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Residential care in Los Angeles: policy and planning for an aging population

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Residential care in Los Angeles: policy and planning for an aging population

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Content Copyright 2020 Stephen Frochen

RESIDENTIAL CARE IN LOS ANGELES:
POLICY AND PLANNING FOR AN AGING POPULATION
by

Stephen Frochen

A Dissertation Presented to the
FACULTY OF THE USC GRADUATE SCHOOL
UNIVERSITY OF SOUTHERN CALIFORNIA
In Partial Fulfillment of the
Requirements of the Degree
DOCTOR OF PHILOSOPHY
GERONTOLOGY

May 2020

ii
ACKNOWLEDGEMENTS

I’m very thankful to Drs. Jennifer Ailshire, Eileen Crimmins, Jon Pynoos, Caroline
Cicero, Reggie Tucker-Seeley, and Susan Enguídanos for supporting me in my research
throughout the USC Davis Ph.D. program and dissertation process. My deepest gratitude,
however, is to Dr. Jennifer Ailshire, who spared no expense in promoting me and my work and
who connected me with resources and professional relationships at every turn of events, all while
respecting my input and making allowances for me in the struggles of the program and
dissertation. I’m particularly grateful to her for regularly and cheerfully funding my conference
travel, purchasing software licenses for me, and meeting my computational needs. I’m fairly
certain that the kind of support and treatment I received at USC Davis under her mentorship are
unheard of in other Ph.D. programs elsewhere.
I would also like to thank the National Institute on Aging (NIA) for supporting me
financially through the Ruth L. Kirschstein Multidisciplinary Research Training Grant in
Gerontology under grant number T32AG000037.
Many thanks to my peers Drs. Connor Sheehan and Seva Rodnyansky for including me
in their own work and supporting me immensely in mine. I hope to collaborate with many other
colleagues as helpful and friendly as both of you in the future.
To my family and friends, I’ve developed a much keener appreciation throughout this
experience for how much I need your support. Mom and dad, thank you for all the trips to
exciting locations to get my mind off of work, especially the Yellowstone National Park and
Mount Rushmore trip after my qualifying exam. Dave and Scott, your friendship has meant the
world as well, especially in recovering from challenges and hardships and celebrating major
milestones, like all the weekend getaways to Yosemite, Palm Springs, San Diego, Death Valley,
iii
Joshua Tree, Sedona, et cetera just to let loose and have a good time. Tom, Phil, Mike, and all
the other guys, our small group meetings and get-togethers have given me strength I didn’t even
know I had. And to Joyce, thank you for supporting me to end.
My sincerest thanks to you all.

iv
TABLE OF CONTENTS
Acknowledgements ......................................................................................................................... ii
List of Tables ................................................................................................................................. vi
List of Figures ............................................................................................................................... vii
Abstract .......................................................................................................................................... ix
Chapter 1- Introduction- Paper 1 .................................................................................................... 1
‘Residential Care for the Elderly: A Review of Long-term Care Development Policy
in California and the United States’

Introduction ......................................................................................................................... 2

Methods ............................................................................................................................... 4

Results ................................................................................................................................. 6

Discussion ......................................................................................................................... 22

References ......................................................................................................................... 24

Tables/Figures ................................................................................................................... 39

Chapter 2- Paper 2 ........................................................................................................................ 41
‘Residential Care in Los Angeles: Evaluation of the Spatial Distribution and
Neighborhood Access to Care among Older Adults’

Introduction ....................................................................................................................... 42

Methods and materials ...................................................................................................... 47

Results ............................................................................................................................... 51

Discussion and conclusion ................................................................................................ 54

References ......................................................................................................................... 62
Tables/Figures ................................................................................................................... 68

Chapter 3- Paper 3 ........................................................................................................................ 72

v
‘Residential Care Development in California: Time Series Analyses of

Facility and Care Capacity Growth’

Introduction ....................................................................................................................... 73

Methods ............................................................................................................................. 75

Results ............................................................................................................................... 78

Discussion ......................................................................................................................... 87

References ......................................................................................................................... 92

Figures ............................................................................................................................... 95

Chapter 4- Paper 4 ...................................................................................................................... 102

‘The eldercare facility ordinance of Los Angeles: A synthetic control analysis of

residential care development and growth’

Introduction ..................................................................................................................... 103

Methods ........................................................................................................................... 107

Results ............................................................................................................................. 115

Discussion ....................................................................................................................... 117

References ....................................................................................................................... 124

Tables/Figures ................................................................................................................. 131

Chapter 5- Conclusion ................................................................................................................ 136

References ................................................................................................................................... 146

vi
LIST OF TABLES

Table 1. California State Programs and Policies Related to Eldercare Facility Development ..... 40

Table 1. Los Angeles County Census Tract Characteristics by Number and Type of
Residential Care Facilities Presented as Mean Percents ............................................................... 69

Table 2. Univariate and Multivariate Zero-Inflated Negative Binomial (ZINB) Regressions
on Total Census Tract Facility Capacity and Age, Race, Poverty, and Disability ....................... 70

Table 1. Synthetic Los Angeles Donor City Weights ................................................................. 131

Table 2. Selected Synthetic Los Angeles V-Matrix Predictor Weights ...................................... 132

vii
LIST OF FIGURES
Figure 1. PRISMA Flow Diagram of Systematic Literature Review ........................................... 39
Figure 2. Timeline of Federal and State Long-Term Care Programs and Policies in the U.S.
and California ................................................................................................................................ 39
Figure 3. Los Angeles Eldercare Facility Permitting Process ...................................................... 40

Figure 1. Hot spot analysis of residential care facility locations in the City of Los Angeles,
1996-2006 ..................................................................................................................................... 68

Figure 2. Hot spot analysis of residential care facility locations in the City of Los Angeles,
2007-2015 ..................................................................................................................................... 68

Figure 3. Predicted facility capacity (beds) by percent of older racial groups in County of
Los Angeles census tracts ............................................................................................................. 71

Figure 1. California Cumulative Facility Growth by Facility Type ............................................. 95

Figure 2. California Cumulative Facility Growth per 10,000 Older Adults ................................. 95

Figure 3. California Cumulative Capacity Growth per 1,000 Older Adults ................................. 96

Figure 4. City of Los Angeles Cumulative Facility Growth by Facility Type ............................. 96

Figure 5. City of Los Angeles Cumulative Facility Growth per 10,000 Older Adults ................. 97

Figure 6. City of Los Angeles Cumulative Capacity Growth per 1,000 Older Adults ................. 97

Figure 7. City of San Diego Cumulative Facility Growth by Facility Type ................................. 98

Figure 8. City of San Diego Cumulative Facility Growth per 10,000 Older Adults .................... 98

Figure 9. City of San Diego Cumulative Capacity Growth per 1,000 Older Adults .................... 99

Figure 10. City of San Jose Cumulative Facility Growth by Facility Type ................................. 99

Figure 11. City of San Jose Cumulative Facility Growth per 10,000 Older Adults ................... 100

Figure 12. City of San Jose Cumulative Capacity Growth per 1,000 Older Adults ................... 100

Figure 13. Yearly Percent Change in Facilities in California, Los Angeles, San Diego, and
San Jose per 10,000 Older Adults Ages 75+ .............................................................................. 101

Figure 14. Yearly Percent Change in Capacity in California, Los Angeles, San Diego, and
San Jose per 1,000 Older Adults Ages 75+ ................................................................................ 101
viii
Figure 1. Synthetic Control Trends: Cumulative Los Angeles and Synthetic Los Angeles
Large Facilities per 10,000 Older Adults 75+ ............................................................................ 131

Figure 2. Permutation Test: Trends of Cumulative Large Assisted Living Facilities and
CCRCs per 10,000 Older Adults 75+ in Los Angeles and Placebo Cities ................................. 132

Figure 3. Los Angeles and Synthetic Control Cities Ratios of Post/Pre-RMSPEs ..................... 133

ix
ABSTRACT

Residential care is an intermediary form of senior housing that bridges the gap between
at-home services and supports and skilled nursing care for functionally impaired older adults.
With a burgeoning older adult population, which is due to double by 2050, the percentage of
older people residing in any form of eldercare housing will more than double by mid-century as
well, increasing the demand of senior housing facilities, putting ever more pressure on cities and
local governments to incentivize, plan, and develop additional residential care facilities for the
impending long-term care need of older adults, particularly in world cities such as Los Angeles.
Unfortunately, little is known about where and how residential care facilities are
developed in Los Angeles, the largest market for the housing type. Most research on residential
care focuses on the cost, service offering, and functional status of older adults in such facilities
and not the institutional frameworks that guide and shape the development of the industry. As
the population of older adults continues to grow, understanding the development and distribution
of residential care facilities will become increasingly important, as residential care helps older
people who are no longer able to stay at home to remain in their neighborhoods (age in place),
reducing isolation, loneliness, and a number of other health outcomes.
The purpose of this dissertation is to test the effectiveness of Los Angeles’ eldercare
facility ordinance on residential care in the city. Los Angeles Planning Staff and City Council
have recognized the importance of these facilities among the city’s large and rapidly growing
aging population and instituted the policy to encourage such development, which theoretically
should have increased the rate of facility development within the jurisdiction since 2006, the year
of its passage. Consequently, the aims of this dissertation in each of three sequential analytical
chapters are to: 1) determine the current state of residential care development in Los Angeles
x
through analysis of the industry’s spatial distribution in the city; 2) examine to what extent
residential care development has increased in Los Angeles and all of California relative to the
long-term care need; and 3) evaluate whether Los Angeles’ Eldercare Facility Ordinance has
increased the rate of residential care development in the city compared to a suitable control
group.
Results show that residential care facilities by type are located and concentrated in
distinct areas of Los Angeles and that the younger old and older Hispanics are least likely to live
in a neighborhood containing residential care as compared to the oldest old in the city. Los
Angeles and California state level outcomes also demonstrate that residential care development
has increased but has slowed in the rate of development, particularly in Los Angeles and
California compared to other large jurisdictions in the state. Finally, results also indicate that the
eldercare facility ordinance of Los Angeles appears to have impacted the growth of residential
care in the city, but only slights and with several qualifications.
Understanding the distribution of residential care and the effectiveness of housing
development policies such as the eldercare facility ordinance on the growth of the industry can
assist planners and policy makers in addressing underserviced areas of long-term care need
through development code ordinances, amendments, general plan guidelines, and other city and
regional planning strategies and policies designed to encourage development of the critical senior
housing type.

1
CHAPTER 1- INTRODUCTION

Title: Residential Care for the Elderly: A Review of Long-Term Care and Development Policy

in California and the United States

Abstract

This literature review outlines the institutional frameworks for long-term care and eldercare
facility development in the United States, focusing on federal and state policies in California
affecting residential care development for the elderly. Research databases were explored,
seeking information on long-term care, residential care, senior housing, affordable housing, land
use, among other search terms. Three main topics were identified in review of the long-term
care and senior housing development literature: 1) the historical context of long-term care in the
U.S., describing the evolution of home and community based services, including residential care
facilities from skilled nursing care, 2) federal programs that subsidize the development of
affordable housing for the elderly, and 3) California State policies that limit municipal
regulations restricting housing development including senior housing to address the current
housing crisis in the state. It finds that federal affordable housing programs are designed
primarily to subsidize service-enriched or mixed occupancy housing complexes that set aside
units for low-income and, in many cases, elderly tenants, whereas state initiatives are devised
mainly to remove regulatory barriers to housing development in local jurisdictions in California,
the largest population and eldercare facility industry in the nation.

2
Introduction
In the history of long-term care (LTC), residential care for the elderly has emerged as an
innovative addition to the services and supports available to functionally impaired older adults in
the United States. Arising out of institutional care, residential care has become an intermediate
form of housing for older adults who can no longer age in place but who desire a less intensive
level of care than what is provided in nursing homes. The underlying intent of the kind of care
offered in such facilities can perhaps be best summed up in the language of the Olmstead
Decision, the landmark Supreme Court ruling at the turn of the 21
st
century prescribing that
people with disabilities, including older adults with functional limitation, should be placed in the
least restrictive care setting possible, given resource constraints and the needs of the broader
disabled community (Olmstead v. L.C., 1999). Although residential care as an industry began
long before the Olmstead Ruling or many of the more progressive policies geared toward
housing for the elderly and the disabled, it originated with the same purpose: LTC offered in a
noninstitutional setting, integrated into the community to preserve the dignity and independence
of older adults. In recent decades, it has expanded to include the institutional settings it
originally stemmed from, many times built into facilities as separate units, such as dementia or
skilled nursing wards. The best example of this multifaceted kind of care are continuing care
retirement communities (CCRCs), which include the full continuum of care, from independent
living, to assisted living with services and assistance with activities of daily living (ADL), and
skilled nursing facilities, often sited in detached buildings as part of larger residential care
developments.
As a noninstitutional network of care that maintains some level of governmental
oversight, what sets residential care apart from other forms of housing for the elderly, however,
3
is its size, diversity, and service offering. Unlike nursing homes, residential care facilities are
regulated at the local level in the U.S., not by the federal government, carrying with them the
character, resilience, and resource limitations unique to their respective states and municipalities,
in which they are planned, developed, and eventually licensed. State governments, in other
words, determine almost entirely how such facilities are developed and administered, through an
individually tailored licensing and regulatory structure that can include the recording of
complaints and allegations of criminal activity as well as site visits to corroborate such
accusations, as is the case of California, the largest market for residential care in the nation
(CDSS, 2017). As a result, across the fifty states, residential care facilities collectively represent
the largest senior housing type for the elderly by number of facilities, operating under a loose
confederation of industry standards and local development policies, the foremost of which are
zoning and land use laws as sanctioned by the federal government (Harris-Kojetin, Sengupta,
Park-Lee, & Valverde, 2013; NCHS, 2016; Village of Euclid, Ohio v. Ambler Reality Co.). In
this respect, residential care as an industry embodies the underlying principle of all planning
activity in the U.S., which tends toward a decentralized form of urban development that responds
to the needs of local communities, guided by the local communities themselves. Although the
average monthly cost of residential care is greater than that of in-home services and supportive
housing, in terms of the services rendered in such facilities and their relative expenses, it is
nearly half the cost of skilled nursing care in the U.S. and offers a wide range of facility types
and sizes of care at various rates (Joint Center for Housing Studies (JCHS), 2014). Among these
are the smaller, more home-like board and care facilities, typically six beds or fewer in size,
which cost much less and normally offer a much smaller staff to resident ratio than larger
facilities, and which represent the vast majority of facilities across the nation. The remaining
4
two types of residential care include assisted living and CCRCs, which are generally larger than
board and care facilities and offer more services and supports but are characteristically more
expensive.
Each of these categories of residential care, as well as all types of housing for the elderly,
experiences a different regulatory load, divided between federal, state, and municipal
government. The research question of this paper, therefore, is to investigate the regulatory
structure of long-term care in the U.S. and at the state level, reviewing the prevailing programs
and policies governing the LTC industry, focusing on residential care and the planning processes
that guide its development in the U.S. and California, the largest share of LTC and residential
care in the nation. It begins by surveying the history of LTC in the United States, then moves to
a more pointed focus on federal laws and programs designed to promote affordable housing for
older adults with functional limitation. It then concludes with a review of California State laws
that encourage and facilitate the development of LTC and senior housing facilities, including
residential care facilities, and the eldercare facility ordinance of Los Angeles, a milestone zoning
regulation that incentivizes eldercare facilities through a series of land use and administrative
policies. Although residential care is a relatively costly form of supportive housing compared to
public housing facilities and other kinds of federally subsidized housing for the elderly discussed
below, this literature review outlines the national and state institutional frameworks for LTC
facility development in general, including policies and programs supportive of residential care
development.
Methods
Because of the complex nature of LTC policy among various levels of government, this
study presents a systematic examination of such policy, providing a brief history of LTC and
5
residential care, the predominant policies at work in publicly funded housing for the elderly, and
the state and local regulations that guide the development of LTC facilities, particularly
residential care facilities.
In reviewing the literature, numerous journal databases were searched, including
ProQuestion Discovery, ProQuest Research Library, Sociology Collection, JSTOR Archival
Journals, PubMed, among others. These databases were explored for research articles;
government, nonprofit, and academic reports; legislation, and literature reviews discussing the
history and policy of LTC in the U.S., focusing on institutional and residential care. Among the
terms used in database searches were history, long-term care, policy, zoning, land use, public
housing, senior housing, housing for the elderly, residential care, aging, among others. Up to
ten articles, reports, or reviews were read per search. Approximately thirty searches were
conducted, followed by examination of the bibliographies of works already found in the initial
searches and general search engine searches, as shown in Figure 1, generating roughly 350
original sources. Saturation of the literature with respect to the development policy and history
of LTC began as three main topics arose in the readings, including the history of long-term care
in general, federal affordable housing programs, and state policies encouraging the development
of eldercare facilities, all three of which accumulated in legislation, federal and state
programmatic literature, and academic publications on each respective topic, with a total of 109
sources included in the text. For example, in reviewing the history of LTC policy, the U.S.
Department of Housing and Urban Development’s (HUD) Section 202 Supportive Housing for
the Elderly Program, the Low Income Housing Tax Credit, and the Housing Choice Vouchers
program, which can be applied to both financing individual housing units for older adults and
development of large scale multifamily complexes, kept reappearing in the reading, as well as
6
the Kerr-Mills Act and the Social Security Amendments of the mid 1950s, which enacted federal
construction support for LTC facilities. Although each of these programs or pieces of legislation
relate to funding for the construction of care facilities, the first three are specific programs that
have subsidized senior housing ventures, while the last two are historic federal funding sources
that fit more appropriately into the narrative of the LTC industry in the U.S. than as active
eldercare facility development programs.
Additionally, since the planning and development of senior housing facilities are
regulated almost exclusively at the state and municipal levels of government, the third topic in
this study, which includes state policies encouraging eldercare facility development, focuses on
development policies in California, the largest market for senior housing facilities in the nation.
Results
A brief history of long-term and residential care in the United States
Reaching as far back in history as the Houses of Tudor and Stuart in Great Britain, the
LTC and residential care industry in the United States has its roots in preindustrial England, in
which a new kind of philanthropy with respect to dwelling places arose as part of legislation
addressing poverty and vagrancy, eventually forming a type of care home known as the
almshouse (Nicholls, 2017). As described by Nicholls, although the development of this form of
housing precipitated the relocation of impoverished people to a state of relative internment, many
of whom were the older poor, it culminated in a network of homes administered by local parishes
that provided accommodations and other resources to the underprivileged. In the period of time
straddling preindustrial and industrial Britain, almshouses ultimately devolved into an unequally
allocated system of halfway houses, typically cropping up in wealthier parishes, which largely
7
functioned as intermediate places of abode for the working poor seeking room and board in
workhouses.
Although the form and character of these houses in England differed from those in early
American settlements, which were often attached to farms as a means of sustenance for the
impoverished and sick, almshouses or county homes continued in colonial America through to
the early 20
th
century in the U.S. (Gaur, 2013). Because such homes commonly suffered
unsanitary conditions, serving as the last resort in care for the chronically ill, in the years leading
up to the New Deal era, they were gradually superseded by the rest home model of care, widely
considered a safer and better administered form of housing for the old and destitute. Beyond
sanitation, the main difference between county homes and rest homes was public versus private
sponsorship. The general model for county homes was local government support as part of
larger farming townships and provinces, whereas rest homes were opened and administered by
religious organizations and cultural groups, endowed by benevolent giving (Gillick, 2017). As
explained by Gillick, by the time the Social Security Act (SSA) of 1935 was passed into law,
almshouses had largely disappeared from the economy due to the appeal of the newer and more
orderly rest homes. And in short order, in the years following the legislation, most remaining
almshouses were driven to closure due to the law’s Old Age Assistance (OAA) program that
excluded older adults in county homes from receiving assistance as part of the program (Gillick,
2018, SSA, 1935). Because of SSA’s compelling disincentive with regard to government
housing for the poor, rest homes became the prevailing mode of LTC for the elderly in the U.S.,
a trend that has continued into the 21
st
century, considering that nursing homes, the modern
equivalent of historic rest homes, are the largest type of LTC facility by number of beds.
8
However, SSA’s comprehensive reforms set only the first of many precedents regarding
LTC and privately operated nursing homes. In the years ensuing the passage of the act, the
federal government extended its financial support to older adults with care needs by adding to
OAA a system of direct payment to care providers as part of the 1950 amendments to SSA
(IOM, 1986; SSA, 1950). The 1950 law additionally overturned SSA’s prohibition of older
adults receiving OAA while residing in public care homes, which were all but outmoded by the
earlier mandate, and set into motion a regulatory structure for licensing nursing homes, in
succession of the 1946 Hill-Burton Act (HBA), an initiative to build and license hospitals and
care centers across the country (Hochban, Ellenbogen, Benson, & Olson, 1981; HBA, 1946).
Amendments to both HBA and SSA in 1954 and 1956, respectively, provided federal funds for
the construction of nonprofit nursing homes and the services provided in them (Moroney &
Kurtz, 1975; SSA, 1956), and other legislation during the same decade added to the federal
funding priority the construction and licensing of privately-owned nursing homes (Regan, 1975).
Further, at the end of the decade, the Kerr-Mills Act succeeded OAA in providing coverage for
older adults not receiving assistance under the program but whose medical payments would be
insurmountable given their income, implementing the first federal program in U.S. history
specifically tailored to older people with medical needs known as the Medical Assistance for the
Aged (MAA) program (Moore & Smith, 2005; KMA, 1960).
As a result of problems related to facility licensing, however, particularly a lack of
consensus regarding standard operating procedures and the enforcement of such procedures, the
federal government ratified a sweeping series of initiatives and laws, intended to foster a safer
regulatory climate surrounding skilled nursing facilities. At the outset, the Special Committee
on Aging (SCA), in 1961, appointed the Public Health Service (PHS) to study and develop
9
guidelines for facility licensure and standards of care, culminating in the Nursing Home
Standards Guide in 1963, the earliest official document providing some level of governance in
the daily operation of nursing homes (Underwood, 1961; HEW, 1963). During the renowned
Moss Committee hearings of 1963 shortly thereafter though, the SCA reported widespread
disparity in the quality of care in nursing homes, the findings of which were a major impetus for
the landmark Medicare and Medicaid Programs of 1965, which further increased federal funding
for nursing home care and which implemented the standards designed by PHS (IOM, 1986; SSA,
1965). Although Medicare and Medicaid brought about the first of many extended care facilities
(ECF), funded by Medicare for acute treatment subsequent to hospitalization and by Medicaid
for additional LTC following a normal period of hospital convalescence, the programs initially
experienced low rates of facility certification in application of PHS’ nursing homes standards
and were eventually forced to relinquish control to states as part of changes to Medicaid in the
1967 Social Security Amendments, abandoning the ECF care model as conceived in the original
legislation. These changes, provided under the Moss Amendments of the law, afforded
flexibility in facility certification to states, which could pick and choose care services under SSA
prior to the 1967 amendments as well as newer services under the most recent amendments
(SSA, 1968). In spite of this, after a series of tragic nursing home incidents receiving heavy
media attention and additional SCA hearings demonstrating shortcomings in adherence to
nursing home standards and the rate of facility certification under the 1967 amendments, the
1974 Social Security Amendments, more commonly known as the Social Services Amendments
of 1974, established a decisive set of regulations regarding nursing home certification and
enforcement of facility standards as a condition to patient coverage under the Medicare and
Medicaid Programs (KFF, 2015; HEW, 1976, SSA, 1975).
10
Since the 1974 amendments, two main branches of regulation have developed in federal
oversight of nursing homes, one further improving the quality of care in and funding for skilled
nursing facilities, and another launching home and community based services (HCBS) as an
alternative to institutional care. The most significant of each is the Omnibus Budget
Reconciliation Act (OBRA) of 1987, including the Nursing Home Reform Act (NHRA), creating
a uniform facility evaluation process, prompted by decades of widespread poor-quality care, and
OBRA of 1981, including the HBCS Waiver Program, providing states the choice of in-home
and community-based care as an alternative to nursing home care for Medicaid eligible older
patients (Walshe & Harrington, 2002; OBRA, 1987; LeBlanc, Tonner, & Harrington, 2000;
OBRA, 1981). Subsequent legislation to OBRA of 1987, including the Patient Protection and
Affordable Care Act (PPACA) of 2010, has amended federal requirements for LTC facilities on
a continual basis, adding to the legislation, among other mandates, a resident bill of rights,
freedom from mistreatment, and pharmaceutical care, along with earlier requirements for
appropriate nutrition, hydration, assistance with incontinence, quality of life, activity and
recreational selection, et cetera (HHS, 2019; KFF, 2013; PPACA, 2010). Successive legislation
to OBRA of 1981 in recent years, including PPACA, has defined and promoted person-centered
care requirements in noninstitutional settings and permitted the alignment of existing waivers for
Medicare and Medicaid qualified (dual eligible) patients with state planning regulations, using a
regular cycle of renewal in combination of the two (HHS, 2014).
Although primarily regulated by individual states (as discussed further with regard to
housing development in the third subsection below), and privately funded by older patients of
means, residential care, a type of HCBS, has received federal sponsorship as part of the waiver
on a limited basis for dual eligible patients since the program’s inception. In California, the
11
waiver program has been in effect since 2006, the most recent renewal of which took place in
2014 as part of state expansion efforts into underserviced counties (CDHCS, 2019). Though
residential care is a much newer arrival among LTC services as opposed to skilled nursing care,
in California it has existed since the mid 1960s, supported predominantly by the private funds of
older adults able to afford the service and underregulated in contrast to nursing home care and
residential care facilities operating under the HCBS Waiver Program (CDSS, 2017). To that
end, the most important piece of legislation signed into law since the beginning of residential
care as an industry in the state is the California Residential Care Facilities for the Elderly Act
(CRCFEA) of 1985, which created a separate regulatory framework comprised of numerous bills
for residential care as opposed to institutional care (CANHR, 2013). The original goal of the act,
according to the advocate organization, was the creation of a multitiered system of care,
responding to the diverse needs of older adults requiring such care. However, as detailed by
CANHR, because of a lack of additional state legislation securing funds for this portion of the
law, specifically its implementation, CDSS, the licensing agency under the act, has been
unsuccessful in devising a graduated care structure and has instead adapted a single licensing
process for all facilities in the state, regardless of size, resources, or patient need. In effect,
CDSS in many cases has turned a blind eye to facilities lacking appropriate medical resources
that have admitted patients with significant care need, presumably because of California’s
growing demand as the largest population of older adults with functional limitation in the nation
as well as the industry’s majority of board and care facilities, which are typically opened and
licensed in single family homes as opposed to medicalized facilities. After decades of residential
care licensing and ten successive bills reforming the industry in the state, the California
Residential Care for the Elderly Reform Act (CRCFEFA) of 2014, sponsored by CANHR and
12
composed of ten separate assembly and state bills, has taken the greatest strides in bolstering the
regulatory structure of facilities, mandating a patient bill of rights, increased training of facility
staff, facility liability insurance, systematic inspections, and punctual investigations following
complaints and allegations of criminal activity, among other requirements (Geraci, Hutchison, &
Brunt, 2015; CANHR, 2019). In this way, although residential care is not designed to provide
the same level of care as skilled nursing facilities, it has become a more trustworthy LTC option
for older adults requiring functional assistance, despite legislative impediments to a system of
facility tiering, which would properly distinguish levels of care need across the state’s inventory
with appropriate levels of oversight.
Federal programs for affordable housing for the elderly
In addition to regulating the skilled nursing care industry, the federal government has
supported public, nonprofit, and privately-owned housing for low-income older persons in the
United States since the early days of the FDR Administration. In the years directly following the
New Deal, as mentioned in the previous subsection, the federal government through OAA began
providing LTC assistance to older adults with functional limitation, indirectly subsidizing care
through disbursements to those living in nursing homes prior to the 1950 Social Security
Amendments, and directly financing it through payments to both public and nonpublic care
providers as part of the newly altered act, as well as in subsequent entitlement programs such as
Medicare and Medicaid (SSA, 1950). During this period of increasing old age health care and
LTC benefits, the Section 202 Supportive Housing for the Elderly Programs as part of the
Housing Act of 1959 was signed into law, the first federal subsidy dedicated exclusively to
affordable senior housing development (HUD, 2019c; HA, 1959). Prior to 1959, the only other
major senior housing subsidy available to developers was the Section 515 Rural Rental Housing
13
Loans Program of the Housing Act of 1949, which has funded loans for multifamily rental
property development in rural communities throughout the nation, servicing low-income older
adults on a limited basis (Stone, 2018; HA, 1949). The Section 202 program for sixty years has
financed loans to nonprofit and private builders to develop reliable and long-standing housing
and services for the elderly as a supportive resource for very low-income older adults with
disability. Since its enactment, the program has funded more than 6,000 developments, housing
in excess of 400,000 elder led families, and has provided rent assistance to waitlisted low-income
older adults based on income, occupancy, and citizenship eligibility requirements (HUD, 2008;
2009; 2012). Among these developments include residential care facilities that have been
converted into service enriched housing (SEH), designed to assist older adults with frailty age in
place, financially supported by Section 223 of the Low-Income Housing Preservation and
Resident Homeownership Act (LIHPRHA) of 1990, and Sections 221 and 236 of the National
Housing Act (NHA) of 1934, (LIHPRHA, 1990; NHA, 1934; HUD, 2019a). Although newer
federal housing programs have become more commonplace in the senior housing development
industry, Section 202 housing has remained until recently an important federal collaboration with
property developers, garnering a reputation as a uniquely age friendly type of living space,
fostering independent living throughout an entire housing structure rather than in only a subset of
units, as in the case mixed occupancy developments, which are typically subsidized through
other federal programs (AARP, 2006).
Since the early Section 202 loan program, however, federal initiatives encouraging the
development of affordable housing for the elderly have expanded into three broad categories,
which are more frequently employed in senior housing development than Section 202 loans due
to their flexible financing, interdependence as federal housing programs, and adaptable
14
occupancy standards in terms of age and income requirements (Scally, Gold, Hedman, Gerken,
& DuBois, 2018; JCHS, 2008). These categories include tax credits supporting property
development, rent subsidy programs, and block grants promoting municipal housing programs
(Turner & Kingsley, 2008). The first of these embodied in the Low Income Housing Tax Credit
(LIHTC) as part of the Tax Reform Act (TRA) of 1986 has become the prevailing method of
publicly financed affordable housing development in the country, having been tested and borne
out of the severely dysfunctional housing market of the 1970s (NLIHC, 2015; TRA, 1986; OCC,
2014). During this time period, as a result of widespread divestment in social programs for the
mentally ill and poor, an entire population of impoverished and at-risk individuals became
homeless in rapid succession and multiplied in number throughout the remaining decade and into
the 80s, whom property owners in turn refused to rent to, opting out of Housing Choice Voucher
(HCV) Section 8 subsidies emanating from the Housing Act of 1937 (part of the second category
of federal housing programs mentioned above) (OCC, 2014; Ray et al., 2018). In an odd turn of
events, the very rent subsidies designed to support low-income families and the homeless in
response to extensive cuts in social and affordable housing development programs beginning in
the Nixon Administration were what provoked affordable housing reform in the mid 80s, leading
to the LIHTC. The main innovation of the program since the tumultuous 70s and 80s has been
private financing, in which venture capitalists receive, in exchange for investment capital, a
“dollar-for-dollar” decrease in the amount of taxes owed to the federal government, in the form
of credits applied to tax returns over the course of a decade (Gudzinas, 2017, p. 1). In this way,
private funding (which is often more fluid than federal advances, the kind employed, for
instance, in Section 202 housing loans) can be generated to offset the regularly insufficient
revenue streams supporting affordable housing development, most often including low rents and
15
return on investment. In recent years, the LIHTC has added an average of 40,000 low-income
housing units for the elderly to the housing market each year, a substantially higher production
rate than Section 202 housing, the next most productive federal housing development subsidy,
which has been in effect nearly twice as long as the LIHTC (HUD, 2015). Additionally, since its
establishment, three recent laws have temporarily augmented the LIHTC by increasing its
investment threshold and federal matching support limit for credits, consisting of the Housing
and Economic Recovery Act (HERA) of 2008, the Tax Credit Assistance Program (TCAP) of
the American Recovery and Reinvestment Act (ARRA) of 2009, and the Consolidated
Appropriations Act (CAA) of 2018, respectively (Gudzinas, 2017; Novogradac & Graff, 2008;
HERA, 2008; ARRA, 2009; CAA, 2018). In contrast, as part of changes to tax law under the
Trump Presidency in 2017, decreases in corporate taxes have rallied low-income housing
advocates toward further housing reform, out of concern that lower taxes may disincentivize
LIHTCs and negate affordable housing efforts in the long term, which depend heavily on the
program (CRS, 2019).
Block grants, the third type of federal housing program discussed above, also arose out of
the turmoil of the 1970s, emanating, as with Section 8 rent subsidies, from the Housing and
Community Development Act (HCDA) of 1974, which created the Community Development
Block Grant Program (CDBG) (HCDA, 1974). As a separate federal program, what sets HCDA
and CDBG apart from rent assistance and tax credit programs is government to government
incentive, wherein HUD awards, on a flexible basis, grants to states and municipalities that
actively produce and preserve affordable housing for vulnerable populations (HUD, 2019b).
Over the years, the program has been amended and enhanced to more equitably apportion
funding across localities and states, a major milestone of which is the Cranston-Gonzalez
16
National Affordable Housing Act (NAHA) of 1990, which established the Comprehensive
Affordable Housing Strategy (CAHS), requiring states and local governments in receipt of block
grants to more effectively distribute resources based on housing needs assessments (NLIHC,
2015; NAHA, 1990). Perhaps the most innovative example of low-income housing block grants
in this respect is the HOME Investment Partnerships Program, as part of NAHA, which allots
affordable housing development funding to prospective states and local governments based on
the following criteria: the amount of unaffordable housing units, how old the housing stock is,
and the poverty rate (CRS, 2014). As the largest block grant in the CDBG program, HOME has
been rated highest among federal subsidies by housing developers as an adaptable investment
leveraging tool that directs public resources to the neediest of cities and neighborhoods,
attracting additional private, local, and state financing in the most flexible manner possible for
receiving communities (Habitat for Humanity, 2016; HUD, 2016).
Conceivably the most pioneering yet currently limited federal initiative for senior
housing development is the Rental Assistance Demonstration (RAD) program as part of the
Consolidated Appropriations Acts of the past several years, which rehabilitates Public Housing
Agency (PHA) properties into privately owned and operated developments through the Section 8
Project-Based Voucher (PBV) rent subsidy and Section 8 Project-Based Rental Assistance
(PBRA) building subsidy programs, descending from the HDCA of 1974 and the HA of 1937,
respectively (CAA, 2019; HUD, 2013; HDCA, 1974; HA, 1937; HUD, 2018). The RAD
program, in effect, reappropriates federal funds allocated to PHAs for the administration and
maintenance of public housing properties, shifting them to tenant and building specific vouchers
in support of these existing properties, salvaging languishing public housing projects eligible for
the program by transferring their ownership to the private sector (Roller & Cassella, 2018). The
17
two main improvements of the initiative are elimination of PHA funding streams that prohibit
private investment in housing ventures, which have long been inadequate in maintaining the
affordable public housing stock and, by consequence, replacement of PHA funding with Section
8 housing vouchers, which are well-known industry subsidies that have leveraged private capital
in affordable housing development since the mid 70s, as with LIHTCs, in addition to other forms
of public assistance, such as Section 202 housing subsidies (HUD, 2019d). The program’s
approach is similar to recent redevelopment efforts supported by the National Housing Trust
Fund (NHTF) of HERA, in which PHAs receive trust funding in addition to LIHTC and Section
8 HCV support to restore dilapidated public housing structures, but without the explicit
understanding of public to private change of ownership (HERA, 2008; CAA, 2012; MHC, 2018).
Since RAD’s onset, nearly 100,000 properties have been converted from PHA administered
projects to nonprofit or privately-owned housing developments, nearly twice as much as the
program’s original cap of 60,000 units, and with the promise of nearly five times that amount
across the program’s duration, according to newly approved appropriations legislation (Cohen,
2017; Roller et al., 2018). Moreover, recent qualitative research has found that newly revitalized
RAD developments show a promising level of quality in their conversion to proprietorship from
public holding, instilling some confidence among affordable housing and senior living advocates
in the program as another viable option for low-income housing development for the elderly
(Hernandez, Moore, & Lazzeroni, 2019).
California state programs and policies related to eldercare facility development
Although long-term care policy in the U.S. is coordinated to varying degrees between
federal and state governments, as explained in the previous section related to tax credits, rent
subsidies, block grant programs for facility development, and the California HCBS waiver
18
program, most state and local development policies remain entirely within the purview of
individual states and their jurisdictions, with little regulatory oversight from the federal
government, except with regard to the financing of such development. Furthermore, within the
state’s regulatory framework for building and development, local municipalities initiate and
oversee almost all aspects of such processes, with limited state intervention as required, as with
recent state interventions in California due to its decades old housing crisis.
As the largest populace, older adult population, and eldercare industry in the country,
consequently, California has suffered a housing emergency for decades, reporting lower and
lower rates of housing production, which have decreased from more than 300,000 units per year
(more than 150,000 multifamily units and a little less than 150,000 single family units) in 1986
to 100,000 units per year (more than 50,000 multifamily units and slightly less than 50,000
single family units) in 2016 (CDHCD, 2018a). Averaging only about 80,000 new units annually
over the past decade, compared to greater than 200,000 each year from the mid 1950s to the turn
of the century, according to CDHCD, California has begun to prioritize the development of
housing as never before, as the state’s population continues to balloon and urban areas continue
to become more dense. As a result, in support of its sizeable infrastructure, terrain, and growing
number of residents, the state has authorized an entire body of legislation and funding
mechanisms intended to increase housing opportunities within its borders, including supportive
housing developments and affordable eldercare facilities. In these respects, California endures as
a noteworthy geography and long-term care industry within the U.S., a demographic and
legislative outlier meriting increased investigation regarding development and the institutional
frameworks supporting the housing industry both locally and nationally.
19
The most recent and drastic of these initiatives, superseding a number of previous
housing growth laws in the state, is the Housing Package of 2017 and subsequent amendments to
the Housing Package in 2018, as shown in Table 1, a collection of state senate and assembly bills
designed to increase funding, project approval, and improve accountability for all forms of
housing development and with special incentives for affordable housing expansion (CDHCD,
2019). Among the more prominent pieces of legislation related to eldercare facility development
in the package are Senate Bill (SB) 35, Streamline Approval Process, which mandates a
simplified and ministerial approval process for infill development in cities not in conformance
with housing supply targets as directed by the bill, with the result that jurisdictions are compelled
to forgo discretionary review of project entitlements, on the condition that such projects are in
compliance with municipal development code (S.B. 35; CDHCD, 2018d); Assembly Bill (AB)
73, Streamline and Incentivize Housing Production, which provides state monetary incentives for
housing sustainability overlay districts in jurisdictions planning to increase their affordable
housing stock, creating a subset of housing development goals within a city district to preserve or
increase housing construction (A.B. 73; CDHCD, 2018b); SB 540, Workforce Housing
Opportunity Zones, which allows city planning departments to approve housing developments
that may present negative impacts to the environment, under the conditions of the California
Environmental Quality Act (CEQA) of 1970 and subsequent reform legislation, in zones
established by the Planning Commission and City Council, without reporting such impacts to the
state (S.B. 540, 2017; A.B. 2045, 1970); AB 678, Housing Accountability Act, which amends
the Housing Accountability Act (HAA) of 1982, requiring cities disapproving low to moderate
income housing projects to provide supporting substantial evidence as to their rationale (A.B.
678, 2017); SB 167, Housing Accountability Act, a companion bill to AB 678, which also
20
amends HAA, imposing a court-ordered fine of $10,000 on municipalities failing to document a
“preponderance of evidence” when rejecting affordable housing developments within their
jurisdictions (S.B. 167, 2017, p. 1); SB 2, Building Jobs and Homes Act, which generates state
funding for municipal plans that encourage affordable housing development and preserve
existing mixed-occupancy developments containing affordable housing units (S.B. 2, 2017;
CDHCD, 2018c); SB 3, Veterans and Affordable Housing Bond Act, which creates bond funding
supporting existing affordable housing developments, infill development, transit-oriented
development, among other housing expansion initiatives (S.B. 3, 2017); AB 1505, Inclusionary
Zoning, which allows jurisdictions to mandate a set amount of affordable housing units prior to
approval of rental housing development (A.B. 1505, 2017); and AB 1521, Preserve the Existing
Affordable Housing Stock, which orders the selling of federally subsidized developments to state
authorized purchasers (A.B. 1521, 2017).
Among the initiatives as part of the 2018 amendments to the 2017 Housing Package are
AB 2372, State Density Bonus Law Floor Area Ratio Bonus, which allows municipal planning
departments to approve multifamily housing developments with a floor area ratio bonus instead
of a unit per acre bonus, allowing bulkier buildings in housing construction, for developments
containing at minimum 20 percent low to moderate income units (A.B. 2372, 2018); AB 3194,
HAA Amendments, which restricts local jurisdictions from denying the approval of housing
projects that conform with general plan requirements and development standards as part of
municipal code requirements (A.B. 3194, 2018); SB 765, SB 35 Amendments, which negates
CEQA directives in ministerial endorsement of entitlement applications as part of infill housing
development under SB 35 (S.B. 765, 2018); AB 2162, Supportive Housing Use “By Right,”
which compels local governments to reclassify supportive housing (housing with supports and
21
services) as a ministerial use in high density residential and mixed use zones (A.B. 2162, 2018);
AB 829, Prohibitions on Local Government Requirements for State Funding Assistance, which
restricts municipalities from arbitrarily demanding City Councilmember approval for supportive
housing development as a condition for state funding support (A.B. 829, 2018); SB 828, Land
Use: Housing Element, which requires local municipalities to allocate more land for residential
development, zoning areas previously set aside for other uses, amending Regional Housing
Needs Assessment (RHNA) procedures as part of the Housing Element Law (HEL) of 1969
(S.B. 828, 2018); and AB 1771, Planning and Zoning: Regional Housing Needs Assessment,
which augments the RHNA process with state supported regional calculations of necessary
housing units to meet the demand of an ever growing population in the state (A.B. 1771, 2018).
While the State of California exerts considerable influence in promoting and enforcing
housing development opportunities, including the construction of eldercare facilities, cities and
counties assume the most responsibility and control of such development. Although numerous
development standard regulations in city municipal codes throughout the state guide and
encourage the growth of eldercare facilities, perhaps the best model of municipal direction that
encourages the growth of eldercare facilities including residential care facilities in California and
elsewhere in the nation is the Los Angeles eldercare facility ordinance of 2006 (Los Angeles,
California, 2006). As the second largest metropolitan area in the U.S., Los Angeles, like the
whole of California, is in the midst of a housing crunch, particularly with regard to housing for
the elderly, and in light of the growing housing demand and the ever increasing older adult
population, Los Angeles City Council approved the ordinance to increase the development of
such facilities. Configured with greater organizational capacity and development incentives than
other senior housing ordinances in the state (such as Anaheim, Escondido, Imperial Beach,
22
Redondo Beach, among others, which mainly offer relief from density, parking, building height,
and setback requirements) (Anaheim, 2009; Escondido, 2011; Imperial Beach, 2009; Redondo
Beach, 2017), the Los Angeles ordinance, an unprecedented land use and administrative
planning tool, was devised to streamline and simplify the permitting and approval process of
eldercare facilities within the jurisdiction, incentivizing such development in three specific and
interrelated ways. First, the ordinance collapses all planning permits related to a proposed
eldercare facility (including, for instance, conditional use permits and variances) into a single
eldercare facility permit administered by a single planner as part of city planning staff,
coordinating the multifaceted development process of large scale multifamily projects in the city.
The policy, additionally, sanctions the development of eldercare facilities in any zone in the city
with the approval of an eldercare facility permit, increasing the potential for eldercare
development beyond residential, commercial, and mixed-use zones in the jurisdiction. Finally,
the ordinance absolves developers of specific plan requirements at the discretion of the Zoning
Administrator, which are additional development regulations in specific city neighborhoods,
granting relief from potentially burdensome development requirements that could disrupt the
approval of such projects. All of these administrative tools, as shown in Figure 3, serve to enable
and accelerate LTC facility development in Los Angeles in a way not promoted in other
California cities in terms of simplified permitting, expanded development opportunities in
previously restricted zones, and relief from specific plan requirements.
Discussion

This review identified the predominant institutional frameworks as part of federal and
California State policies that encourage senior housing development. It revealed three general
topics arising out of the literature with respect to residential care, including the history of LTC
23
and residential care, federal programs that subsidize affordable housing in the U.S., and state
policies in California that limit restrictions to senior housing development in state jurisdictions.
This review summarized advances made by the federal government and California State
Legislature in increasing senior housing development opportunities at the state and national
level, including skilled nursing facilities, service enriched housing, and affordable supportive
housing for the elderly. It also demonstrated that federal affordable housing programs are geared
mainly toward the funding of senior housing projects, whereas state and local housing policies
and programs are designed primarily to remove regulatory barriers to housing development at the
municipal level of government. Researchers, policy makers, and developers can use this
research to identify affordable housing programs for the elderly at the national level and as a
basis for research on housing growth legislation in California and other states not addressed in
this study.

24
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39
Figure 1. PRISMA Flow Diagram of Systematic Literature Review

Figure 2. Timeline of Federal and State Long-Term Care Programs and Policies in the U.S. and
California

40

Figure 3. Los Angeles Eldercare Facility Permitting Process

41
CHAPTER 2

Title: Residential Care in Los Angeles: Evaluating the Spatial Distribution and Neighborhood

Access to Care among Older Adults

Abstract

Residential care has increased in number of facilities and has grown in density in urban areas, yet
it is disproportionately dispersed in cities and only beginning to meet the current long-term care
need of older adults as an alternative to institutional and in-home care. California State
Department of Social Services residential care facility data were linked with Los Angeles County
census tract data to examine the spatial distribution of facilities through hot spot analysis of small
and large clusters of facilities and zero-inflated negative binomial regression of census tract
facility counts on older age and race groups, older disabled adults, and older adults in poverty in
the area. The results show clusters of large facilities west of downtown Los Angeles and clusters
of small facilities in the northern suburbs of the city in the San Fernando Valley. Increases in pre-
and early-retired adults and older Hispanics in census tracts are associated with the greatest
decreases in facility tract capacity in the area, whereas increases in the oldest old and older
disabled adults are associated with the greatest increases. Understanding spatial disparities in
residential care can help local agencies and developers plan and partner in more intentional and
equitable development of facilities. The greatest opportunity for such development may lie in
institutional tools for eldercare facility development such as the eldercare facility ordinance of
Los Angeles and development of board and care facilities in residential zones of Los Angeles and
other cities.

42
Introduction

As the population of older adults in the United States continues to grow, projected to total
more than 80 million by 2050, providing safe and affordable housing for the elderly has become
a critical task for policy makers, states, local authorities, and service providers. Although helping
older people stay in their homes for as long as possible (aging in place) has been the prevailing
strategy for housing the nation’s aging population, considering the difficulty of moving from
home in old age and costliness of long-term care (LTC) (Joint Center for Housing Studies
(JCHS), 2014), residential and skilled nursing care present housing opportunities for older adults
unable to stay at home. The current provision of residential and institutional care, however, is
sharply below demand with respect to present and growing densities of older adults in the U.S.,
reflecting the broader challenge of unsustainable housing development, which is not at pace with
population growth in heavily urbanized states such as California (Lewis and Neiman, 2002).
Understanding the spatial distribution of care facilities relative to current concentrations of older
populations can help local and regional service providers, planners, and developers identify and
anticipate existing and future care need, particularly in underserviced areas.
Residential care, the umbrella term for non-institutional supportive housing for
functionally impaired older adults, has become an alternative to in-home services and supports as
well as costly nursing home care. Through the lens of social economics, residential care has
become a full-fledged and highly specialized service, concentrating in different sizes and shapes
in areas of significant need (Pugh, 1986), typically in communities densely populated by older
adults who can afford the cost. Three main kinds of residential care exist, differentiated by size,
location, and services offered: 1) assisted living, 2) board and care, and 3) the full-service
continuing care retirement community (CCRC). Both assisted living and board and care
43
facilities serve as a segue between at-home, independent living, and nursing home care, aiding in
completion of basic and instrumental activities of daily living (ADL), such as bathing and
dressing, providing medication administration, as well as offering concierge services such as
meals and transportation (A Place for Mom, 2017). Board and care facilities tend to be smaller
(six or fewer beds) and are typically located in residential communities, most often in single-
family detached units (California Department of Social Services (CDSS), 2017). In contrast,
assisted living facilities are larger operations, which can be located anywhere in cities, including
residential as well as commercial, industrial, and other zoned areas (CDSS, 2017). Compared to
assisted living, board and care is limited in service offering due to facility size and economies of
scale. The CCRC is the rarest kind of residential care and represents a newer, full spectrum
service housing type for the elderly, which typically offers independent living and nursing home
care in addition to assisted living. CCRCs tend to be the largest facilities by number of beds and
are the most expensive, designed to house older adults from the onset of functional limitation to
death in skilled nursing care.
Despite the number of choices available, however, older adults often find it necessary to
leave their neighborhoods and communities of longstanding residence to access such care and
can experience difficulties in leaving familiar settings, family, and friends, such as loneliness and
seclusion in residential and institutional care, particularly older adults who belong to minority
and at-risk groups (Leggett et al., 2011). Limited care options in neighborhoods can compromise
immediate social networks, forcing the elderly into unsupportive communities, and moves away
from home neighborhoods can lead to poor health outcomes among oldest old adults, the age
demographic typically residing in such care environments, which, in turn, are mainly inhabited
by sick, disabled, and cognitively impaired older people (Jungers, 2011).
44
A primary reason for older adults moving away from their communities for residential
care is variability in neighborhood composition and access to care by gender, socioeconomic
status (SES), disability, and racial makeup from location to location (Agency for Healthcare
Research and Quality (AHRQ), 2017). Across the U.S., roughly 70% of assisted living residents
are female, and more than 70% are functionally impaired, requiring assistance with at least one
ADL (Caffrey et al., 2012). For low-income and impoverished elderly, care accessibility is
limited and will likely grow more tenuous due to increasing poverty since the early part of the
century (Bass-Haugen, 2009). Access to board and care and other forms of non-institutional
housing among poor and severely disabled older people is growing in the state under programs
such as the Assisted Living Waiver, a type of Home and Community-Based Services (HBCS)
waiver in California, but is still minimal (Cimino, 2017). Additionally, although the proportion
of minorities in residential care is increasing, access to care among elder minorities is still much
lower than older Whites (Howard et al., 2002). Taken together, the current state of affairs in
residential care is one of low supply and incremental growth among functionally impaired and
disadvantaged older adults, many of whom currently or will require financial support for HCBS
as an alternative to costly institutional care. Consequently, a critical need exists in identifying
geographical areas lacking in residential care and other forms of LTC as well as better
understanding of the distribution of local and regional care capacity with respect to the
impending or conceivable need.
Los Angeles: The premier residential care city

At the center of a slowly increasing and disproportionately distributed residential care
industry sits the global city and major elderly enclave of Los Angeles. The Los Angeles
metropolitan area is the second largest in the United States, and its county is the largest by total
45
and older adult population in the nation (U.S. Census Bureau, 2018). As a world city, it is a
powerful commercial core, the global capital of the entertainment industry and hub for banking,
manufacturing, importing and exporting, and construction, including the development of care
facilities. Nearly half of residential care facilities in the United States are in the west, most of
them in California, and with the greatest concentration in the Los Angeles area (Harris-Kojetin et
al., 2013). Under the leadership of Mayor Eric Garcetti, Los Angeles has become an innovator
of policies supporting such housing for the elderly, joining the ranks of other ambitious cities
across the country and world in the American Association of Retired Person’s (AARP) Network
of Age-Friendly Communities and the World Health Organization’s (WHO) Global Network of
Age-Friendly Cities as part of its Age-Friendly City Initiative: Purposeful Aging LA (Executive
Directive No. 17, 2016). The initiative ordered the formation of the Purposeful Aging Task
Force in the city, a pioneering collaborative advisory body composed of Los Angeles City and
County representatives and staff, AARP researchers, and university faculty, which created the
Age-Friendly Action Plan, culminating in a comprehensive set of recommendations to City and
County Councils and staff for implementation (Purposeful Aging Los Angeles, 2018). The
plan’s recommendations encourage a number of housing opportunities for the elderly, including
increased development of accessory dwelling units, rent assistance, home retrofitting programs
and supports, and diversity of housing types promoted by novel planning and zoning policies in
communities across the city and county. The latest of these policies is the city’s eldercare
facility ordinance, which, as discussed later in this paper, simplifies the permitting process of
residential and institutional care facilities in the municipality and allows their development in
any zone of the city through application and approval of the eldercare facility unified permit (Los
Angeles, California, 2006). The Age-Friendly City Initiative, Age-Friendly Action plan’s
46
housing recommendations, and City Municipal Code’s eldercare facility ordinance were all
devised, in part, to ease the burden of Los Angeles’ expensive housing market, one of the
costliest in the nation due to the city and county’s severely undersupplied housing stock.
According to the website of the Office of Los Angeles Mayor Eric Garcetti, such policies were
also created to add housing options in the city, including increased development of residential
care facilities, to keep older adults in their homes and communities for as long as possible, a
principal concern of City Council, considering the city’s current housing economy.
Although Los Angeles is at the forefront of the residential care industry, with Los
Angeles County accounting for approximately 1,300 facilities, representing about 17% of
facilities in the state and 5% across the U.S., the potential need for such facilities appears to be
outstripping the supply (CDSS, 2017). Over 7,000 licensed residential care facilities in
California provide only 29 beds per 1,000 seniors age 60 and older in urban areas, with even
lower ratios in suburban and rural areas (The SCAN Foundation, 2011). Facilities in Los
Angeles County supply approximately 26 beds per 1,000 seniors 60 and older (CDSS, 2017). At
the national level, about 60% of impoverished, community-dwelling older adults, report some
level of unmet care need (Komisar, Feder, and Kasper, 2005), totaling 4.8 million older people,
translating into more than 100 older adults per 1,000 with unmet care need who could benefit
from residential care, which is much less costly than institutional care (Medicaid, 2018). These
numbers shed light on the misalignment of supply and need for residential care among the
elderly in the city, county, and state.
Considering the sizeable elderly, residential care, and growing age-friendly presence in
Los Angeles, we present a spatial analysis of residential care facilities and census tracts in the
city and county, assessing the potential need of such care and whether the industry is equitably
47
meeting it. To understand the spatial distribution of residential care facilities, we tested for
spatial clustering among residential care facilities in Los Angeles, using the Getis-Ord Gi*
statistic. In evaluating the supply of residential care relative to the care need, we estimated the
relationship between residential care capacity and the proportions of total elderly population,
minority elderly, disabled elderly, and elderly in poverty, at the census tract level, using zero-
inflated negative binomial regression.
Methods and materials
Data and measures

We obtained residential care facility data from the CDSS data portal (CDSS, 2017).
CDSS variables used in this study are geographic location, first license date, number of facilities,
and capacity (number of facility beds). CDSS data were geocoded to create a point feature in
ArcGIS. As of December 31, 2015, the data sample included 1,306 licensed facilities in Los
Angeles County, of which 487 were in the City of Los Angeles.
To measure the care need of facilities, we used 2015 American Community Survey
(ACS) 5-year estimates of the distribution of age, disability, race, and poverty in the population
by census tract (Social Explorer, 2015). We created tract-level measures of the proportion of
older adults ages 55 to 64, 65 to 74, 75 to 84, and 85 and older; the proportion of White, Black,
Asian, and Hispanic older adults; the proportion of older adults with income at or below the
poverty level; and the proportion of older adults with disability, measured as having hearing,
cognitive, ambulatory, self-care, or independent living difficulty (Social Explorer, 2018). Age
groups are a percentage of total tract population, whereas older race groups, older adults in
poverty, and older disabled adults are a percentage of total tract older adult population.
48
We linked ACS 5-year estimates to Los Angeles County census tract TIGER shapefiles
using ArcGIS, joining facility point features data with ACS 5-year estimate census tract data. We
then calculated census tract mean percentages by older adult age groups; older Whites, Blacks,
Asians, and Hispanics; and older adults in poverty and with a disability by the number and type
of facilities per census tract.
Analysis

Spatial clustering analysis

To evaluate the spatial distribution of residential care facilities, we conducted spatial
clustering or hot spot analysis using the Getis-Ord Gi* statistic to pinpoint statistically
significant clusters of small and large residential care facilities. Ding et al. (2015) and Chen and
Greene (2012) used similar techniques in analyzing clustering and dispersion of health-related
point data. Additionally, Varady et al. (2010) analyzed Housing Choice Voucher recipient hot
spots, showing densities of poverty and race. To our knowledge, however, no studies have
utilized hot spot analysis on residential care facilities to assess the distribution, access to, and
capacity of such facilities. We restricted the hot spot analysis to the City of Los Angeles, the
most densely populated and heavily developed area in the county, comparing clusters across two
time periods, totaling two decades of development: 1) 1996–2006 and 2) 2007–2015. We
selected these two periods in order to examine the distribution of facilities by size before and
after changes to the Los Angeles City municipal code in 2006 that expanded opportunities for
development of residential care facilities in the city and streamlined the facility development
permitting process through a new eldercare facility ordinance (Los Angeles, California, 2006).
Although the CDSS dataset records residential care facilities registered as early as 1971 in the
City of Los Angeles, many fewer facilities existed prior to 1996, representing a facility count and
49
stock incomparable to the number developed in the decades following. Facilities in both time
periods analyzed are color-coded and symbolized, layered on top of City of Los Angeles census
tract choropleth maps of oldest old women (ages 75 and older) with at least one disability per
square mile, depicting areas with concentrations of small board and care facilities differentiated
from large assisted living and CCRC facilities relative to areas of need. Hot spot analysis is used
only to determine whether facilities are randomly dispersed by size across the city over the past
two decades of residential care development. In other words, it is not used to show a causal
relationship between the eldercare facility ordinance and an uptake in residential care
development in the city.
We used the ESRI ArcGIS 10.4 Getis-Ord Gi* spatial statistics tool, which calculates
equation (1), as provided on Esri’s website as of 2017:
G
i
*
=
∑ w
i,j
x
j
-X
"∑ w
i,j
n
j=1
n
j=1
S
#
!n∑ w
i,j
2 n
j=1
-(∑ w
i,j
)
n
j=1
2
#
n-1
, where X
"
=
∑ x
j
n
j=1
n
and S=
#
∑ x
j
2 n
j=1
n
- (X
"
)
2
(1)
where xj represents the respective capacity value for facility j, wi,j is the spatial weight between
facility i and j, and n is the total number of facilities. The Gi* statistic compares each facility’s
capacity as part of its immediate neighborhood, weighted relative to its surroundings as
conceptualized by a pre-set distance band, to the entire dataset. The spatial weight is
implemented by a spatial weights matrix, composed of values of one if facility i falls within the
distance band and zero if not. In this case, the distance band is one mile. If a facility and others
near it are statistically smaller or larger in size than the rest of the facilities within a 1-mile
distance band, they are marked as a hotspot.

50
Supply and need mismatch analysis

We performed two analyses to evaluate the supply of residential care facilities in Los
Angeles County relative to potential demand in the area, defined using census tract boundaries.
First, the number and type of residential care facilities were compared by age, race, poverty
level, and disability by census tract. Second, negative binomial (NB) regression models were
utilized to examine the relationship between residential care facility capacity and tract-level
population characteristics. The dependent variable (number of facilities per tract) was heavily
positively skewed, with more tracts with no facilities, and therefore no beds (n = 1,692), than
tracts with any beds (n = 654). Because more than 70% of tracts had no beds, we used the zero-
inflated negative binomial (ZINB) extension of NB. The NB model assumes that both census
tracts with beds and tracts with no beds derive from equivalent data-generating processes. ZINB
relaxes this assumption, computing the probability of census tracts with zero facility beds in the
certain zero count category as opposed to the uncertain zero count, given the predictor variable,
total population per square mile, that gauges the density of residential development in each tract.
ZINB regression is a two-stage model, the first stage accounting for many census tracts
with no facilities and therefore no beds, or the repeated absence of tracts with no beds, and the
second stage evaluating tracts with beds related to predictors (Long, 1997; Cameron & Trivedi,
2013). The first stage of the regression was a logit model, predicting census tracts with no beds,
leaving census tracts with beds for the second stage, which was a NB model, regressing tract
facility beds against explanatory variables. The first stage of the model predicted census tracts
with zero facilities using tract population density. Incidence rate ratios (IRR), generated in the
second stage of the model, represent the percent change of tract facility beds associated with a 1-
percent increase in age groups; older Whites, Blacks, Asians, and Hispanics; and older adults in
51
poverty or with disability. We preferred the ZINB to a zero-inflated Poisson (ZIP) model, since
the dispersion of the dependent variable, tract facility beds, was much greater than the mean in
measuring additional variance unaccounted for in the probability distribution. ZINB allowed for
greater variance or overdispersion of data than ZIP or its underlying Poisson model, both of
which assume equal mean and variance (Rodriguez, 2013). The model tested a parametrized
distribution of a discrete random dependent variable, total census tract facility capacity or beds,
with an unobservable α dispersion parameter in addition to observable βs. As suggested on the
UCLA Institute for Digital Research and Education’s website as of 2017, we computed the ZIP
goodness of fit test, comparing ZINB and ZIP regression, which indicated that ZINB regression
is preferred to ZIP. We ran univariate ZINB regressions to describe each of the explanatory
factors with respect to the dependent variable, tract facility beds. Multivariate ZINB regression
was also calculated to show interdependence among the predictors and control for race, poverty,
and disability. To interpret ZINB coefficient results, we computed marginal effects of each
predictor upon tract facility beds.
Results

Spatial clustering analysis

Figures 1 and 2 show the distribution of residential care facilities and hot spots of both
small and large facilities in the City of Los Angeles, between 1996–2006 and 2007–2015,
respectively. Larger facilities in both periods cluster in Central Los Angeles, in relatively older
and denser neighborhoods that are closer to the Downtown Los Angeles central business district.
Smaller facilities in both periods cluster in the more suburban San Fernando Valley, northwest of
the city center. Very few facilities are located in South Los Angeles, a traditionally African-
American and increasingly Hispanic area. Two other patterns stand out from the clustering
52
analysis results. First, between periods, hot spots shift westward for both small and large
facilities, away from downtown Los Angeles, and toward lower-density, higher income
neighborhoods. Second, we observe hot spots of large facilities along the boundary with inner-
ring suburbs such as West Hollywood, Beverly Hills, Santa Monica, and Culver City, in Central
and West Los Angeles. While these cities do not have their own eldercare facility ordinance, and
their facilities were excluded from the analysis, they do have active business districts, and many
of their neighborhoods are home to significant concentrations of older and higher-income adults.
Supply and need mismatch analysis

Table 1 shows Los Angeles County census tract demographic descriptive statistics by
number and type of facilities. Total percentages are displayed for all of Los Angeles County and
for tracts with no residential care facilities, one or more facilities, and tracts with at least one
type of facility, whether board and care, assisted living, and CCRC. Board and care facilities are
the most common residential care facility type in the county, with more than 500 census tracts
containing one or more of them. Assisted living facilities occur in over 200 tracts, while CCRCs
are quite rare, occurring in only 18 tracts.
The number of facilities in census tracts is positively associated with the percentage of
older adults in every age range. Census tracts with CCRCs have higher percentages of older
adults of every age range than tracts with assisted living facilities. Assisted living facilities have
higher proportions of older adults than tracts with board and care homes. Facility types are not
mutually exclusive, meaning one or two types of residential care facility other than the type in
question can be in the same tract. As the number of facilities increases in census tracts, so does
the proportion of White and Asian older adults. In contrast, the number of facilities is inversely
associated with the proportion of older Blacks, Hispanics, and older adults in poverty. Tracts
53
with at least one CCRC tend to have higher proportions of White elderly and lower proportions
of Hispanic and Black elderly. There is no clear pattern between percent of residents with a
disability and the number of facilities or type of facilities per tract.
We find that age, percent Hispanic elderly, and percent elderly with disability are
statistically significant independent variables influencing residential care facility capacity. Table
2 displays results of univariate and multivariate ZINB regressions models. Univariate regression
results show that capacity tends to be 20% greater in tracts with higher proportions of adults over
age 75 and 50% higher in tracts with higher proportions of adults over age 85. Higher
proportions of Whites, higher elderly poverty rates, and higher elderly disability also increase
facility capacity, but by very small magnitudes. In contrast, percent elderly Black and Hispanic
reduce facility capacity to a small degree.
Multivariate analysis controls for age, race, poverty, and disability among older adults,
revealing altered relationships and levels of statistical significance between the independent
variables and tract facility capacity compared to univariate analysis. For instance, analyzing the
effect of these variables together reveals a change in the direction of the relationship between
capacity and older adults ages 65-74 compared to univariate analysis, moving from a 3%
increase in capacity with no statistical significance to an 8% decrease with a statistical
significance of p < .001. Further, ZINB regression of capacity on the percent of older adults ages
75-84 loses significance in multivariate analysis and decreases from a 20% (p < .001) increase in
capacity to only 4% (no statistical significance), the most dramatic shift in magnitude between
coefficients in either type of model. A 1 percentage point increase in the proportion of older
adults ages 55-64, 65-74, and 85 is associated with a decrease of 6% (p < .01), 8% (p < .01), and
an increase of 43% (p < .001) in capacity, respectively. Additionally, as older Hispanics and
54
disabled older adults increase 1-percent in tracts, capacity decreases 2% (p < .001) and increases
2% (p < .01), separately. No statistically significant relationship exists between capacity and
older racial groups besides Hispanics and older adults in poverty.
Figure 3 shows predicted facility capacity in beds by the percent of older Whites, Blacks,
Asians, and Hispanics. Predictions were run separately for percentages of older age groups and
older race, disability, and poverty categories, as the percentage of oldest old adults (by total
population) reaches to only about 40% as compared to other covariates such as older race groups,
older adults in poverty, and older adults with disability (by total number of older adults), which
totals 100% in certain census tract estimates. Older Whites and Asians have more direct
geographic access to residential care, benefitting from greater tract capacity, on average, as either
group increases in proportion across county tracts, relative to older Blacks and Hispanics, who
have less access. Older Hispanics suffer the greatest disparity in access to care among all groups.
As their proportion increases in census tracts, predicted bed capacity decreases from 15 to
roughly 3 beds. Older Blacks have less access to residential care as well but not to the same
extent as older Hispanics. Predicted capacity decreases from 9 to 5 beds as the percentage of
older Blacks increases from 10% to 90% in census tracts.
Discussion and conclusion

This article demonstrates that the spatial distribution of facilities meets the needs of some
but not all communities of older adults in the Los Angeles area. Examining the location and
capacity of care facilities in the City of Los Angeles reveals geographic disparities in both their
placement and clustering, the mapping and analysis of which provides a concise yet
comprehensive view of residential care in the area.
55
The location and statistically significant clustering of small and large facilities is
indicative of the broader physical and social geography of the city and county. In plotting the
position of facilities in the city, we depict not only underlying patterns of residential
development but also areas supportive of residential care. The results reveal not only hot spots of
large facilities but the area of the city where the majority of facilities are located, namely, the
San Fernando Valley. All other facilities, even though they may be particularly large, are
positioned in western Los Angeles, and to a smaller degree, South Los Angeles and the area just
north of the downtown area. This geography of residential care in the city makes sense, as larger
populations and institutional planning frameworks for residential development encourage larger
facilities near larger, denser populations and smaller facilities near smaller, more dispersed
populations. Residential zones attract smaller, board and care facilities in lower density
neighborhoods, whereas commercial zones attract larger, assisted living facilities
and CCRCs in higher density areas. The greatest disparities, however, take place between the
north and south of Los Angeles, in the suburbs of the city, where the size of most facilities is six
beds or fewer. These facilities are overwhelmingly the majority, not only at the city and county
level, represented by the dataset in this study, but across the state as well, and they tend to
populate in single family home neighborhoods.
However, not all neighborhoods contain these or other larger facilities. The most visually
striking lack of residential care in the city, as shown in Figures 1 and 2, are the highly affluent
neighborhoods northwest of Beverly Hills, including the Hollywood Hills, Beverly Glen, Bel
Air, and extremely low income areas, including South Los Angeles and the downtown area. Each
of these areas is a residential care desert, but based on age and income characteristics (Mapping
L.A. Neighborhoods, 2017a; 2017c), they lack a care presence for different reasons. For
56
instance, in Bel Air, where median income is more than $200,000 and homeownership is very
high (Mapping L.A. Neighborhoods, 2017b), developing neighborhood care facilities in the area
would be extremely costly and impractical, considering lot size, price, and zoning. In the most
impoverished areas of Central and South Los Angeles, on the other hand, including the
downtown area and Watts, median income is roughly $15,000 and $25,000, respectively, and
crime is high (Mapping L.A. Neighborhoods, 2017d; 2017e), which may render the area
unattractive to potential developers and investors who desire to make a profit on their ventures as
well as older adults seeking safe care.
County level results portray the broader picture of residential care in the city and beyond
and help to explain it in terms of tract facility count, type, and most importantly, capacity, the
best measure of access to care, compared to facilities, which vary widely by number of beds.
Influenced both by the number and type of facilities per tract, tract capacity follows a slightly
more dispersed Poisson distribution, which places the bulk of facility counts toward the lower
end of the data distribution, among small, six-bed facilities. In such a distribution, demographics
associated with smaller counts and therefore smaller facilities generate the greatest pull on
capacity. In other words, in Los Angeles County, tracts with small to medium sized facilities, as
opposed to even the largest facilities, are more apt to provide the most capacity and access to
residential care for nearby populations.
Although we find more and larger facilities in areas with greater proportions of older
Whites and Asians and fewer facilities in places with greater percentages of older Blacks,
Hispanics, and older people in poverty, the fully adjusted model reveals that age and race are the
most influential predictors of tract capacity, particularly older adults 85 years and up and older
Hispanics. For the oldest old, this could be the result of residential care development targeting
57
existing or growing aging populations, older adults moving to areas abundant in residential care,
or older adults living in such facilities. Considering the magnitude and statistical significance of
the results, almost unquestionably, this reflects the number of functionally-impaired oldest old
receiving care in these facilities, who are still part of the non-institutionalized population and
counted in census estimates. This may also be the case with older disabled adults, who predict
more tract capacity at a much lower rate compared to the oldest old and who may represent the
majority of the population residing in residential care in the county.
Regarding Hispanic older adults, the relationship is less intuitive. In both descriptive,
regression, and marginal analyses, fewer older Hispanics live in tracts with more facilities, larger
facilities, and more total capacity. At least a couple of residential and sociocultural consequences
can be inferred from these outcomes. First, older Hispanics, on average, have less access to
residential care and may need to move away from their home neighborhoods more often when
seeking such care than other racial groups. Second, as a result, older Hispanic adults may need to
rely more heavily on informal care or at-home services and supports (Greene and Monahan,
1984; Min and Barrio, 2009; McGarry, Temkin-Greener, and Li, 2014), given other cultural and
socioeconomic factors, than older Whites, Blacks, and Asians.
For the recently retired and pre-retired, however, the results show even less promising
access to residential care, which from a service planning perspective should cause concern and
generate interest in more public investment in residential care. Among older adults ages 55-64
and 65-74, the percentage of beds in Los Angeles census tracts decreases four times as much as
the percentage older Hispanics. The prevalence of functional limitation is much lower among
preretirement and early retirement age adults compared to the oldest old (Covinsky et al., 2009),
so lower capacity is to be expected in census tracts with more older adults ages 55-74. Fewer
58
beds, in other words, can be expected in tracts growing into old age, compared to tracts already
heavily populated by the oldest old, who may have moved out of their neighborhoods to access
care rather than finding it nearby.
Ideally, care facilities should be placed as near as possible to both existing and impending
need, although many factors obstruct such intentional development, including land availability,
zoning, cost, et cetera. The exception to this are board and care facilities, those with six beds or
fewer, which are typically located in lower density single-family home neighborhoods and may
be easier to site than larger facilities due to residential property availability, as explained on the
California Registry website as of 2018. Very little literature is available on these facilities and
their importance as the most frequent facility type and largest service provider by number of
facilities and beds in the residential care industry. In underserviced areas, such as Hispanic
communities, board and care facilities may represent the most immediate solution to increasing
affordable care capacity near the need.
As Los Angeles County, the largest by both total and older adult population in the U.S.,
continues to age, providing non-institutional LTC options such as residential care will become an
important priority for service providers catering to functionally impaired older people who can
no longer live at home but who wish to stay in their neighborhoods. Increasing access to care is
of utmost importance, even if it is less geographically targeted toward those in need, including
the recently retired, pre-retired, and older Hispanics, considering the sheer number of older
people who will require such care in the future and the physical and psychological benefits of
social support, social engagement, and living in familiar surroundings.
To this end, Los Angeles City Council passed an eldercare facility ordinance,
streamlining the permitting process of residential care facilities and enabling easier siting of
59
them in the city, implemented at the end of 2006 (Los Angeles, California, 2006). The ordinance
provides relief from specific plan requirements, an added level of development regulation to the
zoning code; allows such facilities in any zone, including residential, commercial, industrial, and
otherwise; reduces parking requirements; and established a unified permit process (Los Angeles,
California, 2006). Land use tools such as the eldercare facility ordinance, which allows
residential care development in all city zones, either by right or through the entitlement process,
can stimulate more purposeful growth in residential care and other forms of LTC, permitting
such development where it formerly was excluded or disincentivized. To our knowledge, no
other major U.S. city has passed such an ordinance. Los Angeles’ regulatory change is designed
to increase the availability of parcels and neighborhoods for residential care facility development
and reduce the regulatory cost of development. No evaluation studies of this ordinance exist to
date, but anecdotal evidence shows moderate uptake in residential care development despite
opposition from neighborhood groups, especially in wealthier, more suburban, and lower-density
areas of the city (LandUse LA, 2013). Through the new ordinance, residential care is allowed in
the city in all residential, commercial, and industrial zones without a planning entitlement, and in
all other zones through a single permitting process, meaning developers are not forced to apply
for multiple planning permits, undertaken by multiple planning staff, which is especially
important in a city as large as Los Angeles. Facilitating and incentivizing the development of
residential care on behalf of land developers and contractors, including retrofitting single-family
homes into board and care facilities, may constitute the most direct means by which cities can
add to the local residential care housing stock and capacity. Such municipal incentives can
reduce application and permitting costs and the amount of time required for entitlement case
management, planning commission and city council public hearings and review, and ultimately
60
project approval. The directional shift from east to west between hot spot analysis timeframes
may imply some initial effect of the eldercare facility ordinance on the geographic dispersion of
residential care facilities in the city. Additionally, evidence of neighborhood-group opposition
may imply that certain neighborhoods have become easier to build in following the ordinance’s
passage (Los Angeles Times, 2013; Fernando Valley Business Journal, 2017; LandUseLA,
2013).
Acquiring single-family homes and retrofitting them into board and care facilities,
however, which represent more than 90% of residential care facilities in the county and state, is
not as prohibitive in terms of capital investment, land area, and timeline as the development of
larger assisted living facilities and CCRCs. Such development can increase city and county care
capacity, while encouraging infill development and redevelopment of vacant properties to reduce
urban sprawl and the environmental footprint. As many advocacy organizations assert, this type
of residential care deserves a closer look by researchers, service providers, and policy makers as
a viable solution to the growing need of non-institutionalized, community-based LTC (California
Registry, 2018; 6 Beds Inc., 2018), particularly in places such as Los Angeles that now have
more welcoming eldercare development policies.
Although careful attention was given to this study, limitations of the analysis include
population aggregation at the census tract level as opposed to the block level, the modifiable area
unit problem, and exclusion of nursing home and hospice care facilities from the dataset. The
geographic level of analysis in this study is Los Angeles County census tracts, which encompass
various populations and residential care facilities, aggregate demographics relative to facility
capacity, and are less precise than blocks. The modifiable areal unit problem in spatial
aggregation, which can contribute to altered outcomes in spatial analysis if polygons are
61
reshaped or rescaled, could lead to inclusion or exclusion of certain areas or neighborhoods in
the City and County of Los Angeles, and consequently modifications in proportions of age, race,
poverty, and disability categories. Few solutions to this problem exist, including comparison of
different scales of geographic aggregation and spatial interpolation, which are beyond the scope
of this study. Further, without nursing home and hospice care facilities in the dataset, both the
hotspot analysis and regression results are incomplete in evaluating the clustering patterns of
care facilities and percent change in care capacity, as the distribution of facilities would change if
these facilities were added. Finally, although both datasets utilized in this study are population
and facility censuses, the spatial and population patterning we observe may not generalize to
other city and county contexts.
Future research using both the city and state dataset will investigate the effectiveness of
the eldercare facility ordinance in the City of Los Angeles, specifically its impact on the rate of
residential care development to another similarly situated city in California. To our knowledge,
Los Angeles is the first and only city to create an ordinance specifically for eldercare facilities as
opposed to group homes or other congregate living facilities. Determining whether the ordinance
has increased residential care capacity at a faster or slower rate than other cities will inform the
City of Los Angeles and developers of the suitability of the use and aid in retooling it for its
intended purpose, which is to increase the rate of development and availability of eldercare
facilities in the jurisdiction.

62
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68
Figure 1. Hot spot analysis of residential care facility locations in the City of Los Angeles,
1996-2006.

Figure 2. Hot spot analysis of residential care facility locations in the City of Los Angeles,
2007-2015.

69

70

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Figure 3. Predicted facility capacity (beds) by percent of older racial groups in County of Los
Angeles census tracts.

72
CHAPTER 3

Title: Residential Care Development in California: Time Series Analyses of Facility and Care

Capacity Growth

Abstract

As the largest share of long term care in the United States, California is a unique setting for
research on the growth of residential care. Although research exists calculating residential care
capacity in California, no studies have evaluated its growth in the state, analyzing trends in
facility development and care capacity over time. We spatially joined the residential care for the
elderly dataset of the California Department of Social Services with linearly interpolated 1990
through 2010 decennial census place data, charting the growth of residential care development
and capacity by number of older adults in all of California and its most prominent cities. From
1996 to 2015, residential care steadily increased in California by number of facilities that have
remained open since licensing and beds relative to the older adult population and is
overwhelmingly characterized by small board and care facilities. The only exceptions to this
general trend are the Cities of San Diego and San Jose, both of which experienced sporadic
development of large assisted living and continuing care retirement communities that have
remained open, followed by lulls in such development, leading to rises and declines in care
capacity per older people over the study period. Understanding city and state development and
capacity growth trends can help local jurisdictions in California and other states discern whether
land use and planning policies, among other development factors, appropriately incentivize
residential care development.

73
Introduction
Due to rapid increases in the older adult population, housing for the elderly is an
increasingly important resource among cities and communities in the United States. Eldercare
facilities have grown in demand as revealed by increases in the development of skilled nursing
care facilities, residential care facilities, hospice care facilities, adult day care centers, and home
health care agencies over the past several decades (California Advocates for Nursing Home
Reform, 2013; Hughes & Smith, 2014; IBISWorld, 2019). However, the number of facilities
built and their capacity as measured in number of beds has not always maintained pace with the
growth of the older adult population (Harrington, Preston, Grant, & Swan, 1992; Doty, Liu, &
Wiener, 1985). Additionally, the number of older people utilizing such facilities has declined in
certain parts of the county, primarily among skilled nursing care, particularly hospital based
skilled nursing facilities, calling into question state and local demand for both institutional and
noninstitutional forms of long-term care (Flynn, 2018; California Healthcare Foundation, 2007).
The largest eldercare industry by facility in the country is residential care, with nearly 30,000
facilities nationally, according to one estimate (NCHS, 2016). Most are located in western states
(Harris-Kojetin, Sengupta, Park-Lee, & Valverde, 2013), and primarily in California, which
claims more than 7,000 of these facilities (California Department of Social Services (CDSS),
2018). As the largest share of residential care in the nation, California is uniquely positioned as
a leader in the industry, offering a broad selection of facility choices for functionally impaired
older adults, including board and care, assisted living, and continuing care retirement
communities (CCRC). With the largest inventory of residential care facilities, the greatest
number of older adults in the country, and the most populace state in terms of total population
74
and number of cities, California is also an ideal location to study trends in state level and
municipal growth in the residential care industry.
The regulatory and licensing agency for residential care facilities in California is CDSS,
which maintains the residential care facility for the elderly (RCFE) dataset, cataloguing facilities
that have been developed and remained licensed since 1967. As the largest continually updated
state dataset of long-term care facilities in the nation (Wiener, Lux, Johnson, & Greene, 2010),
the RCFE dataset provides a detailed and historical look at an industry fifty years in the making.
As detailed by Wiener et al., the California residential care industry, as inventoried in the RCFE
dataset, contains more than twice as many facilities as the next two largest state residential care
industries in terms of facilities combined, including Michigan and Washington, and nearly three
times as many beds as the next two largest state markets in terms of beds combined, including
Florida and Pennsylvania. Linked with population information, including decennial census and
American Community Survey (ACS) data, as is common in the evaluation of LTC service
provision and capacity, the dataset offers a rich and versatile collection of administrative and
spatial information on the location and quality of residential care facilities and the geographies
that surround them. Understanding the growth of residential care in California and the dataset
that tracks its growth is critical, as the older adult population and the LTC industry in the state,
the largest in the nation, continues to grow, and as institutional care in the state has declined,
particularly among hospital based skilled nursing care (California Healthcare Foundation, 2007).
In view of California’s importance in the residential care industry and the CDSS dataset
that describes residential care facilities in the state, the aim of this study is to present a
descriptive analysis of the dataset, including our expansion of it, which links state facility data
with decennial census place or city demographic information as a means of conducting statewide
75
and local spatial analyses of facilities as well as track residential care development longitudinally
across prominent jurisdictions in the state. We also present time series analyses of the growth of
residential care in the state and its most prominent cities by number of facilities, types of
facilities, and capacity as measured by the number of beds.
Methods
Data
We downloaded the RCFE dataset from the CDSS Community Care Licensing Division
(CCLD) in 2017, the California departmental agency that curates and updates the dataset, to
capture residential care development trends from 1996 to 2015, the timeframe of the study. The
RCFE dataset is a census of all facilities in the state that have maintained their license since
opening, excluding facilities closed more than five years past the dataset’s date of access from
CDSS. The dataset is updated weekly and serves primarily as a tool for consumers searching for
facilities in which to receive care.
We then geocoded the addresses of residential care facilities as a point shapefile in
ArcGIS, spatially joining them with California Topologically Integrated Geographic Encoding
and Referencing (TIGER) 2010 place shapefiles, linked with 1990 through 2010 decennial
census place age demographics (ages 65+, 75+, and 85+). We linearly interpolated census
variables between decennial censuses 1990, 2000, and 2010, estimating age demographics in
California census places across the study period 1996 to 2015. As of late 2017, the facility
census included 11,353 facilities, 7,412 of which were licensed, 3,384 closed, 54 on probation,
and 503 pending license. The analytical sample, consisting of facilities joined with census
places, those located in incorporated cities throughout the state, contains 9,883 facilities, 6,132 of
which are licensed, 3,216 closed, 53 on probation, and 488 pending license. The primary
76
distinction between the state census of residential care (11,353) and the analytical sample (9,883)
are nearly 1,500 facilities that fall outside of municipal areas under county jurisdiction.
Measures
We created three different variables measuring residential care growth, including
cumulative facility growth by type, facilities per older adults (ages 65+, 75+, and 85+), and
facility capacity per older adults (ages 65+, 75+, and 85+), aggregated at the state and city level
of measurement. We also generated two measures of change to compare California’s residential
care growth to each city examined, including yearly percent change in facilities per older adults
(ages 75+) and yearly percent change in capacity per older adults (ages 75+). Implementing the
growth and change variables in four different geographies over the study timeframe, we analyzed
facility and capacity growth in all of California and in its three largest municipalities, Los
Angeles, San Diego, and San Jose.
Analyses
We describe the CDSS residential care facility for the elderly dataset, briefly explaining
its importance and function as a search tool for older adults seeking long-term care (LTC)
outside of the home but away from institutional settings such as nursing homes. We also
elucidate its usability in conducting research on residential care in the state, detailing the
dataset’s most important variables and the quality of information in such fields, including facility
address and capacity, as employed in this study. Inspection, complaint, site visit, and citation
variables are described in less detail, as they are not utilized as variables in this study. We also
explain our expansion of the dataset, including decennial census variables at the census place or
city level, demonstrating how the expanded dataset can be used for spatial and demographic
analyses in various locations.
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We then present time series analyses to demonstrate growth and change in growth in
residential care development at the state level and in its largest cities in terms of population and
residential care facilities, including Los Angeles, San Diego, and San Jose. We chart these
analyses by cumulative number and percent change in facilities, facility capacity as measured by
number of beds, and facility type as indicated by the dataset’s facility nomenclature for
continuing care retirement communities (CCRC), as well as number of beds, differentiating
board and care facilities (six beds or fewer) from assisted living facilities (seven beds or more).
Although there may exist board and care facilities with more than six beds and assisted living
facilities with fewer than seven beds in California, the six bed facility has been recognized as an
industry standard in measuring the size and service offering of the more personal and homelike
board and care facility setting across the state (6Beds, Inc., 2019). Further, the State of
California allows by right in municipalities the development of facilities with six or fewer beds
in residential zones, denoting that cities in the state do not require a planning entitlement or
permit for such small facilities, the kind of permit typically reserved for larger scale and
potentially disruptive development that would necessarily activate planning commission and city
council discretionary review and community involvement; facilities with seven or more beds are
consequently subjected to such a permitting process in cities across the state (CHSC, 1973; Los
Angeles, California, 2000; San Diego, California, 1989; San Jose, California, 2000). In other
words, facilities with six or fewer beds are usually opened in existing housing units in residential
zones across the state that do not require any actual land development, as is most likely the case
with facilities containing seven or more beds. Coding board and care facilities with six or fewer
beds is the best approximation of the break point between board and care and assisted living
facilities, as the dataset only distinguishes between all residential care facilities and CCRCs.
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Facility growth as measured by total facilities and total capacity in these analyses is normalized
by the growth of older adults in cities, displaying facilities per 10,000 older adults (ages 65+,
75+, and 85+) and capacity in facility beds per 1,000 older adults, the typical standardization of
the number of facilities and facility capacity at the municipal, county, and state level. Growth
change is computed as the percent change in facilities per older adults and capacity per older
adults by year, comparing the rate of change in California to each of its largest three cities across
the study period.
Results
The RCFE dataset
The RCFE dataset is a publicly available administrative record of residential care
facilities that are open, closed, on probation, and pending license in California. These data are
presented to the public for the purpose of searching for facilities in which older adults can
receive LTC. Although the dataset is an exhaustive list of facilities currently open and closed,
CDSS removes from the dataset facilities that have closed more than five years from the
dataset’s date of access in the CCLD’s data portal. For older adults seeking LTC and the details
of the quality of such care, it provides useful information on facilities open and pending license
for planning LTC, as well as facilities on probation due to complaints and allegations of criminal
activity, ostensibly for the purpose of avoiding such facilities. For licensed facilities on
probation and not on probation, the administrative record maintains information on the dates and
number of facility visitations related to complaints and allegations of criminal misconduct, as
well as the state statute associated with the real or potential infraction or crime committed in
residential care facilities. These fields provide interested parties with a historical record of the
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performance or problems related to services rendered in open facilities or those that have been
closed for up to five years.
Although the dataset is primarily intended for use by consumers, it can also be utilized by
researchers in analyzing current or historical service provision, quality, and as presented in this
article, growth in residential care by facility and capacity across various geographies. Perhaps
the most conspicuous benefit of the dataset is its listing of facility addresses, which allows
researchers with access to geographical information systems (GIS) to organize facilities by
location for analysis, in neighborhoods at the census block or track level of analysis, in cities by
census places, and at the county level. Another crucial piece of information in the dataset is the
facility capacity field, which records the number of beds in each facility. Pairing the location of
facilities with their capacity permits researchers to track California’s residential care service
provision in various places, as analyzed and illustrated in map form by Frochen, Ailshire, and
Rodnyansky (2019) in Los Angeles neighborhoods, or over time for geographical comparison
and statewide assessment of the potential LTC need of the housing type. As the older adult
population continues to grow along with LTC facilities in support of the functional limitation and
disability of the elderly, administrative records such as this dataset will become increasingly
important in exhibiting and forecasting the collective service provision of residential care in
California and other states at the city, county, and state level.
In addition to the RCFE dataset, we expanded the facility data to include census place or
city classification and age demographic information using the spatial join tool in ArcGIS. We
attached to each facility the census place Federal Information Processing System (FIPS) code for
city identification in time series analyses, the results of which are illustrated in the next
subsection. Joining RCFE and census place variables, and variables from other geographical
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aggregations, provides researchers the ability to examine not only residential care facilities but
the social contexts that surround them as well. Depending on the availability of variable
information at different aggregations of census data, including most often census blocks, tracts,
places, and counties, facilities and their surrounding geographies can be analyzed at very small
or rather large geographical scales, providing an assortment of study scales from which to select
for analysis. At all levels of aggregation, age demographics are available for download from the
Census Bureau’s American Fact Finder, including old age demographics at the census place level
of analysis, the scale used in this study (U.S. Census Bureau, 2019).
Time series analyses of residential care growth in California
As shown in Figure 1, Among the 11,353 residential care facilities in the CDSS RCFE
census of facilities in California, 6,435 facilities were licensed or on probation as of 2015. This
number grew from 728 in 1996, illustrating the cumulative growth of facilities that have
remained open across the study period. The overwhelming majority of facilities in the state are
classified as board and care, those categorized in the analysis as six beds or fewer. In the last
year of the study period, board and care facilities represented nearly 80% of all facilities in
California, demonstrating how fundamental these smaller and more intimate facilities are to the
residential care industry in the state. These facilities grew in size during the timeframe from a
little more than 400 to nearly 5,000. Assisted living and CCRC facilities also experienced
growth in the two-decade period, rising from nearly 300 to nearly 1,500. Board and care
facilities in the state underwent a ten-fold increase in growth over the time period, while assisted
living and CCRC facilities experienced only a five-fold increase.
Figure 2 shows the cumulative growth of facilities that have remained open across the
study period per number of older adults at different age ranges, representing the residential care
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facility offering among older people in the state. By far, facilities are most plentiful among older
adults ages 85+ due to their small size as a population compared to the younger age ranges. Just
over 90 facilities per 10,000 oldest old adults existed in California as of 2015, growing from
more than 20 at the beginning of the study period. A little more than 30 facilities per 10,000
older adults ages 75+ were developed in California at the end of the timeframe, whereas 14 per
10,000 older adults ages 65+ existed by 2015, growing from just over four and two, respectively.
Although tracking the growth of facilities provides a reasonable approximation of the
level of residential care service provision in the state, the number of beds per older adults
provides an even more accurate evaluation of care provision, accounting for both large and small
facilities that can vary in size from hundreds of beds to only two per facility. Figure 3 shows the
growth of residential care facility beds per older adults at different age ranges, displaying the
total capacity of care in California among older people during the time period. As with facilities,
older adults ages 85+ experience a larger distribution of beds among their demographic, reaching
to nearly 250 per 1,000 in 2015, compared to nearly 80 and 40 per 1,000 for older adults ages
75+ and 65+, respectively. Among the oldest old, ages 85+, the rate of growth is greatest, risen
from just over 20 to 250 beds per 1,000 across the two decades of time. For older adults ages
65+ and 75+, the rate is much less pronounced, growing from approximately 10 and 20 to nearly
40 and 80 per 1,000 older adults, respectively.
For the purposes of investigating census place or city level residential care growth, and as
a precursor to land use and planning policy studies of facility growth in California, the context of
which is primarily at the municipal level, the following analyses illustrate cumulative facility and
capacity growth in the state’s three largest census places or cities by population and residential
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care presence. Because California facility and capacity counts represent the state’s residential
care stock in urban and rural areas combined, city level trends of facility and capacity growth per
older adults tend toward a lower ratio, with many more older people in cities sharing a common
municipal residential care supply relative to the broader industry in the state. These three cities
include Los Angeles, San Diego, and San Jose. All previous evaluation studies of residential
care provision in California take place at the county level of aggregation (SCAN, 2011), which is
useful for broader statewide assessments of care provision and need but which is less targeted in
analyzing the largest and densest clusters of facilities in the state. Metropolitan analyses of
facilities, on the other hand, represent in large part the central cores of LTC, including residential
care, particularly among the state’s most prominent cities, and are a better representation of the
geographies in which the residential care industry is most closely bound.
Figure 4 shows the growth of Los Angeles facilities that have stayed open since licensing
by facility type. As the largest city in the state by population, Los Angeles is the largest cluster
of municipal residential care facilities, containing nearly 500 in 2015. This number grew from
approximately 50 at the beginning of the study period, indicating a ten-fold increase in facility
growth across the timeframe. As in the case of all facilities in California, the board and care
industry in Los Angeles overwhelmingly represents the bulk of facilities, accounting for nearly
82% of them in the jurisdiction. The remaining number of facilities in the city are assisted living
or CCRC facilities. Board and care facilities increased in number from roughly 40 to 400 from
1996 to 2015, while assisted living and CCRCs grew from about 20 to 90.
As shown in Figure 5, the cumulative growth of facilities in Los Angeles by number of
older adults at different age ranges varies widely but similarly to state trends, with facilities
being distributed greatest among older adults ages 85+, followed in order by older adults ages
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75+ and 65+. Facilities per 10,000 older adults ages 85+ increased from a little more than ten to
roughly 70 across the timeframe, whereas facilities per 10,000 older adults ages 75+ and 65+
grew from approximately three to 25 and nearly two to 12, respectively.
Figure 6 shows the growth of residential care facility beds per older adults in different
age categories, showing the total capacity of care in Los Angeles across the study period. Older
adults ages 85+ show a larger distribution of beds, as in the case of all facilities in California,
growing to roughly 160 per 1,000 in 2015. In contrast, approximately 50 and 25 per 1,000
facility beds per older adults were accounted for among older people ages 75+ and 65+,
respectively. Among older people ages 85+, facilities grew the most from 1996 to 2015,
increasing from about 50 to 160 beds per 1,000 in the twenty-year period. Among older adults
ages 75+ and 65+, the rate is much smaller, as in the state analysis, rising to 54 from about 12
and 25 from about 5 per 1,000 older adults, respectively.
Moving to the second largest city and residential care market in California, Figure 7
illustrates facility growth by type of facility in San Diego. As a sizeable municipality in the
state, San Diego contains one of the largest clusters of facilities, reaching to nearly 180 in 2015.
At the beginning of the study timeframe, the city had only 35 facilities, showing a growth rate of
facilities that have remained licensed since opening of more than 500%. As with all of
California, the board and care industry in San Diego accounts for the vast majority of facilities
and represents the same proportion as Los Angeles, 82% in the municipality. Board and care
facilities grew from about 30 to 150 during the two-decade period of time, while assisted living
and CCRCs increased to 30 from about 10.
As shown in Figure 8, similar to Los Angeles and all of California, cumulative facility
growth by older adults differs considerably among older age ranges, with facilities divided most
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abundantly among the oldest old, ages 85+. Facilities per 10,000 older adults ages 85+ grew
from about 30 to a little more than 70 across the timeframe, while facilities per 10,000 older
adults ages 75+ and 65+ grew from approximately six to 25 and nearly three to 12, respectively,
demonstrating a greater facility to older adult ratio in the early part of the study period than at the
end, compared to Los Angeles.
As with Los Angeles and all of California, charting the growth of residential care facility
capacity per older adults at different ages in San Diego provides a more detailed view of the
industry in the city. As in previous graphs of state and municipal capacity per older people,
older adults ages 85+ are far less represented than younger age groups in the analysis due to
mortality, and are therefore spread thinner among facility beds, claiming more capacity in ratios
of beds to older adults in their age group. In San Diego, however, as shown in Figure 9, the
trend in growth of capacity per oldest old adults shows an irregular spike at the beginning of the
study period, then a subsequent decline and lull prior to another uptake at the end of the
timeframe. A closer look at the underlying data reveals that two rather large CCRC facilities
were developed in the early part of the time period, along with a handful of assisted living and
board and care facilities, dramatically and abruptly increasing residential care capacity in the
city, followed by years of smaller residential care facility development. Although facilities
continued to be developed in the jurisdiction after the CCRCs in question, increases in older
adults ages 85+ outstripped growth in facility beds, leading to a decline in beds per older people
in the middle part of the study period followed by a slight increase toward the end.
Consequently, the rate of growth of capacity increased from nearly 100 to roughly 250 beds per
1,000 older adults ages 85+ from 1996 to 2000, dipping to as low as 201 in 2011, and then
increasing again to almost 240 per 1,000 older adults at the end of the timeframe. Though much
85
less severe, older adults ages 75+ and 65+ show a similar trend, an initial increase followed by
stability or small decline in the middle of the study period and then slight increase in the end as
well. Among older people ages 75+ and 65+ in the time period, the rate rises from roughly 20 to
84 and from about ten and forty beds per 1,000 older adults, respectively.
Continuing on to the third largest city and residential care presence in the state, Figure 10
shows growth of facilities that have stayed open since licensing in San Jose by type of facility.
San Jose, one of the largest cities in California, possesses a considerable number of facilities,
which grew to almost 150 at the end of the study period. In 1996, the city had just under 30
facilities, signifying a five-fold increase in facilities across the twenty year time period. Similar
to Los Angeles, San Diego, and the entire state, board and care facilities in the city represent the
lion’s share of municipal facilities, more so than the proportion of board and care to all facilities
in the other cities analyzed in this study. The board and care industry represents approximately
87% of all facilities in San Jose, as shown by the small margin between all facilities and board
and care facilities in the figure, particularly in the early years of the time period. As illustrated in
the graph, in subsequent years, the board and care facilities in the city grew from nearly 30 to
almost 130 across the time frame. Assisted living and CCRCs, on the other hand, increased to
two nearly 20.
Like Los Angeles, San Diego, and all of California, cumulative growth of facilities in San
Jose diverges considerably in magnitude among the three older age groups, with older adults
ages 85+ representing once more the smallest number relative to facilities, thereby apportioning
the most facilities comparatively to their demographic as in analyses of previous cities. As
demonstrated in Figure 11, facilities per 10,000 older adults ages 85+ grew from just over 40 to a
little more than 100 over the twenty year time period, while facilities per 10,000 older adults
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ages 75+ and 65+ increased from about ten and four to approximately 30 and 14, respectively,
which is nearly identical to state facility per older adult ratios but larger than those of either Los
Angeles or San Diego.
Unlike the cumulative capacity growth of Los Angeles and San Diego, which show stable
increase in the former and initial increase followed by decline in the latter, San Jose’s growth of
capacity casts a pattern of repetitive increase followed by decrease, moving steadily upward with
each rise and fall over the study timeframe due to a smaller sample size than Los Angeles. As in
the case of San Diego, early and substantial gains in residential care capacity followed by much
smaller rates of facility development portray the pattern of capacity growth in San Jose with each
increase and subsequent decrease in the ratio of beds to older adults across the two decades of
cumulative growth. As shown in Figure 12, among older adults ages 85+, this upward but
unstable pattern of growth is most distinct, growing from nearly 50 to 170 over the twenty year
period. Among older adults ages 75+ and 65+, which follow a similarly unstable yet less
noticeable upward trajectory of growth, the ratio of capacity to their respective age groups is
approximately ten to 50 and five to 21.
Although tracking cumulative facility growth by type of facility and cumulative capacity
by number of older adults in California and its largest cities provides in depth service provision
analyses among the various locations, plotting yearly percent change of facility growth and
capacity per older adults, representing the long-term care need, allows for state and city
comparisons of such growth. Figure 13 shows yearly percent changes in facilities in California,
Los Angeles, San Diego, and San Jose per 10,000 older adults ages 75+, demonstrating changes
in the rate of development in the state and its municipalities per the typical age demographic
residing in such facilities. While there exists a range of values in the change of growth in each
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geography, San Jose displaying the greatest variability of the four, a general downward trend of
change can be seen in California and Los Angeles, revealing a proliferation of facilities in the
early part of the study period and a slower increase toward the latter end in the two areas. San
Diego and San Jose, on the other hand, show a much more uniform rate of change on average
from beginning to end, displaying low levels of change from 1997 to 2015, despite great
increases in the interim.
Figure 14 shows percent changes in capacity across the state and its largest cities, which
demonstrates great fluctuation in all four geographies examined in the early part of the study
period. Changes in capacity also vary widely in San Diego and San Jose throughout the entire
timeframe, as in Figure 13, starting at higher levels of change relative to California and Los
Angeles and continuing to fluctuate mildly. This higher and comparatively unstable level of
development is best exemplified by the sporadic increases in large scale development in San Jose
and the precipitous increase in development in San Diego early on as indicated in Figures 9 and
12, even as California and Los Angeles grew at a slower rate, despite larger capacity overall and
remaining relatively stable throughout the timeframe.
Discussion
This study shows that residential care has increased by number of facilities and care
capacity as measured in number of beds in the state and its largest cities between 1996 and 2015
but has experienced ostensibly differential rates of development depending on location. In the
four separate geographies analyzed in this article (California and the Cities of Los Angeles, San
Diego, and San Jose), the size of the older adult population and residential care presence in each
determines in large part the stability of residential care facility and capacity growth. In other
words, the larger the geography in question, the more older adults, facilities, and beds exist in
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each, and the less sensitive the ratios of facilities and beds to older adults are to changes in
residential care development.
The best example of this are the differences in capacity per 1,000 older adults ages 85+
between Los Angeles, San Diego, and San Jose across the two decade timeframe. In each
respective trend, Los Angeles shows the most steady increase in capacity, whereas San Diego
shows sharp increase followed by steady decline and while San Jose shows intermittent increase
and decrease in upward growth. What these particular trends highlight, due to smaller
populations of older adults ages 85+ compared to the younger and larger older age
demographics, is the sometimes unpredictable nature of residential care facility development, as
well as all forms of development in cities. In San Diego, we observe a surge of residential care
capacity in the early part of the study period due to the development of rather large assisted
living and CCRC facilities, followed by years of normal development, relatively speaking.
However, due to a constantly increasing older adult population, as modeled by linear
interpolation of decennial census place older age demographics, San Diego experiences decline
in capacity per older adults in later years, even as it added a great many beds to its inventory at
the beginning of the timeframe. In the case of San Jose, the same is true, although the pattern
repeats itself several times and to a smaller degree than in San Diego. At different times across
the study period, San Jose adds to its inventory several large facilities that significantly increase
the ratio of beds to oldest old adults but encounters alternating years of smaller scale
development, primarily board and care facilities.
However, in comparing the development of residential care in each of these jurisdictions
as well as all of California, analyses of yearly change in facility growth belie the surge in
development in San Diego in the early part of the study period and the overall fluctuating pattern
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in San Jose, revealing an underlying stagnation of development from start to finish, similar to
Los Angeles and California, which demonstrate much greater rates of initial facility growth
followed by a lower yearly percent change in facility growth. Residential care in California and
its greatest cities, in other words, has either decreased or remained static in its rate of
development over the two decade period, which is not the most promising news considering the
exponential increase of older adults already in progress in the state and nation.
While these trends are not necessarily encouraging from a service provision point of
view, as they demonstrate growth in care capacity with diminishing rates of development relative
to an aging society, each analysis calls into question the nature and process of facility
development in each respective jurisdiction. More specifically, these trends challenge whether
institutional frameworks for eldercare development, including residential care, in each city are
adequate or appropriate. Put another way, the erratic ratios of capacity per older adults in San
Diego and San Jose cast a critical eye on the planning and land use policies, community ideals
with respect to eldercare facilities (including not in my back yard (NIMBY) organizations), city
council and planning commission political composition in an era of partisan politics, and land
availability of both municipalities, prompting interest in whether these and other cities should
intervene and incentivize such development, allowing for community participation and input as
well as the prioritization of jurisdictional care needs and resource allocation. Because the
development of residential care and other forms of eldercare in California is regulated at the
municipal level of government, mainly planning and community development departments, with
the assistance of planning commissions, city councils, and community stakeholders, can make
inroads toward a more critically informed eldercare development environment with regard to the
residential care and senior housing industry in general. In other words, although political and
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social ideology may play a part in shaping state and national long-term care issues in general,
housing and eldercare facility development are guided largely by local planning policies,
physical and geographical constraints, and communities of people concerned with preserving the
character, safety, and livability of their respective neighborhoods, which can cut across party
lines and socioeconomic groups in remarkably unpredictable ways. Almost certainly, however,
more affluent areas demonstrate greater political involvement and activism with respect to
housing and eldercare facility development than less resourced neighborhoods in certain
locations. At present, only Los Angeles out of the three cities investigated in this study
possesses a planning policy specifically geared toward eldercare facilities, including residential
care facilities, that incentivizes such development through a unified permitting process and
allowance of facility development in any zone in the city, given the approval of an eldercare
facility permit (Los Angeles, California, 2006). As the older adult population continues to grow,
other cities will likely enact similar policies, encouraging the development of these facilities for
older people who require support with functional limitation and disability outside of the home
and away from institutional LTC settings.
Although this study was subjected to methodological rigor, a number of limitations exist
in the analyses. First, the RCFE dataset only contains facilities that have remained open since
licensing and those that have closed within five years of accessing the dataset. Consequently,
this investigation’s analyses do not track all development, licensing, and subsequent closures of
facilities across the entire study timeframe, only facilities that have remained continuously open
as of late 2017. In other words, the actual number of facilities developed and beds provided
across the study period, net the number of facility closures each year, is more than likely
different from what is recorded in the RCFE dataset, with large scale facilities tending to remain
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in the administrative dataset longer than small facilities, six beds or fewer, which typically
experience more turnover than sizeable, corporate developments. Further, decennial census
place age demographics were linearly interpolated across three decades of data, meaning that
intervening years of age variables were estimated and may not represent that actual count of
older people in all of California, Los Angeles, San Diego, and San Jose over the timeframe.
Both of these limitations may affect the accuracy of the ratios of facilities and beds to older
adults across the period of study.
Future study is needed to evaluate not only facility development and care capacity but the
number of facility inspections, visitations, and complaints logged in the RCFE dataset as a means
of investigating care quality relative to the demographic makeup of local geographies.

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Figure 1. California Cumulative Facility Growth by Facility Type

Figure 2. California Cumulative Facility Growth per 10,000 Older Adults

0
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Figure 3. California Cumulative Capacity Growth per 1,000 Older Adults

Figure 4. City of Los Angeles Cumulative Facility Growth by Facility Type

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Figure 5. City of Los Angeles Cumulative Facility Growth per 10,000 Older Adults

Figure 6. City of Los Angeles Cumulative Capacity Growth per 1,000 Older Adults

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Figure 7. City of San Diego Cumulative Facility Growth by Facility Type

Figure 8. City of San Diego Cumulative Facility Growth per 10,000 Older Adults

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Figure 9. City of San Diego Cumulative Capacity Growth per 1,000 Older Adults

Figure 10. City of San Jose Cumulative Facility Growth by Facility Type

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Figure 11. City of San Jose Cumulative Facility Growth per 10,000 Older Adults

Figure 12. City of San Jose Cumulative Capacity Growth per 1,000 Older Adults

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Figure 13. Yearly Percent Change in Facilities in California, Los Angeles, San Diego, and San
Jose per 10,000 Older Adults Ages 75+

Figure 14. Yearly Percent Change in Capacity in California, Los Angeles, San Diego, and San
Jose per 1,000 Older Adults Ages 75+

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CHAPTER 4

Title: The eldercare facility ordinance of Los Angeles: A synthetic control analysis of residential

care development and growth.

Abstract
As the population continues to age, residential care is growing as an industry by number of
facilities. The 2006 eldercare facility ordinance of Los Angeles was designed to encourage such
growth by creating a streamlined permitting process for senior housing facilities, allowing such
facilities in any zone in the city with an eldercare facility permit, and absolving developers of
specific plan requirements in specific city neighborhoods at the discretion of city planning staff.
California State Department of Social Services residential care facility data were linked with
census place data to compare the pre and post effect of the ordinance on the number of assisted
living facilities developed in Los Angeles to a comparison group of cities using synthetic control
analysis. Facilities licensed between 1996 and 2015 in Los Angeles were matched to a synthetic
cohort of cities based on key residential care and housing characteristics for counterfactual
comparison. Normalized by older adults 75 years of age and older, Los Angeles shows a slight
advantage of development in excess of the synthetic control in posttreatment. Understanding the
ordinance can assist city staff and decision makers in retooling the policy for increased
productivity of eldercare development or duplication of the law in other municipalities.

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Introduction
As the demand for long term care (LTC) and supportive housing facilities (SHF) for the
elderly continues to grow in the United States, federal, state, and local government have
increasingly intervened on behalf of local communities through policy implementations
encouraging such facilities. Development and support of these communities is an important
governmental priority, as housing for the elderly, particularly affordable senior housing, serves
as an organized platform of supportive service delivery, an economy of scale from which
functionally impaired older adults who can no longer stay at home can receive LTC and to which
service providers can contribute resources in a targeted fashion (Harper, 2017).
Some of the most well-known federal initiatives in this regard are the U.S. Department of
Housing and Urban Development’s (HUD) Section 202 Supportive Housing for the Elderly
Program, the Low Income Housing Tax Credit (LIHTC), and Housing Choice Vouchers (HCV)
program, which assist local developers in creating housing facilities geared, at least in part,
toward low income and many times frail older adults (HUD, 2019a; HUD, 2019b; HUD, 2019c).
One of the more innovative federal policies draws upon the HCV program, which reallocates
HUD rental assistance from inaccessible public housing developments, transferring the funding
to approved private developments with supportive services for older adults (Harper, 2017; Stone,
2018). A variety of state funding programs exist as well, as in California, the location of this
study’s analysis, including the Housing for a Healthy California program, the Multifamily
Housing Program, and Planning Grants as part of Senate Bill (SB) 2, all of which provide
competitive grants for affordable housing, aiming to create communities for at risk populations
such as impoverished and disabled older adults (CHCD, 2019a; CHCD, 2019b; CHCD, 2019c).
State planning and zoning legislation in California has also begun to support LTC facilities,
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including SB 35, Streamline Approval Process; Assembly Bill (AB) 73, Streamline and
Incentivize Housing Production; and SB 540, Workforce Housing Opportunity Zones. These
laws collectively mandate the streamlining of housing project approval, create project streamline
overlay districts, encourage development of infill projects and facilities with a certain percentage
of low-income units, and restrain environmental impact project review on a discretionary basis
(S.B. 35, 2017; A.B. 73, 2017; S.B. 540, 2017).
However, while federal and state agencies can incentivize affordable senior housing
facilities and even mandate certain aspects of their development, local government exerts the
most influence on the creation of such communities. Local government and municipal code,
ideally, capture the historical and communal values of a given town or city, which govern,
among other facets of municipal management, the regulation of private property in community
development departments, including senior housing developments, through planning and zoning
policy. Regulation of this kind lends to local government a well-fitting and decentralized
apparatus for the review and approval of necessary facilities such as large scale housing projects
for the elderly but can also engender complications from a larger, state wide service provision
perspective, as in groups of cities with more restrictive development codes with respect to senior
housing. Such local planning and development policy can also generate rare opportunities for
case studies of a particular policy and its location contrasted with a comparison group of one or
more locations.
Perhaps the best example of a local policy that encourages senior housing development
while providing opportunity for analyzing its success relative to a comparison group is the
eldercare facility ordinance of the City of Los Angeles. Approved by City Council in 2006, the
ordinance is an unprecedented land use and administrative planning policy designed to promote
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all forms of eldercare and LTC facilities, including skilled nursing care, residential care, adult
day care, hospice care, and SHF communities, which is not in use to the same extent in any other
city in California. As a result, Los Angeles’ eldercare facility ordinance is the flagship eldercare
municipal directive in California, which incentivizes senior housing development in the city
through three primary means. These include:
1) A streamlined review process for eldercare facilities that consolidates all planning
entitlements associated with a prospective eldercare development (whether variances,
conditional use permits, etc.) into one permit, which is assigned to a single planner for a
single review process, otherwise known as the unified eldercare facility permit,
2) Allowance of such facilities in any zone in Los Angeles, not just residential or
commercial zones, with an approved unified eldercare facility permit, and
3) Absolution of specific plan requirements on the part of developers, an additional layer
of development policy in specific neighborhoods of the city, providing administrative
relief from potentially prohibitive development policies at the discretion of the Los
Angeles Planning Department (Los Angeles, California, 2006).
These three measures as part of the ordinance were designed to increase the likelihood and
efficiency of senior housing project permitting and development, with the intended purpose of
expanding the senior housing stock and capacity in the jurisdiction relative to the increasing
older adult population.
Residential care in Los Angeles and California
In evaluating the effectiveness of local development policies such as the eldercare facility
ordinance of Los Angeles, arguably the most suitable type of eldercare development to employ
in case study analysis of facility development and growth in Los Angeles is residential care, the
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largest eldercare industry by number of facilities nationally, with between 30,000 and 40,000
communities in the United States (NHCS, 2016; Wiener, Lux, Johnson, & Greene, 2010), and
with nearly 7,500 facilities in California alone (CDSS, 2018). Not only is residential care the
largest subindustry of LTC in the nation and state, it is also the most diverse form of eldercare in
the U.S., providing LTC in smaller, home-like living spaces of board and care facilities, which
are typically opened and licensed in existing single family homes, as well as assisted living and
continuing care retirement communities (CCRC), which can range in size from only dozens to
hundreds of beds as large scale development projects requiring municipal project review and
permitting. As the most plentiful and varied form of LTC in California and Los Angeles,
residential care facilities are developed and licensed at a higher rate than other kinds of LTC,
such as skilled nursing care, adult day care, and hospice care, and are therefore more appropriate
in measuring increases in facility development in the wake of policies encouraging such growth
compared to other locations in the state.
Considering the importance of California, the largest LTC industry and provider of
residential care facilities in the nation, and Los Angeles, the second largest metropolitan area in
the country and sponsoring jurisdiction of the eldercare facility ordinance, the aim of this study is
to test the impact of the eldercare facility ordinance on housing type, presenting a synthetic
control analysis of residential care development in Los Angeles relative to a comparison group of
cities in the state. We analyze the growth of residential care facilities in Los Angeles, measuring
its increase relative to a synthetic cohort of municipalities carefully matched to Los Angeles on
key housing demographics in the pretreatment period, before the approval of the ordinance in
2006, for counterfactual comparison during the posttreatment period. We also present placebo
and falsification tests for the purpose of statistical inference as a means of evaluating whether the
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effect observed in Los Angeles, the treated city, is substantial compared to randomly selected
cities in the synthetic cohort.
Methods
Data
The residential care for the elderly (RCFE) dataset was downloaded from the California
Department of Social Services’ (CDSS) Community Care Licensing Division (CCLD), the
department and agency responsible for managing the dataset. The RCFE dataset is a complete
list of facilities currently open and licensed as well as facilities that closed up to five years from
accessing the CCLD data portal, those on probation, and those pending license. The primary
purpose of this publicly available dataset is for consumer research of licensed and recently closed
facilities.
After downloading the dataset, we geocoded facilities, assigning spatial coordinates to
facility addresses and linking each with a corresponding 2010 census designated place or city in
ArcGIS, joining facility data to decennial census place area, age, housing, and income
demographics accessed in the National Historical Geographical Information System (NHGIS)
(NHGIS, 2019). The sample of facilities joined to census place demographic variables totals
nearly 10,000 facilities, more than 6,000 of which are licensed, more than 3,000 of which are
closed, and with nearly 500 pending license. This sample is smaller than the complete census of
facilities originally accessed in the CCLD data portal in late 2017, the margin of which fall
outside of incorporated city boundaries in county territories and were not included in this
investigation. Facilities were then categorized by type, using the RCFE dataset’s existing
classification system that distinguishes CCRCs from all other residential care facilities, and by
sorting facilities 25 beds or fewer in size from all others without a CCRC designation that are 26
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or more beds in capacity, generating classifications for small and large assisted living facilities,
respectively. The purpose of this categorization scheme is to demarcate smaller facilities exempt
from planning permits by California State Law, conceptualized here as board and care facilities
six beds or fewer in size, from assisted living facilities and CCRCs of greater size, and test only
large facilities as suggested in the literature (CHSC, 1973).
We then linearly interpolated census place demographics between decennial census years
1990, 2000, and 2010, extrapolating data to 2015 and delineating a study period of decennial and
intermediately estimated census variables ranging from 1996 to 2015, linking facilities by their
dates of license with each corresponding census variable year. This study timeframe was
constructed to produce an approximately equal amount of time in the pretreatment and
posttreatment periods, before and after the eldercare facility ordinance of 2006 in Los Angeles.
The facility sample was then narrowed to include only assisted living facilities and
CCRCs, those 7 beds or larger that require a planning permit, if developed, according to state
law. By excluding all facilities six beds or fewer, those coded as board and care facilities in this
study, we removed the bulk of facilities that typically undergo the most turnover in the dataset,
which cannot be accounted for in this analysis due to the limitations of the data. The limitation
in question here is that the RCFE dataset only catalogues facilities that have remained open since
initial licensing as well as all facilities closed within five years prior to the dataset’s date of
access. Board and care facilities are opened mainly in single family residences by proprietors
often less equipped to shoulder the costs associated with the delivery of LTC, and these smaller
facilities experience higher closure rates than assisted living and CCRC communities by virtue of
their sheer size as a subindustry of residential care (Wiener, 2019); for instance, board and care
facilities represent roughly 78% of the RCFE dataset but accounted for 82% of closures between
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2012 and 2017 (CDSS, 2018). Further, all residential care facilities 25 beds or fewer were also
excluded from the sample, as facilities 16 to 26 beds and larger in size are characterized as large
facilities, those that would almost certainly require city planning approval and would survive the
LTC market as economies of scale, remaining in the dataset over the study time period; utilizing
the conservative upper bound of 26 beds in describing large facilities from the literature, we
modeled large facilities as 26 beds or greater in capacity (CHCF, 2002; CANHR, 2019; AARP,
2012). Removing these facilities from the dataset restricted the analytical sample to a little more
than 1,500 facilities 26 beds or larger that likely experienced many fewer closures across
hundreds of cities in California between 1996 and 2015, which represent as best as possible
residential care development requiring a permit in the state that could benefit from the ordinance.
Measures
Dependent variable
The dependent variable utilized in this study is large facilities (26 beds or greater) per
10,000 older adults ages 75+ in census places, the target age demographic for residential care
development (Steadfast Companies, 2019), and the standard normalization of large eldercare
facilities in population studies (Bowblis, 2012).
Control variables
We selected control variables representing key theoretical and residential care factors,
influencing the development of the eldercare housing type. The devised control variables
include total housing units per total population, the percentage of each city with a slope of 15%
or less and not containing any hydrological features (derived from city digital elevation model
(DEM) raster datasets from USGS and paired with the National Hydrography Dataset (NHD))
(USGS, 2014; USGS, 2016), median household income, total number of white people per total
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population, total number of people ages 45 to 64 per total population, total number of assisted
living and CCRCs (both large and small facilities) per 10,000 older adults ages 75+, total number
of assisted living and CCRCs (both large and small facilities) by square mileage, as well as a
lagged dependent variable in year 2005 before treatment of Los Angeles’ eldercare facility
ordinance in 2006, as suggested by the model diagnostic literature (Abadie, Diamond, &
Hainmueller, 2010; McClelland & Gault, 2017). Ashok, Klöbner, Pfeifer, and Schieler (2015)
further advise against using lagged dependent variables in every year prior to pretreatment as a
set of predictor variables in synthetic control analysis, as doing so negates the predictive value of
all other covariates. These controls represent principal factors theorized as influential in the
supply of housing (Davidoff, 2016), including housing units by population denoting the density
of the existing and growing housing stock (Smersh, Smith, & Schwartz, 2003); land
developability or the amount of total land in cities on which housing could be developed, even if
currently developed (Saiz, 2010); median household income or buying power (Steadfast
Companies, 2019); the relative amount of white people in each city, the overwhelming racial
majority in assisted living facilities and CCRCs (Ball, Perkins, Hollingsworth, Whittington, &
King, 2009); the relative number of people ages 45 to 64, representing the family members of
older adults able to pay for such housing (Steadfast Companies, 2019); and the number of
assisted living facilities and CCRCs, both small and large, normalized by the older population
and area in each municipality. Although the literature suggests housing costs, such as median
monthly mortgage payment and rent, are also important factors, they were not available at the
city level of analysis over the time frame in this study (Saiz, 2010). Although older adults ages
75+ with an income of more than $35,000 and adults ages 45 to 65 with an income in excess of
$75,000 are the target demographics for those who normally receive care in residential care
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development and those who pay for it, respectively, due to lack of access to income by age
demographic data for census places in NHGIS, the dataset’s best controls for age and income are
the number of older adults ages 75+, the number of adults ages 45-64, and median household
income (Steadfast Companies, 2019).
Synthetic control analysis
Synthetic control analysis evaluates the impact of policy interventions in jurisdictions or
governments, measured against a synthetic cohort of governments, matching the two on key
demographic and economic variables in pretreatment for comparison in posttreatment. It creates
a hypothetical comparison group, aiming to imitate the treated unit as closely as possible prior to
the intervention to estimate the difference in outcomes subsequent to the policy enactment.
In contrast to difference in differences research methodology, which assumes that treated
and control cities maintain parallel yet disparate levels of growth during pretreatment with
respect to the outcome, the synthetic control method equalizes the growth of the treated city and
that of the average of untreated cities that collectively imitate its growth. In this way, we sought
to make identical the context of Los Angeles and its synthetic control prior to treatment,
measuring the effect of the policy in the difference between the two in posttreatment (Hu et al.,
2017). In comparing Los Angeles to other large cities in California in terms of size and
demographic makeup, many potential control cities show different but unparallel facility growth
rates during pretreatment. San Diego, for example, the next largest city in California, roughly a
fourth of the size of Los Angles, shows rapid increase followed by a plateau in growth before the
ordinance in preliminary analyses, whereas Los Angeles shows steady growth up until the
passage of the ordinance.
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After organizing the dataset longitudinally, collapsing all large assisted living facilities
and CCRCs and their combined capacity, along with interpolated census place demographics,
across the study timeframe in each city, we created a comparison group of control cities as part
of the synthetic cohort. The outcome of cumulative large facility growth normalized by 10,000
older adults ages 75+ in Los Angeles was matched to a cohort of donor cities, jurisdictions
combined to create the synthetic control, in the state prior to treatment, yielding an average
growth trend of cumulative facilities per older adults contributed from each respective control
city that best represents facility growth in Los Angeles before the eldercare facility ordinance of
2006 (Abadie et al., 2010). The respective weights of cities contributing to the average synthetic
control trend were computed based on the V-weight matrix of the aforementioned predictor
variable weights, the collection of estimated weights measuring the relative impact of each
control variable, selected to simulate the residential care and development environment of Los
Angeles (McClelland & Gault, 2017).
Thus, the outcome of cumulative growth in large facilities per 10,000 older adults ages
75+ was incorporated into the synthetic control model, along with control variables representing
key residential care and development characteristics of Los Angeles. We identified Los Angeles
as the treated city in the design and extracted the best mixture and weighting of cohort cities
based on predictor variables to assemble a synthetic or hypothetical Los Angeles without an
ordinance from the untreated donor pool for comparison. Finally, we charted the coincident
levels of cumulative facility growth normalized by number of older adults between Los Angeles
and its synthetic control during pretreatment and the difference between the two in posttreatment,
in which facility growth in the synthetic control diverges from that of Los Angeles, no longer
constrained to the treated unit by virtue of the control variables.
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Inference, diagnostics, and permutation tests
We generated pre and post root mean squared prediction errors (RMSPE) for the
outcomes among treated and untreated cities, the primary inferential diagnostics for falsification
testing in synthetic control analysis. According to Abadie, Diamond, and Hainmueller (2015),
RMSPEs measure lack of model fit between the outcome trajectory for any city and its synthetic
cohort of cities. In other words, RMSPEs calculate the difference between the actual growth of
normalized large facilities per older adults in every city across the study timeframe and its
respective synthetic control, including pre- and posttreatment RMSPEs for Los Angeles and its
synthetic control, as well as every weighted city in the synthetic cohort and its own synthetic
control, matched to Los Angeles on key residential care and development characteristics. In
building the model, we iteratively constructed a set of predictor variables, as discussed
previously, that predicted the growth of facilities per older people as produced by Los Angeles
during pretreatment, made up of weighted control cities contributing to the synthetic trend
estimate. Consequently, we matched the trends of Los Angeles and the synthetic control based
on control variables as closely as possible during pretreatment, generating in the process a pre-
RMSPE for Los Angeles and each control city, indicating goodness of fit between each city and
its respective synthetic cohort prior to treatment. The smaller the pre-RMSPE value, the better
the fit between the two (Johnson, 2013).
We then estimated Post-RMSPEs, indicating the difference between each city’s actual
facility growth trend in posttreatment and its respective synthetic control, and calculated the ratio
of post-RMSPEs to pre-RMSPEs, measuring the relationship between the effect size of the
intervention in posttreatment and the fit of actual and synthetic cities in pretreatment. The larger
the post/pre-RMSPE ratio for each city, the greater the indication that each actual city
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experienced a large effect in posttreatment after being closely matched to the control in
pretreatment (Turkova & Donze, 2017). As suggested by Abadie, Diamond, and Hainmueller
(2015), in selecting donor cities to incorporate into RMSPE calculations for each municipality,
we permitted control cities only with an RMSPE no more than three times the value of the actual
city’s RMSPE. We subsequently compared Los Angeles’ post/pre-RMSPE ratio to that of every
other control city, ranking each from highest to lowest and calculating the probability of Los
Angeles’ rank in the order. The higher Los Angeles’ rank, the lower the likelihood that the
difference between pre- and posttreatment growth of large facilities in the city occurred by
chance.
Finally, a permutation test was conducted to illustrate the trends of large facility growth
in Los Angeles and every weighted control city that contributed to the synthetic control trend,
modeling and graphing for visual comparison the trends of each control city as part of the
synthetic cohort before and after approval of the ordinance. Although studies comparing
municipal policies can present enormous disparities in outcomes and control variables, the
synthetic control method blends collections of governments into a single comparison group that
best reproduces the treated unit based on carefully selected control parameters, despite the size,
demographic makeup, or geography of any one control city in the group. Along with Los
Angeles, the placebos in the graphic represent the individual trends of cities whose weights were
collectively applied to the average trend of the synthetic control estimate, based on key predictor
variables selected to mimic the residential care and development environment in Los Angeles
during pretreatment (Galiani & Quistorff, 2016).

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Results
Figure 1 shows the growth of large facilities in Los Angeles over the study period
compared to Los Angeles’ synthetic control. The illustration reveals that Los Angeles produced
in the posttreatment period a widening surplus of facilities in comparison to its control, reaching
nearly one facility per 10,000 older adults 75 ages and older by 2015, a small difference in
residential care development subsequent to the eldercare facility ordinance of 2006. Prior to the
ordinance, actual and synthetic Los Angeles, or the synthetic cohort, followed a nearly identical
upward trajectory of facility growth, indicating goodness of fit in evaluating the two in
posttreatment. However, in the early part of the pretreatment period, both show a bit of
separation before converging in later preintervention years, demonstrating the lagged dependent
variable in 2005, as recommended by the diagnostic literature.
Table 1 displays the cities contributing to the weighted synthetic control in order of
weight. Twenty-seven cities comprise the control, most of which exert negligible influence
individually on synthetic Los Angeles’ trend estimate, while a handful strongly influence it. The
four most highly weighted cities in the control share a border with Los Angeles, which is to be
expected, considering that events and places nearer to each other tend to share more in common
than those farther away. Although seemingly inconsequential, the smaller weighted cities when
combined significantly alter and align the synthetic control estimate with Los Angeles’ trend,
without which the Los Angeles and synthetic cohort trends match far less well. Multiple
iterations of predictor variables (in preparation for the analysis shown here) produced various
arrangements of cities and weights that, prior to the current model specification, could not
imitate Los Angeles’ trend during pretreatment as successfully.
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Likewise, in order of variable and year, Table 2 enumerates the most significant variables
in selecting weighted cities for estimating the synthetic control growth trend, as generated in the
method’s V-matrix output. Out of more than 60 predictor variables in this analysis, balanced
between actual and synthetic Los Angeles as shown in Table 3 in Appendix A, 10 make up the
vast majority of weight. The most influential of these is lagged housing units per square mile in
2001, representing more than a third of the weight. The next most weighty lagged predictor
variables include housing units per square mile in 2000 and 2002, followed by the percent of
people ages 45 to 64 per total population in 1999 and 2004. Densities of housing and a younger
population who can financially assist older family members and friends in buying into residential
care, as conceptualized in this study, appear to exercise the greatest sway in the selection of
synthetic cohort cities and thereby the synthetic control trend estimate. However, although the
remaining predictor variables carry little weight compared to those most heavily weighted in
Table 2, they jointly constitute, in addition to the other predictors in the model, the ideal model
configuration. Previous iterations of the model that excluded these lower weighted variables (not
shown here) were unable to achieve the same degree of overlap between Los Angeles and its
synthetic control as advised by synthetic control guidelines. The model specification exhibited
in Figure 1 and Tables 1, 2, and 3 (in Appendix A) produced a pre-RMSPE value of .06, the
lowest of any other iteration in the analysis without utilizing yearly lagged dependent variables
during pretreatment as cautioned in the literature, suggesting optimal model fit.
Figure 2 displays the permutations of Los Angeles’ facility growth trend along with every
weighted control city as untreated placebo units. Many of the placebo cities show sporadic
growth followed by lulls in facility development, sometimes far in excess of Los Angeles’ output
of large facilities per 10,000 older adults ages 75 and up. Balancing these more productive
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placebos are cities with no development of large facilities during pretreatment followed by leaps
in growth or no development whatsoever. In fact, the highest weighted control city in the
analysis, East Los Angeles, contains no large facilities at all. Provided the model’s predictor
variables, both extremely productive and unproductive cities in terms of large facilities per older
adults were combined into a weighted average of yearly development designed to mimic Los
Angeles’s trend prior to passage of the eldercare facility ordinance of 2006, performing the task
remarkably well. Although some of the most highly weighted untreated cities in the synthetic
cohort show no development over the entire study period, with a larger assortment of smaller
weighted placebos exhibiting a great many more facilities per older adults than Los Angeles at
certain years during the timeframe, the synthetic control’s low pre-RMSPE and predictor
variable balance shown in Table 3 in Appendix A strongly support it as a comparison group to
Los Angeles’s facility growth.
Figure 3 illustrates the ratios of post/pre-RMSPEs for Los Angeles and each city as part
of its synthetic cohort, indicating the relationship between the effect size of the intervention in
Los Angeles and the model’s goodness of fit, and the same for each placebo city contributing to
the control. Among all weighted cities in the analysis, Los Angeles ranked second with a ratio of
6.85. Taking the quotient of Los Angeles’ rank to the number of cities in the ordered lineup of
ratios, the effect size observed in posttreatment between actual and synthetic Los Angeles given
model fit renders a p-value of .07. In other words, the likelihood that Los Angeles’ excess of
facility growth compared to synthetic Los Angeles in posttreatment occurred by chance is 7%.
Discussion
This study shows that the development of large assisted living facilities and CCRCs in
Los Angeles increased after passage of the eldercare facility ordinance of 2006, more so than a
118
similarly situated synthetic control of untreated cities, suggesting that ordinance had an impact
on the development of residential care in Los Angeles. Although Los Angeles develops by far
the most residential care facilities in California, producing nearly three times as many facilities
as San Diego, the next largest city in the state, a normalized ratio of facilities to older adults 75
years of age and older, the typical demographic residing in such facilities, reveals a small but
noteworthy increase of facilities in Los Angeles, almost one facility per 10,000 older adults more
than its synthetic control. Understanding the effectiveness of the ordinance as analyzed in this
study can inform city planning staff and local decisions makers as to its suitability in increasing
residential care development and how it could be retooled for better usefulness or reproduced in
other locations as appropriate.
We made this comparison by creating a hypothetical Los Angeles, constructed from
many other cities in the state, and structured based on key independent variables informed by the
residential care and development literature. As mentioned earlier, the highest weighted cities in
the analysis lie directly adjacent to Los Angeles, sharing many of the same geographical
characteristics as their neighboring and sprawling municipality, citing the first law of geography
and the spatial sciences, that things closer to one another are more apt to behave like each other
(Tobler, 1970). Other lesser weighted cities arrived in the analysis from much farther distances
across the state, one of which from the uttermost northern reaches of the Bay Area, in semi-rural
wine country, the City of Napa. Although the assemblage of these placebo cities may appear at
face value a crude hodgepodge of dissimilar locations, collectively they represent and mirror in a
theoretical sense, based on key assisted living and development predictors, the housing
environment of Los Angeles without an eldercare facility ordinance for posttreatment
comparison. Despite the fact that many of the placebo cities’ municipal codes contain language
119
about eldercare or senior housing facilities, some of them even stipulating that proposed senior
housing developments must be subjected to discretionary review through a formal conditional
permitting process, none of them has approved an ordinance even vaguely comparable to the
eldercare facility ordinance of Los Angeles, which strongly incentivizes residential care
development and all other forms of senior housing in the jurisdiction, as discussed in the
introduction of this study.
As such, the weighted grouping of these cities based on carefully selected predictor
variables renders a control trend that supports the effectiveness of Los Angeles producing more
facilities per older adults than it would have without the eldercare facility ordinance, with a few
qualifications. First, although the statistical significance of Los Angeles’ growth trend compared
to the control presents a reasonable level certainty regarding the outcome following the
intervention, it does not represent the typical upper bound of 5% used in most analytical studies.
Put differently, the likelihood of observing by the chance the difference between Los Angeles’
growth trend and the counterfactual in posttreatment is slightly higher than normally accepted in
empirical research, an inherit weakness in this investigation. Among the cities with either
greater or similar statistical significance due to their positions in the RMSPE ratio ranking,
including Monterey Park and Bell, the synthetic control of either city far exceeded the placebo in
the counterfactual analysis of facilities per older adults and was closely matched to the city in
pretreatment. This indicates that while the effect size was large and the model was well fitted in
either analysis, each city underperformed its respective control, producing as little as one and as
much as three facilities per 10,000 older adults less than the counterfactual. In contrast, while
the effect size relative to the goodness of fit in the Los Angeles analysis was smaller than
Monterey Park and only a little larger than Bell, Los Angeles produced more than its control in
120
posttreatment, outstripping the other two compared to their counterfactuals, although not
outranking them in terms of RMSPE ratios.
Second, as stated previously, some of the highest weighted cities in the analysis contain
no development of large facilities across the entire study timeframe, a seemingly odd occurrence
among control cities, but not so odd when contrasted with the entire sample of census designated
places joined to CDSS RCFE data, as originally generated for this study. Among the 27 cities
used in forming synthetic Los Angeles, 21% contain no facilities, whereas approximately 19% of
cities across the state also contain no cities, a relatively comparable set of figures. As indicated
in the results, many of the highest weighted cities contain no facilities and therefore no facility
growth during the study period but were matched to Los Angeles as heavily influential
contributors to the control trend due to corresponding housing densities and concentrations of
middle aged people. Consequently, while these highly weighted cities contribute no facilities to
the synthetic control estimate, they represent some of the only locations in the state organized
and developed like Los Angeles, given the selected predictor variables, disposing them as prime
and appropriate candidates for the weighted control despite their complete lack of large scale
residential care development.
Third and perhaps most importantly, the results in posttreatment hinge exclusively on the
mixture of the dependent and predictor variables, lagged or averaged over the pretreatment
period. Slight changes to the dependent variable or its predictors in this regard can present
completely different results subsequent to the intervention. For instance, conceptualizing
housing growth as the number of non-board and care facilities, those seven beds or larger in size
across the study period (not shown in this study), muddies the results, and in tandem with other
changes in model specification, flips them outright. Accordingly, modeling the growth of these
121
small facilities in addition to lagging the dependent variable and other somewhat extraneous
predictor variables, such as housing vacancy and occupancy rates, in every year prior to
treatment renders a counterfactual in excess of Los Angeles’ facility growth trend. As advised
by the housing and residential care literature, the following model specification results in a Los
Angeles large facility growth trend in excess of the counterfactual, as presented in this article:
Conceptualizing large assisted living and CCRCs as 26 beds or greater in size, those that could
benefit, if developed, during the permitting stage of facility construction from the Los Angeles
eldercare facility ordinance; lagging the dependent variable solely in 2005, the year prior to
treatment; and removing independent variables that do not bear directly on the development or
purchase of residential care in treated or untreated cities (Steadfast Companies, 2019; Abadie,
Diamond, & Hainmueller, 2010; McClelland & Gault, 2017; Smersh, Smith, & Schwartz, 2003;
Saiz, 2010; Ball, Perkins, Hollingsworth, Whittington, & King, 2009).
Considering the said model specification and the results of the study that show only slight
increase of residential care development in Los Angeles after the approval of the ordinance
compared to the synthetic cohort, a number of implications come to the forefront of the
investigation, including the viability of this land use law and its potential implementation in
other municipalities and broader levels of government. While the analyses of this study do seem
to indicate an increase in development following the ordinance, allowing the qualifications just
mentioned, developers and senior housing practitioners have voiced an opposite viewpoint,
claiming the land use law has had no effect in the jurisdiction, either in general or due to other
land use tools that more or less attenuate its effect on supportive senior housing development (A.
Friedrich, personal communication, January 30, 2020; J. Ailshire, personal communication,
March 22, 2020). Since this is the first study to examine the effectiveness of such an ordinance,
122
future research on the usefulness of this and other senior housing ordinances is advised prior to
widespread advocacy of the eldercare facility ordinance of Los Angeles in its current form in
jurisdictions across California, at the state level of government, and beyond, although rethinking
the ordinance in Los Angeles nearly 15 years after its passage is advised, along with advocating
for local eldercare land use policies tailor made for each respective city.
Additionally, given the aforementioned caveats to this study’s results, the following
limitations warrant consideration as well. The first of these is the scope of the study, limited to
Californian cities, which individually (apart from Los Angeles) cannot serve as a control to
facility growth in a city as large as Los Angeles, the second largest metropolitan area in the
nation. Because residential care as an industry is regulated at the state level, comparing and
contrasting the senior housing type across state boundaries is extremely difficult, even with
harmonized data in select national studies of residential care, sampled at the facility level of
observation on a limited basis. Ideally, residential care data from states with other large
sprawling cities such as Phoenix, Arizona; Houston, Texas; Jacksonville, Florida; among others,
could be crafted into a control or incorporated as part of a weighted control to formulate a more
naturally occurring comparison to Los Angeles’ development environment for counterfactual
analysis. However, as residential care remains mainly a local affair, such comparisons must wait
for future studies using state harmonized housing datasets. Additionally, the CDSS residential
care dataset joined with census designated places in this study only catalogues facilities that have
remained in operation since the last date of license as well as those that have closed within five
years of downloading the dataset, which is updated weekly. As a result, the analysis of this study
could not track all licensing and closures of facilities across the entire study timeframe and
thereby all residential care development. However, as conceptualized in this study, large
123
facilities are subject to far fewer closures than smaller facilities, particularly six-bed board and
care facilities, and are less likely to have opened and closed prior to 2012, the five-year marker
for the purging of closure data in the dataset. Future investigation of residential care trends will
necessitate state datasets that track all licensing and closures of facilities, inventorying all
opening and shutting down of facilities whether small or large as well as all development of large
facilities requiring permitting and planning approval prior to construction.

124
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131
Figure 1. Synthetic Control Trends: Cumulative Los Angeles and Synthetic Los Angeles Large
Facilities per 10,000 Older Adults 75+

Table 1. Synthetic Los Angeles Donor City Weights

City Weight

City Weight
Pasadena 0.001

Santa Clara 0.023
West Hollywood 0.002

Sacramento 0.025
Laguna Hills 0.003

Monrovia 0.028
Florin 0.006

Bell 0.029
Moreno Valley 0.006

Citrus Heights 0.033
South San Francisco 0.007

Downey 0.033
Daly City 0.010

Napa 0.034
Irvine 0.013

Monterey Park 0.040
San Mateo 0.013

Palm Desert 0.043
San Pablo 0.015

Compton 0.062
Madera 0.018

Willowbrook 0.105
Westminster 0.018

Glendale 0.176
Oakland 0.020

East Los Angeles 0.217
Long Beach 0.021

0
0.5
1
1.5
2
2.5
3
3.5
4
4.5
1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015
Los Angeles and Synthetic Los Angeles Large Facilities per 10,000 Older Adults 75+
Los Angeles Synthetic Los Angeles
Los Angeles Eldercare Facility Ordinance
132

Table 2. Selected Synthetic Los Angeles V-Matrix Predictor Weights

Selected Predictor Variable Weight
Total Housing Units per Square Mile (1999) 0.01
Total Housing Units per Square Mile (2000) 0.14
Total Housing Units per Square Mile (2001) 0.35
Total Housing Units per Square Mile (2003) 0.14
Total Housing Units per Square Mile (2005) 0.01
Percent People Ages 45-64 per Total Population (1998) 0.05
Percent People Ages 45-64 per Total Population (1999) 0.09
Percent People Ages 45-64 per Total Population (2002) 0.04
Percent People Ages 45-64 per Total Population (2004) 0.12
Percent People Ages 45-64 per Total Population (2005) 0.03

Figure 2. Permutation Test: Trends of Cumulative Large Assisted Living Facilities and CCRCs
per 10,000 Older Adults 75+ in Los Angeles and Placebo Cities

Los Angeles Eldercare Facility Ordinance
0
2
4
6
8
10
12
14
1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015
Permutation Test: Large Assisted Living and CCRCs per 10,000 Older Adults 75+
Los Angeles Eldercare Facility Ordinance
133
Figure 3. Los Angeles and Synthetic Control Cities Ratios of Post/Pre-RMSPEs

0 1 2 3 4 5 6 7 8 9
Compton
Palm Desert
San Pablo
San Mateo
South San Francisco
Irvine
Monrovia
Long Beach
Madera
Napa
Oakland
Santa Clara
Florin
Glendale
Citrus Heights
Daly City
Moreno Valley
Laguna Hills
West Hollywood
Westminster
Pasadena
Downey
Sacramento
Willowbrook
East Los Angeles
Bell
Los Angeles
Monterey Park
RMSPE Ratios of Los Angeles and Synthetic Control Cities
134
Appendix A

Table 3. Los Angeles and Synthetic Los Angeles Predictor Balance

Synthetic
Los Angeles Los Angeles
Large Facilities per 10,000 Older Adults 75+ (2005) 2.01 2.01
Mid-Sized and Large Facilities per 10,000 Older Adults 75+ (1996) 1.19 1.20
Mid-Sized and Large Facilities per 10,000 Older Adults 75+ (1997) 1.23 1.23
Mid-Sized and Large Facilities per 10,000 Older Adults 75+ (1998) 1.33 1.34
Mid-Sized and Large Facilities per 10,000 Older Adults 75+ (1999) 1.55 1.54
Mid-Sized and Large Facilities per 10,000 Older Adults 75+ (2000) 1.53 1.58
Mid-Sized and Large Facilities per 10,000 Older Adults 75+ (2001) 1.75 1.74
Mid-Sized and Large Facilities per 10,000 Older Adults 75+ (2002) 1.96 1.96
Mid-Sized and Large Facilities per 10,000 Older Adults 75+ (2003) 2.00 1.99
Mid-Sized and Large Facilities per 10,000 Older Adults 75+ (2004) 2.09 2.09
Mid-Sized and Large Facilities per 10,000 Older Adults 75+ (2005) 2.29 2.42
Mid-Sized and Large Facilities per Square Mile (1996) 0.04 0.04
Mid-Sized and Large Facilities per Square Mile (1997) 0.04 0.04
Mid-Sized and Large Facilities per Square Mile (1998) 0.04 0.05
Mid-Sized and Large Facilities per Square Mile (1999) 0.05 0.05
Mid-Sized and Large Facilities per Square Mile (2000) 0.05 0.05
Mid-Sized and Large Facilities per Square Mile (2001) 0.06 0.06
Mid-Sized and Large Facilities per Square Mile (2002) 0.07 0.07
Mid-Sized and Large Facilities per Square Mile (2003) 0.07 0.07
Mid-Sized and Large Facilities per Square Mile (2004) 0.07 0.07
Mid-Sized and Large Facilities per Square Mile (2005) 0.08 0.08
Total Housing Units per Square Mile (1996) 2,671 2,671
Total Housing Units per Square Mile (1997) 2,676 2,676
Total Housing Units per Square Mile (1998) 2,680 2,680
Total Housing Units per Square Mile (1999) 2,685 2,685
Total Housing Units per Square Mile (2000) 2,690 2,690
Total Housing Units per Square Mile (2001) 2,702 2,702
Total Housing Units per Square Mile (2002) 2,715 2,715
Total Housing Units per Square Mile (2003) 2,727 2,727
Total Housing Units per Square Mile (2004) 2,739 2,739
Total Housing Units per Square Mile (2005) 2,752 2,752
Percent Whites per Total Population (1996) 49% 49%
Percent Whites per Total Population (1997) 49% 49%
Percent Whites per Total Population (1998) 48% 48%
Percent Whites per Total Population (1999) 48% 48%
135
Percent Whites per Total Population (2000) 47% 47%
Percent Whites per Total Population (2001) 47% 47%
Percent Whites per Total Population (2002) 48% 48%
Percent Whites per Total Population (2003) 48% 48%
Percent Whites per Total Population (2004) 48% 48%
Percent Whites per Total Population (2005) 48% 49%
Percent People Ages 45-64 per Total Population (1996) 18% 18%
Percent People Ages 45-64 per Total Population (1997) 18% 18%
Percent People Ages 45-64 per Total Population (1998) 18% 18%
Percent People Ages 45-64 per Total Population (1999) 18% 18%
Percent People Ages 45-64 per Total Population (2000) 19% 19%
Percent People Ages 45-64 per Total Population (2001) 19% 19%
Percent People Ages 45-64 per Total Population (2002) 20% 20%
Percent People Ages 45-64 per Total Population (2003) 20% 20%
Percent People Ages 45-64 per Total Population (2004) 20% 20%
Percent People Ages 45-64 per Total Population (2005) 21% 21%
Median Household Income (1996) $34,382 $35,289
Median Household Income (1997) $34,958 $36,138
Median Household Income (1998) $35,535 $36,987
Median Household Income (1999) $36,111 $37,475
Median Household Income (2000) $36,687 $36,686
Median Household Income (2001) $37,993 $39,791
Median Household Income (2002) $39,299 $40,896
Median Household Income (2003) $40,604 $42,002
Median Household Income (2004) $41,910 $43,107
Median Household Income (2005) $43,216 $44,212
Percent of City with < 15% Slope and Not a Hydrological Feature 80% 86%

136
CHAPTER 5- CONCLUSION

The previous four chapters of this dissertation represent four separate research articles on
residential care in California, a new and quickly growing form of long-term care (LTC) and for
older adults who can no longer remain in their homes due to functional limitation but who do not
require skilled nursing care. As a whole, the four articles signify a broader effort to identify a
testable policy affecting housing for the elderly, investigate the current state of affairs with
respect to a specific type of senior housing promoted by the policy, and then test the policy’s
effectiveness in supporting it. Residential care as a type of senior housing was selected for
analysis in these articles given its growing prominence as a housing type out of institutional care
by number of facilities, and less importantly, readily accessible and easily geocoded (assigned
geographical coordinates for spatial analysis) address data. Although nursing homes still
marginally produce more beds per older adults than the residential care industry, the latter has
outproduced skilled nursing care in the western U.S. in terms of facilities for years now,
particularly in California and its largest metropolitan area, Los Angeles. Residential care is the
largest form of housing for the elderly by far by number of facilities in the state and has been
more naturally integrated into residential environments than other forms of senior housing due to
small and mid-sized board and care and assisted living facilities that organically crop up in low
density residential neighborhoods and commercial areas across the state. Understanding
residential care in California, the largest state provider in the industry, is critical, as more and
more older adults are buying into the housing type.
Perhaps more fundamental, however, is understanding how residential care in California
currently fares under the institutional auspices that guide its development, a theme originally
conceived of as part of the broader corpus of these articles, the culminating research question of
137
these four papers. Although a plethora of policies are introduced and described in the first article
of this dissertation, which is a comprehensive literature review of federal and California policies
promoting all forms of senior housing, the article ultimately reveals an unprecedented municipal
policy in the state’s largest city, Los Angeles, the Eldercare Facility Ordinance of 2006, that
strongly encourages senior housing development in the jurisdiction, as progressively discussed in
all four studies of the dissertation. This concluding chapter briefly reviews each article as part of
the larger work, describing the main findings of each paper in distinct sections of the chapter and
offering final concluding remarks on residential care and the effectiveness of the policy in
promoting its development.
Residential care for the elderly: A review of long-term care and development policy in California
and the United States
This introductory chapter of the dissertation serves two primary purposes. The first is to
introduce the Eldercare Facility Ordinance of Los Angeles, the land use law tested in the final
analytical chapter of the work, and the second is to provide a comprehensive review of the LTC
and residential care literature in the U.S., framing the body of work within a governmental and
policy-oriented context.
The article is broken down into three main sections as informed by the housing literature,
including the history of LTC as an industry, federal affordable housing programs in the United
States, and California state policies that encourage the development of residential care and other
forms of eldercare facilities. The review provides a lengthy prologue into LTC, spanning as far
back in history as the almshouses of Great Britain, and culminating in some of the more recent
pieces of reform legislation coordinating LTC among the federal government and states in the
historical section of the paper, such as the Home and Community Based Services (HBCS)
138
Waiver Program of the Omnibus Budget Reconciliation Act (OBRA) of 1981, the Patient
Protection and Reconciliation Act (PPACA) of 2010, and the California Residential Care for the
Elderly Reform Act (CRCFERA) of 2014, the most recent regulatory bill related to residential
care in the state. In the next section, it then outlines the bulk of federal policies and programs
supporting the development of affordable housing for the elderly across the nation, grouping the
majority of such programs into three categories, including low-income housing tax credits
(LIHTC) in support of affordable housing development, rent subsidy programs, and block grants
supporting city and county housing programs. The final analytical section of the chapter reviews
California housing policies, many of which were passed into law as recently as two to three years
ago due to the heightening housing crisis in the state, and concludes with an overview of the Los
Angeles Eldercare Facility Ordinance of 2006 that strongly incentivizes the growth of senior
housing in the jurisdiction through three primary regulatory structures, as outlined in the paper.
Although this introductory literature review describes in detail the ordinance and how it
encourages the growth of eldercare facilities, the fourth and final analytical chapter of this
dissertation explains the ordinance in greater detail, communicating its importance as a land use
tool in expanding senior housing resources in Los Angeles as compared to other cities untreated
with an eldercare facility ordinance throughout the state. Examination of the literature in this
chapter shows that federal affordable housing programs function mainly as subsidies for low-
income senior housing development, whereas California state and local housing policies, at least
in recent years, act mainly to streamline existing planning review processes and absolve
developers of sometimes costly and time consuming administrative policies.

139
Residential care in Los Angeles: Evaluating the spatial distribution and neighborhood access to
care among older adults
In this first analytical chapter, the City of Los Angeles is introduced as the study area of
the dissertation and the premier residential care city, the largest municipal provider of the
housing type in California and the western United States. The article also portrays the growing
need of the housing type in the city and state, even as residential care continues to grow as an
industry, as indicated in the next analytical chapter of the work. The paper continues by
introducing the dissertation’s dataset, the California Department of Social Services (CDSS)
residential care for the elderly (RCFE) dataset that is used in the remaining analytical chapters of
the body of work. The primary purpose of this chapter is to spatially and cartographically
analyze the current state of residential care in Los Angeles, demonstrating the location of
facilities relative to the potential need and the relationship between the number of beds provided
in city neighborhoods and key old age groups.
Two main statistical approaches comprise this chapter, including spatial clustering and
supply and need mismatch analyses. The spatial clustering analysis utilizes the Getis-Ord Gi*
statistic, measuring the grouping of extreme phenomena or events in various geographical
aggregations of analysis, in this case, very small or large sized facilities within the boundaries of
the city in two separate timeframes, as shown in the article’s two maps of the jurisdiction. The
supply and need mismatch analysis measures the relationship between the percent change of
neighborhood or census tract residential care facility beds and a 1-percent increase in a number
of old age groups, using incident rate ratios (IRR) as generated through zero-inflated negative
binomial (ZINB) regression. A secondary component of the mismatch analysis is the calculation
of marginal effects on the number of beds as predicted among the old age groups. This portion
140
of the analysis illustrates the predicted trends of residential care facility beds in Los Angeles
census tracts by the proportion of old age groups residing in them, showing how residential care,
as measured through the number of beds in census tracts, either increases or decreases in
neighborhoods as a function of the percentage of older adults living in them. Taken as a whole,
the findings of the study show concentrations of large facilities near the central urban core of Los
Angeles, clusters of small facilities near the outer suburban neighborhoods of the city, many
more beds in census tracts containing large communities of older adults ages 85 and up, and a
disadvantage in access to care for older Hispanic people across the jurisdiction. The findings
reveal, more specifically, clusters of large facilities in and around the highest concentrations of
older women 75 years of age and older with at least one disability per square mile,
concentrations of large facilities east of the downtown area and near the neighboring cities of
West Hollywood and Beverly Hills, and clusters of small facilities in suburban neighborhoods
less populated with older women with disability, primarily in the San Fernando Valley, an
expansive residential area in the northern portion of the city. Additionally, as demonstrated in
the regression results of the study, the number of residential care beds per census tract increases
nearly 50% for every 1-percent increase in oldest old adults, and decreases more gradually in
neighborhoods inhabited by Hispanic older adults, a 2-percent decrease in beds for every 1-
percent increase among the demographic. Examination of geographical access to residential care
in the city reveals not only differential access to care among older age and race groups but the
precise locations and clustering of facilities both small and large in the city, depicting the spatial
distribution of the LTC type across one of the largest jurisdictions in the state and nation and
showing how geographic and old age demographic characteristics influence the location of
residential care facilities of various sizes and types.
141

Residential care development in California: Time series analyses of facility and care
capacity growth
In this second analytical chapter, the CDSS RCFE dataset is brought into high relief as a
statewide dataset that, paired with interpolated census designated place variables, can be used for
the calculation of residential care service provision trends over time. As a descriptive paper, this
study also portrays the dataset in a more critical light, describing its importance as an
administrative record and census of residential care facilities in the state, the primary use of
which is consumer research of facilities, and as employed in this paper, data analytics. The main
purpose of this study is to introduce the RCFE dataset as a record that can be used for time series
analysis of service provision trends and ultimately a longitudinal dataset as a precursor to the
third and final analytical paper of this dissertation, comparing facility development trends in Los
Angeles to a control of cities in the state.
Five measures make up this chapter’s set of time series analyses, including total
cumulative facility growth by facility type; total cumulative facility growth per 10,000 older
adults ages 65, 75, and 85 and older; and total cumulative care capacity growth as measured in
number of beds per 1,000 older adults ages 65, 75, and 85 and older; percent change in number
of facilities per 10,000 older adults; and percent change in capacity in beds per 1,000 older adults
from 1996 to 2015, the timeframe of the study. Each of these measures is applied in separate
analyses to the entire state of California and its three largest cities, including Los Angeles, San
Diego, and San Jose. The total cumulative facility growth measure displays the respective
upward trend of board and care homes, assisted living facilities, and continuing care retirement
communities (CCRC) in California and its three largest cities, demonstrating first and foremost
142
that the residential care industry has experienced an increase in facilities in each of the study
areas across the timeframe. The total cumulative facility growth per older adults time series
trend adds to the previous analysis normalization of the facility growth measure by old age
demographics utilized in LTC service provision analyses. Of these normalized facility growth
variables, facilities per oldest old adults, 85 years of age and older, illustrates the greatest
volatility in each of the jurisdictions and state due to smaller numbers of the age demographic in
California. The total cumulative care capacity per older adults variable, the typical measure for
care provision in the LTC industry, shows the growth of beds per older adults in each city and
across the state. As with the normalized facility growth variables, normalized care capacity per
oldest old adults exhibits the greatest instability among the different capacity measures by city
and California, as there are fewer older adults ages 85 and up relative to the younger age groups.
As a group, these time series analyses predominantly reveal steady growth of facilities and beds
in the state and its three largest cities. However, in select cases, they also demonstrate odd
patterns of development. For example, San Diego’s cumulative care capacity measure indicates
initial precipitous growth in the first few years of the study period, followed by slow decline
until the end of the timeframe. This odd growth trend in San Diego uncovers an anomalous
series of extremely large facility developments in the jurisdiction in the early portion of the study
period, trailed by years of smaller scale residential care development, which coinciding with
increases in the older adult population, lowers the capacity growth trend until the latter end of the
study period. Investigation of facility and capacity growth trends in California and its leading
cities, in general, shows concurrent increase in the residential care stock across the state but also
exceptional cases of sharp and intermittent growth caused by unusually productive years
throughout the study period. Percent change in number of facilities per older adults indicates an
143
overall slowdown in development in all of California and Los Angeles across the timeframe but
a more or less level rate of development from beginning to end of the study period in San Diego
and San Jose, despite drastic changes in the interim. Finally, percent change in number of beds
per older adults shows that the greatest increases in development in each geography occurred in
the early part of the time frame, only to decrease to a low and stagnant level for the remaining
part of the study timeframe.
The eldercare facility ordinance of Los Angeles: A synthetic control analysis of residential care
development and growth
This final analytical chapter brings the dissertation to its investigative conclusion, serving
as the closing methodological paper and culminating experiment of the chapters in testing the
Eldercare Facility Ordinance of Los Angeles, as discussed throughout the entire work. The
article describes the ordinance and the importance of residential care in Los Angeles, the
principal study area of the investigation, in the fullest detail in the dissertation. It then tests the
law in Los Angeles using a control group of cities in synthetic control analysis. The primary
purpose of this chapter is to measure the effect of the natural experiment of the Eldercare Facility
Ordinance in Los Angeles and present the diagnostic results to determine the inference of the
experiment’s results, as the synthetic control method is a new and somewhat unorthodox
statistical technique in research on housing for the elderly.
As assembled in the analysis from the previous chapter, the number of large facilities, 26
beds or greater in size, per 10,000 older adults ages 75 and older in Los Angeles is compared in
this chapter to a synthetic control of weighted cities, drawn into the control based on key
residential care and development characteristics. These predictor characteristics include the
number of mid-sized to large facilities per older adults 75 years and older, the number of mid-
144
sized to large facilities per square mile, total housing units per square mile, the proportion of
whites to total population, the proportion of adults ages 45 to 64 to total population, and median
household income, all lagged in every year prior to the intervention of the ordinance. Other
predictor variables used in the selection of control cities include the percentage of municipalities
with a slope of less than 15% and with no hydrological features, averaged over each year during
the pretreatment period, and the dependent variable lagged only in the year prior to pretreatment,
as suggested by the literature. A group of 27 cities comprise the control used for comparison
with Los Angeles, the most highly weighted of which include East Los Angeles, Glendale, and
Willowbrook. Additionally, among the dozens of lagged predictor variables used in the selection
of cities as part of the control, total housing units per square mile and the proportion of adults
ages 45 to 64 appear to influence the model most. Designed iteratively to match the cumulative
large facility growth trend of Los Angeles in pretreatment, minimizing the root mean squared
prediction errors (RMSPE), the difference between the actual growth trend of Los Angeles and
its control, the current model specification shows in posttreatment a slight increase of nearly one
facility per 10,000 older adults 75 years of age and older in Los Angeles relative to the synthetic
control. Ratios of post- and pre-RMSPE values, which approximate the relationship between
differences seen in posttreatment relative to the fit between actual and synthetic Los Angeles
prior to passage of the ordinance are then presented, indicating in rank order the probability of
each city’s growth trend compared to its own control happening by chance. As explained in the
article, the likelihood of Los Angeles’ growth trend in posttreatment relative to the control
occurring by chance is 7%, a reasonable level of certainty regarding the effectiveness of the
Eldercare Facility Ordinance in producing more facilities in the jurisdiction subsequent to
passage of the law. Empirical investigation of residential care and the facility ordinance in Los
145
Angeles indicates successful policy implementation of the unprecedented land use tool in
increasing the residential care housing stock, and by association, perhaps all forms of LTC.
Conclusion
The previous four chapters of this dissertation accomplish three important tasks,
including identifying a testable policy influencing the development of residential care for the
elderly in Los Angeles, analyzing the current state of residential care in the city and state, and
testing the policy’s effectiveness in supporting its growth in the jurisdiction. Taken as a whole,
this dissertation describes the Los Angeles Eldercare Facility Ordinance of 2006, shows that
residential care is disproportionately distributed among city neighborhoods and old age groups
and growing in California and its largest cities, and indicates that the ordinance is increasing the
growth of residential care in the city compared to a suitable comparison group of Californian
cities.

146
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Abstract (if available)

Abstract Residential care is an intermediary form of senior housing that bridges the gap between at-home services and supports and skilled nursing care for functionally impaired older adults. With a burgeoning older adult population, which is due to double by 2050, the percentage of older people residing in any form of eldercare housing will more than double by mid-century as well, increasing the demand of senior housing facilities, putting ever more pressure on cities and local governments to incentivize, plan, and develop additional residential care facilities for the impending long-term care need of older adults, particularly in world cities such as Los Angeles. ❧ Unfortunately, little is known about where and how residential care facilities are developed in Los Angeles, the largest market for the housing type. Most research on residential care focuses on the cost, service offering, and functional status of older adults in such facilities and not the institutional frameworks that guide and shape the development of the industry. As the population of older adults continues to grow, understanding the development and distribution of residential care facilities will become increasingly important, as residential care helps older people who are no longer able to stay at home to remain in their neighborhoods (age in place), reducing isolation, loneliness, and a number of other health outcomes. ❧ The purpose of this dissertation is to test the effectiveness of Los Angeles’ eldercare facility ordinance on residential care in the city. Los Angeles Planning Staff and City Council have recognized the importance of these facilities among the city’s large and rapidly growing aging population and instituted the policy to encourage such development, which theoretically should have increased the rate of facility development within the jurisdiction since 2006, the year of its passage. Consequently, the aims of this dissertation in each of three sequential analytical chapters are to: 1) determine the current state of residential care development in Los Angeles through analysis of the industry’s spatial distribution in the city

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Asset Metadata

Creator Frochen, Stephen (author)

Core Title Residential care in Los Angeles: policy and planning for an aging population

School Leonard Davis School of Gerontology

Degree Doctor of Philosophy

Degree Program Gerontology

Publication Date 05/04/2020

Defense Date 03/24/2020

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

Tag eldercare facilities,facility development,OAI-PMH Harvest,Planning,policy evaluation,residential care,senior housing,spatial analysis,spatial distribution

Language English

Contributor Electronically uploaded by the author (provenance)

Advisor Ailshire, Jennifer (committee chair), Cicero, Caroline (committee member), Pynoos, Jon (committee member), Tucker-Seeley, Reginald (committee member)

Creator Email frochen@usc.edu,sfrochen@yahoo.com

Permanent Link (DOI) https://doi.org/10.25549/usctheses-c89-298686

Unique identifier UC11664056

Identifier etd-FrochenSte-8425.pdf (filename),usctheses-c89-298686 (legacy record id)

Legacy Identifier etd-FrochenSte-8425.pdf

Dmrecord 298686

Document Type Dissertation

Rights Frochen, Stephen

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 a...

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