Satisfaction with local "public goods" and services: the effects of household income and privatization in southern California

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Content
SATISFACTION WITH LOCAL “PUBLIC GOODS” AND SERVICES: THE
EFFECTS OF HOUSEHOLD INCOME AND PRIVATIZATION IN SOUTHERN
CALIFORNIA

by

Huanghai Li

____________________________________________________________________

A Dissertation Presented to the
FACULTY OF THE GRADUATE SCHOOL
UNIVERSITY OF SOUTHERN CALIFORNIA
In Partial Fulfillment of the
Requirements for the Degree
DOCTOR OF PHILOSOPHY
(PLANNING)

December 2008

Copyright 2008 Huanghai Li
ii
DEDICATION
To my mentor and doctoral advisor, Professor Peter Gordon, with respect and
thanks. Without him, I would have given up my doctoral studies three years ago,
and would never have completed this dissertation.
iii
TABLE OF CONTENTS
Dedication ii
List of Tables iv
List of Figures ix
Abstract x
Chapter One: Theoretical Background 1
Chapter Two: Research Questions & Test Hypotheses 17
Chapter Three: Research Design 25
Chapter Four: Test Results & Discussion 39
Chapter Five: Conclusions 91
References 96
Appendix I: Survey Questionnaire 99
Appendix II: Empirical Tests on the 2005 American Housing Survey 104
National Data
Appendix III: Additional Two-Sample Nonparametric Tests on 118
My Data Sample
iv
LIST OF TABLES
Table 1: Populations and shares of largest U.S. cities, 1900-2000 3
Table 2: Size distribution of the cities in the U.S., 1970-2004 6
Table 3: Partition of the sampled homes 19
Table 4: Definitions for the partition categories 20
Table 5: Partition of homeowners by HOA status and by local area 22
incorporation status

Table 6: Municipal services surveyed 28
Table 7: General demographic statistics for surveyed areas 29
Table 8: Number of valid observations across the five counties in my sample 30
Table 9: Distribution of the homes in my sample (1) 30
Table 10: Distribution of the homes in my sample (2) 30
Table 11: Distribution of the homes in my sample (3) 31
Table 12: Largest and small cities and CDPs in my sample 31
Table 13: The surveyed Census block groups in my sample 32
Table 14: Largest and smallest Census block group 32
Table 15: Translation from monthly HOA assessment ranges to estimated 34
annual assessment amounts

Table 16: Translation from annual property tax ranges to estimated annual 35
property tax amounts

Table 17: Descriptive statistics and correlation matrix of the key variables 36
Table 18: t-tests between non-HOA and HOA responses 37
Table 19: Interpretation of the variable “Street Repair” 39
v
Table 20: Distribution of “Rating of neighborhood as place to live” in the 41
AHS sample

Table 21: Distribution of “Street repairs” in the AHS sample 41
Table 22: Two-sample test results of the AHS data 42
Table 23: Hedonic regression with basic hedonic variables, HOA dummy 44
and property tax rate on the AHS southern California sub-sample

Table 24: Hedonic regression with basic hedonic variables, HOA fee rate 44
and property tax rate on the AHS southern California sub-sample

Table 25: Distribution of homeowners’ satisfaction score for each surveyed 46
municipal service in my sample

Table 26: Given income level (above- vs. below-median), compare non-HOA 49
with HOA responses

Table 27: Test results of service rankings comparison between non-HOAs and 50
HOAs

Table 28: Given above-median-income and place, compare non-HOA and 52
HOA responses

Table 29: Given below-median-income and place, compare non-HOA and 53
HOA responses

Table 30: Given above-median-income and Los Angeles city, comparing 54
non-HOA and HOA responses

Table 31: Test results of service rankings comparison among different places 58
Table 32: Given above-median-income and HOA status, compare big city vs. 59
homevoter city responses

Table 33: Given below-median-income and HOA status, comparing big city vs. 60
homevoter city responses

Table 34: Given above-median-income and HOA status, comparing homevoter 61
city vs. unincorporated area responses

vi
Table 35: Given below-median-income and HOA status, compare homevoter 62
city vs. unincorporated area responses

Table 36: Given above-median-income and HOA status, compare big city vs. 63
unincorporated area responses

Table 37: Given below-median-income and HOA status, compare big city vs. 64
unincorporated area responses

Table 38: Given above-median-income and HOA status, comparing 65
Los Angeles city vs. big city responses

Table 39: Given above-median-income and HOA status, comparing 66
Los Angeles city vs. homevoter city responses

Table 40: Given above-median-income and HOA status, comparing 67
Los Angeles city vs. unincorporated area responses

Table 41: Given below-median-income and HOA status, comparing 68
Los Angeles city vs. big city responses

Table 42: Given below-median-income and HOA status, comparing 69
Los Angeles city vs. homevoter city responses

Table 43: Given below-median-income and HOA status, comparing 70
Los Angeles city vs. unincorporated area responses

Table 44: Given HOA status, comparing incorporated city vs. unincorporated 74
area responses

Table 45: Comparing non-HOA incorporated city vs. HOA unincorporated 75
area responses

Table 46: Hedonic regression with basic hedonic variables and HOA dummy 78
variable

Table 47: Hedonic regression with basic hedonic variables, HOA dummy and 80
cost rate variable

Table 48: Hedonic regression with basic hedonic variables, place dummy, 82
HOA dummy and cost rate variables; reference group is Los Angeles city

vii
Table 49: Hedonic regression with basic hedonic variables, place dummy, 85
HOA assessment rate and property tax rate variable; reference group is
Los Angeles city

Table 50: Hedonic regression with basic hedonic variables, cost rate and the 87
interaction terms between HOA status and incorporation status variable;
reference group is HOAs in unincorporated areas

Table 51: Summary of the empirical study in southern California 91
Table A1: Percentage of HOA housing units 106
Table A2: Distribution of structure type 106
Table A3: Comparisons between the U.S. and southern California 106
Table A4: Distribution of “Rating of neighborhood as place to live” in the 107
AHS sample

Table A5: Construction scheme for the variable “Street Repair” 109
Table A6: Distribution of “Street repairs” in the AHS sample 109
Table A7: Distribution of HOA and non-HOA homes of the AHS southern 110
California sub-sample

Table A8: Annualized monthly assessment fees relative to property value, 111
where the reported assessment fee is greater than zero

Table A9: Summary statistics and correlation matrix of some other variables 111
Table A10: t tests between non-HOA and HOA homes in the AHS southern 112
California sub-sample

Table A11: Two-sample test results of the AHS data 114
Table A12: Hedonic regression with basic hedonic variables, HOA dummy 115
and property tax rate on the AHS southern California sub-sample

Table A13: Hedonic regression with basic hedonic variables, HOA fee rate 116
and property tax rate on the AHS southern California sub-sample

Table A14: Definition for High and Low Home Value 118
viii
Table A15: Given high home values and HOA status, comparing big city vs. 119
homevoter city responses

Table A16: Given high home value and HOA status, comparing big city vs. 120
unincorporated area responses

Table A17: Given high home value and HOA status, compare homevoter city 121
vs. unincorporated area responses

Table A18: Given low home values and HOA status, compare big city vs. 122
homevoter city responses

Table A19: Given low home value and HOA status, compare big city vs. 123
unincorporated area responses

Table A20: Given low home value and HOA status, compare homevoter city 124
vs. unincorporated area responses

Table A21: Given high home value and place, compare non-HOA and HOA 125
responses

Table A22: Given low home value and place, comparing non-HOA and HOA 126
responses
ix
LIST OF FIGURES
Figure 1: Percent of total population living in metropolitan areas and in their 3
central cities and suburbs, United States, 1910-2000

Figure 2: Comparisons of above-median-income homeowners’ rankings for 56
services at different places (living in either HOAs or non-HOAs)

Figure 3: Comparisons of the below-median-income non-HOA homeowners’ 57
rankings for services at different places

Figure 4: Comparisons of the below-median-income HOA homeowners’ 57
rankings for services provided in different places

Figure 5: Effects on housing prices of the interactions between HOA status 88
and incorporation status
x
ABSTRACT
I consider several research questions in this dissertation, including whether
household income and the privatization of municipal service provision matter when
explaining homeowners’ expressed satisfaction with local “public” services and
homeowners’ choice of local governance form. It will become clear that what are
often thought of as traditional local public or municipal services can be supplied by
private entities. Indeed that is a key part of this investigation. To simplify, I will not
always refer to them as so-called public services (or municipal), but will continue to
use conventional labels with the understanding that these goods and services can and
often are privately supplied. I also address the question whether traditional local
public governments and private homeowners’ associations (HOAs) are complements
or substitutes.
To properly treat these questions, I implemented a questionnaire survey of a
sample of homeowners in the five counties of the Los Angeles metropolitan area of
southern California. I conducted various empirical tests on the sample data, as well
as on 2005 American Housing Survey National data.
Based on the empirical test results from my survey data, I find that in
southern California, the interaction of income and privatization matters in terms of
homeowners’ reported satisfaction with their municipal services and hence the
interaction affects their choice of local governance form. This is a test of the well
known Tiebout model for southern California. Residents shop for the municipal
services made available to them in the local metro area and compare many public
xi
and private alternatives. An important finding is that above-median-income
homeowners (defined here as the homeowners whose household incomes are higher
than the median household income in the Los Angeles area) find preferred options in
both markets. Below-median-income homeowners are more likely to find preferred
packages from private suppliers.
Also based on the empirical results from my survey data, I find that in
southern California, it appears that public local governments and private HOAs are
not substitutes, but complements. This follows from the finding that private
suppliers in incorporated areas are preferred to private suppliers in unincorporated
areas. A private HOA in an incorporated city is the most preferred local governance
form, especially for below-median-income homeowners.
Empirical tests on the 2005 AHS National data showed that in the nation,
privatization of neighborhoods improves affected homeowners’ degree of
satisfaction with their neighborhoods and street repair services, regardless of
homeowners’ household income level.

1
CHAPTER ONE
THEORETICAL BACKGROUND
Samuelson (1954) addressed the classic economic problem of possibly
inefficient market supply of public goods. Since then, two innovative proposals have
been advanced to deal with Samuelson’s problem: Tiebout’s spatial clubs and
Buchanan’s entrepreneurial clubs. As Webster and Lai (2001) have pointed out,
“there are similarities between Tiebout’s and Buchanan’s approaches. Both explore
institutional arrangements for efficient supply of collective goods other than
Samuelson’s two extremes: private goods efficiently supplied by a perfectly
competitive market and public goods supplied by state-organized collective action”.
1

Tiebout’s idea is that competition among units of local governments could result in
the delivery of public goods roughly as efficiently as a market solution. “Buchanan’s
idea was that entrepreneurial clubs can supply excludable public goods efficiently”.
2

Researchers in institutional economics have agreed that “individuals and
firms seek co-operative opportunities with lower rather than higher transaction costs;
and that as they seek exchange partners, [some] orders emerge spontaneously from
their cost-minimizing behavior. … Institutions emerge to reduce transaction costs
and more generally, the costs of voluntary co-operation. Markets are institutions that
reduce the costs of organizing a multitude of individual transactions. Government
edicts, policy and regulations are institutions that reduce the costs of collective

1
“Property Rights, Planning and Markets”, Webster and Lai, 2001, p. 106.

2
“Property Rights, Planning and Markets”, Webster and Lai, 2001, p. 106.
2
transactions” (Webster and Lai, 2001). In the case of public finance, people agree
that they can benefit from some forms of institutional arrangements so that public
goods can be efficiently supplied. The spontaneous order for local public goods can
take either the form of local government regulations or the form of market
institutions. Parallel to Tiebout’s and Buchanan’s ideas are Fischel’s “Homevoter
City Hypothesis” (2001 and 2004) and Nelson’s privatization of local governments
(1999, 2004, 2005 and 2006a) about the institutions for supplying and managing
local public goods.
William Fischel’s “Homevoter City Hypothesis”
Most Americans now live in a suburban “small” city, away from large central
cities often associated with urban congestion, pollution, crime and so on, and
commute to work, often in another municipality. Since the 1920s, ever less costly
motor vehicle use has liberated more and more people including most of the middle
classes from the central city and the use of fixed route transport. The following
figure shows that there has been a clear trend in which more and more people live in
suburbs.
Population shares of the biggest city (New York), the top 20 and 75 biggest
cities in the U.S. have been steadily falling since 1940s, although their absolute
populations have grown over time (Table 1). This is true in spite major of
annexations by each.
3

Figure 1: Percent of total population living in metropolitan areas and in their central
cities and suburbs, United States, 1910-2000 (percent)

Source: Hobbs, Frank and Nicole Stoops, 2002, Demographic Trends in the 20
th
century, U.S. Census
Bureau, Census 2000 Special Reports, Series CENSR-4

Table 1: Populations and shares of largest U.S. cities, 1900-2000

1900 1920 1940 1950 1960 1980 2000
NYC 3 5.62 7.455 7.892 7.782 7.072 8.008
Top 20 11.971 19.487 25.026 27.516 28.092 27.304 30.944
Top 75 16.766 28.101 36.178 40.748 43.977 44.645 53.467
US 76.094 106.461 131.954 151.325 179.979 227.225 282.224
NYC Share 4.52% 5.28% 5.65% 5.22% 4.32% 3.11% 2.84%
Top 20 Share 15.73% 18.30% 18.97% 18.18% 15.61% 12.02% 10.96%
Top 75 Share 22.03% 26.40% 27.42% 26.93% 24.43% 19.65% 18.24%

Population unit: millions
Source: U.S. Census
4
Where most people work and where they reside are no longer the same, and
their political loyalties can became divided. The growth of homeownership and
owners’ demand for protection of their assets from various threats has come to define
local governments’ main mission (Fischel 2001). In his book “The Homevoter
Hypothesis”, Fischel proposes the “Homevoter City Hypothesis” (HCH, hereafter)
with these elements:
1. Home values are the largest part of most people’s asset portfolio;
2. The riskiness of homeownership is uninsurable;
3. Public events like taxes and spending affect the value of homes.
Therefore, homeowners’ deep concern with their homes’ values
understandably dominates local politics, including zoning, local taxation, municipal
affairs, and so on. Fischel follows Tiebout’s model, in which residents “vote with
their feet” among communities to select their preferred bundle of municipal services,
taxes and fees.
In short, as Fischel writes, “whether Tiebout resolved this [Samuelson’s]
theoretical problem is not entirely clear, but his model nowadays stands for the
proposition that local government provision of geographically isolated public goods
is superior to provision of the same goods by larger, more centralized units of
governments.”
3

Then why do people prefer small cities? Implicit in Fischel’s HCH is the idea
that a city is a homevoter city if the city is not big so that the local government is

3
“The Homevoter Hypothesis”, Fischel, 2001, p. 58.
5
able to easily identify the median voter’s wants and to implement corresponding
local policies to satisfy the median voter’s demands. If a city is too big with too
diversified desires among homevoters, a local government will be unable to achieve
efficient allocations.
How “small” a city is one that can be classified as a “homevoter city”?
Fischel did not give a clear answer. Can the available data be helpful? One can get a
good picture of the size distribution of the cities in the U.S., by analyzing the U.S.
Census Bureau’s internal database on individual local government finances. The
data describe four kinds of local municipalities, namely CITY, TOWN,
TOWNSHIP, and BOROUGH. The population sum of these groups together
accounts for about 50-60 percent of the U.S. total population in recent years. As
Table 2 shows, the median size of all the U.S. cities is estimated to be a population
of about 10,000. Only a quarter of all the U.S. cities have a population over 30,000.
Most cities in the U.S. are small cities. About half of the U.S. total population lives
in cities with less than 80,000 population.
There were 19,296 “cities” in the U.S., counted by the U.S. Advisory
Commission on Intergovernmental Relations (ACIR 1993) according to Census
definitions. However, as Fischel (2001) pointed out that this is too low a count of
local governments whose authority is general, as opposed to limited-authority special
districts and independent school districts. He added a count of townships in states
where they have general taxing and regulatory authority to the Census Bureau’s
count of cities, and came up with a total number of about 25,000 municipalities.
6
Table 2: Size distribution of the cities in the U.S., 1970-2004

Year 1970 1980 1990 2000 2004
Sample Size (# of Obs) 4,616 6,910 10,901 5,370 4,102
Total Population of the
Sample
117,468,806 137,341,925 160,000,651 154,616,738 148,167,136
Sample % of the U.S. Total
Population
58% 60% 63% 55% 51%
25 Percentile Size of City 2,068 1,610 900 2,109 2,764
Median Size of City 7,257 5,549 2,860 7,937 10,733
Mean Size of City 25,448 19,876 14,678 28,793 36,121
75 Percentile Size of City 19,597 15,856 10,580 23,682 29,941
Maximum Size of City 7,895,563 7,071,030 7,352,700 8,008,278 8,084,316
% of Population in Cities
with Population <=30K
25% 30% 32% 22% 18%
% of Population in Cities
with Population >=80K
53% 47% 46% 56% 61%

Source: U.S. Census (ftp://ftp2.census.gov/pub/outgoing/govs/special60/).

Robert Nelson’s Version of the Tiebout Model
On the other hand and in light of the strong assumptions required to achieve a
perfect Tiebout world, Nelson (2006a) suggests that “it might be possible to realize a
very rough approximation, if there were a much wider flexibility in local
governmental forms and boundaries than exist at present”. He envisions a practical
low-cost path to approach Tiebout’s perfect world by replacing the municipality as
an institutional form with private HOAs, not only for new developments, but also for
existing neighborhoods. According to Nelson, “rather than physically moving to a
7
new area at a high cost, a group of people already living in a neighborhood might be
free to secede to form their own unit of local government and thus obtain the
collective services they want at this scale without exorbitant transaction costs”.
4

Nelson argues that neighborhood privatization confers a wider range of possibilities
that offer a much wider variety of local governmental forms and boundaries than
exist at present. The private prerogatives of neighborhood associations can help
approximate a Tiebout world with low transaction costs. Here are HOAs’ strong
points stressed by Nelson (2004):
• The ability to discriminate in admitting residents to the neighborhood in
various ways that would not be acceptable for a municipal government.
• The ability to sell rights of entry into the neighborhood.
• The ability to enter into many forms of commercial activities within (or,
in concept, outside of) the neighborhood.
• The ability to make a commitment to undertake future actions that would
be legally binding and enforceable in court for the lifetime of the
association.
• The ability to hire a new employee or dismiss an existing employee under
the same legal rules as a business corporation or other private firm.
• The ability to create a private HOA of virtually any size or shape as an
exercise of the private rights of the owner of an appropriate parcel of
land.

4
“Welcome to the New – and Private – Neighborhood”, Nelson, 2006, p. 41.
8
Overall, Nelson (2005) notes, “neighborhood associations because they are
private have wider freedom to innovate in matters of governance”.
5
There are fewer
constitutional constraints on a private organization, as compared with a local
government in the public sector. For example, a local public government is bound by
the one person/one vote rulings of the U.S. Supreme Court. A neighborhood
association, in contrast, has the choice to allocate voting rights according various
definitions of the extent of property ownership.
Nelson’s libertarian tone should not be surprising; Nelson’s “private
neighborhoods” shares similarity with the “private protective associations” proposed
by the libertarian philosopher Robert Nozick (1974). Similar arguments for replacing
traditional municipal governance with private HOAs can also be found in other
researches, for instance, Robert Dilger (1992), and Beito, Gordon and Tabarrok
(2002). It is suggested that transferring the functions of traditional municipalities to
the private sphere increases efficiency by subjecting them to market discipline and
also increases liberties through freedom of contract and consumer sovereignty.
The private HOA form of governance has been experiencing rapid growth in
the much of the United States over the past three decades. In 1970, there were only a
few hundred such developments nationwide, and only about one percent of all
Americans belonged to private community associations.
6
However, it is estimated
by the Community Associations Institute that by 2004, there were about 18 percent

5
“The Private Neighborhood and the Transformation of Local Government”, Nelson, 2005, p. 437.

6
Estimated by Urban Land Institute.
9
of Americans, amounting to 55 million people, living in 275,000 neighborhood
associations. More than 50 percent of new housing units nationwide built from 1980
to 2000 were within a legal framework of private collective ownership.
7
More than
1.25 million Americans today serve on the board of directors of an HOA.
It is evident that in the past three decades, HOAs have taken on some of the
traditional functions of public local governments, in part because competing
demands for existing tax revenues makes it hard for traditional local municipalities
and counties to provide many of the services their citizens desire. Some
conventional governments are happy to have HOA owners take responsibility for
providing public goods, letting the conventional government avoid responsibility of
these developments. “Modern Community associations are the product of a single
event (the taxpayer revolt)” (Rybczynski, 2007). For example, in 1978, California
taxpayers approved Proposition 13 that resulted in a cap (one percent) on property
tax rates in the state, thereby reducing them by an average of 57 percent. In addition
to lowering property taxes, the initiative also contained language requiring a two-
thirds majority in both legislative houses for future increases in all state tax rates or
amounts of revenue collected. The initiative spread nationally, and within five years
over half of the states had adopted similar legislation. As Rybczynski (2007)
observes, “Twenty five years ago, [developers would] built parks, green spaces, and
retention ponds and when the projects were finished [they would] deed all these
public spaces to the municipality…. The taxpayer revolt changed all that. Municiapl

7
In the state of California at present, 60 percent of all new housing is being built in a community
association. See T. M. Gordon (2004).
10
governments everywhere refused to take responsibility for maintaining public spaces
in new developments… Henceforth public amenities would remain the responsibility
… of homeowners.” An empirical study by McKenzie (1998) shows that most of the
variance in common-interest development construction by county in California is
explained by variations in housing prices and government indebtedness (emphasis
added), which seems to support Rybczynski’s argument.
The rise of private community associations is significant in the United States
in the last quarter of the twentieth century. The popularity of private HOAs, given
their financial disincentive in the form of double taxation (that is, both the property
taxes and HOA assessments), implies some significant benefits for HOAs. One of
these benefits might be that private HOAs offer better municipal services for their
members, and thus higher home values in the housing market. However, this field
has received only limited attention thus far from urban scholars; especially in the
empirical side, there are only several papers on this topic. For example, Agan and
Tabarrok (2005) in their Virginia study found that the market value of a home in a
private HOA was at least 5–6 percent more than a similar home nearby not governed
by a HOA. LaCour-Little and Malpezzi (2001) found empirical evidence in
Missouri of higher home values in gated private communities than otherwise similar
homes, controlling other hedonic characteristics.
8

In light of this, Nelson and others have pointed out that since the 1960s, the
historic role of local governments has de facto increasingly been privatized at the

8
According to my survey data (Table 11), about 96 percent of gated communities were HOAs, and 40
percent of HOAs involved gated communities.
11
neighborhood level. They suggest that homeowner associations are replacing
municipalities, that private HOAs prompt a “revolution” in local governance - the
authority of traditional municipalities to provide local “public” goods and land-use
regulation will be shifted to neighborhood associations.
The Debate between Fischel and Nelson
However, Fischel (2004) argued that community associations are not
substitutes for municipal governance but, for the case of homevoter cities,
complements. Homeowners’ ultimate goal is to protect the values of their homes, so
they will seek to implement any of a number of tools, including forming private
HOAs. Restrictive covenants help them deal with externality problems that affect
their home values to a large degree. Where HOAs are not sufficient to achieve the
goal, both homeowners and developers find that local public governments can help.
Fischel (2004) argues that “one of the functions of existing neighborhood
associations is to see to it that zoning laws are enforced outside their [HOAs] own
borders…. The associations help lower the transaction costs of dealing with complex
zoning issues. They are more effective than individual homeowners because their
greater numbers and common interests make government officials pay more
attention”.
9
HOA members are interested in what happens to land use outside of the
boundaries as well as within them (NIMBY).
Fischel (2004) concludes that “it would be a bad idea to displace municipal
governance entirely with private governance. This is not because private governance

9
“Revolution or Evolution?”, Fischel, Regulation, Summer 2004, p. 50.
12
is undesirable, but because municipalities provide an important function in the U.S.
federal system of governance that neighborhood associations would be unable to
fulfill unless they simply became municipalities”.
10

Critics of private communities and the controversial role of income
On the other hand, there naturally has been skepticism about Nelson’s
sweeping proposal of privatizing older neighborhoods. For example, the HOA voting
system could dramatically depart from the current one-person-one-vote to an
allocation of voting rights according to property ownership if neighborhoods were to
be privatized according to Nelson’s proposal. Whether the proposed voting system is
deemed fair or not will prompt controversy. In addition, as some scholars argue (for
example, McKenzie 1994), the privatized services and infrastructures provided by
HOAs are placed outside the realm of large-scale democratic control and civil
liberties, which gives rise to the potential for abuse of power and invites conflict.
One implication of McKenzie’s argument is that the phenomena of the rapid growth
of HOAs would be short-lived. However, empirical studies do not seem to support
the claims of McKenzie and alike. For example, T. M. Gordon (2004) finds that
planned developments are more diverse with respect to income than their image
might suggest and that planned developments do not exhibit markedly different
patterns of voting behavior once other relevant characteristics are taken into account,
among some other empirical findings on this issue.

10
“Revolution or Evolution?”, Fischel, Regulation, Summer 2004, p. 48.
13
Some social commentators have referred to the spread of common interest
developments (CID) as the “secession of the successful” (Reich, 1991). McKenzie
(1994) populated a special name for private HOAs: “Privatopia”. This literature
implies that the privatization of neighborhoods would lead to unfair allocation of
municipal services: richer people would get plenty of municipal services, whereas
poorer people might have little or even no municipal services.
However, Nelson (2004) writes about private HOAs’ potential contributions
towards revitalizing low-income neighborhoods. Nelson argues that the
neighborhood privatization would benefit not only “the well-off residents of new
developments in the outer suburbs but the poorer residents of existing neighborhoods
in inner cities who would gain a much higher degree of control over their own
immediate environments”
11
(emphasis added). He maintains that the current situation
for many low-income groups is that they “have been denied access to new housing
opportunities in attractive locations within their economic means. … [Therefore], the
creation of private HOAs in older neighborhoods would create market incentives for
the redevelopment of many deteriorated [low-income] neighborhoods in existing
cities and inner suburbs”.
12

What is of particular interest in this dissertation is the relationship and
comparison between traditional municipal governments and private HOAs. In other
words, which local governance form is more appreciated by residents, public or

11
“The Private Neighborhood”, Nelson, Regulation, Summer 2004, p. 46.

12
“The Private Neighborhood”, Nelson, Regulation, Summer 2004, p. 45.
14
private? Are they substitutes or complements? What is the role of income in people’s
choice of local governance form? These are important research topics.
Both the HCH interpretation of settlement patterns and the privatization of
neighborhoods “emerged from the substantial amount of financial assets that most
people have in their homes and the fact that home values cannot be insured or
diversified. Even people who do not care what happens next door have to consider
what prospective homebuyers may think, … [because] most homeowners do not own
any other asset of comparable size”.
13
The risk of “the tragedy of neighborhood
commons” is large. Neighborhood quality and neighborhood transition are a
“commons”, which suggests the potential for a “tragedy of commons”. “Without the
ability to exclude other uses, higher-quality neighborhoods would be invaded by
lower-quality free-riders’ uses. The higher-quality uses would be detracted from that
higher quality by the free-riding. If higher-quality neighborhoods knew in advance
about this tragedy, they would never create such neighborhoods with high
environment quality in the first place”.
14
Private HOAs and homevoter cites are two
possible effective ways of dealing with this classic economic problem, the possibly
inefficient market supply of public goods. Which approach is more efficient in
providing more appreciated public goods and therefore enhancing home values is
millions of American families’ concern. The standard prisoner dilemma analysis
only points to a problem. The discussion cited here evokes the idea of spontaneous

13
“Revolution or Evolution?”, Fischel, Regulation, Summer 2004, p. 51.

14
“The Private Neighborhood”, Nelson, Regulation, Summer 2004, p. 41.
15
order responses to the difficulty and points to evolving institutions that address the
problem.
In addition, municipalities are an important layer in the U.S. federal system
of governance. In 1999, total civilian employment in the United States was about 135
million, of which 20.2 million (15 percent) worked for a government. Of this 20.2
million, 13 percent worked for the federal government (not counting uniformed
military personnel), 24 percent for state governments, and 63 percent for local
governments, which include schools and other special districts (Joint Economic
Committee 2000). The employment in local governments accounted for about 9.4
percent of the total employment nationwide. Moreover, Fischel (2001) estimates that
there were about 25,000 municipalities in the United States. The attraction of
“home-rule” rests on the idea that, compared with federal and state governments,
local-governments are closest to the people. But the revolutionary neighborhood
privatization proposed by Nelson suggests a profound shake-up effect on the U.S.
federal system of governance.
To address this major development in urban governance that has to date
received only limited attention from urban scholars, this dissertation is organized as
follows. In the next section, I present my research questions, and formulate some test
hypotheses. Then, I discuss the data and methodology used. Two sets of data were
used in this study, including the data from a questionnaire survey in southern
California and the 2005 AHS National data. Next, I report the results of various
empirical tests on both sets of data, trying to understand how homeowners feel about
16
their municipal services. The final section discussed conclusions and ideas for
further work.
17
CHAPTER TWO
RESEARCH QUESTIONS & TEST HYPOTHESES
To contribute to this research topic, I raise this question: in line with
Tiebout’s well known model, i.e., people “vote with their feet”, to what extent are
homeowners satisfied with the choices they have made? Tiebout’s (1956) is an
idealized model in that he assumes perfect homeowner information, land developers’
perfect knowledge about the preference of home purchasers, zero moving costs,
small economies of scale in local services, easy formation and secession of local
municipalities, etc. However, there are many constraints in the real world that
prevent Tiebout’s model from full-scale realization (see, for example, Nelson
(2006a)). A natural question to ask is how homeowners are actually doing by “voting
with their feet”. Therefore, I selected the Los Angeles metropolitan area as a test
case, and investigated how real homeowners judged the municipal services provided
by various forms of local governance.

Research Question #1:
How satisfied are homeowners with their municipal services?

This research question is essentially the motivation for this dissertation. It
motivated me to conduct a questionnaire survey on some homeowners across the five
counties of southern California in 2007, regarding their degree of satisfaction with
municipal services they received. This fresh and first-hand data, together with a
18
large housing transaction database from DataQuick, also made possible an empirical
study of comparisons of many aspects between traditional local public governments
and private HOAs, in terms of homeowners’ degree of satisfaction with their
municipal services, as well as in terms of housing values. As mentioned above, there
are currently very few empirical studies in this important field of urban economics.
Furthermore, the geographic areas used for available studies were very limited, for
example, only five zip codes in Agan and Tabarrok (2005). Therefore, I want to
conduct an empirical test for a broader geographic area.
In addition, the fundamental role of income inspires me to conduct empirical
tests on how homeowners in different income groups feel about the municipal
services provided by traditional municipalities versus private HOAs. Does income
affect homeowners’ perspective about private communities? Are private HOAs the
exclusionary products particularly designed for richer people? Empirical test results
should be able to offer better understanding in this subject.

Research Question #2:
Does the interaction of privatization and income matter in providing
homeowners with higher-rated municipal services, that is, are
homeowners in private communities relatively more satisfied with
their municipal services than their peers in traditional public
municipalities, controlling for other attributes such as household
income and/or place? Is a home in a private HOA valued more than
19
a similar home outside a private HOA, controlling for household
income, place, and other standard hedonic characteristics?

In this dissertation, I also want to test Fischel’s “Homevoter City
Hypothesis”, via the third research Question.

Research Question #3:
Does the size of traditional municipalities matter, in terms of
homeowners’ degree of satisfaction with their municipal services?
Are similar homes at different places valued the same by the market,
with the control of other hedonic characteristics?

In order to formulate the test hypotheses for Research Question #2 and #3, I
divided the surveyed homeowners in southern California into the partitions presented
in Table 3.

Table 3: Partition of the sampled homes

non-HOA HOA non-HOA HOA
L.A. [1] [5] [9] [13]
Big City [2] [6] [10] [14]
Homevoter City [3] [7] [11] [15]
Unincorporated Area [4] [8] [12] [16]
Above-median-income Below-median-income

20
Table 4 defines the terminologies in Table 3. As mentioned above, a city is
deemed to be a homevoter city if the city is small so that the local government is able
to easily identify median voters’ will and thus to implement corresponding local
policies to satisfy the median voters. But no formal cut-off size has ever been
specified. In the survey area, Los Angeles city (with a population of over 3.6
million, Census 2000) and Long Beach city (with a population of 460,000, Census
2000) are unlikely to be homevoter cities. In this study, homevoter cities are
presumed to be those with a population of less than 80,000, while non-homevoter
cities are those above this cutoff line.

Table 4: Definitions for the partition categories

Terminology Definition
Los Angeles City The biggest city in southern California
Big City City Population (Census 2000) > 80K, excluding L.A.
Homevoter City City Population (Census 2000) less then 80K
Unincorporated Unincorporated area
Above-median-income Homeowners Household Income (Census 2000) > $60K
Below-median-income Homeowners Household Income (Census 2000) <= $60K
non-HOA Housing unit self-reported not in an HOA
HOA Housing unit self-reported in an HOA

Test Hypotheses for Research Question #2:
The null hypothesis for testing Research Question #2 is that given household
income level (and place), homeowners in private HOAs would have about the same
21
degree of satisfaction with the surveyed municipal services as homeowners in non-
HOAs. A home in a private HOA would be valued by the market about the same as a
home with no HOA governance and otherwise similar characteristics.
For example, given above-median income, comparing non-HOA (Category
[1] – [4] in Table 3 as a group) with HOA (Category [5] – [8] in Table 3 as another
group) should not yield statistical difference in terms of degree of satisfaction with
the municipal services or in terms of home values. Another example could be the
comparison between non-HOA (Category [2] in Table 3) and HOA (Category [6] in
Table 3), given above-median income and big city.
Test Hypotheses for Research Question #3:
The null hypothesis for testing Research Question #3 is that given household
income level and HOA status, homeowners at different places would have about the
same degree of satisfaction with the surveyed municipal services. Homes at different
places with otherwise similar characteristics would be valued by the market about
the same.
For example, given above-median income and living in a private HOA, the
comparison between Big City (Category [6] in Table 3) and Homevoter City
(Category [7] in Table 3) should not produce statistical difference either in terms of
degree of satisfaction with municipal services or in terms of home values.
Finally, in light of the debate between Nelson and Fischel, I also proceed to
empirically test the following research question:

22
Research Question #4:
Are traditional public local governments and private HOAs
complements or substitutes?

Test Hypotheses for Research Question $4:
Test Hypothesis 4.1: Private HOAs are substitutes for traditional public
municipalities.
To test this hypothesis, I first define Substitute as “Replace”
15
and
Complement as “Augment”
16
. Then I divided homeowners into the following four
new categories (Table 5):
a. Non-HOA homeowners in incorporated cities
b. HOA homeowners in incorporated cities
c. Non-HOA homeowners in unincorporated areas
d. HOA homeowners in unincorporated areas

Table 5: Partition of homeowners by HOA status and by local area incorporation
status

non-HOA HOA
Incorporated City a b
Unincorporated Area c d

15
Nelson used the word “Replace” at the title of his 1999 paper, “Privatizing the Neighborhood: A
Proposal to Replace Zoning with Private Collective Property Rights to Existing Neighborhoods”.

16
“Augmentation” in Fischel’s word (page. 51, Fischel, 2004).
23
Hypothesis 4.1 implies to the following two possibilities, with all other things
equal,
i. (b)
≈
(d). HOA homeowners should report no difference between
living in incorporated cities and in unincorporated areas in terms of their
degree of satisfaction with their municipal services, or an HOA housing
unit in an incorporated city should be valued about the same as a similar
HOA housing unit in an unincorporated area;
ii. (d)
≥
(a). HOA homeowners living in unincorporated areas should
report degrees of satisfaction with their municipal services equivalent to
or higher than non-HOA homeowners in incorporated cities, or an HOA
housing unit in an unincorporated area should be valued equivalent to or
more than a non-HOA housing unit with similar other characteristics in
an incorporated city.
Test Hypothesis 4.2: Private HOAs and traditional public municipalities are
complements. Test Hypothesis 4.2 implies that, with all other things equal,
iii. (b) is the most appreciated. An HOA in an incorporated city is
homeowners’ highest scored choice of local governance form. Or an
HOA housing unit in an incorporated city should have the highest
valuation among the housing units with similar other characteristics in all
the categories in Table 5.

24
In the next section, I will discuss the research design for testing these
hypotheses.
25
CHAPTER THREE
RESEARCH DESIGN
Measurement for the efficiency of the providing of municipal services
A measurable indicator of the adequacy of providing municipal services
would be homeowners’ reported degree of satisfaction with their municipal services.
According to Webster and Lai (2001), institutional orders emerge spontaneously
from widespread cost-minimizing behavior. Here, the cost-minimizing could be
understood equivalently to either:
i. minimizing the total cost to provide a given level of municipal services;
or
ii. given the total cost of municipal services, maximizing homeowners’
degree of satisfaction.
A given level of municipal services is very difficult to standardize and
quantify, because people have widely varying wants and different households
consume different combinations of municipal services. Therefore, in this research, I
consider homeowners’ stated degree of satisfaction with the municipal services
provided by different forms of local governance as the measurement for the
efficiency of those local governance forms.
To simplify the empirical research, two assumptions are made: First,
homeowners take into account the monetary costs they pay for the municipal
services when rating their degree of satisfaction with the services. Second, people
favor a certain form of municipal governance, either private or public or both, if they
26
are satisfied with the package of municipal services offered by that municipal
governance form. Based on these two assumptions, and other things equal, if
homeowners, taking into consideration the monetary costs involved, are satisfied
with the municipal services provided by some form of local governance,
homeowners will move to or create or support that form of local governance.
Collection of homeowners’ degree of satisfaction with municipal services
I conducted a survey on the residents in the Los Angeles metropolitan area to
obtain sample data on homeowners’ degree of satisfaction with the municipal
services they received. This was via mailed questionnaires
17
sent directly to
homeowners in the five Los Angeles areal counties: Los Angeles, Orange, Riverside,
San Bernardino, and Ventura. In 2007, I sent out questionnaires to 6,125
homeowners across the five counties, and received slightly over 500 responses, 490
of which were usable observations. The usable response rate was eight percent. From
here on, I refer as “my sample” to these 490 usable observations.
Before the formal survey, I sent out trial questionnaires to 1,500 homeowners
across the five counties as a pilot test. The main database was a large housing
transactions data provided by DataQuick. This database included detailed individual
information for more than 200,000 housing units that were transacted in the year of
2001 in the five counties of southern California, information including home address,
transaction value and housing characteristics. In constructing the survey sample, I
only considered single-family residential housing units, and excluded

17
The questionnaire used is attached in Appendix 1.
27
condominiums, co-ops, mobile/manufactured homes and such other residential types.
The subjects in the pilot test were randomly selected households from the database. I
received 75 responses from the pilot test with the responses rate of only five percent.
Thirteen of the respondents (about 17 percent) reportedly belonged to private HOAs.
The number of HOA homeowners was very small.
Based on the results of the pilot test, I made several improvements to the
formal survey. I offered a reward of $2 for each respondent to boost the response
rate. In order to obtain a sample with balanced representation of HOA and non-HOA
housing units, I used a new method to select the survey subjects. I first identified the
home addresses that could possibly belong to an HOA. I purchased the data on
registered HOAs in California from HOA-Info (2007), which maintained the address
information of the presidents of most HOAs in California. Next, I created a sub-
database of the home addresses in the main database within two blocks on the same
street from the identified addresses of the presidents of the HOAs in the five
counties. Finally, I randomly selected survey subjects from the sub-database.
There are two advantages of obtaining the survey subject sample in this way.
First, the responses were approximately evenly distributed between HOA and non-
HOA homes. In my sample, the percentage of reported HOA homes among the
respondents was about 46 percent. Second, the sample homes were supposed to be
located very close to each other (that is, within two blocks on the same street). Thus,
I have head-to-head comparisons on homeowners’ degree of satisfaction with local
28
services. This to some degree controlled for the influence of location and amenities
of the surveyed homes.
Finally, I sent out questionnaires and asked the homeowners to provide
information about the form of “local government” (including the local city, county,
HOA, special district, or homeowner) that supplied the specified 16 types of services
(Table 6), and respondents’ degree of satisfaction with each service, on a scale of
from 1 to 10, with 10 being Excellent and 1 being Unacceptable.

Table 6: Municipal services surveyed

Municipal Services Surveyed
Trash Collection
Gardening
Street cleaning
Street lighting and repair
Security
Painting/outside maintenance
Parking lot repair
Gates or fences
Landscaping
Recreation facilities, like gym
Indoor community center
Swimming pools/tennis courts
Lake or beach
Playground/tot lot
Tree, Lawn care in common areas
Water or sewer

29
The questionnaire data
My survey area is the five counties of southern California. Table 7 shows
some background statistics about these five counties.

Table 7: General demographic statistics for surveyed areas

County 1990 2000 1990 * 2000 ** 1990 2000
Los Angeles 8,863,164 9,519,338 $34,965 $42,189 $223,800 $209,300
Orange 2,410,556 2,846,289 $45,922 $58,820 $250,300 $270,000
Riverside 1,170,413 1,545,387 $33,081 $42,887 $138,800 $146,500
San Bernardino 1,418,380 1,709,434 $33,443 $42,066 $128,500 $131,500
Ventura 669,016 753,197 $45,612 $59,666 $243,500 $248,700
*: in 1989 Dollar
**: in 1999 Dollar
Population
Median Household
Income
Median Housing Value

Source: U.S. Census

The homes in my sample were located across the five counties approximately
proportional to each county’s population. The number of observations in the San
Bernardino County was moderately under-represented, probably due to low
percentage of HOA homes in that county and random sample selection error. Table 8
shows the distribution of these 490 homes across the five counties.
Table 9 - 11 divide the sample homes into different combination of partitions.
Note that most homes in gated communities belonged to private HOAs. However,
more than half of the HOA homes were not in gated communities.
30
Table 8: Number of valid observations across the five counties in my sample

County # of Obs
Los Angeles 326
Orange 64
Riverside 57
San Bernardino 19
Ventura 24
Total 490

Table 9: Distribution of the homes in my sample (1)

non-HOA HOA Total
City 228 177 405
47% 36% 83%
Unincorporated Area 35 50 85
7% 10% 17%
Total 263 227 490
54% 46% 100%

Table 10: Distribution of the homes in my sample (2)

non-HOA HOA non-HOA HOA
L.A. 17 17 48 5 87
Big City 46 46 52 28 172
Homevoter City 32 47 33 34 146
Unincorporated Area 15 16 20 34 85
Total 110 126 153 101 490
Above-median-income Below-median-income
Total

31
Table 11: Distribution of the homes in my sample (3)

Non-HOA HOA Total
Non-Gated 251 131 382 (81%)
Gated 4 88 92 (19%)
Total 255 (54%) 219 (46%) 474

Note that there were only five below-median-income HOA homes in Los
Angeles city (Table 10). This created the problem of not enough observations for
some two-sample tests with the group of below-median-income HOA homes in Los
Angeles city. Nevertheless, the definitions defined in Table 4 were the best way of
dividing the sample into the sixteen categories presented in Table 3.
The homes in my sample were located across 114 places, including 98
(incorporated) cities and 16 unincorporated areas. Table 12 shows the largest and
smallest city and CDP (Census Designated Area, in Census’ terminology, that means
unincorporated area) in my sample.

Table 12: Largest and small cities and CDPs in my sample

Place Name
Population
(Census 2000)
# of Sample Obs.
in the place
Largest City Los Angeles city 3,694,834 87
Smallest City Rolling Hills city 1,871 2
Largest CDP South Whittier CDP 55,047 8
Smallest CDP Sunnyslope CDP 4,290 1
CDP: Census Designated Place, i.e., unincorporated area

32
The homes in my sample were distributed across 401 Census block groups.
Table 13 shows the distribution of population, median household income and median
home value across the 401 census block groups in my sample. Table 14 shows the
largest and smallest Census block group in my sample. The maximum number of
surveyed homes in a Census block group is eight (not shown in the table).

Table 13: The surveyed Census block groups in my sample

Block Group Minimum Q1 Mean Median Q3 Maximum
Population 235 1,126 1,840 1,531 2,337 14,568
Median Household
Income
20,341 45,807 65,601 58,864 80,876 200,001
Median Home
Value
53,100 178,700 295,414 238,800 370,700 1,000,001

Table 14: Largest and smallest Census block group

Census Block
Group
Name
Population
(Census 2000)
# of Sample Obs.
in the Group
Largest
Block Group 1, Census Tract
432.21, Riverside County, CA
14,568 1
Smallest
Block Group 4, Census Tract
2032, Los Angeles County, CA
235 1

It is fair to say that my sample is a good representation of the homes in
southern California in terms of geographic distribution.
33
My sample data have the detailed individual information of home address,
transaction value, and housing characteristics in the year of 2001, characteristics
including lot size, living area, number of bathrooms, number of bedrooms, etc., for
each surveyed home. In addition, I matched the home addresses to ArcView (a
popular Geographic Information System) and obtained the information of each
housing unit’s distance to the coast. From the questionnaire survey, I obtained
information on whether a housing unit belongs to an HOA or not (that is, “HOA
status”).
With the detailed home addresses, I located the place (city or unincorporated
area) and Census block group of each surveyed home by using the Census American
FactFinder - Census 2000 Summary File 3 (SF3) sample data.
18
Based on the place
information, I was able to construct the Place dummy variables.
19

Next, I obtained data on some socio-economic characteristics (e.g., income)
for each surveyed homeowner. These were proxied by Census block group data (that
is, median household income of a Census block group). Census block group data
should be good proxies for individual homeowners’ socio-economic characteristics,

18
Although the American Community Survey (ACS) provides annually updated data on the
characteristics of population and housing (the most recent data is 2006 ACS), every year the ACS
only supports the release of single-year estimates for geographic areas with populations of 65,000 or
more. The ACS will accumulate sample over 3-year and 5-year intervals to produce estimates for
smaller geographic areas including census tracts and block groups. However, ACS at this time does
not identify anything that doesn’t have at least a minimum of 65,000 persons. In the ACS 2006, the
smallest surveyed geographic area is places (such as cities), or county subdivisions (MCDs). There
should be much variation of the socio-economic characteristics among the households in such broad
geographic areas.

19
Specifically, there were four types of places in the sample, namely Los Angeles city, big city except
Los Angeles city, homevoter city and unincorporated area.
34
because of the homogeneity of the population within a small Census block group
(typically 1,000 - 2,000 persons) thanks to Tiebout-sorting.
Finally, I constructed some other key variables, including the HOA
assessment rate, property tax rate, and cost rate, as follows.
• HOA assessment rate
In my questionnaire, I did not ask the surveyed homeowners about the
specific dollar amount of their HOA monthly assessments; instead, I provided dollar
amount ranges for their selection (“Assess_M” is the variable name in my sample
data). Based on this information, I estimated the annual HOA assessment amount for
each surveyed home by using the scheme presented in Table 15. To measure the
cost of HOA assessments relative to home values, I created the new variable of
annual HOA assessment rate (“FeeRate”), which is equal to the estimated annual
assessment amount in 2007 divided by home transaction value in 2001.

Table 15: Translation from monthly HOA assessment ranges to estimated annual
assessment amounts

Value for "Assess_M"
in the questionnaire Meaning
Mid-point Monthly
Assessment
Annual
Assessment
1 less than $30 $20 $240
2 $30 - $79 $55 $660
3 $80 - $149 $115 $1,380
4 $150 - $199 $175 $2,100
5 $200 - $299 $250 $3,000
6 $300 - $399 $350 $4,200
7 $400 - $499 $450 $5,400
8 above $500 $600 $7,200

35
• Property tax rate
Similar to the HOA assessments, I provided dollar amount ranges of property
tax for surveyed homeowners’ selection (“Tax” is the variable name in my sample
data). Then, I constructed the new variable annual property tax rate “taxR” by first
translating each range into a “mid-point annual property tax” (Table 16) and then
dividing it by home transaction value in the year 2001.

Table 16: Translation from annual property tax ranges to estimated annual property
tax amounts

Value for "Tax "
in the questionnaire Meaning
Mid-point Annual
Property Tax
1 less than $500 $300
2 $500 - $999 $750
3 $1000 - $1499 $1,250
4 $1500 - $2999 $2,250
5 $3000 - $4999 $4,000
6 $5000 - $7999 $6,500
7 over $8000 $10,000

• Cost rate
By combining the annual HOA assessment rate and property tax rate for each
housing unit, I created the new variable “CostRate”. The purpose of constructing this
variable was to avoid possible problems caused by colinearity in some of the
multivariate regression model specifications, which I will discussed shortly.
36
Descriptive statistics of my sample homes (Table 17)
An average home in my sample had the following characteristics:
a. Located 18 miles from the Pacific coast;
b. 10,000 square feet of the lot size, and 2,000 square feet of living area;
c. 3 bedrooms and 2 bathrooms;
d. Annual property tax payment 1.5 percent relative to home value;
e. 0.3 percent of annual HOA assessment relative to home value, if in an
HOA.
20

Table 17: Descriptive statistics and correlation matrix of the key variables

Statistic LOTSIZE SQFT Dcoast * BATH BEDRMS FeeRate taxR
MEAN 9,913 1,983 1.82 2.33 3.14 0.29 1.46
STD 14,080 970 1.58 1.05 1.01 0.60 0.68
Corr Matrix LOTSIZE SQFT Dcoast * BATH BEDRMS FeeRate taxR
LOTSIZE 1
SQFT 0.521 1
Dcoast * 0.267 0.016 1
BATH 0.219 0.752 -0.068 1
BEDRMS 0.346 0.726 0.014 0.631 1
FeeRate -0.322 0.049 0.014 0.265 -0.007 1
taxR -0.168 -0.142 0.151 -0.048 -0.075 0.083 1
* Unit of Distance to Coast = 10 miles

Note: The coefficients of correlation in the matrix are Spearman’s (nonparametric) rank
correlations.

The correlation matrix shows that the variable SQFT (living area) is highly
correlated with BATH, BEDRMS, and LOTSIZE. Therefore, I did not include

20
Please keep in mind that Home Value was transaction value recorded in 2001, while the data of
both HOA assessments and property taxes were collected in 2007.
37
SQFT in all of the regressions presented in the next section of Test Results and
Discussion. Except this one, the other correlation coefficients are small. Notably
both the variables HOA assessment rate (FeeRate) and property tax rate (taxR) are
nearly uncorrelated with the other variables.
I further ran several two-sample t-tests among the variables distance to the
coast, home transaction value, lot size and living area between non-HOA and HOA
housing units (Table 18). I ran these t-tests to investigate the possibilities such as
whether HOA homes were clustered close to the Pacific coast. It turned out that there
was no significant difference between non-HOA and HOA homes in terms of
distance to the coast. The other t-tests show that an HOA housing unit is valued
slightly more than a non-HOA housing unit, and the lot size of an HOA unit is
slightly larger than a non-HOA unit, although both relationships are not significant.
The only significant relationship here is that in my sample, the living area of an
HOA unit is larger than in a non-HOA unit.

Table 18: t-tests between non-HOA and HOA responses

Mean # of Obs Mean # of Obs Mean # of Obs Mean # of Obs
non-HOA 1.777 262 384,598 262 9,250 262 1,855 262
HOA 1.872 196 426,678 196 10,800 196 2,153 196
t Value
Pr > |t|
DF
Variances
Method
* Unit of Distance to Coast = 10 miles
Equal Unequal Unequal Unequal
365 384 230 456
0.5384 0.1340 0.2993 0.0011
DCoast VALUE LOTSIZE SQFT
-0.62 -1.50 -1.04 -3.29
Satterthwaite Satterthwaite Satterthwaite Pooled

38
Empirical methodologies
Two standard methodologies were used in this research, namely two-sample
nonparametric tests and multivariate regressions. I compared the homeowners living
in various local governance forms (traditional municipality versus private HOA, or
non-homevoter city versus homevoter city versus unincorporated area), given their
household income level (above- versus below-median-income), in terms of their
degree of satisfaction with available municipal services, by applying two-sample
nonparametric tests. Nonparametric tests rather than conventional t tests were used,
because the scores of satisfaction degree are ordinal and because the samples were
small for some tests, which implies non-normal distribution of the data.
Multivariate regressions were also carried out to examine the effects on home
values of private versus public governance form (i.e., HOA or not) and of different
city types (non-homevoter city, homevoter city, or unincorporated area), while
controlling other relevant factors, including housing hedonic characteristics, property
tax, the monthly assessments for HOA homeowners, and respondents’ economic
attributes, such as household income.
In next section, I present the results of two-sample nonparametric tests and
multivariate regressions.
39
CHAPTER FOUR
TEST RESULTS & DISCUSSION
The first test involved two-sample nonparametric tests and multivariate
regressions on the 2005 American Housing Survey National data, which acted as a
backdrop for my research on southern California. Therefore, I will first present the
empirical test results on the AHS data, and then focus on my questionnaire data.
1. Test results on the 2005 AHS National data
Details of my research on the 2005 AHS National data are in Appendix II.
Here I only present some key results.
I used two variables from the AHS data that are related to homeowners’
degree of satisfaction with their municipal services, namely “Rating of
neighborhood” (directly taken from the AHS variable HOWN) and “Street Repair”
(constructed from the AHS variable EROAD). The values of “Rating of
neighborhood” takes from 1 to 10 (1 is worst, and 10 is best). The interpretation of
“Street Repair” is shown in Table 19.

Table 19: Interpretation of the variable “Street Repair”

Variable Score Interpretation
Street Repair 4
Homeowner answers "No repair work" to "Roads within 1/2 block
need repairs?"
3
Homeowner answers "Minor repair work" to "Roads within 1/2 block
need repairs?"
2
Homeowner answers "Major repair work" to "Roads within 1/2 block
need repairs?"

40
Frequency distributions of these two variables are in Table 20 and 21. It
appears that most homeowners across the country were satisfied with their
neighborhood:
• Seventy six percent of the homeowners surveyed by AHS in 2005 gave
overall ratings about their neighborhood equal to or higher than 8.
• Eighty nine percent of the homeowners rated their neighborhood equal to
or higher than 7.
• The rating distribution is skewed to the right, i.e., higher satisfaction.
In addition, most homeowners across the country were satisfied with the
street repairs municipal service. Sixty-five percent of the surveyed homeowners
reported that no repair work was needed for roads within 1/2 block of their home.
Only five percent of the surveyed homeowners reported that major repair work was
needed for roads within 1/2 block of their residence.
41

Table 20: Distribution of “Rating of neighborhood as place to live” in the AHS
sample

Rating of
Neighborhood
Frequency Percent
Cumulative
Frequency
Cumulative
Frequency
1 88 0.36 88 0.36
2 91 0.37 179 0.73
3 146 0.6 325 1.33
4 237 0.97 562 2.3
5 1,068 4.37 1,630 6.67
6 1,174 4.8 2,804 11.47
7 3,082 12.61 5,886 24.08
8 6,886 28.17 12,772 52.24
9 4,531 18.53 17,303 70.77
10 7,145 29.23 24,448 100
Missing 480
Total 24,928

Table 21: Distribution of “Street repairs” in the AHS sample

Answer
Street Repairs
Rating
Frequency Percent
Cumulative
Frequency
Cumulative
Frequency
Major repair work 2 1,284 5.27 1,284 5.27
Minor repair work 3 7,160 29.37 8,444 34.63
No repair work 4 15,938 65.37 24,382 100
Missing 546
Total 24,928

42

Table 22: Two-sample test results of the AHS data
Household
Income Mean P-value # of Obs Mean P-value # of Obs
$120K and up non-HOA 8.465 2706 3.638 2709
HOA 8.648 0.0096 901 3.814 0.0000 898
[$100K - 120K) non-HOA 8.301 1573 3.610 1568
HOA 8.578 0.0026 374 3.787 0.0000 371
[$80K - 100K) non-HOA 8.271 2194 3.598 2193
HOA 8.414 0.1574 473 3.789 0.0000 475
[$60K - 80K) non-HOA 8.189 3333 3.591 3322
HOA 8.492 0.0003 506 3.782 0.0000 505
[$50K - 60K) non-HOA 8.237 1885 3.573 1878
HOA 8.512 0.0059 256 3.760 0.0000 258
[$40K - 50K) non-HOA 8.159 2031 3.564 2031
HOA 8.788 0.0000 231 3.748 0.0000 230
[$35K - 40K) non-HOA 8.206 1010 3.546 1003
HOA 8.624 0.0068 101 3.707 0.0047 99
[$30K - 35K) non-HOA 8.222 1069 3.549 1059
HOA 8.362 0.4264 94 3.787 0.0002 94
[$25K - 30K) non-HOA 8.170 1025 3.520 1023
HOA 8.871 0.0003 93 3.696 0.0119 92
[$20K - 25K) non-HOA 8.250 1035 3.532 1024
HOA 8.651 0.2098 83 3.682 0.0636 85
[$10K - 20K) non-HOA 8.251 1926 3.518 1918
HOA 8.686 0.0055 121 3.754 0.0001 122
Below $10K non-HOA 8.231 1336 3.509 1331
HOA 8.880 0.0025 92 3.819 0.0000 94
Rating of Neighborhood Street Repairs

43
AHS: Two-sample nonparametric test results (Table 22)
The results of the two-sample tests on the 2005 AHS national sample show
that privatization of neighborhood did improve homeowners’ degree of satisfaction
with their neighborhoods and street repair services, regardless of homeowners’
household income level. Across the country in the year of 2005, homeowners living
in HOAs were consistently and mostly significantly (at the 10 percent level) more
satisfied with their neighborhoods and the municipal service of street repairs, in all
twelve income groups. Twenty one out of all the twenty four tests indicated that
homeowners living in HOAs were significantly more satisfied than their non-HOA
counterparties.
AHS southern California
21
: Multivariate regression results (Table 23 – 24)
It is worthwhile to point out that the variable Home Value in the AHS data is
not transaction value, but rather householders’ self-reported “current market value of
unit”. We should expect some estimation error associated with this variable when
interpreting the regression results.
The first observation from Table 23 and 24 is that the variable “log of Lot
Size” was not significant and had the wrong (negative) sign. This contradicts the
literature on housing price hedonic analysis. This problem is probably due to the
inaccurate estimation of the “current market value of unit”, which was self-reported
by householders. Therefore, we should be cautious when interpreting the regression
results from the AHS data.

21
I only ran regressions on the southern California sub-sample of the AHS data, because there was too
much geographic heterogeneity among homes across the country.
44
Table 23: Hedonic regression with basic hedonic variables, HOA dummy and
property tax rate on the AHS southern California sub-sample

Dependent logValue logValue logValue
All Obs. Above-Median-Income Below-Median-Income
Adj. R2 0.2187 0.2025 0.1836
# of Obs. 730 366 364
Intercept 12.696 12.784 12.812
(50) (37.46) (35.23)
logLot -0.016 0.013 -0.039
(-0.57) (0.35) (-0.98)
Bathrms 0.307 0.296 0.261
(8.97) (6.86) (4.99)
BedRms 0.039 -0.006 0.052
(1.33) (-0.16) (1.2)
HOA Dummy 0.206 0.080 0.370
(2.71) (0.91) (2.91)
taxR -0.244 -0.326 -0.215
(-5.94) (-5.21) (-4.03)

Table 24: Hedonic regression with basic hedonic variables, HOA fee rate and
property tax rate on the AHS southern California sub-sample

Dependent logValue logValue logValue
All Obs. Above-Median-Income Below-Median-Income
Adj. R2 0.2112 0.2013 0.1668
# of Obs. 730 366 364
Intercept 12.722 12.779 12.867
(49.85) (37.38) (34.76)
logLot -0.020 0.013 -0.048
(-0.72) (0.36) (-1.19)
Bathrms 0.321 0.304 0.286
(9.43) (7.1) (5.45)
BedRms 0.037 -0.008 0.051
(1.28) (-0.22) (1.17)
FeeRate 0.192 -0.190 0.504
(0.62) (-0.5) (1.05)
taxR -0.243 -0.324 -0.217
(-5.85) (-5.16) (-3.98)

45
Table 23 indicates that the housing market in southern California priced a
premium for HOA units, for all observations in the sample and for the below-
median-income group.
Table 24 shows that the property tax was significantly negatively capitalized
into home values, consistent with the literature on housing price hedonic analysis.
However, there was no capitalization of the HOA assessments into home values,
which again contradicts the existing relevant literature.
Based on the empirical tests on 2005 AHS National data, I concluded that
from the national perspective, privatization of neighborhoods improves homeowners’
degree of satisfaction with their neighborhoods and the street repairs service,
regardless of homeowners’ household income level. Moreover, the southern
California housing market in general priced a premium for housing units in private
HOAs, controlling for other hedonic characteristics.
2. Test results on my survey data
In my questionnaire survey, half of the surveyed homeowners rated more
than ten out of the sixteen municipal services that were surveyed. On average the
surveyed homeowners gave scores for 9.5 services.
Table 25 shows the distribution of homeowners’ satisfaction score for each
surveyed municipal service. I observe that most homeowners were satisfied with the
municipal services they received. Half of the homeowners in my sample gave
satisfaction scores equal to or higher than 8 for fifteen out of the sixteen surveyed
46
services.
22
Seventy five percent of the surveyed homeowners scored equal to or
higher than 6 for thirteen out of sixteen surveyed services.
23
For each service, the
satisfaction score distribution is skewed to the right, i.e., higher satisfaction. This
quick summary result corroborates the idea that in a free society, individuals make
choices that work for them, even when it comes to local “public” goods.

Table 25: Distribution of homeowners’ satisfaction score for each surveyed
municipal service in my sample

Satisfaction Score # of Obs. Q1 Mean Median Q3 Max
Trash Collection 481 8 8.49 9 10 10
Gardening 346 7 7.79 8 10 10
Street cleaning 455 6 7.49 8 9 10
Street lighting and
repair
442 7 7.67 8 10 10
Security 298 5 7.04 8 9 10
Painting/outside
maintenance
277 6 7.47 8 9 10
Parking lot repair 225 6 7.28 8 9 10
Gates or fences 273 6 7.34 8 9 10
Landscaping 321 7 7.59 8 9 10
Recreation
facilities, like gym
156 5 6.96 7 9 10
Indoor community
center
148 5 6.93 8 9 10
Swimming
pools/tennis courts
217 6 7.37 8 9 10
Lake or beach 111 6 7.10 8 10 10
Playground/tot lot 161 6 7.42 8 9 10
Tree, Lawn care in
common areas
310 6 7.31 8 9 10
Water or sewer 432 7 8.12 8 10 10

22
The median score for “Recreation facilities, like gym” is 7, the only median score lower than 8.

23
“Security”, “Recreation facilities, like gym”, and “Indoor community center” have Q1 scores of 5.
47
My sample: Two-sample nonparametric test results
Note that in all the tables on two-sample nonparametric test results, the test
variable is surveyed homeowners’ degree of satisfaction with each municipal service.
The p-values are two-tailed, that is, I was not privileged to assume which test sample
would be more satisfied before the test. The font is Bold and Red where there was a
significant difference (two tailed p-value < 10 percent) between the two test samples
and there were more than ten observations in both test sample.
Test Results I: Given income level, compare non-HOA and HOA service
rankings (Table 26)
Table 26 shows that in the five counties of southern California, homeowners
in different income groups (above- versus below-median-income homeowners)
responded very differently in terms of their satisfaction scores with the municipal
services received. Living in private HOAs seems to be a higher-rated choice for
below-median-income homeowners. However, above-median-income homeowners
living in non-HOAs reported higher degrees of satisfaction with their municipal
services than those in HOAs.
Above-median-income non-HOA homeowners had significantly higher
satisfaction (at the 10 percent level) with seven municipal services among the sixteen
surveyed services. At the 15 percent level, there were four more services with which
above-median-income non-HOA homeowners indicated significantly higher
48
satisfaction.
24
Among all sixteen two-sample tests, this result consistently showed up
in fifteen tests; that is, positive signs with above-median-income non-HOA
homeowners in all tests except one.
However, below-median-income HOA homeowners reported significantly
higher satisfaction with four municipal services (at the 10 percent level) than their
below-median-income peers living in non-HOAs. Two other tests indicated higher
degrees of satisfaction with below-median-income HOA homeowners at the 15
percent level.
To summarize, in the five counties of southern California, income is an
important factor in determining how different local governance forms (public versus
private) affect homeowners’ degree of satisfaction with their municipal services. The
null hypothesis for Research Question #2 was rejected. Test Result I can be
summarized in Table 27.

24
The three services were Gardening, Street cleaning, Playground/tot lot, and Tree, lawn care in
common areas.
49
Table 26: Given income level (above- vs. below-median), compare non-HOA with
HOA responses

Mean P-value # of Obs Mean P-value # of Obs
non-HOA 8.75 0.2451 109 8.31 148
HOA 8.44 121 8.78 0.0422 72
non-HOA 8.05 0.1452 64 7.66 88
HOA 7.47 97 8.05 0.5070 65
non-HOA 8.19 0.1414 101 6.84 144
HOA 7.69 116 7.75 0.0116 67
non-HOA 8.20 0.0419 97 7.24 139
HOA 7.60 114 8.30 0.0054 63
non-HOA 8.11 0.0116 55 6.84 0.3615 81
HOA 7.11 81 6.85 0.7282 54
non-HOA 8.35 0.0109 46 7.34 0.7991 67
HOA 7.44 82 7.44 0.8297 52
non-HOA 8.44 0.0205 27 6.34 47
HOA 7.40 70 7.78 0.0048 50
non-HOA 8.59 0.0005 39 6.97 65
HOA 7.08 83 7.65 0.1053 57
non-HOA 8.02 0.0342 55 7.45 76
HOA 7.26 95 7.60 0.8198 63
non-HOA 6.84 25 7.09 0.1324 46
HOA 7.35 0.3044 46 6.22 23
non-HOA 7.48 0.5028 25 7.02 0.3421 46
HOA 6.92 38 6.27 22
non-HOA 7.94 0.3129 36 7.02 49
HOA 7.44 61 7.16 0.8455 43
non-HOA 8.00 0.7403 25 6.66 0.8297 41
HOA 7.77 22 6.27 15
non-HOA 8.40 0.1058 35 7.15 0.8824 53
HOA 7.56 41 7.06 18
non-HOA 7.96 0.0110 51 6.88 73
HOA 7.03 96 7.58 0.1273 59
non-HOA 8.51 0.1066 97 7.89 132
HOA 8.00 107 8.31 0.1780 64
Tree, Lawn care in
common areas
Water or sewer
Above-median-income Below-median-income
Indoor community
center
Swimming
pools/tennis courts
Security
Painting/outside
maintenance
Lake or beach
Playground/tot lot
Parking lot repair
Gates or fences
Landscaping
Recreation
facilities, like gym
Trash Collection
Gardening
Street cleaning
Street lighting and
repair

50
Table 27: Test results of service rankings comparison between non-HOAs and HOAs

Degree of Satisfaction HOA non-HOA
Above-median-income homeowners Higher-rated
Below-median-income homeowners Higher-rated

It seems that private HOAs represent an efficient local governance form in
providing higher-rated municipal services for below-median-income homeowners.
However, the test on above-median-income homeowners showed interesting
opposite results. It deserves further investigation to find out why above-median-
income non-HOA homeowners scored higher degrees of satisfaction with their
municipal services than those in HOAs. Provisionally we can say that median voters
make their wishes heard when they are non-poor.
Test Results II: Given Income level and Place, compare non-HOA and HOA
service rankings (Table 28 - 30)
25

Test Result II reports the comparison between non-HOA and HOA
homeowners’ satisfaction scores with the surveyed municipal services, while
controlling income level (above- versus below-median household income) and place
(big city, homevoter city, unincorporated area, or Los Angeles city).
In the above-median-income group except the case of Los Angeles city, non-
HOA services consistently received higher ratings than HOA services in most of the

25
Again, the p-values are two-tailed in Table 24 - 25. The font is Bold and Red where there is a
significant difference (two tailed p-value < 10 percent) between the two test samples and where the
numbers of observations are double-digit.
51
two-sample tests, that is, there were forty two out of all the forty eight tests that
showed positive signs with the test sample of non-HOA against HOA. Furthermore,
ten tests revealed that the above-median-income non-HOA homeowners were
significantly (at the 10 percent level) more satisfied with the municipal services than
their peers.
For below-median-income homeowners, the majority of the tests indicate that
those living in private HOAs were more satisfied than those living in non-HOAs.
Especially among homevoter cities or unincorporated areas, the below-median-
income homeowners were apparently better off in terms of municipal service
provision by living in HOAs. Eight tests suggested significantly higher scores for
HOA against non-HOA homeowners, among all the thirty two two-sample tests on
the below-median-income homeowners living in homevoter cities or unincorporated
areas. The results were mixed in the case of residence in a big city.
The tests regarding Los Angeles city were showed no significant differences
for the above-median-income homeowners, while the tests on the below-median-
income homeowners in Los Angeles city were not reported due to too small sample
sizes of the below-median-income HOA homeowners in Los Angeles city.
Again, in the five counties of southern California, income is an important
factor in determining how different local governance forms (public versus private)
affect homeowners’ degree of satisfaction with their municipal services. The null
hypothesis for Research Question #2 was rejected. In short, Test Results II repeated
Test Results I (see Table 27, repeated here).
52
Table 27: Test results of service rankings comparison between non-HOAs and HOAs

Degree of Satisfaction HOA non-HOA
Above-median-income homeowners Higher-rated
Below-median-income homeowners Higher-rated

Table 28: Given above-median-income and place, compare non-HOA and HOA
responses

Above-median-income
Mean P-value # of Obs Mean P-value # of Obs Mean P-value # of Obs
non-HOA 9.02 0.1549 45 8.53 32 9.00 0.4768 15
HOA 8.29 45 8.66 0.8818 47 8.33 15
non-HOA 8.26 0.1913 23 7.53 19 9.07 0.0043 14
HOA 7.39 31 7.90 0.4167 41 6.79 14
non-HOA 8.41 0.0623 41 8.44 0.2894 32 8.23 0.7054 13
HOA 7.60 40 8.02 45 8.00 15
non-HOA 8.70 0.0104 40 8.27 0.4580 30 8.31 0.6141 13
HOA 7.38 42 7.93 42 7.64 14
non-HOA 7.56 0.2592 18 8.72 0.0391 18 8.75 0.0981 12
HOA 6.61 28 7.37 30 7.00 13
non-HOA 8.09 0.2605 11 8.53 0.0643 17 8.33 0.2296 12
HOA 7.16 32 7.50 32 7.18 11
non-HOA 7.33 0.8002 3 8.62 0.1399 13 8.57 0.4290 7
HOA 6.96 26 7.79 28 7.73 11
non-HOA 7.83 0.3780 12 8.46 0.1038 13 9.56 0.0069 9
HOA 6.93 28 7.21 34 6.67 12
non-HOA 7.75 0.2707 16 7.84 0.7475 19 8.85 0.0273 13
HOA 7.12 34 7.62 37 6.92 13
non-HOA 7.14 7 7.30 0.9126 10 6.60 5
HOA 7.33 0.8864 21 7.06 17 8.20 0.3095 5
non-HOA 7.40 0.6118 5 7.54 0.5585 13 7.00 4
HOA 6.93 15 6.93 15 7.33 0.8762 6
non-HOA 7.60 0.6309 10 7.67 0.9961 15 8.40 0.7980 5
HOA 7.45 22 7.52 29 7.75 8
non-HOA 6.25 8 9.18 0.1698 11 8.67 0.5000 3
HOA 7.64 0.4697 11 7.17 6 6.00 1
non-HOA 8.44 0.1663 9 8.53 0.1871 15 8.25 0.4772 8
HOA 7.24 17 7.73 15 6.83 6
non-HOA 8.11 0.1016 19 8.00 0.3019 16 8.33 0.0680 9
HOA 6.92 36 7.45 40 6.67 12
non-HOA 8.85 0.0639 40 8.50 0.8286 30 8.36 0.2405 14
HOA 8.07 41 8.36 39 7.38 13
Tree, Lawn care in
common areas
Water or sewer
Big City Homevoter City
Indoor community
center
Swimming
pools/tennis courts
Playground/tot lot
Parking lot repair
Gates or fences
Landscaping
Lake or beach
Trash Collection
Gardening
Street cleaning
Unincorporated
Recreation
facilities, like gym
Street lighting and
repair
Security
Painting/outside
maintenance

53

Table 29: Given below-median-income and place, compare non-HOA and HOA
responses

Below-median-income
Mean P-value # of Obs Mean P-value # of Obs Mean P-value # of Obs
non-HOA 8.47 51 8.16 32 7.58 19
HOA 8.68 0.7456 28 8.88 0.0616 34 8.12 0.4623 33
non-HOA 7.54 24 8.00 20 7.47 17
HOA 7.92 0.4687 25 8.30 0.5881 33 8.28 0.4071 32
non-HOA 7.59 49 7.52 33 5.56 18
HOA 7.81 0.7666 26 8.28 0.0698 32 6.72 0.1757 29
non-HOA 7.94 48 7.50 32 6.17 18
HOA 8.23 0.7433 26 8.76 0.0226 29 6.87 0.3787 31
non-HOA 7.33 0.0231 24 7.71 0.7557 14 6.00 0.9312 14
HOA 5.90 20 7.59 27 5.93 28
non-HOA 7.67 0.6776 18 7.17 12 6.62 13
HOA 7.27 22 7.52 0.7137 25 6.83 0.8166 30
non-HOA 7.15 13 6.83 12 5.33 9
HOA 7.27 0.7945 22 8.32 0.0359 25 6.68 0.2740 31
non-HOA 6.88 16 7.31 13 6.62 13
HOA 7.41 0.4849 22 8.03 0.3361 29 6.87 0.7621 31
non-HOA 7.41 0.7525 22 7.93 15 6.85 13
HOA 7.21 24 8.03 0.7158 32 8.24 0.0780 33
non-HOA 7.80 0.0220 15 7.45 0.2957 11 5.67 9
HOA 5.33 9 6.60 10 7.06 0.4340 18
non-HOA 7.80 0.0967 15 7.73 0.0314 11 5.67 9
HOA 5.29 7 6.27 11 7.21 0.2327 19
non-HOA 8.20 0.0146 15 7.23 13 5.33 9
HOA 6.37 19 8.00 0.2555 20 7.54 0.0431 28
non-HOA 7.50 0.0462 18 6.73 11 3.75 4
HOA 5.20 5 6.86 0.3880 7 6.89 0.1343 9
non-HOA 7.79 0.2760 19 7.43 14 5.56 9
HOA 6.20 5 7.44 0.7661 9 6.59 0.3548 17
non-HOA 7.36 1.0000 25 7.40 15 5.40 10
HOA 7.26 23 7.93 0.3865 30 7.75 0.0227 32
non-HOA 8.29 0.7709 45 8.25 28 6.17 18
HOA 8.25 24 8.81 0.4086 31 7.79 0.0201 34
Trash Collection
Gardening
Unincorporated
Landscaping
Recreation
facilities, like gym
Street cleaning
Street lighting and
repair
Security
Painting/outside
maintenance
Tree, Lawn care in
common areas
Water or sewer
Big City Homevoter City
Indoor community
center
Swimming
pools/tennis courts
Lake or beach
Playground/tot lot
Parking lot repair
Gates or fences

54
Table 30: Given above-median-income and Los Angeles city, comparing non-HOA
and HOA responses

Above-median-income
Mean P-value # of Obs
non-HOA 8.24 17
HOA 8.38 0.9573 16
non-HOA 6.88 8
HOA 7.31 0.4036 13
non-HOA 7.00 0.9467 15
HOA 6.82 17
non-HOA 6.50 14
HOA 7.35 0.5584 17
non-HOA 6.86 7
HOA 7.42 0.7791 12
non-HOA 8.33 0.9730 6
HOA 8.33 0.9730 9
non-HOA 8.50 0.4939 4
HOA 7.14 7
non-HOA 9.00 0.2304 5
HOA 7.80 10
non-HOA 7.57 0.7921 7
HOA 7.15 13
non-HOA 5.00 3
HOA 7.75 0.2286 4
non-HOA 8.00 0.8000 3
HOA 6.33 3
non-HOA 8.83 0.7000 6
HOA 7.00 4
non-HOA 7.67 3
HOA 9.50 0.1714 4
non-HOA 8.00 3
HOA 10.00 0.1000 3
non-HOA 7.00 0.5730 7
HOA 6.40 10
non-HOA 7.62 0.6951 13
HOA 7.56 16
Tree, Lawn care in
common areas
Water or sewer
LA City
Indoor community
center
Swimming
pools/tennis courts
Lake or beach
Playground/tot lot
Parking lot repair
Gates or fences
Landscaping
Recreation
facilities, like gym
Street cleaning
Street lighting and
repair
Security
Painting/outside
maintenance
Trash Collection
Gardening

Note: Because there were only five observations in the group of below-median-income HOA
homeowners in Los Angeles city, the test results on this group were not reported.
55
Test Results III: Given income level and HOA status, comparing service
rankings at different places (Table 32 - 43)
Similar to the previous examples, the p-values are two-tailed. That is, I could
not assume which sample would report larger scores. The font is Bold and Red
where there was significant difference (two tailed p-value < 10 percent) between the
two test samples and where the numbers of observations were double-digits for both
test samples.
From the Test Result III, the first observation is that only 8 out of all the 192
two-sample tests on the above-median-income homeowners showed statistically
significant difference at the 10 percent level. Among these eight tests with significant
results, five were the tests regarding residents in Los Angeles city. In brief, there was
almost no statistically significant difference in the satisfaction scores of the above-
median-income homeowners across all places (Los Angeles city, big cities,
homevoter city, and unincorporated area) with all the sixteen surveyed services, no
matter the above-median-income homeowners were in either HOAs or non-HOAs
(Figure 2). However, it seemed that Los Angeles city was the worst place to live in
terms of the quality of municipal services. People choose to live in Los Angeles city,
probably because of other reasons, such as various agglomeration benefits offerred
by the large city.
It seems that in the five counties of southern California, place did not matter
to above-median-income homeowners (living in either HOAs or non-HOAs) in terms
of their degrees of satisfaction with the municipal services. Better off homeowners
56
probably had sufficient economic means to insist on and obtain desired municipal
services no matter where they resided.

Figure 2: Comparisons of above-median-income homeowners’ rankings for services
at different places (living in either HOAs or non-HOAs)

Big City
≈
Homevoter City
≈
Unincorporated Area
≈
Los Angeles City

However, there were a lot more interesting results in the tests on the below-
median-income homeowners. For the below-median-income homeowners in non-
HOAs, both big cities and homevoter cities offered universally higher-rated
municipal services than either unincorporated areas or Los Angeles city, that is, all
the tests showed consistent positive signs with big city or homevoter city against
unincorporated area or Los Angeles city. In the cases of big city versus
unincorporated area, six out of sixteen surveyed municipal services provided by big
cities were rated with significantly (at the 10 percent level of statistical significance)
higher scores than those for unincorporated areas. Three other tests with less then ten
observations in the unincorporated area test sample indicated that residence in the
big city scored significantly higher ratings than that in the unincorporated area at the
10 percent level. The tests of homevoter city versus unincorporated area
demonstrated similar results, including five tests significant at the 10 percent level
showing homevoter city received higher ratings than unincorporated area, and three
other tests significant at the 15 percent level with less than ten observations in
57
unincorporated areas also favoring homevoter city residence. However, there was no
significant difference in satisfaction scores between big city and homevoter city. The
relationships are graphed and shown in Figure 3.

Figure 3: Comparisons of the below-median-income non-HOA homeowners’
rankings for services at different places

Homevoter City
≈
Big City > Unincorporated Area
≈
Los Angeles City

For below-median-income HOA homeowners, homevoter-city services were
universally higher-rated than big-city services; there were consistent positive signs
with homevoter city residents against big city residents in all of the sixteen two-
sample tests. HOA homeowners in homevoter cities were significantly more
satisfied with three out of sixteen municipal services than those HOA homeowners in
big cities. In the case of homevoter cities versus unincorporated areas, homevoter
cities had significantly higher scores for seven services. However, the test results of
big city versus unincorporated area were mixed in the case of below-median-income
HOA homeowners. Figure 4 shows these results.

Figure 4: Comparisons of the below-median-income HOA homeowners’ rankings for
services provided in different places

Homevoter City > Big City
≈
Unincorporated Area
58
Test Result III is summarized in Table 31. Income did matter in terms of
homeowners’ degree of satisfaction with the municipal services provided by
different places/local municipal forms: Above-median-income homeowners by
definition had sufficient economic means to satisfy their desire for satisfactory
municipal services no matter which place/local governance form they chose to live
in. However, it seemed that living in homevoter cities was the best choice for below-
median-income homeowners, because they gave the highest scores for the municipal
services provided by homevoter cities. The null hypotheses for Research Question #3
were rejected, and Fischel’s HCH was sustained by the evidence.

Table 31: Test results of service rankings comparison among different places

Degree of Satisfaction Homevoter City
non-Homevoter
City
Unincorporated
Area
L.A.
Above-median-income
homeowners
Below-median-income
homeowners
Highest-rated
No significant difference

59
Table 32: Given above-median-income and HOA status, compare big city vs.
homevoter city responses

Mean P-value # of Obs Mean P-value # of Obs
Big City 9.02 0.2852 45 8.29 45
Homevoter City 8.53 32 8.66 0.6792 47
Big City 8.26 0.2279 23 7.39 31
Homevoter City 7.53 19 7.90 0.4519 41
Big City 8.41 41 7.60 40
Homevoter City 8.44 0.8084 32 8.02 0.4802 45
Big City 8.70 0.2654 40 7.38 42
Homevoter City 8.27 30 7.93 0.4475 42
Big City 7.56 18 6.61 28
Homevoter City 8.72 0.3120 18 7.37 0.3391 30
Big City 8.09 11 7.16 32
Homevoter City 8.53 0.3782 17 7.50 0.5077 32
Big City 7.33 3 6.96 26
Homevoter City 8.62 0.4946 13 7.79 0.2555 28
Big City 7.83 12 6.93 28
Homevoter City 8.46 0.4169 13 7.21 0.3891 34
Big City 7.75 16 7.12 34
Homevoter City 7.84 0.6914 19 7.62 0.5874 37
Big City 7.14 7 7.33 0.6019 21
Homevoter City 7.30 0.9571 10 7.06 17
Big City 7.40 5 6.93 0.8110 15
Homevoter City 7.54 0.6612 13 6.93 0.8110 15
Big City 7.60 10 7.45 22
Homevoter City 7.67 0.6431 15 7.52 0.8972 29
Big City 6.25 8 7.64 0.8287 11
Homevoter City 9.18 0.0492 11 7.17 6
Big City 8.44 9 7.24 17
Homevoter City 8.53 1.0000 15 7.73 0.6528 15
Big City 8.11 0.7826 19 6.92 36
Homevoter City 8.00 16 7.45 0.4455 40
Big City 8.85 0.2959 40 8.07 41
Homevoter City 8.50 30 8.36 0.5694 39
Above-median-inc. HOA Above-median-inc. non-HOA
Lake or beach
Playground/tot lot
Tree, Lawn care in
common areas
Water or sewer
Landscaping
Recreation
facilities, like gym
Indoor community
center
Swimming
pools/tennis courts
Security
Painting/outside
maintenance
Parking lot repair
Gates or fences
Trash Collection
Gardening
Street cleaning
Street lighting and
repair

60
Table 33: Given below-median-income and HOA status, comparing big city vs.
homevoter city responses

Mean P-value # of Obs Mean P-value # of Obs
Big City 8.47 0.3367 51 8.68 28
Homevoter City 8.16 32 8.88 0.7274 34
Big City 7.54 24 7.92 25
Homevoter City 8.00 0.3294 20 8.30 0.2986 33
Big City 7.59 0.5513 49 7.81 26
Homevoter City 7.52 33 8.28 0.5124 32
Big City 7.94 0.2849 48 8.23 26
Homevoter City 7.50 32 8.76 0.3575 29
Big City 7.33 24 5.90 20
Homevoter City 7.71 0.7087 14 7.59 0.0054 27
Big City 7.67 0.6474 18 7.27 22
Homevoter City 7.17 12 7.52 0.7766 25
Big City 7.15 0.9419 13 7.27 22
Homevoter City 6.83 12 8.32 0.0796 25
Big City 6.88 16 7.41 22
Homevoter City 7.31 0.7184 13 8.03 0.3433 29
Big City 7.41 22 7.21 24
Homevoter City 7.93 0.7104 15 8.03 0.3208 32
Big City 7.80 0.9083 15 5.33 9
Homevoter City 7.45 11 6.60 0.2621 10
Big City 7.80 0.7277 15 5.29 7
Homevoter City 7.73 11 6.27 0.6061 11
Big City 8.20 0.4201 15 6.37 19
Homevoter City 7.23 13 8.00 0.0094 20
Big City 7.50 0.4520 18 5.20 5
Homevoter City 6.73 11 6.86 0.3106 7
Big City 7.79 0.3767 19 6.20 5
Homevoter City 7.43 14 7.44 0.4231 9
Big City 7.36 25 7.26 23
Homevoter City 7.40 0.8380 15 7.93 0.3822 30
Big City 8.29 0.9181 45 8.25 24
Homevoter City 8.25 28 8.81 0.2125 31
Below-median-inc. HOA Below-median-inc. non-HOA
Lake or beach
Playground/tot lot
Tree, Lawn care in
common areas
Water or sewer
Landscaping
Recreation
facilities, like gym
Indoor community
center
Swimming
pools/tennis courts
Security
Painting/outside
maintenance
Parking lot repair
Gates or fences
Trash Collection
Gardening
Street cleaning
Street lighting and
repair

61
Table 34: Given above-median-income and HOA status, comparing homevoter city
vs. unincorporated area responses

Mean P-value # of Obs Mean P-value # of Obs
Homevoter City 8.53 32 8.66 0.6938 47
Unincorporated 9.00 0.4971 15 8.33 15
Homevoter City 7.53 19 7.90 0.0964 41
Unincorporated 9.07 0.0196 14 6.79 14
Homevoter City 8.44 0.7332 32 8.02 0.4534 45
Unincorporated 8.23 13 8.00 15
Homevoter City 8.27 30 7.93 0.9968 42
Unincorporated 8.31 0.9402 13 7.64 14
Homevoter City 8.72 18 7.37 0.7071 30
Unincorporated 8.75 0.8796 12 7.00 13
Homevoter City 8.53 0.9535 17 7.50 0.7229 32
Unincorporated 8.33 12 7.18 11
Homevoter City 8.62 0.9288 13 7.79 0.8741 28
Unincorporated 8.57 7 7.73 11
Homevoter City 8.46 13 7.21 0.8162 34
Unincorporated 9.56 0.2064 9 6.67 12
Homevoter City 7.84 19 7.62 0.4630 37
Unincorporated 8.85 0.0947 13 6.92 13
Homevoter City 7.30 0.5834 10 7.06 17
Unincorporated 6.60 5 8.20 0.3210 5
Homevoter City 7.54 0.6714 13 6.93 15
Unincorporated 7.00 4 7.33 0.5965 6
Homevoter City 7.67 15 7.52 29
Unincorporated 8.40 0.4240 5 7.75 0.5973 8
Homevoter City 9.18 0.4505 11 7.17 0.7143 6
Unincorporated 8.67 3 6.00 1
Homevoter City 8.53 0.9034 15 7.73 0.6312 15
Unincorporated 8.25 8 6.83 6
Homevoter City 8.00 16 7.45 0.4191 40
Unincorporated 8.33 0.5337 9 6.67 12
Homevoter City 8.50 0.8899 30 8.36 0.2145 39
Unincorporated 8.36 14 7.38 13
Above-median-inc. non-HOA Above-median-inc. HOA
Lake or beach
Playground/tot lot
Tree, Lawn care in
common areas
Water or sewer
Landscaping
Recreation
facilities, like gym
Indoor community
center
Swimming
pools/tennis courts
Security
Painting/outside
maintenance
Parking lot repair
Gates or fences
Trash Collection
Gardening
Street cleaning
Street lighting and
repair

62
Table 35: Given below-median-income and HOA status, compare homevoter city vs.
unincorporated area responses

Mean P-value # of Obs Mean P-value # of Obs
Homevoter City 8.16 0.3591 32 8.88 0.0836 34
Unincorporated 7.58 19 8.12 33
Homevoter City 8.00 0.5838 20 8.30 0.7956 33
Unincorporated 7.47 17 8.28 32
Homevoter City 7.52 0.0079 33 8.28 0.0089 32
Unincorporated 5.56 18 6.72 29
Homevoter City 7.50 0.0611 32 8.76 0.0007 29
Unincorporated 6.17 18 6.87 31
Homevoter City 7.71 0.0526 14 7.59 0.0065 27
Unincorporated 6.00 14 5.93 28
Homevoter City 7.17 0.5057 12 7.52 0.1973 25
Unincorporated 6.62 13 6.83 30
Homevoter City 6.83 0.2951 12 8.32 0.0139 25
Unincorporated 5.33 9 6.68 31
Homevoter City 7.31 0.6154 13 8.03 0.0494 29
Unincorporated 6.62 13 6.87 31
Homevoter City 7.93 0.3137 15 8.03 32
Unincorporated 6.85 13 8.24 0.6499 33
Homevoter City 7.45 0.2753 11 6.60 10
Unincorporated 5.67 9 7.06 0.9360 18
Homevoter City 7.73 0.1458 11 6.27 11
Unincorporated 5.67 9 7.21 0.2025 19
Homevoter City 7.23 0.1211 13 8.00 0.3340 20
Unincorporated 5.33 9 7.54 28
Homevoter City 6.73 0.1407 11 6.86 7
Unincorporated 3.75 4 6.89 0.6993 9
Homevoter City 7.43 0.1807 14 7.44 0.3426 9
Unincorporated 5.56 9 6.59 17
Homevoter City 7.40 0.1065 15 7.93 0.4739 30
Unincorporated 5.40 10 7.75 32
Homevoter City 8.25 0.0013 28 8.81 0.0048 31
Unincorporated 6.17 18 7.79 34
Below-median-inc. HOA Below-median-inc. non-HOA
Lake or beach
Playground/tot lot
Tree, Lawn care in
common areas
Water or sewer
Landscaping
Recreation
facilities, like gym
Indoor community
center
Swimming
pools/tennis courts
Security
Painting/outside
maintenance
Parking lot repair
Gates or fences
Trash Collection
Gardening
Street cleaning
Street lighting and
repair

63
Table 36: Given above-median-income and HOA status, compare big city vs.
unincorporated area responses

Mean P-value # of Obs Mean P-value # of Obs
Big City 9.02 0.7378 45 8.29 45
Unincorporated 9.00 15 8.33 0.9125 15
Big City 8.26 23 7.39 0.3122 31
Unincorporated 9.07 0.1492 14 6.79 14
Big City 8.41 0.6382 41 7.60 40
Unincorporated 8.23 13 8.00 0.2115 15
Big City 8.70 0.4889 40 7.38 42
Unincorporated 8.31 13 7.64 0.6503 14
Big City 7.56 18 6.61 28
Unincorporated 8.75 0.3443 12 7.00 0.7427 13
Big City 8.09 11 7.16 32
Unincorporated 8.33 0.4632 12 7.18 0.9357 11
Big City 7.33 3 6.96 26
Unincorporated 8.57 0.5417 7 7.73 0.2458 11
Big City 7.83 12 6.93 0.7266 28
Unincorporated 9.56 0.0299 9 6.67 12
Big City 7.75 16 7.12 0.7239 34
Unincorporated 8.85 0.2094 13 6.92 13
Big City 7.14 0.7361 7 7.33 21
Unincorporated 6.60 5 8.20 0.5352 5
Big City 7.40 0.7143 5 6.93 15
Unincorporated 7.00 4 7.33 0.8563 6
Big City 7.60 10 7.45 22
Unincorporated 8.40 0.7453 5 7.75 0.5027 8
Big City 6.25 8 7.64 0.6667 11
Unincorporated 8.67 0.4970 3 6.00 1
Big City 8.44 0.9449 9 7.24 0.8917 17
Unincorporated 8.25 8 6.83 6
Big City 8.11 19 6.92 0.8990 36
Unincorporated 8.33 0.9729 9 6.67 12
Big City 8.85 0.5444 40 8.07 0.4003 41
Unincorporated 8.36 14 7.38 13
Above-median-inc. non-HOA Above-median-inc. HOA
Lake or beach
Playground/tot lot
Tree, Lawn care in
common areas
Water or sewer
Landscaping
Recreation
facilities, like gym
Indoor community
center
Swimming
pools/tennis courts
Security
Painting/outside
maintenance
Parking lot repair
Gates or fences
Trash Collection
Gardening
Street cleaning
Street lighting and
repair

64
Table 37: Given below-median-income and HOA status, compare big city vs.
unincorporated area responses

Mean P-value # of Obs Mean P-value # of Obs
Big City 8.47 0.1088 51 8.68 0.2225 28
Unincorporated 7.58 19 8.12 33
Big City 7.54 0.8981 24 7.92 25
Unincorporated 7.47 17 8.28 0.4244 32
Big City 7.59 0.0057 49 7.81 0.0713 26
Unincorporated 5.56 18 6.72 29
Big City 7.94 0.0097 48 8.23 0.0183 26
Unincorporated 6.17 18 6.87 31
Big City 7.33 0.0973 24 5.90 20
Unincorporated 6.00 14 5.93 0.8710 28
Big City 7.67 0.2664 18 7.27 0.3985 22
Unincorporated 6.62 13 6.83 30
Big City 7.15 0.2099 13 7.27 0.5159 22
Unincorporated 5.33 9 6.68 31
Big City 6.88 0.7630 16 7.41 0.4093 22
Unincorporated 6.62 13 6.87 31
Big City 7.41 0.5225 22 7.21 24
Unincorporated 6.85 13 8.24 0.1712 33
Big City 7.80 0.1536 15 5.33 9
Unincorporated 5.67 9 7.06 0.1001 18
Big City 7.80 0.1482 15 5.29 7
Unincorporated 5.67 9 7.21 0.2303 19
Big City 8.20 0.0198 15 6.37 19
Unincorporated 5.33 9 7.54 0.0634 28
Big City 7.50 0.0373 18 5.20 5
Unincorporated 3.75 4 6.89 0.3227 9
Big City 7.79 0.0423 19 6.20 5
Unincorporated 5.56 9 6.59 0.8675 17
Big City 7.36 0.0668 25 7.26 23
Unincorporated 5.40 10 7.75 0.6990 32
Big City 8.29 0.0009 45 8.25 0.2271 24
Unincorporated 6.17 18 7.79 34
Below-median-inc. HOA Below-median-inc. non-HOA
Lake or beach
Playground/tot lot
Tree, Lawn care in
common areas
Water or sewer
Landscaping
Recreation
facilities, like gym
Indoor community
center
Swimming
pools/tennis courts
Security
Painting/outside
maintenance
Parking lot repair
Gates or fences
Trash Collection
Gardening
Street cleaning
Street lighting and
repair

65
Table 38: Given above-median-income and HOA status, comparing Los Angeles city
vs. big city responses

Mean P-value # of Obs Mean P-value # of Obs
LA City 8.24 17 8.38 0.7888 16
Big City 9.02 0.1683 45 8.29 45
LA City 6.88 8 7.31 13
Big City 8.26 0.0530 23 7.39 0.9055 31
LA City 7.00 15 6.82 17
Big City 8.41 0.0552 41 7.60 0.5244 40
LA City 6.50 14 7.35 17
Big City 8.70 0.0245 40 7.38 0.9932 42
LA City 6.86 7 7.42 0.7409 12
Big City 7.56 0.4239 18 6.61 28
LA City 8.33 0.5972 6 8.33 0.1784 9
Big City 8.09 11 7.16 32
LA City 8.50 0.8571 4 7.14 0.6069 7
Big City 7.33 3 6.96 26
LA City 9.00 0.2109 5 7.80 0.3155 10
Big City 7.83 12 6.93 28
LA City 7.57 7 7.15 0.9806 13
Big City 7.75 0.5630 16 7.12 34
LA City 5.00 3 7.75 0.2585 4
Big City 7.14 0.3575 7 7.33 21
LA City 8.00 1.0000 3 6.33 3
Big City 7.40 5 6.93 1.0000 15
LA City 8.83 0.5734 6 7.00 4
Big City 7.60 10 7.45 0.6899 22
LA City 7.67 0.9182 3 9.50 0.2495 4
Big City 6.25 8 7.64 11
LA City 8.00 3 10.00 0.0373 3
Big City 8.44 0.5077 9 7.24 17
LA City 7.00 7 6.40 10
Big City 8.11 0.3561 19 6.92 0.5510 36
LA City 7.62 13 7.56 16
Big City 8.85 0.0085 40 8.07 0.4799 41
Above-median-inc. HOA Above-median-inc. non-HOA
Lake or beach
Security
Painting/outside
maintenance
Parking lot repair
Gates or fences
Trash Collection
Gardening
Water or sewer
Landscaping
Recreation
facilities, like gym
Indoor community
center
Swimming
pools/tennis courts
Street cleaning
Street lighting and
repair
Playground/tot lot
Tree, Lawn care in
common areas

66
Table 39: Given above-median-income and HOA status, comparing Los Angeles city
vs. homevoter city responses

Mean P-value # of Obs Mean P-value # of Obs
LA City 8.24 17 8.38 16
Homevoter City 8.53 0.6815 32 8.66 0.5262 47
LA City 6.88 8 7.31 13
Homevoter City 7.53 0.4186 19 7.90 0.4556 41
LA City 7.00 15 6.82 17
Homevoter City 8.44 0.0582 32 8.02 0.2397 45
LA City 6.50 14 7.35 17
Homevoter City 8.27 0.1095 30 7.93 0.5590 42
LA City 6.86 7 7.42 0.3665 12
Homevoter City 8.72 0.0325 18 7.37 30
LA City 8.33 6 8.33 0.3531 9
Homevoter City 8.53 0.8270 17 7.50 32
LA City 8.50 4 7.14 7
Homevoter City 8.62 0.7233 13 7.79 0.8000 28
LA City 9.00 0.8357 5 7.80 0.6772 10
Homevoter City 8.46 13 7.21 34
LA City 7.57 7 7.15 13
Homevoter City 7.84 0.7657 19 7.62 0.5780 37
LA City 5.00 3 7.75 0.3356 4
Homevoter City 7.30 0.2639 10 7.06 17
LA City 8.00 0.7838 3 6.33 3
Homevoter City 7.54 13 6.93 0.9519 15
LA City 8.83 0.1766 6 7.00 4
Homevoter City 7.67 15 7.52 0.8448 29
LA City 7.67 3 9.50 0.2233 4
Homevoter City 9.18 0.0939 11 7.17 6
LA City 8.00 3 10.00 0.0491 3
Homevoter City 8.53 0.4212 15 7.73 15
LA City 7.00 7 6.40 10
Homevoter City 8.00 0.4409 16 7.45 0.2042 40
LA City 7.62 13 7.56 16
Homevoter City 8.50 0.0610 30 8.36 0.2362 39
Above-median-inc. HOA Above-median-inc. non-HOA
Lake or beach
Security
Painting/outside
maintenance
Parking lot repair
Gates or fences
Trash Collection
Gardening
Water or sewer
Landscaping
Recreation
facilities, like gym
Indoor community
center
Swimming
pools/tennis courts
Street cleaning
Street lighting and
repair
Playground/tot lot
Tree, Lawn care in
common areas

67
Table 40: Given above-median-income and HOA status, comparing Los Angeles city
vs. unincorporated area responses

Mean P-value # of Obs Mean P-value # of Obs
LA City 8.24 17 8.38 0.9341 16
Unincorporated 9.00 0.3656 15 8.33 15
LA City 6.88 8 7.31 0.4910 13
Unincorporated 9.07 0.0045 14 6.79 14
LA City 7.00 15 6.82 17
Unincorporated 8.23 0.2048 13 8.00 0.1951 15
LA City 6.50 14 7.35 17
Unincorporated 8.31 0.1642 13 7.64 0.7162 14
LA City 6.86 7 7.42 0.9118 12
Unincorporated 8.75 0.0424 12 7.00 13
LA City 8.33 0.8042 6 8.33 0.3277 9
Unincorporated 8.33 0.8042 12 7.18 11
LA City 8.50 4 7.14 7
Unincorporated 8.57 0.8421 7 7.73 0.7102 11
LA City 9.00 5 7.80 0.5459 10
Unincorporated 9.56 0.2961 9 6.67 12
LA City 7.57 7 7.15 0.8967 13
Unincorporated 8.85 0.0998 13 6.92 13
LA City 5.00 3 7.75 4
Unincorporated 6.60 0.5412 5 8.20 0.6858 5
LA City 8.00 0.7110 3 6.33 3
Unincorporated 7.00 4 7.33 1.0000 6
LA City 8.83 1.0000 6 7.00 4
Unincorporated 8.40 5 7.75 0.8622 8
LA City 7.67 3 9.50 0.2636 4
Unincorporated 8.67 0.6428 3 6.00 1
LA City 8.00 3 10.00 0.1198 3
Unincorporated 8.25 0.6015 8 6.83 6
LA City 7.00 7 6.40 10
Unincorporated 8.33 0.3052 9 6.67 0.7366 12
LA City 7.62 13 7.56 0.9458 16
Unincorporated 8.36 0.1627 14 7.38 13
Above-median-inc. HOA Above-median-inc. non-HOA
Lake or beach
Security
Painting/outside
maintenance
Parking lot repair
Gates or fences
Trash Collection
Gardening
Water or sewer
Landscaping
Recreation
facilities, like gym
Indoor community
center
Swimming
pools/tennis courts
Street cleaning
Street lighting and
repair
Playground/tot lot
Tree, Lawn care in
common areas

68
Table 41: Given below-median-income and HOA status, comparing Los Angeles city
vs. big city responses

Mean P-value # of Obs
LA City 8.38 47
Big City 8.47 0.3240 51
LA City 7.39 28
Big City 7.54 0.7280 24
LA City 5.91 45
Big City 7.59 0.0010 49
LA City 6.55 42
Big City 7.94 0.0067 48
LA City 6.23 30
Big City 7.33 0.0826 24
LA City 7.32 25
Big City 7.67 0.8799 18
LA City 5.50 14
Big City 7.15 0.2091 13
LA City 6.79 24
Big City 6.88 0.9220 16
LA City 7.30 27
Big City 7.41 0.9674 22
LA City 6.42 12
Big City 7.80 0.2992 15
LA City 5.92 12
Big City 7.80 0.0743 15
LA City 6.15 13
Big City 8.20 0.0319 15
LA City 5.56 9
Big City 7.50 0.2963 18
LA City 6.58 12
Big City 7.79 0.3292 19
LA City 6.42 24
Big City 7.36 0.3218 25
LA City 7.81 42
Big City 8.29 0.0943 45
Trash Collection
Gardening
Below-median-inc. non-HOA
Water or sewer
Landscaping
Recreation
facilities, like gym
Indoor community
center
Swimming
pools/tennis courts
Lake or beach
Street cleaning
Street lighting and
repair
Playground/tot lot
Tree, Lawn care in
common areas
Security
Painting/outside
maintenance
Parking lot repair
Gates or fences

Note: Because there were only five observations in the group of below-median-income HOA
homeowners in Los Angeles city, the test results on this group were not reported. The same for Table
42 and 43.
69
Table 42: Given below-median-income and HOA status, comparing Los Angeles city
vs. homevoter city responses

Mean P-value # of Obs
LA City 8.38 0.8238 47
Homevoter City 8.16 32
LA City 7.39 28
Homevoter City 8.00 0.7553 20
LA City 5.91 45
Homevoter City 7.52 0.0029 33
LA City 6.55 42
Homevoter City 7.50 0.0953 32
LA City 6.23 30
Homevoter City 7.71 0.0518 14
LA City 7.32 0.8175 25
Homevoter City 7.17 12
LA City 5.50 14
Homevoter City 6.83 0.2521 12
LA City 6.79 24
Homevoter City 7.31 0.6993 13
LA City 7.30 27
Homevoter City 7.93 0.7085 15
LA City 6.42 12
Homevoter City 7.45 0.4396 11
LA City 5.92 12
Homevoter City 7.73 0.0393 11
LA City 6.15 13
Homevoter City 7.23 0.1747 13
LA City 5.56 9
Homevoter City 6.73 0.6993 11
LA City 6.58 12
Homevoter City 7.43 0.6841 14
LA City 6.42 24
Homevoter City 7.40 0.2979 15
LA City 7.81 42
Homevoter City 8.25 0.0280 28
Water or sewer
Landscaping
Recreation
facilities, like gym
Indoor community
center
Swimming
pools/tennis courts
Playground/tot lot
Tree, Lawn care in
common areas
Below-median-inc. non-HOA
Lake or beach
Security
Painting/outside
maintenance
Parking lot repair
Gates or fences
Trash Collection
Gardening
Street cleaning
Street lighting and
repair

70
Table 43: Given below-median-income and HOA status, comparing Los Angeles city
vs. unincorporated area responses

Mean P-value # of Obs
LA City 8.38 0.2418 47
Unincorporated 7.58 19
LA City 7.39 28
Unincorporated 7.47 0.8750 17
LA City 5.91 0.6775 45
Unincorporated 5.56 18
LA City 6.55 0.6011 42
Unincorporated 6.17 18
LA City 6.23 0.7885 30
Unincorporated 6.00 14
LA City 7.32 0.3225 25
Unincorporated 6.62 13
LA City 5.50 0.9745 14
Unincorporated 5.33 9
LA City 6.79 0.7590 24
Unincorporated 6.62 13
LA City 7.30 0.5349 27
Unincorporated 6.85 13
LA City 6.42 0.6398 12
Unincorporated 5.67 9
LA City 5.92 0.9714 12
Unincorporated 5.67 9
LA City 6.15 0.6364 13
Unincorporated 5.33 9
LA City 5.56 0.4233 9
Unincorporated 3.75 4
LA City 6.58 0.4274 12
Unincorporated 5.56 9
LA City 6.42 0.3502 24
Unincorporated 5.40 10
LA City 7.81 0.0076 42
Unincorporated 6.17 18
Trash Collection
Gardening
Below-median-inc. non-HOA
Water or sewer
Landscaping
Recreation
facilities, like gym
Indoor community
center
Swimming
pools/tennis courts
Lake or beach
Street cleaning
Street lighting and
repair
Playground/tot lot
Tree, Lawn care in
common areas
Security
Painting/outside
maintenance
Parking lot repair
Gates or fences

71
Test Results IV: Various tests for Research Question #4 (Table 44 – 45, 35 & 37)
Table 44: Given HOA status, comparing service rankings between incorporated
city and unincorporated area responses
For HOA homeowners (the HOA panel in Table 44), incorporated cities
offered higher-rated municipal services than unincorporated areas. Among the
sixteen municipal services, HOA homeowners rated significantly (at the 10 percent
level) higher degrees of satisfaction with three municipal services provided by
incorporated cities than unincorporated areas.
26
The other tests indicated no
significant relationship. In short, the municipal form of HOA in incorporated cities
was rated higher than were HOA services in unincorporated areas -- again in terms
of homeowners’ reported degree of satisfaction; expectation (i) under Hypothesis 4.1
was rejected.
27

On the other hand, the tests on non-HOA homeowners (the non-HOA panel
in Table 44) could act as controls for the tests on HOA homeowners. The tests on
non-HOA homeowners showed that there was not much difference between
incorporated cities and unincorporated areas in terms of degree of satisfaction with
the municipal services they offered. Only one test (water and sewers) indicated
statistical significance at the 10 percent level. Combined with the above test results
on HOA homeowners, it appears that private municipal suppliers in incorporated
areas are preferred to private suppliers in unincorporated areas. HOAs in

26
These three municipal services are Street lighting and repair, Security, and Water and sewer.

27
Hypothesis 4.1 and the expectation (i) are defined in the second section.
72
incorporated cities offered the most highly rated municipal services among the four
categories defined in Table 5. Briefly, services provided by HOAs in incorporated
areas were the highest rated, and expectation (iii) under Hypothesis 4.2 was
confirmed.
28

Table 45: Comparing service rankings between non HOA incorporated cities
and HOA unincorporated area responses
The test results in Table 45 clearly demonstrate that non-HOA homeowners
living in incorporated cities prompted ratings of higher degree of satisfaction from
municipal services than those living in private HOAs in unincorporated areas. Four
tests showed that non-HOA homeowners in incorporated cities scored statistically
significantly (at the 10 percent level) higher ratings for their municipal services than
HOA homeowners in unincorporated areas, while the other tests showed no
significant relationship. The empirical evidence indicates that services provided to
residents in non-HOAs in an incorporated city were rated more highly than for
HOAs in unincorporated area, and the expectation (ii) under Hypothesis 4.1 was
rejected.
29

It makes sense to investigate homevoter city residents and big city residents
separately for the case of the below-median-income group, because it was shown that
there was no statistically significant difference among all the places for the above-
median-income homeowners in terms of their degree of satisfaction with their

28
Hypothesis 4.2 and the expectation (iii) are defined in the second section.

29
Hypothesis 4.1 and expectation (ii) are defined in the second section.
73
municipal services. In addition, homevoter cities provided higher-rated municipal
services than big cities. Therefore, Table 35 and 36 from the two-sample Test
Results III are repeated here.
Table 35: Given below-median-income and HOA status, comparing service
rankings between homevoter city and unincorporated area responses
Table 35 revealed the same findings as Table 44, but the test results in Table
35 included greater statistical significance. Homevoter cities offered higher-rated
municipal services than unincorporated areas in seven of the sixteen municipal
services (at the 10 percent level) for below-median-income HOA homeowners.
Clearly, expectation (i) under Hypothesis 4.1 was rejected. Additionally, only four
tests indicated that homevoter cities were higher-rated than unincorporated areas for
the below-median-income non-HOA homeowners, whereas three more tests (with
the total of seven tests) suggested that homevoter cities were significantly higher-
rated than unincorporated areas for the below-median-income HOA homeowners. In
other words, HOAs in Incorporated Area were highest rated among the four
municipal forms defined in Table 5, and expectation (iii) under Hypothesis 4.2 was
confirmed.
Table 37: Given below-median-income and HOA status, comparing service
rankings between big city and unincorporated area responses
The test results in Table 37 are not clear. It seems that big cities provided
higher-rated (at the 10 percent statistical significance level) municipal services than
unincorporated areas for the below-median-income non-HOA homeowners.
74
However, the test results for the below-median-income HOA homeowners were
mixed.

Table 44: Given HOA status, comparing incorporated city vs. unincorporated area
responses

Mean P-value # of Obs Mean P-value # of Obs
Incorporated 8.51 0.4687 224 8.60 0.1461 175
Unincorporated 8.21 34 8.19 48
Incorporated 7.67 122 7.80 147
Unincorporated 8.19 0.1483 31 7.83 0.9691 46
Incorporated 7.47 0.1162 215 7.76 0.2295 165
Unincorporated 6.68 31 7.16 44
Incorporated 7.68 0.1656 206 7.92 0.0335 160
Unincorporated 7.06 31 7.11 45
Incorporated 7.32 1.0000 111 6.99 0.0896 120
Unincorporated 7.27 26 6.27 41
Incorporated 7.76 0.6658 89 7.45 0.1276 122
Unincorporated 7.44 25 6.93 41
Incorporated 7.12 0.8542 59 7.56 0.2524 108
Unincorporated 6.75 16 6.95 42
Incorporated 7.43 83 7.38 0.2920 125
Unincorporated 7.82 0.3638 22 6.81 43
Incorporated 7.59 106 7.45 143
Unincorporated 7.85 0.4752 26 7.87 0.2228 46
Incorporated 7.14 0.1586 58 6.87 61
Unincorporated 6.00 14 7.30 0.6293 23
Incorporated 7.32 0.1636 59 6.53 51
Unincorporated 6.08 13 7.24 0.2731 25
Incorporated 7.51 0.2715 72 7.33 95
Unincorporated 6.43 14 7.58 0.5340 36
Incorporated 7.22 0.4110 60 7.24 0.4762 34
Unincorporated 5.86 7 6.80 10
Incorporated 7.76 0.2828 72 7.49 0.1936 49
Unincorporated 6.82 17 6.65 23
Incorporated 7.36 0.5301 106 7.30 141
Unincorporated 6.79 19 7.45 0.8532 44
Incorporated 8.28 0.0144 198 8.25 0.0241 155
Unincorporated 7.13 32 7.68 47
non-HOA HOA
Lake or beach
Playground/tot lot
Tree, Lawn care in
common areas
Water or sewer
Landscaping
Recreation
facilities, like gym
Indoor community
center
Swimming
pools/tennis courts
Security
Painting/outside
maintenance
Parking lot repair
Gates or fences
Trash Collection
Gardening
Street cleaning
Street lighting and
repair

75
Table 45: Comparing non-HOA incorporated city vs. HOA unincorporated area
responses

Mean P-value # of Obs
non-HOA Incorporated 8.51 0.3199 224
HOA Unncorporated 8.19 48
non-HOA Incorporated 7.67 122
HOA Unncorporated 7.83 0.7924 46
non-HOA Incorporated 7.47 0.5757 215
HOA Unncorporated 7.16 44
non-HOA Incorporated 7.68 0.0915 206
HOA Unncorporated 7.11 45
non-HOA Incorporated 7.32 0.0168 111
HOA Unncorporated 6.27 41
non-HOA Incorporated 7.76 0.0201 89
HOA Unncorporated 6.93 41
non-HOA Incorporated 7.12 0.7722 59
HOA Unncorporated 6.95 42
non-HOA Incorporated 7.43 0.2180 83
HOA Unncorporated 6.81 43
non-HOA Incorporated 7.59 106
HOA Unncorporated 7.87 0.5620 46
non-HOA Incorporated 7.14 58
HOA Unncorporated 7.30 0.8318 23
non-HOA Incorporated 7.32 0.9842 59
HOA Unncorporated 7.24 25
non-HOA Incorporated 7.51 72
HOA Unncorporated 7.58 0.8736 36
non-HOA Incorporated 7.22 0.5976 60
HOA Unncorporated 6.80 10
non-HOA Incorporated 7.76 0.1059 72
HOA Unncorporated 6.65 23
non-HOA Incorporated 7.36 106
HOA Unncorporated 7.45 0.9113 44
non-HOA Incorporated 8.28 0.0227 198
HOA Unncorporated 7.68 47
Trash Collection
Gardening
Street cleaning
Street lighting and
repair
Security
Painting/outside
maintenance
Parking lot repair
Gates or fences
Landscaping
Recreation
facilities, like gym
Indoor community
center
Swimming
pools/tennis courts
Lake or beach
Playground/tot lot
Tree, Lawn care in
common areas
Water or sewer

76
Conclusions from two-sample nonparametric tests
Combining all the above two-sample test results, I can report (1) that the
interaction of privatization and income did matter in terms of homeowners’ degree of
satisfaction with their municipal services and (2) that privatization of traditional
municipal functions improved the perceived quality of these services for the below-
median-income people, in the five counties of southern California.
i. The surveyed below-median-income homeowners preferred services in
private HOAs, while non-HOA services were rated more highly by the
surveyed above-median-income homeowners.
ii. For above-median-income homeowners, place did not matter in terms of the
perceived quality of municipal services. It is plausible that the above-
median-income homeowners had sufficient economic means and were able
to obtain the municipal services up to their desired level in light of the
available options in the Los Angeles area, no matter what kind of place they
resided in, either Los Angeles city, a homevoter city or an unincorporated
area.
iii. For the surveyed below-median-income homeowners, living in a homevoter
city offered the most highly rated services. Fischel’s HCH was confirmed
by the empirical evidence on the below-median-income homeowners in
southern California.
iv. The empirical evidence from Test Results IV suggests that in southern
California, private HOAs and traditional public municipalities seemed not
77
substitutes but rather complements. Private suppliers in incorporated areas
were preferred to private suppliers in unincorporated areas. Private HOAs in
an incorporated city was the preferred local governance form, especially for
below-median-income homeowners.
v. Forming private HOAs seemed to be a market response of providing higher-
rated municipal services for below-median-income homeowners because
their limited economic means frustrated them in gaining satisfactory
municipal services, perhaps in light of the diminished supply of local public
goods in California. Private HOAs and homevoter cities (conventional small
cities) could be efficient local governance forms for below-median-income
households.
My sample: Multivariate regressions results
Note that all the regressions reported are semi-log hedonic regressions, that
is, the variables of home value and lot size were transformed to log scale, while the
other variables were left in their normal scale. Also note that in all the regression
models, I included four basic hedonic characteristic variables: log of lot size,
distance to the coast, number of bathrooms, and number of bedrooms.
Case 1: Simplest Case - with HOA dummy (Table 46)
The HOA dummy variable was only marginally significant (positive sign, t-
ratio of 1.48 and p-value of 0.14) in the regression that included all observations,
while insignificant for both the above- and below-median-income sub-samples (t-
ratio of 0.68 and -0.29 respectively). This model specification suggested that the
78
market overall marginally priced HOA housing units. The statistical insignificance
and opposite signs of the HOA dummy in the two income groups suggested the
heterogeneity of the two groups.

Table 46: Hedonic regression with basic hedonic variables and HOA dummy
variable

Dependent logValue logValue logValue
All Obs. Above Median Income Below Median Income
Adj. R2 0.4811 0.502 0.3088
# of Obs. 458 234 224
Intercept 9.554 10.022 9.846
(39.87) (34.25) (22.5)
logLot 0.313 0.296 0.263
(10.73) (8.47) (4.9)
DCoast -0.133 -0.151 -0.097
(-9.99) (-8.26) (-4.93)
Bath 0.160 0.151 0.134
(5.97) (4.6) (3.04)
BedRms 0.074 0.028 0.095
(2.77) (0.86) (2.15)
HOA Dummy 0.066 0.036 -0.023
(1.48) (0.68) (-0.29)

Case 2: With HOA dummy and cost rate (Table 47)
When assessed services cost rates (the annual property tax rate plus HOA
assessment rate) were added to the hedonic regression model, the HOA dummy
variable became significantly positive for all observations and for the above-median-
income group. For the below-median-income group, the HOA dummy variable
79
became positive but remained insignificant. It seemed that the market positively
priced HOA housing units. However, homeowners of different income levels
behaved differently: other things equal, above-median-income homeowners paid for
living in private HOAs,
30
while there was no significant evidence showing that
below-median-income homeowners paid a premium for HOA housing units (t-ratio
of 0.96 and p-value of 0.34).
The hedonic model of Case 2 is better than Case 1. The adjusted R-squared of
Case 2 increased to 0.539 from 0.481 in Case 1. The coefficient of the cost rate
variable was negative and highly significant, consistent with the literature in that
housing costs, such as property tax and HOA assessments, are negatively capitalized
into housing prices. Also note that the four basic hedonic variables (including log of
lot size, distance to the coast, number of bathrooms, and number of bedrooms) had
the expected signs. These hedonic variables except “number of bedrooms” were
highly statistically significant.
31
The magnitudes of these four basic hedonic
variables were also stable in both Case 2 and Case 1.

30
The t-ratio for the HOA dummy was 2.27 and p-value 0.024.

31
The statistical insignificance of the variable “number of bedrooms” may be due to its high
correlation with the variable “number of bathrooms”. To be consistent with the literature, I still kept
both variables in all the models, because a camp of the literature on hedonic modeling maintains that a
variable should be kept in the model if there is economic justification of keeping it even if its
estimated coefficient is insignificant.
80

Table 47: Hedonic regression with basic hedonic variables, HOA dummy and cost
rate variable

Dependent logValue logValue logValue
All Obs. Above Median Income Below Median Income
Adj. R2 0.539 0.5555 0.3898
# of Obs. 395 200 195
Intercept 10.719 11.022 10.933
(36.95) (29.05) (22.63)
logLot 0.218 0.219 0.172
(6.66) (5.35) (3.04)
DCoast -0.107 -0.105 -0.095
(-7.8) (-5.51) (-4.65)
Bath 0.179 0.163 0.162
(6.77) (4.94) (3.73)
BedRms 0.033 -0.009 0.066
(1.23) (-0.28) (1.48)
HOA Dummy 0.154 0.130 0.082
(3.24) (2.27) (0.96)
CostRate -0.183 -0.217 -0.155
(-7) (-4.78) (-4.54)

81
Case 3: With place dummies, HOA dummy and cost rate;
reference group is Los Angeles city (Table 48)
Case 3 showed that the same home is valued significantly more in a
homevoter city or in Los Angeles city than in a big city (non-homevoter city) or an
unincorporated area, with the other things equal. This result seems to support
Fischel’s hypothesis. Location in Los Angeles city seemingly offered housing
consumers agglomeration benefits, which made a housing unit in Los Angeles city
valued more than a comparable housing unit in a non-homevoter city or an
unincorporated area.
Adding the place dummies improved the model fit by a very small amount,
with the adjusted R-squared increased to 0.566 from 0.539 in Case 2. Also note that
adding the place dummies did not affect the sign, magnitude, and statistical
significance of all the other variables. The models in Case 1 - 3 were stable and made
good economic sense.
Again, homeowners in different income groups behaved differently: other
things equal, the above-median-income homeowners paid for living in HOAs (t-ratio
of 2.31), while the below-median-income homeowners did not pay a significant
premium for HOA housing units (t-ratio of 1.31 and p-value of 0.19).
82

Table 48: Hedonic regression with basic hedonic variables, place dummy, HOA
dummy and cost rate variables; reference group is Los Angeles city

Dependent logValue logValue logValue
All Obs. Above Median Income Below Median Income
Adj. R2 0.5657 0.5775 0.4306
# of Obs. 395 200 195
Intercept 10.777 11.135 11.124
(36.25) (27.88) (22.72)
logLot 0.220 0.219 0.166
(6.77) (5.37) (2.94)
DCoast -0.111 -0.101 -0.104
(-7.91) (-5.26) (-4.84)
Bath 0.173 0.157 0.158
(6.73) (4.82) (3.75)
BedRms 0.038 -0.002 0.066
(1.47) (-0.07) (1.54)
HOA Dummy 0.161 0.129 0.113
(3.42) (2.31) (1.31)
CostRate -0.192 -0.211 -0.177
(-7.49) (-4.73) (-5.29)
Big City -0.133 -0.195 -0.191
(-2.29) (-2.26) (-2.3)
Homevoter City 0.041 -0.063 0.046
(0.67) (-0.71) (0.51)
Unincorprated -0.248 -0.283 -0.265
(-3.38) (-2.75) (-2.48)

83
Case 4: With place dummies, HOA assessment rate and property tax rate;
reference group is Los Angeles city (Table 49)
Case 4 showed that both HOA assessments (in the case of private HOAs) and
property taxes were negatively capitalized into home values for all the observations
in my sample. Case 4 also indicates that there was difference between the above-
median-income and below-median-income homeowners in capitalizing the negative
values of HOA assessments when paying for HOA housing units. The estimated
coefficient of the FeeRate variable for the above-median-income group was not
significant (with the t-ratio of only -1.26 and p-value of 0.21), whereas significant
for the below-median-income group (t-ratio of -2.76 and p-value 0.006).
This seems to provide an explanation for the two-sample tests results in the
previous sub-section, that is, in the above-median-income group, non-HOA
homeowners revealed statistically significantly higher degrees of satisfaction with
municipal services than HOA homeowners, whereas the below-median-income HOA
homeowners were more satisfied than their non-HOA counterparts:
• Above-median-income homeowners did not significantly discount HOA
fees into their purchase prices when they bought the homes in private HOAs.
In other words, there was no significant evidence of negative capitalization of
HOA assessments for the above-median-income homeowners. However, they
paid to live in private HOAs.
32
Therefore, we can only speculate that the
above-median-income homeowners’ expectation for living in HOAs was

32
Refer to the significant estimated coefficient of the HOA dummy variable (t-ratio of 2.31) in Case
3.
84
relatively too high and thus they became disappointed later (experienced
buyers’ remorse), resulting in lower degrees of satisfaction with the
municipal services than those above-median-income homeowners living in
non-HOAs.
• As a sharp contrast, the below-median-income people, given their limited
budget, paid more attention to the monetary costs of living in private HOAs,
and therefore significantly discounted the HOA fees into their home prices.
Moreover, they did not pay for living in HOAs.
33
As a result, the below-
median-income HOA homeowners were relatively more satisfied with the
municipal services than their non-HOA peers, thanks to the superiority of
private HOAs in providing local public goods.

33
Also refer to the insignificant estimated coefficient of the HOA dummy variable (t-ratio of 31 and
p-value of 0.19) in Case 3.
85

Table 49: Hedonic regression with basic hedonic variables, place dummy, HOA
assessment rate and property tax rate variable; reference group is Los Angeles city

Dependent logValue logValue logValue
All Obs. Above-Median-Income Below-Median-Income
Adj. R2 0.5556 0.5719 0.4257
# of Obs. 395 200 195
Intercept 10.679 10.917 11.203
(34.52) (26.24) (22.35)
logLot 0.229 0.244 0.153
(6.62) (5.61) (2.64)
DCoast -0.112 -0.102 -0.101
(-7.86) (-5.27) (-4.69)
Bath 0.192 0.163 0.170
(7.62) (4.97) (4.14)
BedRms 0.038 0.004 0.065
(1.45) (0.11) (1.52)
FeeRate -0.110 -0.086 -0.146
(-2.67) (-1.26) (-2.76)
taxR -0.191 -0.220 -0.170
(-6.43) (-4.42) (-4.43)
Big -0.105 -0.191 -0.168
(-1.8) (-2.21) (-2.06)
Homevoter 0.067 -0.060 0.069
(1.09) (-0.68) (0.77)
Unincorporated -0.230 -0.287 -0.248
(-3.12) (-2.78) (-2.33)

86
Case 5: With cost rate and the interaction between HOA status and
incorporation status; reference group is HOAs in unincorporated areas (Table
50)
The model specification in Table 50 makes economic sense. Similar to the
previous regression examples, four basic hedonic variables (namely, log of lot size,
distance to the Pacific coast, umber of bathrooms and number of bedrooms) and cost
rate (the summation of annual property tax rate and HOA assessment rate) were
included in the models in Table 50. These five key variables had the right signs and
were highly significant (except for the number of bedrooms variable). The
magnitudes of these five key variables’ coefficients were consistent with those
estimated in the previous models. The adjusted R-squared measurement was
relatively high, 0.606 for the model with all observations.
The regression results for the interaction terms can be summarized in Figure
5. The coefficient of HOA homes in incorporated cities was highly significantly
positive (t-ratio of 2.89 and p value of 0.004), relative to the reference group of HOA
homes in unincorporated areas. Clearly, the municipal form of HOA in an
incorporated city was higher-rated than in an HOA in an unincorporated urea, and
expectation (i) under Hypothesis 4.1 was rejected. In addition, from Figure 5, it is
clear that HOA homes in incorporated cities had the highest valuation, when
controlling for other characteristics, that is, HOA in an incorporated city was the best
among the four municipal forms defined in Table 5, and the expectation (iii) under
Hypothesis 4.2 was confirmed.
87

Table 50: Hedonic regression with basic hedonic variables, cost rate and the
interaction terms between HOA status and incorporation status variable; reference
group is HOAs in unincorporated areas

Dependent logValue logValue logValue
All Obs.
Above-Median-
Income
Below-Median-
Income
Adj. R2 0.6058 0.5807 0.4963
# of Obs. 395 200 195
Intercept 10.440 11.028 10.597
(36.11) (26.39) (24.15)
logLot 0.237 0.222 0.189
(7.63) (5.47) (3.64)
DCoast -0.106 -0.100 -0.096
(-8.07) (-5.34) (-5.01)
BATH 0.181 0.164 0.162
(7.17) (5.06) (4.08)
BEDRMS 0.041 0.001 0.064
(1.61) (0.04) (1.6)
CostRate -0.168 -0.207 -0.137
(-7.88) (-4.88) (-5.23)
nonHOAxIncorporated 0.089 -0.054 0.197
(1.19) (-0.48) (1.90)
nonHOAxUnincorporated -0.186 -0.378 -0.021
(-1.92) (-2.66) (-0.16)
HOAxIncorporated 0.204 0.031 0.241
(2.89) (0.29) (2.48)

88
Figure 5: Effects on housing prices of the interactions between HOA status and
incorporation status

Moreover, in the model for the below-median-income homeowners, the
coefficient of non-HOA homes in incorporated cities was positive relative to the
reference group of HOA homes in unincorporated areas, with marginal statistical
significance (t-ratio of 1.90 and p value of 0.058). However, in the model with all
observations, the coefficient of non-HOA homes in incorporated cities was positive
but insignificant. The results indicated that the municipal form of non-HOA in an
incorporated city was better than HOA in a unincorporated area, and the expectation
(ii) under Hypothesis 4.1 was rejected.
34

The regression results from Case 5 show that traditional public local
governments and private HOAs are not substitutes but rather complements to each
other.

34
The expectations of the hypotheses are defined in the second section.
HOA homes in
incorporated
cities
(b)
>
Non-HOA homes
in incorporated
cities
(a)
≥

HOA homes in
unincorporated
areas
(d)
>
Non-HOA homes
in unincorporated
areas
(c)
89
Conclusions from the multivariate regressions
In the five counties of southern California,
i. Housing units in private HOAs were priced with a premium in the overall
housing market. However, different income groups responded differently to
living in private HOAs.
ii. Above-median-income homeowners paid a premium for living in private
HOAs, whereas below-median-income homeowners did not pay a significant
premium for HOA housing units, controlling the influences of the other
hedonic variables.
iii. There was no significant negative capitalization of HOA assessments into
home prices when the above-median-income homeowners paid for their
HOA homes. Combined with (ii), it is conjectured that above-median-
income homeowners’ expectations for living in private HOAs were
relatively too high and hence became disappointed later about their
municipal services.
iv. Below-median-income people paid more attention to the monetary costs of
living in private HOAs, and therefore significantly discounted the HOA fees
into their home purchase prices. As a result, below-median-income HOA
homeowners were relatively more satisfied with their municipal services
than their non-HOA peers.
v. Fischel’s Homevoter City Hypothesis was confirmed by the empirical tests.
90
vi. The evidences suggest that traditional public local governments and private
HOAs are not substitutes but rather complements to each other.
91
CHAPTER FIVE
CONCLUSIONS
The empirical study results are summarized in Table 51.

Table 51: Summary of the empirical study in southern California

Research Question Empirical Evidence Conclusion
#1. How satisfied are
homeowners with their municipal
services?
Table 25
Most homeowners are satisfied with their
municipal services.
Two-sample Test Results I (Table 26),
II (Table 28-30), and III (Table 31-42).
Regression Case 2 (Table 47), Case 3
(Table 48), and Case 4 (Table 49).
Two-sample Test Results III (Table 31-
42).
Regression Case 3 (Table 48) and
Case 4 (Table 49).
Two-sample Test Results IV (Table 44-
45, 34 and 36).
Regression Case 5 (Table 50).
#4. Are traditional public local
governments and private HOAs
complements or substitutes?
Private HOAs and traditional public local
governments are not substitutes but rather
complements.
#2. Does the interaction of
privatization and income matter
in providing homeowners with
better municipal services?
The interaction of privatization and income
does matter in providing homeowners with
better municipal services. Forming private
HOAs seems to be a good choice for below-
median-income homeowners.
#3. Does the size of traditional
municipalities matter in terms of
the quality of municipal
services?
Homevoter city is the best municipal form
for below-median-income homeowners,
while place does not matter for above-
median-income homeowners.

Most homeowners are satisfied with their municipal services. My survey
sample data indicate that in the five local counties of southern California, half of the
homeowners gave satisfaction scores equal to or higher than 8 for fifteen out of all
the sixteen surveyed municipal services.
35, 36,

37
These results suggest that in a free

35
The rating scale is from 1 to 10, 10 being Excellent and 1 being totally Unacceptable.

36
The only exception was “Recreation facilities, like gym”; the median satisfaction score was 7 in my
sample.

37
In the AHS 2005 National data, seventy six percent of the surveyed homeowners rated for their
neighborhood equal to or higher than 8, on the scale from 1 to 10 rating with 10 the best and 1 the
92
society, individuals make choices that work for them, even when it comes to local
public goods.
Income matters in terms of homeowners’ degree of satisfaction with the
municipal services in southern California. In this test case, the below-median-
income homeowners living in private HOAs scored higher degrees of satisfaction
with their municipal services than those below-median-income people living in non-
HOAs. However, for the above-median-income homeowners, living in non-HOAs
seemed to be a better choice.
Privatization does matter in providing higher-rated municipal services
for below-median-income homeowners. The empirical evidences from southern
California demonstrate that private HOAs provided enhanced municipal services for
the below-median-income homeowners, in terms of both homeowners’ higher degree
of satisfaction and the positive effect on housing values. This could be understood as
a market response to the specific local circumstances in southern California. The
problem of limited local property tax in California following the adoption of
Proposition 13 in 1978 may make it hard for traditional public municipalities to
provide all the services their citizens desire. Below-median-income residents who
want new services or higher levels of existing services given their limited economic
means can form or buy into private HOAs that administer affordable desired
municipal services. The proliferation of private HOAs in California thus exemplifies
a real-world application of the Tiebout’s idealized model.

worst. Regarding the municipal service of street repairs, sixty five percent of homeowners across the
country reported that no repair work was needed for roads within half block of their home.
93
Empirical tests on the AHS 2005 National data demonstrate that from the
national perspective, privatization of neighborhoods improves homeowners’ degrees
of satisfaction with their neighborhoods and the service of street repairs, regardless
of homeowners’ household income level.
Income also matters in terms of homeowners’ valuation of housing units
in private HOAs. For southern California, the empirical findings show that above-
median-income homeowners paid a premium for living in private HOAs, while
below-median-income homeowners did not pay a significant premium for HOA
housing units, controlling for the influences of other hedonic variables. In addition,
there was no significant negative capitalization of HOA assessments into home
values when the above-median-income homeowners paid for their homes in private
HOAs. On the other hand, the below-median-income people significantly discounted
the HOA monthly fees into their home prices. The negative capitalization of HOA
assessments translated into a lower purchase price.
In southern California, traditional public local governments and private
HOAs are not substitutes but rather complements. The empirical test results from
my survey data in southern California show that private suppliers in incorporated
areas were preferred to private suppliers in unincorporated areas. A private HOAs in
incorporated city was the highest rated local governance form, especially for the
below-median-income homeowners.
It is sound policy practice to give homeowners more choices in the menu of
municipal service providers. Many Americans say they want less government at the
94
national or state level, but in their neighborhoods they seem to want more
governance. Private homes constitute nearly all of most people’s nonretirement
assets. Homeowners can insure their homes against fire or theft, but they cannot
insure their home values from negative externalities.
38
Public local governance and
private contractual governance are thought to be effective ways of managing
neighborhood commons externality problems. The most widely used way to protect
residential home values is to choose a favorable municipal arrangement, other things
equal. In this way, homeowners “vote with their feet,” moving to communities that
provides them with the preferred mix of home prices, property taxes (and fees) and
municipal services to mitigate the uninsurable risks of their homes.
In this dissertation I asked the questions: to what extent are homeowners
satisfied with the municipal choices they have made, and does the interaction of
privatization and income affect homeowners’ choice of local governance forms? The
empirical evidence suggests that the recent rapid growth of the local governance
form of private HOAs is a new choice in homeowners’ menu of municipal service
providers and that it is particularly a better choice for below-median-income
homeowners in southern California. In terms of the supply of local public goods, the
power of an elaborated Tiebout model is revealed.
I want to end this dissertation with a recent poignant news story. The Los
Angeles Times on January 17, 2008 reported that “only three blocks from the
Imperial Courts public housing project, along a stretch of land once used as a

38
Examples of negative externalities are crime, noise, eroded streets, etc.
95
neighborhood dump, 44 homes [priced from the mid-$400,000s] are rising in Watts.”
Watts has struggled for decades to overcome grinding poverty, crime and a
dangerous reputation of gangster activities. Annual median household incomes in the
community ranged from $12,000 to $25,000 as of the 2000 Census - well below the
city’s median of nearly $37,000. The new development is typically a private
common-interest development in a poor neighborhood. As the Los Angeles Times
reported, “Lee, the developer, said he remained confident that people will buy the
homes, repeating a pattern in Boyle Heights, Bell Gardens, Huntington Park and
Commerce - other low-income areas where the company has built subdivisions.”
Lee’s past developments in low-income neighborhoods were favorably accepted in
the marketplace, which could be attributed to the combination of proper home prices
and relatively nice neighborhood (including amenities and municipal services).
Whether this is a new successful development model or not remains to be tested by
the market.
96
REFERENCES
Agan, A., and A. Tabarrok (2005). “What Are Private Governments Worth?”.
Regulation, Vol. 28, No. 3 (Fall, 2005), pp. 14-17

American Institute of Planners (1976). “Survey of State Land Use Planning
Activity”. Report to the U.S. Department of Housing and Urban
Development. Washington, DC

Beito, D., P. Gordon, and A. Tabarrok (2002). The Voluntary City: Choice,
Community, and Civil Society. Ann Arbor, MI: University of Michigan

Buchanan, J. M. (1965). “An Economic Theory of Clubs”. Economica, New Series,
Vol. 32, No. 125 (Feb., 1965), pp. 1-14

Dilger, R. (1992). Neighborhood Politics: Residential Community Associations and
American Governance. New York: New York University

Eagle, S. J. (1999). “Privatizing Urban Land Use Regulation: The Problem of
Consent”. 7 George Mason Law Review, 905-921 (1999)

Ellickson, R. (1998). “New Institutions for Old Neighborhoods”. Volume 48, No. 1
of the Duke Law Journal, 75-110 (1998)

Fischel, W. A. (2001). The Homevoter Hypothesis: How Home Values Influence
Local Government Taxation, School Finance, and Land-Use Policies.
Cambridge, MA: Harvard University Press

Fischel, W. A. (2004). “Revolution or Evolution? Do Homeowners Association
Members Benefit from – or Even Want – the Dissolution of Municipalities
and Local Zoning”. Regulation, Summer 2004

Gordon, T. M. (2004). “Planned Developments in California: Private Communities
and Public Life”. Public Policy Institute of California, 2004

Korngold, G. (2001). “The Emergence of Private Land Use Controls in Large-Scale
Subdivisions: The Companion Story to Village of Euclid v. Ambler Realty
Co.”. Case Western Reserve Law Review v51 no4 p617-43 Summ 2001

LaCour-Little, M., and S. Malpezzi (2001). “Gated Communities and Property
Values”. Wisconsin-Madison CULER working paper

97
Lo, C. Y. H. (1990). Small Property versus Big Government: Social Origins of the
Property Tax Revolt. Berkeley: University of California Press

Malpezzi, S. (1996). “Housing Prices, Externalities, and Regulation in U.S.
Metropolitan Areas”. Journal of Housing Research, Volume 7, Issue 2, 209-
241

McKenzie, E. (1994). Privatopia: Homeowner Associations and the Rise of
Residential Private Government. New Haven, CT, and London: Yale
University Press

McKenzie, E. (1998). “Homeowner Associations and California Politics: An
Exploratory Analysis”. Urban Affairs Review, 34(1): 52-75

McKenzie, E. (2006). “The Dynamics of Privatopia: Private Residential Governance
in the USA”. Private Cities: Global and Local Perspectives, edited by
George Glasze, Chris Webster, and Klaus Frantz. Abingdon, Oxon:
Routledge

National Multi Housing Council (1982). “Rent Control Activities through May 31,
1982”. Washington, DC

Nelson, R. H. (1999). “Privatizing the Neighborhood: A Proposal to Replace Zoning
with Private Collective Property Rights to Existing Neighborhoods”. George
Mason Law Review, Volume 7, Summer 1999

Nelson, R. H. (2004). “The Private Neighborhood: Will Homeowners Associations
Lead to A Revolution in Local Government?”. Regulation, Summer 2004

Nelson, R. H. (2005). The Private Neighborhood and the Transformation of Local
Government, Washington, D.C.: The Urban Institute Press, 2005

Nelson, R. H. (2006a). “Welcome to the New – and Private – Neighborhood: Local
Government in a World of Postmodern Pluralism”. Reason, Volume 37, No.
11, April 2006

Nelson, R. H. (2006b). “Postmodern Politics in Action: Ten Signposts, Past and
Present, to the Coming Decentralized Political Order”. Reason, Volume 37,
No. 11, April 2006

Nozick, R. (1974). Anarchy and Utopia. New York: Basic Books

98
Reich, R. B. (1991). “Secession of the Successful”. New York Times, January 20,
1991, Section 6, p. 16

Rybczynski, Witold (2007). Last Harvest: From Cornfield to New Town. New York:
Simon & Schuster, Inc.

Samuelson, P. A. (1954). “The Pure Theory of Public Expenditures”. Review of
Economics and Statistics, 36 (November): 387-389

Tiebout, C. M. (1956). “A Pure Theory of Local Expenditures”. Journal of Political
Economy, 64 (October): 416-424

Webster, C, and L. Lai (2001). Property Rights, Planning and Markets: Managing
Spontaneous Cities. Edward Elgar

Weiss, M. A. (1987). The Rise of the Community Builders: The American Real
Estate Industry and Urban Land Planning. New York: Columbia University
Press
99
APPENDIX I: SURVEY QUESTIONNAIRE
Dear Homeowner:
Hello, my name is Huanghai Li, graduate student at the School of Policy,
Planning and Development at University of Southern California. I am doing
research on the housing market in southern California under Professor Peter
Gordon’s supervising (http://www-rcf.usc.edu/~pgordon). Your responses to the
following questions would greatly help me in my work. None of the questions are of
a personal nature but all will be kept in confidence anyway.
There are only nine questions in this questionnaire, which won’t take more
than 5 minutes to complete. You may skip any question/questions if you feel
uncomfortable to answer. Your support of my research is greatly appreciated! If you
respond to the questionnaire, a $2 reward will be mailed to you. Two dollars is
trivial, but it is a sincere “THANK YOU” from a student.

Yours truly,
Huanghai Li
November 2007
100
Questionnaire

Section I: The Residence

Question #1:

Number of bedrooms [ ]
Number of bathrooms [ ]
Lot size (square foot) [ ]
Area (square foot) [ ]

Question #2: Is this residence in a gated community?
□ Yes
□ No

Section II: Information about Homeowners’ Association
1

Question #3: Are you a member of a homeowners' association?
□ Yes
□ No

Question #4: If yes, is membership required at this address?
□ Yes
□ No

If you answered No to either Question #3 or #4, please skip Question #5 and #6, and
go directly to Section III. Thank you.

Question #5: If you answered Yes to either Question #3 or #4, in what range is your
monthly membership assessment today?
□ less than $30
□ $30 - $79
□ $80 - $149
□ $150 - $199
□ $200 - $299
□ $300 - $399
□ $400 - $499
□ above $500
101

Question #6a: If you answered Yes to either Question #3 or #4, over the last five
years, have there been special assessments
2
at this property?
□ Yes
□ No

Question #6b: If you answered Yes to Question #6a, has the total of special
assessments paid by you over the last five years exceeded $1,000?
□ Yes
□ No

Section III: Municipal Services

Question #7: Please look at the list of services below and check whether they are
provided by the local city, the county, the homeowners’ association (if you live in
one), the special district
3
, or yourself (homeowner).

Municipal Services City County Homeowners’
Association
Special
District
Home
Owner
Not
sure
Trash Collection
□ □ □ □ □ □
Gardening
□ □ □ □ □ □
Street cleaning
□ □ □ □ □ □
Street lighting and repair
□ □ □ □ □ □
Security
□ □ □ □ □ □
Painting/outside
maintenance
□ □ □ □ □ □
Parking lot repair
□ □ □ □ □ □
Gates or fences
□ □ □ □ □ □
Landscaping
□ □ □ □ □ □
Recreation facilities, like
gym
□ □ □ □ □ □
Indoor community center
□ □ □ □ □ □
Swimming pools/tennis
courts
□ □ □ □ □ □
Lake or beach
□ □ □ □ □ □
Playground/tot lot
□ □ □ □ □ □
Tree, Lawn care in
common areas
□ □ □ □ □ □
Water or sewer
□ □ □ □ □ □

102
Question #8: Please indicate your level of satisfaction with these services, on a scale
of from 1 to 10, with 10 being Excellent and 1 being Unacceptable.

Municipal Services Degree of Satisfaction
Trash Collection [ ]
Gardening [ ]
Street cleaning [ ]
Street lighting and repair [ ]
Security [ ]
Painting/outside maintenance [ ]
Parking lot repair [ ]
Gates or fences [ ]
Landscaping [ ]
Recreation facilities, such as gym [ ]
Indoor community center [ ]
Swimming pools/tennis courts [ ]
Lake or beach [ ]
Playground/tot lot [ ]
Tree, lawn care in common areas [ ]
Water or sewer [ ]

Section IV: Property Tax
Question #9: In what range was your most recent annual property tax bill for this
property?
□ less than $500
□ $500 - $999
□ $1,000 - $1,499
□ $1,500 - $2,999
□ $3,000 - $4,999
□ $5,000 - $7,999
□ over $8,000

Thank you for your great help!
If you have any question, please contact me at: huanghal@usc.edu (email), or PO
BOX 7386, Los Angeles, CA 90007 (postal mail).
Please allow 4 - 8 weeks to process the reward.

103
Notes:
1
For your information, a homeowners’ association (HOA) is an organization
comprised of all owners of units in a housing development, and often is governed by
a board. Homeowners’ associations collect fees, fines, and other assessments from
homeowners, maintain the common areas of the development, and enforce the
association's governing documents (the so-called CC&R, i.e., Conditions, Covenant
and Restrictions). These may include detailed rules regarding construction and
maintenance of individual homes. The common areas maintained and governed may
include landscaping, common buildings (e.g., clubhouses) and recreational facilities
such as swimming pools, common walls in attached housing developments, and
infrastructure such as streets, mailboxes, sidewalks, and parking lots. Nevertheless,
only owners -- who need not be residents -- are allowed to vote in elections to choose
the board. Residents of the community who are not owners (e.g., renters) do not
typically receive a vote.

2
Often, a homeowners’ association collects special assessments from all its members
in addition to regular monthly fees. Assessments can be made to cover legal
expenses for a judgement against the homeowners’ association, to repair damage
from a natural disaster, or to make improvements.

3
Special districts are a form of local government created by a local community to
meet a specific need. Inadequate tax bases and competing demands for existing taxes
make it hard for cities and counties to provide all the services their citizens desire.
When residents or landowners want new services or higher levels of existing
services, they can form a district to pay for and administer them.

I will use this series number to track your home address: 204968
104
APPENDIX II: EMPIRICAL TESTS ON THE 2005 AMERICAN HOUSING
SURVEY NATIONAL DATA
I also carried out the same empirical tests on the 2005 American Housing
Survey (AHS) National data as I did on my own survey data in southern California.
The AHS collects data on the Nation’s housing, including apartments, single-family
homes, mobile homes, and vacant housing units. The survey covers many topics,
including household characteristics, income, housing and neighborhood quality,
housing costs, equipment and fuels, size of housing unit, etc. National data are
collected in odd numbered years, and covers about 55,000 housing units. I used the
most recent 2005 National data.
Construction of the AHS sample for this research
To utilize the AHS data source, I first of all needed to identify whether a
surveyed home belonged to an HOA or not. The following question asked by AHS
helped.
IFFEE =
Condo/co-op/assoc/mobile home park fee required

Long description:
Is there a required condo/co-op association fee?
Do you pay any required mobile home park fee?
Are there any required mobile home park fee?
Is there a required homeowner’s association fee?

The responses to this question take the following values:
105
IFFEE Interpretation
1 Yes
2 No
B Not applicable
D Don't Know
R Refused

I only considered homeowners that answered Yes or No to this question, in
order to get rid of the noise caused by the answer “B”, “D”, or “R” to this question.
The 2005 AHS National data includes 69,020 observations of homes
nationwide. I created an AHS sample for my research purpose using the following
filter:
a. Consider single-family residential housing units only, and exclude
condominiums, co-ops, mobile and manufactured homes.
39

b. Consider only homes of two structure types: one-unit building detached from
any other building, or one-unit building attached to one or more buildings.
c. Consider only homeowners that answered Yes or No to the Question
“Condo/co-op/assoc/mobile home park fee required?”.
d. Consider only homeowners that reported their household income.
After this filtering, the sample size was down to 24,928. I simply refer to this sample
as the AHS sample.

39
This is consistent with the construction of my own sample.
106
Table A1 and A2 show some frequency distributions of the AHS sample.

Table A1: Percentage of HOA housing units

HOA Frequency Percent
Cumulative
Frequency
Cumulative
Frequency
0 21,575 86.55 21,575 86.55
1 3,353 13.45 24,928 100
Total 24,928

Table A2: Distribution of structure type

Structure Type Frequency Percent
Cumulative
Frequency
Cumulative
Frequency
One-unit building, detached
from any other building
23,960 96.12 23,960 96.12
One-unit building, attached to
one or more buildings
968 3.88 24,928 100
Total 24,928

To give readers some idea about the AHS sample, Table A3 shows some
summary statistics for the U.S. and southern California in the year of 2005.

Table A3: Comparisons between the U.S. and southern California

U.S. Southern California
Population** 288,378,137 16,914,762
PMSAs of 1980 design * 145 4
AHS 2005 Sample Size* 24,928 761
Median Household Income* 58,086 71,445
Median House Value* 173,000 520,000
* Estimates from American Housing Survey National 2005
** Population estimates from American Community Survey 2005

107
Variables for two-sample nonparametric tests
I used two measurable key variables related to homeowners’ degree of
satisfaction with their municipal services. One, “Rating of neighborhood” (variable
name: HOWN), was taken directly from AHS.
HOWN =
Rating of neighborhood as place to live
0 No neighborhood
1:10 Rating (10 is best, 1 is worst)
B Not applicable
D Don't Know
R Refused
. Not reported

Long description:
How would you rate your neighborhood on a scale of 1-10?

The values of this variable takes from 1 to 10 (1 is worst, and 10 is best). The
frequency distribution of this rating is shown in Table A4.

Table A4: Distribution of “Rating of neighborhood as place to live” in the AHS
sample

Rating of
Neighborhood
Frequency Percent
Cumulative
Frequency
Cumulative
Frequency
1 88 0.36 88 0.36
2 91 0.37 179 0.73
3 146 0.6 325 1.33
4 237 0.97 562 2.3
5 1,068 4.37 1,630 6.67
6 1,174 4.8 2,804 11.47
7 3,082 12.61 5,886 24.08
8 6,886 28.17 12,772 52.24
9 4,531 18.53 17,303 70.77
10 7,145 29.23 24,448 100
Missing 480
Total 24,928

108
It appears that most homeowners across the country were satisfied with their
neighborhood:
• Seventy six percent of the homeowners surveyed by AHS in 2005 gave
overall ratings about their neighborhood equal to or higher than 8.
• Eighty nine percent of the homeowners rated their neighborhood equal to
or higher than 7. The rating distribution is skewed to the right, i.e., higher
satisfaction.
This is consistent with the finding from my sample.
The other key variable “Street Repair” was constructed from the AHS
variable EROAD.
EROAD =
Roads within 1/2 block need repairs
1 Major repair work
2 Minor repair work
3 No repair work
4 No streets within half a block
B Not applicable
D Don't Know
R Refused
Blank Not reported

Long description:
What is the condition of the streets within half a block of this building?
Do these streets need major repairs, minor repairs or no repair work?

I constructed the new variable “Street Repair” using the scheme in Table A5. The
distribution of this variable is shown in Table A6.
109
Table A5: Construction scheme for the variable “Street Repair”

Variable Score Interpretation
Street
Repair
4 Homeowner answers "No repair work" to "Roads within 1/2 block
need repairs?"
3 Homeowner answers "Minor repair work" to "Roads within 1/2 block
need repairs?"
2 Homeowner answers "Major repair work" to "Roads within 1/2 block
need repairs?"

Table A6: Distribution of “Street repairs” in the AHS sample

Answer
Street Repairs
Rating
Frequency Percent
Cumulative
Frequency
Cumulative
Frequency
Major repair work 2 1,284 5.27 1,284 5.27
Minor repair work 3 7,160 29.37 8,444 34.63
No repair work 4 15,938 65.37 24,382 100
Missing 546
Total 24,928

Again, most homeowners across the country were satisfied with the
municipal service of street repairs. Sixty-five percent of the surveyed homeowners
reported that no repair work was needed for roads within 1/2 block of their home.
Only five percent of the surveyed homeowners reported that major repair work was
needed for roads within 1/2 block of their residence.

110
Data for multivariate regressions
I only ran hedonic regressions on the southern California sub-sample of the
AHS data, because there is too much geographic heterogeneity of the homes across
the country. To construct the AHS southern California sub-sample, I applied the
following finer filter to the AHS sample:
a. Select from the AHS sample only those observations in the four PMSAs in
southern California, which approximately match my sample in the five local
counties.
b. The self-reported current market value of unit ≥ 0.
In the AHS southern California sub-sample, the percentage of HOA units was
small, 7.62 percent (Table A7), probably because there were many condominium
HOA units in southern California and these units were excluded to form the sample.

Table A7: Distribution of HOA and non-HOA homes of the AHS southern
California sub-sample

hoa Frequency Percent
Cumulative
Frequency
Cumulative
Frequency
HOA 58 7.62 58 100
non-HOA 703 92.38 761 100

The average HOA fee rate was 0.18 percent in the AHS southern California
sub-sample (Table A8), which is lower than the average HOA fee rate (0.29 percent)
in my sample. This should be expected, because in my sample, the HOA fees were
111
for the year of 2007 while the home transaction values were recorded in the year of
2001. Generally speaking, the AHS southern California sub-sample data is
consistent with my survey sample data. Table A9 shows some summary statistics
and correlation matrix for some other variables in the AHS southern California sub-
sample.

Table A8: Annualized monthly assessment fees relative to property value, where the
reported assessment fee is greater than zero.

FeeRate
# of Obs 56
Min 0.004
25% Percentile 0.059
Median 0.127
Mean 0.181
75% Percentile 0.227
Max 0.703
Std Deviation 0.167
unit: percent

Table A9: Summary statistics and correlation matrix of some other variables

Statistic LOT BathRms BedRms taxR %
MEAN 15,701 2.05 3.27 0.51
STD 60,688 0.77 0.88 0.49
# of Obs 761 761 761 730
Corr Matrix LOT BathRms BedRms taxR
LOT 1
BathRms 0.181 1
BedRms 0.138 0.612 1
taxR 0.002 0.082 0.016 1

Note: The coefficients of correlation in the matrix are Spearman’s (nonparametric)
rank correlations.
112
I also conducted some two-sample t tests between non-HOA and HOA on
several key variables (Table A10). There was no significant difference of the lot size
or property tax between non-HOA and HOA homes. However, in the AHS southern
California sub-sample, HOA homes were in general valued significantly more than
non-HOA homes.

Table A10: t tests between non-HOA and HOA homes in the AHS southern
California sub-sample

Mean # of Obs Mean # of Obs Mean # of Obs
non-HOA 628,121 703 15,931 703 0.5012 673
HOA 879,557 58 12,903 58 0.6096 57
t Value
Pr > |t|
DF
Variances
Method Pooled Satterthwaite
<.0001 0.4977
VALUE LOT
-4.45 0.68
Unequal Equal
759 109
Unequal
Satterthwaite
taxR %
-1.16
0.2524
60.3

Test results and discussion
The settings of the two-sample nonparametric tests and multivariate
regressions for the AHS data are basically the same as for my survey data. However,
it is worthwhile to point out the following differences between the two data sets:
First, most importantly, the variable Home Value in the AHS data is not
transaction value, but rather householders’ self-reported “current market value of
113
unit”. We should expect some estimation error associated with this variable when
interpreting the regression results.
Second, I partitioned the AHS sample into more income groups (that is
twelve groups), because there is much more homeowners heterogeneity across the
country and at the same time the AHS sample has a lot more observations.
Third, the AHS data do not provide detailed information about the place (city
or unincorporated area) of each surveyed homes to protect privacy. Therefore, I was
not able to construct the place dummies or the interaction terms for regressions as I
did with my sample data.

• Two-sample nonparametric tests results (Table A11)
Across the country in the year of 2005, homeowners living in HOAs were
consistently and mostly significantly (at the 10 percent level) more satisfied with
their neighborhoods and the municipal service of street repairs, in all twelve income
groups. The signs were universally positive for the HOA test sample in all the tests.
Twenty one out of all the twenty four tests indicated that homeowners living in
HOAs were significantly more satisfied than their non-HOA counterparties.
114
Table A11: Two-sample test results of the AHS data

Household
Income Mean P-value # of Obs Mean P-value # of Obs
$120K and up non-HOA 8.465 2706 3.638 2709
HOA 8.648 0.0096 901 3.814 0.0000 898
[$100K - 120K) non-HOA 8.301 1573 3.610 1568
HOA 8.578 0.0026 374 3.787 0.0000 371
[$80K - 100K) non-HOA 8.271 2194 3.598 2193
HOA 8.414 0.1574 473 3.789 0.0000 475
[$60K - 80K) non-HOA 8.189 3333 3.591 3322
HOA 8.492 0.0003 506 3.782 0.0000 505
[$50K - 60K) non-HOA 8.237 1885 3.573 1878
HOA 8.512 0.0059 256 3.760 0.0000 258
[$40K - 50K) non-HOA 8.159 2031 3.564 2031
HOA 8.788 0.0000 231 3.748 0.0000 230
[$35K - 40K) non-HOA 8.206 1010 3.546 1003
HOA 8.624 0.0068 101 3.707 0.0047 99
[$30K - 35K) non-HOA 8.222 1069 3.549 1059
HOA 8.362 0.4264 94 3.787 0.0002 94
[$25K - 30K) non-HOA 8.170 1025 3.520 1023
HOA 8.871 0.0003 93 3.696 0.0119 92
[$20K - 25K) non-HOA 8.250 1035 3.532 1024
HOA 8.651 0.2098 83 3.682 0.0636 85
[$10K - 20K) non-HOA 8.251 1926 3.518 1918
HOA 8.686 0.0055 121 3.754 0.0001 122
Below $10K non-HOA 8.231 1336 3.509 1331
HOA 8.880 0.0025 92 3.819 0.0000 94
Rating of Neighborhood Street Repairs

I concluded from the results of the two-sample tests on the 2005 AHS
national sample that privatization of neighborhood did improve homeowners’ degree
of satisfaction with their neighborhoods and street repair services, regardless of
homeowners’ income level.
115
• Multivariate regressions results
There was no data for the variable of “distance to the coast” in the AHS
southern California sub-sample, because there was no detailed address of each
surveyed home in the AHS data. Therefore, in the two hedonic regressions that
follow (Table A12 and A13), the basic hedonic variables are log of Lot size, Number
of bathrooms, and Number of bedrooms.

Table A12: Hedonic regression with basic hedonic variables, HOA dummy and
property tax rate on the AHS southern California sub-sample

Dependent logValue logValue logValue
All Obs. Above-Median-Income Below-Median-Income
Adj. R2 0.2187 0.2025 0.1836
# of Obs. 730 366 364
Intercept 12.696 12.784 12.812
(50) (37.46) (35.23)
logLot -0.016 0.013 -0.039
(-0.57) (0.35) (-0.98)
Bathrms 0.307 0.296 0.261
(8.97) (6.86) (4.99)
BedRms 0.039 -0.006 0.052
(1.33) (-0.16) (1.2)
HOA Dummy 0.206 0.080 0.370
(2.71) (0.91) (2.91)
taxR -0.244 -0.326 -0.215
(-5.94) (-5.21) (-4.03)

116
Table A13: Hedonic regression with basic hedonic variables, HOA fee rate and
property tax rate on the AHS southern California sub-sample

Dependent logValue logValue logValue
All Obs. Above-Median-Income Below-Median-Income
Adj. R2 0.2112 0.2013 0.1668
# of Obs. 730 366 364
Intercept 12.722 12.779 12.867
(49.85) (37.38) (34.76)
logLot -0.020 0.013 -0.048
(-0.72) (0.36) (-1.19)
Bathrms 0.321 0.304 0.286
(9.43) (7.1) (5.45)
BedRms 0.037 -0.008 0.051
(1.28) (-0.22) (1.17)
FeeRate 0.192 -0.190 0.504
(0.62) (-0.5) (1.05)
taxR -0.243 -0.324 -0.217
(-5.85) (-5.16) (-3.98)

The first observation from Table A12 and A13 is that the variable “log of Lot
Size” was not significant and had the wrong (negative) sign. This contradicts both
the literature on housing prices hedonic analysis and the hedonic regression results
from my sample data. This problem is probably due to the inaccurate estimation of
the “current market value of unit”, which was self-reported by householders.
Therefore, we should be cautious when interpreting the regression results from the
AHS data.
117
Table A12 indicates that the housing market in southern California priced a
premium for HOA units, for all observations in the sample and for the below-
median-income group.
Table A13 shows that the property tax was significantly negatively
capitalized into home values, consistent with the literature on housing price hedonic
analysis. However, there was no capitalization of the HOA assessments into home
values, which again contradicts both the literature and the research results from my
survey sample.
The takeaway from the empirical tests on 2005 American Housing Survey
National data is that from the national perspective, privatization of neighborhoods
improves homeowners’ degree of satisfaction with their neighborhoods and the street
repairs service, regardless of homeowners’ household income level.
118
APPENDIX III: ADDITIONAL TWO-SAMPLE NONPARAMETRIC TESTS ON
MY DATA SAMPLE (TABLE A15-A22)
In this appendix, I include some additional two-sample nonparametric tests
on my sample according to the partition of High versus Low home value
(terminologies defined in Table A14), with the combination of other partitions. The
definition of the other partitions of place, income and HOA status are defined in
Table 4 in the second section.

Table A14: Definition for High and Low Home Value

Terminology Definition
High Home Value Home Value (Census 2000) > $240K
Low Home Value Home Value (Census 2000) <= $240K

119
Table A15: Given high home values and HOA status, comparing big city vs.
homevoter city responses

Mean P-value # of Obs Mean P-value # of Obs
Big City 8.71 0.5806 45 8.67 0.5852 33
Homevoter City 8.49 35 8.55 42
Big City 7.70 23 8.09 0.8116 22
Homevoter City 8.00 0.6170 22 7.89 38
Big City 8.08 40 7.69 29
Homevoter City 8.47 0.4307 36 8.08 0.4070 39
Big City 8.33 39 7.90 30
Homevoter City 8.34 0.8644 35 8.14 0.7686 37
Big City 8.00 18 7.21 19
Homevoter City 8.30 0.6259 20 7.50 0.4400 28
Big City 7.67 12 7.65 0.6372 20
Homevoter City 7.94 0.5939 17 7.13 30
Big City 6.33 3 7.50 14
Homevoter City 8.13 0.1667 15 7.71 0.7667 24
Big City 7.55 11 7.42 0.8619 19
Homevoter City 8.20 0.3833 15 7.07 30
Big City 7.63 16 7.17 23
Homevoter City 7.80 0.8187 20 7.50 0.6990 34
Big City 7.17 6 7.50 0.3283 16
Homevoter City 7.82 0.4883 11 6.69 13
Big City 7.75 4 7.73 0.3630 11
Homevoter City 7.93 0.8529 14 6.77 13
Big City 7.63 8 7.69 0.7559 13
Homevoter City 8.00 0.6055 16 7.69 0.7559 26
Big City 7.00 8 7.75 0.8516 8
Homevoter City 8.85 0.1196 13 7.71 7
Big City 8.55 0.7010 11 7.50 12
Homevoter City 8.53 17 8.33 0.3032 12
Big City 7.89 18 7.26 23
Homevoter City 8.16 0.6023 19 7.49 0.6736 35
Big City 8.56 39 8.00 29
Homevoter City 8.61 0.9892 31 8.22 0.4689 36
High Home Value HOA High Home Value non-HOA
Lake or beach
Playground/tot lot
Security
Painting/outside
maintenance
Parking lot repair
Gates or fences
Trash Collection
Gardening
Street cleaning
Street lighting and
repair
Tree, Lawn care in
common areas
Water or sewer
Landscaping
Recreation
facilities, like gym
Indoor community
center
Swimming
pools/tennis courts

120
Table A16: Given high home value and HOA status, comparing big city vs.
unincorporated area responses

Mean P-value # of Obs Mean P-value # of Obs
Big City 8.71 45 8.67 0.0908 33
Unincorporated 8.88 0.9983 8 7.56 9
Big City 7.70 23 8.09 0.0194 22
Unincorporated 8.75 0.1769 8 6.11 9
Big City 8.08 40 7.69 0.9521 29
Unincorporated 8.25 0.7824 8 7.11 9
Big City 8.33 0.8392 39 7.90 0.2872 30
Unincorporated 8.13 8 6.63 8
Big City 8.00 18 7.21 0.2588 19
Unincorporated 8.17 0.9913 6 5.88 8
Big City 7.67 12 7.65 0.3924 20
Unincorporated 8.00 0.6864 6 6.75 8
Big City 6.33 3 7.50 0.6022 14
Unincorporated 8.50 0.2000 4 6.67 6
Big City 7.55 11 7.42 0.6122 19
Unincorporated 9.75 0.0535 4 6.29 7
Big City 7.63 16 7.17 0.5497 23
Unincorporated 8.57 0.3941 7 6.56 9
Big City 7.17 0.1548 6 7.50 16
Unincorporated 5.33 3 8.00 1.0000 2
Big City 7.75 0.0667 4 7.73 0.5165 11
Unincorporated 5.00 2 6.33 3
Big City 7.63 8 7.69 0.8428 13
Unincorporated 7.67 0.9152 3 7.40 5
Big City 7.00 8 7.75 0.7778 8
Unincorporated 8.00 1.0000 1 6.00 1
Big City 8.55 0.7005 11 7.50 0.1099 12
Unincorporated 8.00 3 4.00 2
Big City 7.89 18 7.26 0.1584 23
Unincorporated 8.00 0.8212 5 5.75 8
Big City 8.56 0.7948 39 8.00 0.2443 29
Unincorporated 7.86 7 6.75 8
Street cleaning
Street lighting and
repair
Tree, Lawn care in
common areas
Water or sewer
Landscaping
Recreation
facilities, like gym
Indoor community
center
Swimming
pools/tennis courts
High Home Value non-HOA High Home Value HOA
Lake or beach
Playground/tot lot
Security
Painting/outside
maintenance
Parking lot repair
Gates or fences
Trash Collection
Gardening

121
Table A17: Given high home value and HOA status, compare homevoter city vs.
unincorporated area responses

Mean P-value # of Obs Mean P-value # of Obs
Homevoter City 8.49 35 8.55 0.1812 42
Unincorporated 8.88 0.6351 8 7.56 9
Homevoter City 8.00 22 7.89 0.0196 38
Unincorporated 8.75 0.2373 8 6.11 9
Homevoter City 8.47 0.9664 36 8.08 0.7105 39
Unincorporated 8.25 8 7.11 9
Homevoter City 8.34 0.9157 35 8.14 0.2304 37
Unincorporated 8.13 8 6.63 8
Homevoter City 8.30 0.7640 20 7.50 0.1176 28
Unincorporated 8.17 6 5.88 8
Homevoter City 7.94 17 7.13 0.6776 30
Unincorporated 8.00 0.8468 6 6.75 8
Homevoter City 8.13 15 7.71 0.3581 24
Unincorporated 8.50 0.7128 4 6.67 6
Homevoter City 8.20 15 7.07 0.6031 30
Unincorporated 9.75 0.1246 4 6.29 7
Homevoter City 7.80 20 7.50 0.3873 34
Unincorporated 8.57 0.2573 7 6.56 9
Homevoter City 7.82 0.0137 11 6.69 13
Unincorporated 5.33 3 8.00 0.4952 2
Homevoter City 7.93 0.0500 14 6.77 0.9679 13
Unincorporated 5.00 2 6.33 3
Homevoter City 8.00 0.8576 16 7.69 0.9885 26
Unincorporated 7.67 3 7.40 5
Homevoter City 8.85 0.9286 13 7.71 0.7500 7
Unincorporated 8.00 1 6.00 1
Homevoter City 8.53 0.7789 17 8.33 0.0220 12
Unincorporated 8.00 3 4.00 2
Homevoter City 8.16 0.9303 19 7.49 0.0477 35
Unincorporated 8.00 5 5.75 8
Homevoter City 8.61 0.9313 31 8.22 0.1051 36
Unincorporated 7.86 7 6.75 8
High Home Value HOA High Home Value non-HOA
Lake or beach
Playground/tot lot
Tree, Lawn care in
common areas
Water or sewer
Landscaping
Recreation
facilities, like gym
Indoor community
center
Swimming
pools/tennis courts
Security
Painting/outside
maintenance
Parking lot repair
Gates or fences
Trash Collection
Gardening
Street cleaning
Street lighting and
repair

122
Table A18: Given low home values and HOA status, compare big city vs. homevoter
city responses

Mean P-value # of Obs Mean P-value # of Obs
Big City 8.75 0.1296 51 8.25 40
Homevoter City 8.17 29 8.97 0.1872 39
Big City 8.08 0.4574 24 7.32 34
Homevoter City 7.47 17 8.28 0.0511 36
Big City 7.88 0.0973 50 7.68 37
Homevoter City 7.34 29 8.18 0.4789 38
Big City 8.24 0.0358 49 7.55 38
Homevoter City 7.26 27 8.41 0.1548 34
Big City 7.00 24 5.72 29
Homevoter City 8.25 0.2598 12 7.45 0.0117 29
Big City 7.94 17 6.94 34
Homevoter City 8.00 0.8350 12 7.93 0.1737 27
Big City 7.38 1.0000 13 6.94 34
Homevoter City 7.20 10 8.31 0.0157 29
Big City 7.12 17 6.97 31
Homevoter City 7.45 0.7573 11 8.06 0.0786 33
Big City 7.50 22 7.14 35
Homevoter City 8.00 0.7014 14 8.11 0.1561 35
Big City 7.75 0.4474 16 5.86 14
Homevoter City 6.90 10 7.07 0.2640 14
Big City 7.69 0.5892 16 5.09 11
Homevoter City 7.20 10 6.54 0.4244 13
Big City 8.12 0.1034 17 6.61 28
Homevoter City 6.75 12 7.74 0.0705 23
Big City 7.17 0.4962 18 6.00 8
Homevoter City 6.67 9 6.17 0.8272 6
Big City 7.65 0.5489 17 6.40 10
Homevoter City 7.25 12 6.92 0.9352 12
Big City 7.54 0.5351 26 6.92 36
Homevoter City 7.00 12 7.83 0.1962 35
Big City 8.54 0.4313 46 8.25 36
Homevoter City 8.11 27 8.91 0.2362 34
Street cleaning
Street lighting and
repair
Tree, Lawn care in
common areas
Water or sewer
Landscaping
Recreation
facilities, like gym
Indoor community
center
Swimming
pools/tennis courts
Low Home Value HOA Low Home Value non-HOA
Lake or beach
Playground/tot lot
Security
Painting/outside
maintenance
Parking lot repair
Gates or fences
Trash Collection
Gardening

123
Table A19: Given low home value and HOA status, compare big city vs.
unincorporated area responses

Mean P-value # of Obs Mean P-value # of Obs
Big City 8.75 0.1119 51 8.25 40
Unincorporated 8.00 26 8.33 0.8970 39
Big City 8.08 0.8387 24 7.32 34
Unincorporated 8.00 23 8.24 0.1191 37
Big City 7.88 0.0041 50 7.68 0.2731 37
Unincorporated 6.13 23 7.17 35
Big City 8.24 0.0110 49 7.55 0.3159 38
Unincorporated 6.70 23 7.22 37
Big City 7.00 0.9860 24 5.72 29
Unincorporated 7.00 0.9860 20 6.36 0.3748 33
Big City 7.94 0.5656 17 6.94 34
Unincorporated 7.26 19 6.97 0.7889 33
Big City 7.38 0.4360 13 6.94 34
Unincorporated 6.17 12 7.00 0.7520 36
Big City 7.12 17 6.97 0.9469 31
Unincorporated 7.39 0.6962 18 6.92 36
Big City 7.50 22 7.14 35
Unincorporated 7.58 0.9946 19 8.19 0.1339 37
Big City 7.75 0.2271 16 5.86 14
Unincorporated 6.18 11 7.24 0.1840 21
Big City 7.69 0.2771 16 5.09 11
Unincorporated 6.27 11 7.36 0.1205 22
Big City 8.12 0.0645 17 6.61 28
Unincorporated 6.09 11 7.61 0.0815 31
Big City 7.17 0.5232 18 6.00 8
Unincorporated 5.50 6 6.89 0.6117 9
Big City 7.65 0.2904 17 6.40 10
Unincorporated 6.57 14 6.90 0.7447 21
Big City 7.54 0.2236 26 6.92 36
Unincorporated 6.36 14 7.83 0.2432 36
Big City 8.54 0.0030 46 8.25 0.1751 36
Unincorporated 6.92 25 7.87 39
Low Home Value non-HOA Low Home Value HOA
Lake or beach
Playground/tot lot
Tree, Lawn care in
common areas
Water or sewer
Landscaping
Recreation
facilities, like gym
Indoor community
center
Swimming
pools/tennis courts
Security
Painting/outside
maintenance
Parking lot repair
Gates or fences
Trash Collection
Gardening
Street cleaning
Street lighting and
repair

124
Table A20: Given low home value and HOA status, compare homevoter city vs.
unincorporated area responses

Mean P-value # of Obs Mean P-value # of Obs
Homevoter City 8.17 0.8665 29 8.97 0.1373 39
Unincorporated 8.00 26 8.33 39
Homevoter City 7.47 17 8.28 0.5995 36
Unincorporated 8.00 0.3912 23 8.24 37
Homevoter City 7.34 0.0857 29 8.18 0.0784 38
Unincorporated 6.13 23 7.17 35
Homevoter City 7.26 0.4030 27 8.41 0.0154 34
Unincorporated 6.70 23 7.22 37
Homevoter City 8.25 0.2831 12 7.45 0.0693 29
Unincorporated 7.00 20 6.36 33
Homevoter City 8.00 0.5213 12 7.93 0.0664 27
Unincorporated 7.26 19 6.97 33
Homevoter City 7.20 0.4843 10 8.31 0.0388 29
Unincorporated 6.17 12 7.00 36
Homevoter City 7.45 0.9192 11 8.06 0.0639 33
Unincorporated 7.39 18 6.92 36
Homevoter City 8.00 0.7923 14 8.11 35
Unincorporated 7.58 19 8.19 0.9839 37
Homevoter City 6.90 0.6830 10 7.07 14
Unincorporated 6.18 11 7.24 0.9666 21
Homevoter City 7.20 0.5056 10 6.54 13
Unincorporated 6.27 11 7.36 0.2598 22
Homevoter City 6.75 0.7495 12 7.74 0.7541 23
Unincorporated 6.09 11 7.61 31
Homevoter City 6.67 0.7518 9 6.17 6
Unincorporated 5.50 6 6.89 0.9600 9
Homevoter City 7.25 0.7470 12 6.92 0.9908 12
Unincorporated 6.57 14 6.90 21
Homevoter City 7.00 0.7118 12 7.83 35
Unincorporated 6.36 14 7.83 0.8238 36
Homevoter City 8.11 0.0395 27 8.91 0.0068 34
Unincorporated 6.92 25 7.87 39
Street cleaning
Street lighting and
repair
Tree, Lawn care in
common areas
Water or sewer
Landscaping
Recreation
facilities, like gym
Indoor community
center
Swimming
pools/tennis courts
Low Home Value HOA Low Home Value non-HOA
Lake or beach
Playground/tot lot
Security
Painting/outside
maintenance
Parking lot repair
Gates or fences
Trash Collection
Gardening

125
Table A21: Given high home value and place, compare non-HOA and HOA
responses

High Home Value
Mean P-value # of Obs Mean P-value # of Obs Mean P-value # of Obs
non-HOA 8.71 0.9933 45 8.49 35 8.88 0.2019 8
HOA 8.67 33 8.55 0.9767 42 7.56 9
non-HOA 7.70 23 8.00 0.8556 22 8.75 0.0330 8
HOA 8.09 0.4870 22 7.89 38 6.11 9
non-HOA 8.08 0.3157 40 8.47 0.1909 36 8.25 0.6140 8
HOA 7.69 29 8.08 39 7.11 9
non-HOA 8.33 0.2636 39 8.34 0.3838 35 8.13 0.2601 8
HOA 7.90 30 8.14 37 6.63 8
non-HOA 8.00 0.2594 18 8.30 0.1391 20 8.17 0.1485 6
HOA 7.21 19 7.50 28 5.88 8
non-HOA 7.67 0.8861 12 7.94 0.1716 17 8.00 0.4519 6
HOA 7.65 20 7.13 30 6.75 8
non-HOA 6.33 3 8.13 0.4532 15 8.50 0.3000 4
HOA 7.50 0.3544 14 7.71 24 6.67 6
non-HOA 7.55 1.0000 11 8.20 0.1890 15 9.75 0.0061 4
HOA 7.42 19 7.07 30 6.29 7
non-HOA 7.63 0.4318 16 7.80 0.6643 20 8.57 0.1112 7
HOA 7.17 23 7.50 34 6.56 9
non-HOA 7.17 6 7.82 0.2693 11 5.33 3
HOA 7.50 0.5765 16 6.69 13 8.00 0.2000 2
non-HOA 7.75 1.0000 4 7.93 0.1924 14 5.00 2
HOA 7.73 11 6.77 13 6.33 0.8000 3
non-HOA 7.63 8 8.00 0.9520 16 7.67 1.0000 3
HOA 7.69 0.8708 13 7.69 26 7.40 5
non-HOA 7.00 8 8.85 0.5716 13 8.00 1
HOA 7.75 0.7009 8 7.71 7 6.00 1
non-HOA 8.55 0.1738 11 8.53 0.9736 17 8.00 0.3000 3
HOA 7.50 12 8.33 12 4.00 2
non-HOA 7.89 0.4194 18 8.16 0.2813 19 8.00 0.0847 5
HOA 7.26 23 7.49 35 5.75 8
non-HOA 8.56 0.2234 39 8.61 0.6099 31 7.86 0.3416 7
HOA 8.00 29 8.22 36 6.75 8
Tree, Lawn care in
common areas
Water or sewer
Big City Homevoter City Unincorporated
Indoor community
center
Swimming
pools/tennis courts
Lake or beach
Playground/tot lot
Parking lot repair
Gates or fences
Landscaping
Recreation
facilities, like gym
Street cleaning
Street lighting and
repair
Security
Painting/outside
maintenance
Trash Collection
Gardening

126
Table A22: Given low home value and place, comparing non-HOA and HOA
responses

Low Home Value
Mean P-value # of Obs Mean P-value # of Obs Mean P-value # of Obs
non-HOA 8.75 0.3385 51 8.17 29 8.00 26
HOA 8.25 40 8.97 0.0423 39 8.33 0.5816 39
non-HOA 8.08 0.2522 24 7.47 17 8.00 23
HOA 7.32 34 8.28 0.2299 36 8.24 0.8475 37
non-HOA 7.88 0.6585 50 7.34 29 6.13 23
HOA 7.68 37 8.18 0.0454 38 7.17 0.1542 35
non-HOA 8.24 0.2374 49 7.26 27 6.70 23
HOA 7.55 38 8.41 0.0181 34 7.22 0.4798 37
non-HOA 7.00 0.0784 24 8.25 0.3531 12 7.00 0.3274 20
HOA 5.72 29 7.45 29 6.36 33
non-HOA 7.94 0.2531 17 8.00 0.8471 12 7.26 0.5067 19
HOA 6.94 34 7.93 27 6.97 33
non-HOA 7.38 0.6905 13 7.20 10 6.17 12
HOA 6.94 34 8.31 0.2136 29 7.00 0.5163 36
non-HOA 7.12 0.9446 17 7.45 11 7.39 0.4384 18
HOA 6.97 31 8.06 0.6523 33 6.92 36
non-HOA 7.50 0.5666 22 8.00 14 7.58 19
HOA 7.14 35 8.11 0.7885 35 8.19 0.5306 37
non-HOA 7.75 0.0751 16 6.90 10 6.18 11
HOA 5.86 14 7.07 1.0000 14 7.24 0.4918 21
non-HOA 7.69 0.0619 16 7.20 0.3100 10 6.27 11
HOA 5.09 11 6.54 13 7.36 0.4254 22
non-HOA 8.12 0.0174 17 6.75 12 6.09 11
HOA 6.61 28 7.74 0.2738 23 7.61 0.1921 31
non-HOA 7.17 0.4308 18 6.67 0.8607 9 5.50 6
HOA 6.00 8 6.17 6 6.89 0.5283 9
non-HOA 7.65 0.3453 17 7.25 0.6398 12 6.57 14
HOA 6.40 10 6.92 12 6.90 0.6808 21
non-HOA 7.54 0.4239 26 7.00 12 6.36 14
HOA 6.92 36 7.83 0.3335 35 7.83 0.1871 36
non-HOA 8.54 0.3878 46 8.11 27 6.92 25
HOA 8.25 36 8.91 0.2882 34 7.87 0.1750 39
Trash Collection
Gardening
Unincorporated
Street cleaning
Street lighting and
repair
Security
Painting/outside
maintenance
Lake or beach
Playground/tot lot
Parking lot repair
Gates or fences
Landscaping
Recreation
facilities, like gym
Tree, Lawn care in
common areas
Water or sewer
Big City Homevoter City
Indoor community
center
Swimming
pools/tennis courts

Asset Metadata

Creator Li, Huanghai (author)

Core Title Satisfaction with local "public goods" and services: the effects of household income and privatization in southern California

Contributor Electronically uploaded by the author (provenance)

School School of Policy, Planning, and Development

Degree Doctor of Philosophy

Degree Program Planning

Publication Date 09/09/2008

Defense Date 07/07/2008

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

Tag Income,local public goods,OAI-PMH Harvest,privatization,Satisfaction,Southern California

Place Name California (states), Los Angeles (city or populated place)

Language English

Advisor Gordon, Peter (committee chair), Moore, Moore, James Elliott, II (committee member), Richardson, Harry W. (committee member)

Creator Email huanghal@usc.edu,peterlihh@yahoo.com

Permanent Link (DOI) https://doi.org/10.25549/usctheses-m1592

Unique identifier UC1440648

Identifier etd-Li-2394 (filename),usctheses-m40 (legacy collection record id),usctheses-c127-108055 (legacy record id),usctheses-m1592 (legacy record id)

Legacy Identifier etd-Li-2394.pdf

Dmrecord 108055

Document Type Dissertation

Rights Li, Huanghai

Type texts

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

Repository Name Libraries, University of Southern California

Repository Location Los Angeles, California

Repository Email uscdl@usc.edu

Abstract (if available)

Abstract I consider several research questions in this dissertation, including whether household income and the privatization of municipal service provision matter when explaining homeowners' expressed satisfaction with local "public" services and homeowners' choice of local governance form. It will become clear that what are often thought of as traditional local public or municipal services can be supplied by private entities. Indeed that is a key part of this investigation. To simplify, I will not always refer to them as so-called public services (or municipal), but will continue to use conventional labels with the understanding that these goods and services can and often are privately supplied. I also address the question whether traditional local public governments and private homeowners' associations (HOAs) are complements or substitutes.