FINAL THESIS - Georgetown University

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.

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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.

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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.

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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

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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

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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

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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.

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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.

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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).

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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

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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

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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.

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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

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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

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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.

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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

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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

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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

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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.

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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


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%


Std: 11.8% Min: 0% Max: 59%

% of Minorities in the Surrounding Census Tract (tminority)


Std: 25.1% Min: 0% Max: 99%


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)


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)


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


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


Std: 14.3


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



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%


Tpoverty Mean: 20.3% Median: 17%



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%



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


dummies PHA-size + demand

PHA-size + 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


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


7.97** p=<0.0001


9.04** p=<0.0001

7.21** p=<0.0001

8.02** p=<0.0001


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


dummies PHA-size + demand+demographic

PHA-size dummies


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


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


1.79 p=0.14

7.50** p=<0.0001


13.55** p=<0.0001

11.86** p=<0.0001

8.92** p=<0.0001


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




PHA-size dummies


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


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


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


0.01 p=0.92

2.15 p=0.15


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


51


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.


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




PHA-size dummies


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


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


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


0.78 p=0.38

3.36 † p=0.07


2.92* p=0.02

6.79** p=<0.0001


8.49** p=<0.0001

17.58** p=<0.0001

10.14** p=<0.0001


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.


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.


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




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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