The Low-Income Housing Tax Credit Program: A Performance Update Analysis

The Low-Income Housing Tax Credit Program:

A Performance Update Analysis A CohnReznick LLP Report NOVEMBER 2014

Introduction This is the third in a series of periodic reports issued by CohnReznick LLP that address the performance of properties financed with federal low-income housing tax credits (housing tax credits) and funds organized to own interest in housing tax credit properties.

To compile and analyze the data required for the assessment, CohnReznick requested participation from every active housing tax credit syndicator and some of the nation’s largest institutional investors. Thirty-two of the 35 housing tax credit syndicators, and three of the nation’s largest investors, participated in the survey. For a complete list of study participants, please refer to Appendix A. CohnReznick analyzed data collected from 18,412 housing tax credit properties, focusing on how they performed during 2011 and 2012. For a more extensive discussion of the methodology employed to collect and analyze property data, please refer to Appendix B. We are grateful to the housing credit industry for its continuing support of CohnReznick’s campaign to promote a deeper understanding of the housing tax credit program, its strengths, and the critical role it plays in the development of affordable housing.

COHNREZNICK LLP November 2014

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Report Restrictions CohnReznick has used information gathered from the housing credit industry participants listed in Appendix A to compile this study. The information provided to us has not been independently tested or verified. As a result, we have relied exclusively on the study participants for the accuracy and completeness of their data. No study can be guaranteed to be 100% accurate, and errors can occur. CohnReznick does not guarantee the completeness or the accuracy of the data submitted by study participants and thus does not accept responsibility for your reliance on this report or any of the information contained herein.

The information contained in this report includes estimations, approximations, and assumptions and is not intended to be legal, accounting, or tax advice. Please consult a lawyer, accountant, or tax advisor before relying on any information contained in this report.

CohnReznick disclaims any liability associated with your reliance on any information contained herein.

To ensure compliance with the requirements imposed by the IRS, we inform you that any U.S. federal tax advice contained in this communication (including any attachments) is not intended or written to be used, and cannot be used, for the purpose of (i) avoiding penalties under the Internal Revenue Code or (ii) promoting, marketing, or recommending to another party any transaction or matter addressed herein.

2 | The Low-Income Housing Tax Credit Program – November 2014

Table of Contents Chapter 1: Executive Summary..

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6

Chapter 2: The Convergence of Housing Tax Credits, Housing

Development, and the Banking Industry.. . . . . . . . . .

. . . . . . . . . 9

Chapter 3: Regulatory Changes Impacting the State of the Equity Market. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

Chapter 4: Fund Investment Performance..

. . . 13

. . . . . . . . . . . . . . . . . . . . . . . . . . 18

Chapter 5: Property Performance.

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27

Chapter 6: Portfolio Composition. .

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 67

Appendices:. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

Appendix A. Acknowledgements. . . . . . . . . . . . . . . . . . .

Appendix B. Survey Methodology. . . . . . . . . . . . . . . . . . .

Appendix C. Glossary.. . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

Appendix D. Property Performance – by State. . . . . . . .

Appendix E. Property Underperformance – by State. .

Appendix F. Property Performance – by MSA.. . . . . . . .

. . . . . 79 . . . . . 79 . . . . . 80 . . . . . 85 . . . . . 87 . . . . . 89 . . . . . 91

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Index of Figures FIGURE 4.1

FIGURE 4.1.1 FIGURE 4.2.1 FIGURE 4.2.2 FIGURE 4.3.1 FIGURE 4.3.2 FIGURE 4.3.3 FIGURE 4.4.1 FIGURE 5.0.1 FIGURE 5.1.1 FIGURE 5.1.2 FIGURE 5.1.3 FIGURE 5.1.4 FIGURE 5.1.5 FIGURE 5.1.6 FIGURE 5.1.7 FIGURE 5.1.8

FIGURE 5.2.1(A) FIGURE 5.2.1(B)

FIGURE 5.2.1(C) FIGURE 5.2.1(D) FIGURE 5.2.1(E)

FIGURE 5.2.2(A) FIGURE 5.2.2(B)

FIGURE 5.2.2(C) FIGURE 5.2.2(D) FIGURE 5.2.3

FIGURE 5.2.4(A) FIGURE 5.2.4(B) FIGURE 5.2.5 FIGURE 5.2.6 FIGURE 5.2.7 FIGURE 5.2.8 FIGURE 5.2.9

FIGURE 5.3.1(A) FIGURE 5.3.1(B) FIGURE 5.3.1(C) FIGURE 5.3.2(A)

Portfolio Composition by Fund Type (Post 1999 Funds) by Gross Equity Gross Equity Percentage of Multi-Investor and Proprietary Funds Since 1999 Gross Equity Price vs. Fund Yield by Year Fund Yield vs. 10-Year Treasury Security Rate by Year Fund Yield Variance by Year Percentage Incidence of Negative Fund Yield Variance Since 1999 Median Fund Yield Variance by Year of Fund Closing Housing Credit Delivery Variance by Investment Type Overall Portfolio Composition Overall Portfolio Performance Overall Portfolio Performance (2008-2012) Median Physical Occupancy Median Debt Coverage Ratio Median Per Unit Cash Flow Median Net Equity Price by Year Placed in Service Net Equity Price vs. Hard Debt Ratio-9% Credit Net Equity Price vs. Hard Debt Ratio-4% Credit Portfolio Distribution by Region Operating Performance by Region 2012 Median Physical Occupancy by Region 2012 Median Debt Coverage Ratio by Region 2012 Median Per Unit Cash flow by Region 2012 Median Physical Occupancy by State 2012 Median Debt Coverage Ratio by State Median Debt Coverage Ratio by State (2008-2012) 2012 Median Per Unit Cash Flow by State Operating Performance by Top 10 Metropolitan Statistical Areas 2011 & 2012 Median Occupancy by Property Age 2011 & 2012 Median DCR by Property Age Operating Performance by Project Size Operating Performance by Credit Type Historical Hard Debt Ratio Trend Operating Performance by Development Type Operating Performance by Tenancy Type Underperformance in 2011 & 2012 Underperformance in 2011 & 2012 by Unit Count 2012 Per Unit Negative Cash Flow Historical Underperformance Trend (2008-2012)

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FIGURE 5.3.2(B) FIGURE 5.3.3

5.3.4(C) 5.3.5(A) 5.3.5(B) 5.3.5(C) 5.3.6 5.3.7 5.3.8 6.1 6.2 6.3 6.4 6.5 6.6 6.7 6.7(A) 6.7(B) 6.8 6.9

FIGURE 5.3.4(A) FIGURE 5.3.4(B) FIGURE FIGURE FIGURE FIGURE FIGURE FIGURE FIGURE FIGURE FIGURE FIGURE FIGURE FIGURE FIGURE FIGURE FIGURE FIGURE FIGURE FIGURE

Historical Underperformance (2008-2012) Chronic Underperformance Distribution of 2012 Physical Occupancy Distribution of 2012 Debt Coverage Ratio Distribution of 2012 Per Unit Cash Flow 2012 Occupancy Underperformance by State 2012 Debt Coverage Ratio Underperformance by State 2012 Per Unit Cash Flow Underperformance by State Cumulative Foreclosure Rate by Year Foreclosure County by Compliance Period Year Reasons Indicated for Foreclosure 2008-2012 Percent Net Equity by Property Age Unit Count by Year Placed in Service Percent Net Equity by Investment Type Percent Net Equity by Credit Type Percent Net Equity by Development Type Percent Net Equity by Tenancy Type Portfolio Composition by Region Percent Net Equity by Region Average Project Size by Region Percent Net Equity by State Net Equity Concentration by Top 10 Metropolitan Statistical Areas

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CHAPTER 1:

Executive Summary

Courtesy of Alliant Capital, Ltd.

W

e sometimes refer to the low-income housing tax credit as a housing program hiding inside an Internal Revenue Code (IRC) section. IRC Section 42, the housing tax credit statute, has now been a feature of the IRC for 28 years, making it the longest-lasting federal housing program. No one could have predicted at its enactment in 1986 that housing credits would become the principal source of financing for the development of affordable housing in this country. In fact, the housing credit program has been responsible for the construction or rehabilitation of more than 2.5 million apartments.1 Since its passage, an efficient capital market for housing credit investments has evolved and, with it, sophisticated information needs from the institutional investors that constitute that market.

• In response to those needs, CohnReznick has undertaken a long-term effort to measure the economic performance of housing tax credit properties. By economic performance, we refer to how well housing credit properties are faring based on the traditional real estate metrics—occupancy, debt coverage ratio (DCR), and per unit cash flow. In addition, we assess whether investors are achieving the investment yields they have been promised and take an in-depth look at properties that are underperforming.

• The data demonstrate that the national portfolio of housing credit properties continues to perform well and that occupancy levels have remained remarkably strong over the years and across every type of project—large or small, urban, rural, or exurban, offering further

1

US Department of Housing and Urban Development, HUD User Database

6 | The Low-Income Housing Tax Credit Program – November 2014

evidence that there is a tremendous affordable housing shortage. The 2011 and 2012 nationwide median physical occupancy rate reached 97%, representing a high water mark for the industry. Normal turnover excluded, housing tax credit properties were nearly fully occupied, a result that should not be surprising given the national shortage of affordable housing.

• The median debt coverage ratio for housing tax credit properties hovered between 1.13 and 1.15 for a significant portion of the last decade before rising to 1.21 in 2009. Our analysis illustrates that the marked improvement in financial performance we witnessed during the recession years continued through 2012. The nationwide median DCR across housing tax credit properties we surveyed climbed to 1.30 in 2012.

• Perhaps the most striking development in the performance data has been the precipitous decline in the percentage of housing credit properties operating below breakeven. A property is operating at breakeven when its net operating income after funding replacement reserves is exactly equal to its debt service. The percentage of housing credit properties operating below breakeven, which reached a high point of 35% in 2005, fell by 43% to 20.2% in 2012.

• As strong as the performance metrics are, it is still the case that one in five properties operates below breakeven. That being said, only 10% of the surveyed properties incurred per unit deficits for the year 2012 in excess of $400. This explains why, in most cases, operating deficits are being funded through a combination of fee deferrals, operating reserves, and voluntary loans made by the general partners or syndicators.

• The cumulative rate of foreclosure in housing tax credit properties has risen somewhat over the years from 0.35% in 20052 to 0.57% in 2010 to 0.63% through the end of 2012.

While this might appear to suggest worsening performance, the bulk of the operating data clearly suggests that the opposite is the case. CohnReznick notes that foreclosure data may have been under-reported in previous years, in part because anecdotal evidence suggests that some of the defunct syndicators experienced a disproportionately higher incidence of foreclosures. Nonetheless, the incidence of foreclosures in housing tax credit properties continues to compare very favorably with the foreclosure rate of market rate multifamily properties and other real estate assets.

• While these data points are impressive, they do not provide an explanation for the nearly universal increase in property performance. As a result, we randomly surveyed 20% of the executives that run asset management departments for the survey respondents.

Their commentary provides a more reliable basis for understanding improved performance than informed speculation on our part. Besides favorable debt leverage and more sophisticated underwriting that continued to positively influence property performance, contributing factors noted by the study participants included lower turnover and collection losses, and a general trend toward stabilization in rent and expense growth.

• “Our portfolio performance very closely mirrors CohnReznick’s data. We have seen occupancy tighten across our portfolio in terms of physical occupancy but, more important, in terms of economic occupancy. The rate of turnover in our properties has steadily decreased over the past five years, which has meant fewer rent skips, better collections, and lower costs related to turning over and re-leasing our apartments.”

2

Ernst & Young, Understanding the Dynamics IV, 2007, Page 3

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• “Property managers are reporting to us that rent collections have improved, which they attribute to employment gains and increasingly tight rental markets.”

• “Several years ago we were witnessing some real spikes in operating expenses, particularly in property insurance premiums, utility costs, and property taxes. More recently, our operating expenses have been more stable, although property taxes remain a problem in communities that are fiscally strapped.”

• “We own properties in some markets where rent increases have not been possible due to the lack of growth in area median income. For the most part, we have been able to manage through the rent ceiling and are optimistic that improving economic conditions will permit us to catch up in the future. In other markets, we have seen growth in rental income as tenants are no longer able to jump to competing properties—there simply are too few alternatives.”

• Whether the improvement in financial performance metrics can be sustained in the coming years will depend on a number of factors, including whether the industry continues to benefit from the historically low interest rate environment that it has enjoyed in recent years. Threats that could impact the program include 1) a version of tax reform that either removes the program or (more likely) “dilutes” its buying power, 2) local property taxation, 3) areas where median income has been flat or decreased, and 4) an increase in construction costs. CohnReznick is committed to conducting similar periodic studies to supply the industry with current and reliable data.

• While the use of technology has made advances, the housing credit industry is still a “horse and buggy” business compared to the quality of data that can be retrieved from the commercial real estate sector. Thus, for example, we received no data or incomplete data from survey participants when we asked them to report: • Mortgage loan defaults • Economic vs. physical occupancy • Affordable Housing Investors Council risk ratings Given the technology now widely available to sponsors and investors, we hope to retrieve and analyze these types of data as well as operating expense data when CohnReznick undertakes the next update.

8 | The Low-Income Housing Tax Credit Program – November 2014

CHAPTER 2:

The Convergence of Housing Tax Credits, Housing Development, and the Banking Industry

T

he Low-Income Housing Tax Credit program reached the 28th anniversary of its enactment in 2014. Adopted in the midst of dramatic changes to the IRC in 1986 and made permanent in 1993, the program has since enjoyed a strong level of bipartisan support in the United States Congress. Moreover, it has become the most significant resource for creating, rehabilitating, and preserving affordable housing in the United States. While an exact unit count has not been compiled, the U.S. Department of Housing and Urban Development estimates that approximately 2.5 million affordable apartment units have been built under the housing tax credit program since 1987. There are many factors that have contributed to making the program successful, including the fact that housing tax credit investments represent a public/private partnership among the federal government, state housing agencies, and the private sector.

Housing tax credits are allocated among the states by the federal government based on the states’ respective populations. Unlike most other tax expenditures, the cost of the housing tax credit program can be calculated with precision because the program’s funding authority is subject to a volume limit. The administration of the program resides primarily with the state credit allocating agencies, which have the authority to determine which projects should be awarded housing credits pursuant to a set of highly transparent procedures. As a result of its local control, the program has proven to be adaptable enough to serve changing housing needs as determined by the states annually. The real charm of the housing tax credit program, compared to every housing program which has preceded it, lies in the reliance on sophisticated capital.

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2.1 How is a Typical Housing Tax Credit Project Financed?

The process begins with a developer gaining control of a developable parcel and initiating the process of securing building permits, environmental clearance, access to utilities, and support of local officials. This can be a multiyear effort in many parts of the country. Once the developer has site control, it can apply for an allocation of housing credits from the relevant state agency. The application process is fraught with its own set of perils because there simply are not enough housing credits available to meet the demand. The states publish their procedures for making allocation decisions annually in a document known as a qualified allocation plan. It is not uncommon for states to turn away three to four applicants for every project that receives a reservation of credits.

Once a developer has a credit reservation in hand, it can negotiate with equity investors, syndication companies, and construction lenders and search for supplemental sources of capital from state and local governments. For most of the past 15 years, the demand for housing credit investments has exceeded the supply. The demand for credits has driven the price at which they trade from $0.42 per $1.00 of housing tax credits in the early years of the program to close to $1.00 per $1.00 of housing tax credits as of the date of publication of this report. The steady progression in housing credit prices has changed the “capital stack” in financing these developments. It is not uncommon for housing credit projects to be financed 75-80% with investor equity, with the balance coming from conventional mortgage financing and, in some cases, “soft” financing from governmental lenders.

This unique combination of capital sources allows housing credit properties to be financed with low levels of “must pay” debt. Ultimately, it is the sparing use of leverage that allows developers to be able to rent these apartments to tenants who could otherwise never hope to live in safe, decent, affordable housing. It is for this reason that the housing credit program is referred to as a capital subsidy.

2.2 How Does the Public/Private Partnership Foster an Efficient Use of the Capital Subsidy?

Compared to rental subsidy programs, the housing tax credit model has proven to be much more efficient.

• State allocating agencies are statutorily obligated to award only enough housing tax credits to make potential developments financially feasible, and the agencies have become very effective at ensuring that the projects to which they award housing credits are not overfinanced.

• In addition to the underwriting that housing credit projects undergo at the state agency level, these developments are underwritten by lenders, investors, and syndicators who acquire, underwrite, and asset manage these investments for institutional investors.

These players typically have sophisticated real estate underwriting platforms that initially supported conventional multifamily or other types of real estate assets. By leveraging their existing underwriting platforms, recruiting talented real estate professionals, and using similarly rigorous underwriting criteria (while acknowledging the uniqueness of this asset class), the affordable housing industry has made significant progress in accurately forecasting rental income and operating expenses.

10 | The Low-Income Housing Tax Credit Program – November 2014

• Before enactment of the housing tax credit program, there were drastic differences in the management of conventional multifamily real estate and affordable housing. Much of the pre-housing credit affordable housing inventory was composed of public housing built in the 1950s and 1960s and rental housing built in the 1960s and 1970s that was financed with a variety of Department of Housing and Urban Development (HUD) subsidies.

The “older assisted inventory,” as it is sometimes referred to, has not stood the test of time because of design flaws, inadequate maintenance, and, in some cases, poor management.

In contrast, housing credit projects tend to look, feel, and operate as if they were conventional multifamily apartments. These properties, as a class, have been more professionally managed by private sector operators and managers.

• In addition to generating tax equity, housing tax credit investments attract private capital from debt providers that would otherwise be disinclined to lend to affordable housing projects. While the debt coverage, typically 1.15-1.20, affords a fairly modest buffer to break even, the lenders that operate in this space understand that the quality of the cash flows in these projects, fueled largely by the demand for the units, is actually quite high.

• Ultimately, the success of housing tax credit investments is collectively “guaranteed” by stakeholders that share common goals. Over time, numerous mechanisms have been built into the development and management processes to hold different participants accountable for their performance, such as payment and performance bonds for general contractors, development completion guarantees for developers, operating deficit guarantees and various tax credit guarantees, and compliance and long-term use restriction requirements for all parties.

2.3. Why Do Institutional Investors Invest in Housing Tax Credit Investments?

Since the mid-1990s, the equity market for housing tax credit investments has been predominantly composed of large, publicly traded companies, most of which are in the banking and financial services sector. As investors and regulators have become increasingly confident in the financial performance of housing tax credit properties as an asset class, the housing tax credit program has become more dependent on the banking sector as a highly reliable source of equity to meet its capital needs. This has been a largely favorable development because banks, for example, filled most of the equity gap created when Fannie Mae and Freddie Mac exited the housing credit market in 2007 and 2008. CohnReznick estimates that approximately $11 billion of capital was committed to housing tax credit investments in 2013, and that the banking sector was the source for approximately 85% of that amount. There are a number of factors that make housing tax credit investments attractive to banks:

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• Increasing after-tax earnings and lowering effective tax rate: Housing credit investors are effectively purchasing a financial asset in the form of a stream of tax benefits (consisting of tax credits and passive losses associated with depreciation and mortgage interest deductions). Investors do not anticipate receiving cash flow distributions, because housing tax credit properties are generally underwritten to slightly above breakeven and developers or syndicators are generally the recipients of any remaining cash flow.

Substantially all of the investors’ returns are expected to be derived from tax benefits.

Banks typically report fairly stable earnings from year to year and are thus predictable federal taxpayers having sufficient taxable income against which to offset tax credits.

The housing tax credit is earned over a 15-year period but is claimed over an accelerated 10-year timeframe, beginning in the year in which the property is placed in service and units are occupied. The ideal housing credit investor is a company with a track record of consistent growth in earnings that is a regular rather than an alternative minimum taxpayer. This has been the profile of the U.S. banking industry for most of the last 28 years, with the exception of rare recession-driven interruptions.

• Satisfying CRA lending and investment test objectives: Banks are obligated, under the Community Reinvestment Act (CRA) regulations, to make loans, provide services, and make investments in low- to moderate-income neighborhoods in those areas in which they conduct business. As a regulatory matter, banks are obligated to operate in a “safe and sound” manner, which requires them to avoid investments that represent potential loss of capital. The strong financial performance track record of housing tax credit investments has historically been an ideal match for bank investors with a conservative focus. There are a limited number of qualified equity investments under CRA regulations, and many of these have less attractive yield and/or risk profiles. Among the available investment options, housing credit investments appear to be a clear investor favorite.

• Achieving a reasonable/superior risk adjusted rate of return: The banks that CohnReznick surveyed have advised us that on a risk-adjusted basis, the yields generated by their housing credit investments are superior to most of their available community development investment alternatives. This is, in part, because banks enjoy a lower cost of funds than other investors, which widens the spread between that cost and the rate of return offered by housing credit investments.

• Enhancing community relations and searching for cross-selling opportunities: Notwithstanding their CRA objectives, U.S. banks have become sophisticated housing tax credit investors and have learned to leverage their equity investments to sell other products and services to the development community. Thus, we increasingly see banks cross-selling other services such as construction financing, letters of credit, permanent loans, and other products to the properties in which they invest.

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CHAPTER 3:

Regulatory Changes Impacting the State of the Equity Market

C

orporate investors typically determine their annual tax credit investment volume based largely on their projected taxable income. Since the global recession, the earnings profile of most financial services firms has become more reliable. Stable earnings in this sector coupled with access to low-cost capital has fueled renewed appetite for tax credit investments. Further, because the supply of housing tax credits is largely fixed by statute and comprehensive tax reform has not been undertaken since 1986, it has been relatively straightforward for a financial institution to forecast tax liabilities and benefits over the 10-year credit delivery period.

As noted, CohnReznick estimates that in 2013 the housing tax credit market saw nearly $11 billion of new investment, 85% of which we believe is attributable to capital provided by the banking sector. The steady flow of capital invested by the national banks has made it possible for the low-income housing tax credit to mature and thrive. While the industry learned that it was over-reliant on the banking sector in 2008 and 2009, it has also become clear that stable tax and regulatory environments ensure the reliable presence of bank investors. The bottom line is that capital from community reinvestment act motivated banks is now the lifeblood of the housing credit program.

Although the tax credit market is thriving, a number of recent regulatory initiatives may affect the future of the equity market for housing tax credits. In 2013, there were policy changes in three areas that may directly impact bank participation in the housing credit market: new regulations under the CRA, Basel III, and new financial reporting rules from the Financial Accounting Standards Board (FASB). The following narrative provides an update on each topic and explores the potential impacts on investor demand going forward.

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3.1. Implication of New CRA Regulation

In November 2013, financial institutions received much anticipated clarification from the Office of the Comptroller of the Currency, the Board of Governors of the Federal Reserve System, and the Federal Deposit Insurance Corporation (the Agencies) in their revised Interagency Questions and Answers Regarding Community Reinvestment (Questions and Answers). The Agencies, among other things, affirmed that community development loans and qualified investments located outside of a bank’s assessment area(s) can receive positive consideration toward meeting the CRA objectives, provided that the “institution is being responsive to the community development needs and opportunities in its assessment area(s).”

While an institution’s performance within its assessment area(s) remains the primary focus of CRA examination, this change in the regulations may begin to address the imbalance in housing credit pricing seen between metropolitan (CRA hot) and non-metropolitan (CRA not) markets. The sometimes stark difference in the “CRA value” of housing credit projects depending on their location was analyzed and documented in a May 2013 CohnReznick study, The Community Reinvestment Act and Its Effect on Housing Tax Credit Pricing.

Differential tax credit pricing results in fewer investment dollars going to affordable housing developments located in CRA not areas and means that some projects, particularly in rural areas, cannot be financed.

Since the new regulations were issued, the agencies have amended their examination procedures to allow institutions to more clearly identify LIHTC investments located in statewide or regional areas that include but may not directly impact their assessment areas.

Implementation of the revisions is already underway, with the Agencies having provided standardized forms and training for CRA examiners. Many banks are waiting to see how their field examiners interpret the revised regulations when giving “credit” for investments in projects acquired by nationwide and regional funds that fall outside their self-defined assessment areas. The largest national banks, which have the most at risk in terms of securing high ratings, have advised CohnReznick that while the Questions and Answers are helpful, they will wait to see CRA exam results before they can be comfortable and confident with how the new rules are being implemented.

3.2 Banking Regulatory Environment

In response to the 2008-2012 global banking crisis, representatives from international banking authorities set forth proposed regulations to address systemic risk resulting from the interconnectedness of the world’s financial system. Under what is commonly referred to as the Basel III capital framework, financial institutions are subject to increasingly stringent minimum capital requirements, limitations on their ability to recognize deferred tax assets in their financial statements, and tougher risk weighting of real estate assets, among other restrictions. The Office of the Comptroller of the Currency and the Board of Governors of the Federal Reserve System published a final rule on October 11, 2013, that established a new regulatory capital framework, which incorporates aspects of Basel III and replaces existing risk-based and leverage capital rules.

14 | The Low-Income Housing Tax Credit Program – November 2014

When developing the new capital regulations, the Agencies recognized the unique nature of community development finance vehicles, such as housing credit investments. The agencies decided that certain of the new regulatory requirements should not apply to equity investments in and mortgage loans to affordable housing projects. For instance, Basel III proposed to apply a 150% risk weighting to so-called High Volatility Commercial Real Estate exposures (HVCRE) with high loan to value, a regulatory change that could have negatively affected financing for housing credit projects. Based in part on advocacy from the housing credit industry and the low foreclosure rates in this asset class, the definition of HVCRE was amended explicitly to exclude loans that finance community development investments.

The final rule also provided preferential capital treatment to equity investments made under the public welfare authority, including housing credit investments. Unlike other equity exposures that could receive risk weights as high as 400%, housing credit investments will be subject to a 100% risk weight.

The risk weighting of a bank’s assets is significant because the final rule increases the minimum capital ratios that financial institutions must satisfy in order to be considered adequately capitalized. The key regulatory measure of capitalization (Tier 1 ratio) compares Tier 1 capital to risk-weighted assets.

Basel III Minimum Capital Requirements 2012

2015

Risk Weighted Assets

500

500

Capital Requirements

x4%

x6%

Minimum Tier 1 Capital

20

30

Under the existing regulatory capital rule, in order to be adequately capitalized, a bank was required to have a minimum Tier 1 ratio of 4% and a 6% ratio to be considered well capitalized. Basel III requires a 4.5% minimum capital ratio for the common equity component of Tier 1 capital.

As a result, financial institutions will be required to reserve a greater percentage of their capital assets. The reduction of available capital could impact the amount of capital available for tax credit investments once it goes into effect after January 1, 2015. Since financial institutions will be required to hold a greater percentage of their assets in liquid, low-yield accounts, banks will need to generate more favorable returns on the capital that is invested. The need for higher returns could drive down tax credit pricing and increase the cost of capital for housing credit projects.

3.3 Implications of the New Accounting Change

In 1994, the Emerging Issues Task Force (EITF) of the FASB considered for the first time how LIHTC investments should be accounted for. As a result of their deliberations, in May of 1995, the Task Force issued EITF 94-1, now referred to as ASC 970, which sets out the general principles for accounting for housing tax credit investments. That guidance provided that LIHTC investments that were not coupled with a yield guarantee must be accounted for under either the cost or equity method of accounting. Under the equity method, an investor’s net income from operations is artificially depressed by depreciation-driven equity losses and impairment adjustments related to project investments. The benefit of housing credits, however, is not reflected in pre-tax income and instead is reflected in the income tax provision.

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Reporting the results of housing credit investments in different sections of the financial statement has made it difficult for the reader of a company’s financial statements to understand the real investment impacts of housing credit investments.

In the fall of 2010, the FASB was persuaded to take up once again the issue of accounting for housing tax credit investments. Consensus was reached in late 2013 and culminated in the issuance of Accounting Standards Update (ASU 2014-01) on January 15, 2014. Most significantly, the ASU 2014-01 established a new accounting method, referred to as the “proportional amortization method,” which represents a significant departure from the equity method. Under the new method, amortization of the investment is reflected as a component of the investor’s income tax expense rather than as a reduction of operating income on its income statement. Under the proportional amortization method, housing credit investments are presented on a “net, net basis” whereby the amortization, tax credits, and other tax benefits are all reflected in the income tax expense line. While the accounting method change will not change bottom-line earnings, the book losses from LIHTC investments will no longer represent a drag on pre-tax net operating income for investors that elect the new method. In addition, bank investors will be able to report lower non-interest expense, an income statement line item, which factors into how their operating efficiency is measured.

Equity Method . • Equity and Impairment losses reduce net operating income • A combination of equity losses and annual impairment is used to fully amortize the investment • Most investment projections include an expectation of impairment in later years

Proportional Amortization • Credits, losses and amortization expense all reflected in income tax expense • Equity loss no longer used • Impairment is only relevant when it occurs when tax credits and other tax benefits no longer expected to be received

ASU 2014-01 has generated much attention and interest from housing credit investors.

Housing credit investors are now obligated to disclose the amount of housing credits and related tax benefits they claim each year, the impact on their income tax expense, and the carrying value of their LIHTC investments (without regard to the accounting method they chose). Implementing the proportional amortization method has proven challenging for most investors with large portfolios. Nonetheless, we believe it will be the preferred method going forward because of its lower administrative burden and the improved income statement presentation compared to the equity method.

16 | The Low-Income Housing Tax Credit Program – November 2014

The ability to use proportional amortization may result in fewer so-called “guaranteed yield� transactions, because the primary motivation for companies that chose to make guaranteed yield investments was to avoid having to use equity accounting. However, since the economic fundamentals of housing credit investing have not changed, we expect that any growth in the investor base will likely be at the margins rather than a fundamental market shift.

Courtesy of RBC Capital Markets Tax Credit Equity Group

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CHAPTER 4:

Fund Investment Performance

G

enerally, an investor contemplating investing in housing tax credits can choose from one of two investment approaches: a direct investment or a syndicated investment. Under the direct investment model, an investor directly owns a limited partner interest in the property partnership that owns the underlying property, with the developer typically assuming the general partner interest. The direct investment approach is typically feasible only for investors that have internal resources dedicated to the acquisition, underwriting, and asset management of housing tax credit properties. As a result, this approach is favored by a handful of institutional investors. The syndicated investment approach enables investors to invest in a fund organized and managed by third-party intermediaries known as syndicators. The investor(s) own the limited partner interest in the fund, with the fund in turn owning the limited partner interest in various property partnerships.

There are two primary investment options when working with a syndicator: proprietary funds and multi-investor funds. In both cases, the syndicator originates potential property investments, performs underwriting, and presents the potential investment to investors.

Proprietary funds are typically sought out by a single investor with a desire for a higher level of control over the location of the properties they finance. The CRA requires banks to make qualified investments in areas in which they collect deposits, and they consequently receive CRA “credit� for doing so. Therefore, one of the primary investment motivations for banks is to earn CRA credit through their housing credit investments. Proprietary funds are a common investment option for institutions that want to focus their capital into very specific locations. The principal advantage of a multi-investor fund is risk diversification. A multi-investor fund can be composed of a number of investors, all of whom share risk and rewards based upon their proportional equity contribution to the fund.

18 | The Low-Income Housing Tax Credit Program – November 2014

Whether the fund has one or many investors, certain tax credit funds are credit-enhanced either by the syndicator or, more typically, by a third-party insurance company. Traditionally, approximately 20% of all tax credit investments were structured with a minimum yield guarantee. In previous years, an advantage of a credit-enhanced housing credit investment, other than its guaranteed minimum return, was the ability to use the so-called effective yield accounting method. However, the disadvantage of a guaranteed fund is that a substantial portion of the investor’s capital is used to finance the guarantee fee, resulting in a substantially lower investment yield. More recently, yield guarantees have become rarer, because of the lack of creditworthy guarantors. In addition, as previously noted in Chapter 3 of this report, the ability of investors to use the proportional amortization method of accounting has stripped guaranteed investments of one of their most beneficial advantages over conventional housing credit investments. CohnReznick suspects that, because of the reasons noted above, guaranteed investments may become less and less common.

Regardless of the chosen investment vehicle, low-income housing tax credit investors are effectively purchasing a financial asset in the form of a stream of tax benefits. Investors do not anticipate receiving cash flow distributions, because housing tax credit properties are generally underwritten to operate just above breakeven and developers or syndicators are generally the recipients of any excess cash flow.

4.1 Investment Performance

Survey respondents were asked to supply CohnReznick with performance data for every low-income housing tax credit fund that they syndicated. Our analysis of fund-level performance data assesses the track record of housing credit funds’ delivery of the originally projected yield and credits to investors versus their actual results.

The yield from housing credit investments is generally measured by the investment’s after-tax internal rate of return (IRR). The IRR is a function of the amount and timing of the projected housing credits and profit/loss versus the timing of the investor’s equity pay-in. In general, housing tax credits are realized on a straight-line basis over a 10-year period. Tax credits not delivered to investors in the first year because of construction or lease-up delays are typically realized in the 11th year (unless there is a permanent tax credit shortfall, which is often covered by basis adjustors). Most housing credit investments are structured with one or more “true-up” provisions to assist with yield maintenance.

For instance, a loss of the time value of credits can be compensated for by a so-called adjustor provision that reduces the investor’s remaining capital contributions to maintain the projected yield. It is important to note that, in addition to the timing of tax credit realization, the composition of the tax benefits (the relative proportion of tax credits to tax losses) is equally important to investors. Traditionally, investors who are sensitive to the negative impact of losses on their earnings were more inclined to invest in 9% tax credit properties with low leverage and less inclined to invest in 4% credit tax-exempt bond transactions that are more highly leveraged. However, given the fact that 4% properties perform roughly the same as 9% properties and in light of the introduction of the proportional amortization method of accounting, the investor bias against 4% properties is likely to disappear.

A CohnReznick Report | 19

Twenty-three survey respondents provided data for 1,140 low-income housing tax credit funds. For purposes of this analysis, we removed all funds that were closed in 1998 or earlier, as the property investments of these funds had already surpassed their 15-year compliance periods as of the effective date of this report. Figure 4.1 illustrates the remaining 893 funds that closed in or later than 1999, organized by fund type and segmented by gross equity and low-income housing tax credits. The average age of the 893 funds presented was eight years as of the effective date of this report.

Portfolio Composition by Fund Type (Post-1999 Funds) by Gross Equity 1.7%

Figure 4.1

FIGURE 4.1

0.3%

Proprietary Multi-Investor

44.7% 53.3%

Guaranteed Public

Of the 893 funds, there were 368 multi-investor funds accounting for 53.3% of the surveyed gross equity with an average fund size of $77.5 million of gross equity. The 486 proprietary funds in the pool accounted for 44.7% of the total fund portfolio gross equity, with an average fund size of $49.7 million of gross equity. The difference in the average size of these funds is driven by the fact that multi-investor funds are typically larger to accommodate the investment objectives of multiple investors. The remaining 39 funds were either guaranteed yield investments or public funds sold to individual investors. These fund types accounted for a total of 2.0% of the total gross equity.

20 | The Low-Income Housing Tax Credit Program – November 2014

Figure 4.1.1 illustrates the historical gross equity percentage split between multi-investor and proprietary fund investments since 1999. Not surprisingly, the percentage of proprietary funds reached its high point at 72.1% in 2009, when the housing credit market was at its lowest point. At the height of the recession, housing credit investors that remained active were almost entirely focused on meeting their CRA obligations, and deployed their capital predominantly through proprietary funds. After the equity market rebounded from the recession, the syndication of multi-investor funds also rebounded, reaching 64.5% in 2012.

Gross Equity Percentage of Multi-Investor and Proprietary Funds Since 1999

Figure 4.1.1 FIGURE 4.1.1

100% 90% 80% 70% 50% 40% 30% 20% 10%

Multi-Investor Fund %

2012

2011

2010

2009

2008

2007

2006

2005

2004

2003

2002

2001

2000

1999

0%

Proprietary Fund %

4.2 Fund Yields

Figure 4.2.1 illustrates the historical relationship between gross equity price and fund investment yields. Dramatic financial and organizational changes within what had been the market’s two largest housing credit investors, Fannie Mae and Freddie Mac, occasioned their exit from the housing credit equity market in 2007 and 2008. In addition to the loss of these government-sponsored enterprises (GSEs) as investors, the devaluation of mortgage securities and subsequent collapse of financial markets severely decreased the demand for tax credit investment among the nation’s largest financial institutions. The cumulative effect of losing the GSEs as investors and losses in the banking sector resulted in a 50% cumulative drop in tax credit demand from the market highs observed in 2006.

A CohnReznick Report | 21

Figure 4.2.1 also demonstrates relatively steady tax credit pricing of between $0.95 and $1.10 per $1.00 of credit despite the relatively drastic fluctuation of yields. During the down years of the national recession when yields exceeded 10%, the median gross equity price dipped to its lowest point, $0.90. While that median will strike some as very high given market conditions, it should be noted that tax credit pricing remained stubbornly high in the “hottest” CRA markets. When the equity market rebounded to pre-recession levels, yields decreased in kind while pricing returned to the high end of the pricing range exhibited in previous years.

Gross Equity Price vs. Fund Yield by Year

FigureFIGURE 4.2.1

$1.20

11.00%

$1.00

10.00% 9.00%

$0.80

8.00%

$0.60

7.00% $0.40

Fund Yield

Gross Equity Price

4.2.1

6.00%

$0.20

5.00%

$0.00

4.00% 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 Gross Equity Price

Annual Median Yield

Figure 4.2.2 illustrates the historical relationship between housing tax credit fund yields and 10-year Treasury security yields (adjusted for an after-tax rate equivalent of a 35% tax rate).

The chart depicts the median originally projected housing tax credit yield by year and the annual trend in 10-year Treasury security yields. In 2006 and 2007, housing credit fund yields approached Treasury yields, but have since increased and were subsequently diverted significantly in the next three years. The rates have since converged to some degree, but housing credit fund yields continue to represent a significant premium over the yield from long-term Treasury securities.

22 | The Low-Income Housing Tax Credit Program – November 2014

Fund Yield vs. 10-Year Treasury Security Rate by Year

FIGURE 4.2.2

Figure 4.2.2

12.00%

Fund Yield

10.00% 8.00% 6.00% 4.00% 2.00% 0.00% 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 Annual Median Yield of Housing Credit Funds

10-Year Treasury Yield (after tax equivalent)

4.3 Yield Variance Analysis

It is important to consider the performance of housing tax credit funds with respect to actual income tax benefits versus originally projected benefits. Investment performance is expressed in terms of yield (calculated based on a quarterly after-tax internal rate of return), overall tax credit delivery, and the initial years of tax credit delivery relative to originally projected amounts.

As we have defined the term, yield variance measures the difference between the originally projected yield at investment closing and the most current yield projection (December 31, 2012, for purposes of our survey). Positive variances indicate the achievement of greater than projected yield. On a weighted average basis (where yield variances for individual funds are aggregated and weighted by equity), survey respondents reported a positive 6.45% variance in meeting yield targets. We removed housing credit funds with credit enhancement (“guaranteed funds�) from this analysis because guaranteed funds are structured with yield maintenance mechanisms that ensure a predictable yield to investors.

While yield is a significant factor for housing credit investors, the individual components of yield computations have a major bearing on their calculation. Yield can be maintained naturally or artificially by pre-negotiated investment provisions in a number of ways.

An investor can receive a more favorable yield in a number of ways: as a result of an underperforming portfolio generating higher losses, if equity pay-in schedules are adjusted to postpone capital contributions, or if so-called adjustor provisions, under which remaining investor capital contributions are reduced to the extent necessary in order to re-establish the target yield.

A CohnReznick Report | 23

Figure 4.3.1 illustrates the variation in fund yield performance based on the year in which the funds were closed.

Fund Yield Variance by Year

FIGURE 4.3.1

Figure 4.3.1 – Fund Yield Variance by Year 20%

15%

Fund Yield Variance

10%

5%

0%

-5%

-10%

-15%

-20%

1999

2000

2001

2002

2003

2004

2005

2006

2007

2008

2009

2010

2011

2012

The graph illustrates the fact that 16.1% of the funds CohnReznick studied reported negative yield variances.3 We totaled the negative yield variances relative to the overall number of funds closed in each year and found that the years during which funds with the highest incidence of negative variance were syndicated in 1999, 2000, 2002, 2004, and 2005. Not coincidentally, those same years have among the highest number of funds syndicated relative to all other years surveyed. Based purely on the percentage of funds with negative yield variances relative to the total number of funds syndicated in the same year, it appears that more recently syndicated funds are generating more favorable results. Indeed, funds closed in years later than 2006 reported fewer than a 12% incidence of negative yield variances.

Figure 4.3.2 illustrates the percentage of funds with negative yield variances sorted by the year in which they initially closed.

3

137 of the 850 funds that provided original and current yield information reported negative yield variations.

24 | The Low-Income Housing Tax Credit Program – November 2014

Percentage Incidence of Negative Fund Yield Variance Since 1999 1999

Percent incidence of negative yield variance

2003

2004

2005

2006

2007

FIGURE 4.3.2

2000

2001

2002

2008

2009

2010

2011

2012

30.6% 33.9%

9.7%

31.3% 17.3% 31.7% 21.9% 16.1% 11.1% 10.2%

5.6%

5.8%

6.7%

5.9%

The data illustrate that “younger” funds with early-stage properties generally perform better.

As such, the most recent years in the table above illustrate very low instances of negative yield variance. Note that any year when fewer than 30 surveyed funds were reported to have closed was removed from our analysis.

Figure 4.3.3 illustrates the median fund yield variance by year closed since 1999.

Median Fund Yield Variance by Year of Fund Closing

FIGURE 4.3.3

8.0% 7.0%

Fund Yield Variance

6.0% 5.0% 4.0% 3.0% 2.0% 1.0%

Median Proprietary Fund Variance

2012

2011

2010

2009

2008

2007

2006

2005

2004

2003

2002

2001

2000

1999

0.0%

Median Multi-Investor Fund Variance

As the table above indicates, aside from a few outlier years since 1999, proprietary funds have reported larger median yield variance than their multi-investor counterparts. We note, however, that the magnitude of the variance between the two fund types could be affected by the manner in which syndicators define “original” yields, especially for proprietary funds that tend to be less specified at closing. We hesitate to draw too many conclusions from this comparison, given the variability between syndicators’ internal processes for tracking proprietary yield data.

A CohnReznick Report | 25

4.4. Housing Credit Variance Analysis

Consistent with CohnReznick’s industry experience, the survey data we examined demonstrated that the aggregate average variance in total housing credits has been less than 1%. Investors were projected to receive $53.0 billion in credits and actually received $52.6 billion through 2012.

The average housing credit investment derives the majority of its benefits (roughly 75%) from housing credits, with the balance coming from passive losses. Because housing tax credits are calculated based on qualified development costs, a property’s future delivery of tax credits is highly predictable. In this context, the timing of tax credit delivery is more likely to create variances, because delays in the construction and lease-up of housing credit properties may result in delayed delivery of housing credits. Our data suggest that such delays, not uncommon in the early years of the program, have become less common over time as the industry’s ability to underwrite credit delivery has improved.

Housing Credit Delivery Variance by Investment Type

FIGURE 4.4.1

Total 1st-Year Housing 2nd-Year Housing 3rd-Year Housing Housing Credit Credit Credit Credit Delivery Variance Delivery Variance Delivery Variance Delivery Variance Total

-0.8%

-7.1%

-10.1%

-6.5%

Proprietary

-1.3%

-3.0%

-6.9%

-5.0%

Multi-investor

-0.5%

-10.8%

-13.1%

-7.9%

Guaranteed

1.0%

-4.5%

-2.1%

1.4%

While the total housing credit delivery variance increased slightly since our last study from a (-0.4%) to (-0.8%), the industry continues to show improvement in projecting first year credits.

There is a significant gap between proprietary and multi-investor funds’ first- and secondyear variance of housing credit delivery. While this gap has decreased since our last performance study, the relationship remains between the two fund types. We presume the difference to be attributable to the fact that proprietary funds are, on average, less specified than their multi-investor counterparts. Because proprietary funds tend to be less specified, comparing actual credit delivery results to original projections may not be as objective an analysis as multi-investor funds, and thus we focus on the track record of multi-investor funds.

26 | The Low-Income Housing Tax Credit Program – November 2014

CHAPTER 5:

Property Performance

Courtesy of Boston Capital

C

ohnReznick solicited data from 35 active housing tax credit syndicators and a number of the nation’s largest housing credit investors, hereinafter referred to as “data providers” or “respondents.” Thirty-two syndicators participated and provided portfolio data, which equates to approximately a 91% response rate. Those who did not participate were predominantly smaller organizations for which the administrative burden of providing data within our desired timeframe as deemed too onerous, or those who were newly formed organizations that manage just a handful of stabilized properties. In addition to the syndicator respondents, three of the largest housing credit investors provided data. In an effort to avoid reconciling property investments held in shared portfolios, we collected only direct investment data from investor respondents. All data were to investors provided by the respondents to CohnReznick on a voluntary and strictly confidential basis.

This chapter summarizes the operating and financial data collected from the respondents for housing tax credit property investments located in each of the 50 states, the District of Columbia, Guam, the U.S. Virgin Islands, and Puerto Rico. The data gathered represented 18,412 housing tax credit properties, an increase of more than 1,000 properties from our most recent study, which analyzed 2008-2010 operating performance. All participants in this study also participated in our 2010 study. The increase in sample size reflected the net increase as additional properties were placed in service and older properties at the end of their respective compliance periods were disposed of. We estimate that this data sample represents approximately 70% of the entire inventory of housing tax credit properties that are actively managed by syndicators and/or investors. The gap between CohnReznick’s data sample and the entire housing tax credit inventory can be attributed to investments made by syndication firms that have left the business, and properties that have reached the expiration of their respective compliance periods and subsequently “cycled out” of the program.

A CohnReznick Report | 27

As can be observed in Figure 5.0.1, the 18,412 properties in CohnReznick’s data sample collectively represented approximately $76 billion in net equity investments and approximately $80 billion in housing tax credits. CohnReznick notes that some respondents did not provide either/or equity and housing credit information for some of the properties in their portfolio.

As such, the net equity and housing credit totals are understated.

Overall Portfolio Composition Survey Total Number of Properties

FIGURE 5.0.1

Stabilized Properties

% of Stabilized

18,412

15,588

84.7%

Number of Units

1,369,239

1,193,244

87.1%

Number of LIHTC Units

1,317,984

1,138,418

86.4%

Housing Credit Net Equity

$

76,117,190,987

$

61,737,621,507

81.1%

Total Housing Credits

$

79,997,624,115

$

64,297,650,209

80.4%

Of the 18,412 properties, 15,588 (84.7%) achieved “stabilized operations” as of December 31, 2012. We characterize a property as having achieved “stabilized operations” when it has completed construction, achieved 100% tax credit occupancy (when all of the tax credit units have been occupied by income-eligible tenants), and has closed on its permanent financing. While the definition of stabilized operations differs slightly among industry participants, CohnReznick has adopted the industry’s consensus definition and does not believe these slight differences are material enough to distort the analysis.

5.1 Portfolio Performance Data—Physical Occupancy, DCR and Per Unit Cash Flow

CohnReznick measured the real estate performance of the surveyed properties by using a number of operating and financial metrics, including: • Physical occupancy, defined as the number of units occupied divided by the number of units available. While we would prefer to report both physical and economic occupancy rates, only a small subset of surveyed syndication firms track economic occupancy data for their portfolios.

• Debt coverage ratio, defined as net operating income less required replacement reserve deposits, divided by mandatory debt service payments.

• Per unit cash flow, defined as the total cash flow generated after deducting debt service payments and required replacement reserve contributions, divided by the total number of units within the property.

28 | The Low-Income Housing Tax Credit Program – November 2014

• Incidence of underperformance, defined as properties operating with less than 90% physical occupancy, less than 1.00 debt coverage ratio, or negative per unit cash flow • Incidence of foreclosure among respondents.

This chapter summarizes the 2011-2012 operating performance data of the 15,588 surveyed stabilized properties. Properties with partial years of stabilized performance in 2011 and 2012 were removed from the dataset, as they could inaccurately represent DCR and cash flow. Figure 5.1.1 summarizes 2011-2012 operating results measured by median physical occupancy, DCR, and per unit cash flow data for the entire stabilized portfolio.

Overall Portfolio Performance

FIGURE 5.1.1

2011

2012

Median Physical Occupancy

97.0%

97.0%

Median Debt Coverage Ratio

1.28

1.30

Median Per Unit Cash Flow

$464

$498

All three major performance metrics show improvement in 2011 and 2012, continuing a consistent trend we have observed for much of the past decade. Included in Figure 5.1.2, the five-year trend of physical occupancy and debt coverage ratio performance data indicated that, in all five years, occupancy remained above 96%, and DCR steadily increased. Physical occupancy reached the 97% mark for the first time since CohnReznick has been collecting performance data, and represents a high-water mark for the industry.

DCR and per unit cash flow have steadily increased, and to our knowledge of previous industry studies, the metrics indicated were at their highest levels to date.4

Overall Portfolio Performance (2008-2012)

4

FIGURE 5.1.2

2008

2009

2010

2011

2012

Median Physical Occupancy

96.4%

96.3%

96.6%

97.0%

97.0%

Median Debt Coverage Ratio

1.15

1.21

1.24

1.28

1.30

Median Per Unit Cash Flow

$250

$341

$419

$464

$498

The Low Income Housing Tax Credit Program at Year 25: An Expanded Look at Its Performance; CohnReznick, LLP December 2012.

A CohnReznick Report | 29

Physical Occupancy Syndicators and investors alike generally underwrite housing tax credit property investments based on the assumption that “effective” or “economic” occupancy will be 93%. The assumed economic loss of 7% takes into account the periodic turnover of units, the ability to lease such units, and losses from rent skips and/or collection problems. While physical occupancy may be calculated at 95%, it is common for housing tax credit properties to lose an additional 1-2% of gross potential rent because of collection problems.

CohnReznick notes that only physical occupancy data have been presented in this report.

Economic occupancy, which is a more valuable metric, is not monitored by a significant number of data providers and thus could not be incorporated in our survey. While physical occupancy was relatively consistent across the country, economic losses may have varied significantly, thereby contributing to differing financial performance among housing credit properties across various geographic segments.

Figure 5.1.3 summarizes the median physical occupancy data for the stabilized properties CohnReznick surveyed for calendar years 2011 and 2012. The data suggested that, in the period of recovery following the recession, as the housing market began to rebound and national unemployment rates improved, median physical occupancy continued to improve. In 2011, the median physical occupancy rate across the surveyed housing tax credit portfolio was 97.0%, which remained steady at 97.0% through 2012.

Median Physical Occupancy

FIGURE 5.1.3

2011 Median Physical Occupancy

97.0%

2012 97.0%

The U.S. Census Bureau published American Housing Survey for the United States 2011, which reported that the national multifamily rental occupancy rate was 90.9% in 2011.5 While the national multifamily occupancy rate has improved slightly since the height of the recession, there still remains a marked difference between occupancy levels in conventional multifamily properties versus housing credit properties. The reasons for the occupancy discrepancy are numerous, but the major driver of the consistently high occupancy rates in housing tax credit properties is the startlingly short supply of low-income housing units in the United States to satisfy the national demand for affordable housing.

5

Source: United States Census Bureau

30 | The Low-Income Housing Tax Credit Program – November 2014

In 2013, the U.S. Census Bureau estimated that 14.5% of the United States’ population, or 45.3 million people, were living in poverty. This is the largest percentage since the census began to quantify this statistic more than 50 years ago, and the number is growing.6 The U.S. Census Bureau defines poverty according to annually calculated income thresholds. In 2012 a family living in poverty was defined as a two-parent, two-child (under 18) household earning less than $28,087 annually. At the same time, the number of extremely low-income renter households (those households earning no more than 30% of area median income) was 10.1 million, representing one in every four renter households. The increasing number of extremely low income households coupled with the net affordable housing units produced presents a troubling housing situation for income-burdened households.7 In a 2013 report, the National Low Income Housing Coalition estimated the deficit of rental units that are both affordable and available for extremely low-income households to be 4.6 million units.8 It is increasingly the case that extremely low-income households have limited affordable housing opportunities in their area.

Debt Coverage Ratio The term “debt coverage” relates to the relationship between net income (effective gross rental income less operating expenses and replacement reserve deposits) and mandatory debt service payments. Thus, for example, an apartment project that reports net rental income of $115,000 and $100,000 of annual mandatory debt service is considered to have a 1.15 DCR. Most lenders require housing tax credit properties to generate net income which produces a debt coverage ratio of at least 1.15 (the underwriting standard) before agreeing to retire a property’s construction loan and extend long-term permanent financing. Some lenders require higher coverage ratios for properties demonstrating lower real estate quality.

The properties CohnReznick surveyed experienced a steady DCR increase from 1.24 in 2010, to 1.28 in 2011 and 1.30 in 2012. Historically, median DCR hovered consistently around 1.15 before increasing to 1.21 in 2009 and increasing significantly again to 1.24 in 2010— a surprising result for some industry observers given the national recession, increased unemployment, and the turmoil in certain housing markets. The data CohnReznick collected in this study suggest that DCR has steadily improved year after year, reaching an all-time high in 2012.

Source: U.S. Census Bureau. Income, Poverty and Health Insurance Coverage in the United States: 2010. www.census.gov/prod/2011pubs/p60-239.pdf.

By “net units produced”, we refer to the number of affordable housing units produced minus the number of affordable units falling into disrepair or cycling out of compliance.

8 Source: National Low Income Housing Coalition http://nlihc.org/sites/default/files/HS_3-1.pdf.

6

7

A CohnReznick Report | 31

Median Debt Coverage Ratio

FIGURE 5.1.4

2011 Median Debt Coverage Ratio

1.28

2012 1.30

Per Unit Cash Flow The level of cash flow that a property generates (expressed here in terms of annual cash flow per apartment unit) closely tracks the property’s DCR; however, to the extent that a property only has “soft” debt, DCR measurements are less relevant. Soft debt refers to mortgage loans made by government agencies or other lenders that require current payments only to the extent that the project has sufficient cash flow (or in some cases, do not require any payments until the maturity of such loans even if there is surplus cash flow).

Accordingly, the number of properties reporting per unit cash flow was larger than the number of properties reporting positive debt coverage.

In the same way that DCRs improved in 2011 and 2012, our data suggest that median per unit cash flow increased in kind. For a large portion of the last decade, housing tax credit properties have reported minimal levels of cash flow averaging between $200 and $250 per unit per annum, after paying hard debt service and making required replacement reserve deposits. In 2008, the median cash flow per unit among more than 15,000 surveyed housing credit properties was $250, which increased to $341 in 2009 and to $419 in 2010.

Based on the data developed for this study, median per unit cash flow continues to trend upward, increasing to $464 in 2011 and $498 in 2012.

While per unit cash flow has doubled in the past six years, the upward trend needs to be put into context. Because the median tax credit project has 73 units, the total sum of cash flow per property—also on a median basis—is still only $36,000 of positive cash flow for the year. Further, any excess cash flow is typically run through the cash flow waterfall specified under the partnership agreement to pay deferred developer fees, asset management fees, or other fees rather than distributed to the partners.

Median Per Unit Cash Flow

Median Per Unit Cash Flow

32 | The Low-Income Housing Tax Credit Program – November 2014

FIGURE 5.1.5

2011

2012

$464

$498

Explanation In our previous study, CohnReznick queried industry experts and participating organizations regarding how the marked improvement in housing credit properties’ performance was possible, particularly in the context of the recession. While none of the explored factors could be singled out as an overriding source, we identified lower hard debt service burden and more sophisticated expense underwriting as the two leading causes for improved property financial performance. In this study, we found that these two factors continued to positively influence the performance of surveyed housing tax credit properties. In addition, some survey participants indicated that better collection efforts have reduced economic vacancy losses across their respective portfolios.

Lower hard debt service burden: As housing tax credit prices have trended upward, the overall surveyed portfolio reflected an increasing number of properties that were financed with little to no hard debt. Figure 5.1.6 illustrates the evolution of tax credit pricing over the past 20 years, measured by the number of capital investors committed to the property partnership, in accordance with a pre-negotiated pay-in schedule, in order to receive one dollar of housing tax credit from such property.

Figure 4.2.1

Median Net Equity Price by Year Placed-in-Service

FIGURE 5.1.6

$1.00 $0.95 $0.85 $0.80 $0.75 $0.70 $0.65 $0.60 $0.55 2012

2011

2010

2009

2008

2007

2006

2005

2004

2003

2002

2001

2000

1999

1998

1997

1996

1995

1994

1993

1992

$0.50 1991

Net Equity Price

$0.90

A CohnReznick Report | 33

At the inception of the housing credit program, equity was raised principally from small investments made by individual investors through public offerings. Beginning in the early 1990s, a corporate equity market began to develop as institutional investors began to understand the asset class, the housing tax credit program was made permanent, and syndicators quickly came to prefer institutional capital as a more efficient way to raise equity. At the national level, housing tax credits traded at net prices as low as $0.50 in the early 1990s, steadily increased to $0.80 per dollar of credit in the early 2000s, and skyrocketed to close to $1.00 at the height of the equity market in 2006. However, as previously mentioned, the exit of Fannie Mae and Freddie Mac and a precipitous decline in the profitability of the largest financial institutions resulted in a meltdown of the housing credit equity market. As a direct consequence, housing tax credit prices fell sharply to an average of $0.74 in 2009, with projects in rural areas fetching as low as $0.62. Pricing has since steadily increased in step with the national economic recovery, and as of the date of this report is averaging $0.94, with pricing routinely exceeding $1.00 in most urban markets.

Housing tax credit prices presented in the following figures are referred to as the “net equity price” because they reflect the direct amount of equity per dollar of credit that was invested to finance the development of these properties. We refer to them as “net” prices because they do not include the costs of raising capital such as fees paid to compensate syndicators for their services, brokerage commissions, and similar costs often collectively referred to as “the load.” The amount of load can vary significantly depending on an investor’s choice of investment vehicles (multi-investor versus proprietary versus direct investment) and the individual syndicator’s business practices.

The years depicted are a function of the year in which the properties are placed in service, as opposed to when the underlying investments are closed and the housing credit prices are determined. Given the development timeline of a typical housing tax credit property, the prices naturally reflect a one- to two-year lag in market price.

Finally, while housing tax credit prices presented are median prices reported by survey respondents, we have observed a price disparity as wide as 35 cents between properties in rural and exurban markets compared to properties located within a CRA assessment area where one or more major bank investors compete to invest in the same property.

Figures 5.1.7 and 5.1.8 illustrate the correlation between tax credit pricing and hard debt ratio among surveyed properties. We presented the 4% housing tax credit properties separately from 9% housing tax credit properties because 4% properties qualify for significantly fewer credits based on the same eligible basis, and thus must be more heavily leveraged than 9% properties.

Though not perfect, a strong inverse relationship exists between the price paid for a property’s housing credits and its level of hard debt. Using 9% housing tax credit properties as an example, in the late 1990s when housing tax credits were traded for less than $0.80 per dollar of credit on a national level, approximately 33% of the permanent sources of funds of housing tax credit properties came from hard debt. Over the past five years, as housing tax credit prices trended upward, the median hard debt ratio observed in 9% housing tax credit properties fell to as low as 15%. More recently, median hard debt in 9% deals has increased.

34 | The Low-Income Housing Tax Credit Program – November 2014

Figure 5.1.7 FIGURE 5.1.7

$1.00

40.0%

$0.95

35.0%

$0.90

30.0%

$0.85

25.0%

$0.80

20.0%

$0.75

15.0%

$0.70

10.0%

$0.65

5.0%

$0.60

0.0%

Hard Debt Ratio

Net equity Price

Net Equity Price vs. Hard Debt Ratio—9% Credit

1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 9% Net Equity Price

Hard Debt Ratio

Net Equity Price vs. Hard Debt Ratio—4% Credit

FigureFIGURE 5.1.8

$1.00

70.0%

$0.95

60.0% 50.0%

$0.85

40.0%

$0.80

30.0%

$0.75

20.0%

$0.70

Hard Debt Ratio

Net Equity Price

$0.90

5.1.8

10.0%

$0.65 $0.60 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 4% Net Equity Price

0.0%

Hard Debt Ratio

On a net equity basis, nearly 27% of the stabilized properties we surveyed were placed in service in 2009 or later, and 74% were placed in service in 2004 or later. As a group, these properties are clearly benefiting from lower levels of hard debt. A cohort of the surveyed properties also refinanced at lower interest rates during the favorable interest rate environment in recent years.

While we do not have a statistical basis for quantifying the impact from refinancing, it is clear that lower leverage and favorable interest rates have served to decrease the hard debt burden on housing tax credit properties.

A CohnReznick Report | 35

We also note that, notwithstanding the data reflecting the favorable effect of lower leverage, a property’s hard debt ratio has, as a single statistic is only one of many contributing factors to a property’s overall performance.

More efficient expense underwriting: CohnReznick’s industry experience and interviews with survey respondents allowed us to conclude that the housing tax credit industry, as a whole, has made significant gains in improving its underwriting of operating expenses. Participants have indicated to us that the availability of benchmarked data from their own portfolios, state credit allocation agencies, and industry data providers have allowed them to improve their expense underwriting, and narrow the level of variance between underwritten and actual expenses typical in the first generation of housing credit properties.

Decreased turnover and improved collection: CohnReznick informally surveyed the key asset management staff of many of the survey respondents who indicated that decreased turnover and improved collections collectively served to reduce economic vacancy losses across their respective portfolios.

In addition, a significant portion of the surveyed properties may have benefited from refinancing in the favorable interest rate environment of the last several years. It remains to be seen whether the improved financial performance can be sustained in the context of a changing interest rate environment, area median income growth constraints, and other factors.

5.2 Segmented Performance Data and Contributing Factors

Housing credit investors and lenders often ask whether property investments in certain geographic areas or of certain types tend to perform better than others. While housing credit investments provide investors with tax credit benefits, they are ultimately equity investments in operating real estate. A major component of the success of any real estate investment is its geographic location. Not surprisingly, CohnReznick found through careful analysis of the data that low-income housing tax credit properties in certain areas have a more favorable operating history than others. CohnReznick stresses that geographic location, while a strong factor in determining an individual property’s success, is just one of a number of factors that will ultimately lead to success or failure of a given low-income housing tax credit property.

Additionally, we segmented the data in a variety of ways in an attempt to identify contributing factors to the success or failure of a given property.

In this chapter we present property operating data according to: • Twelve regional areas • All 50 states • Metropolitan statistical areas (MSAs) to the extent that a meaningful sample size could be obtained • Age • Size • Credit type • Hard debt leverage ratio • Development type • Tenancy type

36 | The Low-Income Housing Tax Credit Program – November 2014

5.2.1 By Region

CohnReznick separated survey properties in the 50 states, the District of Columbia, Guam, the U.S. Virgin Islands, and Puerto Rico into 12 regions with similar geographic profiles that most ideally classified the country. Figure 5.2.1(A) illustrates the average equity level in each region as a percentage of the overall stabilized portfolio.

Portfolio Distribution by Region Region Number

Constituent States

FIGURE 5.2.1(A)

% of Stabilized Portfolio

Region 1

CA, OR, WA

17.6%

Region 2

AK, HI

0.4%

Region 3

ID, MT, WY

0.7%

Region 4

AZ, CO, NM, NV, UT

4.5%

Region 5

MN, ND, SD

2.1%

Region 6

IA, KS, NE, MO

4.3%

Region 7

IN, IL, MI, OH, WI

Region 8

AR, OK, TX

Region 9

AL, FL, GA, LA, MS

Region 10

KY, NC, SC, TN, VA, WV

Region 11

CT, DC, DE, MA, MD, ME, NH, NJ, NY, PA, RI, VT

Region 12

GU, PR, VI

14.2% 7.2% 13.5% 8.2% 26.1% 0.9%

A CohnReznick Report | 37

Figure 5.2.1(B) illustrates the 2011 and 2012 operating performance (occupancy, DCR, and per unit cash flow) data for stabilized properties in the surveyed portfolio segmented by region.

Operating Performance by Region

FIGURE 5.2.1(B)

Median Physical Occupancy Median Debt Coverage Ratio Median Per Unit Cash Flow Region Number

2011

2012

2011

2012

2011

2012

Region 1

97.9%

98.0%

1.36

1.34

$

802

$ 753

Region 2

97.2%

97.5%

1.28

1.22

$

778

$ 789

Region 3

96.5%

96.7%

1.19

1.25

$

412

$ 465

Region 4

97.0%

97.0%

1.27

1.29

$

592

$ 580

Region 5

98.0%

98.0%

1.36

1.38

$

666

$ 745

Region 6

96.4%

96.3%

1.20

1.22

$

248

$ 294

Region 7

96.0%

96.2%

1.21

1.23

$

348

$ 384

Region 8

95.1%

95.8%

1.18

1.23

$

295

$ 364

Region 9

95.8%

96.0%

1.27

1.23

$

350

$ 331

Region 10

97.0%

97.0%

1.26

1.27

$

425

$ 436

Region 11

97.8%

98.0%

1.38

1.46

$

623

$ 726

Region 12

100.0%

99.9%

1.27

1.31

$

473

$ 553

Seven of the 12 regions reported 2012 median occupancy rates that were greater than or equal to the 97.0% overall portfolio median. The highest performing region measured by occupancy rate was Region 12, which includes Puerto Rico, the U.S. Virgin Islands, and Guam. The 95 stabilized properties in this region reported near 100% occupancy in 2012.

The survey data support our experience that properties located in Puerto Rico, the U.S. Virgin Islands, and Guam consistently operate at or close to 100% occupancy because of the scarcity of affordable housing in these areas. Regional occupancy data suggest that there has not been a meaningful variance from the overall portfolio trend. Consistently high occupancy suggests that the demand for affordable housing is not concentrated in the east and west coasts, as some have suggested, but is the case virtually everywhere in the United States.

38 | The Low-Income Housing Tax Credit Program – November 2014

Figures 5.2.1(C) – (E) illustrate each region’s 2012 median occupancy rate, DCR, and per unit cash flow on a national map. Regions are colored such that each performance range is indicated with a different color.

2012 Median Physical Occupancy by Region

96.7%

FIGURE 5.2.1(C)

98.0%

98.0%

98.0%

96.2% 96.3% 97.0%

97.0%

95.8%

96.0%

97.5%

2012 Median Occupancy by Region 95.9% and below

96.0% to 96.7%

96.8% to 97.5%

97.6% to 97.9%

98.0% and above

Aside from Region 12, which we previously discussed, it’s not surprising that the east and west coasts, representing Regions 1 and 11, were found to have the strongest occupancy performance. Inasmuch as these two regions have the largest representation of properties in the survey sample, their performance has had the largest influence on overall national portfolio performance. Not unlike the results of the previous performance studies, the Southeast and Midwest regions reported occupancy rates that were slightly below the portfolio median. However, it may come as a surprise to some that Region 5, composed of Minnesota, North Dakota, and South Dakota, reported regional occupancy rates that were consistent with the traditionally strong-performing coastal regions. While Minnesota’s performance is buoyed considerably by the number of strong-performing properties in the Minneapolis and St. Paul markets, North Dakota is more of a surprise. We attribute some of the increased performance in North Dakota, and by inclusion the entire Region 5, to the influx of tenant base and economic prosperity brought on by the North Dakota oil boom.

9

Region 12, which consists of Puerto Rico, the US Virgin Islands and Guam account for a very small portion of the overall portfolio, and have not been included in the maps in this report.

A CohnReznick Report | 39

Since the discovery and extraction of oil from the Bakken formation in North Dakota in 2006, enough jobs have been created to give the state the lowest unemployment rate in the country, and provided the state (which has less than 1 million inhabitants) a $1 billion budget surplus. We expect the oil boom to continue to have a positive impact on the performance of housing credit properties in North Dakota for the foreseeable future.

Region 8, composed of Texas, Arkansas, and Oklahoma, had the least favorable regional occupancy at 95.8%. As discussed in the state breakdowns, Arkansas and Oklahoma reported the lowest median occupancy rates in 2011, although both marginally improved in 2012.

Only four of the 12 regions reported 2012 median DCRs that were greater than or equal to the 1.30 overall portfolio median. Once again the northeast and west coast, representing Regions 1 and 11, were found to have the strongest DCR performance, and the fact that these two regions have the largest representation of properties in the survey sample means their performance has had the largest influence on overall national portfolio performance.

Median Debt Coverage Ratio by Region

FIGURE 5.2.1(D)

1.38

1.23 1.34

1.46

1.23 1.22 1.29

1.27

1.23

1.23

1.22

2012 Median DCR by Region 1.17 and below

1.18 to 1.19

1.20 to 1.25

40 | The Low-Income Housing Tax Credit Program – November 2014

1.26 to 1.30

1.31 and above

Six of the 12 regions reported 2012 median per unit cash flow that were greater than or equal to the $498 overall portfolio median. The central section of the country, illustrated here by Region 6, continues to lag behind the rest of the country with regard to per unit cash flow. At $294 per unit, Region 6 reported significantly less cash flow per unit than the national median in 2012. It is no surprise once again that the west coast and the northeast, Regions 1 and 11, reported two of the four highest regional per unit cash flows at $753 and $726, respectively.

2012 Median Per Unit Cash Flow by Region

$465

FIGURE 5.2.1(E)

$745

$753

$726

$384 $294 $580

$436

$364

$331

$789

2012 Median Per Unit Cash Flow $0 to $100

$101 to $250

$251 to $500

$501 to $1,000

$1,000 and above

5.2.2 By State

CohnReznick further segmented operating data for the surveyed properties according to their distribution among the 50 states, Puerto Rico, the U.S. Virgin Islands, and Guam.

California, New York, Texas, Florida, and Illinois collectively accounted for nearly 40% of the overall portfolio based on the volume of equity investment in surveyed properties.

Properties located in Alaska, Guam, Hawaii, Montana, South Dakota, U.S. Virgin Islands, and Wyoming all have net equity investment that represent small amounts of the overall portfolio.

A CohnReznick Report | 41

2012 Median Physical Occupancy by State

FIGURE 5.2.2(A)

WA

ME ND

MT

MN

OR

NY

WI

SD

ID

MI

WY

PA

IA

NE

OH

NV

IL UT

CA

IN WV

CO

KS

VA

KY

MO

NH VT MA RI CT NJ DE MD DC

NC TN OK

AZ

NM

SC

AR MS

TX

AL

GA

LA FL

AK

HI

2012 Median Occupancy by State 90% and below

90.1% to 95.6%

95.61% to 96.7%

96.71% to 99.0%

99.1% and above

Figure 5.2.2(A) illustrates each state’s 2012 median occupancy rate. The states were grouped and color-coded based on each state’s median occupancy percentage.

On a state level, median occupancy rates among surveyed properties ranged from just above 94% to 100%. Puerto Rico reported median 2012 occupancy of 100%, which is impressive considering that the sample size was more than 80 stabilized properties.

California reported the fifth-highest median occupancy at 98.1%, which is a continuation of the consistent trend of high occupancy in that state. This is an impressive figure given the fact that the California subset includes more than 1,400 stabilized properties. New York is not far behind at 98.0% median occupancy for 2012, constituting a pool of nearly 1,400 properties. While the demand for affordable housing is almost universal, the level of unmet demand is clearly highest in the most populous states.

In 2012, 13 states reported median occupancy that was greater than or equal to 98% and they accounted for more than 5,000 properties. An additional 17 states reported median occupancy that was greater than or equal to 97%, the national portfolio median.

42 | The Low-Income Housing Tax Credit Program – November 2014

Figure 5.2.2(B) illustrates each state’s 2012 median debt coverage ratio. All states reported 2012 median DCR in excess of 1.15 except for Alaska, Arkansas, and Georgia, which reported statewide median debt coverage ratios of 1.04, 1.11, and 1.08, respectively.

2012 Median Debt Coverage Ratio by State

FIGURE 5.2.2(B)

WA

ME ND

MT

MN

OR

WI

SD

ID

MI

WY

PA

IA

NE

OH

NV

IL UT

CA

NH VT MA RI CT

NY

IN WV

CO

KS

VA

KY

MO

NJ DE MD DC

NC TN AZ

OK NM

SC

AR MS

TX

AL

GA

LA FL

AK

HI

2012 Median DCR by State 1.00 and below

1.01 to 1.10

1.11 to 1.20

1.21 to 1.30

1.31 and above

Remarkably, 47 states encompassing more than 14,000 properties reported median DCR of 1.20 or greater in 2012, which is a very impressive statistic and representative of the overall strength of the housing tax credit industry.

As some will recall, 2010 was the first year in which all 50 states, Puerto Rico, Guam, or the U.S. Virgin Islands reported overall median DCR of greater than 1.00. Based on our analysis of the data, DCRs in most states appear to be improving, and the fact that no states were operating below breakeven in 2011 or 2012 is another positive indicator that the success of the housing credit program is national in scope. Only three states reported DCR decreases between 2011 and 2012 of 5% or greater. Guam and Alaska are two of the three, and contain a total of less than 40 stabilized properties. The smaller sample size is more sensitive to the impact of individual properties’ effect on the overall median. The statewide weighted average percent change in DCR between 2011 and 2012 was 1.8%, which is another favorable indicator.10 10

Each state’s percentage change in DCR between 2011 and 2012 was weighted by the number of stabilized properties reporting data in each state.

A CohnReznick Report | 43

We also observed the DCR trend of each state from 2008 to 2012. Over that period, Alaska reported marked decline, exhibiting negative variation of greater than 10%. Arkansas, Louisiana, and Maine also reported declines, but were each less than 5%. The other 49 surveyed states and territories all reported DCR growth averaging 14.4%.

Figure 5.2.2(C) illustrates the median state DCR trend for the years 2008-2012.

Median DCR Trend by State (2008-2012)

Median Debt Coverage Ratio Less than 1.00

1.00-1.15

1.16-1.30

1.31-1.50

44 | The Low-Income Housing Tax Credit Program – November 2014

Greater than 1.50

FIGURE 5.2.2(C)

Figure 5.2.2(D) illustrates each state’s 2012 median per unit cash flow. Hawaii, Guam, and the U.S. Virgin Islands each had 2012 median per unit cash flow in excess of $1,200, which we attribute to the disproportionate number of projects in these areas that receive rental subsidy payments. Housing credit projects in Georgia and Arkansas reported median cash flow per unit of less than $120, the two lowest of the 50 states, measured solely by per unit cash flow.

Despite the general trend of improvement that coincides with the overall portfolio trend from 2011 to 2012, these states’ cash flows were significantly less than the overall portfolio median.

2012 Median Per Unit Cash Flow by State

FIGURE 5.2.2(D)

WA

ME ND

MT

MN

OR

MI

WY

PA

IA

NE

IL UT

IN WV

CO

NJ DE MD DC

OH

NV CA

NH VT MA RI CT

NY

WI

SD

ID

KS

VA

KY

MO

NC TN AZ

OK NM

SC

AR MS

TX

AL

GA

LA FL

AK

HI

2012 Median Per Unit Cash Flow by State $0 to $100

$101 to $250

$251 to $500

$501 to $1000

$1001 and above

A CohnReznick Report | 45

5.2.3 By MSA

Figure 5.2.3 summarizes the operating performance data for stabilized properties segmented by the top 10 MSAs. The top 10 MSAs were selected based on the total number of properties located within each MSA. However, Appendix F includes property operating and financial performance data for all metropolitan statistical areas where a meaningful sample size could be obtained. The top 10n MSAs collectively represent 22.5% of the total number of properties surveyed.

Operating Performance by Top 10 MSA’s Median Physical Occupancy Top 10 MSAs

FIGURE 5.2.3

Median Debt Coverage Ratio 2012

Median Per Unit Cash Flow

2011

2012

2011

2011

2012

New York-Newark-Jersey City, NY-NJ-PA

98.0%

98.0%

1.46

1.52

$ 942

$ 1,086

Los Angeles-Long Beach-Anaheim, CA

98.4%

98.9%

1.45

1.47

$ 1,091

$ 1,218

Chicago-Naperville-Elgin, IL-IN-WI

97.0%

97.2%

1.29

1.28

$ 609

$ 697

Washington-Arlington-Alexandria, DC-VA-MD-WV

98.0%

97.5%

1.35

1.37

$ 1,058

$ 1,160

Philadelphia-Camden-Wilmington, PA-NJ-DE-MD

97.9%

98.0%

1.32

1.37

$ 329

$ 396

Boston-Cambridge-Newton, MA-NH

98.6%

98.5%

1.35

1.41

$ 760

$ 990

Minneapolis-St. Paul-Bloomington, MN-WI

97.7%

98.0%

1.33

1.35

$ 873

$ 911

Seattle-Tacoma-Bellevue, WA

97.0%

97.4%

1.32

1.34

$ 755

$ 780

San Francisco-Oakland-Hayward, CA

97.3%

98.2%

1.32

1.34

$ 1,135

$ 910

Detroit-Warren-Dearborn, MI

95.1%

95.0%

1.01

0.91

$ 46

$ (29)

All top 10 MSAs, apart from Detroit, had median occupancy, DCR and per unit cash flow rates that were consistent with or greater than the national portfolio median. Detroit’s performance metrics place it at the bottom of the top 10 MSAs. While the median statistics for the United States and most states improved year-over-year between 2011 and 2012, Detroit, given its troubled local economy, appears to have moved in the opposite direction.

In 2012, the median DCR for the Detroit MSA fell below breakeven to 0.87, which is significantly below the 1.20 median DCR for the State of Michigan.

46 | The Low-Income Housing Tax Credit Program – November 2014

5.2.4 By Age

The following graphs illustrate how the 2011 and 2012 operating and financial performance data differed based on the year in which a property was originally placed in service (PIS).

CohnReznick chose not to present data for properties placed in service during 2011 and 2012 because of the relatively small size of the stabilized sample during the aforementioned years. For purposes of this report, we have used “the PIS” date and “property age” interchangeably.

Based on Figure 5.2.4(A) below, occupancy by property age is clustered within the 96-98% range, indicating that property age has not been a material driver of occupancy rates, as the difference in this range is minimal. The general trend, however, is that properties placed in service in the last five years have performed better from an occupancy perspective.

Everything else being equal, newly constructed or rehabilitated properties tend to offer more comprehensive amenities and have better curb appeal, and are thus more likely to attract a higher tax credit qualified rent.

Figure 5.2.4 (a)

2011 and 2012 Median Occupancy by Property Age

FIGURE 5.2.4(A)

98.50% 98.00% 97.50% 97.00% 96.50% 96.00%

2011 Median Occupancy

2011

2010

2009

2008

2007

2006

2005

2004

2003

2002

2001

2000

1999

95.50%

2012 Median Occupancy

Surveyed properties displayed more variability in DCR and per unit cash flow based on their distribution along the age spectrum. There is no linear relationship suggesting that older properties tend to underperform newer ones financially. Quite the contrary, properties placed in service in the late 1990s reported DCRs as strong as the younger cohort of properties.

Many investors have expressed concern that older properties will demonstrate weaker cash flows because of the impact of deferred maintenance and/or decreased ability to compete with newer properties. While there certainly are some housing credit projects with deferred maintenance, these issues tend to be modest in scope and are overwhelmed by the scarcity of affordable units.

A CohnReznick Report | 47

Figure 5.2.4 (b)

2011 and 2012 Median DCR by Property Age

FIGURE 5.2.4(B)

1.80 1.70 1.60 1.50 1.40 1.30 1.20

2011 Median Debt Coverage Ratio

2011

2010

2009

2008

2007

2006

2005

2004

2003

2002

2001

2000

1999

1.10

2012 Median Debt Coverage Ratio

Figure 5.2.4 (c)

2011 and 2012 Median Per Unit Cash Flow by Property Age FIGURE 5.2.4(C) $1,100 $1,000 $900 $800 $700 $600 $500 $400 $300

2011 Median Per Unit Cash Flow

2011

2010

2009

2008

2007

2006

2005

2004

2003

2002

2001

2000

1999

$200

2012 Median Per Unit Cash Flow

In general, housing tax credit properties placed in service during the past five years generated greater than historical median cash flows. This is consistent with our findings, as these younger properties have realized the benefit of low levels of leverage and relatively inexpensive financing. Respondents noted that the positive improvement in DCRs and cash flows is attributable to the fact that there tend to be fewer “surprises” in the early years of a property’s operation once it is stabilized and when its debt has been converted to permanent financing (and resized properly if necessary).

48 | The Low-Income Housing Tax Credit Program – November 2014

5.2.5 By Size

Figure 5.2.5 presents the operating performance data of surveyed housing credit properties grouped by size, e.g., number of apartment units per property. On average, stabilized housing tax credit properties comprise 69 apartment units per property, reflecting a slight decrease from the 72 apartment units per property reported in CohnReznick’s previous performance study. Those projects containing between 51 and 100 apartment units per property were found to have operating performance metrics that most closely mirror that of the entire surveyed portfolio. The difference in performance metrics based on unit count are summarized below.

Operating Performance by Project Size

FIGURE 5.2.5

Median Physical Occupancy Median Debt Coverage Ratio Median Per Unit Cash Flow Number of Units

2011

2012

2011

2012

2011

2012

0 - 25

97.0%

97.0%

1.26

1.29

$

315

$ 356

26 - 50

97.0%

97.2%

1.27

1.28

$

381

$ 415

51 - 100

97.0%

97.3%

1.27

1.29

$

509

$ 553

101 - 200

97.0%

97.0%

1.32

1.33

$

747

$ 758

201 - 300

95.1%

95.6%

1.24

1.25

$

637

$ 729

301 or more

96.3%

95.8%

1.31

1.34

$

724

$ 829

The median occupancy rate for the subset of properties with more than 200 units was slightly lower than the national median. Modest turnover can be less impactful at larger properties, and as such there can be less pressure on management to fill vacant units immediately. This scenario plays a role in the lower than median occupancy at properties with more than 200 units. Not surprisingly, the smaller properties reported less per unit cash flow than their larger counterparts. Despite lower-than-median occupancy, larger properties tend to benefit from economies of scale with regard to spreading fixed expenses over more units, and the greater efficiency can significantly impact cash flow.

5.2.6 By Credit Type

The data reflected in Figure 5.2.6 summarize the operating performance data for stabilized properties segmented by credit type. Data providers were presented with three options to classify the tax credit type for each property by 9%, 4%, and 4% & 9% housing tax credits.

However, many respondents did not represent that their properties were classified as 4% & 9%; thus the sample size for 4% & 9% tax credit types was very small. For purposes of this report, CohnReznick merged the subset of acquisition/rehabilitation properties that qualify for both 4% and 9% credits into the 9% category.

A CohnReznick Report | 49

As shown below, the median occupancy rate for stabilized 9% credit properties was remarkably consistent with 4% credit properties in 2011 and 2012.

Operating Performance by Credit Type

FIGURE 5.2.6

Median Physical Occupancy Median Debt Coverage Ratio Median Per Unit Cash Flow Credit Type

2011

2012

2011

2012

2011

2012

4% Tax Credits

97.0%

97.1%

1.27

1.27

$

642

$ 671

9% Tax Credits

97.0%

97.0%

1.28

1.30

$

411

$ 444

While we have not observed significant differences between the operating performance of 4% versus 9% properties in terms of occupancy and DCR, the 4% properties we surveyed reported consistently higher levels of cash flow than their 9% counterparts. We attribute this to the fact that properties financed with tax-exempt bonds are generally larger and thus have the ability to distribute their fixed costs over a wider base of apartment units.

We note in addition that 4% projects that have been financed with so-called low floater bonds are reporting very high levels of cash flow given the continuing favorable interest rate environment.

5.2.7 By Hard Debt Leverage Ratio

The data reflected in Figure 5.2.7 summarize the trend of historical hard debt leverage ratio by credit type. We speculated that lower leverage was an important contributing factor to improved DCRs and cash flow.

Figure 5.2.7

Historical Hard Debt Ratio Trend

FIGURE 5.2.7

60.0%

Hard Debt Ratio

50.0% 40.0% 30.0% 20.0%

4% Hard Debt Ratio

50 | The Low-Income Housing Tax Credit Program – November 2014

9% Hard Debt Ratio

2012

2011

2010

2009

2008

2007

2006

2005

2004

2003

2002

2001

2000

1999

1998

1997

10.0%

Notwithstanding the data reflecting the favorable effect of lower leverage, in actual practice a property’s hard debt ratio, as a single determinant, has little bearing on its overall performance.

Cash flow levels are often considerably higher in larger properties financed with tax-exempt bonds and 4% tax credits that are more heavily leveraged than most 9% properties. The average 4% property in our survey has 116 apartments units, almost twice the size of the average 9% property with 62 units. As previously noted, smaller properties have fewer units over which to distribute their fixed costs and, as a result, they are more sensitive to debt burden and perform more predictably with lower levels of debt.

5.2.8 By Development Type

In previous studies, new construction properties tended to report the strongest operating metrics. However, in 2011 and 2012, rehabilitation properties were the strongest performers.

We suspect that this is a function of the fact that an increasing percentage of acquisition rehab projects are supported by Section 8 rental subsidy contracts.

Historic rehabilitation properties and historic properties with both new construction and rehab elements, referred to here as “mixed” properties, reported below-average operating results. While the performance of these sub-sets is less favorable, their sample size is small, consisting of only 271 properties, and is more sensitive to a handful of “bad apples” spoiling the bunch. The fact that historic buildings adapted for use as low-income housing do not perform as well as other property types should not be surprising. Historic buildings formerly used as school houses or for manufacturing often take more time to lease and, because their physical plants are less efficient, they experience higher utility expenses and maintenance costs.

Operating Performance by Development Type

FIGURE 5.2.8

Median Physical Occupancy Median Debt Coverage Ratio Median Per Unit Cash Flow Development Type

2011

2012

2011

2012

2011

2012

Historic Rehab

95.6%

95.5%

1.24

1.23

$

239

$

145

New Construction

97.0%

97.0%

1.26

1.28

$

450

$

471

Rehab

97.0%

97.0%

1.31

1.33

$

507

$

555

Mixed

94.7%

95.1%

0.96

0.98

$

(22)

$

23

A CohnReznick Report | 51

5.2.9 By Tenancy Type

Based on CohnReznick’s experience, housing tax credit properties set aside for senior tenants have historically reported somewhat stronger operating results than properties rented to other types of tenants. The results of our survey were consistent with that trend: senior-only properties (24.2% of the total) outperformed the overall portfolio in 2011-2012 by all measures (occupancy, DCR, and per unit cash flow). These results are not surprising, given that senior properties traditionally report lower turnover ratios as well as lower operating expenses. However, the strong performance of properties serving tenants with special needs is not as intuitive. It has been our experience that special needs properties, while among the most challenging to manage, can generate higher levels of operating income because these projects tend to attract multiple sources of subsidy and are commonly undertaken by nonprofit syndicators, many of whom have chosen to underwrite these projects conservatively and to dedicate more experienced staff to manage and monitor these properties.

Operating Performance by Tenancy Type

FIGURE 5.2.9

Median Physical Occupancy Median Debt Coverage Ratio Median Per Unit Cash Flow Tenancy Type

2011

2012

2011

2012

2011

2012

Family

96.6%

96.8%

1.25

1.27

$

442

$

467

Senior

97.9%

98.0%

1.32

1.36

$

521

$

558

Special Needs

97.0%

96.7%

1.30

1.40

$

344

$

464

Other

97.4%

98.0%

1.37

1.34

$

495

$

571

5.3 A Close-Up View of Underperforming Properties

Given the tremendous demand and historically high occupancy rates associated with affordable housing properties, we are often asked to explain how it is that some housing credit projects fail. In the effort to analyze the failure rate in these properties, CohnReznick analyzed the data obtained from respondents by isolating a cohort of properties as “underperforming” versus “performing.” We define underperforming properties as those properties reporting one of the following criteria: • Physical occupancy levels below 90% • A debt coverage ratio below 1.00 • Insufficient cash flow to cover operating expenses The properties identified as underperforming have been further segmented to identify those that have reported operating issues versus those that have reported technical issues.

52 | The Low-Income Housing Tax Credit Program – November 2014

Operating underperformance refers to cases where a property suffers from low occupancy, operating deficits, or physical plant issues such as deferred maintenance. Herein lies the similarity between housing tax credit properties and market-rate or any other real estate rental assets: Housing tax credit properties are effectively a real estate asset unto themselves and are measured, in some ways, in the same manner that their non-tax-incented counterparts are measured. Syndicators and investors commonly maintain what is referred to as a “watch list” in connection with their asset management procedures. Watch lists track properties with performance measures to ensure that “problem” properties are more closely monitored. Watch list criteria can vary from syndicator to syndicator; however, most respondents adopted the criteria established by the Affordable Housing Investors Council (AHIC)11 as a baseline for measuring underperformance. Pursuant to AHIC standards, a property investment reporting below 90% economic occupancy or below 1.00 DCR should be placed on a watch list for close monitoring, in addition to being observed for other performance matters.

Because housing tax credit properties must conform to certain statutory requirements, they are also subject to rigorous compliance tests and layers of oversight by the IRS and state housing agencies. Given the added burden of these requirements, housing credit properties bear somewhat higher administrative costs than conventional multifamily apartment projects. Failure to meet these requirements can have significant negative consequences for investors. For this reason we treat properties failing to comply with housing tax credit program requirements as properties that are technically underperforming.

There are limitations to CohnReznick’s analysis of technical underperformance because, like most studies preceding it, the focus of our work has been on stabilized properties. Thus, the report does not address construction or lease-up risks nor does it offer indicators related to properties that were unable to come to fruition because of other development-stage challenges.

The fact that some housing tax credit properties experience underperformance can be attributed to a number of reasons. Low occupancy can be attributed to: soft market conditions, competition from other properties in close proximity to the housing credit property, ineffective tenant screening resulting in high eviction rates, and deteriorating property conditions rendering some of the property’s units uninhabitable or inferior to the competition. Although this chapter explores the common symptoms of underperformance of housing tax credit properties, diagnosing the underlying causes for underperformance tends to be a case-by-case exercise.

11

http://www.ahic.org

A CohnReznick Report | 53

In addition to the static information presented, the report presents analysis related to both the duration and magnitude of underperformance. Clearly, chronic underperformance deserves more attention than pure operating volatility, as persistent underperformance is more likely to cause a loss on investment, while operating volatility often results in a temporary drop in occupancy or DCR. Assuming all other indicators remain constant, should an investor be more concerned about a portfolio where 35% of the properties report below 1.00 DCR with an average per unit annual deficit of $100 or a portfolio where only 15% of the properties report below 1.00 DCR with annual deficits that are much higher? In our experience, the length and the magnitude of operating deficits has proven to be much more important than the number of properties reporting deficits.

5.3.1 Underperformance

As reflected in Figure 5.3.1(A), for 2012, 7.6% (measured by net equity) operated at below 90% physical occupancy, 18.6% operated at or below breakeven, and 20.2% incurred operating deficits. As previously noted, the incidence of properties reporting negative cash flow generally corresponds to the incidence of properties reporting debt coverage below 1.00, with the exception of properties financed exclusively with soft debt. Furthermore, the spread between properties reporting occupancy and cash flow challenges indicates that high occupancy does not necessarily guarantee strong financial performance. While low occupancy is often a key driver of operating deficits, these deficits may be the result of a multitude of issues, including spikes in operating expenses, rent concessions, and higher than normal turnover.

Underperformance in 2011 and 2012

FIGURE 5.3.1(A)

% of Net Equity

% of Net Properties

2011

2012

2011

2012

Below 90% Physical Occupancy

8.5%

7.6%

10.9%

9.8%

Below 1.00 DCR

20.5%

18.6%

23.2%

21.7%

Below $0 Per Unit Cash Flow

21.4%

20.2%

24.4%

23.1%

In our analysis of property size, CohnReznick isolated the cohort of underperforming properties as a percentage of the total number of properties (as opposed to a percentage of net equity). Figure 5.3.1(B) indicates that, as expected, properties with a higher number of units tend generally to withstand operating challenges more easily. In addition, equity investors tend to pay a premium to invest in larger properties, and premium pricing generally translates to lower levels of debt per apartment.

54 | The Low-Income Housing Tax Credit Program – November 2014

Of the properties reporting negative cash flow during 2012, 53.7% were smaller properties with 50 or less units. While this group had a disproportionate share in the incidence of operating deficits, the magnitude of these deficits tends to be less significant and, indeed, are often not material amounts.

2012 % Underperforming by Property Size

FIGURE 5.3.1(B)

% Underperformance Occupancy

Debt Coverage Ratio

Per Unit Cash Flow

Number of Units

2011

2012

2011

2012

2011

2012

0 - 25

12.0%

10.9%

29.6%

28.4%

30.9%

29.2%

26 - 50

8.5%

7.7%

25.8%

24.5%

26.5%

24.5%

51 - 100

7.0%

6.1%

19.5%

17.0%

20.8%

19.3%

101 - 200

8.0%

6.8%

16.4%

15.1%

16.4%

16.6%

201 - 300

13.4%

13.0%

21.2%

18.1%

22.2%

19.9%

9.1%

9.7%

18.4%

16.6%

18.2%

17.0%

301 or more

2012 Per Unit Cash Flow Underperformance

by Property Size

Figure 5.3.1 (c) FIGURE 5.3.1(C)

Number Of Units Per Property

301 or more 201 - 300 101 - 200 51 - 100 26 - 50 0 - 25 0%

20%

40%

Underperforming %

60%

80%

100%

Performing %

A CohnReznick Report | 55

5.3.2 Historical Underperformance Trend 2008-2012

During the economic downturn, a common concern expressed by regulators and other stakeholders was worry about the potentially negative effect of national economic conditions on the health of the housing tax credit inventory. However, the data that CohnReznick collected on property performance clearly demonstrated that property operations in LIHTC properties did not decline during the recession. For example, during 2008 and 2009, the percentage of underperforming properties was largely consistent with that of prerecession years. As such, during 2008, 11.9% of the properties surveyed operated at below 90% occupancy, 32.2% reported below 1.00 debt coverage, and 33.4% generated net operating income that was insufficient to cover their mandatory debt service payments and replacement reserve contributions.

Historical Underperformance Trend (2008-2012)

FIGURE 5.3.2(A)

% of Net Equity 2008

2009

2010

2011

2012

Below 90% Physical Occupancy

11.9%

12.6%

9.5%

8.5%

7.6%

Below 1.00 DCR

32.2%

27.6%

24.6%

20.5%

18.6%

Below $0 Per Unit Cash Flow

33.4%

27.8%

24.7%

21.4%

20.2%

Emerging from the national recession in 2010, as the national economy improved, so too did the incidence of underperformance among housing credit properties. A number of factors can be credited with influencing this statistic, including increased demand for affordable housing, lower volatility in operating expenses, lower turnover (with lower turnover costs), and the fact that many properties, somewhat counterintuitively, were able to increase rents.

56 | The Low-Income Housing Tax Credit Program – November 2014

Figure 5.3.2 (b)

Historical Underperformance Trend

FIGURE 5.3.2(B)

35.0% 30.0%

% of Net Equity

25.0% 20.0% 15.0% 10.0% 5.0% 2008

2009

Below 90% Physical Occupancy

2010 Below 1.00 DCR

2011

2012

Below $0 Per Unit Cash Flow

Figure 5.3.2(B) illustrates the improving trend in underperformance since 2008.

It would be difficult to underestimate the importance of the dramatic decrease in the number of projects operating with negative cash flow. In the 10-year period from 2002 to 2012, the percentage of properties operating below breakeven fell from 35.7% to 20.2%.

5.3.3 Chronic Underperformance

To account for the fact that housing tax credit properties, like other types of real estate, are vulnerable to operating volatility in varying degrees, CohnReznick assessed the incidence of underperformance in consecutive years. We summarized properties with less than 90% physical occupancy consecutively using two time periods: 2011 alone, and 2011-2012.

Across the entire portfolio, only 8.5% of properties reported below 90% occupancy in 2011, and an even more modest 4.0% reported less than 90% occupancy during both 2011 and 2012. We attribute this to the sharp improvement in the demand for affordable housing due to families transitioning out of single-family homes and net in-migration. As with occupancy, properties reporting debt coverage below 1.00 and negative cash flow for sustained periods of time represent a more modest fraction of total properties than the ratio of properties reporting operating deficits for a single year.

A CohnReznick Report | 57

Chronic Underperformance

FIGURE 5.3.3

% of Net Equity

% of Properties

2011

2011 & 2012

2011

2012

Below 90% Physical Occupancy

8.5%

4.0%

10.94%

9.82%

Below 1.00 DCR

20.5%

11.9%

23.15%

21.71%

Below $0 Per Unit Cash Flow

21.4%

12.4%

24.36%

23.13%

5.3.4 Magnitude of Underperformance

CohnReznick plotted the distribution of properties reporting underperformance measured by occupancy rate, DCR, and per unit cash flow in order to ascertain the magnitude of underperformance. Of the 7.6% properties reporting occupancy below 90% during 2012, 6.2% were clustered within the 80-90% range. Measured by physical occupancy, only 1.4% of the surveyed stabilized properties were considered extreme underperformers reporting less than 80% occupancy for 2012.

Figure 5.3.4(a)

Distribution of 2012 Physical Occupancy

FIGURE 5.3.4(A)

60%

52.9%

50% 40% 30% 18.0%

20% 10% 0%

0.2%

0.2%

1.0%

1.7%

<60%

60% to 69.9%

70% to 79.9%

80% to 84.9%

21.5%

4.4% 85% to 89.9%

90% to 94.9%

95% to 97%

>97%

Occupancy Ranges

An indicator of the magnitude of underperformance in affordable housing properties is the fact that less than 1% of the properties placed in service since the inception of the housing credit program have been lost to foreclosure. The low risk of foreclosure, given the modest level of housing tax credit property cash flows, can be understood by focusing on the incidence of chronic underperforming properties as well as the relatively nominal level of negative cash flow deficits in properties that have them.

58 | The Low-Income Housing Tax Credit Program â&#x20AC;&#x201C; November 2014

While 18.6% of surveyed housing credit properties experienced negative cash flow in 2012, as shown in Figure 5.3.4(B), 8.3% of the total properties were operating between 0.80 and 0.99 DCR in 2012. A modest 4.8% of properties reported DCRs of less than 0.50.

Figure 5.3.4 (b)

Distribution of 2012 Debt Coverage Ratio

FIGURE 5.3.4(B)

60%

56.5%

50% 40% 30% 20% 10%

14.7% 4.8%

1.2%

1.8%

2.5%

3.4%

.50% to .59%

.60% to .69%

.70% to .79%

.80% to .89%

0% <.50%

10.2%

4.9% .90% to 1.00% to 1.15% to >1.25% .99% 1.149% 1.25%

Debt Coverage Ratio Ranges Only 10.6% of the surveyed properties reported cash flow deficits that CohnReznick regards as material in amount (i.e., more than $400 per unit), with the vast majority of properties operating with per unit cash flow of more than $200. The low incidence of severely underperforming properties helps to explain that, in many cases, operating deficits incurred at low-income housing tax credit properties were funded through fee deferrals, operating deficit guarantee and reserves, or advances from the general partner or syndicators.

Distribution of 2012 Per Unit Cash Flow

Figure 5.3.4 (c) FIGURE 5.3.4(C)

80% 69.0%

70% 60% 50% 40% 30% 20% 10% 0%

10.6%

-$400

4.1%

5.5%

-$400 to -$199.99

-$200 to -$1

10.8%

$0 to $200

>$200

Per Unit Cash Flow Ranges

A CohnReznick Report | 59

5.3.5 Underperformance by State

Following is a series of maps illustrating the percentage of 2012 underperformance by state.

2012 Occupancy Underperformance by State (Percentage below 90% Physical Occupancy)

FIGURE 5.3.5(A)

WA

ME ND

MT

MN

OR

MI

WY

PA

IA

NE

OH

NV

IL UT

CA

NH VT MA RI CT

NY

WI

SD

ID

IN WV

CO

KS

VA

KY

MO

NJ DE MD DC

NC TN OK

AZ

NM

SC

AR MS

TX

AL

GA

LA FL

AK

HI

2012 Occupancy Underperformance 20.1% and above

16.1% – 20.0%

10.1%-16.0%

6.1%-10%

6.0% and below

The states shaded in gray have reported the highest incidence of occupancy issues, and those shaded in orange are the states that report the highest occupancy levels. In keeping with our previous experience, the states located along the East Coast and West Coast tend to report the highest levels of occupancy.

60 | The Low-Income Housing Tax Credit Program – November 2014

The map illustrates that the incidence of underperformance was highest in five states that took longer to recover from the economic downturn.

2012 Debt Coverage Ratio Underperformance by State (Percentage below 1.00 DCR) FIGURE 5.3.5(B) WA

ME ND

MT

MN

OR

WI

SD

ID

NY MI

WY

PA

IA

NE

OH

NV

IL UT

CA

IN WV

CO

KS

VA

KY

MO

NH VT MA RI CT NJ DE MD DC

NC TN OK

AZ

NM

SC

AR MS

TX

AL

GA

LA FL

AK

HI

2012 Occupancy Underperformance 20.1% and above

16.1% â&#x20AC;&#x201C; 20.0%

10.1%-16.0%

6.1%-10%

6.0% and below

A CohnReznick Report | 61

2012 Per Unit Cash Flow Underperformance by State (Percentage below $0 Per Unit Cash Flow)

96.7%

FIGURE 5.3.5(C)

98.0%

98.0%

98.0%

96.2% 96.3% 97.0%

97.0%

95.8%

96.0%

97.5%

2012 Median Occupancy by Region 95.9% and below

96.0% to 96.7%

96.8% to 97.5%

97.6% to 97.9%

98.0% and above

As previously noted, the overall incidence of underperformance has decreased annually across every metric since 2009. We note, however, that the improving trend is on a national portfolio basisâ&#x20AC;&#x201D;as the previous maps indicate, there are still states with high concentrations of underperforming properties. For instance, 23 states reported occupancy underperformance that was greater than the 7.6% national figure. Five states reported occupancy underperformance that was more than double the national figure. Arkansas reported the highest incidence of occupancy underperformance at 23.4%, which is not surprising given its 95.8% 2012 median occupancy rate discussed previously. States with only a handful of properties like Guam, Hawaii, Puerto Rico, and the U.S. Virgin Islands reported no incidence of occupancy underperformance. Washington, New York, and the District of Columbia all reported occupancy underperformance of 2.5% or less, which is particularly impressive given the fact that these states account for more than $11.6 billion of net equity.

62 | The Low-Income Housing Tax Credit Program â&#x20AC;&#x201C; November 2014

Eighteen states reported debt coverage ratio underperformance that was greater than the 18.6% national median. Aside from the U.S. Virgin Islands, Alaska reported the highest incidence of DCR underperformance at 33.8%. Pennsylvania, South Dakota, and Wyoming all reported DCR underperformance of 5% or less. Twenty-two states reported per unit cash flow underperformance that was greater than the 20.2% national figure. Once again, Arkansas reported the highest incidence of per unit cash flow underperformance at 36.6%, which is consistent with its $99 per unit 2012 median cash flow as identified above.

5.3.6 Foreclosure

The prevailing concern among housing tax credit investors continues to be the risk of foreclosure. If the owner of a qualifying housing tax credit project forfeits title to the property because of foreclosure or by tending a deed in lieu of foreclosure, the transfer is treated as a sale of the property. As a technical matter, this transfer generates housing tax credit recapture. A recapture event prompted by foreclosure results in the loss of one-third of the housing credits previously claimed in addition to 100% of any projected future housing tax credits.

While the foreclosure of housing tax credit properties has been rare, the potential impact to investors can be significant. Historically, properties lost to foreclosure reported large and sustained cash flow deficits. The incidence of chronic deficits may be attributed to low occupancy levels, poor sponsorship, and defective construction, among other issues.

However, in large part because of the flexibility and variability with which affordable housing investments can be financially supported or restructured, a remarkably low number of properties fall victim to foreclosure in any given year.

CohnReznick asked respondents to report the number of properties they have lost to foreclosure, including circumstances in which a deed may have been tended in lieu of foreclosure. Respondents reported that, of a total of 18,412 properties surveyed, 117 properties were foreclosed and, of that number, almost half were foreclosed during the period 2008â&#x20AC;&#x201C;2012. This number translates to an aggregate foreclosure rate of 0.63% calculated by number of properties. As previously noted, however, we believe the number of foreclosures may be understated because CohnReznick was unable to obtain data it might have obtained in previous years from syndication firms that have left the business or become inactive. CohnReznick has reason to believe, strictly on an anecdotal basis, that the incidence of property foreclosure has been higher among these firms than for the rest of the industry. However, because we lack precise information concerning the size and number of foreclosures in such firmsâ&#x20AC;&#x2122; respective portfolios, any estimate we might make on the potential impact to the overall industry data would require speculation on our part.

CohnReznick believes that the firms we surveyed represent the core of the housing tax credit industry and that their care in financing and asset managing their investments is an important part of why the foreclosure rate of housing tax credit properties continues to be so low.

A CohnReznick Report | 63

CohnReznick plotted the cumulative number of foreclosures on a yearly basis. The year in which foreclosures occurred was reported for 90 of the 117 foreclosed properties. To derive the yearly cumulative rate, CohnReznick divided the number of foreclosures through yearend by the total number of properties placed in service on or before the corresponding year and distributed the “missing 27” properties evenly over the years.

Figure 5.3.6

Cumulative Foreclosure Rate by Year

FIGURE 5.3.6

0.80% 0.70% 0.60% 0.50% 0.40% 0.30% 0.20% 0.10% 2013

2012

2011

2010

2009

2008

2007

2006

2005

2004

2003

2002

2001

2000

1999

1998

1997

0.0%

Cumulative Foreclosure Rate

While the increasing rate of foreclosure appears disconcerting, the foreclosure rate needs to be analyzed in its proper context. The number of housing credit projects has been building over many years, because the program’s “take-up rate” was modest in the early years. It has been our experience that an increasing number of housing credit syndicators have become “comfortable” with allowing properties that are nearing the end of their compliance period (or beyond it in some cases) to go into foreclosure when they are convinced that the financial impact to investors is not material to them.

Further, while foreclosure can be a catastrophic event for developers and lenders, the financial consequences for investors tend to be much less significant than would be the case for investors impacted by a “conventional” foreclosure. Based on the data collected, the median year in which tax credit properties are foreclosed was year 11 of the 15-year compliance period. While a foreclosure results in the loss of one-third of the housing credits previously claimed, 100% of any future housing tax credits, and interest at the penalty rate, actual financial impact tends to be cushioned by developers and guarantors that are often obligated to make investors whole pursuant to a recapture guarantee. More to the point, since foreclosures are typically forestalled to the latter years of the compliance period, most investors will have recovered most if not all of their capital from the tax benefits that are not required.

64 | The Low-Income Housing Tax Credit Program – November 2014

Figure 5.3.7

Foreclosure Count by Compliance Period Year

FIGURE 5.3.7

14

Foreclosure Count

12 10 8 6 4 2 0 1

2

3

4

5 6 7 8 9 10 Year of the Compliance Period

11

12

13

14

15

Between 2008 and 2012, 73 foreclosures were reported by respondents, representing a marked uptick in foreclosure activity. As noted above, this needs to be evaluated in proper context given the fact that there were fewer properties in service before 2008. In 71 of the 73 cases, survey participants cited one of the factors listed in Figure 5.3.8 as the principal cause of the project’s failure. Perhaps not surprisingly, the national recession tended to exacerbate already weak demand in a handful of “soft markets” around the country.

While the great majority of developers with troubled properties stand behind them financially, it is the non-performing general partners that continue to be the leading cause of foreclosure among housing credit properties, accounting for 26.5% of all of the foreclosures reported by data providers.

A CohnReznick Report | 65

Reasons Indicated for Foreclosure 2008-2012

Figure 5.3.8 FIGURE 5.3.8

9.9% 7.0%

Soft market condition

5.6%

40.8%

Non-performaing general partner Ineffective property management Natural disaster

8.5%

Construction issue

28.2%

Other

We note that the term â&#x20AC;&#x153;construction issueâ&#x20AC;? above refers to newly constructed or rehabilitated properties where construction defects were so severe as to render a significant number of units uninhabitable. While we have analyzed the factors leading to and causing foreclosure activity, it is important to reiterate that, at 0.63%, the rate of foreclosure in housing tax credit properties continues to be much lower than any other real estate asset class with which we are familiar.

66 | The Low-Income Housing Tax Credit Program â&#x20AC;&#x201C; November 2014

CHAPTER 6:

Portfolio Composition

Courtesy of City Real Estate Advisors

W

e next turn our attention to summarizing the composition of the stabilized property portfolio that comprises 15,588 housing tax credit properties segmented by age, project size, investment type, and other characteristics. Unless otherwise noted, all percentages are expressed on a net equity basis.

6.1 Portfolio Composition by Property Age

Figure 6.1 illustrates the portfolioâ&#x20AC;&#x2122;s composition by age group. The median age of the properties in the stabilized portfolio is nine years (properties placed in service in 2005). An average-size housing tax credit new construction development may take 12 months to complete construction, 6 months to lease up, and another 6 months to achieve stabilized operations. As such, it often takes an average housing tax credit property 12 months from the point in time it is first placed in service to the point at which it achieves stabilized operations.

On a median basis, the stabilized properties reported approximately six full years of stabilized Figure 6.1 operating history by the end of the survey period (December 31, 2012).

Percent Equity by Property Age

FIGURE 6.1

1.3%

4.4%

20.1%

26.8%

5 years or less 6 -10 years 11 - 15 years 16 years or older Not specified

47.4%

A CohnReznick Report | 67

6.2. Portfolio Composition by Unit Count

According to our survey, the average stabilized housing tax credit property placed in service in 2012 was composed of 69 units. We suspect, however, that the properties placed in service in 2012 were an incomplete sample because some properties had not yet stabilized.

The typical 4% housing credit property averaged 90 units, while 9% housing credit properties averaged 48 units. The size of a typical 4% property, measured solely by unit count, provides a larger base to share fixed operating expenses. This observation served to explain in part why, despite the fact that 4% properties tend to be more highly leveraged than their 9% counterparts, 4% properties collectively tend to report better operating performance.

6.2 Figure 6.2 illustrates the trend of average units per property by year placed in Figure service.

Unit Count by Year Placed in Service

FIGURE 6.2

100

Number of Units Per Property

90 80 70 60 50 40 30 20 10

68 | The Low-Income Housing Tax Credit Program â&#x20AC;&#x201C; November 2014

2012

2011

2010

2009

2008

2007

2006

2005

2004

2003

2002

2001

2000

1999

1998

1997

1996

1995

1994

1993

1992

1991

1990

0

6.3 Portfolio Composition by Investment Type

Throughout the history of the housing tax credit market, credits were syndicated through the sale of equity investments in public funds, direct investments, proprietary funds, and multi-investor funds. The term “public funds” refers to publicly registered offerings marketed to individual investors that were the major source of equity financing in the early years of the housing tax credit program. Beginning in the early 1990s, institutional investors began to represent the dominant share of the housing tax credit equity market, making public funds an increasingly rare and ultimately extinct investment vehicle for raising capital.

“Direct investments” refers to investments made by a single corporate investor directly into a project partnership, as opposed to investing through a fund managed by a thirdparty syndicator. We caution that direct investments account for a smaller portion of our dataset than we would have expected because of incomplete information and/or lack of participation of the largest direct investors. In future reports, CohnReznick intends to capture data for a larger portion of this segment of the market. Currently, most equity investments in our dataset are made through third-party intermediaries or syndicators who raise investor capital, acquire equity investments in housing tax credit projects, and provide long-term asset management services.

Figure 6.3

Percent Net Equity by Investment Type

0.3%

6.2%

0.1%

FIGURE 6.3

2.0%

7.5% Direct Multi-Investor Proprietary

32.2%

Public

51.7%

Exchange Guaranteed Not Specific

A CohnReznick Report | 69

Property investments made by multi-investor funds constitute the majority of the properties surveyed. Survey respondents indicated that multi-investor funds represented 51.7% of the total equity financing on a stabilized net equity basis. Proprietary fund investments account for the second highest market share, with 32.2% of net equity.

The term “guaranteed investments” refers to housing credit investments that were sold together with a yield guarantor from an insurance company or investment banking firm.

These investments were more popular during the years 1995-2005 but have become a relatively rare execution in more recent years.

6.4. Portfolio Composition by Credit Type

As previously noted, the housing tax credit statute provides for two types of housing tax credits: 9% and 4% credits. Projects that are conventionally financed and are awarded housing tax credit allocations are eligible for 9% credits. Generally speaking, an owner of a housing tax credit property may claim housing tax credits equal to 9% of the project’s qualified costs each year for 10 years. Conversely, properties that are financed in whole or in part by the issuance of tax-exempt bonds may claim a 4% tax credit for 10 years, based again on qualified housing expenditures. As a general matter, 9% projects are more heavily financed by investor equity and thus have a more modest level of hard debt to service. Tax-exempt bond projects that qualify for 4% credits generate significantly lower levels of tax credit equity and thus require higher debt levels (albeit at lower tax-exempt interest rates).

70 | The Low-Income Housing Tax Credit Program – November 2014

Figure 6.4 illustrates the incidence of 4% and 9% credit properties as a percentage of the total inventory of housing tax credit properties surveyed. More than 1,700 out of the 18,412 properties surveyed did not include a credit type designation. It appears that most of this latter group are made up of so-called acquisition rehabs, which feature both 9% and 4% credits.

Figure 6.4 As shown below, 9% properties accounted for 69.42% of the net equity surveyed, 22.19% was invested in 4% properties, and the remaining 8.39% was unspecified..

Percent Net Equity by Credit Type

FIGURE 6.4

8.4% 9% credit 4% credit

22.2%

Not Specified

69.4%

A CohnReznick Report | 71

6.5 Portfolio Composition By Development Type

CohnReznick requested that respondents specify whether each property investment was either newly constructed or a rehabilitation of an existing property. Newly constructed properties accounted for 64.8% of the net equity surveyed, and rehabilitated properties accounted for 33.6% of net equity surveyed, 1.4% of which were rehabilitations of historic structures and the remaining 0.2% were mixed or unspecified development types. With respect to financing, our data suggest that the average new construction development was financed with $4.2 million of net equity, while the average rehabilitation property required $3.5 million of net equity.

Figure 6.5

Percent Net Equity by Development Type 1.4%

FIGURE 6.5

0.1% 0.1%

New construction Rehab

33.6%

Historic Rehab

64.8%

72 | The Low-Income Housing Tax Credit Program â&#x20AC;&#x201C; November 2014

Mixed Not Specified

6.6 Portfolio Composition by Tenancy Type

Figure 6.6 illustrates our finding that family properties account for 70.52% of all properties surveyed: Senior properties accounted for 24.20%; special needs properties accounted for another 1.61%; and the remaining 3.67% were mixed tenancies, single-room occupancies, or properties for which respondents did not specify a tenancy type. While the percentage of senior properties increased slightly since our last report, the data suggested that there have been no significant changes in terms of the relative allocation of capital among the various tenancy types.

Figure 6.6

Percent Net Equity by Development Type

FIGURE 6.6

0.1% 1.3% 2.0% 0.3% 1.6%

Family Senior

24.2%

Special Needs SRO

70.5%

Mixed Other Not Specified

The term “special needs” displayed in Figure 6.6 refers to properties that have been set aside for unique tenancy groups. The relative distribution of special needs or “supportive housing” projects is a function of each state’s assessment of its most critical housing needs—and how best to serve tenants with significant housing challenges, such as the homeless or tenants with physical and/or mental handicaps.

A CohnReznick Report | 73

6.7 Portfolio Composition by Region

Figure 6.7 summarizes the housing credit portfolio net equity by 12 CohnReznick-defined regions of the country. CohnReznick bundled the 50 states, the District of Columbia, Guam, the U.S. Virgin Islands, and Puerto Rico into 12 regions consisting of similar geographic composition that most ideally grouped areas of the country. The regions in Figure 6.7 are arranged in descending order of total housing credit net equity.

Portfolio Distribution by Region Number of Properties

FIGURE 6.7

Housing Credit Net Equity

Housing Credits

Region Number

Survey Total

Stabilized Properties

Survey Total

Stabilized Properties

Survey Total

Stabilized Properties

Region 1

2,428

2,113

$13,075,914,110

$10,841,470,263

$13,222,618,598

$10,884.699,463

Region 2

69

55

$397,415,716

$269,555,281

$419,113,584

$281,250,020

Region 3

252

186

$588,039,795

$442,487,844

$700,208,591

$523,121,789

Region 4

845

732

$3,538,360,801

$2,805,328,027

$3,900,823,098

$3,098,716,851

Region 5

600

517

$1,561,473,380

$1,321,670,955

$1,783,928,911

$1,508,751,976

Region 6

1,263

1,064

$3,213,946,289

$2,683,761,102

$3,426,479,847

$2,811,103,518

Region 7

2,934

2,504

$11,142,625,726

$8,764,500,762

$12,749,335,728

$9,871,891,880

Region 8

1,530

1,193

$5,846,840,929

$4,455,936,868

$5,925,971,657

$4,377,487,258

Region 9

2,093

1,727

$9,656,630,513

$8,354,453,302

$10,072,104,966

$8,646,829,244

Region 10

2,310

1,928

$6,304,132,194

$5,082,496,672

$7,219,266,262

$5,724,183,289

Region 11

3,965

3,467

$20,072,183,401

$16,116,601,265

$19,736,708,581

$15,906,626,780

Region 12

109

95

$699,782,936

$583,710,936

$836,510,609

$662,988,140

74 | The Low-Income Housing Tax Credit Program â&#x20AC;&#x201C; November 2014

As noted in Figure 6.7(A), Region 11 accounted for the greatest amount of total net equity, more than 26% of the entire portfolio. Region 1 encompassed the second highest amount of equity at more than 17% of the portfolio, followed by Region 7, which equaled roughly 14% of the portfolio net equity. Region 2 had the least amount of portfolio net equity (less than 1%), and comprised only 69 properties (54 of which are stabilized).

Figure 6.7 (a)

Percent Net Equity by Region

4.5% 4.3%

FIGURE 6.7(A)

0.7% 0.9% 2.1% 0.4% Region 1 Region 2

26.2%

7.2%

Region 3 Region 4 Region 5 Region 6

13.5%

Region 7

14.3% 8.2%

Region 8 Region 9 Region 10

17.7%

Region 11 Region 12

A CohnReznick Report | 75

Figure 6.7(B) illustrates the distribution of the average property sizes by region. Since our last report, there have been only minor fluctuations to the average number of units by region.

Figure 6.7 (b)

Unit Count by Year Placed in Service

FIGURE 6.7(B)

100

91

90

88

80

80

73

70

85 72

68

66

60

49

47

50

63

38

40 30 20 10 0

on

i eg

R

1

on

R

i eg

2

on

R

i eg

3

on

i eg

R

4

on

R

i eg

5

on

R

i eg

6

on

i eg

R

7

on

R

i eg

8

on

R

i eg

9 Re

Average Number of Units Per Property

76 | The Low-Income Housing Tax Credit Program â&#x20AC;&#x201C; November 2014

on gi

10 Re

on gi

11 Re

on gi

12

6.8 Portfolio Composition by State

CohnReznick segmented the portfolio property data by all 50 states, Puerto Rico, the U.S. Virgin Islands, and Guam. The top five states, measured by total equity capital, were California, New York, Texas, Florida, and Illinois, which collectively accounted for more than 40.2% of the overall portfolio. Montana, Alaska, South Dakota, Guam, and the U.S. Virgin Islands represented the bottom five in terms of total capital, each accounting for 0.2% or less of the overall portfolio.

Percent Net Equity by State

FIGURE 6.8

WA

ME ND

MT

MN

OR

NY

WI

SD

ID

MI

WY

PA

IA

NE

OH

NV

IL UT

CA

IN WV

CO

KS

VA

KY

MO

NH VT MA RI CT NJ DE MD DC

NC TN AZ

OK NM

SC

AR MS

TX

AL

GA

LA FL

AK

HI

2012 Percent Net Equity 1% and below

1.01% to 2.3%

2.31% to 4%

4.01% to 10%

10.01% and above

A CohnReznick Report | 77

6.9 Portfolio Composition by MSA

The question has been raised from time to time as to whether a disproportionate level of housing tax credits are being allocated to the nation’s largest cities. Figure 6.9 illustrates the capital concentration data for properties located in the top 10 metropolitan statistical areas. The general concept of a MSA is that of a large population center, together with adjacent communities that have a high degree of social and economic integration. MSAs may comprise one or more entire counties, except in New England, where cities and towns are the basic geographic units. At times, MSAs can also include cities or counties from different states; the Washington-Arlington-Alexandria, DC-VA-MD-WV MSA is a prime example.

As shown below, 23.9% of the total housing tax credit equity we surveyed was concentrated in properties located within the 10 largest MSAs. CohnReznick notes that the percentage of total housing tax credit equity closely correlates to the aggregate population of residents in the 10 MSAs versus the rest of the U.S. population. We conclude therefore that no statistical basis exists for the claim that housing credits are being disproportionately allocated to the nation’s major urban centers.

Figure 6.9

FIGURE 6.9

$7B $6B $5B $4B $3B $2B $1B MI it, tro De

,W A tle at

Se

Mi

am

i, F

L

MA

Bo

sto

n,

C n, D ng to

PA

Wa shi

lph ia,

ila de Ph

Sa

nF

ran

cis

ica

co

go

,C

, IL

A Ch

,C

An ge

les

J-P -N Lo s

NY rk, Yo w Ne

A

0 A

Housing Credit Net Equity

Net Equity Concentration by Top 10 Metropolitan Statistical Areas

Appendix F Outlines the Overall Portfolio Composition of All MSAs. One observation we keep turning to is how consistent the performance of housing credit properties is across every category and sub-category. At the end of the day, “good” performance in housing credit properties is ultimately a function of occupancy. While some observers believe the crisis in affordable housing is centered in our major metropolitan areas, the fact is that virtually every community in the country needs more affordable housing. This report confirms that the supply/demand equation in affordable housing continues to move in the wrong direction and begs the question of whether the housing credit program is due for resizing.

78 | The Low-Income Housing Tax Credit Program – November 2014

APPENDIX A

Acknowledgments CohnReznick would like to thank the following organizations for contributing data and financial support for the study: • AEGON USA Realty Advisors

• National Equity Fund

• Alliant Capital

• Northern New England Housing Investment Fund

• Bank of America • Boston Financial Investment Management • City Real Estate Advisors • Community Affordable Housing Equity Corporation

• Ohio Capital Corporation for Housing • PNC Multifamily Capital • R4 Capital LLC • Raymond James

• Enterprise Community Investment

• RBC Capital Markets

• First Sterling

• Red Stone Equity Partners

• Great Lakes Capital Funds

• Regions Bank

• Housing Vermont

• The Richman Group Affordable Housing Corporation

• Hunt Capital Partners • Hudson Housing • Massachusetts Housing Investment Corporation • Merritt Community Capital

• Stratford Capital • The Summit Group • SunTrust Community Development Corporation

• Michel Associates

• Virginia Community Development Corporation

• Midwest Housing Equity Group

• U.S. Bank

• National Development Council

• WNC Associates

CohnReznick also thanks the National Association of State and Local Equity Funds for making a financial contribution on behalf of its member organizations.

A CohnReznick Report | 79

APPENDIX B

Survey Methodology

T

his report represents the third in a series of studies undertaken by CohnReznick concerning the Low Income Housing Tax Credit program. In December 2013, CohnReznick transmitted data requests to 43 organizations, including all active housing credit syndicators known to the Firm and a number of the nation’s largest housing credit investors. Investor respondents were asked to provide data limited to direct investments and fund-level performance to mitigate what would otherwise be a large overlap of properties’ data assembled from participating syndicators’portfolios.

CohnReznick believes that 18,412 properties, the sample size represented in this study, are in excess of 70% of the housing credit properties placed in service since the inception of the program that are being actively asset-managed by syndicators and/or investors.

By “actively” managed, we refer to those properties that are within their compliance periods (or just beyond), for which an asset manager would produce quarterly or annual reports. We suspect the gap between CohnReznick’s dataset and 100% of all properties was largely a result of defunct syndicators, as well as properties placed in service in the earlier years of the housing credit program that have reached the end of their compliance periods, have been disposed of, and have “cycled out” of the program. Additionally, direct investments account for a smaller portion of our dataset than we would have expected because of incomplete information and/or lack of participation of the largest direct investors. Direct investments are investments made by a single corporate investor directly into a project partnership as opposed to investing through a fund managed by a third-party syndicator. In future reports we plan to capture data for a larger portion of this segment of the market. We believe that the sample size represented in the study provides a statistically meaningful basis for our analysis and findings.

Data Collection

A participant solicitation email and data collection template was sent to the aforementioned organizations on December 2, 2013. Respondents were initially requested to return the data collection template no later than January 15, 2014. However a few participating respondents indicated that they lacked sufficient time to complete the survey properly, and they were offered a deadline extension. All contacts, whether made by telephone or email, were recorded in response contact logs.

Data Collection Template

The following shows the main data points requested from each participating investor and syndicator. Instructions were attached to each collection field to minimize interpretation.

Contact information for CohnReznick professionals was supplied along with the collection template for questions related to the data request.

Where applicable, audited financial data were requested and were represented as having been furnished in that form. However, CohnReznick did not perform any independent validation as to whether the data were indeed audited.

80 | The Low-Income Housing Tax Credit Program – November 2014

DATA FIELDS

DEFINITION/EXPLANATION

PROPERTY INVESTMENT IDENTIFICATION

Fund identification

Provide the name of the fund each property belongs to. In cases where property interest is split among multiple funds, please assign the property to the fund where the majority is assigned.

Investment type

Select from: Direct, Proprietary, Multi-investor, Guaranteed, or Public.

Property name

Provide the name of each property or a unique identification number from your database which permits future identification.

Property address

Enter the street address, city, 2-letter state abbreviation, and 5-digit zip code. When possible please enter the MSA for each property as presented by the U.S. Census Bureau. (http://www.census.gov/population/metro/data/def.html)

Type of credit

Select from: 4%, 9%, 4%/9%.

Total net equity (federal LIHTC only)

Please enter the actual dollar amount (e.g., $2,000,000 instead of $2 million) and enter equity (to be) contributed for federal LIHTC credits only. Please use closing projected amount or actual amount.

Total projected federal LIHTC

Enter 10-year total. Please use closing projected or actual amount (to be consistent with Total Net Equity column).

Calculated price per federal LIHTC

Please confirm that the calculated Price per Credit is consistent with your records. If not, please confirm your entries in Columns J & K.

Property status

Select from: Pre-Construction, Construction, Lease-up, Pre-stabilization (leased-up but not yet stabilized), Stabilization (perm loan conversion).

Year placed in service

Enter 4-digit year; enter projected PIS year if not yet in service. If this is a multiple building project, with more than one PIS date, enter the date the first building was placed in service.

First year of credit delivery

Enter the first year of housing credit delivery. Enter the projected year if not yet delivering credits.

Type of development

Select from: New Construction, Rehab, Acq/Rehab, New Construction/ Rehab, Historic Rehab, and Other.

Type of tenancy

Select from: Family, Senior, Special Needs, Family/Special Needs, Senior/Special Needs, and Other.

Total number of units

Enter the total number of units; include manager’s and market rate units.

Total number of LIHTC units

Enter the total number of LIHTC units, including manager’s unit.

Project based rental assistance

If the property benefits from rental assistance please enter Y; if not, please enter N.

Type of rental assistance

Select from: Section 8, RD, ACC, Other, NA. Choose the major assistance type if more than one is received. NOTE: This field will automatically be grayed if “N” is entered in the corresponding row in column T.

Hard debt

Enter Y if the property is financed with hard debt; enter N if the property has no hard debt.

Type of financing

Select from: conventional-insured, conventional-uninsured, bonds, RD.

Type of interest rate

Select from: fixed, variable, variable with hedge.

Hard debt ratio

Enter % (hard debt / total project costs). Enter 0.0% if project has no hard debt.

Average physical occupancy Enter average physical occupancy per 2011 year-end audit. For projects (2011-2012) that did not have a full year of stabilized operation, please provide the average over the stabilized period.

A CohnReznick Report | 81

DATA FIELDS

DEFINITION/EXPLANATION

PROPERTY INVESTMENT IDENTIFICATION

Average debt coverage ratio (2011-2012)

Provide the year-end debt coverage ratio per audited financials. DCR is defined as: net operating income minus required replacement reserve contributions divided by the mandatory debt service payments.

NOTE: enter NA if property has no hard debt.

Average per unit cash flow Provide the year-end per unit cash flow. Per unit cash flow is defined as: net (2011-2012) operating income minus required replacement reserve contributions and mandatory debt service payments, if any, then divide by the total number of units.

AHIC watchlist

Select from: “Y” if the property is on your organization’s current watch list based on AHIC standards.

AHIC rating

Enter the property’s corresponding AHIC rating: A, B, C, D, F.

Secondary transaction

Indicate Y if the property was involved in, or acquired through, a secondary transaction. (Note: Please exclude re-syndications for preservation.)

Year of secondary transaction Please provide the 4-digit year in which the secondary transaction occurred.

FORECLOSURE DATA

Property name

Please provide a unique identifier for each foreclosed property if you are not comfortable providing its legal name.

Address

Enter the street address, city, 2-letter state abbreviation, and 5-digit Zip Code.

Hard debt ratio

Enter % (hard debt / total project costs). Enter 0.0% if project has no hard debt.

Total net equity (federal LIHTC only)

Please enter the actual dollar amount (e.g., $2,000,000 instead of $2 million) and enter equity (to be) contributed for federal LIHTC credits only. Please use closing projected amount or actual amount.

Total projected federal LIHTC

Enter 10-year total. Please use closing projected or actual amount (to be consistent with Total Net Equity column).

Year placed in service

Enter 4-digit year; enter projected PIS year if not yet in service. If this is a multiple building project, with more than one PIS date, enter the date the first building was placed in service.

First year of credit delivery

Enter the first year of housing credit delivery. Enter the projected year if not yet delivering credits.

Year of foreclosure

Enter the 4-digit year of foreclosure.

Federal LIHTC lost to foreclosure

Enter the total federal LIHTC lost to foreclosure.

Was the LP covered by a recapture guarantee

Enter Y or N.

Reason for foreclosure

Please select from the drop-down menu the primary reason for foreclosure: soft market condition, ineffective property management, non-performing general partner, fraud, underwriting issue, natural disaster, construction issue, other.

82 | The Low-Income Housing Tax Credit Program – November 2014

DATA FIELDS

DEFINITION/EXPLANATION

FUND IDENTIFICATION AND PERFORMANCE DATA

Fund identification

Provide the name for each fund or a unique identification number from your database that permits future identification.

Fund type

Select from: Direct, Proprietary, Multi-investor, Guaranteed, Public.

Year closed

Enter 4-digit year of fund closing.

Total gross equity

Please enter the actual dollar amount (i.e., $2,000,000 instead of $2 million).

Please use closing projected or actual amount.

Net equity invested in properties

Please provide the net equity invested in the properties of each fund.

Original projected IRR

Enter IRR projected at fund closing with necessary adjustment for property removal/addition.

IRR (as of 12/31/2012)

Enter the 2012 year-end IRR.

Total projected LIHTC at closing

Provide the 10-year total amount of federal LIHTC projected to be available to the investor limited partner at fund closing.

Total LIHTC as of 12/31/2012

Provide the 10-year total amount of LIHTC delivered (or projected to be delivered based on the latest information such as 8609s) to the investor limited partner.

Originally projected 1st, 2nd, Provide the annual amount of federal LIHTC projected to be available to and 3rd year LIHTC the investor limited partner at fund closing; specify the amount for each of the first three years.

Total actual 1st, 2nd, 3rd year Provide the annual amount of federal LIHTC delivered (or projected to be LIHTC as of 12/31/2012 delivered based on the latest information such as 8609s) to the investor limited partner; specify the amount for each of the first three years.

Initial working capital reserve balance

Please enter the initial WCR balance. This means the initial balance when fully funded.

Working capital reserve balance as of 12/31/2012

Please enter the WCR balance as of 12/31/2012.

Do you anticipate any residual value in the fund?

Enter Y or N.

Do you anticipate any net proceeds to the investors?

Enter Y or N.

Estimated net proceeds to the investors

Select from: $0-$250k, $250k-$500k, $500k-$1M, $1M+.

Do you anticipate any preservation of re-syndication opportunities in the fund?

Enter Y or N.

A CohnReznick Report | 83

Data Processing

The receipt of a completed survey questionnaire and any relevant comments made by the respondents were recorded in the contact logs. All questionnaires were first analyzed for data completeness and systematic errors for reasons such as misinterpretation. If questionnaires were returned with incomplete data, respondents were contacted immediately to determine the possibility of providing missing data and, in limited circumstances, the consequences of participants being unable to accommodate the entire data request. Other follow-up activities were conducted to ensure data integrity. Upon completion of the first round processing, data were compiled, filtered, and normalized.

Each data element provided was then uploaded to an Access database maintained by CohnReznick. The database was built in a completely confidential manner to ensure that no individual data points or groups of individual data points could be attributed to any data provider. The data were loaded into the database to ensure the consistency of field data types and to allow for flexible and repeatable calculation.

Data entered into the database were checked for arithmetical errors, and flagged for any large discrepancies between the current and previous yearsâ&#x20AC;&#x2122; data for trend warnings. Based on industry standards and a lengthy, programmatic filtering system designed by CohnReznick, outliers that could skew the study results were screened and later removed from the affected calculations. Based on predefined data outputs and calculation definitions, CohnReznick ran queries and wrote scripts to perform calculations and group datasets (e.g., linking Zip Codes to applicable MSAs) for segmentation analysis. Aggregated data and outputs were re-exported into an Excel template for further testing and quality control analysis.

84 | The Low-Income Housing Tax Credit Program â&#x20AC;&#x201C; November 2014

APPENDIX C

Glossary Credit type

There are two types of low-income housing tax credits under the Internal Revenue Code § 42: the 9% credits are available to support new construction or rehabilitation projects that are not considered federally subsidized; the 4% credits are available to support new construction or rehabilitation projects that are financed with taxexempt bonds, or the acquisition costs of existing buildings. While the actual value varies based on a number of factors, the 9% and 4% credits are designed to subsidize 70% and 30% of the low-income unit costs in a project.

Community Reinvestment The Community Reinvestment Act was enacted in 1997 to ensure Act (CRA): that banks and other depositary institutions help meet the credit needs of the communities in which they operate. For more information about CRA, please reference CohnReznick’s study of the program: http://www.cohnreznick.com/sites/default/files/ CohnReznick_CRAStudy.pdf.

Debt coverage ratio

Net operating income (effective gross operating income minus operating expenses) minus required replacement reserve contributions, divided by mandatory debt service payments.

Direct investment

Investors make equity investments directly into a property partnership as opposed to investing through a fund managed by a third-party intermediary.

Economic occupancy

Collected gross rental income divided by gross potential rental income.

Foreclosure

The legal process by which a mortgagee or other lien holder obtains, either by court order or by operation of law, a termination of a mortgagor’s right to a property usually as a result of default.

Guaranteed investment Investors make equity investments to an investment fund (which, in turn, owns interest in multiple property partnerships) organized by a third-party intermediary. Under a guaranteed investment structure, the yield, as contractually agreed upon, is guaranteed by a creditworthy entity for a premium.

Metropolitan statistical areas (MSAs)

A geographical region with relatively high population density at its core and close economic ties throughout the area. MSAs are defined by the U.S. Office of Management and Budget, and used by the U.S. Census Bureau and other U.S. government agencies for statistical purposes.

Multi-investor investment Multiple investors jointly make equity investments into an investment fund (which, in turn, owns interest in multiple property partnerships) organized by a third-party intermediary, and thus share investment benefits and risks.

Net equity

The amount of equity raised from “allocating” housing credits to investors. Net equity is distinguished from gross equity by excluding the “load” (i.e., fees charged by syndicators for underwriting and managing the investment fund) and the capital set aside for reserves.

A CohnReznick Report | 85

Physical occupancy

The number of occupied units divided by the total number of rentable units in a given property.

Placed-in-service

The date when the property is ready for its intended use; a housing credit property can either claim credits beginning the year it is placed in service (provided that units are occupied by income qualified tenants) or defer the beginning of the credit period to the following year.

Proprietary investment

A single investor makes equity investments and assumes the limited partner role in an investment fund (which, in turn, owns interest in multiple property partnerships) organized by a third-party intermediary.

Public investment

Investment funds commonly seen in the early years (pre-early 1990s) of the housing credit program when investment capital was primarily derived from individual investors.

Qualified occupancy

All of the housing credit units have been leased to tenants who have been income-certified and deemed eligible to occupy such units.

Recapture

Housing credit properties are subject to a 15-year compliance period that extends five years beyond the credit period. Credits may be recaptured during the 15-year compliance period if the property ceases to qualify as a housing credit property or ceases to be occupied by qualified tenants. The amount of recapture will be calculated based on two-thirds of the previously claimed credits plus applicable interest charges.

Soft debt

Mortgage loans where payments are subject to available cash flow.

Stabilized operations

Definitions among syndicators can differ; however, for purposes of this report we consider stabilized operations to be properties that have completed construction, achieved 100% qualified occupancy, and closed on permanent financing.

State allocating agencies State or local agencies that have the authority to allocate federal low-income housing tax credits to a property.

86 | The Low-Income Housing Tax Credit Program â&#x20AC;&#x201C; November 2014

APPENDIX D

Property Performance by State MEDIAN PHYSICAL OCCUPANCY

MEDIAN DEBT COVERAGE RATIO

MEDIAN PER UNIT CASH FLOW

STATE

2011

2012

2011

2012

2011

2012

AK

97.0%

97.0%

1.12

1.04

$212

$202

AL

97.0%

97.0%

1.37

1.31

$345

$316

AR

95.3%

95.8%

1.13

1.11

$149

$99

AZ

95.8%

95.7%

1.18

1.22

$361

$389

CA

98.0%

98.1%

1.36

1.34

$934

$863

CO

97.9%

98.0%

1.23

1.26

$663

$657

CT

96.9%

96.0%

1.15

1.15

$454

$280

DC

98.0%

97.5%

1.21

1.28

$606

$681

DE

98.9%

98.0%

1.48

1.54

$1,188

$1,164

FL

95.0%

95.8%

1.29

1.26

$594

$568

GA

95.5%

95.4%

1.08

1.08

$83

$115

GU

94.0%

95.0%

1.57

1.31

$1,991

$1,232

HI

97.9%

98.3%

1.50

1.46

$1,849

$1,397

IA

95.3%

96.2%

1.26

1.25

$363

$371

ID

95.5%

96.1%

1.13

1.20

$173

$344

IL

96.6%

97.0%

1.27

1.26

$436

$508

IN

94.9%

95.3%

1.19

1.22

$233

$296

KS

97.0%

96.1%

1.14

1.19

$201

$191

KY

96.0%

96.6%

1.07

1.21

$133

$309

LA

96.0%

96.0%

1.30

1.27

$411

$339

MA

98.1%

98.2%

1.33

1.43

$618

$723

MD

98.0%

98.0%

1.44

1.49

$760

$771

ME

96.4%

97.2%

1.23

1.24

$365

$390

MI

95.2%

95.0%

1.18

1.20

$341

$334

MN

98.0%

98.0%

1.40

1.38

$835

$815

MO

96.0%

95.9%

1.16

1.18

$190

$242

MS

96.0%

96.0%

1.32

1.26

$359

$390

MT

97.3%

96.7%

1.24

1.32

$468

$458

NC

97.7%

97.9%

1.32

1.30

$471

$470

ND

98.8%

98.8%

1.21

1.30

$460

$577

NE

98.0%

97.0%

1.20

1.28

$290

$399

NH

97.3%

96.7%

1.46

1.55

$875

$783

NJ

98.0%

98.0%

1.24

1.30

$338

$418

NM

97.1%

97.0%

1.36

1.39

$605

$610

NV

95.2%

96.3%

1.28

1.28

$573

$589

NY

97.9%

98.0%

1.53

1.56

$847

$968

OH

97.5%

97.3%

1.24

1.24

$350

$325

OK

94.9%

95.0%

1.16

1.25

$253

$304

OR

97.7%

97.8%

1.26

1.27

$425

$453

PA

97.6%

98.0%

1.30

1.49

$306

$355

PR

100.0%

100.0%

1.23

1.31

$377

$537

RI

97.2%

97.0%

1.36

1.47

$692

$820

A CohnReznick Report | 87

STATE

MEDIAN PHYSICAL OCCUPANCY 2011 2012

MEDIAN DEBT COVERAGE RATIO 2011 2012

MEDIAN PER UNIT CASH FLOW 2011 2012

SC

96.8%

97.0%

1.18

1.22

$299

$376

SD

95.1%

96.1%

1.38

1.40

$511

$574

TN

95.0%

96.2%

1.16

1.17

$279

$327

TX

95.3%

96.0%

1.20

1.26

$388

$453

UT

97.4%

97.0%

1.35

1.31

$680

$636

VA

97.1%

97.0%

1.31

1.32

$687

$706

VI

97.1%

98.0%

1.50

1.77

$914

$1,611

VT

97.0%

97.6%

1.29

1.39

$468

$542

WA

97.0%

97.2%

1.36

1.37

$596

$664

WI

96.0%

95.9%

1.18

1.25

$385

$519

WV

95.3%

95.8%

1.31

1.21

$351

$255

WY

96.0%

97.0%

1.18

1.20

$473

$512

88 | The Low-Income Housing Tax Credit Program – November 2014

APPENDIX E

Property Underperformance

by State STATE

MEDIAN PHYSICAL OCCUPANCY 2012

MEDIAN DEBT COVERAGE RATIO 2012

MEDIAN PER UNIT CASH FLOW 2012

AK

4.6%

33.8%

33.5%

AL

6.8%

18.4%

22.9%

AR

23.4%

30.9%

36.6%

AZ

11.8%

21.4%

22.8%

CA

3.2%

9.9%

12.6%

CO

3.1%

5.3%

10.4%

CT

9.2%

15.0%

20.9%

DC

2.4%

11.1%

14.8%

DE

4.6%

18.2%

12.3%

FL

14.6%

17.1%

18.6%

GA

12.9%

28.1%

35.6%

GU

0.0%

0.0%

0.0%

HI

0.0%

8.1%

4.4%

IA

9.1%

11.8%

15.3%

ID

6.8%

19.3%

22.0%

IL

7.6%

21.3%

22.9%

IN

7.9%

18.3%

24.9%

KS

12.1%

22.7%

29.7%

KY

13.1%

22.4%

29.7%

LA

9.9%

12.6%

16.4%

MA

3.2%

7.3%

12.7%

MD

5.2%

6.6%

9.2%

ME

4.1%

15.9%

22.4%

MI

13.8%

31.6%

34.1%

MN

2.9%

11.4%

14.2%

MO

16.2%

26.7%

34.1%

MS

21.2%

21.1%

23.4%

MT

8.9%

6.9%

7.8%

NC

5.3%

11.6%

15.0%

ND

5.1%

8.3%

12.8%

NE

6.7%

16.3%

19.0%

NH

7.0%

10.2%

13.3%

NJ

5.1%

13.2%

28.6%

NM

13.8%

12.5%

18.8%

NV

18.8%

19.8%

28.7%

NY

2.5%

5.9%

11.8%

OH

6.2%

20.8%

26.6%

OK

12.8%

18.2%

25.8%

OR

2.9%

12.1%

19.2%

PA

3.9%

5.0%

24.1%

PR

0.0%

2.3%

5.8%

RI

16.5%

19.0%

19.9%

A CohnReznick Report | 89

STATE

MEDIAN PHYSICAL OCCUPANCY 2012

MEDIAN DEBT COVERAGE RATIO 2012

MEDIAN PER UNIT CASH FLOW 2012

SC

3.7%

22.4%

SD

2.8%

4.4%

7.3%

TN

10.7%

20.9%

27.5%

TX

12.6%

17.4%

19.4%

UT

5.2%

6.5%

8.1%

VA

6.2%

11.0%

12.3%

VI

0.0%

36.2%

36.2%

VT

5.5%

6.2%

10.7%

WA

2.5%

5.5%

9.6%

WI

7.9%

16.7%

19.3%

WV

12.7%

18.9%

23.1%

WY

9.1%

2.0%

2.0%

90 | The Low-Income Housing Tax Credit Program – November 2014

24.0%

APPENDIX F

Property Performance by MSA MEDIAN PHYSICAL OCCUPANCY STATE

MSA

2011

2012

MEDIAN DEBT COVERAGE RATIO

MEDIAN PER UNIT CASH FLOW

2011

2012

2011

2012

AK

Anchorage, AK

97.5%

97.0%

1.18

1.08

$316

$601

AL

Auburn-Opelika, AL

99.0%

97.4%

1.01

1.28

$20

$23

AL

Birmingham-Hoover, AL

97.4%

97.3%

1.12

1.31

$83

$165

AL

Daphne-Fairhope-Foley, AL

95.0%

97.0%

1.53

1.47

$846

$731

AL

Decatur, AL

98.2%

96.9%

1.44

1.14

$352

$138

AL

Florence-Muscle Shoals, AL

96.4%

96.9%

1.65

1.60

$364

$484

AL

Gadsden, AL

93.5%

95.1%

1.56

1.85

$150

$529

AL

Huntsville, AL

97.0%

96.4%

1.47

1.60

$455

$426

AL

Mobile, AL

97.5%

98.4%

1.23

1.17

$466

$512

AL

Montgomery, AL

95.7%

95.5%

1.20

1.16

$314

$293

AL

Scottsboro, AL

94.8%

95.6%

2.36

1.79

$426

$487

AL

Selma, AL

98.5%

99.0%

1.53

1.48

$592

$623

AL

Troy, AL

95.0%

97.0%

1.23

1.28

$209

$287

AL

Tuscaloosa, AL

97.4%

98.2%

1.50

1.56

$401

$453

AR

Blytheville, AR

98.3%

98.4%

1.47

1.78

$144

$280

AR

Fayetteville-Springdale-Rogers, AR-MO

96.6%

98.4%

1.01

1.11

$58

$123

AR

Forrest City, AR

96.9%

98.9%

1.59

1.43

$1,181

$1,007

AR

Fort Smith, AR-OK

93.4%

91.7%

1.06

0.85

$46

-$386

AR

Harrison, AR

96.5%

100.0%

1.07

1.26

$520

$227

AR

Jonesboro, AR

97.9%

95.1%

1.06

1.21

$271

$222

AR

Little Rock-North Little Rock-Conway, AR

95.7%

95.0%

1.06

1.07

$105

$30

AR

Mountain Home, AR

95.3%

98.3%

1.32

1.28

$302

$313

AR

Pine Bluff, AR

92.3%

92.7%

0.97

1.01

-$105

-$108

AR

Searcy, AR

97.2%

93.8%

1.33

1.01

$264

-$4

AZ

Flagstaff, AZ

90.6%

96.1%

1.12

1.33

$132

$631

AZ

Lake Havasu City-Kingman, AZ

95.3%

97.0%

1.16

1.02

$278

$41

AZ

Nogales, AZ

95.6%

95.2%

1.47

1.21

$696

$741

AZ

Payson, AZ

95.9%

97.0%

2.39

1.67

$873

$398

AZ

Phoenix-Mesa-Scottsdale, AZ

95.5%

95.0%

1.13

1.14

$136

$241

AZ

Prescott, AZ

95.5%

95.0%

1.00

1.32

$71

$374

A CohnReznick Report | 91

MEDIAN PHYSICAL OCCUPANCY STATE

MSA

2011

AZ

Safford, AZ

94.6%

AZ

Show Low, AZ

AZ

MEDIAN PER UNIT CASH FLOW

2011

2012

2011

95.1%

1.78

1.57

$1,074

$657

95.5%

95.8%

1.18

1.11

$604

$338

Sierra Vista-Douglas, AZ

97.2%

96.4%

1.56

1.48

$621

$834

AZ

Tucson, AZ

97.0%

96.7%

1.01

0.96

$614

$393

AZ

Yuma, AZ

98.5%

98.1%

1.16

1.07

$491

$222

CA

Bakersfield, CA

98.2%

97.6%

1.30

1.21

$543

$386

CA

Chico, CA

100.0%

99.1%

1.53

1.51

$843

$1,553

CA

El Centro, CA

98.3%

97.9%

1.09

1.27

$69

$513

CA

Eureka-Arcata-Fortuna, CA

98.0%

98.0%

1.60

1.14

$1,001

$611

CA

Fresno, CA

97.1%

97.4%

1.25

1.27

$630

$661

CA

Hanford-Corcoran, CA

99.4%

98.4%

1.38

1.49

$1,631

$936

CA

Los Angeles-Long Beach-Anaheim, CA

98.4%

98.9%

1.45

1.47

$1,091

$1,218

CA

Madera, CA

98.1%

98.6%

1.48

1.31

$888

$834

CA

Merced, CA

94.0%

95.4%

1.36

1.09

$792

-$77

CA

Modesto, CA

97.7%

96.7%

1.30

1.24

$625

$665

CA

Napa, CA

99.0%

97.0%

1.52

1.28

$1,036

$1,477

CA

Oxnard-Thousand Oaks-Ventura, CA

99.3%

100.0%

1.24

1.31

$1,299

$1,603

CA

Red Bluff, CA

95.6%

92.6%

1.27

1.03

$435

-$53

CA

Redding, CA

98.0%

98.0%

1.28

0.97

$336

$481

CA

Riverside-San Bernardino-Ontario, CA

98.6%

98.0%

1.37

1.28

$780

$615

CA

Sacramento--Roseville--Arden-Arcade, CA

96.2%

96.3%

1.20

1.24

$708

$566

CA

Salinas, CA

98.6%

99.0%

1.40

1.34

$1,544

$1,414

CA

San Diego-Carlsbad, CA

98.0%

98.9%

1.40

1.36

$1,239

$1,189

CA

San Francisco-Oakland-Hayward, CA

97.3%

98.1%

1.32

1.34

$1,135

$910

CA

San Jose-Sunnyvale-Santa Clara, CA

97.9%

98.9%

1.36

1.37

$1,386

$1,416

CA

San Luis Obispo-Paso Robles-Arroyo Grande, CA 98.3%

99.1%

1.39

1.49

$884

$1,119

CA

Santa Cruz-Watsonville, CA

98.0%

98.9%

1.88

1.76

$1,746

$1,610

CA

Santa Maria-Santa Barbara, CA

99.0%

99.3%

1.33

1.34

$1,971

$1,026

CA

Santa Rosa, CA

98.3%

98.4%

1.37

1.36

$1,011

$923

CA

Stockton-Lodi, CA

94.8%

92.8%

1.35

1.41

$376

$200

92 | The Low-Income Housing Tax Credit Program â&#x20AC;&#x201C; November 2014

2012

MEDIAN DEBT COVERAGE RATIO

2012

MEDIAN PHYSICAL OCCUPANCY STATE

MSA

CA

MEDIAN DEBT COVERAGE RATIO

MEDIAN PER UNIT CASH FLOW

2011

2012

2011

2012

2011

2012

Truckee-Grass Valley, CA

97.6%

97.3%

1.07

1.31

$33

$615

CA

Vallejo-Fairfield, CA

94.0%

95.0%

1.39

1.48

$511

$701

CA

Visalia-Porterville, CA

97.2%

95.0%

1.34

1.15

$587

$544

CO

Boulder, CO

98.9%

99.0%

1.17

1.54

$1,037

$1,417

CO

Ca単on City, CO

96.0%

100.0%

0.42

0.01

$1,406

$1,788

CO

Colorado Springs, CO

95.4%

95.8%

1.15

1.14

$681

$619

CO

Denver-Aurora-Lakewood, CO

97.7%

97.9%

1.23

1.23

$660

$657

CO

Durango, CO

98.9%

98.4%

1.51

1.65

$1,392

$1,239

CO

Fort Collins, CO

99.0%

98.6%

1.29

1.34

$1,085

$584

CO

Grand Junction, CO

97.3%

98.9%

1.13

1.06

$359

$163

CO

Greeley, CO

99.0%

98.2%

1.22

1.48

$184

$566

CO

Pueblo, CO

99.5%

98.5%

1.44

1.34

$862

$539

CT

Bridgeport-Stamford-Norwalk, CT

97.9%

98.0%

1.30

1.10

$414

$302

CT

Hartford-West Hartford-East Hartford, CT

96.3%

96.0%

1.12

1.16

$348

$175

CT

New Haven-Milford, CT

97.8%

96.0%

1.36

1.16

$1,042

$738

CT

Norwich-New London, CT

94.7%

96.0%

1.32

1.65

$1,060

$1,498

DC

Washington-Arlington-Alexandria, DC-VA-MD-WV 98.0%

97.5%

1.35

1.37

$1,058

$1,160

DE

Dover, DE

96.3%

99.7%

1.49

1.60

$807

$661

FL

Arcadia, FL

80.5%

82.9%

0.26

0.42

-$437

-$689

FL

Cape Coral-Fort Myers, FL

92.9%

93.0%

1.15

1.06

$149

$310

FL

Deltona-Daytona Beach-Ormond Beach, FL

96.7%

95.0%

1.36

1.31

$625

$487

FL

Gainesville, FL

93.0%

88.8%

1.13

0.86

-$111

-$790

FL

Jacksonville, FL

94.7%

94.5%

1.23

1.26

$389

$490

FL

Lakeland-Winter Haven, FL

95.0%

94.7%

1.35

1.00

$563

$281

FL

Miami-Fort Lauderdale-West Palm Beach, FL

96.2%

97.0%

1.45

1.39

$1,067

$1,266

FL

Naples-Immokalee-Marco Island, FL

94.0%

92.0%

1.12

1.26

$231

$452

FL

North Port-Sarasota-Bradenton, FL

94.6%

96.0%

1.20

1.40

$464

$1,073

FL

Orlando-Kissimmee-Sanford, FL

94.5%

96.4%

1.20

1.26

$519

$845

FL

Palm Bay-Melbourne-Titusville, FL

92.0%

95.8%

0.88

1.41

-$193

-$93

FL

Panama City, FL

95.5%

96.2%

1.35

1.15

$976

$487

A CohnReznick Report | 93

MEDIAN PHYSICAL OCCUPANCY STATE

MSA

FL

MEDIAN DEBT COVERAGE RATIO

MEDIAN PER UNIT CASH FLOW

2011

2012

2011

2012

2011

Pensacola-Ferry Pass-Brent, FL

94.0%

92.9%

1.23

1.07

$244

$59

FL

Port St. Lucie, FL

91.8%

91.7%

1.10

1.03

$982

$45

FL

Punta Gorda, FL

96.8%

97.7%

1.23

1.03

$453

$13

FL

Sebastian-Vero Beach, FL

92.9%

88.0%

0.89

1.10

-$262

-$101

FL

Tallahassee, FL

94.0%

94.5%

1.21

1.11

$724

$144

FL

Tampa-St. Petersburg-Clearwater, FL

93.3%

96.3%

1.31

1.29

$448

$506

FL

Wauchula, FL

91.0%

91.0%

0.43

0.42

-$254

-$467

GA

Albany, GA

97.7%

92.5%

1.08

1.33

$62

$173

GA

Americus, GA

97.5%

95.6%

2.51

1.92

$126

$93

GA

Atlanta-Sandy Springs-Roswell, GA

94.9%

94.5%

0.97

1.01

-$93

-$1

GA

Augusta-Richmond County, GA-SC

97.1%

96.2%

1.14

1.05

$202

$55

GA

Bainbridge, GA

99.4%

96.4%

1.18

1.32

$152

$273

GA

Calhoun, GA

86.7%

98.2%

1.59

1.70

$426

$562

GA

Columbus, GA-AL

95.2%

96.2%

0.99

0.97

-$11

-$62

GA

Macon, GA

98.8%

98.3%

0.68

1.07

$201

$861

GA

Savannah, GA

94.4%

95.8%

1.32

1.11

$285

$194

GA

Valdosta, GA

95.8%

96.4%

0.79

1.04

-$101

$52

HI

Urban Honolulu, HI

97.5%

99.1%

1.47

1.52

$1,965

$1,397

IA

Cedar Rapids, IA

92.6%

94.0%

1.57

1.38

$166

$5

IA

Clinton, IA

96.1%

98.3%

1.00

1.52

$110

$272

IA

Davenport-Moline-Rock Island, IA-IL

95.1%

96.2%

1.18

1.12

$182

$204

IA

Des Moines-West Des Moines, IA

96.2%

96.7%

1.25

1.21

$601

$655

IA

Dubuque, IA

98.2%

97.0%

1.13

1.15

$568

$431

IA

Fort Madison-Keokuk, IA-IL-MO

95.8%

93.4%

1.37

1.95

$131

$572

IA

Iowa City, IA

94.0%

94.7%

1.29

1.12

$241

$198

IA

Mason City, IA

93.0%

93.4%

1.36

1.32

$530

$329

IA

Muscatine, IA

96.6%

97.9%

1.48

1.47

$699

$575

IA

Oskaloosa, IA

94.7%

96.9%

1.76

1.69

$577

$736

IA

Sioux City, IA-NE-SD

95.0%

96.4%

1.29

1.24

$391

$715

IA

Waterloo-Cedar Falls, IA

96.9%

98.0%

2.34

1.81

$496

$641

94 | The Low-Income Housing Tax Credit Program â&#x20AC;&#x201C; November 2014

2012

MEDIAN PHYSICAL OCCUPANCY STATE

MSA

ID

MEDIAN DEBT COVERAGE RATIO

MEDIAN PER UNIT CASH FLOW

2011

2012

2011

2012

2011

2012

Boise City, ID

95.1%

95.9%

1.03

1.05

$39

$24

ID

Coeur d'Alene, ID

97.1%

97.3%

1.31

1.32

$664

$583

ID

Lewiston, ID-WA

99.2%

98.3%

1.26

1.21

$462

$370

ID

Logan, UT-ID

99.2%

97.5%

2.32

1.27

$907

$290

IL

Bloomington, IL

89.8%

97.1%

0.98

1.17

$38

$574

IL

Carbondale-Marion, IL

96.2%

97.5%

0.98

0.95

-$271

$13

IL

Charleston-Mattoon, IL

97.5%

96.0%

1.08

1.13

$111

$177

IL

Chicago-Naperville-Elgin, IL-IN-WI

97.0%

97.2%

1.29

1.28

$609

$697

IL

Decatur, IL

98.0%

98.0%

1.77

2.05

$1,720

$2,132

IL

Galesburg, IL

96.0%

94.0%

1.58

1.79

$566

$716

IL

Ottawa-Peru, IL

95.8%

100.0%

1.60

1.69

$563

$603

IL

Peoria, IL

96.0%

97.1%

1.96

1.97

$419

$477

IL

Rockford, IL

94.1%

92.0%

1.07

1.26

$334

$667

IL

Springfield, IL

96.0%

97.0%

1.53

1.25

$641

$540

IL

Sterling, IL

97.2%

97.1%

1.13

1.00

$150

$95

IN

Auburn, IN

92.6%

91.3%

0.89

0.98

-$198

-$34

IN

Elkhart-Goshen, IN

94.0%

96.5%

0.99

1.35

$54

$142

IN

Evansville, IN-KY

97.0%

97.0%

1.41

1.53

-$100

$544

IN

Fort Wayne, IN

96.0%

95.4%

1.29

1.37

$533

$591

IN

Indianapolis-Carmel-Anderson, IN

94.4%

95.7%

1.07

1.19

$92

$263

IN

Kokomo, IN

98.0%

95.0%

0.94

0.84

-$29

-$281

IN

Lafayette-West Lafayette, IN

96.3%

95.3%

1.08

1.41

$163

$597

IN

Marion, IN

95.1%

96.4%

1.20

0.92

-$365

$273

IN

Muncie, IN

96.9%

96.7%

1.34

1.62

$171

$422

IN

South Bend-Mishawaka, IN-MI

96.0%

96.0%

1.27

1.35

$662

$487

KS

Coffeyville, KS

96.6%

91.3%

1.58

1.08

$505

$62

KS

Great Bend, KS

99.0%

99.0%

1.57

1.25

$389

$267

KS

Hays, KS

99.0%

100.0%

1.38

1.55

$769

$463

KS

Hutchinson, KS

100.0%

99.0%

1.09

0.85

$84

-$55

KS

Lawrence, KS

95.7%

98.0%

0.99

0.84

$134

$139

A CohnReznick Report | 95

MEDIAN PHYSICAL OCCUPANCY STATE

MSA

KS

MEDIAN DEBT COVERAGE RATIO

2011

2012

2011

Manhattan, KS

96.4%

95.7%

1.40

1.41

$152

$219

KS

Parsons, KS

98.0%

95.0%

0.93

0.52

$377

-$193

KS

Salina, KS

98.4%

97.9%

1.34

1.28

$627

$807

KS

Topeka, KS

97.1%

96.0%

1.21

1.13

$421

$208

KS

Wichita, KS

93.8%

95.0%

1.07

1.26

$66

$304

KY

Bowling Green, KY

96.0%

97.0%

1.26

1.40

$403

$484

KY

Danville, KY

87.5%

93.8%

0.74

0.82

-$409

-$363

KY

Elizabethtown-Fort Knox, KY

99.0%

95.6%

1.10

1.61

$245

$415

KY

Glasgow, KY

85.0%

89.6%

1.00

1.03

-$103

$116

KY

Lexington-Fayette, KY

97.3%

96.3%

0.99

1.11

$96

$452

KY

London, KY

99.0%

98.3%

1.70

2.01

$696

$861

KY

Louisville/Jefferson County, KY-IN

93.4%

94.9%

0.96

1.02

$2

$40

KY

Owensboro, KY

98.0%

99.3%

2.04

7.80

$1,036

$1,233

LA

Alexandria, LA

96.7%

97.0%

1.40

1.27

$450

$496

LA

Bastrop, LA

97.9%

97.5%

1.87

1.81

$963

$413

LA

Baton Rouge, LA

95.7%

95.0%

1.26

1.12

$404

$106

LA

Fort Polk South, LA

89.0%

82.0%

1.29

1.53

$127

$298

LA

Hammond, LA

97.0%

98.1%

1.77

1.89

$365

$456

LA

Houma-Thibodaux, LA

96.8%

95.1%

1.42

1.45

$960

$495

LA

Lafayette, LA

97.3%

96.2%

1.36

1.54

$616

$904

LA

Lake Charles, LA

95.2%

96.6%

1.37

1.40

$892

$1,092

LA

Monroe, LA

96.9%

96.0%

1.51

1.35

$578

$460

LA

Natchitoches, LA

96.2%

93.1%

1.35

1.16

$427

$273

LA

New Orleans-Metairie, LA

96.0%

95.8%

1.48

1.33

$759

$522

LA

Opelousas, LA

97.3%

96.2%

1.25

1.26

$337

$432

LA

Ruston, LA

97.7%

96.8%

1.57

1.44

$484

$619

LA

Shreveport-Bossier City, LA

95.2%

97.0%

1.19

1.18

$228

$253

MA

Barnstable Town, MA

98.2%

99.7%

1.60

1.62

$87

$1,359

MA

Boston-Cambridge-Newton, MA-NH

98.6%

98.5%

1.35

1.41

$760

$990

MA

Pittsfield, MA

94.7%

92.8%

1.29

1.39

$263

$501

96 | The Low-Income Housing Tax Credit Program â&#x20AC;&#x201C; November 2014

2012

MEDIAN PER UNIT CASH FLOW 2011

2012

MEDIAN PHYSICAL OCCUPANCY STATE

MSA

MA

MEDIAN DEBT COVERAGE RATIO

MEDIAN PER UNIT CASH FLOW

2011

2012

2011

2012

2011

2012

Springfield, MA

97.8%

97.8%

1.31

1.49

$536

$664

MA

Worcester, MA-CT

99.3%

97.0%

1.39

1.33

$866

$727

MD

Baltimore-Columbia-Towson, MD

98.0%

98.5%

1.46

1.57

$650

$782

MD

California-Lexington Park, MD

94.6%

98.6%

1.00

1.42

$14

$222

MD

Cambridge, MD

96.0%

96.5%

1.49

1.23

$704

$358

MD

Cumberland, MD-WV

98.0%

97.0%

1.29

1.15

$311

$252

MD

Hagerstown-Martinsburg, MD-WV

95.0%

97.0%

1.76

1.63

$1,007

$650

MD

Salisbury, MD-DE

98.5%

98.3%

1.68

1.64

$713

$832

ME

Augusta-Waterville, ME

94.2%

97.2%

1.34

1.00

$140

$760

ME

Bangor, ME

97.3%

97.0%

1.94

1.20

$987

-$100

ME

Lewiston-Auburn, ME

97.4%

94.6%

1.18

1.26

$902

$510

ME

Portland-South Portland, ME

96.5%

97.6%

1.25

1.32

$522

$588

MI

Adrian, MI

93.0%

89.5%

1.14

1.18

$446

$210

MI

Alma, MI

90.0%

92.0%

0.69

0.94

$178

-$15

MI

Ann Arbor, MI

97.0%

98.5%

1.38

1.58

$484

$744

MI

Battle Creek, MI

91.0%

94.8%

1.17

0.90

$370

-$251

MI

Bay City, MI

92.6%

97.5%

1.29

1.40

$700

$579

MI

Big Rapids, MI

95.0%

94.1%

1.31

1.42

$420

$519

MI

Cadillac, MI

95.8%

95.8%

1.56

1.45

$805

$643

MI

Coldwater, MI

97.0%

90.0%

1.43

1.31

$348

$517

MI

Detroit-Warren-Dearborn, MI

95.1%

95.0%

1.01

0.91

$46

-$29

MI

Escanaba, MI

98.0%

93.8%

1.44

1.40

$743

$718

MI

Flint, MI

96.4%

96.0%

1.15

1.11

$263

$197

MI

Grand Rapids-Wyoming, MI

96.2%

97.0%

1.22

1.39

$317

$495

MI

Hillsdale, MI

92.5%

92.5%

1.07

1.25

$112

$592

MI

Holland, MI

95.0%

96.0%

1.12

1.06

$546

$327

MI

Ionia, MI

96.0%

93.2%

1.32

1.32

$442

$361

MI

Jackson, MI

96.9%

96.0%

1.13

1.11

$1,094

$1,003

MI

Kalamazoo-Portage, MI

96.9%

97.0%

1.34

1.28

$662

$660

MI

Lansing-East Lansing, MI

92.0%

94.4%

1.25

1.20

$392

$523

A CohnReznick Report | 97

MEDIAN PHYSICAL OCCUPANCY STATE

MSA

MI

MEDIAN DEBT COVERAGE RATIO

MEDIAN PER UNIT CASH FLOW

2011

2012

2011

2012

2011

Marquette, MI

97.0%

98.0%

1.24

1.07

$285

$184

MI

Midland, MI

96.0%

95.2%

1.11

1.22

$186

$590

MI

Mount Pleasant, MI

97.0%

96.0%

1.32

1.26

$543

$453

MI

Muskegon, MI

94.0%

96.0%

1.12

1.15

$418

$399

MI

Niles-Benton Harbor, MI

97.0%

98.0%

1.40

1.41

$780

$666

MI

Owosso, MI

87.5%

91.3%

1.42

1.49

$109

$762

MI

Saginaw, MI

93.9%

94.6%

1.21

1.19

$244

$306

MI

Sault Ste. Marie, MI

94.0%

96.5%

1.92

1.69

$2,029

$1,848

MI

Traverse City, MI

96.0%

95.0%

1.25

1.32

$1,221

$948

MN

Bemidji, MN

97.9%

100.0%

1.25

1.71

$444

$713

MN

Brainerd, MN

100.0%

100.0%

1.61

1.48

$880

$680

MN

Duluth, MN-WI

97.5%

98.4%

1.33

1.28

$846

$851

MN

Mankato-North Mankato, MN

97.9%

97.6%

1.63

1.65

$1,118

$1,119

MN

Minneapolis-St. Paul-Bloomington, MN-WI

97.7%

98.0%

1.33

1.35

$873

$911

MN

Rochester, MN

100.0%

99.1%

1.48

1.69

$872

$1,543

MN

St. Cloud, MN

96.3%

97.0%

1.09

1.22

$165

$568

MO

Branson, MO

95.2%

92.3%

1.38

1.12

$186

$29

MO

Cape Girardeau, MO-IL

97.0%

96.5%

2.31

2.03

$912

$813

MO

Columbia, MO

98.0%

96.9%

1.08

1.54

$128

$1,102

MO

Jefferson City, MO

93.5%

93.0%

0.60

0.92

-$360

-$182

MO

Joplin, MO

97.5%

96.9%

1.38

1.09

$272

$358

MO

Kansas City, MO-KS

96.0%

96.9%

1.11

1.21

$233

$312

MO

Springfield, MO

96.4%

97.0%

1.38

1.48

$457

$690

MO

St. Joseph, MO-KS

93.4%

92.1%

0.89

0.99

-$300

-$22

MO

St. Louis, MO-IL

96.0%

95.5%

1.15

1.13

$103

$136

MS

Cleveland, MS

97.3%

99.0%

1.45

1.52

$732

$743

MS

Columbus, MS

100.0%

96.4%

1.62

1.14

$126

$123

MS

Greenwood, MS

95.8%

93.8%

1.25

1.06

$475

$438

MS

Gulfport-Biloxi-Pascagoula, MS

92.2%

93.0%

1.29

1.21

$653

$423

MS

Hattiesburg, MS

97.0%

98.0%

1.40

1.73

$561

$798

98 | The Low-Income Housing Tax Credit Program â&#x20AC;&#x201C; November 2014

2012

MEDIAN PHYSICAL OCCUPANCY STATE

MSA

MS

MEDIAN DEBT COVERAGE RATIO

MEDIAN PER UNIT CASH FLOW

2011

2012

2011

2012

2011

2012

Indianola, MS

99.0%

100.0%

0.77

0.68

-$239

-$111

MS

Jackson, MS

97.0%

96.0%

1.54

1.52

$734

$739

MS

Laurel, MS

96.5%

92.5%

1.20

1.36

$258

$378

MS

Meridian, MS

96.0%

96.5%

1.32

1.79

$346

-$249

MS

Natchez, MS-LA

98.0%

97.0%

1.09

1.13

$184

$128

MT

Billings, MT

97.4%

96.5%

1.30

1.17

$599

$539

MT

Bozeman, MT

96.5%

97.6%

1.11

1.21

$489

$384

MT

Helena, MT

99.0%

99.0%

1.27

1.31

$233

$323

MT

Kalispell, MT

98.3%

100.0%

1.24

1.39

$378

$372

MT

Missoula, MT

99.6%

97.9%

1.92

2.12

$947

$1,167

NC

Asheville, NC

98.2%

97.2%

1.26

1.28

$241

$316

NC

Burlington, NC

95.0%

93.1%

1.32

1.20

$458

$301

NC

Charlotte-Concord-Gastonia, NC-SC

97.0%

97.9%

1.09

1.23

$329

$438

NC

Durham-Chapel Hill, NC

97.0%

97.0%

1.13

1.03

$206

$96

NC

Fayetteville, NC

95.8%

95.7%

1.53

1.67

$495

$562

NC

Forest City, NC

97.3%

98.7%

1.61

1.34

$957

$694

NC

Greensboro-High Point, NC

96.6%

97.0%

1.18

1.21

$375

$464

NC

Greenville, NC

96.0%

97.0%

1.59

1.51

$763

$1,083

NC

Henderson, NC

96.8%

96.2%

2.22

1.76

$1,737

$960

NC

Hickory-Lenoir-Morganton, NC

96.8%

94.6%

1.08

1.04

$290

$139

NC

Jacksonville, NC

98.0%

99.2%

1.53

1.45

$587

$340

NC

Kinston, NC

98.0%

96.4%

1.46

1.43

$446

$309

NC

Laurinburg, NC

97.6%

99.2%

1.32

1.38

$818

$780

NC

Lumberton, NC

98.2%

98.4%

1.88

1.64

$587

$509

NC

Morehead City, NC

99.1%

99.7%

1.53

1.50

$474

$498

NC

New Bern, NC

99.0%

93.5%

1.17

1.42

$191

$278

NC

Pinehurst-Southern Pines, NC

94.7%

93.8%

1.20

1.04

$225

$51

NC

Raleigh, NC

98.0%

98.1%

1.33

1.38

$571

$643

NC

Roanoke Rapids, NC

97.7%

99.4%

1.15

1.46

$280

$852

NC

Rockingham, NC

98.1%

100.0%

1.39

1.36

$769

$560

A CohnReznick Report | 99

MEDIAN PHYSICAL OCCUPANCY STATE

MSA

NC

Rocky Mount, NC

NC

2011

MEDIAN DEBT COVERAGE RATIO

MEDIAN PER UNIT CASH FLOW

2012

2011

2012

2011

99.0%

96.9%

1.01

1.45

$1,023

$1,185

Sanford, NC

96.0%

95.0%

1.37

1.00

$616

$9

NC

Shelby, NC

97.7%

98.0%

1.19

1.15

$455

$300

NC

Washington, NC

100.0%

99.0%

3.58

3.81

$1,010

$1,264

NC

Wilmington, NC

98.5%

97.9%

1.33

0.97

$665

$151

NC

Winston-Salem, NC

96.9%

97.5%

1.16

1.21

$270

$271

ND

Bismarck, ND

98.9%

100.0%

1.43

1.61

$462

$487

ND

Fargo, ND-MN

98.5%

98.3%

1.23

1.21

$811

$578

ND

Grand Forks, ND-MN

97.9%

95.9%

1.28

1.80

$528

$1,214

NE

Beatrice, NE

92.0%

92.5%

1.12

1.21

$189

$151

NE

Fremont, NE

98.3%

100.0%

1.72

1.52

$817

$756

NE

Grand Island, NE

99.0%

98.0%

1.47

1.93

$511

$763

NE

Hastings, NE

94.2%

97.0%

0.50

1.38

-$540

$343

NE

Lincoln, NE

98.8%

99.0%

1.34

1.27

$185

$267

NE

North Platte, NE

94.0%

95.0%

1.13

1.25

$139

$279

NE

Omaha-Council Bluffs, NE-IA

97.0%

97.0%

1.12

1.27

$302

$413

NE

Scottsbluff, NE

98.5%

95.7%

1.04

1.28

$189

$639

NH

Claremont-Lebanon, NH-VT

97.0%

96.6%

1.27

1.49

$328

$356

NH

Concord, NH

97.7%

96.8%

2.23

1.77

$1,608

$1,217

NH

Keene, NH

95.8%

95.2%

1.22

1.08

$383

$306

NH

Laconia, NH

97.2%

94.0%

1.76

1.39

$696

$529

NH

Manchester-Nashua, NH

97.0%

95.8%

1.38

1.40

$1,074

$668

NJ

Atlantic City-Hammonton, NJ

98.0%

98.3%

1.44

1.24

$329

$314

NJ

Trenton, NJ

97.2%

95.5%

1.04

1.21

-$70

$308

NJ

Vineland-Bridgeton, NJ

97.6%

98.0%

0.90

1.66

$61

$501

NM

Albuquerque, NM

96.8%

96.4%

1.18

1.24

$569

$274

NM

Clovis, NM

98.8%

96.0%

1.70

1.37

$701

$400

NM

Deming, NM

97.1%

96.6%

1.45

1.60

$639

$770

NM

Farmington, NM

98.3%

96.0%

1.24

1.29

$470

$723

NM

Gallup, NM

97.5%

98.0%

1.57

1.40

$753

$836

100 | The Low-Income Housing Tax Credit Program â&#x20AC;&#x201C; November 2014

2012

MEDIAN PHYSICAL OCCUPANCY STATE

MSA

NM

MEDIAN DEBT COVERAGE RATIO

MEDIAN PER UNIT CASH FLOW

2011

2012

2011

2012

2011

2012

Las Cruces, NM

99.0%

99.0%

1.56

1.70

$533

$1,052

NM

Santa Fe, NM

96.6%

96.6%

1.23

1.14

$592

$567

NM

Taos, NM

97.0%

96.4%

1.12

1.18

$122

$289

NV

Elko, NV

95.5%

95.0%

1.64

1.39

$968

$811

NV

Fernley, NV

92.9%

100.0%

0.89

1.38

$296

$609

NV

Las Vegas-Henderson-Paradise, NV

95.0%

97.0%

1.15

1.25

$182

$525

NV

Reno, NV

96.0%

95.9%

1.23

1.25

$542

$724

NV

Winnemucca, NV

95.0%

98.3%

1.59

1.96

$872

$957

NY

Albany-Schenectady-Troy, NY

98.0%

98.6%

1.81

2.09

$974

$871

NY

Binghamton, NY

98.0%

97.9%

1.90

3.38

$486

$790

NY

Buffalo-Cheektowaga-Niagara Falls, NY

97.0%

97.6%

1.35

1.43

$639

$800

NY

Corning, NY

93.5%

95.0%

1.08

1.17

$417

$284

NY

Elmira, NY

98.0%

96.9%

2.78

1.16

$403

$98

NY

Glens Falls, NY

95.6%

97.5%

1.60

1.87

$336

$429

NY

Ithaca, NY

100.0%

98.7%

1.65

1.61

$1,096

$1,495

NY

Jamestown-Dunkirk-Fredonia, NY

93.4%

96.7%

0.53

2.52

$39

$509

NY

Kingston, NY

99.5%

99.0%

1.44

1.39

$472

$500

NY

Malone, NY

97.2%

100.0%

-1.57

-0.47

-$1,207

-$623

NY

New York-Newark-Jersey City, NY-NJ-PA

98.0%

98.0%

1.46

1.52

$942

$1,086

NY

Olean, NY

96.0%

98.6%

2.65

6.99

$407

$1,152

NY

Rochester, NY

97.1%

96.9%

1.45

1.46

$802

$729

NY

Syracuse, NY

95.8%

97.0%

1.98

1.58

$748

$798

NY

Utica-Rome, NY

97.0%

95.2%

1.39

1.43

$401

$707

NY

Watertown-Fort Drum, NY

98.8%

97.3%

1.44

1.61

$1,493

$1,490

OH

Akron, OH

97.7%

97.6%

1.34

1.35

$368

$577

OH

Ashtabula, OH

98.2%

98.6%

1.71

1.68

$1,190

$864

OH

Bucyrus, OH

93.5%

93.9%

1.06

1.12

-$269

-$76

OH

Cambridge, OH

96.4%

98.6%

1.31

1.34

$993

$959

OH

Canton-Massillon, OH

97.8%

97.6%

1.32

1.19

$368

$236

OH

Cincinnati, OH-KY-IN

96.9%

97.0%

1.34

1.45

$287

$425

A CohnReznick Report | 101

MEDIAN PHYSICAL OCCUPANCY STATE

MSA

OH

MEDIAN DEBT COVERAGE RATIO

MEDIAN PER UNIT CASH FLOW

2011

2012

2011

2012

2011

Cleveland-Elyria, OH

98.5%

98.3%

1.21

1.12

$363

$250

OH

Columbus, OH

97.7%

97.6%

1.35

1.24

$648

$328

OH

Dayton, OH

96.5%

95.9%

1.18

1.23

$429

$534

OH

Jackson, OH

95.5%

95.5%

1.65

1.37

$229

$261

OH

Mansfield, OH

94.0%

95.3%

1.06

1.08

$24

$62

OH

Springfield, OH

96.5%

94.2%

1.13

1.16

$157

$265

OH

Tiffin, OH

95.4%

95.2%

1.14

1.02

$175

-$33

OH

Toledo, OH

95.8%

96.5%

1.26

1.13

$408

$167

OH

Washington Court House, OH

96.1%

94.8%

1.14

1.30

$434

$849

OH

Youngstown-Warren-Boardman, OH-PA

98.3%

96.7%

1.27

1.05

$221

$100

OH

Zanesville, OH

99.0%

98.7%

1.00

1.38

-$5

$626

OK

Lawton, OK

90.3%

94.4%

1.02

1.03

$357

$77

OK

McAlester, OK

96.7%

96.8%

1.63

1.82

$751

$989

OK

Muskogee, OK

97.0%

92.8%

1.70

1.30

$694

$409

OK

Oklahoma City, OK

96.0%

97.7%

1.30

1.39

$480

$453

OK

Shawnee, OK

95.1%

95.0%

1.28

1.36

$582

$639

OK

Stillwater, OK

90.5%

91.6%

1.16

1.14

$195

$235

OK

Tahlequah, OK

95.0%

95.0%

1.09

0.83

$93

-$114

OK

Tulsa, OK

95.2%

97.5%

1.12

1.08

$146

$250

OR

Albany, OR

97.7%

96.9%

1.22

1.27

$289

$273

OR

Bend-Redmond, OR

97.6%

96.4%

1.35

1.41

$874

$943

OR

Eugene, OR

98.6%

98.2%

1.27

1.47

$360

$697

OR

Hermiston-Pendleton, OR

96.4%

96.9%

1.45

1.28

$367

$226

OR

Medford, OR

98.5%

97.9%

1.24

1.26

$538

$408

OR

Newport, OR

96.1%

96.3%

1.16

0.96

$269

-$72

OR

Portland-Vancouver-Hillsboro, OR-WA

97.9%

98.1%

1.31

1.25

$566

$581

OR

Roseburg, OR

90.7%

95.4%

0.88

1.11

-$181

$73

OR

Salem, OR

98.1%

99.2%

1.38

1.50

$543

$520

PA

Allentown-Bethlehem-Easton, PA-NJ

98.0%

98.3%

1.65

1.61

$436

$751

PA

Bloomsburg-Berwick, PA

96.7%

97.6%

1.19

1.40

$6

$237

102 | The Low-Income Housing Tax Credit Program â&#x20AC;&#x201C; November 2014

2012

MEDIAN PHYSICAL OCCUPANCY STATE

MSA

PA

MEDIAN DEBT COVERAGE RATIO

MEDIAN PER UNIT CASH FLOW

2011

2012

2011

2012

2011

2012

Chambersburg-Waynesboro, PA

97.1%

97.3%

2.00

5.07

$603

$588

PA

Erie, PA

95.8%

98.4%

1.07

0.90

$24

$0

PA

Harrisburg-Carlisle, PA

97.0%

98.3%

2.84

3.14

$916

$1,116

PA

Lancaster, PA

97.5%

96.0%

1.65

1.21

$451

$134

PA

Philadelphia-Camden-Wilmington, PA-NJ-DE-MD

97.8%

98.0%

1.32

1.37

$329

$396

PA

Pittsburgh, PA

97.6%

97.9%

1.13

1.43

$157

$411

PA

Pottsville, PA

99.0%

97.5%

0.61

1.58

-$975

$330

PA

Reading, PA

99.7%

99.4%

3.18

2.33

$146

$459

PA

Scranton--Wilkes-Barre--Hazleton, PA

98.5%

99.1%

1.35

1.50

$558

$900

PA

State College, PA

97.8%

98.7%

1.88

1.94

$756

$1,012

PA

York-Hanover, PA

95.8%

98.8%

1.30

1.98

$428

$950

PR

Aguadilla-Isabela, PR

100.0%

100.0%

1.13

1.20

$191

$546

PR

Mayag端ez, PR

98.5%

98.0%

1.71

1.49

$634

$537

PR

Ponce, PR

99.0%

99.5%

2.44

1.92

$1,205

$1,026

PR

San Juan-Carolina-Caguas, PR

100.0%

100.0%

1.31

1.32

$387

$522

RI

Providence-Warwick, RI-MA

97.0%

97.1%

1.36

1.46

$527

$772

SC

Charleston-North Charleston, SC

96.4%

98.0%

1.09

1.13

$84

$251

SC

Columbia, SC

97.1%

96.4%

1.16

1.08

$372

$115

SC

Florence, SC

97.8%

100.0%

1.08

1.03

$375

$459

SC

Gaffney, SC

92.0%

92.4%

2.06

1.07

$926

$1,074

SC

Georgetown, SC

98.0%

95.2%

1.17

1.38

$287

$556

SC

Greenville-Anderson-Mauldin, SC

96.4%

97.3%

1.36

1.32

$506

$482

SC

Greenwood, SC

92.0%

90.6%

1.05

1.05

$107

$152

SC

Hilton Head Island-Bluffton-Beaufort, SC

94.5%

96.0%

0.98

1.13

-$85

$228

SC

Myrtle Beach-Conway-No. Myrtle Beach, SC-NC

96.9%

97.4%

1.21

1.26

$369

$592

SC

Newberry, SC

93.1%

93.8%

1.02

1.53

$185

$220

SC

Orangeburg, SC

94.7%

94.6%

1.18

1.37

$320

$636

SC

Spartanburg, SC

96.4%

95.9%

1.25

1.75

$320

$500

SC

Sumter, SC

95.8%

95.7%

1.16

1.08

$132

$51

SD

Rapid City, SD

95.6%

96.4%

1.42

1.40

$669

$622

A CohnReznick Report | 103

MEDIAN PHYSICAL OCCUPANCY STATE

MSA

SD

MEDIAN DEBT COVERAGE RATIO

MEDIAN PER UNIT CASH FLOW

2011

2012

2011

2012

2011

Sioux Falls, SD

93.6%

96.5%

1.13

1.11

$301

$640

TN

Chattanooga, TN-GA

97.0%

97.7%

1.27

1.18

$285

$404

TN

Clarksville, TN-KY

91.4%

92.2%

1.24

1.07

$327

$128

TN

Jackson, TN

94.4%

94.7%

1.00

0.75

$88

-$88

TN

Johnson City, TN

98.4%

99.0%

1.10

1.01

$381

$70

TN

Kingsport-Bristol-Bristol, TN-VA

98.6%

98.2%

1.21

1.15

$242

$141

TN

Knoxville, TN

94.7%

95.3%

1.17

1.29

$341

$342

TN

Memphis, TN-MS-AR

94.0%

96.2%

1.09

1.13

$222

$260

TN

Nashville-Davidson--Murfreesboro--Franklin, TN

94.5%

96.1%

1.15

1.11

$302

$473

TX

Amarillo, TX

94.2%

94.9%

1.25

0.82

$291

-$270

TX

Austin-Round Rock, TX

95.2%

97.9%

1.12

1.19

$328

$715

TX

Beaumont-Port Arthur, TX

96.0%

94.1%

1.32

1.32

$424

$472

TX

Brownsville-Harlingen, TX

97.3%

96.8%

1.55

1.62

$742

$1,054

TX

College Station-Bryan, TX

95.7%

92.1%

1.40

1.47

$0

$559

TX

Corpus Christi, TX

96.7%

96.9%

1.28

1.28

$454

$605

TX

Dallas-Fort Worth-Arlington, TX

93.8%

95.6%

1.06

1.09

$137

$170

TX

El Paso, TX

97.0%

98.0%

1.64

1.74

$901

$1,017

TX

Houston-The Woodlands-Sugar Land, TX

94.0%

95.0%

1.19

1.17

$288

$285

TX

Killeen-Temple, TX

95.8%

95.4%

1.52

1.48

$982

$868

TX

Longview, TX

95.1%

94.0%

1.64

1.44

$1,024

$758

TX

Lubbock, TX

87.1%

92.1%

0.69

1.08

$60

$152

TX

McAllen-Edinburg-Mission, TX

98.5%

98.4%

1.28

1.36

$601

$618

TX

Odessa, TX

98.2%

99.5%

1.44

2.21

$565

$2,077

TX

San Antonio-New Braunfels, TX

95.7%

95.4%

1.14

1.20

$361

$450

TX

Texarkana, TX-AR

93.4%

92.3%

1.05

0.98

$75

-$70

TX

Tyler, TX

97.0%

97.0%

1.50

1.28

$466

$259

TX

Waco, TX

96.1%

96.0%

1.27

1.28

$625

$802

TX

Wichita Falls, TX

96.2%

96.7%

1.53

1.53

$778

$810

UT

Ogden-Clearfield, UT

96.4%

96.0%

1.14

1.29

$642

$918

UT

Provo-Orem, UT

99.3%

99.8%

1.40

1.38

$828

$1,061

104 | The Low-Income Housing Tax Credit Program â&#x20AC;&#x201C; November 2014

2012

MEDIAN PHYSICAL OCCUPANCY STATE

MSA

UT

MEDIAN DEBT COVERAGE RATIO

MEDIAN PER UNIT CASH FLOW

2011

2012

2011

2012

2011

2012

Salt Lake City, UT

97.0%

97.0%

1.31

1.35

$573

$599

UT

St. George, UT

97.9%

98.6%

1.79

1.51

$1,129

$815

VA

Big Stone Gap, VA

95.5%

94.8%

1.33

1.24

$166

$125

VA

Blacksburg-Christiansburg-Radford, VA

98.3%

96.1%

1.39

1.43

$648

$671

VA

Bluefield, WV-VA

94.0%

96.0%

1.31

1.44

$262

$619

VA

Charlottesville, VA

97.8%

97.4%

1.33

1.43

$729

$353

VA

Danville, VA

96.1%

97.0%

0.99

1.06

$478

$267

VA

Harrisonburg, VA

95.0%

95.5%

1.05

1.05

$134

$188

VA

Lynchburg, VA

92.7%

96.3%

1.33

1.40

$795

$1,088

VA

Richmond, VA

96.0%

96.0%

1.19

1.19

$696

$488

VA

Roanoke, VA

96.3%

98.1%

1.22

1.23

$631

$670

VA

Staunton-Waynesboro, VA

88.7%

95.5%

0.96

0.96

$147

$124

VA

Virginia Beach-Norfolk-Newport News, VA-NC

97.8%

97.8%

1.45

1.44

$983

$852

VT

Barre, VT

94.5%

94.5%

1.33

1.18

$429

$401

VT

Bennington, VT

97.5%

98.5%

1.11

1.68

$289

$491

VT

Burlington-South Burlington, VT

97.0%

97.8%

1.41

1.42

$876

$976

VT

Rutland, VT

98.0%

99.2%

1.09

1.06

$48

$237

WA

Bellingham, WA

98.9%

98.0%

1.26

1.75

$484

$803

WA

Bremerton-Silverdale, WA

95.5%

98.0%

1.35

1.30

$631

$722

WA

Centralia, WA

98.1%

96.0%

1.32

1.30

$449

$388

WA

Kennewick-Richland, WA

96.9%

96.6%

1.87

1.74

$1,268

$974

WA

Moses Lake, WA

96.2%

96.6%

1.51

1.47

$647

$693

WA

Mount Vernon-Anacortes, WA

99.0%

98.8%

1.32

1.30

$330

$427

WA

Olympia-Tumwater, WA

95.1%

94.0%

1.40

1.41

$807

$468

WA

Port Angeles, WA

98.5%

95.8%

2.39

2.16

$933

$988

WA

Seattle-Tacoma-Bellevue, WA

97.1%

97.4%

1.32

1.34

$755

$780

WA

Spokane-Spokane Valley, WA

94.3%

93.4%

1.14

1.35

$201

$290

WA

Walla Walla, WA

98.0%

99.0%

1.98

1.68

$939

$652

WA

Wenatchee, WA

94.2%

98.5%

1.96

1.72

$1,025

$1,207

WA

Yakima, WA

97.5%

96.2%

1.33

1.43

$413

$475

A CohnReznick Report | 105

MEDIAN PHYSICAL OCCUPANCY STATE

MSA

WI

MEDIAN DEBT COVERAGE RATIO

MEDIAN PER UNIT CASH FLOW

2011

2012

2011

2012

2011

Appleton, WI

95.9%

97.3%

1.31

1.26

$595

$561

WI

Eau Claire, WI

93.4%

94.9%

1.63

1.48

$707

$272

WI

Fond du Lac, WI

99.2%

96.9%

1.02

1.24

$150

$442

WI

Green Bay, WI

97.0%

95.0%

1.25

1.40

$1,197

$710

WI

Janesville-Beloit, WI

94.8%

96.6%

1.02

1.15

$28

$341

WI

Madison, WI

97.0%

96.0%

1.21

1.23

$659

$776

WI

Milwaukee-Waukesha-West Allis, WI

95.8%

95.8%

1.15

1.18

$307

$513

WI

Oshkosh-Neenah, WI

98.4%

98.0%

1.76

1.38

$1,569

$781

WI

Racine, WI

95.0%

94.8%

1.11

1.28

$321

$940

WI

Shawano, WI

97.9%

96.5%

0.78

0.68

$200

$222

WI

Stevens Point, WI

98.0%

96.7%

1.08

1.24

$241

$547

WI

Wausau, WI

96.5%

95.7%

1.08

1.07

$194

$284

WI

Wisconsin Rapids-Marshfield, WI

93.3%

93.6%

0.72

1.08

-$69

$1,007

WV

Beckley, WV

94.6%

95.2%

1.37

1.40

$229

$333

WV

Charleston, WV

96.0%

96.2%

1.19

1.13

$288

$195

WV

Clarksburg, WV

96.3%

91.7%

1.25

0.91

$81

$8

WV

Huntington-Ashland, WV-KY-OH

98.3%

96.0%

1.12

1.18

$155

$158

WV

Morgantown, WV

96.6%

97.8%

1.30

1.40

$544

$1,118

WV

Parkersburg-Vienna, WV

95.8%

96.5%

1.65

1.17

$562

$365

WV

Weirton-Steubenville, WV-OH

97.0%

98.0%

1.31

1.31

$383

$337

WV

Wheeling, WV-OH

99.3%

99.6%

1.05

1.27

$435

$508

WY

Casper, WY

97.5%

100.0%

1.08

1.98

$266

$1,372

WY

Cheyenne, WY

96.0%

96.4%

1.45

1.33

$550

$516

WY

Gillette, WY

92.0%

93.0%

1.06

1.17

$239

$527

WY

Riverton, WY

96.6%

98.5%

1.09

1.09

$351

$657

106 | The Low-Income Housing Tax Credit Program â&#x20AC;&#x201C; November 2014

2012

About Us About the Tax Credit Investment Services Group

The Tax Credit Investment Services (TCIS) group is a dedicated business unit within CohnReznick focused on evaluating and advising clients on tax-advantaged investments, including low-income housing, historic rehabilitation, new markets and renewable energy.

As a group made up of experts with a fairly narrow industry focus, TCIS covers a variety of consulting areas, including investment due diligence, investment and business strategy, and industry benchmarking research for the benefit of investor and syndicator communities.

The TCIS team is composed of a multidisciplinary group of professionals, including CPAs, attorneys, financial analysts, and other professionals with experience as state housing finance agency and commercial real estate executives. CohnReznick’s TCIS team members have authored a number of affordable housing industry studies, speak regularly at industry conferences, and have been widely quoted in the financial press concerning tax credit investments.

In addition to the professional experience of TCIS team members, the group’s clients benefit from the knowledge and experience of hundreds of CohnReznick audit, tax, and consulting professionals working on investment tax credit transactions on a daily basis.

For more information about TCIS, please visit www.cohnreznick.com/tcis.

To contact TCIS, please call 1-617-648-1400 or write to: CohnReznick – TCIS One Boston Place, Suite 500 Boston, MA 02108 TCIS@CohnReznick.com

About CohnReznick

CohnReznick LLP is one of the top accounting, tax, and advisory firms in the United States, combining the resources and technical expertise of a national firm with the hands-on, entrepreneurial approach that today’s dynamic business environment demands.

Headquartered in New York, NY, and with offices nationwide, CohnReznick serves a large number of diverse industries and offers specialized services for middle market and Fortune 1000 companies, private equity and financial services firms, government contractors, government agencies, and not-for-profit organizations. The Firm, with origins dating back to 1919, has more than 2,700 employees including nearly 300 partners and is a member of Nexia International, a global network of independent accountancy, tax, and business advisors. For more information, visit www.cohnreznick.com.

A CohnReznick Report | 107

CohnReznick LLP Š 2014

This has been prepared for information purposes and general guidance only and does not constitute professional advice. You should not act upon the information contained in this publication without obtaining specific professional advice. No representation or warranty (express or implied) is made as to the accuracy or completeness of the information contained in this publication, and CohnReznick LLP, its members, employees and agents accept no liability, and disclaim all responsibility, for the consequences of you or anyone else acting, or refraining to act, in reliance on the information contained in this publication or for any decision based on it.

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