.5 LA/SoCal 75+

76

% of Total

County
Census
Tract
No. of
Occupied
Housing
Units
No. of
Workers aged
16 or over
Housing Units:
Lacking
Complete
Plumbing or
Kitchen
Housing
Units:
Overcrowded
Housing Units:
Cost Burdened
Workers:
Longer Than
Average
Commute
Location/
General Area
% of
households
in Low-
Income
category

2396.02 875 1210 3.4 30.9 43.4 68.6 LA/SoCal 75+

1200.3 700 1091 3.4 22.9 45.6 54.9 LA/SoCal 75+

1282.1 1520 2086 3.3 30.9 49 54.3 LA/SoCal 75+

2112.02 920 1393 3.8 21.1 45 62.4 LA/SoCal 75+

2118.04 1310 1177 4.2 16.8 46.6 57.7 LA/SoCal 75+

2121.01 1415 1829 2.8 15.5 48.4 60.1 LA/SoCal 50-75

5331.05 640 1100 2.3 36.7 48.3 53.6 LA/SoCal 75+

1231.03 1545 1883 4.9 14.6 46.6 53.5 LA/SoCal 50-75

2119.21 1025 1125 3.9 15.6 51.2 57.2 LA/SoCal 75+

2037.2 1010 1596 3 30.2 40.6 60 LA/SoCal 75+

1905.2 1645 1815 2.4 19.8 42.2 53.6 LA/SoCal 75+

1916.2 1200 1234 12.4 20.8 44.2 63.9 LA/SoCal 75+

2123.03 1410 1780 3.9 28.4 42.1 60.3 LA/SoCal 75+

1912.03 1170 1146 2.6 24.4 42.3 59.4 LA/SoCal 75+

1279.1 1485 2536 2.7 25.9 49.8 52.2 LA/SoCal 75+

3025.05 1495 1607 9.7 17.1 46.5 61.9 LA/SoCal 75+

2371.02 895 1177 3.4 20.1 46.4 73.1 LA/SoCal 75+

5018.03 1430 1651 4.5 23.4 49.7 52.2 LA/SoCal 75+

5018.04 730 904 6.8 18.5 44.5 58.8 LA/SoCal 50-75

1241.05 930 1160 3.8 19.2 42.9 52.7 LA/SoCal 50-75

1277.12 1190 1363 3.8 15.1 53.4 67 LA/SoCal 75+

77

% of Total

County
Census
Tract
No. of
Occupied
Housing
Units
No. of
Workers aged
16 or over
Housing Units:
Lacking
Complete
Plumbing or
Kitchen
Housing
Units:
Overcrowded
Housing Units:
Cost Burdened
Workers:
Longer Than
Average
Commute
Location/
General Area
% of
households
in Low-
Income
category

2088.01 1255 1537 8.8 29.1 40.6 67.9 LA/SoCal 75+

2187.02 790 868 5.1 18.9 50.6 65 LA/SoCal 75+

1278.05 1140 1800 7.9 17.5 42.5 64 LA/SoCal 50-75

5511.01 1195 1563 2.4 15.9 42.3 52.8 LA/SoCal 50-75

2117.03 2055 2858 4.1 13.8 45.5 60.4 LA/SoCal 50-75

5342.02 1240 1948 3.6 33.9 41.5 61.6 LA/SoCal 75+

5348.03 1150 1928 3 22.6 44.8 53.4 LA/SoCal 75+

6006.02 740 985 2.7 28.9 44.6 58.8 LA/SoCal 75+

5402.03 1280 2052 2.3 21.1 48 54.5 LA/SoCal 75+

6009.12 1500 2488 2.7 14 45.7 56.5 LA/SoCal 50-75

5409.01 1145 1850 3.9 23.1 45.9 62.2 LA/SoCal 50-75

2225 1350 2103 3.3 21.9 44.4 65.2 LA/SoCal 50-75

2383.2 1130 1192 4.9 23 43.4 68.3 LA/SoCal 75+

2123.06 1175 1453 2.6 23.8 42.5 59.9 LA/SoCal 75+

2126.2 1845 2352 2.4 28.5 50.4 55.5 LA/SoCal 75+

2242 735 1187 8.2 29.3 42 59 LA/SoCal 75+

2362.02 2520 2451 3.4 15.5 56.7 62 LA/SoCal 75+

2283.2 715 1393 3.5 33.6 46.2 53.4 LA/SoCal 75+

2288 1215 1983 2.9 27.2 45.7 59.4 LA/SoCal 75+

2409 1435 2143 4.9 22 46 59.2 LA/SoCal 75+

2411.2 1305 1727 3.1 24.5 53.6 65.8 LA/SoCal 75+

78

% of Total

County
Census
Tract
No. of
Occupied
Housing
Units
No. of
Workers aged
16 or over
Housing Units:
Lacking
Complete
Plumbing or
Kitchen
Housing
Units:
Overcrowded
Housing Units:
Cost Burdened
Workers:
Longer Than
Average
Commute
Location/
General Area
% of
households
in Low-
Income
category

2316 2000 2695 2.2 19.8 43 57.5 LA/SoCal 75+

2319 1450 1875 3.1 24.5 53.4 57.8 LA/SoCal 75+

2422 1645 1930 3.3 23.3 42.6 64.9 LA/SoCal 75+

2431 1420 1635 3.2 19.7 48.9 60.8 LA/SoCal 75+

5703.01 2255 3267 8.2 18.8 43.7 54.3 LA/SoCal 75+

5336.01 1100 2013 2.7 22.3 45.5 54 LA/SoCal 75+
4809.02 1325 2122 2.6 13.6 41.9 58.6 LA/SoCal 50-75

79
Appendix B: Margin of Error (Most Impacted Census Tracts)

Census
Tract
Total No. of
Occupied Units
MOE
Estimate:
Lacks Plumbing
MOE
% Lacks Plumbing
MOE
Estimate:
Overcrowded
MOE
% Overcrowded
MOE
Estimate:
Cost Burdened
MOE
% Cost Burdened
MOE
Total No. of
Workers
MOE
Estimate: Long
Commute
MOE
% Long
Commute
MOE

1926.10 1435 88 50 47.5 3.5 0.2 405 124.9 28.2 1.7 655 142.5 45.6 2.8 1968 255 1209 245.8 61.4 8
1927 1040 95 45 27.3 4.3 0.4 270 104.7 26 2.4 465 128.1 44.7 4.1 1800 322 1176 271 65.3 11.7
1957.20 895 53 35 30.5 3.9 0.2 120 65.5 13.4 0.8 370 86.5 41.3 2.4 1226 165 690 151.5 56.3 7.6
1958.02 935 51 25 24.2 2.7 0.1 130 55.2 13.9 0.8 380 82.2 40.6 2.2 1316 141 706 112.1 53.6 5.7
1283.02 1455 60 35 35.1 2.4 0.1 335 106.8 23 1 660 133.8 45.4 1.9 2081 205 1156 206.4 55.6 5.5
2134.02 1505 122 45 39.8 3 0.2 534 133 35.5 2.9 615 136.4 40.9 3.3 2036 259 1302 263.9 63.9 8.1
2324 1905 84 55 43.3 2.9 0.1 310 106.7 16.3 0.7 870 183.4 45.7 2 2924 297 1819 279.7 62.2 6.3
2328 955 41 30 29.5 3.1 0.1 255 69.3 26.7 1.1 445 89 46.6 2 1539 228 949 171.1 61.7 9.1
2393.10 1020 58 49 43.2 4.8 0.3 255 80.7 25 1.4 430 104.7 42.2 2.4 1582 220 1004 183.7 63.5 8.8
2216.02 920 26 45 34.2 4.9 0.1 239 56.9 26 0.7 395 81.4 42.9 1.2 1106 167 688 150.4 62.2 9.4
2349.02 1390 123 50 46.4 3.6 0.3 190 100.4 13.7 1.2 860 176.4 61.9 5.5 1530 292 865 227.7 56.5 10.8
2396.02 875 47 30 29.5 3.4 0.2 270 75.9 30.9 1.7 380 90.4 43.4 2.3 1210 148 830 151.5 68.6 8.4
1200.30 700 48 24 23.3 3.4 0.2 160 59.4 22.9 1.6 319 74.3 45.6 3.1 1091 169 599 139.7 54.9 8.5
1282.10 1520 59 50 36.2 3.3 0.1 470 116.1 30.9 1.2 745 137.2 49 1.9 2086 228 1133 210 54.3 5.9
2112.02 920 48 35 29.5 3.8 0.2 194 55.3 21.1 1.1 414 75.9 45 2.3 1393 153 869 139.1 62.4 6.9
2118.04 1310 82 55 45.6 4.2 0.3 220 85.3 16.8 1.1 610 149.3 46.6 2.9 1177 194 679 179.4 57.7 9.5
2121.01 1415 59 40 47.5 2.8 0.1 220 79.8 15.5 0.7 685 149 48.4 2 1829 259 1100 238.9 60.1 8.5

80
Census
Tract
Total No. of
Occupied Units
MOE
Estimate:
Lacks Plumbing
MOE
% Lacks Plumbing
MOE
Estimate:
Overcrowded
MOE
% Overcrowded
MOE
Estimate:
Cost Burdened
MOE
% Cost Burdened
MOE
Total No. of
Workers
MOE
Estimate: Long
Commute
MOE
% Long
Commute
MOE

5331.05 640 38 15 29.5 2.3 0.1 235 83.1 36.7 2.2 309 91.1 48.3 2.9 1100 168 590 131 53.6 8.2
1231.03 1545 55 75 48 4.9 0.2 225 101.7 14.6 0.5 720 148.5 46.6 1.7 1883 278 1007 212.1 53.5 7.9
264.04 720 42 20 26.8 2.8 0.2 145 61.1 20.1 1.2 295 85.6 41 2.4 1380 216 849 155.7 61.5 9.6
6 565 48 40 25.6 7.1 0.6 85 39.8 15 1.3 259 64 45.8 3.9 711 109 387 111.8 54.4 8.3
879.02 1240 66 55 34.7 4.4 0.2 305 114.1 24.6 1.3 530 131.5 42.7 2.3 2265 323 1188 265.4 52.5 7.5
878.06 1575 58 75 46.2 4.8 0.2 210 102.4 13.3 0.5 785 187.2 49.8 1.8 2267 282 1192 283.5 52.6 6.5
2119.21 1025 66 40 36.1 3.9 0.3 160 74.7 15.6 1 525 143.9 51.2 3.3 1125 168 644 202.4 57.2 8.6
2037.20 1010 63 30 40.8 3 0.2 305 94 30.2 1.9 410 123.1 40.6 2.5 1596 298 958 225.6 60 11.2
1905.20 1645 55 40 34.2 2.4 0.1 325 89.8 19.8 0.7 694 151.5 42.2 1.4 1815 230 973 205.3 53.6 6.8
1916.20 1200 46 149 53.9 12.4 0.5 249 87.8 20.8 0.8 530 129.3 44.2 1.7 1234 214 788 190.2 63.9 11.1
2123.03 1410 73 55 49.5 3.9 0.2 400 119.3 28.4 1.5 594 128.6 42.1 2.2 1780 234 1074 234.4 60.3 7.9
1912.03 1170 71 30 30.5 2.6 0.2 285 108.2 24.4 1.5 495 127 42.3 2.6 1146 174 681 202.1 59.4 9
1279.10 1485 90 40 49 2.7 0.2 384 126.4 25.9 1.6 740 163.6 49.8 3 2536 337 1323 248.6 52.2 6.9
3025.05 1495 72 145 63.2 9.7 0.5 255 94.6 17.1 0.8 695 148.2 46.5 2.2 1607 219 994 199.6 61.9 8.4
2371.02 895 53 30 30.5 3.4 0.2 180 65.8 20.1 1.2 415 96.3 46.4 2.7 1177 162 860 154.6 73.1 10.1
5018.03 1430 68 65 41.8 4.5 0.2 335 106.3 23.4 1.1 710 144.2 49.7 2.4 1651 212 861 176.6 52.2 6.7
5018.04 730 40 50 30.5 6.8 0.4 135 49.7 18.5 1 325 73.5 44.5 2.4 904 116 532 108.6 58.8 7.6
1241.05 930 47 35 30.5 3.8 0.2 179 71.1 19.2 1 399 98.5 42.9 2.2 1160 149 611 122.9 52.7 6.8
1277.12 1190 45 45 50.4 3.8 0.1 180 80.8 15.1 0.6 635 159.6 53.4 2 1363 238 913 206.5 67 11.7
2088.01 1255 75 110 48.5 8.8 0.5 365 102.6 29.1 1.7 510 123.6 40.6 2.4 1537 268 1043 265.8 67.9 11.8
2187.02 790 39 40 34.2 5.1 0.3 149 59.8 18.9 0.9 400 83.7 50.6 2.5 868 145 564 121.9 65 10.9

81
Census
Tract
Total No. of
Occupied Units
MOE
Estimate:
Lacks Plumbing
MOE
% Lacks Plumbing
MOE
Estimate:
Overcrowded
MOE
% Overcrowded
MOE
Estimate:
Cost Burdened
MOE
% Cost Burdened
MOE
Total No. of
Workers
MOE
Estimate: Long
Commute
MOE
% Long
Commute
MOE

1278.05 1140 56 90 75 7.9 0.4 200 90.1 17.5 0.9 485 152 42.5 2.1 1800 217 1152 234.9 64 7.7
5511.01 1195 40 29 28.2 2.4 0.1 190 82.3 15.9 0.5 505 136.6 42.3 1.4 1563 182 825 172.6 52.8 6.1
2117.03 2055 95 85 57.3 4.1 0.2 284 104.2 13.8 0.6 935 178.6 45.5 2.1 2858 331 1725 305.4 60.4 7
5342.02 1240 58 45 38.9 3.6 0.2 420 103.4 33.9 1.6 515 130.2 41.5 1.9 1948 301 1199 245.8 61.6 9.5
5348.03 1150 55 35 40.2 3 0.1 260 100.1 22.6 1.1 515 143 44.8 2.1 1928 277 1030 230.9 53.4 7.7
6006.02 740 44 20 21.6 2.7 0.2 214 60.4 28.9 1.7 330 79.1 44.6 2.7 985 152 579 128.4 58.8 9.1
5402.03 1280 66 30 38 2.3 0.1 270 91 21.1 1.1 615 145.6 48 2.5 2052 224 1119 240.5 54.5 6
6009.12 1500 68 40 48.1 2.7 0.1 210 79.4 14 0.6 685 144.5 45.7 2.1 2488 301 1406 261.6 56.5 6.8
5409.01 1145 83 45 46 3.9 0.3 265 109.8 23.1 1.7 525 137 45.9 3.3 1850 301 1150 299.1 62.2 10.1
2225 1350 53 45 42.5 3.3 0.1 295 90.8 21.9 0.9 600 145.6 44.4 1.7 2103 267 1372 258.3 65.2 8.3
2383.20 1130 61 55 54.3 4.9 0.3 260 93.9 23 1.2 490 128.7 43.4 2.3 1192 161 814 166.3 68.3 9.2
2123.06 1175 67 30 24.2 2.6 0.1 280 88.3 23.8 1.4 499 113.2 42.5 2.4 1453 228 870 233.8 59.9 9.4
2126.20 1845 69 45 45.6 2.4 0.1 525 143.1 28.5 1.1 930 184.1 50.4 1.9 2352 260 1305 243.7 55.5 6.1
2242 735 33 60 36.1 8.2 0.4 215 58.6 29.3 1.3 309 82 42 1.9 1187 177 700 149.2 59 8.8
2362.02 2520 119 85 72 3.4 0.2 390 152.1 15.5 0.7 1430 267.7 56.7 2.7 2451 325 1519 313 62 8.2
2283.20 715 51 25 29.5 3.5 0.3 240 72.9 33.6 2.4 330 76.5 46.2 3.3 1393 240 744 160.5 53.4 9.2
2288 1215 57 35 45.3 2.9 0.1 330 104.1 27.2 1.3 555 136.8 45.7 2.1 1983 235 1177 228 59.4 7
2409 1435 67 70 74.3 4.9 0.2 315 118 22 1 660 166.8 46 2.2 2143 373 1269 293.1 59.2 10.3
2411.20 1305 50 40 40.2 3.1 0.1 320 108.7 24.5 0.9 700 148.8 53.6 2.1 1727 342 1137 263.2 65.8 13
2316 2000 100 44 40.2 2.2 0.1 395 130.1 19.8 1 860 178.1 43 2.2 2695 307 1549 263.7 57.5 6.5
2319 1450 91 45 52.8 3.1 0.2 355 126.8 24.5 1.5 775 175.8 53.4 3.4 1875 265 1084 277.2 57.8 8.2

82
Census
Tract
Total No. of
Occupied Units
MOE
Estimate:
Lacks Plumbing
MOE
% Lacks Plumbing
MOE
Estimate:
Overcrowded
MOE
% Overcrowded
MOE
Estimate:
Cost Burdened
MOE
% Cost Burdened
MOE
Total No. of
Workers
MOE
Estimate: Long
Commute
MOE
% Long
Commute
MOE

2422 1645 58 55 50.9 3.3 0.1 384 113.8 23.3 0.8 700 149 42.6 1.5 1930 280 1253 242.3 64.9 9.4
2431 1420 71 45 52.8 3.2 0.2 280 103.2 19.7 1 695 161.9 48.9 2.4 1635 259 994 191.8 60.8 9.6
5703.01 2255 68 185 116.3 8.2 0.3 425 169.3 18.8 0.6 985 270.2 43.7 1.3 3267 390 1775 397.2 54.3 6.5
3790 1830 115 80 68.6 4.4 0.3 300 102 16.4 1 760 179.1 41.5 2.6 2455 301 1286 286.2 52.4 6.4
5336.01 1100 56 30 34.2 2.7 0.1 245 85.6 22.3 1.1 500 131.3 45.5 2.3 2013 243 1088 237.9 54 6.5
4809.02 1325 80 35 32.3 2.6 0.2 180 74.8 13.6 0.8 555 137.7 41.9 2.5 2122 251 1244 271.6 58.6 6.9

Abstract (if available)

Abstract Short of becoming homeless, everyone must live somewhere, but the circumstances leading to an individual’s choice of housing can be complex. Housing choices represent both personal factors and outside influences and are often wrapped up in the overly simplified concept of “housing affordability.” In California, the unaffordability of housing is particularly acute. This thesis uniquely combined multiple datasets from the US Census Bureau and the US Department of Housing and Urban Development to classify areas of the state according to the number of select housing-related challenges that residents experienced as a result of their housing accommodations. The challenges were then mapped, individually and collectively, to observe the geographic distribution of the phenomena. This innovative method supplements the 30-percent ratio (of housing costs to income) methodology traditionally used to denote housing affordability and adds a visual and spatial display of housing challenges at a statewide level and in several focus areas that have been negatively impacted by the current housing crisis. Finally, a review is provided of existing and potential solutions to the four housing challenges investigated. The results may be of interest to affordable housing providers, legislators, and even residents.

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Asset Metadata

Creator Graham, Lucresia (author)

Core Title A cartographic exploration of census data on select housing challenges among California residents

School College of Letters, Arts and Sciences

Degree Master of Science

Degree Program Geographic Information Science and Technology

Degree Conferral Date 2021-12

Publication Date 09/29/2021

Defense Date 08/18/2021

Publisher University of Southern California (original), University of Southern California. Libraries (digital)

Tag ACS data,American Community Survey data,California,Cartography,census,CHAS data,commute,Comprehensive Housing Affordability Strategy data,GIS,Housing,housing affordability,housing challenges,housing cost burden,housing costs,housing problems,HUD,hypermobility,mapping,OAI-PMH Harvest,overcrowding,Plumbing,spatial analysis,unaffordability

Format application/pdf (imt)

Language English

Contributor Electronically uploaded by the author (provenance)

Advisor Swift, Jennifer N. (committee chair), Ruddell, Darren (committee member), Wilson, John P. (committee member)

Creator Email lucresia@usc.edu,lucypendl@gmail.com

Permanent Link (DOI) https://doi.org/10.25549/usctheses-oUC16011582

Unique identifier UC16011582

Legacy Identifier etd-GrahamLucr-10114

Document Type Thesis

Format application/pdf (imt)

Rights Graham, Lucresia

Type texts

Source University of Southern California (contributing entity), University of Southern California Dissertations and Theses (collection)

Access Conditions The author retains rights to his/her dissertation, thesis or other graduate work according to U.S. copyright law. Electronic access is being provided by the USC Libraries in agreement with the author, as the original true and official version of the work, but does not grant the reader permission to use the work if the desired use is covered by copyright. It is the author, as rights holder, who must provide use permission if such use is covered by copyright. The original signature page accompanying the original submission of the work to the USC Libraries is retained by the USC Libraries and a copy of it may be obtained by authorized requesters contacting the repository e-mail address given.

Repository Name University of Southern California Digital Library

Repository Location USC Digital Library, University of Southern California, University Park Campus MC 2810, 3434 South Grand Avenue, 2nd Floor, Los Angeles, California 90089-2810, USA

Repository Email cisadmin@lib.usc.edu