An Expanded Look at Its Performance

Transcription

The Low-Income Housing
Tax Credit Program at Year 25:
An Expanded Look at
Its Performance
A CohnReznick LLP Report
December 2012
Introduction
This is the second in a series of periodic reports issued by CohnReznick LLP that addresses
the performance of properties financed with low-income housing tax credits (housing tax
credits). The housing tax credit program was enacted as a way to stimulate the development of affordable housing for low- to moderate-income families. Over the past 26 years,
the program has been studied by various interested parties such as the General Accounting
Office (GAO) and various congressional oversight committees to determine whether:
• The program was meeting the original intent of Congress
• The program should be made permanent
• The program could be made more efficient.
Given the scrutiny that federal income tax expenditures are undergoing, this is an appropriate time to examine whether housing tax credit properties are meeting their financial
obligations and the needs of the markets they serve. While the report was not undertaken
for this particular purpose, the data collected provides most of the information that would
be required to assess the housing tax credit program. To compile and analyze the data
required for the assessment, CohnReznick requested the participation of 40 investment
sponsors and the nation’s largest institutional investors. Nearly every sponsor of housing
tax credit investments, and many of the nation’s largest investors, participated in the
survey. For a complete list of study participants, please refer to Appendix A. With the assistance of Integratec, our affiliated real estate services and software solutions company,
CohnReznick examined data collected from the financial statements of 17,118 apartment
properties. While CohnReznick examined operating data for every housing credit property
without regard to the year in which the property was placed in service, focus was on the
manner in which housing credit properties performed during the economic downturn from
2008 to 2010. For a more extensive discussion of the methodology employed to collect and
analyze property data, please refer to Appendix B.
This report represents an expansion of CohnReznick’s first housing credit property study
that was published in August 2011. A phased approach allowed CohnReznick to supply
much needed recent industry data while still operating within the timeframe necessary to
perform a current yet increasingly rigorous analysis of the data collected. In addition to the
inclusion of 800 additional properties, the enclosed report contains an expanded analysis
of the August 2011 study and provides:
• Additional analysis of the various trends CohnReznick identified in different parts
of the country
• Fundamental reasons why the housing tax credit portfolio exhibited better financial
results in 2008–2010 and why certain properties underperform
• How the performance of housing tax credit funds has improved for the benefit
of investors over the years.
We are grateful to the many firms that supported CohnReznick’s effort in promoting a deeper
understanding of the housing tax credit program, its strengths, opportunities for improvement
and the critical role the program plays in the development of affordable housing.
CohnReznick LLP
December 2012
A CohnReznick Report | 1
Report Restrictions
The initial report on the performance of low-income housing tax credit properties (“The
Low-Income Housing Tax Credit Program at Year 25: A Current Look at its Performance”)
was published by Reznick Group, P.C. in August 2011. In October 2012, Reznick Group,
P.C. combined with J.H. Cohn, LLP to form CohnReznick, LLP, which is now the 11th largest
accounting, tax and advisory firm in the United States. Accordingly, this follow-up report is
issued by CohnReznick, LLP.
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 warrant 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
Table of Contents
Chapter 1: Executive Summary..
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7
Chapter 2: Portfolio Performance. . . . . . . . . . . . . . . . . . .
2.1. Physical Occupancy. . . . . . . . . . . . . .
2.2. Debt Coverage Ratio. . . . . . . . . . . . .
2.3. Per-Unit Cash Flow. . . . . . . . . . . . . . . .
2.4. Possible Explanations for Improved
Financial Metrics. . . . . . . . . . . . . . . . . .
. . . . . . . . . . . . . . . . 13
. . . . . . . . . . . . . . . . 15
. . . . . . . . . . . . . . . . 16
. . . . . . . . . . . . . . . . 17
. . . . . . . . . . . . . . . . 18
Chapter 3: Portfolio Performance by Segments.. . . . . . . . . . . . . . . . . . .
3.1. Segmentation Analysis – by Property Age. . . . . . . .
3.2. Segmentation Analysis – by Property Size . . . . . . . .
3.3. Segmentation Analysis – by Investment Type. . . . .
3.4. Segmentation Analysis – by Credit Type. . . . . . . . . .
3.5. Segmentation Analysis – by Development Type.. .
3.6. Segmentation Analysis – by Tenancy Type.. . . . . . .
3.7. Geographic Segmentation Analysis . . . . . . . . . . . . .
3.7.1 Geographic Segmentation Analysis – by Region. .
3.7.2. Geographic Segmentation Analysis – by State.. . .
3.7.3 Geographic Segmentation Analysis
– by Top 10 MSAs. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Chapter 4:
Nonperforming Properties. . . . . . . . . . . . . . . .
4.1. Operating Underperformance. . . . .
4.1.1. Underperformance in 2010. . . . . . . .
4.1.2. Historical Trend (2008–2010). . . . . . . .
4.1.3. Chronic Underperformance. . . . . . .
4.1.4. Magnitude of Underperformance. .
4.1.5. Underperformance – by State.. . . . .
4.2. Foreclosure. . . . . . . . . . . . . . . . . . . . . . .
4.3. Technical Underperformance. . . . . .
Chapter 5:
Fund Investment Performance. . . . . . . . . .
5.1. Introduction. . . . . . . . . . . . . . . . . . . . .
5.2. Fund Yields. . . . . . . . . . . . . . . . . . . . . .
5.3. Yield Variance Analysis. . . . . . . . . . .
5.4. Housing Credit Variance Analysis. .
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Chapter 6:
Portfolio Composition. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
6.1. Portfolio Composition – by Property Age. . . . . . .
6.2. Portfolio Composition – by Property Size. . . . . . . .
6.3. Portfolio Composition – by Investment Type. . . .
6.4. Portfolio Composition – by Credit Type. . . . . . . . .
6.5. Portfolio Composition – by Development Type..
6.6. Portfolio Composition – by Tenancy Type.. . . . . .
6.7. Portfolio Composition – by Region. . . . . . . . . . . . .
6.8. Portfolio Composition – by State. . . . . . . . . . . . . . .
6.9. Portfolio Composition – by MSA. . . . . . . . . . . . . . .
Appendices:. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Appendix A. Acknowledgments. . . . . . . . . . . . . . . . . . . . .
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. . . . . . . .
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Index of Figures
Figure 2.0.1 Overall Portfolio Composition.. . . . . . . . . . . . . . . . . . . . . . . . . . .
Overall Portfolio Performance (2008–2010). . . . . . . . . . . . . . . . . .
2.0.3 Overall Portfolio Performance (2008–2010)
– Comparison of August 2011 and Current Report. . . . . . . . . . . .
2.1 Median Physical Occupancy (2008–2010). . . . . . . . . . . . . . . . . .
2.2 Median Debt Coverage Ratio (2008–2010).. . . . . . . . . . . . . . . . .
2.3 Median Per-Unit Cash Flow (2008–2010). . . . . . . . . . . . . . . . . . . .
2.4.1 Net Equity Price by Year Placed in Service. . . . . . . . . . . . . . . . . .
2.4.2 Net Equity Price vs. Hard Debt Ratio by Year
Placed-in-Service – 9% Housing Tax Credit Properties. . . . . . . . . .
2.4.3 Net Equity Price vs. Hard Debt Ratio by Year
Placed-in-Service – 4% Housing Tax Credit Properties. . . . . . . . . .
2.4.4 Hypothetical Housing Tax Credit Project
Debt Service Calculation. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
3.1.1(a) 2010 Median Physical Occupancy by Year Placed-in-Service. . .
3.1.1(b) 2010 Median Debt Coverage Ratio by Year Placed-in-Service. . .
3.1.1(c) 2010 Median Per Unit Cash Flow by Year Placed-in-Service. . . . .
3.1.1(D) 2008/2009 Median Debt Coverage Ratio
by Year Placed-in-Service. . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
3.1.1(E) 2008/2009 Median Per Unit Cash Flow by Year Placed-in-Service.
3.2 Operating Performance by Project Size. . . . . . . . . . . . . . . . . . . .
3.3 Operating Performance by Investment Type. . . . . . . . . . . . . . . .
3.4 Operating Performance by Credit Type. . . . . . . . . . . . . . . . . . . .
3.5 Operating Performance by Development Type. . . . . . . . . . . . . .
3.6 Operating Performance by Tenancy Type. . . . . . . . . . . . . . . . . .
3.7.1 Portfolio Distribution by Region. . . . . . . . . . . . . . . . . . . . . . . . . . .
3.7.1(a) Operating Performance by Region. . . . . . . . . . . . . . . . . . . . . . .
3.7.1(b) 2010 Median Physical Occupancy by Region. . . . . . . . . . . . . . .
3.7.1(c) 2010 Median Debt Coverage Ratio by Region . . . . . . . . . . . . . .
3.7.1(d) 2010 Median Per Unit Cash Flow by Region. . . . . . . . . . . . . . . . .
3.7.2(a) 2010 Median Physical Occupancy by State. . . . . . . . . . . . . . . . .
3.7.2(b) 2010 Median Debt Coverage Ratio by State. . . . . . . . . . . . . . . .
3.7.2(c) 2010 Median Per Unit Cash Flow by State. . . . . . . . . . . . . . . . . . .
3.7.3 Operating Performance by Top 10 MSAs. . . . . . . . . . . . . . . . . . .
4.1.1 2010 Underperformance. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
4.1.1(a) 2010 Per Unit Cash Flow Performance by Property Size. . . . . . . . .
4.1.2 Underperformance (2008–2010).. . . . . . . . . . . . . . . . . . . . . . . . .
4.1.3 Chronic Underperformance.. . . . . . . . . . . . . . . . . . . . . . . . . . . .
4.1.4(a) Distribution of 2010 Physical Occupancy. . . . . . . . . . . . . . . . . . .
4.1.4(b) Distribution of 2010 Debt Coverage Ratio.. . . . . . . . . . . . . . . . . .
4.1.4(c) Distribution of 2010 Per Unit Cash Flow. . . . . . . . . . . . . . . . . . . . .
4.1.4(D) Operating Deficit Funding Sources for 2010 Property Deficits. . . .
4.1.5(a) 2010 Occupancy Underperformance by State
(Percent Below 90% Physical Occupancy). . . . . . . . . . . . . . . . . .
4.1.5(b) 2010 Debt Coverage Ratio Underperformance
by State (Percent Below 1.00 DCR). . . . . . . . . . . . . . . . . . . . . . .
Figure 2.0.2 Figure
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Figure 4.1.5(c) Figure
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2010 Per Unit Cash Flow Underperformance
by State (Percent Below $0 Per Unit Cash Flow). . . . . . . . . . .
4.1.5(d) Chronic Occupancy Underperformance by State
(Below 90% Occupancy in All Three Years 2008–2010). . . . . .
4.1.5(e) Chronic DCR Underperformance by State
(Below 1.00 DCR in All Three Years 2008–2010). . . . . . . . . . . .
4.1.5(f) Chronic Cash Flow Underperformance by State (Below
$0 Per Unit Cash Flow in All Three Years 2008–2010). . . . . . . . .
4.2.1 Cumulative Foreclosure Rate by Year.. . . . . . . . . . . . . . . . . .
5.1.1 Total Surveyed Gross Equity by Fund Type (Post 1994 Funds). .
5.2(a) Gross Equity Price vs. Fund Yield by Year. . . . . . . . . . . . . . . .
5.2(b) Surveyed Housing Tax Credit Fund Yield vs. 10-Year
Treasury Security Rate (after tax equivalent). . . . . . . . . . . . .
5.3 Fund Yield Variance by Year. . . . . . . . . . . . . . . . . . . . . . . . .
5.4.1 Housing Credit Delivery Variance by Investment Type. . . . . .
5.4.2 Initial Years’ Housing Credit Delivery Variance
by Year Fund Closed. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
6.1 Net Equity by Year Placed-in-Service. . . . . . . . . . . . . . . . . . .
6.1(a) Percent Net Equity by Property Age
(Years since Placed-in-Service, as of 12/31/2010). . . . . . . . . .
6.2 Average Project Size by Year Placed in Service. . . . . . . . . . .
6.2(a) Average Project Size by Net Equity, Credit Type
and Year Placed-in-Service. . . . . . . . . . . . . . . . . . . . . . . . . .
6.3 Percent Net Equity by Investment Type. . . . . . . . . . . . . . . . .
6.4 Percent Net Equity by Credit Type. . . . . . . . . . . . . . . . . . . . .
6.5 Percent Net Equity by Development Type. . . . . . . . . . . . . . .
6.6 Percent Net Equity by Tenancy Type. . . . . . . . . . . . . . . . . . .
6.7 Portfolio Composition by Region. . . . . . . . . . . . . . . . . . . . . .
6.7(a) Percent Net Equity by Region. . . . . . . . . . . . . . . . . . . . . . . .
6.7(b) Average Project Size by Region. . . . . . . . . . . . . . . . . . . . . . .
6.8 Percent Net Equity by State. . . . . . . . . . . . . . . . . . . . . . . . . .
6.9 Net Equity Concentration among Top 10 MSA’s. . . . . . . . . . .
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Chapter 1:
Executive Summary
Background
T
he Low-Income Housing Tax Credit program
reached the 25th anniversary of its enactment
in 2011. Adopted in the midst of dramatic
changes to the Internal Revenue Code in 1986,
the program has since enjoyed a strong level of
bipartisan support in the United States Congress.
Following are some of the many features that
make the housing tax credit program unique.
• The cost of the housing tax credit program to the federal government is fixed and
determinable by statute. The program is subject to a volume limit that permits its cost,
unlike most tax expenditures, to be calculated with precision, thus ensuring that it cannot
become a “runaway” government program.
• Housing tax credits are divided among the states based on their respective populations. The
determination of which projects are to be awarded housing credit allocations is made by
state housing credit agencies 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 established by the states rather than by the federal government.
• For the last 15 years, the demand for housing tax credits has exceeded supply almost
every year. This imbalance between the supply and demand for housing credits has
resulted in a highly efficient use of tax credit dollars as a tool to finance the construction
of new affordable housing and the rehabilitation of older affordable housing complexes.
• Over the course of the past decade, the occupancy level in housing tax credit properties has consistently been approximately 96%. Given the normal turnover of rental units,
this means that housing credit properties are effectively fully occupied. An inventory of
25,000+ fully occupied properties is directly attributable to the number of U.S. households
that are “rent burdened.” The traditional measure for severe rent burden is when more
than 50% of household income is required for housing. In 2010, 20.2 million U.S. households
fell into this category.1
• In addition to the housing credit program, there are other federal housing programs
designed to maintain the affordability of rents for low-income tenants. The housing credit
program is unique because it functions as a capital subsidy to stimulate the production
of new affordable housing and represents a public-private partnership.
The housing tax credit program has been, by any measure, a resounding success. A
number of previous studies of the housing tax credit program have documented the
program’s favorable record of:
• Serving income-qualified tenants at restricted rents
• Operating with an exceedingly low number of properties being lost to foreclosure
• Maintaining high levels of occupancy.
1
Source: Joint Center for Housing Studies of Harvard University. “The State of the Nation’s Housing 2012.”
The housing tax credit program has developed a strong track record for delivering quality
housing to low-income families, meeting the expectations of institutional investors and
maintaining a cumulative foreclosure rate that is less than 1%, which is more fully described
in the report. State housing credit agencies are statutorily obligated to award only enough
housing tax credits to make potential developments financially feasible, and the allocators
have been effective at ensuring that projects to which they award housing credits have
not been overfinanced. With statutory rent restrictions constraining the income potential
of housing credit projects, one consequence of this statutory obligation is that housing tax
credit properties are underwritten with very little margin for error in generating sufficient
net operating income. Accordingly, when operating expenses are higher than projected
or when rents are marginally lower than expected, housing tax credit properties may
produce just enough or slightly less cash flow than is needed to service their mortgage
debt. In recent years, as much as 35% of all housing tax credit properties have operated
below break-even, albeit often by fairly small amounts.2 At the same time, studies have
confirmed that the rate of foreclosure for housing tax credit properties has been very low in
the aggregate. The apparent contradiction between these two data points – break-even
or slightly below break-even net operating income in conjunction with an overall low foreclosure rate – is discussed in this report.
The tension between these two economic realities has left some investors with the impression that housing credit investments are riskier than was previously understood. The
concern held by some parties with respect to this issue has been further exacerbated by
the challenges to the national economy occasioned by the near meltdown of the financial services sector and the negative economic developments that followed it. Addressing
the question of whether the number of struggling housing credit properties may have
escalated and whether the incidence of foreclosures has increased in recent years was a
central objective of CohnReznick’s August 2011 report. Our analysis of the data suggests
that there has been no material deterioration in housing credit property performance and
that certain operating performance metrics significantly improved from 2008 to 2010.
Survey Findings
CohnReznick achieved strong industry participation in its efforts to compile operating data
for this report. Yielding a 95% overall response rate, 32 organizations chose to participate
in our August 2011 study and an additional six participated in this study. All analytics have
been updated to include the additional property data from the new participants.
Cumulatively, study respondents provided CohnReznick with operating data for 17,118
housing credit properties, 90% of which achieved stabilized operations as of December 31,
2010. On average, stabilized properties in the surveyed pool CohnReznick analyzed had
operated for approximately seven years as of December 31, 2010. This pool represents
approximately $73 billion in housing tax credits approximately $62 billion in equity contribution from investors to finance property development.3
2
3
Source: Ernst & Young. “Understanding the Dynamics V.”
The net equity and credit figures are slightly understated in value and slightly mismatched as a result of missing data from either
or both data fields. We estimate that the Housing Credit Net Equity figure may be understated by approximately $1 billion, and
the Total Housing Credits figure may be understated by approximately $1.7 billion.
Our analysis of housing credit property performance is based on the three most important
metrics for measuring property operations:
• Physical occupancy
• Debt coverage ratio
• Per-unit net cash flow.
The August 2011 study included data for 16,356 properties; the current report contains data
for 17,118 properties, representing an increase of 762 properties (4.5% of the total pool). In
comparison to CohnReznick’s August 2011 study, the expansion in the number of properties
included in our data sample had rather limited impact on the change in overall industry
performance. We attribute this limited impact to the large scale of the August 2011 data
sample, which provided a solid statistical basis to support the findings. The limited impact
of increasing the data sample is also partly attributed to the fact that trends identified in
the August 2011 study appear to be common across individual participants’ respective
property portfolios.
CohnReznick reports the following operating results from the data collected from respondents:
• Housing tax credit properties typically require economic occupancy of at least 87–89%
(or physical occupancy of approximately 89-91%) to attain break-even operations. In
recent years, the median occupancy in housing credit properties has reliably been
approximately 96%. Notwithstanding the national recession and sharp increase in unemployment, median occupancy in housing credit properties was 96.4%, 96.3% and 96.6%
in 2008, 2009 and 2010, respectively. As previously noted, high occupancy rates are
another indicator of the tremendous imbalance between the increasing demand and
short supply of affordable housing properties. Many survey respondents we interviewed
following publication of our August 2011 report noted that unfavorable economic conditions led to enlarged tenant bases across properties in their affordable housing portfolios.
• The median debt coverage ratio (DCR) for housing tax credit properties has hovered
between 1.13 and 1.15 for a significant portion of the past decade. The data indicate that
debt coverage ratios climbed from these levels to 1.21 in 2009 and 1.24 in 2010. Similar to
positive trends in the DCR, annual net cash flow per apartment unit was $250 in 2008, $341
in 2009 and reached $419 in 2010. As more fully described in Section 2.4, CohnReznick
made significant efforts aimed at answering the question “What made this improvement
possible?” including designing quantitative analysis to test our suppositions and interviewing industry experts to draw on their experience. Out of many possible explanations
for the growth in debt coverage ratios and cash flow, we point out two important factors:
better expense underwriting practices and a more favorable mix of debt to equity, which
characterizes most of the properties developed over the past five years.
• CohnReznick prepared further analyses of the manner in which metric variances are
distributed by segmenting properties according to construction type, property age and
property size, in addition to other segmentation categories. CohnReznick has not observed
material differences in operating results based on segmentation comparisons. Historically,
the most significant difference in property performance has been attributed to certain
geographical areas, particularly the Midwest. As a result, this report focuses on the influence that geography has on property performance by region, state and metropolitan
statistical area (MSA). CohnReznick found through careful analysis of the performance
data that low-income housing tax credit properties in certain areas tend to have more
favorable operating histories than others. Nonetheless, with rare exceptions, properties
in virtually all markets collectively showed improved financial performance from 2008 to
2010. In fact, 2010 is the first year in which every one of the 50 states reported a median
debt coverage ratio of greater than 1.00 for the entire state. However, certain markets
remain fragile and report a disproportionately larger share of both underperforming properties and persistent operating deficits. In addition to regional and state-level performance
metrics, the report includes performance data generated for MSAs for which a meaningful
sample size could be obtained. Operating data at the MSA level show more volatility
because of a smaller sample size. Though CohnReznick is making the study’s MSA data
available, we caution readers to refrain from drawing conclusions about an MSA based
solely on the enclosed MSA information. We encourage readers to contact CohnReznick
professionals to assess the data provided in the appropriate context. Operating performance by MSA is included as Appendix F.
• In addition to studying operating performance for the entire cohort of surveyed properties, CohnReznick adhered to current industry practice for isolating “performing” as
opposed to “underperforming” properties. We identify properties as underperforming
when they report occupancy levels below 90% and/or debt coverage ratios below 1.0.
It was within the group of underperforming properties that CohnReznick observed the
most significant change in operating results from 2008 to 2010.
The subset of underperforming properties that report occupancy challenges has been
perhaps the most volatile of the data points we analyzed on a year-to-year basis. Before
2008, the percentage of properties reporting below 90% occupancy (on a net equity
versus property count basis) ranged from a low of 11.5% to a high of 18%. However,
the percentage of properties reporting below 90% occupancy dropped to 11.9% in
2008, increased slightly to 12.6% in 2009 and decreased to 9.5% in 2010. There are
certain markets, particularly in parts of the Midwest, where the inherent rent advantage
between rents charged by housing tax credit versus market-rate properties has been
nominal. CohnReznick has observed continued chronic occupancy challenges in some
of these markets. In a few such states, over 20% of the housing credit portfolio operated
with physical occupancy below 90% during 2010, representing more than twice the
national percentage.
Paralleling the overall increase in occupancy in 2010, the percentage of properties
reporting negative cash flow and/or negative debt coverage actually decreased from
2008 to 2010. The percentage of properties operating below break-even, which has traditionally been the statistic of greatest concern for investors, has historically been as high as
35%; based on the data collected, this percentage was 33.4% in 2008, 27.8% in 2009 and
24.7% in 2010. The decrease in properties operating below break-even from 2008 to 2010
is clearly a favorable trend, all the more so because it was achieved during an economic
downturn. As one might expect, in those states where an above-average number of
properties report occupancy challenges, an above-average number of properties report
negative cash flow. For instance, while housing credit portfolios in Georgia and Indiana
both followed the national trend of improved financial performance, more than 40% of
their respective surveyed portfolios incurred operating deficits in 2010. We note, however,
that no state reported a median DCR of below 1.0 in 2010.
The vast majority of housing tax credit properties that slip into one of the underperforming categories do so for just a year and return to profitable operation in the
following year. Properties that report occupancy and cash flow challenges for three or
more consecutive years (characterized as “chronic” underperformers) are therefore
fairly unusual. In some cases, these properties are unable to struggle back to breakeven despite changing property management companies, funding large deficits for
multiple years or trying to restructure property debt. These properties are deemed to
have “structural” deficits because of serious physical plant issues, high area crime rates
or similar issues that cannot easily be corrected.
Measured against the total pool of underperforming properties, CohnReznick’s data
suggest that the percentage of properties reporting chronic underperformance for
each consecutive year from 2008 to 2010 was 13.9% for negative debt coverage and
just 3.9% for those properties with occupancy rates below 90%. There is an apparent
contradiction between the percentage of properties reporting deficits in comparison to
the remarkably low foreclosure rate for housing tax credit properties. However, because
underperforming properties tend to underperform for only short periods of time and
because deficits tend not to be significant in most underperforming properties, it is easier
to understand why the cumulative foreclosure rate remains less than 1%.
• Historically, a great deal of attention has been given to the relatively small number
of housing tax credit properties foreclosed upon by their lenders. It appears that this
particular data point may have been understated, in part because some of the larger
syndicators were using their own capital to support troubled properties in order to avoid
foreclosure. This practice became less prevalent in the years from 2002 to 2006, when
investor equity became relatively easy to obtain. As a result, the rate of foreclosures
in housing tax credit properties has increased in small increments in recent years. The
respondents CohnReznick surveyed reported that 98 of the total property count of 17,118
experienced foreclosure through the end of 2010, an aggregate foreclosure rate of
0.57% measured by property count. Approximately 50% of the stated foreclosures were
reported to have occurred between 2008 and 2010. Thus, although operating performance generally improved, the rate of foreclosure from 2008 to 2010 still increased,
suggesting that challenging economic conditions may have disproportionately affected
chronically underperforming properties during those years.
Clearly, the number of foreclosures has been underreported as a result of incomplete
data. Over the past 10 years, a minimum of eight syndication firms closed operations or
became inactive. CohnReznick believes, on the basis of anecdotal evidence, that some
of those firms experienced a disproportionately higher incidence of foreclosures. We
were not able to pinpoint the number of foreclosed properties syndicated by these firms,
nor were we able to ascertain the total number of properties that had been syndicated
by these firms. As a result, any attempt to estimate the impact that the property portfolios syndicated by these firms might have on the industry foreclosure rate would require
speculation on our part. Rather than abandon the methodology CohnReznick adopted
to undertake this study, and compromise its results, we have confined the scope of our
observations to the results we received from study respondents.
Based upon respondents’ data, while the number and rate of foreclosures increased
incrementally from 2008 to 2010, the incidence of foreclosures in housing tax credit
properties continued to compare very favorably with the foreclosure rate of market
rate multifamily properties and other real estate asset groups. Based on the data we
collected, tax credit properties were foreclosed, on average, just shy of year 11 of the
15-year compliance period. As such, while foreclosure is a catastrophic event, the
financial impact to investors is much less significant than it is to the property’s lenders.
• Virtually all surveyed properties have been syndicated to investors through one of
several types of investment funds: direct, proprietary, multi-investor and others. In addition to operational data for property-level investments, survey respondents were asked
to supply CohnReznick with performance data for every low-income housing tax credit
fund that they syndicated to date. This analysis was intended to assess the track record
of low-income housing tax credit funds in terms of delivering the originally projected
yield and housing credits to investors. On a weighted average basis, survey respondents
reported a positive 6.0% variance in meeting yield targets, i.e., the actual yield was 6%
higher than the projected yield at investment closing. That being said, the composition
of yield is as, if not more, important than the yield figure alone. This is discussed in greater
detail in Chapter 5 of the report.
• Consistent with CohnReznick’s industry experience, the relative variance in housing
credit delivery versus the projected credits over the entire 10-year housing credit period
tends to be very small. While developers and syndicators tend to overestimate timing of
credit delivery, the data suggest that syndicators have become more accurate in their
forecasts of tax credit timing than they were in the housing credit program’s early years.
Whether improved DCR and cash flow 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.
CohnReznick is committed to conducting similar studies periodically in order to supply the
industry with current and reliable data.
Chapter 2:
Portfolio Performance
C
ohnReznick solicited data from 40 currently 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 organizations chose to participate in the August 2011 study, and
an additional six participated in the current study, resulting in a 95% overall
response rate (see Appendix A). In an effort to avoid the administrative
burden of reconciling property investments held in shared portfolios, we
collected only direct investment and fund investment performance data
from investor participants. All data was provided by the respondents to
CohnReznick on a voluntary and strictly confidential basis.
This report 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. After adjusting for property investments where equity investments in the same property were held in multiple funds, the data
gathered to support the August 2011 report represented 16,356 housing tax credit properties. An additional 762 properties have expanded the dataset for a total of 17,118 housing
tax credit properties represented in this report. We believe 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 100% of all housing tax credit properties is largely attributable to investments made by
defunct syndicators, and properties that have reached the expiration of their respective
compliance periods and subsequently “cycled out” of the program.
As can be observed in Figure 2.0.1, the 17,118 properties in CohnReznick’s data sample
collectively represent approximately $62 billion in net equity investments and approximately $73 billion in housing tax credits.
Of the 17,118 properties, 15,399 (90%) reached “stabilized operations” as of December
31, 2010. We define stabilized operations as properties that have completed construction, achieved 100% tax credit qualified occupancy (i.e., all of the tax credit units have
been occupied by income-eligible tenants) and the property has closed its permanent
financing. While the definition of stabilized operations differs slightly among industry participants, CohnReznick defines stabilized operations using the industry’s consensus definition
and does not believe these slight differences are significant enough to distort our analysis.
As can be observed in Figure 2.0.1, the 15,399 stabilized properties collectively represent
approximately 83% of the survey sample on a housing credit net equity basis. Per property,
stabilized investments included in this report averaged 72.4 apartment units, $3.4 million in
net equity investment and $3.9 million in total housing credits.
Overall Portfolio Composition
Figure 2.0.1
Survey Total
Number of Properties
Stabilized Properties
% of Stabilized
17,118
15,399
90.0%
1,264,353
1,114,928
88.2%
Housing Credit Net Equity
$62,363,416,612
$51,711,320,179
82.9%
Total Housing Credits
$73,155,492,616
$60,255,560,568
82.4%
Number of Units
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 within a property
• 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 amount of cash flow generated by each property after
deducting debt service payments and required replacement reserve contributions
• Incidence of noncompliance
• Incidence of foreclosure.
This chapter summarizes the 2008–2010 operating performance data of the 15,399 stabilized properties. Figure 2.0.2 summarizes 2008–2010 operating results measured by median
physical occupancy, DCR and per-unit cash flow data for the entire stabilized portfolio.
While physical occupancy remained consistently strong from 2008 to 2010, DCR and perunit cash flow trended upward in 2008, 2009 and 2010.
Overall Portfolio Performance (2008–2010)
2008
Figure 2.0.2
2009
2010
Median Physical Occupancy
96.4%
96.3%
96.6%
Median Debt Coverage Ratio
1.15
1.21
1.24
Median Per Unit Cash Flow
$250
$341
$419
As previously discussed, the expansion of CohnReznick’s dataset from the August 2011
to the current report has had a limited impact on the overall industry performance data
because of the sheer size of the August 2011 report. However, for purposes of comparison,
Figure 2.0.3 below represents the data differences between the two reports.
Overall Portfolio Performance (2008–2010)
– Comparison of August 2011 and Current Report
Median Physical
Occupancy
Median Debt
Coverage Ratio
2008
2009
2010
2008
2009
August 2011 report
96.4%
96.3%
96.6%
1.15
1.19
Current report
96.4%
96.3%
96.6%
1.15
1.21
2010
Figure 2.0.3
Median Per Unit
Cash Flow
2008
2009
2010
1.24
$246
$335
$412
1.24
$250
$341
$419
2.1. 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 resulting 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.
Figure 2.1 summarizes the median physical occupancy data for the stabilized properties CohnReznick surveyed for calendar years 2008 through 2010. The data suggest that,
notwithstanding the recent recession, the troubled housing sector and increased unemployment, median occupancy remained consistently robust, with only minor fluctuations
from year to year. The 2008 median occupancy rate of 96.4% decreased slightly to 96.3%
in 2009 but rebounded and subsequently increased to 96.6% in 2010.
Median Physical Occupancy (2008–2010)
2008
Median Physical Occupancy
Figure 2.1
2009
96.4%
2010
96.3%
96.6%
In contrast to the housing credit portfolio, the U.S. Census Bureau recently published data
suggesting that conventional multifamily properties were negatively impacted by the
recession. The Bureau reported that the national multifamily rental vacancy rate climbed
from 9.6% in the fourth quarter of 2007 to 10.1% in the fourth quarter of 2008 and increased
further to 10.7% in 2009 before returning to the pre-recession level of 9.4% during the fourth
quarter of 2010.4 The reasons for the difference in occupancy levels are numerous, but the
major driver of the consistently high occupancy rates in housing tax credit properties is
that the United States simply does not have enough low-income housing units to satisfy the
national demand for affordable housing. In fact, the recent downturn in the economy may
have created a more pressing need for low-income housing than ever before. During this
downturn, housing tax credit property production contracted to half its pre-recession level
as a result of the halving of the housing credit market. In a recently published report, the
National Low Income Housing Coalition estimated the deficit of rental units that are both
affordable and available for extremely low-income households (those earning up to 30% of
area median income [AMI]), to be 6.8 million units in 2010.5
We note that only physical occupancy data have been presented in this report. Economic
occupancy, while meaningful, is not monitored by a significant portion of data providers
and was thus excluded from the survey. Furthermore, 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.
2.2. 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 a debt
coverage ratio of at least 1.15 (the industry standard) before agreeing to retire a property’s
construction loan and extend long-term permanent financing. In addition, some lenders
require higher coverage ratios for properties demonstrating lower real estate quality.
4
5
Source: U.S. Census Bureau American Housing Survey. http://www.census.gov/hhes/www/housing/hvs.
Source: National Low Income Housing Coalition. http://nlihc.org/sites/default/files/HousingSpotlight2-1.pdf.
The properties CohnReznick surveyed experienced a steady increase in DCR from 2008
to 2010, at a pace that was more pronounced than the positive trend in occupancy
rates. In 2008, median DCR was 1.15, which is consistent with previous studies based on
various industry sources6 and coincides with the current industry standard. Furthermore, the
median DCR increased to 1.21 in 2009 and increased 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.
Median Debt Coverage Ratio (2008–2010)
2008
Median Debt Coverage Ratio
Figure 2.2
2009
1.15
2010
1.21
1.24
The improvement in 2008–2010 DCR was pervasive. The same positive trend was identified across virtually every state, property type and financing type and was reflected in the
data supplied by almost all individual data providers. Enterprise Community Investment,
Inc. (Enterprise), one of the nation’s largest nonprofit housing credit syndicators and a
participant in our study, published a November 2010 report examining the operating
performance of 1,545 housing tax credit properties in its own portfolio.7 Enterprise’s report
concluded that, “Whether using the weighted average method (calculating one DCR for
the entire aggregate portfolio) or portfolio median, DCR results were consistently in the 1.05
to 1.12 range between 2004 and 2008. In 2009, there was an 8% increase in DCR to 1.20.”
2.3. 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 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 debt coverage.
In the same way that DCRs improved from 2008 to 2010, the data suggest that median
cash flow per unit increased year over year from 2008 to 2010. Since 2002, cash flow after
paying hard debt service was minimal averaging between $200 and $250 per unit per
annum (PUPA), or $20 per unit per month. However, the 2008 median cash flow per unit of
$250 increased to $341 in 2009 and to $419 in 2010. As previously noted, while these annual
increases may appear dramatic, they represent growth in net income per apartment of
less than $10 per month.
S ource: Ernst & Young. “Understanding the Dynamics V,” reporting the median DCR of 1.14 for 12,064 housing credit properties
in calendar year 2006.
7
Source: Enterprise Community Investment, Inc. “Asset Management LIHTC Portfolio Trends Analysis – November 2010.”
6
Median Per Unit Cash Flow (2008–2010)
2008
Median Per Unit Cash Flow
Figure 2.3
2009
$250
2010
$341
$419
Along with improved financial and operating performance, the incidence of underperforming properties with respect to per-unit cash flow decreased consistently from 2008 to
2010. The most significant improvement was the lower percentage of housing tax credit
properties operating below break-even. Based on the data collected, the percentage of
properties operating below breakeven was 33.7% in 2008, 28.3% in 2009 and 25.2% in 2010.
2.4. Possible Explanations for Improved Financial Metrics
Since publishing the August 2011 report, CohnReznick committed to answering the question
“how is this improvement possible?” Accordingly, CohnReznick interviewed industry experts
to draw on their experience and designed quantitative analyses to test possible factors that
point to the improvement in performance metrics of housing tax credit properties.
There are many possible factors that could have contributed to improved operating and
financial performance of a typical housing tax credit property. These factors include:
• Higher rental rates
• Lower occupancy turnover or collection losses
• Lower hard debt service levels
• Lower than projected operating expenses or better expense underwriting practices.
However, none of these factors can be singled out as a principal or overriding source for
improved operations. Of the various causes explored, CohnReznick found that more efficient expense underwriting and more favorable debt-to-equity ratios are the two primary
contributors to improved performance.
Lower hard debt service: In CohnReznick’s August 2011 report, we speculated that the
marked increase in the number of properties carrying lower leverage was an important
contributing factor to improved DCRs and cash flow.
As housing tax credit prices have trended upward, the overall portfolio reflects an
increasing number of properties that have been financed with little to no hard debt. This
is not surprising, since a more favorable equity-to-debt mix is a direct result of higher tax
credit pricing.
Figure 2.4.1. illustrates the evolution of net equity pricing over the past 20 years, measured
by the amount of capital investors committed to, in accordance with a pre-negotiated
pay-in schedule, in order to receive one dollar of tax credit. At the inception of the
housing credit program, equity was raised principally from relatively small investments
by individual investors through public offerings. Beginning in 1992 and 1993, a corporate
equity market began to develop, and the housing tax credit program was made permanent in 1993. Institutional investors began to understand the asset class, and syndicators
quickly came to realize that raising capital from institutional investors was a more efficient way to raise equity. At the national level, housing tax credits initially traded at net
prices as low as $0.50 per dollar of credit, steadily increased to $0.85 per dollar of credit in
2004 and skyrocketed to close to $1.00 at the height of the equity market between 2006
and 2007. However, the exit of Fannie Mae and Freddie Mac and a precipitous decline
in the profitability of the largest financial services companies resulted in a meltdown of
the housing credit equity market. As a direct consequence, housing tax credit prices fell
sharply to $0.70-$0.75 before increasing gradually again at the end of 2010 and in earnest
in 2011.
Net Equity Price by Year Placed-in-Service
Figure 2.4.1
housing credit net equity price
$1.00
$0.90
$0.80
$0.70
$0.60
$0.50
$0.40
1991
1993
1992
1995
1994
1997
1996
1999
1998
2001
2000
2003
2002
2005
2004
2007
2006
2009
2008
2010
Readers should note the following:
• Housing tax credit prices presented in Figure 2.4.1 are described as the “net equity
price” because they reflect the direct amount of equity per dollar of credit that will be
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 in Figure 2.4.1 naturally reflect a 1- to 2-year lag in market price.
• Finally, while the housing tax credit prices in Figure 2.4.1 are median prices reported
by survey respondents, a price disparity as wide as 35 cents between properties in
certain markets can be observed based on whether the property is located within
the Community Reinvestment Act (CRA) assessment area where one or more investors compete for investment in the same property. The CRA and its effect on housing
tax credit pricing will be analyzed in a separate report that will be published in the first
quarter of 2013.
Figures 2.4.2 and 2.4.3 illustrate the impact of higher tax credit pricing on the debt-toequity mix (expressed as a hard debt ratio) of housing tax credit properties placed in
service between 1997 and 2011. The right Y-axis shows the median hard debt ratio of the
surveyed housing tax credit properties, while the left Y-axis shows the median housing tax
credit price for properties placed in service within the same time period. We presented the
4% housing tax credit properties separately from 9% housing tax credit properties because
4% properties tend to be much more heavily leveraged than 9% properties.
Though not perfect, a strong inverse relationship exists between a given property’s tax
credit price and its level of hard debt. Using 9% tax credit properties as an example, in the
late 1990s when median housing tax credit equity pricing was in the mid- to high 70 cents
range, one-third of the permanent financing of housing tax credit properties was provided
by conventional “hard” debt. In recent years, as housing tax credit prices began to benefit
from much lower leverage, only one-fifth of permanent financing is made up of hard debt.
Figure 2.4.2
$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%
5.0%
$0.65
1997
1998
1999
2000
2001
2002
2003
Net Equity Price
2004
2005
2006
2007
Hard Debt Ratio
2008
2009
2010
Hard Debt ratio
Net Equity Price vs. Hard Debt Ratio by Year
Placed-in-Service – 9% Housing Tax Credit Properties
Figure 2.4.3
$1.00
70.0%
$0.95
60.0%
$0.90
50.0%
$0.85
40.0%
$0.80
30.0%
$0.75
20.0%
$0.70
10.0%
Hard Debt ratio
Net Equity Price vs. Hard Debt Ratio by Year
Placed-in-Service – 4% Housing Tax Credit Properties
0.0%
$0.65
1997
1998
1999
2000
2001
2002
2003
Net Equity Price
2004
2005
2006
2007
2008
2009
2010
Hard Debt Ratio
The following hypothetical example illustrates the scale of the potential impact on debt
coverage from lower debt burdens using the following assumptions:
• Number of units = 72
• Per unit total development cost = $200,000
• Total development cost = $14,400,000
• Property bears a conventional first mortgage equal to 18% of cost (which is the median
hard debt for surveyed projects placed in service in 2009).
Hypothetical Housing Tax Credit Project
Debt Service Calculation
Figure 2.4.4
Scenario A
(33% leveraged)
Total development costs
Hard debt ratio
Total hard debt
Scenario B
(18% leveraged)
$14,400,000
33%
Interest rate
$4,752,000
$14,400,000
18%
6.50%
$2,592,000
6.50%
Annual debt service
$360,430
$196,598
Per unit per month debt service
$417
$228
Compared with the hard debt burden typical of a housing credit property developed in
1997 (33% leverage – Scenario A), the reduction in debt service (Scenario B) equates to a
substantial debt service savings of $189 per unit per month.
It is worth noting that by 2010, greater than 34% of the surveyed properties were placed
in service within the previous five years. As a group, these properties are clearly benefiting
from lower levels of hard debt.
Notwithstanding the data reflecting the favorable effect of lower leverage, a property’s
hard debt ratio has, as a single statistic, little bearing on a property’s overall performance.
Indeed, cash flow levels are often considerably higher in larger properties financed with
tax-exempt bonds and 4% tax credits. The average 4% property in our survey has 116
apartments units, almost twice the size of the average 9% property with 59 units. Smaller
properties have fewer units over which to distribute their fixed costs. As a result, they are
more sensitive to debt levels and perform more predictably with lower levels of debt.
Operating expenses: Reduced insurance premiums and lower utility bills resulting from
individual metering and/or energy efficiencies are two frequently cited reasons for unexpected operating expense savings in recent years. One of the nation’s largest syndicators,
and a participant in this study, believes “the increase in DCR is a result of both revenue and
expense improvements.”8 Across this syndicator’s portfolio, median total revenue increased
4.5% from 2008 to 2009, relative to the 2% underwritten rate, while expenses increased only
2.2% relative to the 3% underwritten rate. Given the combination of higher-than-underwritten rental income and lower-than-underwritten operating expenses, the syndicator’s
portfolio realized a 9.2% increase in net operating income. The syndicator explained that
expense savings were most notable in insurance (decrease of 6.4%), real estate taxes
(decrease of 5.1%) and utilities (increase of only 1.6%) between 2008 and 2009. While the
types of cost savings realized in the syndicator’s portfolio are consistent with other industry
participants, we searched for additional confirmation of this observation and found that
the National Apartment Association (NAA) reported that in the subsidized housing subset,
average operating expenses of $4,441 per unit in 2008 fell to $4,319 per unit in 2009 and
rose to $4,856 per unit in 2010. The NAA has an extensive database of multifamily properties consisting of 1.1 million units and does an annual survey of operating expenses.9 The
NAA database includes 60,326 units identified as “subsidized affordable housing units.”
Unfortunately, there is no way to break this subset down further into housing credit properties versus properties developed under pre-housing credit subsidy programs (Secs. 8, 221
(d)(4), 236, etc.). Nonetheless, NAA’s survey and findings are useful for corroborating our
own analysis.
Perhaps more important than lower operating expenses, CohnReznick’s industry experience and interviews with survey respondents led us to believe that the housing tax credit
industry, as a whole, has come a long way in improving its underwriting of operating
expenses. Syndicators, for instance, indicated that the availability of benchmarked data
from their own portfolios, state credit allocation agencies and industry data providers have
helped them improve their expense underwriting.
8
9
Source: National Apartment Association. “2011 Survey of Operating Income & Expenses in Rental Apartment Communities.”
Other contributing factors: Lenders of permanent financing for housing tax credit properties
typically require a 1.15 to 1.20 DCR at conversion of the loan from a construction loan to a
permanent loan. During the recent equity market meltdown, when the housing credit industry
was able to attract only half of its prerecession level of equity, lenders and investors began to
require more stringent underwriting terms, including a DCR of 1.20 to 1.25 and more favorable
guarantee and reserve protections. Accordingly, while a 1.24 median DCR may seem high,
higher DCRs suggests a greater ability to generated projected cash flow needs.
Many industry participants believed that favorable interest rates and the abundance of subsidies, like the Section 1602 program made available in the last few years, played a part in the
improvement of property financial performance. However, the full impact of many of these
newer programs cannot yet be measured with operating performance data, as the properties developed under these programs have only just begun to achieve stabilized operations.
Factors found to have minimal to no bearing on improved financial metrics: While high
physical occupancy is an indicator of the demand for affordable housing, it is not an
influential one with respect to the 2008-2010 improvement in performance metrics, as
occupancy remained within a very narrow band from 96% to 96.9% over the past decade.
Higher rental rates: Because of the sheer volume, CohnReznick did not attempt to collect
rent rate data. However, participants’ senior asset management staff were interviewed
from a cross-section of data providers regarding their portfolio’s ability to achieve rent
increases during the survey years.
Survey respondents concurred that the decrease in workforce employment, the decreasing
homeownership rate and an overriding sense of financial instability led to an enlarged renter
pool and increased competition for rental housing from 2008–2010. However, none of the
interviewed respondents observed a direct correlation between higher demand and rent
increases over the last few years. In some markets, affordable housing properties reduced
rents to guard against pricing pressure from market rate rentals or condominium conversions.
In other markets, affordable housing properties experienced less pricing pressure because of
decreased competition from homeownership, and thus either eliminated concession offerings or implemented rent increases without jeopardizing occupancy.
Lower occupancy turnover and collection losses: Because a significant portion of survey
respondents do not track economic occupancy or have such data readily available,
CohnReznick collected only physical occupancy data. After an initial analysis of the
occupancy data and other metrics, we speculated that turnover rates and turnoverrelated costs might have decreased. Interestingly, upon further research and interviews
with industry participants, this has not proven to be the case. While tenant retention efforts
coupled with declining homeownership rates, particularly among first-time homebuyers,
may have resulted in reduced turnover levels in select properties, many asset managers
reported that turnover rates had increased for a variety of reasons, including tenants losing
or switching jobs and tenants moving more frequently to reap the benefits of a property’s
first month’s concessions in a practice referred to as “concession shopping.”
We lack a basis for predicting whether the improvement in property operations is a trend
likely to sustain itself or is a short-term phenomenon. However, since the data suggest that
the improvements in expense underwriting and the favorable impact of lower leverage are
more clearly visible in “younger” properties, it is reasonable to assume that more favorable debt coverage ratios will be maintained for the foreseeable future. CohnReznick is
committed to conducting similar studies periodically to supply the industry with current and
reliable data.
Chapter 3:
Portfolio Performance
by Segments
H
ousing credit investors and lenders frequently
question whether property investments
in certain geographical areas, construction
types, tenancy types, financing types or other
segmented criteria tend to perform better
than others. This chapter reviews the 2008–2010
trends in occupancy, debt coverage and cash
flow according to property age, property size
and other attributes. However, the single most
important expansion from the segmentation
analysis in CohnReznick’s August 2011 report is the geographic
segmentation performance data we herein present by region, state and
metropolitan statistical area.
3.1. Segmentation Analysis – by Property Age
The following graphs illustrate how the 2010 operating and financial performance data
may have differed based on the year in which a property was originally placed in service.
CohnReznick chose not to present data for properties placed in service during 2009 and
2010 due to the relatively small size of the stabilized sample during the aforementioned
years. For purposes of this report, we have used “placed-in-service” date and “property
age” interchangeably.
Based on Figure 3.1.1(A) below, occupancy by property age is clustered within the 95.5%
to 97.5% range, indicating that property age has not been a material driver of occupancy
rates as the difference of this range is minimal.
2010 Median
physical Occupancy
2010 Median Physical Occupancy
by Year Placed-in-Service
Figure 3.1.1(a)
98%
97%
96%
95%
1990
1992
1994
1996
1998
2000
2002
2004
2006
2008
Surveyed properties display a wider spread in DCR and per-unit cash flow along the age
spectrum; however, CohnReznick observes that there is no linear relationship suggesting
that older properties tend to underperform newer ones financially.
2010 Median Debt Coverage Ratio
Figure 3.1.1(B)
2010 Median Debt Coverage Ratio
1.50
1.40
1.30
1.20
1.10
1.00
1990
1992
1994
1996
1998
2000
2002
2004
2010 Median Per Unit Cash Flow
2006
2008
Figure 3.1.1(C)
2010 Median per unit cash flow
$700
$600
$500
$400
$300
$200
1990
1992
1994
1996
1998
2000
2002
2004
2006
2008
In general, housing tax credit properties placed in service during the past five years generated greater than average 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. A survey respondent noted in its report that “another factor in the DCR improvement is that the newer projects entering the portfolio are larger in size and have healthier net
operating income (NOI) than the portfolio as a whole.”10 Further, some respondents noted
that the positive improvement in DCRs and cash flows may be attributed to the fact that
there tends to be fewer “surprises” in the first several 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). Some respondents believe that as properties age, deferred maintenance
could become a compounded issue over time and cause decreased DCRs and cash flow.
10
The following two figures illustrate that stronger financial performance metrics were
observed in nearly every age group of surveyed properties from 2008 to 2009.
2008/2009 Median Debt Coverage Ratio
Figure 3.1.1(d)
2008/2009 Median
debt coverage ratio
1.45
1.35
1.25
1.15
1.05
1990
1992
1994
1996
1998
2009 DCR
2000
2002
2004
2008 DCR
2008/2009 Median Per Unit Cash Flow
2008/2009 Median Per Unit Cash Flow
2006
Figure 3.1.1(e)
$600
$500
$400
$300
$200
$100
1990
1992
1994
1996
2009 Per Unit Cash Flow
1998
2000
2002
2004
2006
2008 Per Unit Cash Flow
3.2. Segmentation Analysis – by Property Size
Figure 3.2 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 are composed of 72 apartment units per property. Those
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.
Operating Performance by Project Size
Number of
Apartment units
per property
Median Physical
Occupancy
Figure 3.2
Median Debt
Coverage Ratio
2008
2009
2010
2008
2009
0–25
96.7%
96.2%
96.7%
1.16
1.21
26–50
96.6%
96.5%
96.7%
1.16
51–100
96.6%
96.8%
97.0%
101–200
96.0%
96.0%
201–300
95.0%
301 or more
Overall
2010
Median Per Unit
Cash Flow
2008
2009
2010
1.28
$199
$271
$371
1.22
1.24
$221
$303
$354
1.16
1.21
1.24
$286
$395
$460
96.1%
1.14
1.20
1.24
$357
$490
$590
94.6%
95.0%
1.11
1.15
1.19
$256
$344
$498
95.0%
95.0%
95.4%
1.15
1.17
1.19
$258
$440
$490
96.4%
96.3%
96.6%
1.15
1.21
1.24
$250
$341
$419
3.3. Segmentation Analysis – by Investment Type
Figure 3.3 summarizes the operating performance data for stabilized properties segmented
by the source of a property’s equity financing, e.g., those acquired by public funds versus
multi-investor funds, and so forth. As noted in Chapter 6, multi-investor fund investments
account for the majority of the portfolio covered by this report. Consistent with the overall
data findings, properties syndicated through multi-investor funds reported an increase in
median occupancy during 2009 and 2010, at 96.4% to 96.6%, respectively. Based on our
findings, property investments held by proprietary funds slightly outperformed the overall
portfolio by the single indicator of occupancy, reflecting the highest 2010 median occupancy of 96.8% among all investment types. In contrast, public funds, consisting of older
property investments, reported a somewhat lower 2010 median occupancy rate of 95.8%.
Both DCR and per-unit cash flow levels for properties acquired by multi-investor funds
mirrored the overall trend as well. However, with slight variations from year to year, direct
investments tend to generate debt coverage and cash flow levels that are slightly lower
than the overall median. We suspect that this is attributable to the fact that the average
size of properties acquired directly is somewhat smaller than average. Other than this
distinction, the differences in operating performance from one investment type to another
are relatively immaterial.
Operating Performance by Investment Type
Median Physical
Occupancy
Median Debt
Coverage Ratio
2008
2009
2010
Direct
95.8%
95.7%
96.2%
1.08
1.12
Multi-investor
96.4%
96.4%
96.6%
1.15
Proprietary
96.8%
96.5%
96.8%
Public
95.8%
95.8%
Overall
96.4%
96.3%
investment Type
2008
2009
2010
Figure 3.3
Median Per Unit
Cash Flow
2008
2009
2010
1.18
$114
$247
$347
1.21
1.25
$240
$345
$420
1.16
1.20
1.24
$262
$352
$414
95.8%
1.16
1.18
1.30
$232
$212
$340
96.6%
1.15
1.21
1.24
$250
$341
$419
Fund-level performance metrics, measured in terms of yield and credit delivery variances,
are discussed in Chapter 5 of this report.
3.4. Segmentation Analysis – by Credit Type
The data reflected in Figure 3.4 summarize the operating performance data for stabilized
properties segmented by credit type. Data providers were presented with the options
to classify the tax credit type for each property by 9% versus 4% housing tax credits and
subsequently further separate the 9% credit properties into two subcategories: 9% new
construction properties and 4% & 9% acquisition and rehabilitation properties. However,
many respondents did not represent that their properties were classified as “4% & 9%” property; 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.
As shown below, the median occupancy rate for stabilized 9% credit properties was on par
with 4% credit properties from 2008 to 2010.
We have not observed meaningful differences between the operating performance of
4% versus 9% properties in terms of DCR. However, 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 apartments. The surveyed 4%
properties averaged 116 units per property while the 9% surveyed properties averaged 59
units per property.
Operating Performance by Credit Type
Median Physical
Occupancy
Median Debt
Coverage Ratio
2008
2009
2010
4% Tax Credits
96.5%
96.3%
96.7%
1.14
1.19
9% Tax Credits
96.5%
96.4%
96.6%
1.15
Overall
96.4%
96.3%
96.6%
1.15
Credit Type
Figure 3.4
2008
2009
2010
Median Per Unit
Cash Flow
2008
2009
2010
1.23
$318
$404
$506
1.21
1.25
$220
$329
$399
1.21
1.24
$250
$341
$419
3.5. Segmentation Analysis – by Development Type
Stabilized new construction properties account for the majority of the properties we
surveyed. As might be expected, based on historical results, newly constructed properties consistently reported stronger operating performance among all development types,
followed by rehabilitated properties and, finally, historic rehabilitation properties (i.e.,
properties qualifying for both housing and historic rehabilitation credits). Historic rehabilitation properties, which account for 480 properties, reported median occupancy of 95.9% in
2010, representing the lowest among all development types. Per-unit cash flow generated
by historic rehabilitation properties was also substantially below average. 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 are often slower to lease and, because their physical plants are less
efficient, tend to experience higher operating and maintenance costs.
Operating Performance by Development Type
Median Physical
Occupancy
Median Debt
Coverage Ratio
Development Type
2008
2009
2010
Historic Rehab
95.7%
95.0%
95.9%
1.05
1.15
1.16
New Construction
96.7%
96.4%
96.8%
1.16
1.20
Rehab
96.0%
96.2%
96.4%
1.16
Mixed
96.0%
95.4%
96.1%
Overall
96.4%
96.3%
96.6%
2008
2009
2010
Figure 3.5
Median Per Unit
Cash Flow
2008
2009
2010
$0
$129
$121
1.23
$273
$334
$422
1.22
1.27
$238
$379
$447
0.88
1.11
1.08
$(113)
$211
$251
1.15
1.21
1.24
$250
$341
$419
3.6. Segmentation Analysis – 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: seniors-only
properties (25% of the total) outperformed the overall portfolio consecutively across 2008 to
2010 and consistently 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 they 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 have dedicated staff managing these properties.
Operating Performance by Tenancy Type
Median Physical
Occupancy
Median Debt
Coverage Ratio
2008
2009
2010
Family
96.0%
96.0%
96.0%
1.14
1.18
Senior
97.7%
97.4%
97.5%
1.20
Special Needs
97.0%
97.0%
97.0%
Other
96.1%
96.4%
Overall
96.4%
96.3%
Tenancy Type
Figure 3.6
2008
2009
2010
Median Per Unit
Cash Flow
2008
2009
2010
1.22
$227
$314
$400
1.27
1.29
$300
$414
$458
1.32
1.37
1.45
$397
$529
$551
96.7%
1.17
1.23
1.22
$111
$271
$310
96.6%
1.15
1.21
1.24
$250
$341
$419
3.7. Geographic Segmentation Analysis
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. A well-conceived development can
succeed nearly anywhere; however, CohnReznick found through careful analysis of the
performance data that low-income housing tax credit properties in certain areas have a
more favorable operating history than others. Nonetheless, with rare exceptions, properties
in virtually all markets showed improved financial performance during the survey period.
In section 3.7 we present property operating data detailed by:
• Twelve regional areas we selected
• State
• The top ten metropolitan statistical areas, ranked by number of properties represented
in the survey.
We further present in Appendix D the property operating and financial performance data
organized by MSA where a meaningful sample size could be obtained. 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.
An analysis of property operating performance by location classification, i.e., urban,
suburban and rural, was conducted but not included in the report. We chose to exclude
this particular analysis from the report because of the lack of consistency among data
providers in how they define and apply location classification terms to their own portfolios.
3.7.1. Geographic Segmentation Analysis – 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. The regions are as follows:
Portfolio Distribution by Region
Region Number
Constituent States
Figure 3.7.1
% of stabilized
portfolio
Region 1
CA, OR, WA
Region 2
AK, HI
0.3%
Region 3
ID, MT, WY
1.3%
Region 4
AZ, CO, NM, NV, UT
4.7%
Region 5
MN, ND, SD
3.4%
Region 6
IA, KS, NE, MO
7.0%
Region 7
IN, IL, MI, OH, WI
Region 8
AR, OK, TX
Region 9
AL, FL, GA, LA, MS
10.6%
Region 10
KY, NC, SC, TN, VA, WV
11.2%
Region 11
CT, DC, DE, MA, MD, ME, NH, NJ, NY, PA, RI, VT
22.4%
Region 12
GU, PR, VI
<0.1%
14.4%
16.0%
8.0%
Figure 3.7.1(A) illustrates the 2008 to 2010 operating performance (occupancy, DCR and
per-unit cash flow) data for stabilized properties in the surveyed portfolio segmented by
region, in descending order of the sample size of housing credit properties.
Operating Performance by Region
Median Physical
Occupancy
Figure 3.7.1(A)
Median Debt
Coverage Ratio
2008
2009
2010
2008
2009
Region 12
99.6%
99.7%
99.4%
1.19
1.23
Region 11
97.0%
97.1%
97.2%
1.19
Region 10
96.4%
96.3%
97.0%
Region 9
95.2%
94.8%
Region 8
95.8%
Region 7
2010
Median Per Unit
Cash Flow
2008
2009
2010
1.23
$438
$485
$521
1.22
1.34
$238
$420
$511
1.15
1.14
1.24
$209
$328
$402
95.6%
1.16
1.06
1.20
$187
$221
$292
95.0%
95.8%
1.16
1.06
1.22
$218
$302
$362
95.3%
95.5%
95.8%
1.02
1.09
1.16
$42
$201
$304
Region 6
95.0%
95.3%
95.8%
1.13
1.12
1.18
$226
$233
$250
Region 5
96.8%
96.7%
97.1%
1.20
1.22
1.33
$470
$614
$604
Region 4
96.5%
96.0%
96.7%
1.17
1.14
1.26
$375
$444
$548
Region 3
95.4%
94.8%
95.0%
1.04
1.00
1.13
$130
$162
$264
Region 2
96.7%
96.8%
97.0%
1.21
1.28
1.30
$735
$1,169
$959
Region 1
97.6%
97.0%
97.4%
1.28
1.27
1.32
$615
$663
$658
Overall
96.4%
96.3%
96.6%
1.15
1.21
1.24
$250
$341
$419
Region 12: GU, PR, VI; Region 11: CT, DC, DE, MA, MD, ME, NH, NJ, NY, PA, RI, VT; Region 10: KY, NC, SC, TN, VA, WV;
Region 9: AL, FL, GA, LA, MS; Region 8: AR, OK, TX; Region 7: IN, IL, MI, OH, WI; Region 6: IA, KS, NE, MO; Region 5: MN,
ND, SD; Region 4: AZ, CO, NM, NV, UT; Region 3: ID, MT, WY; Region 2: AK, HI; Region 1: CA, OR, WA.
Seven of the 12 regions reported 2010 median occupancy rates that were higher than the
96.6% 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 101
stabilized properties in this region reported near 100% median occupancy, a rate significantly more favorable than the other regional occupancy rates and greater than the
national median. 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 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.
Given the disproportionately smaller size of properties in Region 3, it is not surprising that it
reported the lowest median occupancy of 95%, as a few vacant units can have a more
drastic impact on the occupancy rates of smaller relative to larger properties.
Figures 3.7.1(B) – (D) illustrate each region’s 2010 median occupancy rate, DCR and perunit cash flow on a national map. Regions are colored such that each performance range
is indicated with a different color.
2010 Median Physical Occupancy by Region
95.0%
Figure 3.7.1(B)
97.1%
97.2%
97.4%
95.9%
95.8%
97.0%
96.7%
95.8%
95.7% and
below
96.6%
95.7%
and below 95.8% to
95.8%
to 96.6%
96.7%
to 97.0%
96.7%
to 97.0%
95.6%
97.1% to
97.1%
to 97.4%
97.4%
97.4%
above
97.4%
andand
above
Not surprisingly, regions with median occupancy rates that were greater than the national
portfolio median are the same ones that report more favorable financial performance,
measured by each region’s respective median debt coverage ratio and per unit cash flow.
Properties located on the East and West Coast, 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. The Southeast and Midwest
states reported occupancy rates that were slightly below the portfolio median.
Region 7 and Region 3 had the least favorable DCRs, although both made significant
improvements in the last few years and are closer to the nationwide median DCR. In 2008,
housing credit properties located in Regions 3 and 7 operated just above DCR break-even
on a regional level. In 2010, the two regions reported improved median DCRs of 1.13 and
1.16, respectively.
2010 Median Debt Coverage Ratio by Region
1.13
Figure 3.7.1(C)
1.34
1.33
1.32
1.16
1.18
1.24
1.26
1.22
1.71 and 1.71
below
and below
1.18 to 1.19
1.18 to 1.19
1.20 to
1.25
1.20
to 1.25
1.2
1.26
1.26toto1.30
1.30
1.30
and
above
1.30
and
above
Across the national housing tax credit portfolio, the 2008 median per-apartment-unit cash
flow was $250 and is generally consistent with what it has been for most of the previous
decade.11 In 2010, the national overall median per-unit cash flow was $419, reflecting
a significant increase from prior years. While per-unit cash flow growth was common
throughout the regions from 2008 to 2010, this trend was most pronounced in the two
11
Source: Ernst & Young. “Understanding the Dynamics V,” citing the trend of DCR over the last decade.
traditionally underperforming regions (Regions 3 and 7), measured solely by DCR and cash
flow. Surveyed housing tax credit properties in Region 7 reported having generated $42 of
median per-unit cash flow during 2008, $201 during 2009 and $304 during 2010. Similarly,
properties in Region 3 reported having generated $130 of median per-unit cash flow during
2008, $162 during 2009 and $264 during 2010. Consistently reporting high levels of cash flow,
Regions 1 and 2 continued to do so during the survey period and reported 2010 median
per-unit cash flows in excess of $500.
2010 Median Per Unit Cash Flow by Region
$264
Figure 3.7.1(D)
$604
$511
$658
$304
$250
$402
$548
$362
$0 to $100
$0 to $100
$101
to $250
$101
to $250
$251 to
to $500
$500
$251
$292
$501
to $1,000
$501
to $1,000
$1,001
and above
$1,001
and above
3.7.2. Geographic Segmentation Analysis – by State
CohnReznick further segmented operating data for the surveyed properties according to
their location in the 50 states, Puerto Rico, the U.S. Virgin Islands and Guam. California, New
York, Texas, Florida and Illinois collectively accounted for more than 42% of the overall portfolio based on the volume of equity investment represented by surveyed properties in the
five states. Properties located in South Dakota, Hawaii, Delaware, Guam and the U.S. Virgin
Islands are the bottom five states in terms of overall net equity investment, representing less
than 1% of the overall portfolio.
Figure 3.7.2(A) illustrates each state’s 2010 median occupancy rate. The states were
grouped and color-coded based on each state’s median occupancy percentage. As
previously discussed, occupancy generally decreased slightly between 2008 and 2009 and
increased in 2010.
2010 Median Physical Occupancy by State
Figure 3.7.2(A)
WA
ME
MT
ND
MN
OR
WI
SD
ID
MI
WY
PA
IA
NE
NV
IL
UT
CA
CO
NH
VT
MA
RI
CT
NY
MO
KS
NJ
DE
MD
DC
OH
IN
WV
VA
KY
NC
TN
AZ
OK
NM
SC
AR
MS
TX
AL
GA
LA
FL
90.0% and90.0%
belowand below 90.1% to90.1%
95.6%to 95.6%
95.61%
to 96.7%
95.61%
to 96.7%
96.71%
96.71%to
to99.0%
99.0%
99.1%
and
above
99.1%
and
above
Hawaii and the U.S. Virgin Islands reported median 2010 occupancy in excess of 99%. Note,
however, that this result may be skewed by the relatively small sample size of less than 15
properties. The next–highest-performing territory was Puerto Rico, which reported 2010
median occupancy of 99.7% for the 88 properties in the pool.
New York, New Jersey and California, which cumulatively total more than 3,200 stabilized properties and more than $15 billion in net equity, reported occupancy equal to or
greater than 97.5% in 2010. These states had a significant impact on the overall national
occupancy rate, and may be significantly responsible for the increase in 2010 median
occupancy. Were these states removed from the national portfolio, the 2010 median
occupancy rate would decrease from 96.6% to 96.1%.
Figure 3.7.2(B) illustrates each state’s 2010 median debt coverage ratio. The majority of
states reported 2010 median DCRs between 1.15 and 1.30. All states reported 2010 median
DCR in excess of 1.10 except for Georgia and Idaho, which reported statewide median
debt coverage ratios of 1.05 and 1.04, respectively.
Of particular significance is that 2010 is the only year of the three-year survey period in
which none of the 50 states, Puerto Rico, Guam or the U.S. Virgin Islands reported overall
median DCR of less than 1.00. In 2008, three states – Idaho, Indiana and Ohio – reported
below break-even median DCRs ranging from 0.85 to 0.99, followed by two states whose
median DCRs were just above 1.00. In 2010, Indiana and Ohio properties operated with
median DCRs in excess of 1.13, and Idaho had a 1.04 median DCR. Based on our analysis
of the data, DCRs in all states appear to be improving, and the fact that no states were
operating below break-even in 2010 is an encouraging sign for the entire low-income
housing tax credit industry. Whether the same level of positive performance can be
sustained in the future remains to be seen.
2010 Median Debt Coverage Ratio by State
Figure 3.7.2(B)
WA
ME
MT
ND
MN
OR
WI
SD
ID
MI
WY
PA
IA
NE
NV
IL
UT
CA
CO
MO
KS
OH
IN
WV
VA
KY
NC
TN
AZ
OK
NM
SC
AR
MS
TX
AL
GA
LA
FL
1.00 or below
1.00 or below
1.011.01
to 1.10
to 1.10
NH
VT
MA
RI
CT
NY
to 1.20
1.111.11
to 1.20
1.21 to 1.30 1.21 to 1.30
1.31 and above
1.31 and above
NJ
DE
MD
DC
Hawaii, Guam and the U.S. Virgin Islands each had 2010 median per-unit cash flow of
greater than $1,400. This is not a surprising result, as properties in these states reported near
100% median occupancy in each of the survey years. Idaho, Georgia and Connecticut
had median cash flow per unit of less than $200 and were the three lowest-performing
states, measured solely by per-unit cash flow. Despite the general trend of improvement
that coincides with the overall portfolio trend from 2008 to 2010, these three states’ cash
flows were less than half of the overall portfolio median.
2010 Median Per Unit Cash Flow by State
Figure 3.7.2(C)
WA
ME
MT
ND
MN
OR
WI
SD
ID
MI
WY
NV
PA
IA
NE
IL
UT
CA
CO
NH
VT
MA
RI
CT
NY
KS
MO
NJ
DE
MD
DC
OH
IN
WV
VA
KY
NC
TN
AZ
OK
NM
SC
AR
MS
TX
AL
GA
LA
FL
$0 to $100
$0 to $100
$101$101
to $250
to $250
$251
$251to
to$500
$500
$501
$1000
$501
to to
$1,000
$1001
above
$1,001
andand
above
3.7.3. Geographic Segmentation Analysis – by Top 10 MSAs
Figure 3.7.3 summarizes the operating performance data for stabilized properties
segmented by the top 10 MSAs, which were selected based on the aggregate net equity
invested in the 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 10 MSAs collectively represent 31.9%
of the total stabilized net equity surveyed.
Nearly all top 10 MSAs, apart from Miami and Detroit, had median occupancy rates that
were consistent with or greater than the national portfolio median. As previously discussed,
occupancy rates decreased slightly from 2008 to 2009 and increased in 2010 while
remaining consistently strong within a very narrow band of 96% to 97%.
Apart from Philadelphia, Miami and Detroit, the top 10 MSAs reported median debt
coverage ratios that were in line with or greater than the national portfolio medians.
Operating Performance by Top 10 MSAs
Median Physical
Occupancy
Figure 3.7.3
Median Debt
Coverage Ratio
2008
2009
2010
2008
2009
2010
New York-Northern
New Jersey-Long
Island, NY-NJ-PA
97.4%
97.7%
97.8%
1.21
1.44
1.46
$251
$557
$675
Los Angeles-Long
Beach-Santa Ana, CA
98.0%
97.5%
97.9%
1.53
1.52
1.44
$982
$1,028
$1,042
San FranciscoOakland-Fremont, CA
97.2%
97.4%
97.5%
1.19
1.23
1.21
$628
$706
$688
Chicago-JolietNaperville, IL-IN-WI
96.4%
97.0%
96.5%
1.16
1.22
1.24
$294
$418
$531
PhiladelphiaCamden-Wilmington,
PA-NJ-DE-MD
96.7%
97.0%
96.7%
1.09
1.12
1.16
$80
$120
$211
WashingtonArlington-Alexandria,
DC-VA-MD-WV
97.0%
96.5%
96.8%
1.17
1.24
1.25
$581
$736
$764
Miami-Fort
LauderdalePompano Beach, FL
96.7%
95.9%
95.6%
1.17
1.15
1.18
$281
$303
$474
Seattle-TacomaBellevue, WA
97.2%
96.4%
97.0%
1.24
1.23
1.19
$441
$474
$395
Boston-CambridgeQuincy, MA-NH
96.5%
97.0%
97.2%
1.15
1.21
1.25
$378
$639
$723
Detroit-WarrenLivonia, MI
94.0%
94.1%
95.0%
0.85
1.00
0.99
-$275
-$54
-$14
Overall
96.4%
96.3%
96.6%
1.15
1.21
1.24
$250
$341
$419
2008
2009
2010
Median Per Unit
Cash Flow
Detroit’s performance metrics place it at the bottom of the top 10 MSAs. The financial
performance of housing tax credit properties in Detroit, while improving, is still significantly
less favorable than that of the other top 10 MSAs and the nationwide portfolio. The 200
stabilized properties located within the Detroit metropolitan area represent 29.4% of all the
surveyed properties in the State of Michigan. While the Detroit properties account for a
large portion of the Michigan properties in the surveyed portfolio, the Detroit properties are
faring significantly worse than the balance of Michigan’s portfolio. Perhaps more so than
any other major American city, Detroit has felt the effects of the recent national recession
in the form of elevated unemployment rates stemming from decline of its manufacturing
base and overall economy.12 However, the trend between 2008 and 2010 suggests that the
operating performance of housing credit properties located in Detroit has been improving,
albeit at a slower pace than the overall portfolio. While CohnReznick has not collected
2011 and early 2012 performance data, positive signs continue to be observed in the
conventional rental market sector in Detroit. In ranking 44 major metropolitan markets,
Marcus & Millichap identified Detroit as having moved up from the 42nd to 38th position among the top MSAs and projects that employment-generated demand will further
increase occupancy by 60 basis points in 2012 to 95%, accompanied by a 3.2% increase in
effective rents.13
ureau of Labor Statistics: The unemployment rate in Wayne County reached a high point of 18.2% in July 2009 and has since
B
improved, but remained high at 11.0% as of May 2012.
13
Source: Marcus & Millichap. “2012 National Apartment Report.”
12
Chapter 4:
Underperforming Properties
G
iven the tremendous demand and historically high occupancy rates
associated with affordable housing units, CohnReznick is often asked
how such properties can fail. In its effort to provide discussion points
related to the failure rate of affordable housing, CohnReznick analyzed
the data obtained from respondents by isolating a cohort of properties as
“underperforming” versus “performing.” Underperforming properties are
those reporting any 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 impediments versus those that have reported technical impediments.
Operating underperformance refers to instances 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 group
unto themselves and therefore 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 assets meeting certain performance measures so that
“problem” properties can be 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)14 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 difficulties.
14
http://www.ahic.org.
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. Because of the added burden of statutory requirements, housing credit
properties bear higher administrative costs than non-tax-incented real estate counterparts.
Accordingly, a property failing to comply with housing tax credit program requirements is
characterized in the report as a property that is technically underperforming.
There are limitations to CohnReznick’s analysis because, as in most studies preceding it, the
focus is 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 financing feasibility issues or other development-stage challenges. The fact
that some housing tax credit properties underperformed can be attributed to a number
of reasons. Specifically, low occupancy can be attributed to: soft market conditions,
competitive properties in close proximity to the housing credit property, ineffective tenant
screening resulting in high eviction rates and deteriorating property conditions rendering
the property uninhabitable or inferior to its competition. Although this chapter explores the
symptoms of underperformance of housing tax credit properties, diagnosing the underlying causes for underperformance is beyond the scope of this report as the sheer size of
the report’s sample renders a deeper dive infeasible.
4.1. Operating Underperformance
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
results in a more likely loss on investment, while operating volatility may result only in a
temporary drop in occupancy or DCR. In addition, the distribution of underperformance
is an interesting indicator. For instance, assuming all other indicators remain constant, it
would be natural to be concerned about a portfolio where 35% of the properties report
below 1.00 DCR with an average per-unit annual deficit of $100 – in comparison to a portfolio where only 15% of the properties report below 1.00 DCR with annual deficits that are
much higher. In practice, however, the magnitude of operating deficits has proven to be
more important than the number of properties reporting deficits.
4.1.1. Underperformance in 2010
As reflected in Figure 4.1.1, calendar year 2010 operations indicate that 9.5% (measured
by net equity) of the capital invested in stabilized housing tax credit properties operated
at below 90% physical occupancy, 24.6% operated at or below break-even and 24.7%
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, while approximately 9.5% of housing tax credit properties operated below
90% occupancy in 2010, 24.6% failed to achieve break-even operations during the same
period. The aforementioned spread 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.
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). Comparison of the two columns in Figure 4.1.1 indicates that, as expected,
properties with a higher number of units tend to withstand operating challenges more
easily by distributing certain fixed costs among a larger number of units. In addition, equity
investors tend to pay a premium to invest in larger properties, and premium pricing translates to lower levels of debt per apartment.
2010 Underperformance
Figure 4.1.1
% of Net Equity
Below 90% Physical Occupancy
% of Properties
9.5%
12.5%
Below 1.00 DCR
24.6%
27.5%
Below $0 Per Unit Cash Flow
24.7%
27.5%
CohnReznick performed a number of analyses to ascertain whether certain property
characteristics, other than location, tend to have any bearing on high incidence of
underperformance. Of the characteristics CohnReznick tested, other than property size, no
other combinations (such as tenancy, development and credit type) had a material effect
on property performance metrics. As shown in Figure 4.1.1(A), smaller-scale properties
containing 50 or fewer apartment units were found to have a disproportionate share in the
incidence of operating deficits.
2010 Per Unit Cash Flow Performance
by Property Size
Figure 4.1.1(A)
Number of Units
Per Property
301–10,000
201–300
101–200
51–100
26–50
0–25
0%
20%
40%
n % Underperforming
60%
n % Performing
80%
100%
4.1.2. Historical Trend (2008–2010)
Industry observers have expressed concern about the potentially negative effect of
national economic conditions on the health of housing tax credit inventory during the
period from 2008 to 2010. However, the data that CohnReznick collected for 2008 to 2010
consistently suggest that this was not the case. For 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. Consistent with the overall median performance data presented in Chapter
3, the number of underperforming properties decreased for two consecutive years after
2008, reaching what appears to be a historically low level in 2010.
Underperformance (2008–2010)
Figure 4.1.2
% of Net Equity
2008
2009
2010
11.9%
12.6%
9.5%
Below 1.00 DCR
32.2%
27.6%
24.6%
33.4%
27.8%
24.7%
In addition to the factors discussed in Chapter 2.4, a variety of other reasons may explain
why housing tax credit properties fared better than their market-rate counterparts during
this period.
• Demand for affordable housing, which has historically been in short supply, tends to
move in the opposite direction of adverse economic conditions.
• A significant portion of the housing tax credit properties surveyed either benefit from property-based rental assistance or serve tenants who possess rental assistance vouchers. For
these tenants, regardless of the gap between their ability to pay pro forma rents, tenants
are responsible for contributing no more than 30% of their adjusted gross income toward
rent, with some or the entire gap being covered by rental assistance. The more subsidy a
property has, the more insulated it becomes from adverse economic conditions.
• Many housing tax credit properties benefit from below-market interest rates or soft
financing. Properties receiving additional subsidies in the form of below-market rates or
soft financing and increasingly high levels of housing credit equity permit project owners
to charge rents that can be significantly below market rate. While housing tax credit
properties are usually underwritten with relatively small operating cushions, housing tax
credit properties are clearly benefiting from lower debt burdens than their market-rate
competitors and earlier generations of housing tax credit properties.
4.1.3 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 three time periods: 2008-2010, 2009-2010
and 2010. Across the entire portfolio, only 6.1% of properties reported below 90% occupancy during both 2009 and 2010, and an even more modest number, 3.9%, reported
below 90% occupancy consecutively from 2008 to 2010. 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.
Chronic Underperformance
Figure 4.1.3
% of Net Equity
2010
2010 and 2009
2010, 2009 and
2008
9.5%
6.1%
3.9%
Below 1.00 DCR
24.6%
17.3%
13.9%
24.7%
16.9%
13.7%
4.1.4. Magnitude of Underperformance
CohnReznick plotted the distribution of properties reporting underperformance by
occupancy rate, DCR and per-unit cash flow in order to ascertain the magnitude of underperformance. Of the 9.5% properties reporting below 90% occupancy during 2010, 7.6%
are clustered within the 80% to 90% range. Measured by physical occupancy, only 1.9% of
the surveyed stabilized properties were considered extreme underperformers reporting less
than 80% occupancy for 2010.
Distribution of 2010 Physical Occupancy
Figure 4.1.4(A)
60%
49.9%
50%
40%
30%
20%
10%
0%
0.3%
0.4%
1.2%
< 60%
60%
to 69%
70%
to 79%
1.8%
80%
to 84%
21.0%
19.7%
90%
to 94%
95%
to 97%
5.7%
85%
to 89%
> 97%
Occupancy Ranges
A significant indicator of the magnitude of underperforming affordable housing properties
is evidenced by the fact that less than 1% of housing tax credit properties placed in service
have been lost to foreclosure. The low risk of foreclosure, given the fragile nature 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. While 25% of surveyed housing credit properties experienced negative cash flow in
2010, as shown in Figure 4.1.3, these properties tended to recover financially fairly quickly, as
temporary issues are managed by their owner-operators. Given the fact that only 14% of the
surveyed properties report cash flow deficits that CohnReznick regards as material (i.e., more
than $400 per unit), it appears that in most cases the property’s developer managed deficits
through a combination of withdrawal from reserves, fee deferrals, short-term suspension of
replacement reserve deposits and loans to the property under guarantee obligations. On
rare occasions, syndicators call upon investors to make additional capital contributions.
Distribution of 2010 Debt Coverage Ratio
Figure 4.1.4(B)
60%
50.5%
50%
40%
30%
20%
10%
0%
14.6%
8.2%
< .50
1.1%
2.0%
2.6%
.50
to .59
.60
to .69
.70
to .79
4.6%
6.1%
.80
to .89
.90
to .99
1.00
to 1.14
10.3%
1.15
to 1.25
> 1.25
DCR Ranges
Distribution of 2010 Per Unit Cash Flow
Figure 4.1.4(C)
70%
65.6%
60%
50%
40%
30%
20%
14.0%
10%
0%
< -$400
4.6%
6.2%
-$400 to -$199
-$200 to -$1
9.6%
$0 to $200
> $200
Per Unit Cash Flow Ranges
A subset of respondents provided information about deficit funding. Respondents were
asked to indicate the main sources of deficit funding, as well as identify the main source of
funding. While there are multiple funding sources for any given property, the table below
identifies the most common sources of funding.
Operating Deficit Funding Sources
for 2010 Property Deficits
Figure 4.1.4(D)
8.4%
1.4%
46.4%
■ GP Advance. . . . . . . . . . . . . . 29.1%
■ Lower Tier Reserve. . . . . . . . 46.4%
14.7%
■ Accrual of Fees. . . . . . . . . . . . 8.4%
■ Upper Tier Reserves. . . . . . . . 1.4%
■ Other. . . . . . . . . . . . . . . . . . . . . . 14.7%
29.1%
4.1.5.Underperformance by State
Following is a series of maps illustrating the percentage of 2010 underperformance by state.
2010 Occupancy Underperformance by State
(Percent Below 90% Physical Occupancy)
Figure 4.1.5(A)
WA
ME
MT
ND
MN
OR
WI
SD
ID
MI
WY
PA
IA
NE
NV
IL
UT
CA
CO
NH
VT
MA
RI
CT
NY
MO
KS
OH
IN
WV
VA
NJ
DE
MD
DC
KY
NC
TN
AZ
OK
NM
SC
AR
MS
TX
AL
GA
LA
FL
and below 6.1% to6.1%
to 10.0%
6.0% and 6.0%
below
10.0%
10.1%
to 16.0%
10.1%
to 16.0%
16.1%
16.1%toto60.0%
60.0%
60.1%
and and
above
60.1%
above
2010 Debt Coverage Ratio Underperformance
by State (Percent Below 1.00 DCR)
Figure 4.1.5(B)
WA
ME
MT
ND
MN
OR
WI
SD
ID
MI
WY
PA
IA
NE
NV
CO
WV
MO
KS
OH
IN
IL
UT
CA
NH
VT
MA
RI
CT
NY
VA
NJ
DE
MD
DC
KY
NC
TN
AZ
OK
NM
SC
AR
MS
TX
AL
GA
LA
FL
17.0% and17.0%
23.0%to 23.0%
23.1%
to 29.0%
23.1%
to 29.0%
29.1% to
29.1%
to 35.0%
35.0%
35.1%
above
35.1%
andand
above
2010 Per Unit Cash Flow Underperformance by State
(Percent Below $0 Per Unit Cash Flow)
Figure 4.1.5(c)
WA
ME
MT
ND
MN
OR
WI
SD
ID
MI
WY
PA
IA
NE
NV
IL
UT
CA
CO
NH
VT
MA
RI
CT
NY
MO
KS
NJ
DE
MD
DC
OH
IN
WV
VA
KY
NC
TN
AZ
OK
NM
SC
AR
MS
TX
AL
GA
LA
FL
15.0% and15.0%
22.0% to 22.0%
22.1%
to 28.0%
22.1%
to 28.0%
28.1%
28.1%toto34.0%
34.0%
34.1%
and
above
34.1%
and
above
States where a greater share of properties failed to achieve 90% occupancy also tend to
struggle with a greater share of properties reporting below break-even operations, relative
to the national portfolio. For instance, 24% of Idaho properties and 20% of Michigan properties were less than 90% occupied during 2010. These two states had 36% and 38% of their
respective portfolios reporting less than a 1.0 debt coverage ratio. Properties in a few other
states like Georgia and Indiana, while having slightly less occupancy underperformance
challenges compared to Idaho or Michigan, appear to have more financial underperformance challenges. In both Georgia and Indiana, more than 40% of the survey properties
incurred operating deficits in 2010, though at the state median level none of the 50 states
reported below 1.0 DCR in 2010.
The following three figures present the incidence of underperformance by state in three
consecutive years, 2008-2010. As noted, 3.9% of all surveyed properties that had at least
three years of stabilized operating history reported below 90% occupancy consecutively
from 2008 to 2010. States with the most significant negative occupancy underperformance include Idaho (17.5%), Michigan (12.3%), West Virginia (11.1%) and Georgia
(11.0%). The percentages in the parentheses following each state as well as those in
Figures 4.1.5(D) and (F) represent the incidence of properties that were less than 90%
occupied in each of the three surveyed years, out of the total surveyed properties in
each state that had at least three years of occupancy data.
Chronic Occupancy Underperformance by State
(Below 90% Occupancy in All Three Years 2008–2010)
Figure 4.1.5(d)
WA
ME
MT
ND
MN
OR
SD
ID
MI
WY
PA
IA
NE
NV
IL
UT
CA
CO
NH
VT
MA
RI
CT
NY
WI
MO
KS
OH
IN
WV
VA
NJ
DE
MD
DC
KY
NC
TN
AZ
OK
NM
SC
AR
MS
TX
AL
GA
LA
FL
1.8% and below1.8% and below
1.9% to 3.9% 1.9% to 3.9% 4.0% to 8.0%
4.0% to 8.0%
8.1% to
19.0%
8.1%
to 19.0%
19.1%
andand
above
19.1%
above
Across the surveyed portfolio, nearly 14% of properties incurred operating deficits in all
three years, 2008-2010. Multiyear persistent deficits suggest that the operational challenges
of this subset of properties may be more difficult to correct. Not surprisingly, chronic financial DCR underperformance was most seen in Georgia (23.6%), Idaho (32.3%), Indiana
(28.5%) and Michigan (30.7%).
Chronic DCR Underperformance by State
(Below 1.00 DCR in All Three Years 2008–2010)
Figure 4.1.5(e)
WA
ME
MT
ND
MN
OR
SD
ID
MI
WY
PA
IA
NE
NV
IL
UT
CA
CO
NH
VT
MA
RI
CT
NY
WI
MO
KS
OH
IN
WV
VA
NJ
DE
MD
DC
KY
NC
TN
AZ
OK
NM
SC
AR
MS
TX
AL
GA
LA
FL
6.0% and 6.0%
below
6.1% to 10.0%
and below
6.1% to 10.0%
10.1%
to 18.0%
10.1%
to 28.0%
18.1%
18.1%toto27.0%
34.0%
27.1%
and
above
27.1%
and
above
de
minimus
minimus
desample
size
sample size
Chronic Cash Flow Underperformance by State
(Below $0 Per Unit Cash Flow in All Three Years 2008–2010) Figure 4.1.5(F)
WA
ME
MT
ND
MN
OR
SD
ID
MI
WY
PA
IA
NE
NV
IL
UT
CA
CO
NH
VT
MA
RI
CT
NY
WI
MO
KS
OH
IN
WV
VA
NJ
DE
MD
DC
KY
NC
TN
AZ
OK
NM
SC
AR
MS
TX
AL
GA
LA
FL
7.0% and 7.0%
below
13.0%to 13.0%
and below 7.1% to 7.1%
13.1%
to 19.0%
13.1%
to 19.0%
19.1%
19.1%to
to27.0%
27.0%
27.1%
and
above
27.1%
and
above
4.2. Foreclosure
The most significant investment risk for housing tax credit investors relates to foreclosure. If
the owner of a qualifying housing tax credit project forfeits title to the property because of
foreclosure or by tendering 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 future housing tax credits. Thus, while
foreclosure of housing tax credit properties has been rare, the potential impact to investors can be financially 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 are foreclosed 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 tendered in lieu of
foreclosure. Respondents reported that out of 17,118 properties surveyed, a total of 98 properties were foreclosed and, of that number, almost half were foreclosed during the period
2008–2010. This number translates to an aggregate foreclosure rate of 0.57% 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 since 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 has been the case
for the rest of the industry. However, because we lack precise information concerning the size
and number of foreclosures in such firms’ 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.
CohnReznick plotted the cumulative number of foreclosures on a yearly basis. The year in
which foreclosures occurred was reported for 81 of the 98 foreclosed properties. To derive
the yearly cumulative rate, CohnReznick divided the number of foreclosures through year
end by the total number of properties placed in service on or before the corresponding
year and distributed the “missing 17” properties evenly over the years.
Cumulative Foreclosure Rate by Year
Figure 4.2.1
Cumulative Foreclosure Rate
0.70%
0.60%
0.50%
0.40%
0.30%
0.20%
0.10%
0.00%
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
2007
2008
2009
2010
While the increasing annual rate of foreclosure might be a cause for concern, the foreclosure rate needs to be presented in its proper context.
• CohnReznick’s industry experience leads us to believe that the foreclosure rate reported
in the early years of the program may have been artificially low. This is due, in part, to
the fact that most foreclosures occur toward the latter years of the 15-year housing tax
credit compliance period. The foreclosure rate in the early years of the program might
have also been masked, to some degree, by the propensity of housing tax credit syndicators to support troubled properties until the end of the housing tax credit period or the
point at which investors’ financial losses are minimal.
• Some of the foreclosures reported during 2008-2010 appear to have had more to do
with financially distressed developers than with fundamental weakness in the underlying
properties in question.
• The most recent increase in the incidence of foreclosures from 2008-2010 appears to
have begun to dissipate. CohnReznick notes that, although corresponding information
is not presented in Figure 4.2.1, survey respondents reported a total of five additional
incidences of foreclosures from January 2011 through approximately the second quarter
of 2011. The number of foreclosures in 2011 is not yet fully reported, but would appear to
be substantially lower than the number of foreclosures in 2010.
• Further, while foreclosure can be a catastrophic event for developers and lenders, the
financial losses to investors tend to be much less significant than in a conventional foreclosure. Based on the data collected, tax credit properties were foreclosed, on average,
in year 10.8 of the 15-year compliance period. While a foreclosure results in the loss of
one-third of the housing credits previously claimed in addition to 100% of any future
housing tax credits, tax credit recapture is not the end result, as the developer and guarantors are often obligated to make investors whole pursuant to a recapture guarantee.
As a practical matter, the effectiveness of the guarantee will, in part, depend on the
creditworthiness of the developer and guarantors.
• Finally, CohnReznick notes that, at 0.57%, the rate of foreclosure in housing tax credit
properties is much lower than is the case for any other real estate investment with which
we are familiar.
4.3. Technical Underperformance
In order for housing tax credit properties to maintain their status as qualified housing tax
credit properties, they must be operated in conformity with a number of statutory provisions collectively referred to as the “compliance rules.” While there are numerous provisions
of Internal Revenue Code §42 that govern how the program is regulated, the most significant are the rules around tenant qualification and rent limitations. While a full explanation
of the aforementioned rules is not within the scope of this report, the following provisions
are significant components of IRC §42:
• In order for a prospective tenant to move into a housing tax credit property, the applicant
must present written verification of household income and assets, and household income
cannot exceed 60% of the area median income level for similarly sized households.
• For tenants that meet the income qualification requirements, the property cannot charge
rents that exceed 30% of the statutory maximum level of income based on household size.
Pursuant to IRC §42, income limits and rent restrictions must be carefully monitored by the
state housing credit agencies; consequently, any failure to comply with the program rules
must, if not resolved in an appropriate period of time, be reported to the IRS.
Survey participants reported that only 65 of the surveyed properties incurred a material
level of noncompliance, yielding a nominal rate of 0.5%. However, we caution that, as
is the case with the foreclosure rate analysis, the rate of noncompliance may be understated because of incomplete data.
Chapter 5:
Fund Investment Performance
5.1. Introduction
I
n addition to operational data for property-level investments, survey
respondents were asked to supply CohnReznick with performance data
for every low-income housing tax credit fund that they had syndicated.
The fund-level performance data analysis assesses the track record of
funds comprised of low-income housing tax credit properties in terms of
delivering the originally projected yield and credits to investors.
Generally speaking, an investor contemplating investing in
housing tax credits can choose from one of two investment
approaches: a direct investment or a syndicated investment. The direct investment approach is typically feasible
only for sophisticated investors with internal resources
dedicated to the acquisition, underwriting and asset
management of housing tax credit properties; therefore, this
approach is favored by a minority of institutional investors.
The syndicated investment approach enables investors to
invest in a fund organized and managed by a third-party
intermediary known as a syndicator. There are two primary
investment options when working with a syndicator: proprietary fund and multi-investor
fund investments. In both fund types, 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 investment. The Community Reinvestment Act requires banks to make investments
in areas in which they collect deposits, and they consequently received CRA “credit” for
doing so. Therefore, one of the primary investment motivations for banks is to earn CRA
“credit” for their investments. Proprietary funds are a common investment option for institutions that want to invest their capital in specific locations and negotiate higher yields from
their investments.
The principal advantage of a multi-investor fund is risk diversification. A multi-investor fund
can be composed of 2 to 15 investors, all of whom share risk and rewards based upon their
proportional equity contribution to the fund. Whether the fund has two or many investors,
certain tax credit funds are credit-enhanced either by the syndicator or, more typically,
by a third-party insurance company. An additional advantage of investing in a creditenhanced housing credit investment, other than a guaranteed return, is the availability of
a method of accounting for the investment known as the effective yield 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. Traditionally, approximately 20% of all tax credit investments were structured with a
minimum yield guarantee. More recently, minimum-yield guarantees have become relatively infrequent, largely because of the lack of creditworthy guarantors.
Low-income housing tax credit investors are effectively purchasing a financial asset in the
form of a stream of tax benefits (consisting of passive losses associated with depreciation
and mortgage interest deductions and tax credits). Investors do not anticipate receiving
cash flow distributions, because housing tax credit properties are generally underwritten to
operate at or slightly above break-even and developers or syndicators are generally the
recipients of cash flow surpluses.
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); the investor’s capital account is written off
when the investor exits the partnership, making an investor’s loss neutral in nominal terms.
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.
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. Investors who are
sensitive to the negative impact of losses on financial statement earnings are more inclined
to invest in 9% tax credit properties with low debt leverage and less inclined to invest in 4%
credit tax-exempt bond transactions that are highly leveraged.
Seventeen survey respondents provided data for 902 low-income housing tax credit funds.
For purposes of this analysis, we removed 62 funds that were closed in 1994 or earlier, as
the property investments of these funds were already beyond their 15-year compliance
periods as of the data collection period of this report. Figure 5.1 illustrates the remaining
840 funds that closed in or later than 1995 organized by fund type and segmented by gross
equity and low-income housing tax credits. Note, the average age of the 840 funds is eight
years as of the data collection period of this report.
Total Surveyed Gross Equity by Fund Type
(Post 1994 Funds)
Figure 5.1
3.8%
37.0%
■ Multi-investor. . . . . . . . . . . . . . 59.2%
■ Proprietary.. . . . . . . . . . . . . . . . 37.0%
■ Guaranteed. . . . . . . . . . . . . . . . 3.8%
59.2%
The 398 multi-investor funds in our sample account for 59.2% of the surveyed fund portfolio
gross equity and had an average fund size of $73.2 million of gross equity. The 416 proprietary funds in the pool sample account for 37.0% of the total fund portfolio gross equity,
with an average fund size of $44.1 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 appetite of multiple investors.
5.2. Fund Yields
Figure 5.2(A) illustrates the historical relationship between tax credit pricing and fund investment yields. The chart clearly demonstrates an upward movement in tax credit pricing
with corresponding decrease in yield. In addition, investment yields, as measured by their
after-tax internal rates of return, steadily decreased from the early 1990s through 2006 with
few exceptions.
Figure 5.2(A)
14.0%
$1.20
12.0%
$1.00
10.0%
$0.80
8.0%
$0.60
6.0%
$0.40
4.0%
$0.20
2.0%
Gross Equity Price
fund Yield
Gross Equity Price vs. Fund Yield by Year
$0.00
1995
1997
1996
1999
1998
2001
2000
2003
2002
Annual Median Yield
2005
2004
2007
2006
2009
2008
2010
Gross Equity Price
Several major aftershocks in the housing credit market had the effect of significantly
lowering demand for housing credit investments during 2007–2009. Dramatic financial and
organizational changes within the two largest housing credit investors, Fannie Mae and
Freddie Mac, in 2006 and 2007 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 equity demand from the market
highs observed in 2006–2009.
Figure 5.2(B) 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.
While 2006 and 2007 housing credit fund yields approached Treasury yields, they increased
and subsequently diverted significantly over the next three years.
Surveyed Housing Tax Credit Fund Yield vs.
10-Year Treasury Security Rate (after tax equivalent)
Figure 5.2(B)
14.0%
Fund Yield
12.0%
10.0%
8.0%
6.0%
4.0%
2.0%
1995
1997
1996
1999
1998
2001
2000
2003
2002
A
nnual Median Yield of Surveyed
Housing Tax Credit Funds
2005
2004
2007
2006
2009
2008
2010
0-Year Treasury Yield
1
(after tax equivalent)
5.3. Yield Variance Analysis
Important to consider is 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.
Yield variance measures the difference between the originally projected yield at investment closing and the most current yield projection as of the survey date, generally as
of December 31, 2010. Positive variances indicate 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 5.97% 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 yield calculations. Yield can be maintained
naturally or “manually” by pre-negotiated investment provisions in a number of ways: An
investor can receive a more favorable yield as a result of an underperforming portfolio
generating higher losses; equity pay-in schedules can postpone capital contributions from
investors; and so-called adjustor provisions, under which remaining investor capital contributions are reduced to the extent necessary in order to re-establish the ratio of capital to
benefits, work to ultimately restore the investment’s yield to projected levels.
Figure 5.3 illustrates the yield variances in housing credit funds based on the year in which
the funds were closed.
Fund Yield Variance by Year
Figure 5.3
20.0%
15.0%
Fund Yield Variance
10.0%
5.0%
0.0%
-5.0%
-10.0%
-15.0%
-20.0%
1995
11 Funds
1997
23 Funds
1996
18 Funds
1999
41 Funds
1998
25 Funds
2001
52 Funds
2000
44 Funds
2003
57 Funds
2002
48 Funds
2005
58 Funds
2004
54 Funds
2007
65 Funds
2006
52 Funds
2009
45 Funds
2008
47 Funds
2010
39 Funds
This graph illustrates the fact that 109 of the 680 funds CohnReznick studied reported negative yield variances. We totaled the negative yield variances relative to the overall number
of funds closed in each year and found that the years in which funds with the highest incidence of negative variances were syndicated were 2002, 2004 and 2007.
5.4. Housing Credit Variance Analysis
Consistent with CohnReznick’s industry experience, the survey data we examined demonstrate that the aggregate average variance in fund yields has been less than 0.50%.
The average housing credit investment derives the majority of its benefits from housing
credits with the balance 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 frequent as the industry’s
underwriting capability has become more efficient.
Housing Credit Delivery Variance by Investment Type
Total
Housing
Credit Delivery
Variance
1st Year Housing
Credit Delivery
Variance
2nd Year
Housing
Credit Delivery
Variance
Figure 5.4.1
3rd Year
Housing
Credit Delivery
Variance
Total
-0.4%
-16.3%
-13.9%
-8.5%
Proprietary
-0.1%
-7.8%
-12.8%
-6.8%
Multi-investor
-0.7%
-21.1%
-14.5%
-9.6%
Guaranteed
0.2%
-11.0%
-14.4%
-4.9%
Figure 5.4.2 graphically illustrates the trend in actual versus projected first-, second- and
third- year housing tax credits.
Initial Years’ Housing Credit Delivery Variance
by Year Fund Closed
Figure 5.4.2
Housing Tax Credit Delivery Variance
30%
15%
0%
15%
30%
45%
60%
1995
1996
1997
1998
1999
■ 1st Year LIHTC Variance
2000
2001
2002
2003
2004
■ 2nd Year LIHTC Variance
2005
2006
2007
2008
■ 3rd Year LIHTC Variance
2009
As illustrated in Figure 5.4.2, survey respondents have historically projected higher than
actual tax credits in the first few years; however, more recently, respondents have been
more accurate at projecting the first few years’ credits. This trend was consistently
observed across the pool of survey respondents. For instance, Enterprise reported that out
of 191 housing tax credit properties it syndicated during 2007–2009, 34% were leased up on
time, 42% were ahead of schedule and the remaining 24% were behind schedule, thereby
incurring negative initial-year credit delivery variances.15
In addition to the fact that housing credits are a function of development costs, they are
realized at a steady level once tax credit occupancy is achieved. To the extent that the
original projection of total housing tax credits or the timing of their delivery falls short, investors are entitled to be “made whole” by adjustor provisions. Finally, properties that struggle
to maintain break-even occupancy tend to generate higher passive losses than initially
projected. Increased loss deductions have the effect of increasing yield, but the negative
impact of losses on financial statements generally makes them unwelcome.
15
Chapter 6:
Portfolio Composition
T
his chapter summarizes the composition of the stabilized property
sample consisting of 15,399 housing tax credit properties by various
segmentation factors. Unless otherwise noted, percentages are expressed
on the basis of stabilized net equity.
6.1. Portfolio Composition – by Property Age
The number of years that have transpired since a housing tax credit property was first
and last placed in service is meaningful from an operating perspective. Older properties
often have physical plant issues or face market competition from more recently developed properties. The composition of the housing tax credit properties surveyed is heavily
weighted (48%) toward properties last placed in service within the past five years. This is a
reflection, in part, on the program’s slow initial activity and the fact that, with exceptions in
select years, 100% of the authorized national housing tax credit allocation has been used.
Figure 6.1 displays the amount of net equity invested in surveyed properties according to
the year that the corresponding property investments in the survey sample were placed
in service.
Net Equity by Year Placed-in-Service
Figure 6.1
$8.0M
Housing Credit Net Equity
$7.0M
$6.0M
$5.0M
$4.0M
$3.0M
$2.0M
$1.0M
$0.0
1990
1992
1991
1994
1993
1996
1995
1998
1997
2000
1999
2002
2001
2004
2003
2006
2005
2008
2007
2010
2009
The median age of stabilized properties in the CohnReznick database is nine years (placed
in service in 2003). The following is a graph indicating the portfolio composition as a
percentage of stabilized properties by age group.
Percent Net Equity by Property Age
(Years since Placed-in-Service, as of 12/31/2010)
Figure 6.1(A)
3.5%
■ 5 years or less.. . . . . . . . . . . . . 52.2%
13.5%
52.2%
■ 6–10 years. . . . . . . . . . . . . . . . . 30.8%
■ 11–15 years. . . . . . . . . . . . . . . . 13.5%
■ 16 years or older. . . . . . . . . . . 3.5%
30.8%
6.2. Portfolio Composition – by Property Size
According to our survey, the average stabilized housing tax credit property is made up
of 72 units. The following figure illustrates the trend of average housing tax credit units per
property by year placed in service.
Average Project Size by Year Placed in Service
Figure 6.2
90
Number of Units/Property
80
70
60
50
40
30
20
10
0
1990
1992
1991
1994
1993
1996
1995
1998
1997
2000
1999
2002
2001
2004
2003
2006
2005
2008
2007
2010
2009
CohnReznick surveyed 4% properties averaging 116 units per property, while 9% properties
averaged 59 units per property. The following table illustrates the trend of the size of both
4% and 9% stabilized properties.
Average Project Size by Net Equity,
Credit Type and Year Placed-in-Service
Figure 6.2(A)
$8.0M
Net Equity (in Millions)
$7.0M
$6.0M
$5.0M
$4.0M
$3.0M
$2.0M
$1.0M
$0.0
1990
1992
1991
1994
1993
1996
1995
1998
1997
2000
1999
■ Average 9% Property Size
2002
2001
2004
2003
2006
2005
■ Average 4% Property Size
2008
2007
2009
6.3. Portfolio Composition – by Investment Type
In the housing tax credit industry, credits have been syndicated through the sale of equity
investments in public funds, direct investments, proprietary funds and multi-investor funds.
“Public funds” refers to the publicly registered offerings 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 increasingly rare and no longer used to raise
capital for this sector. “Direct investments” refers to investments made by a single investor
directly into a project partnership as opposed to investing through a fund managed by
a third party. Direct investments make up a smaller portion of the market because they
require the use of internal resources to monitor real estate operations and compliance with
housing tax credit program rules. Currently, most equity investments 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.
Percent Net Equity by Investment Type
6.3%
■ Direct. . . . . . . . . . . . . . . . . . . . . . . 6.3%
5.2%
54.5%
Figure 6.3
■ Multi-investor. . . . . . . . . . . . . . 54.5%
0.7%
■ Proprietary.. . . . . . . . . . . . . . . . 33.3%
■ Public. . . . . . . . . . . . . . . . . . . . . . . 0.7%
33.3%
■ Not Specified. . . . . . . . . . . . . . . 5.2%
CohnReznick notes that property investments made by multi-investor funds constitute the
majority of the properties surveyed. Survey respondents indicated that multi-investor funds
represented 54.6% of the total equity financing on a stabilized net equity basis. Proprietary
fund investments account for the second highest market share, with 33.3% of net equity. In
recent years, as the housing credit equity market declined sharply, investors still active in
the housing tax credit market placed a disproportionate amount of their capital through
proprietary funds. CohnReznick notes that this trend began to reverse in 2010 with the
recovery of the equity market.
6.4. Portfolio Composition – by Credit Type
The housing tax credit statute provides for two types of housing tax credits – 9% and 4%
housing tax 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 heavily financed with
investor equity and thus have a modest level of hard debt financing to service. Tax-exempt
bond projects that qualify for 4% credits generate significantly lower levels of tax credit
equity and require higher debt levels (albeit at lower tax-exempt interest rates).
Figure 6.4 displays the inventory of housing tax credit properties surveyed and divides them
into 9% and 4% housing credit types. Participants did not distinguish between credit types
for approximately 2,000 out of the 17,118 properties surveyed. As a result, CohnReznick
excluded the 2,000 unidentified properties from this analysis.
As shown below, 9% properties account for 71.3% of the net equity surveyed, with the
remaining 28.7% invested in 4% properties.
Percent Net Equity by Credit Type
71.3%
28.7%
Figure 6.4
■ 9% Housing Tax Credits
■ 4% Housing Tax Credits
6.5. Portfolio Composition – By Development Type
CohnReznick requested that respondents specify whether their property investments
represented new construction or the rehabilitation of older, existing properties. Newly
constructed properties accounted for 68.2% of the net equity surveyed, and rehabilitated
properties accounted for 27.9% of net equity surveyed, with the remaining 4% being properties that represent the rehabilitation of historic structures or properties involving mixed
development types. With respect to financing, our data reflect that the average new
construction development was financed with $3.7 million of net equity, and the average
rehabilitation property required $2.8 million of net equity.
Percent Net Equity by Development Type
Figure 6.5
2.4%
1.5%
■ New Construction. . . . . . . . . 68.2%
■ Rehab. . . . . . . . . . . . . . . . . . . . . 27.9%
68.2%
■ Historic Rehab. . . . . . . . . . . . . . 2.4%
27.9%
■ Mixed. . . . . . . . . . . . . . . . . . . . . . . 1.5%
6.6. Portfolio Composition – By Tenancy Type
“Special needs” displayed in Figure 6.6 refers to properties that are set aside for unique
tenancy groups. This determination is based on the state’s assessment of its most critical
housing needs—principally tenants with significant housing challenges, such as the homeless or tenants with physical handicaps.
The data show that family properties account for 73.3% of all properties surveyed; senior
properties account for 21.5%; special needs properties account for another 2.5%; and the
remaining 2.7% either have mixed tenancies or respondents did not specify tenancy type
in their responses. The data suggested that there has been no meaningful variance in
terms of investment size among the various tenancy types.
Percent Net Equity by Tenancy Type
Figure 6.6
2.7%
2.5%
73.3%
■ Family.. . . . . . . . . . . . . . . . . . . . . 73.3%
■ Senior. . . . . . . . . . . . . . . . . . . . . . 21.5%
21.5%
■ Special Needs. . . . . . . . . . . . . . 2.5%
■ Mixed or Not Specified. . . . . 2.7%
6.7. Portfolio Composition – by Region
The data reflected in Figure 6.7 summarizes the housing credit net equity by 12
CohnReznick-defined regions of the country. The regions in Figure 6.7 are arranged in
descending order by greatest to least in total housing credit net equity.
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 are composed of the following:
Portfolio Composition by Region
Number of
Properties
Region
Number
Survey
Total
Figure 6.7
Number of Units
Stabilizied
Properties
Survey
Total
Stabilizied
Properties
Housing Credit
Net Equity
Survey
Total
Stabilizied
Properties
Total Housing
Credits
Survey
Total
Stabilizied
Properties
Region 1
2,437
2,215
187,862
170,013 $12,418,490,575 $10,734,283,353 $12,983,060,878 $11,112,035,053
Region 2
54
44
4,469
2,622 $222,981,536 $139,716,992 $311,641,942 $167,453,573
Region 3
229
207
9,194
8,308 $501,254,208 $429,595,451 $594,558,443 $500,530,163
Region 4
788
716
57,883
52,515 $2,814,098,250 $2,371,709,285 $3,325,049,639 $2,808,278,287
Region 5
588
529
28,021
23,912 $1,345,586,660 $1,112,170,776 $1,608,104,263 $1,343,679,514
Region 6
1,219
1,081
63,372
53,513 $2,938,908,795 $2,246,460,634 $3,249,299,591 $2,433,270,659
Region 7
2,717
2,447
191,677
170,755 $8,947,816,963 $7,347,578,906 $10,873,359,834 $8,761,727,408
Region 8
1,338
1,226
123,421
107,997 $4,281,728,653 $3,565,145,154 $5,050,959,624 $4,296,055,907
Region 9
1,828
1,622
181,509
157,474 $7,995,325,242 $6,493,879,063 $9,394,008,999 $7,630,196,093
Region 10
1,897
1,712
119,966
106,790 $4,516,058,259 $3,861,332,569 $5,331,090,420 $4,552,484,846
Region 11
3,818
3,437
282,094
248,108 $15,515,157,446 $12,747,593,847 $19,128,578,259 $15,714,700,408
Region 12
116
101
9,831
8,241 $640,389,440 $484,449,104 $1,006,361,855 $711,930,795
Region 1: CA, OR, WA; Region 2: AK, HI; Region 3: ID, MT, WY; Region 4: AZ, CO, NM, NV, UT; Region 5: MN, ND, SD;
Region 6: IA, KS, NE, MO; 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.
Percent Net Equity by Region
Figure 6.7(A)
1%< 1%
21%
5%
25%
■ Region 3. . . . . . . . . . . . . . . . . . . . . . 1%
2%
■ Region 4. . . . . . . . . . . . . . . . . . . . . . 5%
■ Region 5. . . . . . . . . . . . . . . . . . . . . . 2%
4%
■ Region 6. . . . . . . . . . . . . . . . . . . . . . 4%
7%
14%
13%
■ Region 1. . . . . . . . . . . . . . . . . . . . . 21%
■ Region 2. . . . . . . . . . . . . . . . . . . . < 1%
■ Region 7. . . . . . . . . . . . . . . . . . . . . 14%
8%
■ Region 8. . . . . . . . . . . . . . . . . . . . . . 7%
■ Region 9. . . . . . . . . . . . . . . . . . . . . 13%
■ Region 10.. . . . . . . . . . . . . . . . . . . . 8%
■ Region 11.. . . . . . . . . . . . . . . . . . . 25%
Region 11 represents the greatest amount of total net equity and accounts for nearly 25%
of the entire portfolio. Region 1 encompasses the second highest amount of equity at
approximately 20% of the portfolio, followed by Region 7, which equals roughly 15% of the
net equity portfolio. Region 2 with the least amount of net equity in the portfolio, is made
up of only 54 properties (44 of which are stabilized) and represents less than 1% of the total
net equity portfolio.
Average Project Size by Region
Figure 6.7(B)
Average Number of Units/Property
120
100
80
88
77
60
45
40
40
70
73
60
97
72
62
50
20
0
Region 1
Region 3
Region 2
Region 5
Region 4
Region 7
Region 6
Region 9
Region 8
Region 11
Region 10
Region 1: CA, OR, WA; Region 2: AK, HI; Region 3: ID, MT, WY; Region 4: AZ, CO, NM, NV, UT; Region 5: MN, ND, SD;
Region 6: IA, KS, NE, MO; 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.
6.8. Portfolio Composition – by State
We 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 net equity, are California, New York, Texas, Florida
and Illinois, collectively accounting for more than 42% of the overall portfolio net equity.
Delaware, South Dakota, Hawaii, Guam and the U.S. Virgin Islands represent the bottom
five in terms of overall net equity, each representing less than 0.2% of the overall portfolio.
Percent Net Equity by State
Figure 6.8
WA
ME
MT
ND
MN
OR
WI
SD
ID
MI
WY
PA
IA
NE
NV
IL
UT
CA
CO
NH
VT
MA
RI
CT
NY
MO
KS
OH
IN
WV
VA
NJ
DE
MD
DC
KY
NC
TN
AZ
OK
NM
SC
AR
MS
TX
AL
GA
LA
FL
1.3% and 1.3
below
and below
1.4% to1.4%
4.0% to 4.0%
4.1%
to 10.0%
4.1%
to 10.0%
10.1% to 16.0%
16.0%
16.1%
andabove
above
16.1%
and
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.
As shown below, 26.3% of the total housing tax credit equity we surveyed was concentrated in properties located within the 10 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.
Net Equity Concentration among Top 10 MSA’s
$7B
Figure 6.9
$6.5
Housing Net Equity
$6B
$5B
$4B
$2.7
$3B
$2.1
$1.9
$2B
$1.5
$1.3
$1.3
$1B
$0
NY-NJ-PA
San Francisco,
CA
Los Angeles,
CA
Philadelphia,
PA
Chicago,
IL
$1.0
Miami,
FL
Washington,
DC
$1.0
$0.8
Boston,
MA
Seattle,
WA
Detroit,
MI
Appendix F outlines the overall portfolio composition of all MSAs.
Appendix A
Acknowledgments
CohnReznick would like to thank the following organizations for
contributing data and financial support for the study:
• AEGON USA Realty Advisors
• Michel Associates
• Alliant Capital
• Midwest Housing Equity Group
• Bank of America
• Mountain Plains Equity Group
• Boston Capital
• National Development Council
• Boston Financial Investment
Management
• National Equity Fund
• Centerline Capital Group
• Northern New England Housing
Investment Fund
• Citibank
• Ohio Capital Corporation for Housing
• City Real Estate Advisors
• PNC Multifamily Capital
• Community Affordable Housing
Equity Corporation
• Raymond James
• Enterprise Community Investment
• First Sterling
• Great Lakes Capital Fund
• Red Capital Group
• RBC Capital Markets
• Red Stone Equity Partners
• Housing Vermont
• The Richman Group Affordable
Housing Corporation
• Hunt Capital Partners
• Stratford Capital
• Hudson Housing
• The Summit Group
• John Hancock
• SunTrust Community Development
Corporation
• J.P. Morgan Chase
• Massachusetts Housing Investment
Corporation
• Merritt Community Capital
• Union Bank of California
• U.S. Bank
• WNC Associates
* CohnReznick also would like to thank the National Association of State and Local Equity
Funds for making a financial contribution on behalf of its member organizations.
Appendix B
Survey Methodology
T
his report represents the second in a series of studies undertaken by the
CohnReznick concerning the Low Income Housing Tax Credit program.
In March 2011, CohnReznick transmitted data requests to 40 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’s first study was published in August 2011. A two-phase approach allowed
CohnReznick to supply much needed current industry data while still operating within the
timeframe necessary to perform an increasingly rigorous analysis of the data. While the first
and second study results were released separately, data were requested and collected once.
Thirty-two organizations chose to participate in the August 2011 study, and an additional six
organizations participated in the current study, resulting in a 95% overall response rate. All
outputs in the current study have been updated to include the additional participant’s data.
CohnReznick believes that 17,118 properties, the sample size represented, is in excess of 70%
of the housing tax credit properties placed in service since 1986 that are being actively assetmanaged by syndicators and/or investors. We suspect that the gap between Reznick’s database
and 100% of all properties ever syndicated was largely a result of defunct syndicators, as well
as properties placed in service in the earlier years of the housing tax credit program that have
approached the expiration of their respective compliance periods and disposed of in ways that
caused them to be “cycled out” of the program. Most important, we believe that the sample size
represented in the study provides a statistically meaningful basis for our analysis and findings.
To substantiate the estimate of our data coverage, we benchmarked our sample size
against the housing tax credit property database maintained by the U.S. Department of
Housing and Urban Development.
CohnReznick Survey vs. HUD Database (1995–2011) 1800
Number of Properties
1500
1200
900
600
300
0
1995
1997
1996
1999
1998
2001
2000
2003
2002
■ CohnReznick Survey
2005
2004
2007
2006
2009
2008
2011
2010
■ HUD Database
Data for this report were compiled and analyzed with the support of Integratec,
CohnReznick’s affiliated real estate services and software solutions company. Integratec’s
involvement in the study was to provide data rollup, filtering, calculation and aggregation/
segmentation services per CohnReznick’s instructions. Integratec’s goal was to provide
CohnReznick with high-quality output in a format that would facilitate further data analysis
and presentation.
The remainder of the appendix outlines the timeline and methodology we followed to
carry out the study.
Data Collection
A participant solicitation letter was mailed to 40 organizations on March 10, 2011,
announcing and requesting support for a housing credit industry study to be undertaken
by the CohnReznick with the assistance of Integratec. The solicitation letter was followed
by a Microsoft Excel data collection template, along with a confidentiality and nondisclosure agreement and acknowledgement of participation. Respondents were initially
requested to return the collection template no later than May 20, 2011. A few participating
respondents who indicated that they lacked sufficient time to complete the survey properly were offered a deadline extension. All contacts, whether made by telephone, email
or mail, were recorded in response contact logs. The six additional participants in this study
submitted their data following publication of the August 2011 report.
For participating organizations that are existing clients of Integratec, we requested that
each respondent grant consent for use of portions of the dataset that Integratec manages
on each respondent’s behalf. Upon receipt of the consent form, Integratec loaded
requested data to our data collection template, then sent it back to respondent for review
and completion of any remaining fields. Respondents that are not clients of Integratec
were instructed to complete the data collection template directly. In either case, data
were transmitted directly by the respondents to CohnReznick.
Questionnaire Design
The following table shows the main data points requested from each participating investor
and syndicator. Instructions were attached to each collection field to minimize interpretations. Contact information of CohnReznick professionals was supplied along with the
collection template for questions related to the data request. CohnReznick believes that
data fields included in the collection template have been carefully designed to allow the
study to be informative, essential and influential.
Where applicable, audited financial data were requested and were represented
as having been furnished in that form. However, neither CohnReznick nor Integratec
performed any independent validation of the data nor did we ascertain that the data
were indeed audited.
Data Fields
Definition/Explanation
Property Investment Identification
Fund identification
Provide the name of the fund or a unique identification number from participant’s
database which permits
future identification.
Investment type
Choose one from the following categories that best describes the investment type of
the fund: direct, proprietary, multi-investor, guaranteed or public.
Property identification
Provide the name of the property or a unique identification number from
participant’s database which permits future identification.
Property address
Provide the property’s street address, city, state (in 2-letter abbreviation) and 5-digit
zip code.
Location type
Choose one from the following categories that best describes the location
characteristics of the property’s location: urban, suburban or rural.
Name of MSA
Provide the name of the Metropolitan or Micropolitan Statistical Area where the
property is located.
Credit type
Choose one from the following categories: 4%, 9% or 4%/9%.
Property status
Choose one from the following categories that best describes the current status
of the property: preconstruction, construction, lease-up (completed construction
but hasn’t achieved 100% qualified occupancy), prestabilization (achieved 100%
qualified occupancy but not yet reached stabilization benchmarks), stabilization or
disposition.
Development type
Choose one from the following categories that best describes the type of the
development: new construction, rehabilitation, acquisition/rehabilitation, historic
rehabilitation, or other.
Tenancy type
Choose one from the following categories that best describes the population the
property has committed to serve: family, senior, special needs, family with special
needs set-aside, senior with special needs set-aside, or other.
Number of units
Provide the total number of housing units the property consists of.
Number of housing
credit units
Provide the total number of units that are eligible for federal
low-income housing tax credits.
Project-based rental
assistance
Indicate whether the property benefits from project-based
rental assistance.
Type of project-based
rental assistance
If the property benefits from project-based rental assistance, choose one from the
following categories that best describes
the type of assistance: Section 8, Rural Development, ACC, or other. If more than
one type of rental assistance program is involved, choose the primary one that
covers the majority of the subsidized units.
Hard debt
Indicate whether the property has permanent debt that requires mandatory debt
service payments regardless of available cash flow.
Hard debt ratio
Provide the percent of property total development costs financed with permanent
hard debt. Leave the field blank if the property has no permanent hard debt.
Year placed in service
Provide the 4-digit year in which the property was or is projected to be placed
in service.
First year of credit
delivery
Provide the 4-dight year in which credit delivery first commenced (or is projected
to commence).
Total housing credit
net equity
Provide the amount of total net equity associated with federal low-income housing
tax credits and (to be) contributed to the property investment.
Total housing credits
to investor
Provide the 10-year total amount of federal low-income housing tax credits
(projected to be) available to the investor limited partner.
Data Fields
Property Performance Data
Physical occupancy for
years 2008 to 2010
Provide the average physical occupancy (average occupancy over the period
during which the property had stabilized operations) for each of the last three years.
Debt coverage ratio
for years
2008 to 2010
Provide the year-end debt coverage ratio (net operating income minus required
replacement reserve contributions then divided by the mandatory debt service
payments) for each of the last three years in accordance with audited financial
statements. Choose “NA” if the property does not have any hard debt. For properties
with partial year stabilized operations during a certain year, provide the average
over the stabilized period.
Per-unit cash
flow for years
2008 to 2010
Provide the year-end per-unit cash flow (net operating income minus required
replacement reserve contributions and mandatory debt service payments, if any, then
divided by the total number of units) for each of the last three years in accordance
with audited financial statements. For properties with partial-year stabilized operations
during a certain year, provide the average over the stabilized period.
On AHIC watch list
If the watch list criteria published by the Affordable Housing Investor’s Council has
been adopted, indicate whether the property is currently on the AHIC watch list.
On internal watch list
Indicate whether the property is currently on participant’s internal watch list.
Operating deficit
funding source(s)
If the property incurred operating deficits during 2010, choose from the following funding
sources (choose all that apply): investor capital call, upper-tier reserve or syndicator
advance, lower-tier reserve or general partner advance, and debt restructuring.
Primary operating deficit
funding source
If the property incurred operating deficits during 2010, choose the single largest
funding source from the following: investor capital call, upper-tier reserve or syndicator
advance, lower-tier reserve or general partner advance, and debt restructuring.
Foreclosure
Indicate whether the property has been foreclosed upon or a deed in lieu of
foreclosure has been tendered.
Year of foreclosure
If the property has been foreclosed upon or a deed in lieu of foreclosure has been
tendered, specify the year in which the foreclosure event occurred.
Noncompliance
Indicate whether the property has ever suffered from credit loss due to
noncompliance issues arising from the IRS or state audit.
Fund Identification and Performance Data
Fund identification
Provide the name of the fund or a unique identification number from participant’s
database that permits future identification.
Investment type
Choose one from the following categories that best describes the investment type of
the fund: direct, proprietary, multi-investor, guaranteed or public.
Year closed
Provide the 4-digit year in which the fund was closed.
Total gross equity
Provide the amount of total gross equity raised from tax credit equity investor.
Total housing credits
to investor
(projected to be) available to the investor limited partner.
Total historic tax credits
to investor
Provide the total amount of federal historic rehabilitation tax credits (projected to
be) available to the investor limited partner.
Total energy tax credits
to investor
Provide the total amount of federal renewable energy tax credits (projected to be)
available to the investor limited partner.
Total other tax credits
to investor
Provide the total amount of any other federal or state tax credits (projected to be)
available to the investor limited partner.
Original IRR
Provide the quarterly investor IRR projected at the time of fund closing, with
necessary adjustment for property removals/additions
Current IRR
Provide the quarterly investor IRR according to the latest asset management or
investor report
Data Fields
As of date for current IRR
Specify the as of date, in the form of xx/xx/xxxx, for the current IRR
Total projected
housing credits
projected to be available to the investor limited partner at fund closing.
Total actual
housing credits
Provide the 10-year total amount of federal low-income housing tax credits delivered
(or projected to be delivered based on the latest information such as 8609s) to the
investor limited partner.
As of date for actual
housing credits
Specify the as of date, in the form of xx/xx/xxxx, for the actual housing credits
Total projected initial
years of housing credits
Provide the annual amount of federal low-income housing tax credits projected to
be available to the investor limited partner at fund closing; specify the amount for
each of the first three years.
Total actual initial years
of housing credits
Provide the annual amount of federal low-income housing tax credits projected
delivered (or projected to be delivered based on the latest information such as 8609s)
to the investor limited partner; specify the amount for each of the first three years.
Current working capital
reserve balance
Provide the current balance of working capital reserve established for the fund.
As of date for actual
housing credits
Specify the as of date, in the form of xx/xx/xxxx, for the current working capital
reserve balance.
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 reviewed
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 a MS SQL Server database jointly
maintained by Integratec and 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 MS SQL
Server 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 years’ 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, Integratec developed SQL scripts to perform calculations and group datasets (e.g.,
linking zip codes to applicable MSAs) for segmentation analysis. Because of the lack of a
median function in the MS SQL Server, Integratec created stored procedures to calculate
median values. Median calculation accuracy was independently checked against Excel
median function calculations. Finally, aggregated data and outputs were re-exported into
an Excel template for further testing and quality control reviews.
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 tax-exempt 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.
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.
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 thirdparty 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, which 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
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
Appendix D
Property Performance by State
Operating Performance by State
Median Physical
Occupancy
State
2008
2009
2010
Median Debt
Coverage Ratio
2008
2009
Median Per Unit
Cash Flow
2010
2008
2009
2010
AK
96.0%
95.5%
96.9%
1.16
1.15
1.21
$505
$481
$642
AL
95.0%
95.1%
95.5%
1.19
1.32
1.35
$201
$269
$297
AR
96.0%
96.0%
96.0%
1.16
1.18
1.17
$179
$245
$250
AZ
95.5%
94.9%
95.8%
1.19
1.23
1.24
$227
$236
$304
CA
97.9%
97.5%
97.9%
1.34
1.36
1.34
$753
$844
$795
CO
96.5%
96.3%
97.2%
1.10
1.15
1.25
$352
$460
$691
CT
96.6%
97.0%
97.0%
1.09
1.18
1.19
$(87)
$233
$123
DC
97.1%
96.9%
96.8%
1.10
1.28
1.25
$359
$705
$540
DE
96.0%
97.0%
96.0%
1.20
1.16
1.19
$271
$240
$329
$329
FL
95.0%
94.6%
95.0%
1.12
1.15
1.16
$231
$259
GA
94.8%
94.8%
95.3%
1.01
1.03
1.05
$18
$29
$67
GU
94.4%
85.1%
89.4%
1.17
1.23
1.45
$548
$735
$1,482
HI
99.0%
99.0%
99.0%
1.41
1.68
1.69
$1,586
$2,496
$2,422
IA
94.4%
94.1%
95.0%
1.11
1.12
1.17
$165
$225
$225
ID
93.8%
94.4%
94.0%
0.95
0.99
1.04
$24
$(7)
$111
IL
96.0%
96.7%
96.1%
1.11
1.22
1.27
$233
$385
$479
IN
94.0%
94.0%
94.4%
0.85
1.05
1.14
$(228)
$77
$212
KS
96.0%
96.0%
96.0%
1.12
1.16
1.13
$234
$287
$216
KY
96.5%
96.2%
96.1%
1.04
1.17
1.32
$4
$230
$352
LA
96.3%
96.0%
96.0%
1.32
1.24
1.25
$389
$384
$503
$537
MA
96.2%
96.7%
97.0%
1.17
1.17
1.27
$369
$421
MD
96.8%
97.0%
97.0%
1.20
1.22
1.26
$298
$387
$480
ME
97.5%
97.4%
97.2%
1.29
1.38
1.40
$360
$432
$545
MI
93.9%
94.0%
95.0%
1.01
1.07
1.11
$23
$154
$240
MN
97.2%
97.0%
97.2%
1.26
1.31
1.36
$554
$688
$691
MO
94.9%
95.3%
95.8%
1.16
1.14
1.21
$236
$224
$287
MS
95.3%
95.0%
96.0%
1.25
1.13
1.32
$284
$207
$430
MT
95.8%
94.4%
95.3%
1.08
1.18
1.27
$161
$391
$447
NA
96.3%
95.1%
96.2%
1.21
1.18
1.17
$445
$274
$230
NC
97.0%
97.0%
97.2%
1.20
1.35
1.36
$259
$495
$495
ND
96.7%
97.4%
97.5%
1.08
1.19
1.25
$356
$411
$409
NE
95.4%
95.8%
96.0%
1.15
1.15
1.24
$272
$216
$336
NH
97.6%
97.0%
97.0%
1.06
1.42
1.47
$129
$627
$749
NJ
97.4%
97.3%
97.6%
1.17
1.22
1.25
$333
$250
$517
NM
95.8%
96.0%
96.6%
1.24
1.26
1.33
$495
$470
$598
NV
96.5%
96.0%
95.3%
1.22
1.19
1.29
$265
$296
$465
NY
97.3%
97.5%
97.5%
1.26
1.45
1.52
$261
$528
$638
$200
OH
96.2%
96.1%
96.1%
0.99
1.05
1.13
$(53)
$62
OK
95.7%
95.5%
96.0%
1.23
1.17
1.24
$288
$278
$277
OR
96.8%
96.1%
96.4%
1.12
1.17
1.20
$268
$287
$372
Median Physical
Occupancy
State
2008
2009
2010
Median Debt
Coverage Ratio
2008
2009
Median Per Unit
Cash Flow
2010
2008
2009
2010
PA
97.0%
97.0%
97.0%
1.16
1.23
1.29
$84
$205
$256
PR
99.9%
99.5%
99.7%
1.18
1.24
1.21
$379
$432
$494
RI
97.0%
97.0%
97.5%
1.18
1.22
1.20
$227
$433
$371
SC
96.0%
96.8%
96.4%
1.13
1.17
1.24
$162
$289
$379
SD
95.0%
95.5%
95.8%
1.17
1.20
1.30
$354
$258
$365
TN
95.6%
94.0%
95.0%
1.01
1.08
1.12
$42
$169
$227
TX
95.4%
95.1%
95.8%
1.14
1.19
1.23
$222
$323
$455
UT
97.7%
97.0%
97.0%
1.20
1.27
1.28
$494
$735
$571
VA
96.3%
96.2%
97.0%
1.17
1.15
1.19
$319
$423
$416
VI
99.0%
99.0%
99.2%
2.39
2.20
2.09
$2,987
$2,539
$2,215
VT
97.5%
97.1%
97.5%
1.13
1.29
1.26
$213
$611
$523
WA
97.0%
96.2%
97.0%
1.24
1.25
1.26
$427
$454
$520
WI
96.0%
95.9%
95.7%
1.03
1.14
1.17
$99
$352
$399
WV
96.0%
95.6%
95.4%
1.18
1.22
1.14
$157
$233
$255
WY
97.5%
96.0%
95.7%
1.15
1.15
1.14
$263
$270
$250
Appendix E
Property Underperformance
by State
2010 Operating and Chronic Underperformance by State
State
Period
Below 90% Physical
Occupancy
Below
1.00 DCR
Below
$0 Cash Flow
Equity %
Equity %
Equity %
AK
2010
11.2%
39.7%
36.0%
AK
Last 3 Years
N/A
34.5%
22.9%
AL
2010
8.1%
20.2%
23.5%
AL
Last 3 Years
5.1%
15.7%
16.0%
AR
2010
18.5%
33.3%
31.6%
AR
Last 3 Years
10.2%
21.2%
18.3%
AZ
2010
15.6%
32.4%
34.3%
AZ
Last 3 Years
6.7%
21.4%
23.9%
CA
2010
4.7%
13.3%
14.0%
CA
Last 3 Years
1.2%
5.8%
5.1%
CO
2010
3.9%
18.6%
19.4%
CO
Last 3 Years
0.2%
7.9%
8.3%
CT
2010
5.0%
33.8%
37.1%
CT
Last 3 Years
0.9%
15.2%
20.0%
DC
2010
6.2%
21.9%
23.9%
DC
Last 3 Years
0.8%
12.3%
11.1%
DE
2010
4.8%
32.5%
31.4%
DE
Last 3 Years
5.1%
16.6%
14.6%
FL
2010
15.2%
28.6%
30.2%
FL
Last 3 Years
10.1%
20.8%
18.7%
GA
2010
17.9%
43.7%
42.6%
GA
Last 3 Years
11.4%
24.6%
23.4%
66.0%
6.6%
5.9%
NA
NA
NA
17.4%
32.0%
33.8%
HI
2010
HI
Last 3 Years
IA
2010
IA
Last 3 Years
8.2%
14.8%
15.7%
ID
2010
24.4%
35.6%
34.9%
ID
Last 3 Years
19.5%
37.6%
30.7%
IL
2010
11.1%
30.1%
30.1%
IL
Last 3 Years
4.3%
12.9%
14.0%
IN
2010
16.5%
41.5%
40.8%
IN
Last 3 Years
9.3%
28.5%
27.4%
KS
2010
17.0%
31.9%
32.4%
KS
Last 3 Years
5.1%
19.6%
20.5%
KY
2010
11.9%
30.1%
31.7%
KY
Last 3 Years
4.4%
13.6%
19.7%
LA
2010
8.3%
20.1%
18.5%
LA
Last 3 Years
2.5%
8.6%
11.7%
MA
2010
6.8%
27.4%
24.8%
MA
Last 3 Years
2.4%
9.4%
8.8%
MD
2010
7.2%
24.7%
22.8%
State
Period
Below 90% Physical
Occupancy
Below
1.00 DCR
Below
$0 Cash Flow
Equity %
Equity %
Equity %
MD
Last 3 Years
2.0%
9.3%
9.3%
ME
2010
8.8%
22.3%
20.3%
ME
Last 3 Years
2.5%
8.0%
2.5%
MI
2010
19.8%
37.8%
38.1%
MI
Last 3 Years
12.6%
30.7%
31.6%
MN
2010
7.8%
18.3%
19.8%
MN
Last 3 Years
2.0%
15.1%
11.8%
MO
2010
18.5%
27.7%
29.1%
MO
Last 3 Years
9.7%
14.0%
14.3%
MS
2010
13.6%
17.3%
20.3%
MS
Last 3 Years
3.7%
15.7%
18.0%
MT
2010
14.5%
24.2%
19.5%
MT
Last 3 Years
4.0%
14.7%
11.7%
NC
2010
6.3%
20.2%
18.4%
NC
Last 3 Years
2.8%
12.4%
11.7%
ND
2010
8.8%
17.9%
13.4%
ND
Last 3 Years
3.2%
15.4%
11.9%
NE
2010
16.1%
24.5%
30.4%
NE
Last 3 Years
6.4%
10.0%
13.3%
NH
2010
5.8%
9.4%
11.4%
NH
Last 3 Years
N/A
5.2%
6.2%
NJ
2010
7.7%
28.3%
28.6%
NJ
Last 3 Years
3.0%
17.0%
16.2%
NM
2010
11.3%
12.8%
11.5%
NM
Last 3 Years
1.1%
7.2%
3.6%
NV
2010
13.0%
23.7%
22.5%
NV
Last 3 Years
2.6%
16.3%
16.1%
NY
2010
4.1%
19.2%
19.8%
NY
Last 3 Years
1.4%
9.0%
10.8%
OH
2010
12.7%
36.7%
37.6%
OH
Last 3 Years
5.8%
21.6%
23.2%
OK
2010
13.6%
28.0%
25.0%
OK
Last 3 Years
4.7%
7.1%
7.7%
OR
2010
7.7%
19.8%
19.0%
OR
Last 3 Years
2.1%
8.2%
8.2%
PA
2010
6.4%
25.3%
26.1%
PA
Last 3 Years
2.0%
19.5%
14.7%
PR
2010
2.5%
9.1%
8.2%
PR
Last 3 Years
3.8%
2.5%
4.2%
RI
2010
7.1%
30.0%
29.2%
RI
Last 3 Years
N/A
14.5%
16.2%
SC
2010
6.2%
28.1%
26.8%
SC
Last 3 Years
2.5%
11.4%
14.9%
State
Period
Below 90% Physical
Occupancy
Below
1.00 DCR
Below
$0 Cash Flow
Equity %
Equity %
Equity %
SD
2010
13.6%
14.8%
SD
Last 3 Years
2.7%
5.1%
4.2%
TN
2010
18.4%
39.0%
36.8%
TN
Last 3 Years
10.7%
32.5%
24.7%
TX
2010
12.9%
29.1%
28.3%
TX
Last 3 Years
5.5%
20.9%
17.2%
UT
2010
5.7%
25.5%
24.7%
UT
Last 3 Years
N/A
8.5%
7.8%
VA
2010
11.2%
25.3%
24.9%
VA
Last 3 Years
2.4%
17.3%
12.3%
VT
2010
1.7%
18.6%
21.6%
VT
Last 3 Years
N/A
N/A
2.3%
WA
2010
5.3%
18.8%
19.5%
WA
Last 3 Years
0.8%
9.5%
8.7%
WI
2010
9.3%
31.0%
29.8%
WI
Last 3 Years
3.3%
21.4%
15.4%
WV
2010
21.0%
28.7%
24.9%
WV
Last 3 Years
12.2%
18.0%
16.6%
WY
2010
18.2%
38.4%
36.5%
WY
Last 3 Years
2.0%
29.6%
28.8%
15.4%
Appendix F
Property Performance by MSA
Operating Performance by MSA
Median Physical
Occupancy
State
MSA
2008
2009
2010
Median Debt
Coverage Ratio
2008
2009
Median Per Unit
Cash Flow
2010
2008
2009
2010
Alabama
BirminghamHoover, AL
97.8%
96.4%
95.8%
1.01
1.07
1.16
$42
$88
$200
Alabama
Daphne-FairhopeFoley, AL
88.3%
89.8%
94.4%
NA
NA
1.15
NA
NA
$238
Alabama
Florence-Muscle
Shoals, AL
97.8%
96.6%
97.4%
1.77
1.71
1.43
$629
$602
$383
Alabama
Huntsville, AL
94.7%
94.6%
94.6%
1.39
1.32
1.40
$406
$358
$337
Alabama
Mobile, AL
95.5%
97.8%
96.9%
1.08
1.32
1.28
$(63)
$574
$497
$222
Alabama
Montgomery, AL
95.0%
96.0%
96.0%
1.06
1.23
1.11
$51
$283
Alabama
Tuscaloosa, AL
92.0%
95.6%
94.8%
0.72
1.22
1.35
$(256)
$177
$77
Alaska
Anchorage, AK
96.0%
95.5%
96.9%
1.16
1.15
1.21
$795
$364
$538
Arizona
Flagstaff, AZ
94.4%
96.9%
98.0%
1.21
1.41
1.37
$104
$340
$708
Arizona
Phoenix-MesaGlendale, AZ
93.0%
92.0%
92.1%
0.96
0.92
1.09
$(99)
$(24)
$(49)
Arizona
Prescott, AZ
95.4%
96.0%
97.7%
1.41
1.28
1.34
$330
$485
$345
Arizona
Show Low, AZ
97.3%
95.8%
95.5%
1.38
1.17
1.07
$590
$318
$190
Arizona
Sierra VistaDouglas, AZ
96.0%
98.5%
97.1%
1.45
1.27
1.26
$758
$293
$313
$322
Arizona
Tucson, AZ
95.7%
96.7%
96.0%
0.94
0.95
1.26
$152
$(118)
Arizona
Yuma, AZ
99.0%
97.5%
97.0%
1.76
1.55
1.42
$998
$789
$583
Arkansas
FayettevilleSpringdaleRogers, AR-MO
95.1%
97.0%
96.7%
1.34
1.43
1.38
$426
$508
$438
Arkansas
Fort Smith, AR-OK
91.7%
95.0%
92.3%
1.14
1.11
1.14
$155
$105
$144
Arkansas
Harrison, AR
97.8%
97.5%
98.3%
1.50
1.41
1.32
$333
$392
$399
Arkansas
Little Rock-North
Little RockConway, AR
95.9%
95.0%
95.2%
1.04
1.08
1.02
$(47)
$63
$(7)
Arkansas
Mountain Home, AR
96.6%
97.2%
96.7%
1.19
1.33
1.23
$454
$444
$136
Arkansas
Texarkana, TXTexarkana, AR
94.9%
94.3%
92.5%
0.89
1.02
1.00
$530
$46
$(40)
California
BakersfieldDelano, CA
98.0%
98.8%
98.6%
1.34
1.27
1.48
$489
$531
$510
California
El Centro, CA
99.2%
99.3%
98.8%
1.39
1.22
1.25
$749
$517
$519
California
Eureka-ArcataFortuna, CA
95.1%
95.5%
97.8%
1.74
1.45
1.74
$1,016
$854
$1,025
California
Fresno, CA
96.3%
96.0%
97.5%
1.25
1.21
1.31
$690
$611
$647
California
HanfordCorcoran, CA
97.8%
97.4%
98.6%
1.29
1.28
1.38
$731
$987
$1,003
California
Los AngelesLong BeachSanta Ana, CA
98.0%
97.5%
97.9%
1.53
1.52
1.44
$982
$1,028
$1,042
California
MaderaChowchilla, CA
97.6%
97.0%
98.7%
1.39
1.93
1.59
$965
$807
$913
California
Merced, CA
98.2%
97.0%
97.0%
1.49
1.27
1.35
$1,019
$397
$486
California
Modesto, CA
98.6%
98.0%
98.4%
1.29
1.09
1.40
$709
$187
$795
California
Oxnard-Thousand
Oaks-Ventura, CA
99.0%
98.5%
98.9%
1.65
1.70
1.53
$2,078
$2,498
$2,002
Median Physical
Occupancy
State
MSA
2008
2009
2010
Median Debt
Coverage Ratio
2008
2009
Median Per Unit
Cash Flow
2010
2008
2009
2010
California
Redding, CA
95.9%
97.8%
95.5%
1.18
1.40
1.06
$378
$559
$35
California
Riverside-San
BernardinoOntario, CA
98.0%
98.0%
98.0%
1.31
1.44
1.38
$646
$894
$661
California
Sacramento—
Arden-Arcade—
Roseville, CA
96.8%
97.0%
97.0%
1.17
1.17
1.18
$460
$456
$685
California
Salinas, CA
98.5%
98.7%
98.2%
1.23
1.34
1.34
$433
$828
$670
California
San DiegoCarlsbad-San
Marcos, CA
97.9%
97.4%
97.9%
1.44
1.46
1.43
$918
$1,188
$1,079
California
San FranciscoOaklandFremont, CA
97.2%
97.4%
97.5%
1.19
1.23
1.21
$628
$706
$688
California
San JoseSunnyvaleSanta Clara, CA
97.8%
97.0%
97.5%
1.21
1.23
1.18
$594
$863
$545
California
San Luis ObispoPaso Robles, CA
100.0%
96.4%
99.7%
1.31
1.38
1.35
$879
$1,099
$1,061
California
Santa BarbaraSanta MariaGoleta, CA
99.6%
97.6%
99.0%
1.16
1.18
1.32
$677
$908
$1,266
California
Santa CruzWatsonville, CA
98.8%
98.0%
98.9%
1.71
1.71
1.82
$1,445
$1,505
$1,222
California
Santa RosaPetaluma, CA
98.0%
98.0%
98.0%
1.52
1.54
1.41
$879
$1,241
$974
California
Stockton, CA
95.9%
92.0%
93.8%
0.84
1.26
1.32
$255
$372
$391
California
Truckee-Grass
Valley, CA
98.6%
98.9%
97.4%
1.35
1.16
1.39
$1,048
$398
$732
California
Vallejo-Fairfield, CA
95.5%
94.6%
95.7%
1.42
1.49
1.15
$845
$1,406
$739
California
VisaliaPorterville, CA
96.7%
96.5%
96.5%
1.49
1.25
1.26
$717
$413
$486
Colorado
Boulder, CO
99.0%
97.2%
97.9%
1.30
1.31
1.40
$808
$980
$1,062
Colorado
Colorado Springs,
CO
95.1%
94.6%
94.9%
1.07
1.11
1.14
$270
$445
$603
Colorado
Denver-AuroraBroomfield, CO
96.3%
96.0%
97.0%
1.07
1.10
1.18
$290
$344
$622
Colorado
Fort CollinsLoveland, CO
98.0%
95.8%
97.5%
1.33
1.35
1.42
$818
$783
$855
Colorado
Pueblo, CO
97.3%
97.8%
97.6%
2.36
1.66
1.64
$1,566
$2,137
$1,561
Connecticut
BridgeportStamfordNorwalk, CT
97.0%
96.8%
97.0%
1.10
1.26
1.12
$43
$74
$(84)
Connecticut
Hartford-West
Hartford-East
Hartford, CT
96.0%
96.1%
96.0%
1.04
1.14
1.05
$(143)
$233
$84
Connecticut
New HavenMilford, CT
97.0%
97.5%
96.0%
0.81
1.05
1.13
$(332)
$32
$(139)
Delaware
Dover, DE
96.0%
98.0%
97.3%
1.05
1.13
1.13
$101
$134
$206
Delaware
Seaford, DE
95.4%
96.0%
96.2%
1.45
1.27
1.28
$476
$348
$329
District of
Columbia
WashingtonArlington-Alexandria,
DC-VA-MD-WV
97.0%
96.5%
96.8%
1.17
1.24
1.25
$581
$736
$764
Median Physical
Occupancy
State
MSA
2008
2009
2010
Median Debt
Coverage Ratio
2008
2009
Median Per Unit
Cash Flow
2010
2008
2009
2010
Florida
Cape Coral-Fort
Myers, FL
95.3%
89.8%
93.5%
1.38
1.04
1.05
$497
$240
$(8)
Florida
Deltona-Daytona
Beach-Ormond
Beach, FL
96.1%
94.8%
95.7%
1.23
1.21
1.24
$207
$280
$218
$597
Florida
Gainesville, FL
95.0%
92.7%
94.4%
0.83
1.23
1.18
$322
$660
Florida
Jacksonville, FL
93.7%
95.7%
95.0%
1.02
1.05
1.05
$(177)
$70
$75
Florida
Lakeland-Winter
Haven, FL
95.3%
95.0%
95.3%
0.89
0.90
1.14
$(222)
$(72)
$107
Florida
Miami-Fort
LauderdalePompano Beach, FL
96.7%
95.9%
95.6%
1.17
1.15
1.18
$281
$303
$474
Florida
Naples-Marco
Island, FL
87.3%
89.5%
93.4%
0.75
1.08
1.15
$(860)
$(658)
$329
Florida
North PortBradentonSarasota, FL
92.7%
91.0%
93.4%
0.94
0.99
1.12
$(116)
$(31)
$164
Florida
OrlandoKissimmeeSanford, FL
94.0%
93.0%
93.9%
1.27
1.19
1.20
$770
$627
$553
Florida
Pensacola-Ferry
Pass-Brent, FL
92.1%
91.5%
93.9%
0.91
0.97
1.12
$(342)
$11
$(102)
Florida
Sebastian-Vero
Beach, FL
91.7%
89.8%
93.2%
0.83
0.57
0.52
$(286)
$(972)
$(1,098)
Florida
Tampa-St.
PetersburgClearwater, FL
94.4%
94.4%
94.3%
1.18
1.27
1.24
$347
$464
$279
Georgia
Atlanta-Sandy
Springs-Marietta, GA
94.1%
92.4%
93.5%
1.00
0.98
0.89
$42
$(38)
$(77)
Georgia
AugustaRichmond
County, GA-SC
96.6%
97.6%
96.0%
0.97
1.15
1.18
$(44)
$144
$153
Georgia
Savannah, GA
91.0%
95.3%
96.0%
0.98
0.96
1.51
$(38)
$73
$898
Georgia
Valdosta, GA
95.7%
97.8%
98.2%
1.03
1.11
1.09
$(39)
$116
$104
Hawaii
Honolulu, HI
99.0%
99.0%
99.0%
1.36
1.64
1.54
$1,586
$2,022
$2,336
Idaho
Boise CityNampa, ID
95.1%
92.5%
95.8%
0.84
0.81
0.85
$(8)
$(383)
$(377)
Idaho
Coeur d'Alene, ID
95.5%
96.5%
94.9%
0.96
1.01
1.14
$(47)
$19
$258
Idaho
Lewiston, ID-WA
97.0%
97.0%
97.8%
1.35
1.33
1.32
$761
$766
$617
Idaho
Twin Falls, ID
91.3%
95.6%
93.8%
0.90
1.14
1.41
$(263)
$190
$515
Illinois
Chicago-JolietNaperville, IL-IN-WI
96.4%
97.0%
96.5%
1.16
1.22
1.24
$294
$418
$531
Illinois
Ottawa-Streator, IL
99.6%
99.9%
95.8%
1.11
1.34
1.72
$506
$389
$910
Illinois
Peoria, IL
94.8%
98.0%
97.4%
0.98
1.06
1.60
$(42)
$242
$880
Illinois
Rockford, IL
94.4%
93.0%
92.9%
0.86
1.08
1.17
$(342)
$300
$670
Illinois
Springfield, IL
95.0%
95.0%
92.5%
1.10
1.17
1.13
$215
$212
$386
Indiana
Evansville, IN-KY
96.0%
97.0%
95.0%
1.46
1.50
1.27
$652
$711
$322
Indiana
Fort Wayne, IN
92.0%
94.5%
92.0%
1.05
1.12
1.05
$231
$209
$119
Indiana
IndianapolisCarmel, IN
92.6%
93.2%
94.0%
0.77
0.99
1.17
$(482)
$49
$284
Indiana
Muncie, IN
94.0%
96.8%
97.9%
0.73
1.56
0.89
$(73)
$497
$72
Median Physical
Occupancy
State
Indiana
MSA
South BendMishawaka, IN-MI
2008
91.5%
2009
93.0%
2010
93.5%
Median Debt
Coverage Ratio
2008
0.81
2009
0.83
Median Per Unit
Cash Flow
2010
2008
1.17
$(422)
2009
2010
$(185)
$159
Iowa
Cedar Rapids, IA
95.4%
90.9%
93.5%
1.33
1.03
1.06
$436
$48
$(31)
Iowa
DavenportMoline-Rock
Island, IA-IL
96.4%
94.6%
95.8%
1.09
1.07
1.12
$204
$174
$134
Iowa
Des Moines-West
Des Moines, IA
94.5%
95.5%
96.0%
1.14
1.09
1.14
$316
$423
$416
Iowa
Iowa City, IA
85.3%
88.8%
91.1%
0.69
1.08
1.40
$(535)
$77
$609
Iowa
Sioux City, IANE-SD
92.1%
95.3%
95.8%
1.11
1.17
1.22
$213
$331
$692
Iowa
Waterloo-Cedar
Falls, IA
92.1%
97.6%
97.7%
1.28
1.27
1.55
$(241)
$95
$312
Kansas
Coffeyville, KS
95.0%
96.9%
96.8%
1.28
1.31
2.06
$286
$282
$897
Kansas
Hutchinson, KS
99.2%
98.6%
98.8%
1.23
1.09
1.08
$281
$124
$137
Kansas
Manhattan, KS
98.0%
96.0%
97.0%
1.30
1.30
1.10
$419
$571
$109
Kansas
Topeka, KS
93.8%
91.0%
92.9%
0.55
1.15
0.97
$(11)
$42
$(46)
Kansas
Wichita, KS
95.9%
96.1%
95.5%
1.27
1.14
1.07
$345
$382
$148
Kentucky
LexingtonFayette, KY
97.7%
96.8%
97.0%
0.96
1.27
1.40
$(306)
$639
$491
Kentucky
Louisville/Jefferson
County, KY-IN
95.1%
95.5%
95.4%
1.17
1.19
1.27
$250
$261
$436
Louisiana
Alexandria, LA
97.3%
95.5%
96.8%
1.83
1.35
1.19
$1,284
$690
$792
Louisiana
Baton Rouge, LA
95.0%
94.8%
95.0%
1.14
1.23
1.14
$-
$303
$344
Louisiana
Hammond, LA
98.8%
98.1%
98.7%
1.71
1.36
1.44
$434
$342
$428
Louisiana
Houma-Bayou
Cane-Thibodaux, LA
97.0%
98.1%
98.9%
1.09
1.20
1.41
$(227)
$190
$385
Louisiana
Lafayette, LA
97.5%
95.0%
98.5%
1.48
1.22
1.36
$733
$330
$681
Louisiana
Lake Charles, LA
98.0%
98.0%
96.1%
1.27
1.23
1.30
$719
$750
$702
Louisiana
Monroe, LA
96.0%
96.0%
98.0%
1.21
1.33
1.33
$399
$464
$526
Louisiana
New OrleansMetairie-Kenner, LA
99.1%
96.0%
95.9%
1.20
1.14
1.07
$(114)
$(25)
$198
Louisiana
OpelousasEunice, LA
95.0%
94.6%
95.9%
1.42
1.56
1.37
$397
$813
$592
Louisiana
Ruston, LA
95.2%
95.8%
96.6%
1.36
1.46
1.46
$393
$710
$1,067
Louisiana
ShreveportBossier City, LA
95.3%
96.9%
97.0%
1.35
1.36
1.37
$767
$702
$976
Maine
Bangor, ME
98.0%
98.5%
97.8%
1.18
1.63
1.83
$290
$102
$490
Maine
Portland-South
PortlandBiddeford, ME
97.6%
97.4%
97.0%
1.28
1.34
1.32
$800
$604
$750
Maryland
BaltimoreTowson, MD
96.4%
97.0%
97.0%
1.20
1.17
1.28
$211
$352
$431
Maryland
Cambridge, MD
97.5%
96.9%
97.6%
1.14
1.79
1.36
$1,076
$580
$508
Maryland
HagerstownMartinsburg,
MD-WV
97.1%
97.5%
98.6%
1.33
1.23
1.50
$372
$325
$981
Maryland
Salisbury, MD
97.0%
96.5%
96.0%
1.12
1.18
1.12
$161
$253
$181
Massachusetts
Barnstable
Town, MA
98.0%
98.5%
99.0%
1.42
1.49
1.60
$800
$538
$506
Median Physical
Occupancy
State
MSA
2008
2009
2010
Median Debt
Coverage Ratio
2008
2009
Median Per Unit
Cash Flow
2010
2008
2009
2010
Massachusetts
BostonCambridgeQuincy, MA-NH
96.5%
97.0%
97.2%
1.15
1.21
1.25
$378
$639
$723
Massachusetts
Pittsfield, MA
96.3%
96.7%
97.0%
1.29
1.15
0.80
$61
$252
$(153)
Massachusetts
Springfield, MA
96.5%
95.8%
96.9%
1.05
1.13
1.27
$61
$232
$231
Massachusetts
Worcester, MA
97.6%
97.3%
98.5%
1.24
1.18
1.19
$763
$566
$591
Michigan
Allegan, MI
93.5%
92.5%
95.0%
0.93
1.06
0.97
$(25)
$256
$(36)
Michigan
Ann Arbor, MI
97.0%
96.9%
96.0%
1.22
1.26
1.15
$368
$285
$518
Michigan
Detroit-WarrenLivonia, MI
94.0%
94.1%
95.0%
0.85
1.00
0.99
$(275)
$(54)
$(14)
Michigan
Flint, MI
92.0%
96.0%
95.8%
1.01
1.12
1.30
$144
$392
$546
Michigan
Grand RapidsWyoming, MI
96.0%
96.0%
97.0%
1.02
1.08
1.09
$46
$220
$232
Michigan
Holland-Grand
Haven, MI
87.0%
90.0%
92.0%
0.62
1.16
1.06
$(771)
$(778)
$248
Michigan
KalamazooPortage, MI
92.0%
94.9%
95.0%
0.76
1.01
1.07
$(403)
$49
$149
Michigan
Lansing-East
Lansing, MI
92.0%
93.0%
93.0%
0.85
0.99
1.07
$(251)
$15
$191
$(343)
Michigan
Midland, MI
96.0%
97.3%
96.0%
0.94
0.83
0.80
$(75)
$(198)
Michigan
Mount Pleasant, MI
96.0%
95.0%
96.5%
1.35
1.37
1.27
$738
$632
$634
Michigan
Muskegon-Norton
Shores, MI
88.7%
93.0%
94.0%
1.01
0.98
1.04
$(58)
$(176)
$137
Michigan
Niles-Benton
Harbor, MI
95.5%
95.0%
96.0%
1.21
0.97
1.02
$525
$50
$(31)
Michigan
Owosso, MI
91.5%
92.5%
97.0%
1.13
1.06
1.20
$377
$186
$477
Michigan
Saginaw-Saginaw
Township North, MI
91.0%
92.0%
94.0%
1.01
1.07
1.18
$27
$233
$397
$822
Michigan
Traverse City, MI
92.0%
90.0%
89.0%
0.99
1.08
1.13
$143
$341
Minnesota
Bemidji, MN
98.6%
98.1%
99.5%
1.43
1.37
1.22
$201
$321
$120
Minnesota
Brainerd, MN
96.0%
98.0%
99.0%
1.64
1.50
1.63
$1,192
$701
$618
Minnesota
Duluth, MN-WI
96.0%
94.6%
94.8%
1.23
1.38
1.59
$648
$675
$1,110
Minnesota
Minneapolis-St.
Paul-Bloomington,
MN-WI
96.6%
96.4%
97.1%
1.15
1.19
1.25
$493
$695
$713
Minnesota
Rochester, MN
96.3%
97.3%
96.9%
1.65
1.52
1.52
$1,177
$989
$588
Minnesota
St. Cloud, MN
97.4%
96.9%
97.8%
1.30
1.30
1.24
$617
$691
$653
Mississippi
Gulfport-Biloxi, MS
96.0%
89.0%
90.0%
1.15
1.37
1.24
$410
$848
$336
Mississippi
Hattiesburg, MS
96.5%
96.3%
98.6%
1.40
1.31
1.63
$378
$318
$374
Mississippi
Jackson, MS
93.6%
94.0%
97.0%
1.07
1.11
1.28
$155
$56
$601
Missouri
Branson, MO
92.9%
91.3%
91.0%
1.16
0.99
1.00
$286
$(10)
$10
Missouri
Jefferson City, MO
87.1%
90.8%
92.9%
0.86
0.72
0.88
$(56)
$(227)
$(156)
$528
Missouri
Joplin, MO
96.6%
95.8%
96.3%
1.32
1.41
1.48
$260
$395
Missouri
Kansas City, MO-KS
95.6%
95.7%
96.4%
1.06
1.09
1.14
$128
$187
$290
Missouri
Springfield, MO
95.3%
96.6%
95.7%
1.46
1.53
1.50
$345
$771
$579
Missouri
St. Louis, MO-IL
95.0%
94.0%
95.0%
1.10
1.12
1.19
$154
$315
$302
Montana
Bozeman, MT
94.8%
86.8%
96.1%
1.09
0.78
1.04
$349
$(563)
$161
Montana
Kalispell, MT
98.0%
97.5%
99.0%
1.41
1.43
1.33
$524
$472
$392
Median Physical
Occupancy
State
MSA
2008
2009
2010
Median Debt
Coverage Ratio
2008
2009
Median Per Unit
Cash Flow
2010
2008
2009
2010
Montana
Missoula, MT
92.6%
94.4%
96.4%
1.21
1.56
2.08
$870
$1,357
$1,061
Nebraska
Grand Island, NE
95.9%
96.1%
96.6%
1.14
1.01
1.33
$246
$93
$507
Nebraska
Lincoln, NE
97.6%
97.9%
97.6%
1.20
1.44
1.39
$632
$892
$870
Nebraska
Omaha-Council
Bluffs, NE-IA
94.1%
94.4%
95.2%
1.12
1.13
1.24
$158
$230
$292
Nevada
Las VegasParadise, NV
97.8%
97.0%
95.8%
1.44
1.26
1.31
$433
$487
$639
Nevada
Reno-Sparks, NV
97.1%
96.0%
95.2%
1.13
1.20
1.05
$112
$213
$120
New
Hampshire
Concord, NH
96.3%
96.0%
99.6%
1.01
1.43
2.04
$37
$980
$1,656
New
Hampshire
Keene, NH
97.6%
95.8%
96.0%
0.64
1.59
1.79
$(117)
$546
$466
New
Hampshire
Lebanon, NH-VT
97.6%
97.5%
96.7%
1.04
1.28
1.24
$68
$535
$522
New
Hampshire
ManchesterNashua, NH
97.0%
97.2%
96.3%
1.26
1.15
1.38
$463
$412
$609
New Jersey
Trenton-Ewing, NJ
98.0%
95.4%
96.2%
1.44
1.03
1.09
$43
$24
$130
New Mexico
Albuquerque, NM
94.5%
96.7%
96.2%
1.27
1.22
1.32
$525
$566
$713
$557
New Mexico
Las Cruces, NM
96.6%
96.0%
97.5%
1.17
1.21
1.39
$366
$426
New Mexico
Santa Fe, NM
94.3%
92.5%
95.9%
1.22
1.14
1.16
$809
$771
$860
New York
AlbanySchenectadyTroy, NY
98.4%
98.0%
97.9%
1.51
1.53
1.57
$922
$808
$990
New York
Binghamton, NY
97.9%
97.0%
97.0%
1.08
1.58
1.34
$359
$208
$277
New York
Buffalo-Niagara
Falls, NY
98.0%
97.3%
97.9%
1.10
1.22
1.16
$114
$357
$516
New York
Glens Falls, NY
98.0%
97.9%
98.2%
1.50
1.45
1.77
$613
$400
$732
New York
JamestownDunkirk-Fredonia, NY
95.6%
95.9%
95.0%
-
1.89
1.26
$(149)
$276
$(9)
New York
Kingston, NY
99.2%
99.0%
99.0%
1.24
1.58
1.80
$426
$670
$674
New York
New York-Northern
New Jersey-Long
Island, NY-NJ-PA
97.4%
97.7%
97.8%
1.21
1.44
1.46
$251
$557
$675
New York
PoughkeepsieNewburghMiddletown, NY
97.1%
97.5%
98.1%
1.04
1.28
1.57
$20
$480
$797
$679
New York
Rochester, NY
96.0%
96.7%
96.4%
1.28
1.30
1.49
$329
$408
New York
Syracuse, NY
98.2%
96.5%
97.7%
1.61
1.68
1.76
$436
$619
$619
New York
Utica-Rome, NY
96.1%
97.0%
97.0%
1.91
1.54
1.71
$517
$609
$539
North
Carolina
Asheville, NC
98.0%
98.1%
98.4%
1.15
1.35
1.50
$158
$357
$486
North
Carolina
CharlotteGastonia-Rock
Hill, NC-SC
96.2%
97.1%
97.0%
1.28
1.53
1.34
$488
$779
$598
North
Carolina
DurhamChapel Hill, NC
96.5%
96.1%
97.9%
0.97
0.91
0.97
$(50)
$(243)
$(208)
North
Carolina
Fayetteville, NC
95.2%
96.4%
96.9%
1.59
1.86
1.49
$669
$786
$531
North
Carolina
GreensboroHigh Point, NC
95.5%
93.5%
97.0%
1.18
1.15
1.15
$93
$169
$210
North
Carolina
Greenville, NC
91.7%
95.3%
95.0%
1.53
1.92
1.84
$374
$879
$883
Median Physical
Occupancy
State
MSA
2008
2009
2010
Median Debt
Coverage Ratio
2008
2009
Median Per Unit
Cash Flow
2010
2008
2009
2010
North
Carolina
Hickory-LenoirMorganton, NC
93.0%
93.3%
96.0%
1.19
1.13
1.35
$274
$180
$481
North
Carolina
Jacksonville, NC
96.5%
96.2%
98.5%
1.04
1.45
1.24
$(10)
$527
$547
North
Carolina
Lumberton, NC
96.9%
99.0%
97.5%
1.33
1.79
1.64
$131
$883
$684
North
Carolina
Raleigh-Cary, NC
96.0%
96.0%
97.0%
1.12
1.21
1.27
$223
$290
$501
North
Carolina
Roanoke
Rapids, NC
94.6%
99.8%
97.8%
1.15
1.65
1.51
$177
$862
$478
North
Carolina
Rocky Mount, NC
99.0%
99.0%
99.1%
1.73
1.89
2.24
$659
$996
$1,377
North
Carolina
Shelby, NC
96.2%
96.8%
97.6%
0.91
1.01
0.96
$(223)
$29
$100
North
Carolina
Wilmington, NC
98.1%
96.0%
95.5%
1.37
1.27
1.28
$422
$342
$535
North
Carolina
Winston-Salem, NC
97.3%
96.7%
95.8%
1.10
1.11
1.05
$124
$197
$88
North
Dakota
Bismarck, ND
94.5%
97.5%
98.3%
1.03
1.27
1.33
$87
$605
$728
North
Dakota
Fargo, ND-MN
97.4%
98.3%
96.4%
1.12
1.08
1.14
$291
$252
$289
Ohio
Akron, OH
97.3%
97.6%
97.6%
0.97
0.88
1.33
$(62)
$(208)
$342
Ohio
CantonMassillon, OH
96.5%
96.3%
97.8%
0.96
0.91
1.18
$(152)
$(161)
$340
Ohio
CincinnatiMiddletown,
OH-KY-IN
94.6%
94.8%
94.0%
0.97
1.03
1.06
$(262)
$39
$42
Ohio
Cleveland-ElyriaMentor, OH
97.2%
97.3%
96.7%
1.00
1.12
1.13
$(85)
$116
$258
Ohio
Columbus, OH
96.9%
96.5%
97.2%
0.90
1.02
1.20
$(76)
$57
$389
Ohio
Dayton, OH
95.8%
95.0%
95.8%
1.10
0.94
1.09
$108
$91
$223
Ohio
Mansfield, OH
97.0%
96.0%
94.6%
1.06
1.19
1.05
$68
$377
$103
Ohio
Springfield, OH
95.4%
95.0%
95.8%
0.90
1.05
0.96
$(139)
$52
$(72)
Ohio
SteubenvilleWeirton, OH-WV
96.7%
95.8%
96.7%
0.95
0.97
1.12
$(94)
$(43)
$231
Ohio
Toledo, OH
98.9%
93.8%
95.4%
1.15
1.00
1.29
$290
$(51)
$387
Ohio
YoungstownWarrenBoardman, OH-PA
95.2%
94.8%
94.9%
0.85
0.86
1.05
$44
$(257)
$104
Oklahoma
Oklahoma City, OK
95.8%
95.5%
96.0%
1.30
1.29
1.25
$368
$465
$372
Oklahoma
Stillwater, OK
95.7%
92.0%
94.3%
1.40
1.08
1.34
$478
$273
$554
Oklahoma
Tulsa, OK
96.0%
96.0%
96.3%
1.26
1.14
1.27
$269
$208
$268
Oregon
Bend, OR
95.7%
87.4%
95.8%
1.46
1.11
1.03
$948
$475
$149
Oregon
EugeneSpringfield, OR
97.9%
97.4%
98.7%
1.34
1.30
1.22
$349
$310
$335
Oregon
Medford, OR
97.1%
96.9%
97.2%
1.34
1.31
1.23
$986
$639
$550
Oregon
PendletonHermiston, OR
95.8%
94.0%
96.4%
1.09
1.27
1.26
$480
$212
$305
Oregon
PortlandVancouverHillsboro, OR-WA
96.7%
95.8%
96.0%
1.14
1.11
1.20
$342
$287
$497
Median Physical
Occupancy
State
MSA
2008
2009
2010
Median Debt
Coverage Ratio
2008
2009
Median Per Unit
Cash Flow
2010
2008
2009
2010
Oregon
Salem, OR
97.4%
96.8%
96.2%
1.08
1.33
1.39
$76
$516
$452
Pennsylvania
AllentownBethlehemEaston, PA-NJ
96.0%
96.7%
97.6%
1.21
1.49
1.46
$647
$331
$275
Pennsylvania
HarrisburgCarlisle, PA
95.5%
92.4%
95.9%
1.30
1.36
1.44
$476
$497
$331
Pennsylvania
PhiladelphiaCamdenWilmington,
PA-NJ-DE-MD
96.7%
97.0%
96.7%
1.09
1.12
1.16
$80
$120
$211
Pennsylvania
Pittsburgh, PA
97.0%
96.5%
97.0%
1.10
1.20
1.36
$(10)
$293
$208
Pennsylvania
Scranton—
Wilkes-Barre, PA
99.0%
98.0%
98.0%
1.43
1.72
1.30
$(180)
$(162)
$438
Pennsylvania
York-Hanover, PA
96.3%
98.4%
97.8%
1.70
1.30
1.49
$358
$395
$546
Puerto Rico
San Juan-CaguasGuaynabo, PR
99.9%
100.0%
100.0%
1.09
1.22
1.23
$340
$388
$495
Rhode
Island
Providence-New
Bedford-Fall River,
RI-MA
97.0%
97.0%
96.8%
1.26
1.26
1.30
$266
$433
$371
South
Carolina
Anderson, SC
96.2%
92.2%
94.6%
1.01
1.12
1.28
$23
$145
$463
South
Carolina
Charleston-North
CharlestonSummerville, SC
96.4%
97.1%
95.9%
0.98
1.06
1.19
$-
$132
$301
South
Carolina
Columbia, SC
96.7%
97.5%
97.0%
1.20
1.19
1.26
$530
$376
$379
South
Carolina
Florence, SC
96.8%
96.3%
98.7%
1.40
1.31
1.51
$399
$260
$705
South
Carolina
GreenvilleMauldin-Easley, SC
95.8%
95.0%
95.5%
1.12
1.16
1.20
$353
$329
$567
South
Carolina
Hilton Head
Island-Beaufort, SC
95.1%
95.4%
94.4%
1.16
1.14
1.14
$163
$372
$331
South
Carolina
Myrtle Beach-North
Myrtle BeachConway, SC
93.8%
96.2%
95.6%
1.29
1.17
1.31
$344
$201
$492
South
Carolina
Spartanburg, SC
96.0%
96.5%
96.0%
0.87
1.05
0.87
$(411)
$(190)
$(165)
South
Dakota
Rapid City, SD
95.3%
95.8%
96.4%
1.17
1.46
1.36
$355
$956
$727
South
Dakota
Sioux Falls, SD
93.9%
92.1%
92.0%
1.18
1.20
1.06
$568
$447
$201
Tennessee
Chattanooga,
TN-GA
97.5%
98.0%
97.4%
1.53
1.00
1.13
$179
$137
$149
Tennessee
Knoxville, TN
94.6%
94.0%
93.9%
0.92
1.08
1.14
$(436)
$171
$115
Tennessee
Memphis,
TN-MS-AR
95.0%
93.9%
95.0%
1.10
1.15
1.22
$33
$324
$281
Tennessee
NashvilleDavidson—
Murfreesboro—
Franklin, TN
97.0%
93.1%
95.9%
1.15
1.09
1.13
$276
$257
$418
Texas
Austin-Round
Rock-San Marcos, TX
94.9%
92.0%
94.6%
1.01
1.01
0.93
$45
$184
$(78)
Texas
Beaumont-Port
Arthur, TX
96.6%
96.5%
96.6%
1.02
1.18
1.38
$56
$312
$466
Median Physical
Occupancy
State
MSA
2008
2009
2010
Median Debt
Coverage Ratio
2008
2009
Median Per Unit
Cash Flow
2010
2008
2009
2010
Texas
BrownsvilleHarlingen, TX
96.4%
97.2%
96.4%
1.23
1.57
1.32
$599
$1,167
$620
Texas
College StationBryan, TX
93.7%
94.6%
94.8%
1.46
1.24
1.25
$510
$139
$480
Texas
Corpus Christi, TX
96.9%
97.0%
96.9%
1.24
1.12
1.23
$622
$258
$426
Texas
Dallas-Fort WorthArlington, TX
94.3%
94.0%
94.0%
1.07
1.06
1.11
$158
$198
$266
Texas
El Paso, TX
97.0%
97.6%
97.9%
1.30
1.42
1.66
$489
$708
$993
Texas
Houston-Sugar
Land-Baytown, TX
95.4%
94.8%
94.9%
1.02
1.09
1.16
$97
$262
$327
Texas
Lubbock, TX
90.0%
91.8%
94.5%
1.13
1.24
1.02
$(66)
$229
$73
Texas
McAllen-EdinburgMission, TX
96.6%
97.9%
98.3%
1.32
1.35
1.24
$708
$647
$642
Texas
San AntonioNew Braunfels, TX
96.2%
95.0%
96.0%
1.02
1.09
1.16
$92
$461
$609
Utah
OgdenClearfield, UT
96.0%
97.0%
97.5%
1.21
1.26
1.55
$510
$712
$1,512
Utah
Salt Lake City, UT
Utah
St. George, UT
97.8%
96.0%
96.3%
1.22
1.20
1.13
$521
$535
$330
100.0%
100.0%
100.0%
1.23
1.83
1.90
$689
$1,215
$1,261
Vermont
Barre, VT
96.9%
96.7%
94.8%
0.88
1.28
1.26
$(249)
$444
$307
Vermont
Burlington-South
Burlington, VT
97.5%
97.9%
98.0%
1.21
1.25
1.32
$404
$666
$800
Virginia
Danville, VA
96.5%
95.1%
96.0%
1.04
0.99
1.11
$175
$469
$216
Virginia
Richmond, VA
94.1%
93.7%
95.0%
1.11
1.07
1.12
$153
$271
$336
Virginia
Roanoke, VA
96.8%
96.8%
97.6%
1.20
1.20
1.03
$71
$171
$43
Virginia
Virginia BeachNorfolk-Newport
News, VA-NC
97.5%
97.0%
97.3%
1.25
1.29
1.34
$491
$584
$745
Washington
Bellingham, WA
97.0%
97.3%
98.5%
1.14
1.30
1.47
$282
$256
$633
Washington
BremertonSilverdale, WA
95.6%
95.0%
96.4%
1.36
1.35
1.29
$667
$770
$641
Washington
Moses Lake, WA
97.0%
96.4%
95.9%
1.22
1.64
1.55
$480
$689
$669
Washington
Mount VernonAnacortes, WA
96.3%
96.1%
98.2%
1.07
0.98
0.91
$118
$268
$(139)
Washington
Seattle-TacomaBellevue, WA
97.1%
96.4%
97.0%
1.24
1.23
1.19
$441
$474
$395
Washington
Spokane, WA
97.0%
99.2%
95.0%
1.42
1.26
1.48
$415
$372
$535
Washington
Yakima, WA
95.0%
95.5%
97.0%
1.23
1.24
1.23
$272
$241
$402
West Virginia
Charleston, WV
98.3%
97.2%
96.5%
1.09
0.98
1.03
$98
$7
$119
West Virginia
Huntington-Ashland,
WV-KY-OH
95.0%
97.0%
98.4%
1.14
1.01
1.30
$161
$313
$355
West Virginia
ParkersburgMarietta-Vienna,
WV-OH
98.5%
94.4%
97.0%
1.16
1.26
1.40
$139
$397
$511
West Virginia
Wheeling, WV-OH
96.2%
97.4%
98.9%
1.14
1.17
1.15
$241
$314
$389
Wisconsin
Janesville, WI
96.0%
96.5%
95.1%
1.10
1.09
1.04
$219
$233
$108
Wisconsin
Madison, WI
95.8%
97.7%
96.3%
1.13
1.15
1.20
$603
$577
$950
Wisconsin
MilwaukeeWaukesha-West
Allis, WI
96.0%
94.6%
95.4%
0.98
1.08
1.10
$39
$218
$201
Wisconsin
Racine, WI
92.6%
90.6%
96.0%
1.15
1.35
1.27
$296
$1,039
$839
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low-income housing, historic rehabilitation, new markets and
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fairly narrow industry focus, TCIS covers a variety of consulting
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An Expanded Look at Its Performance

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