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Rental Housing After Subsidy

Permanent Link: http://ufdc.ufl.edu/UFE0042168/00001

Material Information

Title: Rental Housing After Subsidy Affordability of Florida's Formerly Subsidized Housing Stock
Physical Description: 1 online resource (61 p.)
Language: english
Creator: Stewart, Caleb
Publisher: University of Florida
Place of Publication: Gainesville, Fla.
Publication Date: 2010

Subjects

Subjects / Keywords: affordable, economic, housing, hud, low
Urban and Regional Planning -- Dissertations, Academic -- UF
Genre: Urban and Regional Planning thesis, M.A.U.R.P.
bibliography   ( marcgt )
theses   ( marcgt )
government publication (state, provincial, terriorial, dependent)   ( marcgt )
born-digital   ( sobekcm )
Electronic Thesis or Dissertation

Notes

Abstract: RENTAL HOUSING AFTER SUBSIDY; AFFORDABILITY OF FLORIDA?S FORMERLY SUBSIDIZED HOUSING STOCK By Caleb Stewart August 2010 Chair: Dawn Jourdan Cochair: Christopher Silver Major: Urban and Regional Planning Much of the nation?s rental housing is affordable to low income families as a result of local, state and national government subsidy programs that help to keep rents low. Property owners receive incentives such as rent subsidies and below market interest rate loans in exchange for keeping rents affordable to low income households for a designated period of time (Center for Housing Policy, 2008). These properties are subject to regular inspections during the designated period of time to ensure that they continue to provide quality housing to their tenants. Physical deterioration is a problem for the subsidized rental housing stock, since it can render a building uninhabitable or force an owner to raise the property?s rents in order to rehabilitate the building. If a property fails an inspection, subsidies and below market interest rate loans attached to the property can be withdrawn, forcing the owner to sell the property or raise rents to market rate. Additionally, as a property?s contract to provide affordable housing expires, an owner can choose to opt out of the subsidy program and raise their rents to market rates (Center for Housing Policy, 2008). The purpose of this research is to explore the extent to which formerly subsidized rental housing developments continue to provide affordable housing after their exit from the assisted housing stock or, conversely, the extent to which the properties are now financially out of reach to low-income tenants. An affordability index was designed for this study in order to gauge the affordability of properties at 60% of the area median income (AMI). The research sought to answer two sets of questions about the current status of Florida?s formerly assisted housing stock: ? What paths do properties take after leaving the assisted housing stock? These paths might include remaining as rental housing, conversion to for-sale condominiums, conversion to condominiums that are available for rent, demolition, or other statuses. What structure, owner, location, and market characteristics are associated with each of these paths? ? How affordable are the rents of properties that have remained rental housing? How do results vary by property characteristics? This study found that the majority of Florida?s formerly assisted housing stock has continued to function as rental properties. Just over 60% of these rental properties have remained affordable at 60% AMI at the property level, although this does not reflect the number of affordable rental units in the State. This study found properties that were formerly funded through Housing and Urban Development (HUD) programs to have a much higher tendency to remain affordable at the 60% AMI level.
General Note: In the series University of Florida Digital Collections.
General Note: Includes vita.
Bibliography: Includes bibliographical references.
Source of Description: Description based on online resource; title from PDF title page.
Source of Description: This bibliographic record is available under the Creative Commons CC0 public domain dedication. The University of Florida Libraries, as creator of this bibliographic record, has waived all rights to it worldwide under copyright law, including all related and neighboring rights, to the extent allowed by law.
Statement of Responsibility: by Caleb Stewart.
Thesis: Thesis (M.A.U.R.P.)--University of Florida, 2010.
Local: Adviser: Jourdan, Dawn.
Local: Co-adviser: Silver, Christopher.
Electronic Access: RESTRICTED TO UF STUDENTS, STAFF, FACULTY, AND ON-CAMPUS USE UNTIL 2012-08-31

Record Information

Source Institution: UFRGP
Rights Management: Applicable rights reserved.
Classification: lcc - LD1780 2010
System ID: UFE0042168:00001

Permanent Link: http://ufdc.ufl.edu/UFE0042168/00001

Material Information

Title: Rental Housing After Subsidy Affordability of Florida's Formerly Subsidized Housing Stock
Physical Description: 1 online resource (61 p.)
Language: english
Creator: Stewart, Caleb
Publisher: University of Florida
Place of Publication: Gainesville, Fla.
Publication Date: 2010

Subjects

Subjects / Keywords: affordable, economic, housing, hud, low
Urban and Regional Planning -- Dissertations, Academic -- UF
Genre: Urban and Regional Planning thesis, M.A.U.R.P.
bibliography   ( marcgt )
theses   ( marcgt )
government publication (state, provincial, terriorial, dependent)   ( marcgt )
born-digital   ( sobekcm )
Electronic Thesis or Dissertation

Notes

Abstract: RENTAL HOUSING AFTER SUBSIDY; AFFORDABILITY OF FLORIDA?S FORMERLY SUBSIDIZED HOUSING STOCK By Caleb Stewart August 2010 Chair: Dawn Jourdan Cochair: Christopher Silver Major: Urban and Regional Planning Much of the nation?s rental housing is affordable to low income families as a result of local, state and national government subsidy programs that help to keep rents low. Property owners receive incentives such as rent subsidies and below market interest rate loans in exchange for keeping rents affordable to low income households for a designated period of time (Center for Housing Policy, 2008). These properties are subject to regular inspections during the designated period of time to ensure that they continue to provide quality housing to their tenants. Physical deterioration is a problem for the subsidized rental housing stock, since it can render a building uninhabitable or force an owner to raise the property?s rents in order to rehabilitate the building. If a property fails an inspection, subsidies and below market interest rate loans attached to the property can be withdrawn, forcing the owner to sell the property or raise rents to market rate. Additionally, as a property?s contract to provide affordable housing expires, an owner can choose to opt out of the subsidy program and raise their rents to market rates (Center for Housing Policy, 2008). The purpose of this research is to explore the extent to which formerly subsidized rental housing developments continue to provide affordable housing after their exit from the assisted housing stock or, conversely, the extent to which the properties are now financially out of reach to low-income tenants. An affordability index was designed for this study in order to gauge the affordability of properties at 60% of the area median income (AMI). The research sought to answer two sets of questions about the current status of Florida?s formerly assisted housing stock: ? What paths do properties take after leaving the assisted housing stock? These paths might include remaining as rental housing, conversion to for-sale condominiums, conversion to condominiums that are available for rent, demolition, or other statuses. What structure, owner, location, and market characteristics are associated with each of these paths? ? How affordable are the rents of properties that have remained rental housing? How do results vary by property characteristics? This study found that the majority of Florida?s formerly assisted housing stock has continued to function as rental properties. Just over 60% of these rental properties have remained affordable at 60% AMI at the property level, although this does not reflect the number of affordable rental units in the State. This study found properties that were formerly funded through Housing and Urban Development (HUD) programs to have a much higher tendency to remain affordable at the 60% AMI level.
General Note: In the series University of Florida Digital Collections.
General Note: Includes vita.
Bibliography: Includes bibliographical references.
Source of Description: Description based on online resource; title from PDF title page.
Source of Description: This bibliographic record is available under the Creative Commons CC0 public domain dedication. The University of Florida Libraries, as creator of this bibliographic record, has waived all rights to it worldwide under copyright law, including all related and neighboring rights, to the extent allowed by law.
Statement of Responsibility: by Caleb Stewart.
Thesis: Thesis (M.A.U.R.P.)--University of Florida, 2010.
Local: Adviser: Jourdan, Dawn.
Local: Co-adviser: Silver, Christopher.
Electronic Access: RESTRICTED TO UF STUDENTS, STAFF, FACULTY, AND ON-CAMPUS USE UNTIL 2012-08-31

Record Information

Source Institution: UFRGP
Rights Management: Applicable rights reserved.
Classification: lcc - LD1780 2010
System ID: UFE0042168:00001


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1 FORMERLY SUBSIDIZED HOUSING STOCK By CALEB STEWART A THESIS PRESENTED TO THE GRADUATE SCHOOL OF THE UNIVERSITY OF FLORIDA IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF MASTER OF ARTS IN URBAN AND REGIONAL PLANNING UNIVERSITY OF FLORIDA 2010

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2 2010 Caleb Stewart

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3 To the memory of Milton Ferguson

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4 ACKNOWLEDGMENTS Thanks to the John D. and Catherine T. MacArthur foundatio n for their support to the Shimberg Center for Housing Studies f or this research. Thanks to my c ommittee members, Dawn Jourdan, Chris topher Silver and Anne Ray for their steady guidance, support and insight with regards to my education and this project. Th anks to by loving wife, Lila for her unwavering support, encouragement and positive energy. Thanks to my parents for their love, interest and support. Thanks to everyone at the Shimberg Center for making sure that I had the support and tools necessary to c omplete this project. Thanks to Laura Abernathy for her assistance during every phase of this research. Thanks, finally, to my dog Diablo for hanging out for the past 11 years.

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5 TABLE OF CONTENTS page ACKNOWLEDGMENTS ................................ ................................ ................................ .. 4 LIST OF TABLES ................................ ................................ ................................ ............ 7 LIST OF FIGURES ................................ ................................ ................................ .......... 8 LIST OF ABBREVIATIONS ................................ ................................ ............................. 9 ABSTRACT ................................ ................................ ................................ ................... 10 CHAPTER 1 INTRODUCTION ................................ ................................ ................................ .... 12 Overview ................................ ................................ ................................ ................. 12 Preservation background ................................ ................................ ........................ 12 Research Questions ................................ ................................ ............................... 15 Methodology ................................ ................................ ................................ ........... 16 Relevant Subsidy Programs ................................ ................................ ................... 17 Federal Housing Programs ................................ ................................ ............... 1 7 The Low Income Housing Tax Credit (LIHTC) ................................ .................. 19 State and Local Housing Bonds ................................ ................................ ....... 21 2 REVIEW OF LITERATURE ................................ ................................ .................... 22 Introduction ................................ ................................ ................................ ............. 22 HUD ................................ ................................ ................................ ........................ 23 United States General Accounting Office ................................ ................................ 25 Community Economic Development Assistance Corporation ................................ 26 California Housing Partnership Corporation Tax Credit Portfolio ............................ 28 Southern California Association of Government ................................ ..................... 29 3 METHODOLOGY ................................ ................................ ................................ ... 31 Introduction ................................ ................................ ................................ ............. 31 Survey Population ................................ ................................ ................................ ... 31 Instrumentation ................................ ................................ ................................ ....... 32 Phone Survey Sheet ................................ ................................ ........................ 32 Phone Survey Spreadsheet ................................ ................................ .............. 35 Data Analysis ................................ ................................ ................................ .......... 35

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6 4 FINDINGS ................................ ................................ ................................ ............... 37 Introduction ................................ ................................ ................................ ............. 37 Descriptive Statistics ................................ ................................ ............................... 37 Funding Sources and Program Types in the LPI ................................ .............. 37 Properties by Year of Loss ................................ ................................ ............... 38 Research Questions ................................ ................................ ............................... 41 Paths Taken After Leaving the Assisted Housing Stock ................................ ... 41 Affordability of LPI Properties ................................ ................................ ........... 43 Regression Model ................................ ................................ ............................ 46 5 CONCLUSIONS AND FURTHER RESEARCH RECOMMENDATIONS ................ 48 Conclusions ................................ ................................ ................................ ............ 48 Suggestions for Future Research ................................ ................................ ........... 49 Detailed Property Variables ................................ ................................ .............. 49 Market Rate Rental Housing Data ................................ ................................ .... 50 Detailed Case Studies ................................ ................................ ...................... 50 Unit Level Affordability Analysis ................................ ................................ ....... 50 APPENDIX A CROSS TABULATION TABLE ................................ ................................ ............... 51 B PHONE SURVEY SHEET ................................ ................................ ...................... 54 C PHONE SURVEY SPREADSHEET CONTENTS ................................ ................... 56 LIST OF REFERENCES ................................ ................................ ............................... 59 BIOGRAPHICAL SKETCH ................................ ................................ ............................ 61

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7 LIST OF TABLES Table page 2 1 Regression model results ................................ ................................ ................... 47

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8 LIST OF FIGURES Figure page 1 1 LPI program types ................................ ................................ .............................. 38 1 2 All properties by year of loss ................................ ................................ ............... 39 1 3 Tax credit/bond properties by year of loss ................................ .......................... 39 1 4 HUD opt outs by year of loss ................................ ................................ .............. 40 1 5 HUD prepayments by year of loss ................................ ................................ ...... 40 1 6 Current status, all properties ................................ ................................ .............. 41 1 7 Current status, tax credit/ bond properties ................................ ........................... 42 1 8 Current status HUD opt out/prepayment properties ................................ ........... 42 1 9 Affordable properties in the LPI ................................ ................................ .......... 43 1 10 Affordable and unaffordable properties by year ................................ .................. 45 1 11 Proportion of affordable rental properties by funding program ........................... 45 1 12 Proportion of affordable rental properties by county ................................ ........... 46

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9 LIST OF ABBREVIATION S AHI Assisted Housing Inventory AMI Area Median Income BMIR Below Market Interest Rate CEDA C Community Economic Development Assistance Corporation CHPC California Housing Partnership Corporation FDIC Federal Deposit Insurance Corporation FHFC Florida Housing Finance Corporation FMR Fair Market Rent GAO General Accounting Office HUD Housing and U rban Development LHFA Local Housing Finance Authority LIHTC Low Income Housing Tax Credit LPI Lost Properties Inventory MRB Mortgage Revenue Bond RD Rural Development REAC Real Estate Assessment Center SCAG Southern California Association of Government US DA United States Department of Agriculture

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10 Abstract of Thesis Presented to the Graduate School of the University of Florida in Partial Fulfillment of the Requirements for the Degree Master of Arts in Urban and Regional Planning RENTAL H OUSING SUBSIDIZED HOUSING STOCK By Caleb Stewart August 2010 Chair: Dawn Jourdan Cochair: Christopher Silver Major: Urban and Regional Planning w income families as a result of local, state and national government subsidy programs that help to keep rents low. Property owners receive incentives such as rent subsidies and below market interest rate loans in exchange for keeping rents affordable to l ow income households for a designated period of time (Center for Housing Policy, 2008). These properties are subject to regular inspections during the designated period of time to ensure that they continue to provide quality housing to their tenants. Physi cal deterioration is a problem for the subsidized rental housing stock, since it can render a building uninhabitable or property fails an inspection, subsidies and bel ow market interest rate loans attached to the property can be withdrawn, forcing the owner to sell the property or raise rents to an owner can choose to opt out of t he subsidy program and raise their rents to market rates (Center for Housing Policy, 2008).

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11 The purpose of this research is to explore the extent to which formerly subsidized rental housing developments continue to provide affordable housing after their exit from the assisted housing stock or, conversely, the extent to which the properties are now financially out of reach to low income tenants. An affordability index was designed for this study in order to gauge the affordability of properties at 60% of t he area median income (AMI). The research sought to answer two sets of questions about the current status of : What paths do properties take after leaving the assisted housing stock? These paths might include remain ing as rental housing, conversion to for sale condominiums, conversion to condominiums that are available for rent, demolition, or other statuses. What structure, owner, location, and market characteristics are associated with each of these paths? How affo rdable are the rents of properties that have remained rental housing? How do results vary by property characteristics? continued to function as rental properties. Just over 60% of these rental properties have remained affordable at 60% AMI at the property level, although this does not reflect the number of affordable rental units in the State. This study found properties that were formerly funded through Housing and Urban De velopment ( HUD ) programs to have a much higher tendency to remain affordable at the 60% AMI level.

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12 CHAPTER 1 INTRODUCTION Overview The purpose of this research is to explore the extent to which formerly subsidized rental housing developments continue to p rovide affordable housing after their exit from the assisted housing stock or, conversely, the extent to which the properties are now financially out of reach to low income tenants. The research will determine whether new use restrictions through preservat ion programs are necessary to ensure that rents for formerly subsidized properties will remain affordable to low income tenants, or if market constraints will keep rents low. The research will also determine what property, owner and neighborhood characteri stics are associated with continued affordability after subsidy. This study sought to determine the status of 436 multifamily rental housing developments in Florida that were previously subsidized by HUD, United States Department of Agriculture ( USDA ) Rura l Development (RD) or Florida Housing Finance Corporation (FHFC) The properties are collected in the Lost Properties Inventory (LPI) database. These properties are no longer affected by income and rent restrictions associated with the subsidy programs in which they had participated. Preservation background of local, state and national government subsidy programs that help to keep rents low. Property owners receive incenti ves such as rent subsidies and below market interest rate loans in exchange for keeping rents affordable to low income households for a designated period of time (Center for Housing Policy, 2008). These properties are

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13 subject to regular inspections during the designated period of time to ensure that they continue to provide quality housing to their tenants. Physical deterioration is a problem for the subsidized rental housing stock, since it can render a building uninhabitable or force an owner to raise the property fails an inspection, subsidies and below market interest rate loans attached to the property can be withdrawn, forcing the owner to sell the property or raise rents to market rate. Addi an owner can choose to opt out of the subsidy program and raise their rents to market rates (Center for Housing Policy, 2008). The risks of opting out and failing out can have devast ating effects on low income households that may not be able to find affordable housing as properties increasingly leave subsidy. The risks of opting out and failing out can have devastating effects on low income households that may not be able to find affo rdable housing as properties increasingly leave subsidy. Since rental housing provides affordable homes to households throughout the which currently provide affordable housing, are increasingly at risk of loss from the current stock of privately owned and publicly subsidized, or assisted housing. The development of methodologies to understand which properties are most at risk, as well as strategies to preserve affordabl e rental housing, can help to reduce and stop the loss

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14 working families, people with disa bilities and the elderly (Center for Housing Policy, 2008). governments are beginning to use a range of techniques and policies. Strategies such as increasing availability of public funds reserved for preservation activities and the compilation of inventories of affordable rental properties to enable the identification of properties at high risk of loss are being employed by many state and local entities (Center for Housing Policy, 2008). Keeping these inventories and examining common characteristics in properties which fail out of subsidy or choose to opt out of affordability programs and agreements may help housing advocates and policy makers identify at risk properties and develop techniques for keeping them affordable. In the State of Florida, more than one quarter of the subsidized rental units are more than 20 years old (The John D. and Catherine T. MacArthur Foundation, 2010). Subsidies on over 43,000 of the units will expire by 2015, and nearly half of the properties with expiring subsidies are located in large urban counties (The John D. and Catherine T. MacArthur Foundation, 2010). Florida currently has an estimated 253,826 units of assisted housing units that carry r ent and income restrictions and 39,434 public housing units. Since there is approximately 907,000 renter households in the State with incomes below 60% AMI, there is clearly a need for preservation in Florida (Ray et al., 2009). Florida currently has an es timated 253,826 units of assisted housing units that carry rent and income restrictions and 39,434 public housing units. Since there is approximately 907,000 renter households in the State with incomes below 60% AMI, there is clearly a n eed for preservatio n in Florida (Ray et a l. 2009).

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15 and available land (The John D. and Catherine T. MacArthur Foundation, 2010). Most of these newer units have been bu ilt by for profit developers and have rents that are unaffordable to tenants with the lowest incomes, rather than non profits with a mission to serve the extremely poor (The John D. and Catherine T. MacArthur Foundation, 2010). Rather than focusing on the construction of new rental housing that may not serve the lowest income families in the state, agencies such as the Florida Housing Finance Corporation have realized that it may be more cost effective to preserve the existing assisted housing stock (The Jo hn D. and Catherine T. MacArthur Foundation, 2010). This study analyzes the characteristics and indicators of those properties in the Lost Properties Inventory ( LPI ) that continue to offer relatively affordable rents and those that do not. The data gather ed gives a clearer picture of which properties leaving subsidy should be the focus of preservation effort s Research Questions The research sought to answer two sets of questions about the current status of LPI properties: What paths do properties take afte r leaving the assisted housing stock? These paths might include remaining as rental housing, conversion to for sale condominiums, conversion to condominiums that are available for rent, demolition, or other statuses. What structure, owner, location, and ma rket characteristics are associated with each of these paths? How affordable are the rents of properties that have remained rental housing ? How do results vary by property characteristics?

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16 Methodology The unit s of analysis were the properties in the LPI. T he LPI consists of 436 properties with 63,414 total units that were formerly listed in the Assisted Housing Inventory (AHI) The Assisted Housing Inventory is a database of affordable rental Housing developments in the State of Florida receiving assistance from federal, state and local programs. The database was created and is maintained by the Shimberg Center for Housing Studies at the University of Florida. Updates to the AHI are provided annually by HUD and RD for properties subsidized at the Federal lev el, Florida Housing Finance Corporation (FHFC) for properties subsidized at the state level and by Local Housing Finance Authorities (LHFAs) for properties subsidized at the local level. A phone survey of property managers and owners for the LPI properties was performed to determine the current status of the properties, particularly their current path and, for those that have remained in the rental inventory, their rent levels. The LPI contains these entries: Properties that appeared in the Assisted Housin g Inventory (AHI) in previous years but are no longer in the AHI; Former Florida Housing Finance Corporation properties that have expired income and rent restrictions, have been foreclosed upon, or are otherwise no longer participating in compliance monito ring. Most are early Low Income Housing Tax Credit and mortgage revenue bond projects; outs. Once the data were in place a s eries of regression analyses were performed with the Current Status variables as dependent variables and the other property characteristics as independent variables. This could determine the property factors most

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17 likely to be associated with the loss of re ntal units or the loss of affordability in units that remain in the rental stock. Relevant Subsidy Programs The following section provides an overview of the types of subsidy relevant to the LPI. Properties listed in the LPI were subsidized by one or more of these programs at one time. Federal Housing Programs From the mid 1960s to the mid 1980s, the Federal Government created affordable rental housing through partnerships with the private sector by providing financial incentives, including interest rate s ubsidies such as Section 236 and Section 221 (d)(3), Below Market Interest Rate (BMIR) and rent subsidies such as Section 8, in exchange for agreements from property owners that they keep their housing units affordable to low income households (Cohen & Lip man, 2007) These subsidies are administered by the Department of Housing and Urban Development (HUD). Millions of federally assisted, privately owned affordable housing units are now located in most communities of the nation including over 60,000 in the state of Florida as a result of these federal programs (Shimberg Center for Housing Studies, 2010c) The Section 8 rental assistance program is the largest of these programs, providing affordable housing to over 1.3 million households (Cohen & Lipman, 2007 ). Some of these properties have increased in value, providing their owners with incentive to opt out of federal affordability programs and convert their housing units to market rate rents. Other properties have suffered physical deterioration over the pa st 30 years and are in need of substantial improvements and rehabilitation (Cohen & Lipman, 2007). Three hundred thousand federally subsidized affordable apartments were lost

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18 from 1995 to 2003 due to conversion to market rate rents or physical deterioratio n (Cohen & Lipman, 2007) Upon the expiration of a Section 8 contract, an owner can choose to opt out of the program and charge market rate re nts for the housing units (Cohen & Lipman, 2007). The U.S. Department of Agriculture (USDA) Rural Development (RD) administers the Section 521 Rental Assistance, Section 514/516 and Section 515 federal subsidy programs (Shimberg Center for Housing Studies 2010 b ) Section 521 Rental Assistance is a project based tenant subsidy program that imposes rent and tenant restrictions (Shimberg Center for Housing Studies, 2010 b ) It is used in conjunction with Section 514/516 or Section 515 (Shimberg Center for Hous ing Studies, 2010 b ). Section 514/516 provides loans and grants to farm workers, family farm organizations, state and local public agencies, and non profit and for profit organizations (Shimberg Center for Housing Studies, 2010 b ) It can be used to finance off farm rental housi ng where it is needed to house income eligible tenants that work on farms as well as on farm rental housing for income eligible tenants (Shimberg Center for Housing Studies, 2010 b ). Section 514/516 can be combined with Section 521 Ren tal Assistance (Shimberg Center for Housing Studies, 2010 b ). Tenants receive priority for this program based on the proportion of income received from farm work (Shimberg Center for Housing Studies, 2010) b Section 515 is a direct loan program that provid es mortgages to non profit and for profit developers at a one percent rate to build rural multi family rental housing (Shimberg Center for Housing Studies, 2010 b ) Loans under this program have 30 year

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19 terms and an amortization period of 50 years (Shimberg Center for Housing Studies, 2010 b ). E ligible tenants include very low, low and moderate income households, with priority to families living in substandard housing (Shimberg Center for Housing Studies, 2010 b ). Tenants can receive rental assistance to restr ict their rent payments to 30 percent of their gross household income in conjunction with the Section 521 Rental Assistance program (Shimberg Center for Housing Studies, 2010 b ). The Low Income Housing Tax Credit (LIHTC) The largest subsidy for affordable r ental housing is the Low Income Housing Tax Credit (LIHTC) (Schwartz, 2006) The LIHTC was established by the Tax Reform Act of 1986 and provides financial incentives for the investment in low income rental housing (Schwartz, 2006) From its inception in 1 986 through 2003, the tax credit program has contributed to the development of more than 1.2 million housing units, accounting for about 28% of all multifamily housing built during that period ( Malpezzi & Vandell, 2002). Tax credits account for almost 150, 000 of the assisted housing units in Florida ( Shimberg Center for Housing Studies, 2010c). Through the LIHTC, investors are allowed to reduce their federal income taxes by a dollar for every dollar of tax credit acquired (Schwartz, 2006) Although investor s can receive the tax credit for 10 years, the property with which the credit is associated must remain affordable for 15 years. The tax credits are awarded to developers who apply for them via designated state agencies, such as state housing finance agenc ies and the population ( Melendez et al. 2008). The amount of the tax credit is based on the cost of the housing development and the proportion of units to be occupied by low income households. Rental housing developments qualify for the credit if at least 40% of their units are affordable to

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20 median income. Many developers designate most units in tax credit developments for low income occupancy in order to maximize the amount of tax credit they receive (Schwartz, 2006). The LIHTC is an important contributing factor in the provision of affordab le rent al housing. Unlike other federal housing programs in which renters pay no more than 30% of their adjusted income on rent with the rest paid out by government, residents of tax credit housing can face rent burdens well above 30% if their incomes are below the Legislation in 1989 required an extended affordability period of 15 additional years (Schwartz, 2006) O wners were given a choice of opting out under certain conditions, therefore the preservation of LIH TC properties as low income housing is not assured (Schwartz, 2006) Conditions such as the inability of the original owners or state housing finance agency to find new buyers can allow rent and income restrictions to be lifted and the owner is allowed to sell the property to the buyer of their choosing. Studies have found that the risk of opting out for properties with tax credits for profit sponsors are part of the ownership struc ture of the projects, the existence of additional affordability restrictions, and the rehabilitation costs associated with the conversion to H ousing developments in need of extensive rehabilitation have a lower ris

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21 opt out; however, the poor condition of the housing units presents an enhanced risk of failing out of the tax credit program (Melendez et al. 2008). State and Local Housing Bonds Some state and lo cal governments sell tax exempt housing bonds, known as Mortgage Revenue Bonds (MRBs) and Multifamily Housing Bonds, using the funds generated to finance low interest mortgages for lower income first time homeowners and the production of affordable multifa mily housing developments for low income families (National Council of State Housing Agencies, 2009) There are over 400 assisted properties that were partially or fully financed with state or local bonds (Shimberg Center for Housing Studies, 2010 c ). The a housing bonds is capped (National Council of State Housing Agencies, 2009) The 2009 state (National Council of State Housing Agencies, 2 009) Developers using state and local bonds to build affordable multifamily housing are required to set aside at least 40% of their units for families earning 60% or less of the area median income (AMI), or 20% for families earning 50% of AMI or less (Nat ional Council of State Housing Agencies, 2009). Although the current national economic crisis has significantly diminished inves tor interest in MRBs, state and local housing bonds have been an important tool in the provision of multifamily affordable hous ing in years past (National Council of State Housing Agencies, 2009). Many of the properties included in this study from the LPI were financed with local or state bonds.

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22 CHAPTER 2 REVIEW OF LITERATURE Introduction The following section reviews past resear ch on indicators associated with properties that may be at risk of leaving the assisted housing stock. There is a fundamental difference between this study and past studies that have dealt with the issue of preservation. This study and past studies have us ed similar independent variables, such as funding programs, unit types and year of construction. However, the dependent variables used in this study, including the paths taken by properties after subsidy and their current rents are new. The difference betw een the studies is that this study analyzed properties after they have left subsidy, while past studies have only exist, preservation risk assessments were reviewed. P ast studies and analyses have attempted to predict properties that would leave subsidy, while this study is unique in its analysis of the entire inventory of properties in the state of Florida that have left assistance. This study seeks to identify unique characteristics of properties in the LPI that do not remain relatively affordable. The identification of these characteristics may help in the construction of risk assessment models and target inventories in the future. This study could make future risk a ssessment models and target inventories more accurate because it helps to identify future affordability trends, not simply near term opt in/opt out decisions. The following chapter reviews past and current methods of analysis, risk assessment models and ta rget inventories. The following section is an

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23 outgrowth of a previous study done by Patricia Roset Zuppa for the Shimberg Center for Housing Studies. HUD HUD applied cross tabulation analysis in order to compare the characteristics of multi family properti es that opted out or prepaid with the characteristics of properties that opted in and those that were foreclosed (HUD 2006a). The analysis examined 22,471 multi family developments funded by HUD under Section 221 (d)(3) BMIR, Section 236 and Section 8 Pro ject Based assistance. The properties were described by percentages, means and medians along the following 6 dimensions: property, owner, financing, location, tenant and physical and financial operation (Roset Zuppa, 2007). An example of a cross tabulatio n is provided in Table 1 of Appendix A. to opt out or prepay: The ratio of subsidized rent to market rent Age of property Type of bedroom units (1 bedroom, 2 bedrooms etc) Located in an area with higher median income Located in an area with higher rent Located in an area with lower poverty rate Located in an area with lower vacancy rate Weak expense to income ratio Since this survey conducted for this study gathered proper ty information such as, unit type, status (foreclosed, condo, rental, etc.) and year built, some aspects of the cross tabulation analysis conducted by HUD may be useful in this study. For example, if it can be determined that multi family properties with m ainly 2 bedroom units were more likely to convert to condominiums in Florida; it would then be possible for future risk

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24 assessment models to score properties with these types of units as being at higher risk than other properties. HUD used a multivariate regression analysis in order to assess whether the findings from the cross tabulation analysis are valid and to enable policymakers to identify the properties that are least likely to renew their rental assistance contracts. ression model were derived from the cross tabular analysis and are provided in Table 2 in Appendix A. The regression model contained 8,992 projects without missing values for all variables, of which 763 projects opted out. The results of the regression ana lysis indicate that most of the variables used are statistically significant and that the variable found to be most significant by the multivariate analyses is the rent to fair market rent (FMR) ratio. The analyses indicate that the lower the rent to FMR r atio, the more likely that a property will opt out (Roset Zuppa, 2007; HUD 2006b). The analyses also concluded that non profit owners of multi family housing are significantly less likely to opt out than other types of owners, proving type of ownership to be another significant variable. The analyses also concluded that properties with the 7 following constant variables are more likely to opt out 100% rental assistance 100% family occupied Having fewer than 50 units Having a unit mix with 3 or less bedrooms Older assisted properties

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2 5 Located in low poverty rate census tracts Located in central city or non metropolitan areas (Roset Zuppa, 2007; HUD 2006b) due to their access to a variety of internal data sources containing detailed property level information not readily available to the public. This property level information provided their analyses with a larger field of variables than many private organizations are able to a chieve (Roset Zuppa, 2007). United States General Accounting Office The United States General Accounting Office (GAO) released a national state by state inventory of multi family properties in 2004. The data fields included in the GAO database related to preservation are Ownership type Inspection score Economic occupancy Maturity date of contract Rental assistance contract expiration Rental assistance contract status Rental assistance utilization rate Number of rental assistance contracts Description o f funding expiration status (GAO, 2004; Roset Zuppa, 2007). The types of ownership include non profit, profit motivated, limited dividend and other. The inspection score is the numeric score related to the physical condition of the property at the time of most recent inspection as determined by the HUD Real Estate Assessment Center (REAC). The economic occupancy refers to income received from the rented units in a property divided by the income that would be received if all units in the development were occupied. The rental assistance utilization rate refers to income from rental assistance payments for rented units divided by income from rental

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26 assistance payments that would be received if all units in the development were occupied. There are three categ status in a ten year window including, mortgage maturing and rental assistance expiring, mortgage maturing only and rental assistance expiring only (GAO, 2004; Roset Zuppa, 2007). Rental assistance contr act expiration and description of funding expiration status are by definition not relevant to the LPI study, since they deal with when a property will leave subsidy and the properties in the LPI are no longer subsidized. The other factors used in the GAO t arget inventory are relevant to this study, since they deal with the previous condition and affordability of the property before they left subsidy. These property factors during subsidy can be compared with property factors of properties that left subsidy. Community Economic Development Assistance Corporation The Community Economic Development Assistance Corporation (CEDAC) has prepared a database of properties in Massachusetts that have or had HUD project based rental assistance and/or state or federally s ubsidized or insured mortgages under HUD Section 236, HUD Section 221(d)(3) Below Market Interest Rate ( BMIR ) or the Massachusetts 13A program. The focus of the database is the year 2010 and properties that are at risk of leaving the inventory by year end. But it also reports on properties that have already been lost, properties that have been preserved until 2011 or later, and properties not considered at risk of loss by 2010 (CEDAC, 2010; Roset Zuppa, 2007). Some of the factors used to determine risk of loss include the following: Risk of loss to market conversion Risk of loss due to physical condition

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27 Risk of loss due to financial viability Market condition opportunity Family units Scale number of units (CEDAC, 2009) Risk of loss to market conversion re increase rents due to conversion to market rate rental housing. A property is considered a higher priority in a strong rental market with no impediments to conversion. A property is considered a lower priorit y when the market is weak or if the property is unable to compete for market rate tenants (CEDAC, 2009). A property is considered a higher priority for risk of loss due to physical condition when it is likely to be lost due to condemnation proceedings or g overnmental action to close the property. A property is considered to be a medium priority if it suffers from significant code and safety issues and is likely to be lost in the next 2 4 years. Properties with a possibility of government action or condemnat ion in the next several years are considered to be a lower priority (CEDAC, 2009). A property is considered a higher priority for risk of loss due to financial viability when the lender has declared default. A property is considered a medium priority when it is not current on loan or covenants but no default has been declared. A property is considered a lower priority when it is financially troubled but is still able to make loan payments (CEDAC, 2009). A property is considered at higher risk of loss due to the type of family units it has when the majority of its units have more than three bedrooms. A property is considered at medium risk of loss when it has general family occupancy. A property is considered at a lower risk of loss when it rents to elderly o ccupancy only (CEDAC, 2009).

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28 A property is considered at a lower risk of loss due to the number of units when it has over 100 units. It is considered a medium risk of loss when it has between 10 and 100 units and low risk if it has fewer than 10 units (CED AC, 2009). Many of these factors are similar to factors used to study and evaluate the LPI properties in the cross tabulation and regression analyses for this study. It should be noted that, with respect to the types of bedroom units, different states may consider different types of bedroom units to be a greater risk depending on the need in that particular state. In the State of Florida there is generally a greater need for one and two bedroom assisted rental units. California Housing Partnership Corporati on Tax Credit Portfolio Because of the risk of these properties becoming market rate, the California Housing Partnership Corporation (CHPC) developed a risk assessment method to ose that were most at risk (Roset Zuppa, 2007). Since properties owned by non profit organization have a low risk profile, they were excluded from the risk assessment. The three major risk factors identified were: 1) additional affordability agreements be yond the expiration of the 15 year affordability period (low risk); 2) for profit general partnerships (high risk); 3) strength of the local housing market (strong market is medium to high risk), calculated by the county median income to the statewide medi an income. The 1987 1989 tax credit inventories were assessed on the risks and classified as: high risk, medium/high risk, medium risk, medium/low risk, and low risk. The conclusion of the assessment was that half of the units were at some level of risk of conversion to market rate within five years (California Housing Partnership Corporation, 2001; Roset Zuppa, 2007).

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29 The CHPC report noted that factors such as project rent compared to market rent could be valuable indicators of risk, but limited availabil ity of data presented an obstacle (California Housing Partnership Corporation, 2001; Roset Zuppa, 2007). median income rent limits set by FHFC, the rents can also be compared w ith market rents as the data becomes available. Many of the other factors used in the CHPC report are relevant to this study, including non profit ownership and strength of local housing markets. Southern California Association of Government The Southern C alifornia Association of Government (SCAG) conducted a risk assessment of federally subsidized units assisted by HUD mortgage subsidies (Section 236, Section 221 (d)(3) and BMIR), HUD rental assistance (project based Section 8) and USDA Section 515. The as sessment focuses on housing units with these types of subsidies since the data was readily available and because three quarters of the total at risk units were assisted under federal programs (Southern California Association of Governments (SCAG), 2000; Ro set Zuppa, 2007). A project is considered to be at high risk when the owner is profit motivated and when Section 8 rent is 105% or less of estimated potential rent in the area of the specific complex. To determine the location specific market rent, a com mercial database of conventional multifamily rental housing was used, which is maintained by the private company RealFacts and which is less generalized and more location specific than FMR. For each expiring Section 8 contract eligible to opt out, the Real Facts database was searched for market rate development of similar size, mix of bedroom types, and modest amenities (SCAG, 2000; Roset Zuppa, 2007).

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30 A project is considered to be at moderate risk when Section 8 rent is between 105% and 120% of estimated po tential market rent in the area of the specific complex. If a project was owned by a non profit organization, a low potential for conversion to market rate was assumed due to the fact that non profit owners are subject to use restrictions that disallow the m from opting out. For this reason, all properties owned by non profit organizations were listed as low risk (SCAG, 2000; Roset Zuppa, 2007). rents is relevant to this stu dy and can be performed as the data becomes available.

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31 CHAPTER 3 METHODOLOGY Introduction The following methodology was designed to examine the paths that formerly assisted properties take after leaving subsidy. The primary tool used in this study consist ed of a telephone survey that determined current rent levels of formerly assisted properties in the State of Florida. Specifically, the methodology examines the current status of formerly assisted properties and whether or not they continue to operate as r ental properties, or have instead been demolished or converted to condominiums. The methodology then compares the rent levels of those properties that have continued to operate as rentals post subsidy with the ir rent levels during subsidy. This chapter wi ll cover the survey population, instrumentation and statistical analysis for the study. Survey Population The list of properties included in the telephone survey was taken from the Lost Properties Inventory (LPI), which is a database of properties that wer e assisted at one time, but have since left subsidy. The LPI excludes all properties listed in the 2009 Assisted Housing Inventory (AHI). The LPI includes non FHFC properties that were listed in the 2004, 2005, 2006, 2007 or 2008 AHI but are no longer in t hat inventory. The LPI also includes FHFC properties that are not listed in the 2009 AHI with the following inactive statuses: Bonds terminated Compliance period expired Foreclosed Loan repaid No longer participating Paid off Released

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32 Reporting requireme nts ceased Sold Properties with the following inactive statuses are excluded from the LPI unless they were also former HUD properties: Rescinded Withdrawn Eligible unfunded application Withdrawn/returned credits Withdrawn/declined CU Duplicate record Fail ed threshold Not required to monitor The LPI also includes HUD properties that predated the 2004 AHI using the HUD Terminated Mortgage database and the Section 8 opt out database. Also included in the LPI are affordable housing developments financed thro ugh LHFAs that have since left subsidy. The list of LHFA properties is kept current through an annual e mail survey conducted by the Shimberg Center. The survey population for this study consisted of the most current version of the LPI, representing the most accurate listing available of formerly assisted housing properties in the State of Florida. The population contained 436 properties that represent over 63,000 units of housing that were formerly affordable and are no longer rent or income restricted. Instrumentation Phone Survey Sheet The survey instrument (Appendix B ) consisted of eight sections and a chart and was approved by the Institutional Review Board prior to the beginning of the survey. The paper survey was administered over the phone and fil led out by the administrator

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33 the surveys were then entered into a spreadsheet for analysis. The first section listed the LPI name and address of the property. This sectio n was filled out by the administrator of the survey based on the most current information listed in the LPI. This information was important to the survey process, since many of the property listings in the LPI database had outdated or missing contact phone numbers. The most current name and address on file for each property was then typed into an internet search engine in an effort to confirm or correct the contact phone number for the property. The second section listed the Shimberg ID of the property be ing surveyed. This section was also filled out by the administrator of the survey based on the most current data in the LPI. The Shimberg ID is a unique identifying number assigned to each property by the Shimberg center. It was important to include this n umber for identification purposes and to accurately combine information learned from the survey with information about each property in existing databases. The third section listed the name and phone number of the contact. The contact in this case was con sidered to be the survey respondent. This information was often used in order to call a property back in situations when the property manager was not available on the first survey attempt. Collection of valid contact information is also important in keepin g the Shimberg Center LPI database current for ease of future surveys. The first question asked the respondent to confirm the current property name and address. The respondent was read the name and address for their property as recorded

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34 in the LPI and as ked to make any corrections. If the name and address were different, the new information was recorded by the survey administrator. The collection of current names and addresses of these properties was important, since many of the properties had undergone n ame changes since leaving subsidy. In many cases their addresses offices. The second question asked for the current use of the property. Possible responses to this questi on included rental, condominium, mixed use, for sale and demolished. Properties that were either condos, for sale or demolished were not typically contacted, rather real estate sales websites and property tax records were used to inform their current statu s. The collection of the current use and status of each property was rental stock after leaving subsidy. The next section of the survey consisted of a chart with colu mns for size /type of unit, number of units and rent. The column for size/type of unit contained six rows including zero bedroom/studio/efficiency, one bedroom, two bedroom, three bedroom, four bedroom and other. The respondent was asked how many of each s ize rental units their property contained and the rent for each type of unit. Many of the properties had a range of rental rates for each type of unit, in which cases the range of rents was recorded by the survey administrator. The collection of this info rmation was important in order to quantify the affordability of each type of unit according to number of bedrooms. It will also be useful in future surveys in order to compare rental rates at these properties over time.

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35 The third question asked for the cur rent target population and whether residency was restricted to said population. The possible responses to this question included elderly, disabled and family. This information was important to the study, since the type of target population and population r estriction on a property can have drastic effects on the rental rates. The fourth question asked whether the current owners of the property were a for profit or non profit corporation. This information is important, since a property with non profit owner ship would likely have relatively cheaper rents than a property owned by a for profit corporation. The fifth question asked whether or not the property accepted Section 8 vouchers. The collection of this information is important, since properties that acc ept vouchers help to provide affordable housing. Phone Survey Spreadsheet A database containing the information recorded from the phone survey was created by entering the data into a Microsoft Excel spreadsheet. The spreadsheet consisted of 49 columns d e scribed in Appendix C Data Analysis A regression analysis of the data collected was performed based on the dependent variable of affordability and seven independent variables. The dependent variable used was rent affordability. Rent affordability of each property was determined county where the property is located. The independent variables used were the following:

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36 Size: properties with less th an 50 units were assigned size one those with 50 99 units were assigned size two those with 100 199 units w ere assigned size three and those with 200 or more units were assigned size four Fedfund: Properties which received some form of HUD funding were assigned a ariable. Properties that received state or local funding where assigned a "0". they were assigned a "0". therwise they were assigned a "0". Term: Properties that left due to termination, foreclosure, or demolition where assigned a "1" otherwise they are assigned a "0". Optout: Properties that left due to an opt out decision where assigned a "1" otherwise they are assigned a "0". Monitor: Properties that left due to no longer required to monitor compliance where assigned a "1" otherwise they are assigned a "0". For each unit size, the 60% AMI rent limit was divided by each housing average r ent. This produced a type of affordability index for each unit type. Each of these results were added together and divided by the total number of units at each property to produce a weighted average of the affordability index of each unit type. Properties at the 60 % AMI rent limit unaffordable at the 60% AMI rent limit.

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37 CHAPTER 4 FINDINGS Introduction This chapter provides a de tailed overview of the multifamily housing stock in the LPI. These properties were formerly funded under federal, state and local programs to make housing units affordable to lower income households. Information such as unit breakdown, rental rates and cur rent status were gathered through the survey outlined in the methodology section Information such as program type, county size and year o f loss are included in the LPI database, created and maintained by the Florida Housing Data Clearinghouse at the Shimberg Center for Housing Studies at the University of Florida. Data gathered during the course of the survey conducted for this study was added to the LPI database. Descriptive Statistics Funding Sources and Program Types in the LPI The formerly subsidized housing developments in the LPI were constructed, owned and managed by non profit and for profit organizations. These private entities owned and managed the properties in the LPI through funding from fe deral, state and/or local programs in order to meet the affordable housing need in the State. The majority of the propert ies in the LPI database were funded th rough Tax Credit/Bond programs The breakdown for types of housing programs associated with the 4 36 properties in the LPI is as follows: 278 former tax credit/bond projects 52 HUD rental assistance opt outs (22 with pre paid HUD mortgages 17 HUD pre paid mortgages without HUD rental assistance opt out 37 RD properties (3 were also tax credit/bond prop erties) 38 FDIC properties 11 other

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38 Tax Credit/Bond properties that are no longer rent or income restricted make up the bulk of the LPI. Most of these properties converted to market rate housing after the expiration of their affordability periods. Tax Cred it and/or Bond properties far outnumber properties built through other funding sources in Florida, therefore it is no surprise that there are so many of these properties in the LPI. There are 278 Tax Credit/Bond properties in the LPI, which make up 63% of the inventory. 69 properties in the inventory were HUD properties, 37 were RD properties and 49 were funded through Federal Deposit Insurance Corporation ( FDIC ) or other sources. Figure 1 1 LPI program t ypes Properties by Year of Loss Loss of properties from the AHI began to increase significantly in 2002, 15 years after the first tax credit projects in 1987. The losses to the inventory have continued to remain at high levels in the years since then. The majority of the losses to the AHI were Tax Credit/ Bond properties. 2004 brought the largest amount of losses to the AHI at close to 70 properties with losses remaining at historical highs in the following 4 years.

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39 This may be attributed to both a robust housing market in the State during that time period and the fact that this is when many of the early tax credit and bond properties reached their subsidy expiration dates 2009 had the least number of losses to the AHI since 2001. Figure 1 2 All properties by year of l oss Figure 1 3 Tax credit/bond pr operties by y ea r of l oss

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40 There are 45 properties repre senting 3 230 units of housing that were lost due to HUD opt outs. The number of housing units lost due to opt outs was the largest in 10 years in 2009 when 522 units of housing were lost. Figure 1 4 HUD opt o uts by year of l oss There are 38 properties in the LPI repres enting 5 273 Units of housing that were lost due to HUD mortgage prepayments many of these also appear in the previous grapph Figure 1 5 HUD prepayments by year of l oss

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41 Research Questions Paths Taken After Leaving the Assisted Housing Stock This study sought to determine the current status of properties that have left the assisted housing stock in Florida. The status might include rental, condo, demolished, vacant, for sale or mix ed use. The current status is known for 227 of the 436 properties in the LPI. The majority of the se properties currently operate as either rentals or condominiums. There are 159 rental properties and 45 properties that converted to condos. This is mirrored in the breakdown of current status of tax c redit and bond properties with 120 of them operating as rentals and 24 condo conversions Of the 62 HUD opt out/prepayment properties, the current status is known for 29 of them. 25 of these are rentals and 6 ha ve converted to condos. Figure 1 6. Current status, all p roperties

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42 Figure 1 7. Current status, tax credit/bond p roperties Figure 1 8. Current status HUD opt out/prepayment p roperties

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43 Affordability of LPI Properties This study also sought to dete rmine how affordable the formerly assisted housing stock has remained, and how their affordability varies by property characteristics. Based on the measure of affordability used to create the regression model, of the 137 properties that had complete unit a nd rental rate information, 84 of them were affordable using the FHFC 60% AMI rent limit ratio. The large proportion of properties that have remained affordable may reflect high vacancies in the current rental housing market. Since the affordability of the se properties used a weighted average, the percentage of affordable properties does not reflect the number of affordable rental units. Figure 1 9. Affordable p roperties in the LPI The majority of the properties for which there is complete data in the LP I database were lost between 2003 and 2008. Between 2003 and 2005, just over 60% of the 54 properties lost remained affordable. Similarly, of the 45 propertie s lost between 2006 and 2008, 62% remained affordable.

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44 As demonstrated in the chart below, a high er proportion of properties formerly funded by HUD tend to remain affordable after subsidy compared to any other funding program. While over 90% of HUD Properties remained affordable, just over 50% of Tax credit/Bond properties remained affordable at 60%AM I. This indicates that during current economic conditions, tax credit and bond properties have continued to provide housing for tenants earning 60% AMI after they leave subsidy. It should also be noted that, over 60% of the tax credit and bond properties w ere funded through bond deals requiring only 20% of their units to be affordable at 80% AMI. This indicates that many of the tax credit and bond properties have tended to serve even lower income populations after subsidy than they did during assistance. T he percentage of HUD properties that remained affordable at 60% AMI may be misleading, since over 60% of these properties served tenants earning less than 30% AMI during subsidy. Currently, none of the HUD properties serve the 30% AMI population and 40% of them are not and bond properties have remain affordable at the rates charged during subsidy when they left assistance, HUD properties have not done so. The implicatio n is that the market has not tended to provide for the population earning 30% or less AMI, even during recessive economic conditions.

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45 Figure 1 1 0. Affordable and unaffordable properties by y ear Figure 1 11. Proportion of affordable rental properties b y funding p rogram Since there are more affordable than unaffordable properties in the LPI, it is no surprise that the number of affordable rental properties by county is equal to or less than the number of unaffordable rental properties. Broward and Hillsb orough Counties are a distinct exception to this trend, with both counties having a disproportionate

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46 number of unaffordable to affordable formerly assisted rental prope rties. Broward County has only three properties in the LPI that are affordable at 60% AM I, while the other 8 LPI properties in Broward are u naffordable. Hillsborough has six affordable properties in the LPI and 13 unaffordable formerly assisted properties. Figure 1 12. Proportion of affordable rental properties by c ounty Regression Model Th e regression model was de veloped and performed by Douglas White at the Shimberg Center for Housing Studies to test the effects of size, federal funding, county size, termination or demolition, opt out and loss of monitoring requirements. After testing the effects of these characteristics on both the current status (rental, condo, demo etc.) and affordability however, the current status variable yielded no statistically significant results for the variables of size, funding type, county size, termination, op t out and monitoring. The model is represented as follows:

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47 y= + 1 size+ 2 fedfund+ 3 largecnty+ 4 medcnty+ 5 term+ 6 optout+ 7 monitor where y=affordability. The results of the model are given below: Table 2 1. Regression model results Explanatory Variable Coefficient Estimate Standard Error P value Intercept 0.9 9816 ** 0.23502 <.0001 Size 0.02726 0.02527 0.2826 Fedfunded 0.02848 0.09055 0.7536 Largecnty 0.35723 0.20744 0.0874 Medcnty 0.37786 0.21003 0.0743 Term 0.12663 0.09982 0.2069 Optout 0.08041 0.8423 0.3415 Monitor 0.21703 ** 0.08435 0.0112 R S quare: 0.2548 Observations: 137 ** indicates significant at the 0.05 level indicates significant at the 0.10 level The regression model showed a relationship between loss of monitoring requirements and affordability. Based on the regression mod el analysis, properties that are no longer required to be monitored for affordability are more likely to be less affordable than properties that leave assistance for other reasons. Although none of the other variables used in the regression analysis yielde d statistically significant results, the model can be used with more detailed variables, such as neighborhood characteristics, for future analysis of the LPI properties

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48 CHAPTER 5 CONCLUSIONS AND FURT HER RESEARCH RECOMME NDATIONS Conclusions This study so ught to answer two sets of questions about the current status of LPI properties: What paths do properties take after leaving the assisted housing stock (rental, condo, demolished etc.)? How affordable are the rents of properties that have remained rental h ousing and how do results vary by property characteristics? Of the 436 properties in the LPI, the majority of the properties are rentals or condominiums. Of the 227 properties for which current status is known, 159 of them are rentals and 45 are condos. Th e type of subsidy program that funded properties did not prove to influence the paths taken after subsidy. The majority of the properties in the LPI were lost between 2002 and 2009, with the majority of these being Tax Credit or Bond properties. The number s of tax credit properties that leave assistance has decreased dramatically in 2009, with fewer than 20 properties lost, compared with close to 70 lost in 2004. This may be a sign of a soft real estate market and these numbers may resume increasing in comi ng years. There are 45 properties representing 3,230 units of housing in the LPI that were lost due to HUD opt outs. The number of housing units lost due to opt outs was the highest in 10 years in 2009 when 522 units of housing were lost. Based on the affo rdability index des igned for this study, just over 60% of the 137 properties for which complete unit and rental rate information was known remained affordable at 60% AMI after leaving assistance. The large proportion of properties that have remained afford able may reflect high vacancies in the current rental housing

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49 market. Since the affordability of these properties used a weighted average, the percentage of affordable properties does not reflect the number of affordable rental units. A higher proportion of properties that were formerly funded by HUD tend to remain affordable after leaving assistance compared with Tax Credit and Bond properties. No affordability levels. Ba sed on the regression model used in this study, there is a relationship between The results of this research indicate that during current economic conditions, properties formerly subsidized through tax credits and bonds have continued to provide housing for tenants earning 60% AMI after subsidy. The market has not, however, provided housing at rates affordable to tenants earning 30% AMI. This is apparent, since, none of the former ly assisted properties, including HUD properties provide housing that is affordable to tenants earning 30% AMI. This research concludes that preservation efforts should focus on the preservation of assisted properties that provide housing to tenants earnin g 30% AMI or less. Furthermore, future planning for affordable housing should focus on the construction of properties that provide for extremely low income tenants. Suggestions for Future Research Detailed Property Variables The regression model designed for this study could be used to analyze more detailed, neighborhood level variables associated with the properties in the LPI.

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50 Market Rate Rental Housing Data analyzed and compared wi th the rent levels of formerly assisted properties. This would give a more accurate assessment of the rent levels of the LPI properties and whether they are following market rate rental housing trends. Detailed Case Studies Detailed studies could be done o n properties that have similar characteristics (number of units, location, etc.) but have taken different paths after subsidy (rental, condo etc.). Detailed studies could also be done on affordable versus unaffordable properties that have similar character istics. Unit Level Affordability Analysis An analysis of the tendencies of different unit types (studio, 1,2, and 3 bedroom) toward affordability could be done on a county by county basis. This would giv e a clearer picture of number of units that stayed a ffordable after subsidy at the county level.

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51 APPENDIX A CROSS TABULATION TABLE Table A 1 Cross tabulation table for physical condition and financial operating characteristics for HUD funded properties (HUD 2006a, 30). Physical and Financial Characteristi cs Opt In Opt out/ Prepays Foreclosure/ Enforcement All Other Total Number of properties 11,126 1,715 2,385 7,245 22,471 Percent of properties 49.5% 7.6% 10.6% 32.2% 100% REAC Physical inspection score (1 100) Median 84.0 78.0 70. 0 87.0 84.0 1 59 13.0% 17.8% 33.5% 10.4% 14.1% 60 69 10.1% 13.9% 16.2% 9.1% 10.5% 70 89 40.1% 42.8% 32.2% 37.1% 39.1% 90 100 36.8% 25.6% 18.1% 43.4% 36.3% Total 100.0% 100.0% 100.0% 100.0% 100.0% REAC Financial perfor mance score (1 100) Median 73.0 70.5 69.0 73.0 73.0 1 59 23.1% 35.5% 36.1% 22.2% 24.3% 60 69 17.2% 13.9% 15.1% 18.7% 17.3% 70 89 47.6% 34.2% 27.0% 45.6% 45.1% 90 100 12.1% 16.4% 21.9% 13.6% 13.3% Total 100.0% 100.0% 100.0% 100.0% 100.0% Expense to income ratio (median) 0.60 0.72 0.80 0.54 0.59 Debt service coverage ratio (median) 1.18 1.24 1.15 1.07 1.14 Quick ratio (median) 0.53 0.43 0.305 0.425 0.4825 Surplus cash level (median) $89.2 $152 .3 $157.6 $134.1 $5.2 Reserve (median) $1,633.7 $991.1 $1,147.2 $1,979.4 $1,669.7 Vacancy rate (median) 1.5% 3.0% 3.5% 1.0% 1.5% Administrative Expenses (median) $102.2 $95.6 $109.0 $106.4 $103.1 Utilities Expenses (median) $56 .4 $56.2 $70.3 $57.7 $57.4 Operating & Maintenance Expenses (median) $110.4 $121.5 $137.4 $96.9 $109.2 Taxes & Insurance Expenses (median) $54.9 $59.3 $62.4 $41.3 $52.5 Total Operating Expenses (median) $330.8 $341.6 $392.0 $311.3 $329.4 Source: 1998 and 1999 REAC Financial Assessment Sub System (FASS) data. Note: Operating expenses are per unit month measures.

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52 Table A 2. Regression model variables for HUD Section 8 opt in and opt out properties (HUD 2006a, 34 35). Variable Va riable Specification Expected Direction of Impact Development size in units Less than 50 units (reference category) 50 99 units 100 199 units 200+ units Unknown. On one hand, conversion to market rate may involve fixed costs; since larger projects hav e lower per unit costs, this may increase their likelihood of opting out. On the other hand, large projects tend to be associated with other physical features that are less attractive to unassisted tenants. Density Percent of 3 bedroom plus units Negat ive. It may be harder to market projects with large units to unassisted tenants because these units may not be physically suitable for higher income singles and couples who could afford market rate units. Family occupancy type Family = 1 Elderly/disable d = 0 Positive. Elderly projects face competition from amenity rich private market projects. Also, the income distribution among elderly and disabled households may not support many market rate units. In other words, family projects are more likely to opt out. Building type Detached or semi detached = 1 Other = 0 Positive. Detached and semi detached projects tend to be associated with other amenities and physical characteristics that are attractive to unassisted tenants. Older Assisted HUD program ty pes Older assisted = 1 Newer assisted = 0 Positive. The older projects often have rents that are below market rate. Ratio of rent to FMR Rent to FMR ratio < 80% 80% < rent to FMR ratio < 100% 100% < rent to FMR ratio < 120% (reference category) 120% < rent to FMR ratio < 130% 130% < rent to FMR ratio < 140% 140% < rent to FMR ratio < 160% Rent to FMR ratio > 160% Negative for projects with rents above local FMR. Projects with rents that are low relative to the FMR may be able to raise rents with littl e effect on vacancy rates. In other words, as rent to FMR ratio increases, we expect the property owner to be less motivated to opt out. Ownership type Nonprofit = 1 For profit or limited dividend = 0 Negative. Nonprofits are less likely to opt out. By definition, for profit owners are motivated to increase revenues. Not federally financed mortgage Not federally financed = 1 Other = 0 Negative This value is a proxy for projects financed by state Housing Finance Agencies (HFAs). HFAs may impose prep ayment and/or opt out restrictions. Neighborhood poverty rate Percent of persons in the surrounding census tract with incomes below poverty threshold in year 2000 Negative Research has shown that tracts with high poverty rates typically have features that make them undesirable places to live and hence are less able to command high rents.

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53 Table 2. continued Variable Variable Specification Expected Direction of Impact 100 percent assisted Projects with 100 percent units receiving HUD assistance =1 Other = 0 Positive A project with a high percentage of unassisted tenants risks high turnover upon conversion to private market status because these tenants will not have enhanced vouchers and may not be able or willing to afford the higher rents. A h igh percentage of assisted tenants implies more opportunity for the owner to raise rents to market levels. Metropolitan location Suburb (reference category) Central city Non metropolitan Negative for central city. We expect owners in central cities to be less likely to opt out because markets may be unable to support unassisted housing. Positive for suburb. Suburban areas tend to have higher income renters to absorb market rate housing. Census division New England Mid Atlantic East North Central West North Central South Atlantic (reference category) East South Central West South Central Mountain Pacific Positive for high rent regions such as New England, Mid Atlantic, and Pacific.

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54 APPENDIX B PHONE SURVEY SHEET 1. Property Name & Address:__________ ____________________________________________ 2. Shimberg ID: 3. Name and phone number of contact: 4. Current property name and address (read latest LPI name and address, if any, to respondent and make corrections): 5. Current use of the development (Rental, condos for sale, condos for rent, mixed use, other) Size/Type of Unit # of Rental Units Rent # of Condo Units Sale Price 0 BR/Studio/Efficiency 1 BR 2 BR 3 BR 4 BR Other 6. Current target population, if any (e.g. elderly, disabled, fam ilies)? Is residency restricted to this population? 7. Current owner a for profit or non profit corporation?

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55 8. Does the property accept Section 8 vouchers?

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56 APPENDIX C PHONE SURVEY SPREADS HEET CONTENTS The Shimberg ID number. A temporary ID number assigned t o properties that lacked Shimberg ID numbers The FHFC key number (for FHFC properties). The HUD property ID number, which is a number assigned to a property by HUD (for HUD properties). The name of the property as listed in the LPI. The address of the pro perty as listed in the LPI. The city where the property is located as listed in the LPI. The zip code where the property is located as listed in the LPI. The county where the property is located as listed in the LPI. Total number of housing units at the pr operty as listed in the LPI. The name of the property according to the phone survey The address of the property according to the phone survey The city where the property is located according to the phone survey The county where the property is located acco rding to the phone survey The zip code where the property is located according to the phone survey The current use or status of the property according to the phone survey (condo, rental, mixed use, vacant, demolished or other) The phone number of the prope rty according to the phone survey The number of 0 bedroom or studio units located at the property according to the phone survey The number of 1 bedroom units lo cated at the property according to the phone survey

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57 The number of 2 bedroom units located at the property according to the phone survey The number of 3 bedroom units located at the property according to the phone survey The number of 4 or more bedroom unit s located at the property according to the phone survey The minimum monthly rent charged per 0 bedroom or studio unit at the property according to the phone survey The maximum monthly rent charged per 0 bedroom or studio unit at the property according to t he phone survey The minimum monthly rent charged per 1 bedroom unit at the property according to the phone survey The maximum monthly rent charged per 2 bedroom unit at the property according to the phone survey The minimum monthly rent charged per 3 bedro om unit at the property according to the phone survey The maximum monthly rent charged per 3 bedroom unit at the property according to the phone survey The minimum monthly rent charged per 4 or more bedroom unit at the property according to the phone surve y The maximum monthly rent charged per 4 or more bedroom unit at the property according to the phone survey The minimum sales price per o bedroom or studio unit at the property according to the survey The maximum sales price per 0 bedroom or studio unit at the property according to the survey The minimum sales price per 1 bedroom unit at the property according to the survey The maximum sales price per 1 bedroom unit at the property according to the survey The minimum sales price per 2 bedroom unit at the pr operty according to the survey

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58 The maximum sales price per 2 bedroom unit at the property according to the survey The minimum sales price per 3 bedroom unit at the property according to the survey The maximum sales price per 3 bedroom unit at the property according to the survey The minimum sales price per 4 or more bedroom unit at the property according to the survey The maximum sales price per 4 or more bedroom unit at the property according to the survey The year the property was built according to the p hone survey Whether the property currently receives subsidies or not according to the phone Whether the property currently accepts Section 8 vouchers according to the phone Whether the property is owned by a non profit corporation or not according to the Any additional notes on the property that are not covered by the spreadsheet columns The minimum and maximum rents entered into the database reflected the range of rental rates per unit at properties that charged a variety of rents for units with the same number of bedrooms. The minimum and maximum rent entered was the same at properties char ging only one rate for units with the same number of bedrooms.

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59 LIST OF REFERENCES "Assisted Housing Inventory: Introduction." Florida Housing Data Clearinghouse 2010. Web. 14 Apr. 2010. . Bogdo n, Amy S., and James R. Follain. An Examination of Three Sets of Indicators of Financial Risk among Multifamily Properties Rep. no. 180. Metropolitan Studies Program Series Occasional Paper, 1996. Print. California Housing Partnership Corporation. The Ta x Credit Turns Fifteen Conversion s Early Tax Credit Portfolio Rep. California Housing Partnership Corporation, 2001. Print. California Housing Partnership Corporation Web Site Web. 14 Apr. 2010. < http://www.chpc.net/ >. CEDAC Risk M atrix. 10 June 2009. Raw data. Community Economic Development Assistance Corporation. Center for Housing Policy. Rental Preservation Rental Housing Preservation 2008. Web. 14 Apr. 2010. < http://www.rentalpreservation.org />. Cohen, Rebecca, and Barbara J. Lipman. Legislative Provisions to Support the Preservation of Affordable Housing Rep. National Preservation Working Group, 2007. Print. "Community Economic Development Assistance Corporation Online Database." CEDAC. Web. 14 June 2010. < http://www.cha pa.org/pdf/CEDACatriskreportJan10.pdf >. "Florida MacArthur Foundation." John D. & Catherine T. MacArthur Foundation MacArthur Foundation Web. 14 Apr. 2010. < http://www.macfound.org/site/c.lkLXJ8MQKrH/b.4991535/k.C529/Florida.htm >. Galster, George, P eter Tatian, and Charlene Wilson. "Alternative Measures for the Financial Condition of the Multifamily Housing Stock." Housing Policy Debate 10.1 (1999): 59 73. Print. "Housing Bonds." NCSHA National Council of State Housing Agencies 2009. Web. 14 Apr. 2010. < http://www.ncsha.org/advocacy issues/housing bonds >. Ling, David C., and Wayne R. Archer. Real Estate Principles a Value Approach Boston (Mass.): McGraw Hill/Irwin, 2005. Print.

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60 Malpezzi, Stephen, and Kerry Vandell. Does the Low Income Housing T ax Credit Increase the Supply of Housing? Publication. Center for Urban Land Economics Research School of Business The University of Wisconsin, 2002. Web. < http://www.bus.wisc.edu/realestate/documents/LIHTC%20Note%20Malpezzi%20 Vandell%20as%20of%20October%2 011%202002.pdf >. Melendez, Edwin, Alex F. Schwartz, and Alexandra DeMontrichard. "Year 15 and Preservation of Tax Credit Housing for Low Income Households: An Assessment of Risk." Housing Studies 2.1 (2008): 67 87. Print. Ray, Anne, Diep Nguyen, William O'Dell, Patricia Roset Zuppa, and Douglas White. The State of Florida's Assisted Rental Housing Rep. Gainesville: Florida Housing Data Clearinghouse Shimberg Center for Housing Studies, 2009. Print. Roset Zuppa, Patricia. Risk Methodologies Draft Documen t. Shimberg Center for Housing Studies, 2007. Print. Schwartz, Alex F. Housing Policy in the United States: an Introduction New York: Routledge, 2006. Print. Shimberg Center for Housing Studies. "Assisted Housing Inventory Lost Properties Inventory." Florida Housing Data Clearinghouse 2010 a Web. 15 Apr. 2010. < http://flhousingdata.shimberg.ufl.edu/a/lpi >. Shimberg Center for Housing Studies. "Assisted Housing Inventory: Introduction." Florida Housing Data Clearinghouse 2010 b Web. 14 Apr. 2010. < htt p://flhousingdata.shimberg.ufl.edu/AHI_User_Guide.html >. Shimberg Center for Housing Studies. "AHI General." Florida Housing Data Clearinghouse 2010c. Web. 15 Apr. 2010. < http://flhousingdata.shimberg.ufl.edu/a/ahi_general >. United States General Accounti ng Office. Multifamily Housing. More Accessible HUD Data Could Help Efforts to Preserve Housing for Low income Tenants Rep. Washington, DC: United States General Accounting Office, 2004. Print. U.S. Department of Housing and Urban Development. MOR Freque ntly Asked Questions Rep. U.S. Department of Housing and Urban Development, 2006b. Web. < http://www.hud.gov/offices/hsg/mfh/rfp/morfaq.pdf >. U.S. Department of Housing and Urban Development. Multifamily Properties: Opting In, Opting out and Remaining Aff ordable Rep. U.S. Department of Housing and Urban Development, 2006a. Print.

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61 BIOGRAPHICAL SKETCH Caleb Stewart received his Master of Arts in Urban and Regional Planning at the University of Florida in the summer of 2010 He previously completed a Bach elor of Arts majoring i n h istory at the University of Florida. Caleb works at the Shimberg Center for Housing Studies researching housing policy with a focus on assisted rental housing stock.