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WHY DO LARGER PUBLIC HOUSING AGENCIES HAVE LONGER WAIT TIMES? UNDERSTANDING APPLICANT DURATION ON THE WAITLIST
A Thesis submitted to the Faculty of the
Graduate School of Arts and Sciences of Georgetown University
in partial fulfillment of the requirements for the degree of
Master of Public Policy
By
Marika Melelani Butler, B.A.
Washington, DC March 30, 2010
ii
WHY DO LARGER PUBLIC HOUSING AGENCIES HAVE LONGER WAIT TIMES? UNDERSTANDING APPLICANT DURATION ON THE WAITLIST
Marika Melelani Butler, B.A.
Thesis Advisor: Joydeep Roy, Ph.D.
ABSTRACT
Stories of lengthy wait lists for subsidized housing assistance are rampant in
newspapers, housing advocacy groups and tenant organizations and this anecdotal
evidence often points to populous cities with large housing agencies as having the
most unreasonable wait times. Is this phenomenon a result of housing agency size or
are there other determinants that lead to longer applicant duration on the waitlist?
This study hypothesized that larger public housing agencies (PHAs) have longer
average wait times for applicants and that within the agencies’ spectrum of
programs, voucher programs have longer average wait times than public housing
programs. Previous studies had either focused solely on waitlist characteristics or
the effect of agency size on management efficiency. The present study provides
quantitative analysis on the relationship between agency size (i.e. the number of
units administered) and average applicant wait time, which is largely absent from
existing literature.
iii
To examine the topic, this study utilized the Department of Housing and
Urban Development’s Picture of Subsidized Households 2000 dataset in combination
with housing demand factors for each locality derived from the 2000 US Census
2000. Four model specifications using multivariate regressions were employed on a
randomized sample of PHAs to ultimately examine the relationship between PHA
size and average applicant wait time for voucher and public housing programs
separately. Housing agency size did lead to significantly longer average wait times
in voucher programs. However, for public housing programs agency size was not a
contributing factor to applicant wait time with housing demand and demographic
factors instead playing a larger role. Analyzing the relevant trends for sub-sections
of the data proved the results to be robust in nature.
Based on the results of this study, policymakers would do well to realize that
while agency size is an explanatory factor in voucher wait time the real issue behind
unreasonably long waiting lists for subsidized housing assistance is the dearth of
available units. If policy makers want to decrease applicant wait time for housing
assistance the best solution lies in increased funding for PHAs with high demand, a
change in HUD protocol to allow funding to go towards construction of new units
instead of capital improvements, and training programs to ameliorate management
efficiency. Until then applicant wait time cannot substantively decline.
iv
I dedicate this thesis to the many people who assisted me in the research and writing process: first and foremost, my thesis adviser, Joydeep Roy, whose patience,
understanding, and advice was invaluable; Eric Gardner, for his help on statistical software; the tremendous faculty and staff of GPPI; my fiancé, Joe; my sister, Elyse; my parents, whose enduring faith in me never wavers; and lastly the Cajun and soul
music that kept me smiling throughout this process.
v
TABLE OF CONTENTS
Chapter 1. Introduction ....................................................................................1 Background on Public Housing Agencies .......................................3
Chapter 2. Prior Studies ...................................................................................8 GAO: Small and Large PHAs Have Similar Views on Housing Reforms..........................................................................................8 NLIHC: A Look at Waiting Lists by Examining HUD Approved Annual Plans................................................................................. 11
Chapter 3. Research Design ........................................................................... 14 Conceptual Framework & Hypothesis........................................... 14 Data Source .................................................................................. 15 Variables ...................................................................................... 16
Control Variables.................................................................... 16 Main Variables of Interest....................................................... 18
Descriptive Statistics..................................................................... 20 Theoretical Model......................................................................... 28
Relationship between PHA Size and Time on the Waiting List30 Chapter 4. Results and Discussion ................................................................. 33
Multivariate Analysis.................................................................... 33 Vouchers................................................................................. 35 Public Housing Units .............................................................. 39
Robustness Checks ....................................................................... 44 Chapter 5. Policy Implications ....................................................................... 57 Chapter 6. Conclusion.................................................................................... 61 Chapter 7. Limitations & Directions for Future Research ............................... 65 Appendix........................................................................................................... 67
Changes Made to Dataset.............................................................. 67 Receiverships................................................................................ 70 Creation of Randomized Sample................................................... 72 Correlations .................................................................................. 74
References......................................................................................................... 76
vi
LIST OF TABLES & CHARTS
Table 1: Overall PHAS and SEMAP Scores by Agency Size............................10 Table 2: Size and Access of PHA Waitlists by Type of Assistance ...................12 Table 3: Frequency of PHAs and Total Units Administered by Agency Size ....21 Table 4: Descriptive Statistics for the Original Dataset and the Random Sample
...........................................................................................................22 Table 5: Frequency of PHAs and Average Wait Times.....................................24 Table 6: Distribution of Public Housing and Voucher Programs in Random
Sample ...............................................................................................25 Table 7: Average Wait Time for Public Housing and Voucher Programs..........26 Table 8: Descriptive Statistics for PHAs Managing Single Program vs. Both
Programs............................................................................................27 Table 9: Summary of Voucher Models.............................................................34 Table 10: Summary of Public Housing Models ..................................................38 Table 11: Models for Sample Restricted to Only PHAs With Both Voucher &
Public Housing Programs ...................................................................47 Table 12: Models for Sample Restricted to PHAs within the Ten States with the
Largest Number of Housing Agencies ................................................50 Table 13: Models for Sample Restricted to Exclude Outliers..............................53 Table 14: Distribution of Missing Observations for Months_Waiting.................68 Chart 1: Map of HUD Receiverships................................................................71 Table 15: Receiverships by Location and Start Date...........................................72 Table 16: Weights & Frequency for Randomized Sample ..................................73
1
Chapter 1. Introduction
Many articles speak of never-ending wait lists for public housing and housing
choice vouchers. Examples of 32-year wait times in New York City are not outliers;
sadly they happen all too often (Watson 2003). Yet while some households wait
decades for subsidized housing assistance, other households have wait times of
approximately one year. As NAHRO reports “a household in New York City may
have to wait several decades, while one in Baltimore of Washington may wait ‘only’
eight years…even among smaller PHAs waiting periods of at least one year are
typical” (Bratt 539). As the number of applicants on waiting lists continues to
increase, applicants waiting for housing assistance are denied the basic necessity of
decent and affordable shelter and this issue needs to be addressed through a
comprehensive housing policy.
Public housing agency wait lists are the best available indicators of housing
need because they display the number of households without affordable housing
options and “demonstrate a vast unmet need for housing assistance” in America
(NLIHC 2003). This is particularly true in localities where the wait lists are longer
than the entire supply of public housing that that locality supports. It is essential to
examine these wait lists so that an honest understanding of the supply and demand
factors affecting publicly assisted housing can be reached. In the wake of the current
2
economic recession and the subprime mortgage crisis, millions of households have
been plunged into economic difficulty, forcing some families into foreclosure while
others face losing their rental units; this crisis has further increased the need for
affordable and subsidized housing. Consequently, times of economic hardship
increase the number of applicants on the waiting list for subsidized housing assistance
thus compounding the already tenuous nature of the waitlists in certain localities.
Previous research and news reports indicate that there are differences in the
amount of time applicants are waiting for public housing assistance and these
differences are seen by many as a result of America’s lack of affordable housing. Due
to the vast variation in applicant duration on the waiting list it is essential to ask, what
are the determinants of this variance in wait time. Rachel Bratt indicates anecdotally
that the differences in wait times could be related to the whether or not the applicant is
applying to a small PHA or a PHA in a larger city, which insinuates that PHA size may
be a factor in the amount of time an applicant remains on the waitlist (Bratt 539).
Since American cities and counties differ in all possible measures; including
demographics, income, and housing options; the present study will focus on examining
the relationship between the size of a public housing agency and the amount of time an
applicant spends on its wait list.
3
This thesis hypothesizes that large public housing agencies will have longer
average waiting periods for applicants as a result of two main factors– management
inefficiency due to size and a more disadvantaged population which increases demand
for affordable housing. To fully examine this relationship this study employs
multivariate OLS regressions with four specific model specifications for both voucher
and public housing programs separately. The methodology controls for factors that
induce affordable housing demand; such as population, median gross rent and
occupancy rate; and the demographic characteristics of the locality to assess how
disadvantaged its population base is. These control variables will clear the noise in
the dataset and allow a thorough examination of the relationship between the number
of units administered by a PHA (an indication of its size) and the length of time an
applicant is on the waiting list for subsidized housing assistance.
Background on Public Housing Agencies The establishment of public housing agencies began with the US Housing Act
of 1937, which allowed cities and counties to create housing agencies through which
the Department of Housing and Urban Development (HUD) would channel funds.
Many contemporary PHAs were founded in the 1940s and 1950s after the enactment of
this law and in the two decades following America saw large increases in public
4
housing and an increased legislative focus on urban issues. During this era of large
public housing growth, PHAs were allocated funding mainly through HUD-sanctioned
formulas, which would then be designated by the city for the construction of affordable
housing units. More recently there has been an expansion of funding sources, which
now relies less on formula funding (CDBG and HOME funds are the exceptions), and
more on a competitive process where PHAs compete against each other to secure
grants (ex: HOPE VI). An additional source of funding are Housing Trust Funds
which are created by the city or county government and funded by the legal fees that
are charged by local governments to transfer documents. This steady stream of income
is then funneled into a Housing Trust Fund, which local governments can utilize to
build affordable housing units. Overall, public housing agencies receive funding from
a variety of sources including formula funding in CDBG and HOME funds, a
competitive bidding process with HOPE VI grants, HUD operating funds and Housing
Trust Funds. However, there is a distinction with CDBG and HOME funds. These
flexible grants are given to the local governments, which have the discretion of
choosing their purpose and these grants may be used on public infrastructure or
surveying populations instead of creating affordable units. Furthermore, once these
funds actually reach the PHA they can be channeled toward building affordable
housing units or for capital improvements on existing public housing. Therefore, not
5
all federal funding even reaches the PHA and the funding that does is not necessarily
used for the construction of new units.
In addition to PHA-run housing units much of contemporary housing assistance
comes in the form of housing choice vouchers. These vouchers are distributed by
HUD to PHAs based on a formula allocation that emphasizes the allotment of the
previous year’s vouchers. The voucher funding is not based on a specific number of
vouchers but on a set fund allocation – meaning that the number of vouchers depends
on the local rents, the resource allocation by management and the utilization rate.
When examining all of the factors affecting the supply of vouchers and public housing
units within a PHA it is obvious that supply is not perfectly correlated with the demand
for affordable housing assistance.
Furthermore, public housing agencies have the interesting distinction of being
autonomous government agencies that receive federal funding, but it is at the discretion
of the cities, townships, counties, and suburbs if they will have a PHA within their
boundary. There is no HUD mandate that maintains that localities must establish
PHAs and this distinction creates difficulties in examining the effect of supply and
demand for affordable housing. Federal funding for public housing, vouchers and
CDBG and HOME grants is definitely an incentive to establish a PHA, but this
incentive is often unused by wealthier communities that have a bias against the
6
development of affordable housing in their neighborhood. This can lead to
neighborhoods that have a great need for affordable housing to lack a public housing
agency within their jurisdiction negating the idea that demand is a sufficient condition
for a PHA.
Even once a PHA is established its size is related to many factors. PHA size is
related to community need for affordable housing, but as described above the needs of
a city may not be sufficiently met by the current affordable housing supply or the city
may completely lack a PHA and thus subsidized units. In addition, PHA size is related
to the level of jurisdiction since multiple PHAs may operate in the same territory. An
example of this is Seattle’s Public Housing Agency and the King County PHA, which
overlap, and therefore one of the entities may have a smaller jurisdiction affecting the
housing units administered by that agency. Finally, the size of the PHA can be a
repercussion of what the locality feels is its need. This can present itself as few
affordable housing opportunities in a community that wants to keep out lower-income
households or a large number of affordable housing opportunities if the city wants to
assist this segment of its population. This discretion on the part of the PHA and the
locality inherently means that there is not a perfect correlation between the size of the
PHA and the need in the jurisdiction.
7
The management and administration of PHAs is decided by the locality. The
mayor, city manager or county executive will appoint the director of the public housing
agency making this position intrinsically political. This can lead to the directorship of
the PHA being a vestige of the patronage system, which can lead to inefficiency and
poor management. Bureaucratic inefficiency in America’s largest cities was pervasive
in the early 20th century as a result of political machines and the patronage system
making it not unlikely that remnants of this inefficiency still exist in today’s most
populous cities. Once appointed, the role of the PHA director is to petition the federal
government for funding grants such as HOPE VI and to allocate vouchers based on the
allotted resources. Even though PHAs are autonomous agencies, HUD can place a
PHA into receivership which means the director is removed due to poor management
and inefficient use of funding.1 During receivership HUD places a proxy director in
the previous director’s place in order to continue funding the PHA so additional
affordable housing is not lost within that jurisdiction. The existence of eight PHAs
currently in receivership demonstrates that efficiency and effective use of funding is
not an overarching principle of PHAs and variance in management does indeed exist.
1 Please see appendix for additional information on receiverships.
8
Chapter 2. Prior Studies
The literature that will be discussed mainly examines public housing agencies
in different contexts. Both prior studies provide a better sense of public housing
agencies by examining opinions on earlier reforms, assessing agency performance
ratings and describing the characteristics of the waitlists. Despite extensive research,
there is no literature that gives a quantitative or conceptual analysis of the effect of
public housing agency size on the average amount of time an applicant is on the wait
list for housing assistance. This thesis will examine this phenomenon and provide
concrete analysis to determine if the size of a public housing agency has an effect on
time spent on the wait list.
GAO: Small and Large PHAs Have Similar Views on Housing Reforms
The GAO report examines the differing views on the housing reforms that took
place in the Quality Housing and Work Responsibility Act (QHWRA) of 1998. The
reforms enacted in the QHWRA created new mandatory reporting procedures, which
increased the workload for public housing agencies. Additionally, the reforms allowed
PHAs to set minimum rents, dropped mandatory priority for homeless individuals on
the waitlists, and gave agencies greater control and more management flexibility.
GAO’s study also examines the differences between the performance ratings for large
and small PHAs, which assess the agencies’ management of HUD housing programs.
9
The examination of performance rating variation between PHAs is extremely relevant
to this study as it demonstrates that management efficiency does vary across PHA size.
The examination of HUD’s performance measurement and risk assessment
scores found that PHAS2 performance ratings for public housing programs did vary
between small and large public housing agencies with larger PHAs receiving more
troubled scores than small PHAs. The better performance of small and medium
agencies was mainly attributable to higher scores on the physical condition of their
units (GAO 2003). While smaller agencies scored higher on the PHAS assessment,
small agencies received lower scores for administering housing choice voucher units.
Small agencies may receive lower SEMAP3 scores due to HUD’s scoring formula,
their lack of economies of scale and lack of experience in managing voucher units
(GAO 2003). This suggests that large PHAs can be more efficient than small PHAs
for certain programs where economies of scales are important and extensive training in
program management is essential. The variances between the PHAS and SEMAP
scores by agency size are displayed in Table 1:
2 PHAS scores are based on the performance of a PHA in managing low-rent units based on physical appearance of the units, financial condition of the agency, effectiveness of management, and resident satisfaction.
3 SEMAP scores measure the efficacy of a PHA at administering housing choice voucher units.
10
Table 1: Overall PHAS and SEMAP Scores by Agency Size
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Table 1 displays that a higher percentage of small and medium PHAs receive a
PHAS designation of high performer than their large agency counterparts. This
signifies that small PHAs are better managers of public housing programs than large
agencies. For SEMAP scores, a larger percentage of small PHAs received a SEMAP
designation of troubled than large PHAs, indicating that large PHAs are better
equipped to manage voucher programs than small PHAs.
Finally, the GAO study examined HUD’s PIC risk-assessment scores, which
measure agencies’ operating risk. The PIC scores are based on three factors:
performance (PHAS or SEMAP score), funding and compliance. In general small
PHAs received lower risk ratings compared to large agencies, which is a result of small
PHAs operating less complex housing programs, having a less complicated funding
system and accounting for a smaller percentage of overall HUD funding (GAO 2003).
11
The variances in the performance and risk assessment scores of public housing
agencies demonstrate that large PHAs are generally viewed as suffering from more
management problems and consequently are higher risk PHAs than small agencies.
However, this fact is reversed within the voucher program with larger PHAs being
more efficient and suffering from less management issues. These findings are
important as they demonstrate the inefficiency of larger PHAs under certain
circumstances, which consequently could result in longer average wait times for those
waiting for subsidized housing assistance.
NLIHC: A Look at Waiting Lists by Examining HUD Approved Annual Plans
The NLIHC report examines waiting lists for public housing assistance by
utilizing HUD approved annual plans. The major findings of the study are in two
areas: size of the waitlist and whether the waitlist is still open to new applicants.
Waiting list size varies widely with a “list of almost 150,000 families waiting for
housing vouchers in New York City, to lists of only a few hundred applicants in some
of the smaller PHAs or those that have had their waiting lists closed to new applicants
for years” (NLIHC 2003). Within the largest PHAs 850,000 families are on the
waiting list for housing vouchers and 460,000 families are on the waiting list for public
housing (NLIHC 2003). These figures demonstrate the massive number of families
waiting for housing assistance, which in many cities far exceeds the capacity of the
12
PHA. For example, the Los Angeles County Community Development Commission
has over 17 times as many families on their waiting list as their number of public
housing units (NLIHC 2003).
In addition as displayed in Table 2, many waiting lists are closed to new
applicants leaving needy families with few options. This phenomenon is more
prevalent within housing voucher programs, displaying the popularity of this program
and its competitive nature. While these figures may be overstated due to families’
ability to be on multiple waiting lists it still demonstrates the mismatch between
demand and supply for affordable housing.
Table 2: Size and Access of PHA Waitlists by Type of Assistance Waiting List
Median Size Waiting List Mean
Size % with Waiting
List Closed % Not Expected to Reopen within One
Year Quintile Housing
Vouchers Public
Housing Housing Vouchers
Public Housing
Housing Vouchers
Public Housing
Housing Vouchers
Public Housing
Q1 0 68 162.2 110.4 16.7% 0.0% 100.0% 0.0%
Q2 166 119 256.6 172.1 16.7% 15.4% 0.0% 50.0%
Q3 313 190 648.3 770.3 75.0% 16.7% 33.3% 50.0%
Q4 342 280 935.5 528.4 53.3% 13.3% 62.5% 50.0%
Q5 1669 918 3491.7 2038.5 53.8% 30.8% 14.3% 75.0%
Large PHAS 6360 1960 18492.7 12209.9 58.7% 28.9% 63.0% 45.5%
Full Sample 881 241 6880 3817.5 40.3% 15.7% 50.0% 52.4%
Source: NLIHC 2003.
The main findings of this study detail the variance in waiting list size between
PHAs and the popularity of voucher programs, which tend to be more competitive and
13
subsequently are more prone to having their wait list closed for a number of years.
These two findings are important to this thesis as they indicate that there are variances
between the size of the public housing agency and the number of people on the waiting
list as well as variances between the number of applicants to both voucher and public
housing programs.
14
Chapter 3. Research Design
Conceptual Framework & Hypothesis This thesis hypothesizes that large public housing agencies have longer average
waiting periods for applicants on the waitlist for both types of subsidized housing
assistance, vouchers and public housing; with a more significant effect felt by voucher
programs. The reasoning behind this conjecture is that voucher programs are more
prevalent in larger PHAs and they tend to have longer wait times than public housing
programs. In general, the underlying reasoning for why larger PHAs will have longer
average wait times is due to two main factors– management inefficiency and a more
disadvantaged population which increases the demand for affordable housing.
To test this hypothesis a randomized sample was created which has both
housing assistance programs. This study ran multivariate regression analysis on the
random sample to test the relationship between the two main variables of interest,
average wait time (months_waiting) and PHA size (ha_size), while controlling for a
variety of other factors at the PHA- and locality-level. The multivariate analysis was
done for vouchers and public housing programs separately and within each program
four model specifications were analyzed. By bifurcating the regression analysis for
vouchers and public housing programs this allowed a full analysis of the effect of PHA
size on the average wait time for both programs individually. The four model
specifications were included to provide further analysis on the effect of the control
15
variables on the variables of interest and identify the pure relationship between housing
agency size and average applicant wait time.
Data Source In order to research this topic two data sources were used – the main source of
data is the HUD Picture of Subsidized Households 2000 dataset. This dataset supplies
PHA size, total units administered, average months on the waiting list, percentage of
population below poverty level in the surrounding census tract, and minorities as a
percentage of the total population within the surrounding census tract. All of the
variables within this dataset are measured at the PHA level and were collected in 1996,
1997, 1998 and 2000. 2000, was the year chosen for this study as it matches the latest
US Census collection date.
In addition to the HUD Picture of Subsidized Households dataset, additional
variables to measure demand for affordable housing are necessary. The housing
demand variables; population, occupancy rate and median gross rent; were collected
from the 2000 US Census and input at the same locality level as the PHA. For
example, if the PHA is for Rockville Maryland the statistics will be for the city of
Rockville and if the PHA is for Montgomery County the statistics will be for all of
Montgomery County. These additional variables are essential as they provide a more
comprehensive picture of the demand for affordable and subsidized housing within the
16
locality. As the housing demand variables needed to be entered individually from the
US Census, the full HUD dataset was curtailed and a randomized sample of PHAs was
formulated.4 The final randomized sample includes all the relevant variables from
each data source and this sample is the basis for the entire methodology employed in
this thesis.
Variables In order to examine the relationship between applicant wait time and agency
size it is necessary to control for both demographic factors and factors that induce
housing demand. These variables are included so that cross-PHA comparisons can be
appropriately made. A detailed description of the control variables and variables of
interest is described below.
Control Variables
City population is measured at the city/county level in thousands of people.
Controlling for the population of the locality is important because larger populations
indicate the presence of more people and the likelihood of a variety of demographics.
The inclusion of population is necessary because large cities are more likely to have
higher numbers of people in poverty and more minorities, both of which can increase
the demand for subsidized housing assistance resulting in longer average wait times.
4 Please see appendix for details on the creation of the randomized sample.
17
The expected relationship between population and average time on the wait list is
positive since larger cities have more people, which could increase the need for
affordable housing.
Occupancy rate is measured at the city/county level and is entered as a whole
number to depict the occupied units as a percentage value of total available units
within the city. High occupancy rates can be an indicator of housing demand since
high occupancy rates signify fewer available housing opportunities. The expected
relationship between occupancy rate and average time on the wait list is positive since
high occupancy rates indicate fewer housing units are available. This is particularly
true because if the available units are not affordable to working class and low-income
households there will still be a high demand for subsidized housing and consequently
longer average wait times on the waiting list.
Median gross rent is measured in dollar units at the city/county level.
Controlling for the median gross rent is important because a high median gross rent
indicates that there are fewer opportunities for low-cost housing. Furthermore, high
median rents can be an indicator of local housing demand since higher than average
rents signify larger demand for housing within this locality, which is consequently
raising the median rent and placing a strain on low-income households. The expected
relationship between median gross rent and average time on the wait list is positive
18
since high median rents indicate fewer available affordable housing units thereby
inducing a higher probability that low-income households will need housing assistance
thus elongating the waitlist.
Percent of minorities is measured for the census tract for each PHA as per the
Picture of Subsidized Housing codebook. The variable was entered as a whole number
depicting the number of minorities as a percentage value of the total population within
the census tract where the HUD assisted households reside. Controlling for the
percentage of minorities within the population is essential because a higher percentage
of minorities is often associated with higher degrees of poverty especially with regard
to blacks and Hispanics. The expected relationship between percent of minorities and
average time on the wait list is positive since localities with a higher percentage of
minorities are more likely to have a higher percentage of residents applying for federal
assistance and are subsequently more likely to use housing assistance.
Main Variables of Interest
PHA size is measured by the number of units (public housing, moderate
rehabilitation, and Section 8 tenant-based vouchers) that the PHA specifically
administers. The HUD Picture of Subsidized Housing Dataset categorizes this variable
into the following categories:
0=not in any agency 1=Agency with 1-99 units
19
2= 100-299 units 3= 300-499 units 4= 500-999 units 5= 1,000-2,999 units 6= 3,000-4,999 units 7= 5,000-9,999 units 8= 10,000-29,999 units 9= 30,000+ units
Within this study the PHA size variable was recoded to create multiple dummy
variables indicating the size of the agency. The dummy variables were recoded as
follows: small PHAs (ha_below300 which constituted categories 1 and 2), small-
medium PHAs (ha_300to999 made up of categories 3 and 4), medium-large PHAs
(ha_1kto10k made up of categories 5, 6, and 7) and finally large PHAs (ha_over10k
which constituted categories 8 and 9). The small PHAs act as the reference group for
the other PHA-size dummy variables in the multivariate analysis. Having PHA-size
dummy variables allows this study to examine the effects of agency size on average
wait time and is this study’s main explanatory variable.
Average length of time on the waitlist is measured by examining the amount of
time each applicant waited prior to receiving a subsidized unit of housing (either public
housing unit or voucher) and then this time is then averaged to give a mean wait time
for the entire PHA measured in months. One important factor to consider is that mean
wait time is only calculated if applicants actually received housing assistance which
20
implies that this statistic excludes applicants that are still on the waiting list since it is
unclear how long these applicants will be waiting for housing assistance.
Descriptive Statistics The dataset utilizes HUD’s Picture of Subsidized Households from 2000, which
was collected prior to the loosening of HUD’s regulatory requirements on small PHAs
that administer fewer than 250 units. Since the data was collected prior to this
regulatory change full disclosure of waiting list characteristics was required for all
PHAs.5 Therefore, any missing observations in the dataset are due to non-compliance
with regulatory requirements not due to separate policies.
Table 3 displays the frequency distribution of the original dataset prior to
creating the randomized sample. It presents the number of PHAs in each size category
as well as the average number of units administered by PHAs of that size category.
The table demonstrates that smaller PHAs are more prevalent accounting for 52.5% of
all the PHAs, but they only account for 6.3% of the total units administered.
Conversely, there are very few large PHAs but they account for a majority of the units
administered.
5 In July 2003, HUD altered its reporting requirements for small PHAs no longer requiring a full annual plan submission and reducing the frequency of performance assessment. This change meant that these small PHAs are no longer required to submit information on their waiting lists on a yearly basis; however, this was not in effect when the my data was collected.
21
Table 3: Frequency of PHAs and Total Units Administered by Agency Size Housing
Agency (HA) Category
Frequency of PHAs in HA
Category
Frequency % of PHAs in
HA Category
Average Number of Units in HA
Category
Total Units Administered in
HA Category
% of Total Units in HA
Category 1 1358 25.3% 51.7 70,245 1.3%
2 1457 27.2% 181.2 264,070 5.0%
3 740 13.8% 385.2 285,084 5.4%
4 819 15.3% 690.4 565,412 10.6%
5 687 12.8% 1,692.0 1,162,372 21.9%
6 134 2.5% 3,869.4 518,496 9.8%
7 105 2.0% 6,923.2 726,935 13.7%
8 59 1.1% 16,038.9 946,294 17.8%
9 8 0.2% 97,274.3 778,194 14.6%
As previously indicated, in order to have a robust dataset with variables on
housing demand it was necessary to include additional variables for occupancy rate,
city or county population, and median gross rent for each PHA from the US Census.
Prior to the addition of these variables the original dataset was sliced to create a
random sample.6 This randomized sample is now the basis for the remaining study
and regression analysis.
Table 4 displays the descriptive statistics for both the original dataset and the
random sample for all of the variables7.
6 Please see appendix for additional information on the creation of the randomized sample. 7 Housing demand variables do not appear in the original dataset as they were individually
entered from Census.
22
Table 4: Descriptive Statistics for the Original Dataset and the Random Sample Original Dataset Random Sample
Name of Variable Mean/Median Value
Std Deviation Min / Max
Mean/Median Value
Std Deviation Min / Max
Months on Waiting List (months_waiting)
Mean: 15.1 Median: 10
Std: 17.6 Min: 0 Max: 520
Mean: 18.2 Median: 14
Std: 15.5 Min: 0 Max: 107
% of Poverty in the Surrounding Census Tract (tpoverty)
Mean: 15.8% Median: 15%
Std: 9.9% Min: 0% Max: 65%
Mean: 18.0% Median: 17%
Std: 11.8% Min: 0% Max: 59%
% of Minorities in the Surrounding Census Tract (tminority)
Mean: 24.9% Median: 15%
Std: 25.1% Min: 0% Max: 99%
Mean: 33.4% Median: 25%
Std: 29.1% Min: 0% Max: 99%
Population (population)
Mean: 378.1 Median: 48.7
Std: 1,042 Min: 0.45 Max: 9,938.4
Occupancy Rate (occupancy_rate)
Mean: 90.9% Median: 91%
Std: 4.7% Min: 44.8% Max: 98.5%
Median Gross Rent (median_gross_rent)
Mean: $526.23 Median: $498
Std: $145.38 Min: $202 Max: $1162
PHA Total Units (pha_total_units)
Mean: 940.3 Median: 250
Std: 5,180 Min: 3 Max: 239,561
Mean: 5,308.6 Median: 757
Std: 19,056 Min: 17 Max: 239,561
Housing Agency Category (ha_size)
Mean: 2.8 Median: 2
Std: 1.7 Min: 1 Max: 9
Mean: 4.51 Median: 4
Std: 2.2 Min: 1 Max: 9
As shown in the above descriptive statistics, the random sample has a longer
average wait time for applicants. This is an indication that larger PHAs had longer
average wait times since the random sample is more heavily weighted toward larger
23
PHAs. In addition to longer average wait times for large PHAs, these agencies also
serve a more disadvantaged and minority population. These two subsets of descriptive
statistics are effective in displaying the variances in population characteristics once
agency size is taken into account.
The differences in average wait time are even more apparent in the following
table, which displays the mean, median, and standard deviation of the variable
months_waiting by PHA size for the random sample. As shown in Table 5, within the
random sample larger PHAs have longer average applicant wait times compared to
small PHAs, with a mean duration of 42.5 months and 8.5 months for large and small
PHAs, respectively.
24
Table 5: Frequency of PHAs and Average Wait Times Months Waiting Variable
Housing Agency (HA) Category
Frequency of PHAs within Category for
Random Sample
Mean / Median (mos)
Standard Deviation
1 28 Mean: 8.5 Median: 5.5
Std: 7.9
2 42 Mean: 9.7 Median: 7.5
Std: 8.0
3 62 Mean: 16 Median: 11
Std: 13.3
4 73 Mean: 16 Median: 13
Std: 14.3
5 62 Mean: 20 Median: 17
Std: 13.8
6 12 Mean: 23 Median: 17.5
Std: 14.4
7 40 Mean: 24.4 Median: 20
Std: 17.2
8 43 Mean: 25.5 Median: 22
Std: 19.2
9 8 Mean: 42.5 Median: 41
Std: 20.7
Total 370 Mean: 18.2 Median: 14
Std: 15.5
Within the original dataset and the random sample there are two types of
programs: public housing programs and voucher programs. A PHA can have either
one or both of these programs; if an agency only has one program it is most commonly
a public housing program. Nonetheless, the majority of public housing agencies have
both a voucher and public housing program. As displayed in Table 6, smaller PHAs
25
have a higher frequency of public housing programs compared to voucher programs.
The following table also displays the distribution of the public housing and voucher
programs within the random sample so one can see the distribution of programs by
PHA size.
Table 6: Distribution of Public Housing and Voucher Programs in Random Sample Voucher Program in Random
Sample Public Housing Program in
Random Sample Overall Random Sample
HA Size
Frequency Percent Cumulative Percent
Frequency Percent Cumulative Percent
Frequency Percent Cumulative Percent
1 7 4% 4% 21 11% 11% 28 8% 8%
2 20 11% 15% 22 12% 23% 42 11% 19%
3 31 17% 32% 31 16% 39% 62 17% 35%
4 37 20% 52% 36 19% 58% 73 20% 55%
5 32 18% 70% 30 16% 74% 62 17% 72%
6 7 4% 74% 5 3% 77% 12 3% 75%
7 21 12% 86% 19 10% 87% 40 11% 86%
8 22 12% 98% 21 11% 98% 43 11% 98%
9 4 2% 100% 4 2% 100% 8 2% 100%
Total 181 189 370
Despite the fact that the same PHA administers both programs the average
amount of time an applicant is on the waiting list for housing assistance can vary
dramatically. The average time on the waitlist is usually longer for voucher programs
and this fact is substantiated within the literature and in the random sample, as shown
in Table 7.
26
Table 7: Average Wait Time for Public Housing and Voucher Programs Public Housing Programs Voucher Programs
Name of Variable
Number of Observations
Mean / Median Standard Deviation
Mean / Median Standard Deviation
Months_waiting Public Housing: 189 Vouchers: 181
Mean: 11.5 mos Median: 9 mos
Std: 9.5 Mean: 25.2 mos Median: 20 mos
Std: 17.4
As shown in Table 7, voucher programs have longer average wait times for
applicants than public housing programs. Applicants to voucher programs may
experience a longer average duration on the waiting list due to the heightened
popularity of this program. This popularity is due to the residents’ increased social
mobility and the improved choices regarding neighborhood and location. Furthermore,
voucher programs are given a HUD goal to use at least 95% of all allocated vouchers
yet many PHAs face barriers in utilizing these vouchers. These barriers are mainly
financial such as PHAs lacking the funding for credit checks, security deposits, and the
cost of transportation to the sites. Furthermore, the PHA may also lack experience in
the rental market, which is an essential component of voucher program management.
The combination of these financial barriers, with a potential lack of experience in the
rental market, and the heightened popularity of this program result in longer average
wait times for these applicants.
27
Since PHAs vary in their program management it is interesting to examine the
difference between PHAs that choose to manage a single program versus PHAs that
choose to simultaneously manage both programs. Table 8 displays the prevalence of
PHAs, within the randomized sample, that manage a single program or concurrently
manage both programs and presents the differences in average wait time, housing
demand and demographic characteristics.
Table 8: Descriptive Statistics for PHAs Managing Single Program vs. Both Programs Name of Variable PHAs with both Public
Housing & Voucher Programs
PHAs with only Public Housing Programs
PHAs with only Voucher Programs
Months_waiting Mean: 18.4 mos Median: 15 mos
Mean: 7.1 mos Median: 5 mos
Mean: 20.5 mos Median: 19.5 mos
Tminority Mean: 44.6% Median: 29%
Mean: 24.9% Median: 18.5%
Mean: 20.8% Median: 11.5%
Tpoverty Mean: 20.3% Median: 17%
Mean: 18.2% Median: 18.5%
Mean: 20.6% Median: 11.5%
Population Mean: 571,874 Median: 77,158
Mean: 33,044 Median: 3,450
Mean: 743,517 Median: 26,038
Occupancy_rate Mean: 91.8% Median: 91.7%
Mean: 89.3% Median: 89.6%
Mean: 89.5% Median: 91.4%
Median_gross_rent Mean: $589.79 Median: $513
Mean: $400.79 Median: $384.5
Mean: $554.77 Median: $524
Number of Observations
302 observations 38 observations 30 observations
As shown in Table 8, PHAs that manage both public housing and voucher
programs have a higher prevalence of disadvantaged populations within their city or
county which results in increased applicant wait time for subsidized housing
28
assistance. Percentage of minorities doubled for PHAs managing both programs,
which strengthens the argument that PHAs managing both voucher and public housing
programs are located within areas suffering from extreme poverty and containing
disadvantaged households. The statistics also display that the PHAs that manage only
public housing programs tend to have smaller populations, lower median rents and
fewer minorities and subsequently benefit from shorter applicant duration on the
waitlist. In contrast, PHAs that manage only voucher programs have larger
populations, higher median rents and longer average wait times. By dividing up the
PHAs, within the randomized sample, it is clear that voucher programs have longer
average wait times and this effect is sustained within PHAs managing both programs.
While these findings are indicative that PHAs managing both programs tend to be
located within areas with higher need for affordable housing and subsequently
experience longer applicant wait times, the small number of observations for PHAs
managing single programs does not provide a large enough sample to make concrete
conclusions.
Theoretical Model The descriptive statistics presented a correlation between larger public housing
agencies and longer average wait times with voucher programs experiencing longer
wait times between the two housing programs. In order to see if this relationship
29
continues when other variables are introduced, this study utilizes multivariate OLS
regression analysis with four separate model specifications.
In each model the main variables of interest are included with the dependent
variable average time on the wait list and independent PHA size dummy variables.
The first model examines only the relationship between the PHA size and average wait
time, with the reference group being small PHAs administering fewer than 300 units.
The second and third models introduce the control variables independently, either the
demographic or the demand variable groups. Finally, the fourth model is fully
unrestricted and includes the main variables of interest and controls for both
demographics and housing demand factors. For each model specification this study
bifurcates the regression by the two types of housing programs: public housing and
voucher programs to determine the individual effect of agency size by program.
• Model 1: Simplified Model Average time on wait list = β0+ β(ha_300to999) + β(ha_1kto10k) + β(ha_over10k)
• Model 2: Simplified Model Including Demand Variables Only Average time on wait list = β0+ β(ha_300to999) + β(ha_1kto10k) + β(ha_over10k) + β(population) + β(occupancy rate)+ β(median gross rent)
• Model 3: Simplified Model Including Demographic Variables Only Average time on wait list = β0+ β(ha_300to999) + β(ha_1kto10k) + β(ha_over10k) + β(%minorities in census tract)
• Model 4: Fully Unrestricted Model Average time on wait list = β0+ β(ha_300to999) + β(ha_1kto10k) + β(ha_over10k) + β(population) + β(occupancy rate)+ β(median gross rent) + β(%minorities in census tract)
30
Relationship between PHA Size and Time on the Waiting List
This study expects that a relationship exists between the size of the public
housing agency and the average amount of time applicants in that PHA are on the
waiting list for subsidized housing assistance. Larger PHAs administer more units, but
they are most often in cities with larger and more disadvantaged populations (i.e. more
concentrated poverty and more minorities). Therefore, cities with larger PHAs usually
have a larger demand for affordable housing, but demand alone is not the deciding
factor in PHA size. Local governments are actually the deciding entity on whether or
not a PHA will be established in that locality. To that point, the local government can
also affect the size of the PHA by lobbying HUD for additional competitive funding,
utilizing the housing trust fund, and channeling CDBG and HOME funds toward
building affordable housing units. This discretion on the part of the locality inherently
means that there is not a perfect correlation between the size of the PHA and the need
in the jurisdiction.
Despite the above qualification, this study’s hypothesis remains that larger
PHAs will have longer average wait times before applicants receive subsidized housing
assistance. This is because cities with a more disadvantaged population may have a
higher demand for affordable housing and without appropriate resources the PHA may
fail to meet this demand due to supply constraints. This mismatch between supply and
31
demand would cause longer wait times for housing assistance; therefore, we can
assume that this phenomenon would be more prevalent in larger cities and
consequently more prevalent in larger PHAs.
The management structure of these larger PHAs may also be more inefficient
leading to longer wait times. This fact is supported by the findings of the GAO study,
which discovered that large PHAs had lower PHAS scores than small PHAs, an
indication of the poor management performance of these larger agencies. Furthermore,
the suggestion that larger PHAs experience more management inefficiency is
maintained by the instance of 5 of the 8 PHAs in receivership being located in larger
cities.8 PHA management is an important factor because if inefficient management
exists housing units may lay vacant for long periods of time when in an alternative
scenario an applicant could have been occupying the unit. Inefficient management can
also result in a large number of vacant units being taken off the rent rolls due to
substandard conditions and severe dilapidation if the PHA has not allocated
rehabilitation funds properly. This type of management inefficiency can currently be
observed within the New York City Housing Authority – an agency which is being
criticized for having nearly 3,300 vacant units outraging housing advocates (Fernandez
2009). In general, large PHAs are more likely to be plagued by housing supply
8 See appendix for additional information on receivership. PHAs in receivership are East St. Louis, New Orleans, Detroit, Kansas City, Chicago, Chester PA, Wellston MO, and the Virgin Islands.
32
deficits and inefficient management, which provides the basis for the hypothesis upon
which this study is predicated - that larger PHAs on average will have a longer wait
time for those applying for housing assistance.
33
Chapter 4. Results and Discussion
Multivariate Analysis As detailed in Tables 9 and 10, four model specifications have been employed
within the multivariate analysis; however, the preferred model specification is Model 4
as it includes all of the relevant control variables and has addressed multicollinearity
with the removal of the poverty variable. This preferred model specification will be
readily used for the discussions of magnitude and statistical significance in the
following sections.
34
Table 9: Summary of Voucher Models
Model 1 Model 2 Model 3 Model 4 Variables PHA-size
dummies PHA-size + demand
PHA-size + demographic
PHA-size + demand+demographic
ha_300to999 8.54 (2.54)** p= 0.001
7.02 (2.12)** p= 0.001
7.35 (2.56)** p= 0.005
6.87 (2.33)** p= 0.004
ha_1Kto10K 16.061 (2.56)** p= <0.0001
14.14 (2.65)** p= <0.0001
14.46 (2.88)** p= <0.0001
13.51 (2.81)** p= <0.0001
ha_over10K 24.05 (4.33)** p= <0.0001
22.02 (4.56)** p= <0.0001
22.55 (4.57)** p= <0.0001
21.69 (4.56)** p= <0.0001
population -0.00055 (0.0008) p= 0.51
-0.00056 (0.0008) p= 0.49
occupancy rate 0.162 (0.125) p= 0.20
0.139 (0.24) p= 0.57
median gross rent 0.022 (0.009)** p= 0.012
0.016 (0.007)* p= 0.036
tminority 0.057 (0.05) p= 0.26
0.06 (0.052) p= 0.254
Joint Significance Tests of: PHA-size dummies 19.26**
p=<0.0001 14.66** p=<0.0001
12.52** p=<0.0001
11.65** p=<0.0001
Demand variables 2.67* p=0.049
2.93* p=0.035
Demographic variables 1.28 p=0.26
1.31 p=0.254
Demand & Demographic variables
2.49* p=0.045
Overall Model 19.26** p=<0.0001
12.47** p=<0.0001
11.78** p=<0.0001
8.93** p=<0.0001
R2 17.28% 21.58% 20.87% 23.33% † statistically significant at p=0.10; * statistically significant at p=0.05; ** statistically significant at p=0.01
35
Vouchers
Discussion of Magnitude:
The above regressions display a very interesting monotonic relationship within
the PHA-size dummy variables. In all four specifications the coefficients for the PHA-
size dummy variables increase monotonically as the range of units administered by the
PHA increases by category (for example moving from PHAs administering 300 to 999
units to PHAs administering 1,000 to 9,999 units). Furthermore, across the
specifications the coefficients remain stable. The increasing coefficients for the PHA-
size dummy variables indicates that as the size of the public housing agency grows the
result is increased applicant wait time for housing choice vouchers.
The magnitudes of the PHA-size coefficients increase as the number of units
administered by the PHA increases. For example: in Model 4, the unrestricted model,
as you move from a small-medium sized PHA (administering between 300 to 999
units) to a medium-large sized PHA (administering between 1,000 and 10,000 units) an
applicant’s wait time is expected to increase on average 6.64 months while a move
from a small PHA (administering between 1 and 299 units) to a small-medium sized
PHA causes an applicant’s wait time to increase on average 6.87 months. Meanwhile
a transition from a medium-large sized PHA to a large PHA (administering over
10,000 units) increases applicant’s average wait time by 8.18 months, holding all other
36
factors constant. This means that the amount of time an applicant must wait for
subsidized housing assistance increases as the housing agency administers more units,
which is demonstrated by the increasingly larger difference between each coefficient
for PHA-size dummy variables.
Median gross rent is the only statistically significant demand variable and
therefore is the only demand variable where magnitude of the coefficient will be
discussed. In Model 4 the coefficient on median gross rent indicates that if median
rent for a city/county increases by $100 per month this is associated with an increase in
average waiting time by 2.6 months. This indicates that the higher the average median
rent is for a county/city the larger the dearth of affordable housing, which propels more
applicants to need subsidized housing creating a longer average wait time for voucher
assistance. With regard to demographic variables, percentage of minorities is not
statistically significant in either model specification, but if it were significant its
coefficient would indicate that if percentage of minorities of a city/county increases by
10 percentage points this is associated with an increase in average waiting time of
approximately 2 weeks.
Discussion of Statistical Significance:
In each of the four model specifications the PHA-size dummy variables were
individually highly statistically significant. In addition, the PHA-size dummy
37
variables are jointly statistically significant in all four models, which indicates that the
size of the PHA is a contributing factor to time on the wait list for voucher applicants.
This fact combined with the previous finding that the coefficients on the PHA-size
dummy variables are increasing as agency size increases demonstrates that as a PHA
administers more units voucher applicants are faced with longer wait times. Since wait
time is an aspect of PHA efficiency this result would contradict the GAO claim that
larger PHAs are more efficient at managing voucher programs. This difference could
be a result of the fact that this regression analysis utilizes wait time as a proxy for
efficiency while the GAO examined SEMAP scores, an overall management scoring
system of voucher programs.
When examining the control variables, there is only one variable that is
statistically significant – median gross rent (p=0.036). The individual significance of
median gross rent is large enough to generate joint statistical significance for the
demand variables despite the insignificance and lack of explanatory power for the
other housing demand variables, population and occupancy rate. For the demographic
variables, tminority is not statistically significant in either model. However, the
individual significance of median gross rent again causes joint statistical significance
(p=0.045) for all of the demand and demographic factors. The joint significance
demonstrates that the demand and demographic variables do have an explanatory effect
38
on applicant wait time for vouchers, but this is mainly due to the statistical significance
of median gross rent.
Table 10: Summary of Public Housing Models
Model 1 Model 2 Model 3 Model 4 Variables PHA-size
dummies PHA-size + demand
PHA-size + demographic
PHA-size + demand+demographic
ha_300to999 3.56 (1.29)* p= 0.006
0.11 (1.28) p= 0.93
3.16 (1.29)* p= 0.015
-0.16 (1.29) p= 0.90
ha_1Kto10K 6.88 (1.59)** p= <0.0001
1.34 (1.74) p= 0.44
4.81 (1.70)** p= 0.005
-0.31 (1.88) p= 0.87
ha_over10K 12.06 (3.11)** p= <0.0001
4.51 (3.18) p= 0.16
8.78 (3.16)** p= 0.006
1.95 (3.44) p= 0.57
population 0.0015 (0.002) p= 0.47
0.0015 (0.002) p= 0.48
occupancy rate 0.091 (0.14) p= 0.52
0.133 (0.14) p= 0.36
median gross rent 0.026 (0.005)** p= <0.0001
0.025 (0.005)** p= <0.0001
tminority 0.057 (0.027)* p= 0.035
0.048 (0.024)* p= 0.046
Joint Significance Tests of: PHA-size dummies 9.33**
p=<0.0001 0.82 p=0.48
4.40** p=0.005
0.20 p=0.898
Demand variables 10.18** p=<0.0001
10.14** p=<0.0001
Demographic variables 4.51* p=0.035
4.03 * p=0.046
Demand & Demographic variables
7.97** p=<0.0001
Overall Model 9.33** p=<0.0001
9.04** p=<0.0001
7.21** p=<0.0001
8.02** p=<0.0001
R2 15.70% 30.00% 17.39% 31.11% † statistically significant at p=0.10; * statistically significant at p=0.05; ** statistically significant at p=0.01
39
Public Housing Units
Discussion of Magnitude:
The above regressions illustrate the very same monotonic relationship within
the PHA-size dummy variables as was previously seen in the voucher model. In all
four specifications the coefficients for the PHA-size dummy variables increase
monotonically; however, the coefficients are increasing by smaller increments than the
voucher model. In addition, unlike the voucher model the coefficients on the PHA-size
dummy variables are not stable.
This increasing monotonic relationship within the PHA-size dummy variables
indicates that as the size of the public housing agency increases it results in increased
waiting time for applicants waiting for public housing units. Since wait time is an
aspect of PHA efficiency this result would confirm the GAO claim that larger PHAs
are less efficient at managing public housing programs. While the present study
examines management efficiency using applicant wait time and a robust regression
analysis, the GAO’s analysis examined which segment of housing agencies had a
higher incidence of troubled PHAS scores to assess management efficiency and did not
control for other factors. Despite the differences in these studies, the results come to a
similar conclusion - that larger PHAs face more management inefficiency, receive
40
more troubled PHAS scores, and consequently applicants endure longer average wait
times.
The incremental increases between the PHA-size categories differ between the
models that include the demand variables (Models 2 and 4) and those models that
exclude the demand variables (Models 1 and 3). Therefore, in order to discuss the
changes in magnitude of the coefficients the discussion has been divided between the
models that include the demand variables and the models that exclude the demand
variables. Within the preferred model, Model 4, the coefficients for small-medium and
medium-large PHAs (agencies administering between 300 and 10,000 units) are
negative but this effect is not statistically significant. Unlike the voucher model, when
demand variables are introduced the PHA-size variables demonstrate no explanatory
power with regard to the applicant wait time. In contrast, the models that exclude the
demand variables, Models 1 and 3, do have significant results for the PHA-size dummy
variables. Within the simplified model, Model 1, moving from a small-medium sized
PHA (administering between 300 to 999 units) to a medium-large sized PHA
(administering between 1,000 and 10,000 units) an applicant’s wait time is expected to
increase on average 3.3 months while a move from a medium-large sized PHA to a
large PHA (administering over 10,000 units) increases applicant’s average wait time
by approximately 5 months, holding all other factors constant.
41
Median gross rent again is the only statistically significant demand variable
mirroring the results in the voucher program where a $100 increase in rent resulted in
an increase in applicant wait time by 2.5 months. Unlike the voucher model,
percentage of minorities is statistically significant for public housing programs with a
10 percentage point increase in minorities being associated with a 2 week increase in
average wait time.
Overall Discussion of Regression Results:
A general examination of the multivariate regression results emphasizes three
main points. First, the results vary greatly between the voucher program and the public
housing program. In the voucher program, the PHA-size variables are individually and
jointly statistically significant in all four model specifications. In the public housing
program, the PHA-size variables are only individually and jointly significant if the
demand variables are excluded from the model. This indicates that the number of units
administered by the public housing agency does have a statistically significant effect
on the time an applicant is on the waiting list for vouchers only. Furthermore, the
larger the number of units administered the longer the additional wait time an applicant
must endure. This is to a much lesser extent for public housing programs than voucher
programs but the same monotonic relationship exists.
42
In general, PHA-size variables had larger and more significant effects on wait
times for voucher programs than public housing programs. This is interesting because
applicant wait time can be considered an efficiency measure, despite its lack of cost-
benefit analysis, and this would insinuate that larger PHAs are less efficient at
managing voucher programs, which does not align with the GAO study. Some may
argue that this divergence is likely due to the fact that the GAO study only examined
SEMAP scores to gauge management efficiency and lacked full regression analysis
with control variables. However, my regression results examine both a fully
unrestricted model with control variables (Model 4) and a simplified model that
includes only housing agency size variables (Model 1) and my findings display that
PHA size is a significant factor in determining applicant wait time for voucher
assistance in both cases. The GAO study analyzed the SEMAP scores of housing
agencies and found that larger agencies received less troubled ratings due to economies
of scale and consequently stated that larger PHAs were more efficient in managing
voucher programs. However, the SEMAP score is a combination of indicators such as
tenant selection procedures, income determination and physical property inspections;
and is in itself a proxy for management efficiency. It is not directly related to the
average wait time that an applicant must wait for a voucher certificate. This is most
likely why there is a discrepancy between larger PHAs receiving less troubled SEMAP
43
scores while my regression results found that larger PHAs had longer average wait
times for voucher assistance.
With regard to other variables that affect applicant wait time the main demand
variable that seems to have a statistically significant effect is median gross rent. In
both the voucher and public housing programs, this variable is statistically significant
in the model specifications that included the demand variables. The statistical
significance of median gross rent implies that as rent increases this affects applicant
wait time because there are more households being priced out of available housing
causing greater demand for low-cost housing and a larger number of applicants on the
wait list. In both voucher and public housing programs an increase in median gross
rent of $100 is associated with approximately 2 months of additional wait time for
subsidized housing assistance. The other housing demand variables, population and
occupancy rate, are both substantively and statistically insignificant.
Regarding demographic characteristics, the percentage of minorities in a
community had a statistically significant effect on average wait time for public housing
programs only. In the voucher program, this variable is statistically insignificant
indicating that demographic characteristics do not individually affect average wait time
when controlling for other variables. However, for public housing programs tminority
was statistically significant in both models implying that as the percentage of
44
minorities in a community increases this will augment the average applicant wait time
for those waiting for public housing units. While voucher certificates can allow
households the option of neighborhood mobility to improve their situation, households
receiving public housing units must remain in the same blighted neighborhoods. The
statistical significance of percentage of minorities for public housing programs may be
a result of the high prevalence of minorities within public housing projects and their
presence coinciding with blighted neighborhood conditions such as lower educational
attainment, inferior public school systems, and higher rates of poverty. All of these
combined factors may have made percentage of minorities associated with longer
average wait times for public housing programs whereas it was not significant for
voucher assistance. Overall the regression results illustrate that agency size is the main
explanatory variable with respect to average applicant wait time for voucher assistance
whereas demographic and housing demand variables – not agency size - were the main
explanatory factors for public housing programs.
Robustness Checks The regression results show that the determinants of applicant wait time are
very different across PHAs that offer voucher certificates and PHAs that offer
subsidized housing units directly. Robustness checks were performed to determine
45
whether the results reflect the underlying or true reality, or whether the results are an
artifact of an overlooked misspecification problem. By splicing the original sample
into smaller restricted samples this study can ensure that the same patterns remain,
which is an indication of the strength of the theoretical model’s explanatory power.
In order to test the robustness of this model three different robustness checks
were performed. First, the sample was spliced to include only PHAs that had both
voucher and public housing programs limiting the sample to 150 observations. This
was done to examine if voucher and public housing programs still differed in their
determinants of applicant wait time when only examining PHAs that administer both
programs. Furthermore, this robustness check controls for factors unique to a PHA
such as efficiency of local governance, political climate and institutional background.
Second, the sample was spliced to include the PHAs of ten states with the
largest number of PHAs limiting the sample to 88 voucher observations and 89 public
housing observations. This robustness check was done to see if there is a state-level
omitted variable inducing bias and creating the differing determinants for vouchers and
public housing programs. By only examining states with a large number of PHAs this
reduces the possibility that PHAs offering vouchers are disproportionately located in
some states while PHAs offering public housing units are disproportionately located in
other states; therefore, resolving the conundrum that state policies could be the
46
differing factor. This robustness check was necessary since panel data for this topic
was unavailable.
Finally, in the last robustness check the original sample’s outliers were
removed to ensure that none of these observations had such a large influence as to
create the difference in determinants for average wait time between vouchers and
public housing programs. The results of the robustness checks are displayed in the
following tables (Tables 11-13).
47
Table 11: Models for Sample Restricted to Only PHAs With Both Voucher & Public Housing Programs
Voucher Models Public Housing Models
Model 1 Model 4 Model 1 Model 4 Variables PHA-size
dummies PHA-size + demand+demographic
PHA-size dummies
PHA-size + demand+demographic
ha_300to999 12.12 (2.90)** p= <0.0001
11.28 (2.64)** p= <0.0001
5.18 (1.37)** p= <0.0001
2.28 (1.22) † p= 0.07
ha_1Kto10K 20.79 (2.81)** p= <0.0001
18.01 (3.48)** p= <0.0001
8.11 (1.63)** p= <0.0001
1.20 (1.86) p= 0.52
ha_over10K 28.11 (4.55)** p= <0.0001
23.46 (4.75)** p= <0.0001
13.12 (3.13)** p= <0.0001
2.99 (3.49) p= 0.39
population 0.00156 (0.002) p= 0.47
0.0014 (0.0021) p= 0.50
occupancy rate -0.083 (0.304) p= 0.79
0.21 (0.19) p= 0.28
median gross rent 0.015 (0.01) p= 0.16
0.023 (0.006)** p= <0.0001
tminority 0.08 (0.06) p= 0.18
0.066 (0.026)* p= 0.014
Joint Significance Tests of: PHA-size dummies 25.27**
p=<0.0001 13.53** p=<0.001
11.86** p=<0.0001
1.32 p=0.27
Demand variables 1.01 p=0.39
9.16** p=<0.0001
Demographic variables
1.86 p=0.18
6.24* p=0.014
Demand & Demographic variables
1.79 p=0.14
7.50** p=<0.0001
Overall Model 25.27** p=<0.0001
13.55** p=<0.0001
11.86** p=<0.0001
8.92** p=<0.0001
R2 18.09% 26.32% 13.36% 30.69% † statistically significant at p=0.10; * statistically significant at p=0.05; ** statistically significant at p=0.01
48
Main Differences in Vouchers:
When examining the main differences between the original model and the
models for the restricted sample containing only PHAs that have both voucher and
public housing programs most of the major patterns remain the same. All of the PHA-
size variables have a monotonic relationship and each specification still has significant
results mirroring the original regression. While population and occupancy rate
remained insignificant, median gross rent became no longer statistically significant in
the restricted sample (p=0.16). Overall the joint significance tests display that the
PHA-size variables are still jointly statistically significant indicating agency size is still
a main determinant of applicant wait time, but the loss of significance for median gross
rent causes the demand variables to no longer be jointly statistical significant, which is
a major divergence from the original model.
Main Differences in Public Housing:
Many of the major patterns seen in the original model are displayed within the
restricted sample except for the monotonic relationship within the PHA-size variables.
Within the restricted sample Model 4 no longer follows the same monotonic
relationship and unlike the original regression results, this specification displays
marginal statistical significance for the small-medium sized PHAs (agencies
administering 300 to 999 units). With regard to the control variables, median gross
49
rent and percentage of minorities continue to be statistically significant in the restricted
sample and their magnitudes remain almost identical to the original regression.
Overall the joint significance tests display that the PHA-size variables lack joint
statistically significance when demand variables are introduced to the specification,
mirroring the original results. Furthermore, emulating the original model the demand
variables are highly jointly statistically significant (p=<0.0001), due to the significance
of median gross rent, and the demographic factors remained jointly significant as well.
50
Table 12: Models for Sample Restricted to PHAs within the Ten States with the Largest Number of Housing Agencies
Voucher Models Public Housing Models
Model 1 Model 4 Model 1 Model 4 Variables PHA-size
dummies PHA-size + demand+demographic
PHA-size dummies
PHA-size + demand+demographic
ha_300to999 7.29 (4.43) † p= 0.10
4.19 (3.95) p= 0.29
2.51 (2.30) p= 0.28
-0.42 (2.21) p= 0.85
ha_1Kto10K 14.01 (3.69)** p= <0.0001
12.70 (4.59)** p= 0.007
7.51 (2.47)** p= 0.003
0.92 (3.01) p= 0.76
ha_over10K 25.16 (6.77)** p= <0.0001
23.54 (7.68)** p= 0.003
12.40 (4.92)* p= 0.014
1.34 (5.42) p= 0.81
population -0.0002 (0.0011) p= 0.84
0.0016 (0.0023) p= 0.51
occupancy rate 0.12 (0.36) p= 0.74
0.114 (0.21) p= 0.59
median gross rent 0.009 (0.012) p= 0.43
0.024 (0.008)** p= 0.005
tminority 0.007 (0.07) p= 0.92
0.048 (0.033) p= 0.15
Joint Significance Tests of: PHA-size dummies 7.22**
p=0.0002 4.53** p=0.006
4.63** p=0.005
0.13 p=0.94
Demand variables 0.32 p=0.81
3.91* p=0.012
Demographic variables
0.01 p=0.92
2.15 p=0.15
Demand & Demographic variables
0.24 p=0.92
3.52* p=0.011
Overall Model 7.22** p=0.0002
3.37** p=0.004
4.63** p=0.005
4.63** p=0.15
R2 16.04% 24.37% 14.70% 28.55% † statistically significant at p=0.10; * statistically significant at p=0.05; ** statistically significant at p=0.01
51
Main Differences in Vouchers:
Some of the major patterns within the model specifications diverge between the
original model and the restricted sample containing PHAs from the 10 states with the
most housing agencies. All of the PHA-size variables still have a monotonic
relationship, however now the smallest PHA-size dummy variable (ha_300to999) is no
longer statistically significant in the unrestricted model. One major difference is that
median gross rent is no longer statistically significant within the restricted sample
thereby causing the demand variables to be jointly insignificant. In addition, the
minority variable loses significance and no longer has any explanatory power (p=0.91).
Overall, in the restricted model only the PHA-size variables are jointly statistically
significant and demonstrate explanatory power with regard to applicant wait time
unlike the original model where median gross rent also had explanatory power.
Main Differences in Public Housing:
Some of the major patterns within the model specifications stay the same for
the regressions run on the restricted sample containing PHAs from the 10 states with
the most housing agencies. All of the PHA-size variables still have a monotonic
relationship; however, now the smallest PHA-size dummy variable (ha_300to999) is
no longer statistically significant within Model 1. Median gross rent remains highly
statistically significant in both regressions; however percentage of minorities becomes
52
insignificant in the restricted sample causing a loss of joint significance for the
demographic characteristics. The other joint significance tests display identical
findings to the original sample. Therefore, the same conclusions can be reached –
PHA-size does not individually affect average wait time for public housing units once
controls in the form of demand variables, in particular median gross rent, are
introduced.
53
Table 13: Models for Sample Restricted to Exclude Outliers
Voucher Models Public Housing Models
Model 1 Model 4 Model 1 Model 4 Variables PHA-size
dummies PHA-size + demand+demographic
PHA-size dummies
PHA-size + demand+demographic
ha_300to999 6.57 (2.11)** p= 0.002
7.01 (2.31)** p= 0.003
4.37 (0.96)** p= <0.0001
1.09 (0.97) p= 0.26
ha_1Kto10K 14.93 (2.32)** p= <0.0001
12.75 (2.59)** p= <0.0001
8.13 (1.38)** p= <0.0001
1.55 (1.75) p= 0.38
ha_over10K 19.47 (3.75)** p= <0.0001
17.71 (3.88)** p= <0.0001
10.97 (3.02)** p= <0.0001
2.33 (3.15) p= 0.46
population -0.00065 (0.0007) p= 0.38
0.0010 (0.0018) p= 0.60
occupancy rate 0.217 (0.22) p= 0.33
0.18 (0.14) p= 0.20
median gross rent 0.015 (0.007)* p= 0.03
0.022 (0.005)** p= <0.0001
tminority 0.04 (0.047) p= 0.38
0.044 (0.024) † p= 0.07
Joint Significance Tests of: PHA-size dummies 18.17**
p=<0.0001 11.45** p=<0.0001
17.58** p=<0.0001
0.50 p=0.68
Demand variables 3.70* p=0.013
8.94** p=<0.0001
Demographic variables
0.78 p=0.38
3.36 † p=0.07
Demand & Demographic variables
2.92* p=0.02
6.79** p=<0.0001
Overall Model 18.17** p=<0.0001
8.49** p=<0.0001
17.58** p=<0.0001
10.14** p=<0.0001
R2 20.89% 22.55% 17.05% 30.96% † statistically significant at p=0.10; * statistically significant at p=0.05; ** statistically significant at p=0.01
54
The outliers excluded in this robustness check were identified using scatter
plots of the average months on the wait list as a function of PHA size. In total eight
PHAs were classified as outliers. 9 Therefore, the restricted sample had 356
observations, of which 175 are voucher programs and 181 are public housing
programs.
Main Differences in Vouchers:
A full examination of the original model and the restricted sample (excluding
outliers) displays that the patterns remain identical. All of the PHA-size variables still
have a monotonic relationship and are statistically significant reflecting the original
regression results. The population, occupancy rate and minority variables remain
statistically insignificant, while median gross rent continues to be significant producing
joint significance for the demand variables. Overall the joint significance tests display
the same patterns as the original model. The joint significance of the PHA-size
variables demonstrates that even with the introduction of control variables the size of
the public housing agency does have an effect on the length of time an applicant is on
the waiting list for vouchers.
Main Differences in Public Housing:
9 The following PHAs were classified as outliers: Glassboro, NJ; Freeport, NY; Chattanooga, TN; Miami Dade, FL; and Philadelphia, PA for voucher programs and Chicago, IL; Hemingford, NE; and Mooresville, NC for public housing programs. In accordance with my prior criteria if a voucher program or public housing program is removed from the sample, the sister program is removed as well.
55
When examining the main differences between the original model and the
restricted sample, which excludes outliers, most of the major patterns remain visible.
All of the PHA-size variables still have a monotonic relationship and each
specification that includes demand variables still has insignificant results for the PHA-
size dummy variables. Of the demand and demographic variables, population and
occupancy rate remain statistically insignificant; however, median gross rent and
percentage of minorities continue to be statistically significant and their magnitudes
remains almost identical to the original regression. Overall the joint significance tests
display similar patterns when analyzing the restricted sample. Therefore, he same
conclusions can be reached – PHA-size does not individually effect average wait time
for public housing units once controls in the form of demand variables, in particular
median gross rent, are introduced. The demand variables are jointly highly statistically
significant.
Overall Discussion of Robustness Checks:
These robustness checks have shown that the original regression results for
both voucher and public housing programs are robust and are not an artifact of a
misspecification problem. The main conclusions being that the determinants of
applicant wait time are extremely different for voucher programs and public housing
programs. The restricted sample of PHAs that manage both voucher and public
56
housing programs and the restricted sample excluding outliers had almost identical
patterns while the restricted sample of PHAs from the top ten states with the most
housing agencies saw some major divergences. This is most likely due to the size of
the restricted sample with only 89 observations for each program; therefore, each
observation has more influence on the coefficients and thus can change significance
levels and coefficient magnitudes.
57
Chapter 5. Policy Implications
The results of the present study provide evidence that public housing agency
size is a contributing factor to applicant wait time for those waiting for voucher
certificates. However, the results for public housing programs are contradictive; with
public housing program wait time mainly being driven by demographic and demand
characteristics. What are the implications of these findings for policymakers? To
answer this question it is necessary to understand that government funding is not
always forthcoming and there are inequities in the placement of public housing
agencies.
Currently, a government chosen formula that allocates funds based on the
previous year’s funding and current budget drives PHA program funding. For voucher
programs, HUD maintains a static total amount of funding; therefore, since funding per
PHA is allocated as an absolute amount and not for a specific number of vouchers this
can actually result in fewer available vouchers when rents increase. For public housing
programs funding is allocated using each agency’s PHAS scores to determine the
allocation of high performer bonuses, but these additional funds are still only
delineated for capital improvements. With the exception of HOPE VI projects, few
new public housing projects are being constructed leaving little leeway for PHAs to
use federal funding to create additional units. The failure of the federal government to
58
expand voucher funding to PHAs with high demand and allow for the creation of
additional subsidized housing units results in a dearth of policy options for PHA
managers dealing with the increased demand for subsidized housing during the current
recession.
Because of the lack of funding made available by the federal government to
expand voucher programs and create new public housing units there are limited options
for policymakers trying to decrease the average time applicants are waiting for
subsidized housing assistance. From the regression results it has been shown that the
larger public housing agencies have longer average wait times for voucher programs
but this same causal relationship can not be deduced for public housing programs.
These results indicate that if applicant wait time can be considered a proxy for
management efficiency then large PHAs are more prone to management inefficiency
for voucher programs. To correct this additional training should be implemented to
correct for management inefficiencies such as slow processes for setting up security
deposits, interviewing landlords and filing necessary paperwork for payments, which
are administrative requirements for voucher programs only. If policymakers attempt to
correct for these management inefficiencies larger PHAs may have the possibility of
having similar applicant wait times to smaller agencies.
59
While supplementing funding to larger PHAs may seem like an easy solution
for the issue of management inefficiency within large PHAs, this ultimately will not
solve the problem of inefficiency or the real problem of unreasonably long wait times
for applicants waiting for subsidized housing assistance. Overall, policymakers must
recognize that long wait times are due to public housing agencies being placed in
neighborhoods with intense need – disadvantaged populations filled with poverty,
unaffordable rent, and higher percentages of minorities. Since localities have the
option of placing a housing agency within their jurisdiction only the localities with
disadvantaged populations elect to make subsidized housing a community priority.
Subsequently this causes PHAs to be created in cities and counties where there will be
high demand for subsidized housing and in times of recession this demand will
increase. To decrease average wait time for applicants policymakers must increase the
number of vouchers and public housing units in areas with high demand; it is
insufficient to simply address the size of housing agencies or implement training
programs to increase program efficiency.
The results of this study demonstrate that the determinants of applicant wait
time vary by program; with agency size being a determining factor for vouchers while
demand and demographic characteristics affect public housing programs.10 This
10 This result is intriguing because it insinuates that PHAs, which administer both programs, can have inefficient management of voucher programs while efficiently managing public housing units.
60
divergence in the determinants of applicant wait time necessitate different policies for
voucher and public housing programs in order to sufficiently address the unreasonably
long wait times suffered by thousands of Americans. The bottom line is larger PHAs
have longer average wait times for subsidized housing assistance because these
agencies are in the communities with the largest amount of need. However, the
instance of agency size having a significant effect on voucher programs proves that
management inefficiency is a very real problem as well, and therefore must be
addressed with training sessions to increase efficiency within voucher management.
The efficacy of program management, while an explanatory factor for voucher
programs, is not the main reason for long applicant wait times overall - it is the lack of
affordable housing in these communities and the failure of the federal government to
remedy this situation. As a result, policymakers would do better to promote additional
funding and expand voucher certificates to cities with the most demand for subsidized
housing assistance, create training modules to increase voucher program efficiency,
and modify HUD funding requirements to include the creation of additional units
instead of focusing only on capital improvements.
61
Chapter 6. Conclusion
Over the past five years housing prices have risen considerably driving
thousands of individuals and families to apply for subsidized housing when faced with
unaffordable housing costs in their community and the recent recession has only made
this phenomenon all the more prevalent. Instead of building additional public housing
units or funding additional vouchers the federal government has focused its resources
toward capital improvements on existing structures and renewing expiring Housing
Choice vouchers. While these measures are important to stem the depletion of
subsidized housing units this funding has done nothing to add new units to the
affordable housing stock.
The present study addresses the question of whether the size of the public
housing agency plays a role in the average amount of time an applicant is on the
waiting list for subsidized housing assistance (both voucher certificates and public
housing units). Previous studies had mainly examined either the efficiency of housing
agency size or analyzed the characteristics of wait list applicants from a qualitative
angle. This study amalgamates these two separate topics together and examines them
in a quantitative manner providing new findings largely absent from previous
literature. A series of model specifications employing a randomized sample of PHAs
estimates the effect of housing agency size on average time on the wait list for both
62
voucher and public housing programs. These model specifications include control
variables for housing demand factors and demographic characteristics of the relevant
census tract to control for locality-specific characteristics and the extent of a
community’s disadvantaged population, which the literature indicated could increase
applicant wait time. The results of these models indicate that applicants waiting for
voucher certificates face longer wait times as a consequence of agency size, while
applicants waiting for public housing units face longer wait times as a result of housing
demand and demographic factors. Applicant wait time for voucher assistance is on
average 22 months longer for housing agencies administering over 10,000 units than
for agencies administering fewer than 300 units. In contrast, applicant wait time for
public housing units is on average only 2 months longer for agencies administering
over 10,000 units in comparison to agencies administering under 300 units and these
results were not statistically significant demonstrating that housing agency size is not a
determining factor of applicant wait time for public housing programs. The evidence
of this analysis, therefore, supports the hypothesis that larger housing agencies have
longer average wait times for applicants waiting for subsidized housing assistance;
however, this relationship only holds true for voucher programs.
In light of the statistically significant results that this study provides, policy
makers should realize that agency size and centralized bureaucracy are not the main
63
factors that need to be addressed with regard to decreasing applicant wait time – it is
the dearth of subsidized housing units available. Applicant duration on the waiting list
is significantly affected by agency size for voucher programs, which is an indication
that bureaucratic size and program management may lengthen wait times. However,
public housing programs do not have a significant relationship between applicant wait
time and agency size. Policy makers need to understand this juxtaposition and
recognize that the same PHAs that efficiently manage public housing programs
inefficiently manage voucher programs; to remedy this situation additional training
must be implemented to ameliorate voucher management. While this analysis has
demonstrated that there is a relationship between housing agency size and applicant
wait time, a policymaker’s long-term goal should be to decrease applicant wait time.
Current government funding for vouchers and public housing units is inadequate and
therefore increased funding needs to be targeted at PHAs in areas with the largest
demand for subsidized housing assistance.
Annu Mangat, of the American Civil Liberties Union, wrote, “People tend to
think of affordable housing as a problem that uniquely affects the poor and homeless.
However, in the last five years . . . we're seeing this problem has spread to working-
class people who are priced out of the market.” Low-income and working class
families can no longer afford housing in many American cities, yet public housing
64
units and Housing Choice vouchers are still viewed by much of the American public as
part of a welfare hand out. This study’s findings aimed to understand the determinants
of applicant duration on the wait list because each year thousands of families struggle
to get by while waiting for subsidized housing assistance. These same families could
have their hopes for affordable shelter realized if policymakers expanded funding to
PHAs in areas of high need, created new affordable housing opportunities and
implemented management training programs. If this study’s policy implications are
implemented the results would be far reaching particularly in times when America is
deeply mired in a recession and demand for affordable housing is at its peak.
65
Chapter 7. Limitations & Directions for Future Research
While a robust analysis of this topic was attempted, the methodology utilized in
this study had its limitations due to time constraints and restricted information. The
most prominent is the lack of HUD-compiled management efficiency scores for each
public housing agency program. The PHAS and SEMAP scores are not publicly
available and frequent attempts to contact HUD proved futile. These management
efficiency scores would have allowed this study to have an additional level of clarity
regarding the relationship between program management and applicant wait time for
subsidized housing assistance. To overcome this limitation, this study utilized
applicant wait time as a proxy for efficiency and found that large voucher programs
had longer average wait times and subsequently were less efficient, while agency size
was not an explanatory factor in applicant wait time for public housing programs. The
previous literature on housing agency size and efficiency supports the exact opposite to
be true. The likely reason for this deviation is the use of applicant wait time as an
efficiency proxy instead of utilizing HUD’s management efficiency scores. The
absence of these variables from the model specifications could possibly alter the
results.
Furthermore, future research would benefit from utilizing the entire universe of
observations for PHAs. Due to constraints with the physical imputation of Census data
66
for the housing demand variables the entire universe of PHAs was cut into a
randomized sample for this study. There are further opportunities to expand upon this
study by potentially examining every PHA within the United States and including
additional housing demand statistics provided by the Census.
67
Appendix
Changes Made to Dataset The following changes were made to the dataset in order to recode missing
variables and remove programs that were not within the population of interest.
Prior to making any changes to the dataset, the original dataset included three
types of programs: public housing, voucher and moderate rehabilitation. Some public
housing agencies have all three programs but others especially the smaller PHAs only
have public housing or public housing and voucher programs. The observations for
moderate rehabilitation have been removed from the dataset because this subset is not
in the population of interest and it is not a large subset of the data (only 664
observations out of 6,412 total). Moderate rehabilitation units are units within a PHA
that are being rehabilitated and are not units that would be occupied by new applicants
on the housing waitlist. Furthermore, the majority of the observations for moderate
rehabilitation was missing the average months on the waiting list and therefore would
be dropped from the dataset during a regression.
Observations that had a -5 or -4 for months_waiting were recoded as missing
variables. Prior to this recoding there were a total of 31 observations that had
months_waiting as a missing variable with nothing coded for each observation. A
variable coded as -5 represents a missing observation due to non-reporting while a -4
represents a missing observation due to suppression (where the cell entry is less than
68
11 for reported families per the HUD A Picture of Subsidized Households codebook).
Prior to recoding these observations as missing, the distribution of the missing values
was examined displaying that there are more missing observations for months_waiting
due to suppression than the ones that are missing due to non-reporting. The table
below displays the distribution of the missing observations within the original dataset.
Table 14: Distribution of Missing Observations for Months_Waiting Overall Distribution of
Missing Observations Distribution of -4 Missing
Values Distribution of -5 Missing
Values
HA Size
Frequency Percent Cumulative Percent
Frequency Percent Cumulative Percent
Frequency Percent Cumulative Percent
1 241 34% 34% 204 35% 35% 29 24% 24%
2 139 19% 53% 101 17% 52% 35 29% 53%
3 72 10% 63% 59 10% 62% 12 10% 63%
4 94 13% 76% 83 14% 76% 11 9% 73%
5 126 18% 94% 106 18% 95% 20 17% 89%
6 24 3% 97% 18 3% 98% 6 5% 94%
7 19 3% 99.7% 12 2% 99.7% 7 6% 100%
8 2 0.3% 100% 2 0.3% 100% 0 0% 100%
Total 717 585 120
Note: Percents may not sum to 100% due to rounding.
The following changes were made to the dataset in order to recode missing
variables and remove programs that were not within the population of interest.
Prior to making any changes to the dataset, the original dataset included three
types of programs: public housing, voucher and moderate rehabilitation. Some public
housing agencies have all three programs but others especially the smaller PHAs only
69
have public housing or public housing and voucher programs. The observations for
moderate rehabilitation have been removed from the dataset because this subset is not
in the population of interest and it is not a large subset of the data (only 664
observations out of 6,412 total). Moderate rehabilitation units are units within a PHA
that are being rehabilitated and are not units that would be occupied by new applicants
on the housing waitlist. Furthermore, the majority of the observations for moderate
rehabilitation was missing the average months on the waiting list and therefore would
be dropped from the dataset during a regression.
Observations that had a -5 or -4 for months_waiting were recoded as missing
variables. Prior to this recoding there were a total of 31 observations that had
months_waiting as a missing variable with nothing coded for each observation. A
variable coded as -5 represents a missing observation due to non-reporting while a -4
represents a missing observation due to suppression (where the cell entry is less than
11 for reported families per the HUD A Picture of Subsidized Households codebook).
Prior to recoding these observations as missing, the distribution of the missing values
was examined displaying that there are more missing observations for months_waiting
due to suppression than the ones that are missing due to non-reporting. The table
below displays the distribution of the missing observations within the original dataset.
70
Receiverships A PHA can be placed in receivership if the agency is badly managed to the
point that HUD removes the agency director. There are multiple types of receivership:
administrative, cooperative endeavor, and judicial. Administrative receivership is a
process where HUD declares a PHA in substantial default of its Annual Contributions
Contract and takes control of the PHA using the powers granted to HUD under the
Housing Act of 1937. In this type of receivership HUD will appoint someone to work
on-site and manage the PHA. A PHA under a Cooperative Endeavor Agreement
operates under a voluntary agreement with HUD and the troubled PHA that defines the
terms and conditions of administrative receivership; however, this type of agreement
facilitates cooperation between HUD and the local government during receivership.
Judicial receiverships are established and monitored by the federal courts with the
District Court appointing a receiver to conduct the PHA’s affairs. Currently on
HUD’s website there are eight PHAs in receivership; four in administrative
receivership, one in a cooperative endeavor agreement, and three in judicial
receivership; as seen in the map below.
71
Chart 1: Map of HUD Receiverships
Source: U.S. Department of Housing and Urban Development
In addition, once a PHA goes into receivership there is a long recovery process
with some PHAs having been in receivership for more that two decades indicating that
inefficient management is not something that is easily resolved. There is a lot of
variance in the duration that PHAs have been in receivership as shown in Table 15.
72
Table 15: Receiverships by Location and Start Date Name Locale Start Date of Receivership Administrative:
East St. Louis Housing Authority East St. Louis, IL October 1985
Wellston Housing Authority Wellston, MO July 1996
Housing Authority of New Orleans New Orleans, LA February 2002
Virgin Islands Housing Authority St. Thomas, VI August 2003
Cooperative Endeavor Agreement: Detroit Housing Commission Detroit, MI July 2005
Judicial: Housing Authority of Kansas City Kansas City, MO 1993
Chester Housing Authority Chester, PA 1994
Chicago Housing Authority Chicago, IL 1987
Creation of Randomized Sample The random sample was created using the following steps. First, a heavier
weight was utilized on the largest PHAs because these are the agencies of interest and
the purpose of this study is to analyze if the largest PHAs have the longest wait times.
Furthermore, the largest PHAs had the fewest number of observations thus
necessitating a higher weight so they are prevalent in the sample. By heavily
weighting the largest PHAs, the sample has a larger percentage of the observations
within these housing agency size categories. Subsequently, weights were assigned: 2%
of small PHAs were selected (HA categories 1 and 2); 5% of small-medium PHAs
were selected (HA categories 3, 4, 5, and 6); and the largest weights were chosen for
73
the largest PHAs (HA categories 7, 8, and 9), with 20%, 50% and 100%, respectively.
Overall 234 observations were chosen for the representative random sample as
displayed in Table 16.
Table 16: Weights & Frequency for Randomized Sample Housing Agency (HA) Category
Frequency of PHAs within HA Category
Percentage Selected for Random Sample
Number for Random Sample
1 1358 2% 27
2 1457 2% 29
3 740 5% 37
4 819 5% 41
5 687 5% 34
6 134 5% 7
7 105 20% 21
8 59 50% 30
9 8 100% 8
Total 234
Once the strata and size of the sample were chosen, a random number generator
was used to assign random numbers to each observation with each HA category.11
After the random numbers were generated for each observation in the strata, the
observations were then sorted by the randomly generated number and the appropriate
number of observations was selected for each specific stratum based on the above
calculations.
11 Website for Random Number Generator used: http://www.random.org/integers/
74
Many PHAs have either a public housing program or a voucher program with a
majority of agencies having both programs. Since the randomized selection process
could pick either one or both of these programs for a particular agency this discrepancy
had to be corrected. For example, the voucher program of Rockville Maryland could
be selected without its public housing program, yet the same PHA runs both of these
programs. In order to correct for this, after randomization the sister program of the
programs selected by the random number generator were also added to the random
sample (i.e. added the voucher program for the PHA if the public housing program was
selected and vice versa). For those PHAs that just administer one program only the
program selected via the random number generator was included in the sample.
Consequently, there are now 370 observations in the random sample due to the
addition of these sister programs.
Correlations Originally the regressions were to include both minority and poverty variables;
however, percentage of poverty in the census tract was excluded due to its high
correlation with the minority variable and because some of its effects were also being
accounted for within the median gross rent variable and thus it was excluded. The
correlation between the control variables is shown below:
75
Voucher Correlation:
Variables Population Occupancy Rate
Median Gross Rent
Tpoverty Tminority
Population 1.00 Occupancy Rate
0.07514 p=0.3148
1.00
Median Gross Rent
0.1496* p=0.044
0.4055** p=<0.0001
1.00
Tpoverty 0.1041 p=0.1887
-0.1846* p=0.0191
-0.1833* p=0.02
1.00
Tminority 0.1939* p=0.0137
-0.0088 p=0.9122
0.0821 p=0.3006
0.8309** p=<0.0001
1.00
Public Housing Correlation:
Variables Population Occupancy Rate
Median Gross Rent
Tpoverty Tminority
Population 1.00 Occupancy Rate
0.1719* P=0.018
1.00
Median Gross Rent
0.3029** P=<0.0001
0.4883** P=<0.0001
1.00
Tpoverty 0.2384** P=0.001
-0.1284 † P=0.079
0.0098 P=0.894
1.00
Tminority 0.3635** P=<0.0001
0.0777 P=0.2891
0.3465** P=<0.0001
0.7698** P=<0.0001
1.00
76
References
Bratt, Rachel G. “Public Housing Authorities: Determining an Appropriate Role in a
National Preservation Strategy” Housing Policy Debate Vol. 2 Issue 2.
Fernandez, Manny. (2009, December 8). Impatience Grows Over Vacancy Rate in
Public Housing. New York Times, Retrieved December 10, 2009 from
http://cityroom.blogs.nytimes.com/2009/12/08/vacancy-rate-in-public-housing-
spurs-impatience/?hp
Government Accountability Office. (2003, October). Public Housing: Small and
Larger Agencies Have Similar Views on Many Recent Housing Reforms.
Report to Ranking Minority Member, Subcommittee on Housing and
Transportation, Committee on Banking, Housing and Urban Affairs, US
Senate.
National Low Income Housing Coalition. (2003, October). A Look at Waiting Lists:
What Can We Learn From HUD Approved Annual Plans? NLIHC Research
Note #04-03. Retrieved October 15, 2009 from www.nlihc.org/doc/04-03.pdf
77
U.S. Department of Housing and Urban Development. Receiverships. Retrieved
December 15, 2009 from
http://www.disasterhousing.gov/offices/pih/oro/rec.cfm
Watson, Jamal. (2003, December 25). Warehousing, Stringer: City Housing Vacancies
Leave Tenants Out in Cold. New York Amsterdam News, Vol. 94 No 52.
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