Crowd Out, Stigma, and the Effect of Place-Based Subsidized Rental

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					                               Crowd Out, Stigma, and the Effect
                           of Place-Based Subsidized Rental Housing


                                                 By

                                        Michael D. Eriksen
                       Department of Economics and Center for Policy Research
                                        Syracuse University
                                  Syracuse, New York 13244-1020
                                       Phone: (315) 443-9054
                                        Fax: (315) 443-1081
                                Email: meriksen@maxwell.syr.edu

                                                 and

                                          Stuart S. Rosenthal
                       Department of Economics and Center for Policy Research
                                         Syracuse University
                                  Syracuse, New York 13244-1020
                                        Phone: (315) 443-3809
                                         Fax: (315) 443-1081
                                 Email: ssrosent@maxwell.syr.edu
                              http://faculty.maxwell.syr.edu/rosenthal/



                                         September 12, 2007




Funding for this project from the John D. and Catherine T. MacArthur Foundation, the Ford Foundation
and the Department of Housing and Urban Development is gratefully acknowledged. We thank Denise
DiPasquale, Gary Engelhardt, Jeffrey Kubik, Edgar Olsen, Erika Poethig, Michael Stegman, Bruce
Weinberg, Johnny Yinger, and seminar participants at the January 2007 AREUEA meetings and Ohio
State University for helpful comments. Any remaining errors are ours alone.
                                                Abstract

The Low-Income Housing Tax Credit (LIHTC) program has ballooned into the largest ever source of
subsidized construction of low-income housing in the United States, accounting for one-third of all recent
multi-family rental construction. Recent proposals in Congress have sought to double the size of the
program. This paper examines the crowd out and stigma/amenity effects of this increasingly important
source of low-income housing. To do so, we apply a unique geographic approach to the data as well as
instrumental variable methods that facilitate identification.

Results indicate that within one-half mile, LIHTC development likely has a positive amenity effect in
low-income areas but a negative stigma effect in high-income areas. These effects attenuate with
distance. Expanding the geographic scope of the analysis to ten mile areas, one-third of LIHTC
development is offset by crowd out of private unsubsidized rental housing construction. This is
consistent with high levels of crowd out found in other markets (e.g. Berry and Waldfogel, 1999) and also
for earlier forms of place-based subsidized housing (e.g. Murray, 1999; Sinai and Waldfogel, 2005).
These patterns raise questions about the viability of the LIHTC program and should be taken into account
when considering its expansion.

Key Words: Crowd Out, Stigma, Subsidized Housing
JEL Codes: H7, H42, R21, R31
        “I rise today to introduce the Affordable Housing Tax Credit Enhancement Act of 2005. … the
        bill would double the current LIHTC [annual allocations], which would yield twice the number of
        affordable units annually. … Today, the LIHTC program is widely regarded as the nation’s most
        successful housing production program resulting in the construction and rehabilitation of more
        than 1.3 million housing units for lower income households. …”

        Statements Submitted to Congressional Record: May 26, 2005
        By Rep. William Jefferson (D-LA)


1. Introduction

        The manner in which housing assistance to the poor is provided remains subject to considerable

and even heated debate: should government invest in people through demand side voucher type programs

(e.g. Section 8 vouchers) or in places through supply side construction subsidies such as public and Low

Income Housing Tax Credit (LIHTC) housing? Against that backdrop, this paper examines the rapidly

growing LIHTC program and highlights two features that have received little attention: what are the

neighborhood-level stigma (or amenity) effects of LIHTC housing – a shifter of demand – and to what

extent does the construction of LIHTC housing crowd out unsubsidized private development – a supply-

side effect. To anticipate, our findings suggest that crowd out rates associated with the LIHTC program

are sufficiently high as to raise questions about the viability of the program, let alone recent proposals to

double its size. Some further context will help to put the LIHTC program in perspective.

        Between the late 1930s and the mid-1980s, the federal government built over one million low-

income units through the public housing program. During the 1980s, construction of public housing units

ended and was replaced by the Low-Income Housing Tax Credit (LIHTC) program. Under different

variants of the LIHTC program between 30 to 91 percent of project construction costs are subsidized.

Those subsidies are financed by the Federal government which allocates tax credits to individual states

based on state population. State housing authorities then reallocate the credits to developers who agree to

adhere to rent ceilings and tenant income limits. From 1987 to 2005, over 1.3 million LIHTC units were

built accounting for roughly one-third of all recent multi-family rental housing constructed. This makes

the LIHTC program the largest supply-side housing subsidy program in the nation’s history. Figure 1
illustrates this point. The figure displays the level of public housing and LIHTC development by decade

over the last 60 years.1 The recent boom in LIHTC development is evident.2

         To assess the crowd out effect of LIHTC housing on unsubsidized rental housing construction,

we combine census tract data from 1970 to 2000 with information on LIHTC units developed since 1987.

Identification of crowd out is based on a simple argument that relies on the geographic scope of the

analysis. Specifically, at the neighborhood level, LIHTC development is likely endogenous because site

selection is sensitive to anticipated capital gains (e.g. Baum-Snow and Marion, 2006) and sharp financial

penalties if a given project is unable to house sufficient numbers of low-income families (Eriksen, 2007).3

However, as the geographic scope of the analysis expands, substitution of new housing capital nets out

across neighborhoods, and LIHTC development becomes exogenous. This implies that non-IV models

should be consistent when the geographic scope of the analysis is sufficiently large. A further implication

is that OLS and IV models should yield different estimates of crowd out for narrow levels of geography,

but similar estimates for broader levels of geography. These and other patterns will be tested.

         To implement this strategy, we estimate the impact of LIHTC development on new, unsubsidized

rental housing development. We do this separately for different levels of geographic scope, from one-half

mile to 10-mile radius circle areas. In all cases we control for MSA fixed effects that enable us to strip

away unobserved MSA-level attributes that might otherwise affect rental housing construction. In

addition, for each level of geography, OLS and GMM estimates are computed. For the latter, we


1
 The public housing data used to create Figure 1 were obtained from analysts at HUD and with assistance from
MacArthur and Abt Associates. These data differ from the publicly available series found at www.huduser.org in
two ways. First, our data contain the year each “project” was placed-in-service from 1937 to 2000. This allows us
to represent new construction of public housing in each decade. Additionally, our data also includes information on
public housing demolitions in the 1990’s. The LIHTC data were obtained from HUD at http://lihtc.huduser.org.
2
 The cost to the federal government in lost tax revenue from the LIHTC program totaled $4.9 billion in 2006. That
loss is expected to increase sharply in the next few years in response to the 40 percent increase in credits allocated
beginning in 2001. Those increases will occur even if current proposals seeking to expand the LIHTC program are
not born out. Additional details on the cost of various forms of low-income housing support are provided in a report
by the U.S. Congress, Joint Committee on Taxation (2005).
3
 LIHTC developers are obliged to fill 40 percent or more of their units with low-income families and are required to
charge rents below a specified ceiling. Non-compliance with these terms results in forfeiture of future tax credits
and also repayment of one-third of previously received allocations plus interest.


                                                                                                                     2
instrument for LIHTC housing between 1987 and 2000 using 1970 housing stock structure types (i.e.

multi-family, single family) and the distribution of bedrooms for both owner- and renter-occupied units.

The intuition behind these instruments is that a past willingness to allow high-density development may

affect the current willingness of the local community to allow further multi-family housing development,

including LIHTC projects.4 An F-test for the joint significance of excluded instruments reported later in

the paper confirm that these instruments are strongly correlated with the level of LIHTC development.

Moreover, in contrast to growth controls, zoning ordinances that limit high-density development need not

affect the level of private rental housing construction provided sufficient open land is available for low-

density development. If such conditions hold, the instruments can be excluded from the structural model.

To test that assumption, Hansen-J (1982) statistics are computed for each level of geography.5 Results

fail to reject the overidentifying restrictions, and especially so for the ten-mile level of geography. This

lends further support for the validity of the instruments.

         Results indicate that for one-half mile radius circle areas OLS estimates of crowd out are

significantly larger than GMM estimates. However, those differences diminish monotonically as the

geographic scope of the analysis is increased. This implies that LIHTC developers tend to locate their

projects in neighborhoods that would ordinarily receive little rental housing construction. In certain

respects, that is consistent with the goals of the LIHTC program. But as noted above, it is important to

also recognize that developers may substitute new housing capital from one neighborhood to another

when building LIHTC projects. As we expand the geographic scope of the analysis to ten mile circle

areas, OLS and GMM estimates become nearly identical. This is consistent with the idea that for

sufficiently large geographic areas LIHTC development is approximately exogenous. Importantly, for the

ten-mile circle area models, our estimates indicate that roughly one-third of LIHTC development is offset


4
 This could be manifested in local zoning laws, for example, that either explicitly allow or preclude high-density
development.
5
Hansen-J tests evaluate the joint null that the instruments are both exogenous and do not belong in the structural
model.



                                                                                                                     3
by crowd out of unsubsidized rental housing construction, an amount large enough to warrant attention

when forming policy

         In certain respects, the high degree of crowd out should not be surprising. Numerous studies in

the literature indicate that housing demand is inelastic while new housing supply is quite elastic.6 A

simple model outlined later in the paper demonstrates that under such market conditions, high rates of

crowd out will occur. On balance, therefore, advocates of LIHTC development need to look beyond

expansion of the total stock of rental housing to justify the LIHTC program. In that regard, it is important

to also recognize that LIHTC development is not exclusively a low-income neighborhood event, and our

paper has something to offer on this point as well.

         As shown in Figure 2a, as of 2000, 16 percent of LIHTC units were located in high-income

census tracts, defined here as tracts in the upper third of their MSA income distribution. Another 28

percent were situated in middle-income communities, with the remaining 56 percent in lower-third

income neighborhoods. The extension of LIHTC housing into middle- and higher-income neighborhoods

differs markedly from public housing. As shown in Figure 2b, 77 percent of public housing units are in

low-income locations with most of the rest in middle-income communities. These summary measures are

strongly suggestive that LIHTC development has expanded low-income housing opportunities in higher

income communities.7

         To shed further light on this feature of the program, we consider the stigma/amenity effects

associated with nearby LIHTC development in high- and low-income communities. Because

stigma/amenity effects likely attenuate sharply with distance, our focus in this portion of the analysis is

primarily on activity in close proximity to LIHTC developments, as within one-half mile. In considering


6
 Using data from the rental housing allowance demand experiment from the 1970s, Hanushek and Quigley (1980)
estimate rental housing price elasticities for Pittsburgh and Phoenix of -0.36 and -0.41, respectively. Other estimates
of the price elasticity of housing demand based on owner-occupied homes range from -0.3 to -1.0. Estimates of the
elasticity of supply of new housing construction are nearly always far above 1. For rental housing construction, the
most reliable supply elasticity estimates are in excess of 3.
7
 In principle, this need not be the case if construction of LIHTC housing in higher-income communities is fully
offset by 100 percent crowd out. However, estimates later in the paper suggest that partial but not full crowd-out is
more likely.


                                                                                                                        4
these issues, it is important to recognize that LIHTC housing is of high-quality relative to most lower-

income housing.8 As a result, LIHTC housing may have a positive amenity effect on nearby low-income

neighborhoods with a corresponding increase in demand for those locations. That would cause

unsubsidized construction of rental housing to increase in the local area, mitigating the impact of crowd

out. On the other hand, it seems likely that LIHTC housing may reduce the appeal of nearby higher

income communities. This could occur either because of fear of publicly supported housing projects in

general, or possibly because of local opposition to the presence of lower income families who occupy

LIHTC units. To the extent that such conditions prevail, NIMBY-type reactions in higher-income

communities would imply that LIHTC development has a negative stigma effect with a corresponding

decrease in demand for the local community. That would cause unsubsidized construction of rental

housing to decrease, exacerbating the impact of crowd out.

        Together, these ideas suggest that LIHTC development may have a much more negative impact

on unsubsidized rental housing construction in nearby high-income communities as compared to nearby

low-income neighborhoods. Moreover, those differences should attenuate as the geographic scope of the

analysis expands and stigma/amenity effects diminish. Additional empirical results reported later in the

paper are consistent with these priors. This suggests that whereas LIHTC development will often be

welcomed in low-income areas, political opposition should be anticipated in higher-income communities.

        The paper proceeds as follows. In the following section, we provide a brief overview of the

LIHTC program as well as previous assessments of the crowd out effects of place-based subsidized rental

housing. Section 3 examines crowd out effects of the LIHTC program, both at a conceptual level and

empirically. Section 4 stratifies the sample into low-, middle-, and high-income areas, and examines

evidence of amenity and stigma effects of LIHTC development. Section 5 concludes.




8
 Cummings and DiPasquale (1999) and DiPasquale, Fricke, and Garcia-Diaz (2003) document this feature of
LIHTC housing.


                                                                                                            5
2. Institutional Context and Prior Estimates of Crowd Out

              This section provides a brief overview of some additional institutional details of the LIHTC

housing program. Also included in this section is a review of previous research on crowd out associated

with place-based subsidized housing.9



2.1 Institutional Context

            Between 1937 and the late 1970’s, low-income housing support was provided almost exclusively

through public ownership and operation of housing built exclusively for low-income families. We refer

here to such housing as “traditional public housing.”10 Typically, occupancy in these projects has been

limited to families near or below poverty levels with tenants paying 30 percent of their gross income

towards rent (Olsen, 2003).

              By the 1980s, two concerns about the public housing program had gained attention. The first

was simply that the government builds, owns, and operates these projects: there are basic questions as to

whether at least some portion of such activity is best left to the private market. In addition, because

public housing projects were spatially concentrated and restricted occupancy to very low-income

residents, they created dense clusters of poverty. Facilitated in part by several horrifying newspapers

accounts, the public came to associate public housing with neighborhood decline, violent crime, and drug-

use (Currie and Yelowitz, 2000). Concerns about adverse peer and neighborhood effects on children

growing up in the projects also gained attention, both in academic and policy circles (see, for example,

Currie and Yelowitz (2000) or Jencks and Mayer (1990)). Primarily for these reasons, the government

stopped virtually all construction of traditional public housing in the early 1980’s and began to demolish




9
    An exhaustive review of the LIHTC and other related housing programs are in Quigley (2000) and Olsen (2003).
10
     Olsen (2003) points out that there are at least 29 different public housing programs.



                                                                                                                   6
the worst performing projects during the 1990s.11 Figure 1 illustrates the construction of traditional

public housing units between 1935 and 2000 along with demolitions in the 1990s.

        The LIHTC program was created as a part of the Tax Reform Act of 1986, both as an alternative

to public housing and to offset the reform’s removal of other tax benefits for owners of rental housing

(U.S. Joint Committee on Taxation, 1987). Different from other low-income housing programs, the

LIHTC program is federally administered by the Internal Revenue Service under Section 42 of the U.S.

Tax code. Figure 1 displays the number of LIHTC units placed in service from 1987 to 2000.

        The premise of the LIHTC program is to create a public-private partnership where the Federal

Government subsidizes between 30 to 91 percent of non-land construction costs for private developers.

In all cases, the subsidy is provided to developers through a 10-year stream of annual nonrefundable

federal tax credits – dollar-for-dollar reductions in federal tax liability (Eriksen, 2007). In exchange for

the subsidy, developers agree to set rents below specified ceilings and to lease a minimum specified share

of the project’s units to low-income families for at least 30 years. In practice, most projects contain 100

percent low-income occupancy owing to how the subsidy is structured and administered (see Eriksen

(2007) for additional details).

        It is also important to note that rent ceilings imposed on LIHTC units occupied by low-income

families are effectively set at 18 percent of MSA median household income as determined by HUD.12

That amount is sufficiently high and the tax credits are sufficiently generous (30 to 91 percent), that

LIHTC units are generally of high quality relative to most other low-income housing (Cummings and

DiPasquale, 1999). It is also important to note that LIHTC occupants typically have a higher income than

residents of traditional public housing (Wallace, 1995).13



11
 In some instances, such as under the HOPE VI program, the structures were remodeled, but in most cases tenants
were usually issued housing vouchers and told to seek housing privately (Jacob, 2004).
12
  The 18 percent limit is derived from two conditions imposed by the program. First, low-income families must earn
incomes below 60 percent of MSA median income, and second, rent must not exceed 30 percent of that value.
13
 For example, Wallace (1995) estimates that only 28 percent of LIHTC residents earn below 50 percent of an area’s
median income compared to 81 percent of those who reside in traditional public housing.


                                                                                                                  7
        These features of the public and LIHTC programs bear importantly on the analysis to follow.

First, traditional public housing has had a negative reputation in the media, and it seems likely that this

could color perceptions of LIHTC housing, especially in higher income communities. Second, LIHTC

housing is relatively high quality in comparison to other low-income housing, and this could foster

positive amenity effects in lower income communities. Third, LIHTC subsidies are so generous that

development in higher income communities may be profitable. Indeed, as shown in Figure 2b, whereas

the majority of public housing is in low-income communities, almost half of LIHTC development is

found in neighborhoods in the upper two-thirds of the income distribution.



2.2 Previous estimates of crowd out from place-based subsidized housing

         The possibility of crowd out arises any time government provides goods and services that are

also offered through the private sector. This has been examined in a variety of markets, including health

insurance, radio, and charitable giving (Culter and Gruber, 1996; Berry and Waldfogel, 1999; Andreoni

and Payne, 2003). Several recent studies have also begun to examine crowd out arising from place-based

subsidized housing, although most do not consider the LIHTC program. The first of these by Murray

(1983, 1999) utilizes national-level aggregate time series data from 1935 to the mid-1980s. These data

pre-date the LIHTC program and are used to assess the impact of public and other earlier forms of

subsidized rental housing construction on unsubsidized housing construction (Murray, 1983) and the

equilibrium stock of housing (Murray, 1999). The general strategy of each paper is to examine whether

construction of subsidized rental housing increases the total stocks of housing on less than a one-for-one

basis. 14 Evidence of such effects would be indicative of crowd out. Murray (1999) finds that subsidized

rental housing programs that target very low income families generate only a small amount of crowd out.

This is consistent with stylized facts that private market developers build little unsubsidized housing for

very low-income families: for crowd out to occur, private markets must first be willing to provide the

14
  More precisely, Murray (1999) estimates the cointegrating relationship between the equilibrium stock of
subsidized and unsubsidized housing stocks over the 1935 to 1987 period.



                                                                                                              8
product. In contrast, Murray (1999) also estimates that between one-third to 100 percent of subsidized

“moderate-income” place-based housing is offset by crowd out of unsubsidized construction. This is

consistent with the idea that in the absence of construction subsidies, the private market would build at

least some moderate income housing.

         Given that LIHTC housing is approximately a moderate income housing program, this is

important. But it should also be emphasized that Murray’s work differs from ours in two very important

respects. First, our empirical design is based on cross-section data and relies on geographic and

instrumental variable methods for identification. Murray’s work is based on aggregate time series data.

Moreover, as noted above, Murray’s data reflect the impact of housing programs that pre-date the LIHTC

program. Because those earlier programs have very different institutional arrangements than LIHTC

development, there is no guarantee that Murray’s results would carry over to the LIHTC program.

        More recently, and closer in structure to this paper, Sinai and Waldfogel (2005) examine crowd

out effects of place-based subsidized rental housing programs on per capita occupied housing units in

1990. When the data are measured at the census place level the OLS estimate of crowd out associated

with place-based subsidized rental housing is roughly 70 percent. However, the size of a census place

varies widely, from rather small to quite large. Especially for smaller census places, it is possible that

OLS estimates may suffer from endogenous location of subsidized housing units for reasons described in

the Introduction.15 When the data are aggregated to the MSA level – at which point concerns about

endogenous location of subsidized housing are greatly diminished – their point estimate of crowd-out

falls to roughly 30 percent. For both levels of geography, LIHTC housing is lumped in with other forms

of place-based subsidized rental housing. This is important because their data on subsidized housing

units (obtained from HUD’s 1996 Picture of Subsidized Housing file) includes roughly 2.8 million place-

based subsidized units, of which only 332,085 are LIHTC units, most of which were not present in 1990,

15
  Sinai and Waldfogel (2005) attempt to instrument for subsidized housing construction using the number of housing
units per capita built before 1940 and also occupied public housing units per capita in 1980. However, results from
the IV models yield quite different estimates of crowd out depending on which of the instruments are included.
Partly for that reason, Sinai and Waldfogel tend to emphasize their non-IV estimates of crowd out.



                                                                                                                 9
the period associated with their dependent variable.16 As with Murray (1983, 1999), therefore, Sinai and

Waldfogel (2005) examine primarily crowd out effects associated with place-based programs that pre-

date the LIHTC program.

        Malpezzi and Vandell (2002) do consider directly the crowd out effects of LIHTC development.

They analyze the impact of 1987-2001 state-level LIHTC allocations on the per capita stock of housing as

measured in 2000 (based on the year-2000 census). Their point estimate implies full crowd out, although

their sample is limited to just 51 state-level observations (including Washington, D.C.) with controls for

14 indicators of demand and supply. As a result, and as recognized by the authors, the standard error on

their estimate of LIHTC effects is several times larger than their corresponding estimated crowd out

effect. They are, therefore, unable to shed much light on the degree of crowd out associated with the

LIHTC program.

        Most recently, Baum-Snow and Marion (2007) examine the impact on LIHTC development given

a census tract’s qualified census tract (QCT) status as defined by HUD. QCT tracts are eligible for

additional subsidies through the LIHTC program making these tracts especially attractive for LIHTC

development, all else equal.17 Baum-Snow and Marion (2007) provide evidence that developers respond

to a tract’s QCT status by shifting development from adjacent tracts to the more heavily subsidized

location. That finding, however, does not necessarily reveal anything about the degree to which LIHTC

development crowds out private unsubsidized construction. On the other hand, their finding underscores

the tendency of developers to substitute capital across neighborhoods in response to competing

development opportunities. As noted in the Introduction, it is for that reason that we believe crowd out

effects are most clearly identified at a relatively broad level of geography for which capital substitution

across neighborhoods largely nets out.

16
  Place-based subsidized units in the 1996 Picture files include Public (1,326,224 units) Section 8 Moderate
Rehabilitation (105,845 units), Section 8 New Construction (897,160 units), Section 236 (447,382 units), and other
placed-based subsidized units (292,237 units). Additionally, the 1996 picture files only report roughly half of the
LIHTC units allocated between 1987 and 1996 (Malpezzi and Vandell, 2002).
17
  LIHTC development in a QCT is eligible for a 30 percent increase in subsidy as compared to those not located in
such a tract.


                                                                                                                  10
        Relative to these studies, the research in this paper makes three important innovations. First, we

directly address the joint effects of stigma and crowd out. Previous studies of crowd out related to

subsidized housing have virtually ignored possible local demand shifts arising from stigma. Second, we

estimate our models multiple times for a range of different levels of geography, from one-half mile to 10-

mile radius circles. This helps not only to address issues about the endogenous construction of subsidized

housing, but also further highlights the different impacts of stigma and crowd out in a manner that we will

make clear later in the paper. Third, previous studies have been hampered by the limited quality of early

data released on the LIHTC program. The LIHTC data used in this study are more current and benefit

from HUD efforts to provide a more complete and accurate accounting of LIHTC development.



3. Crowd Out

3.1 Conceptual Model

          Suppose initially that stigma/amenity effects do not occur in response to LIHTC development.

As argued in the Introduction, this is likely a good approximation for larger geographic areas. This also

implies that housing demand is unaffected by the LIHTC program. Consider now Figure 3a which

portrays the market for newly built rental housing. Abstracting from some of the details of the LIHTC

program, the program subsidizes construction up to an exogenously given state-level allocation. This

implies an outward shift in aggregate supply causing equilibrium rents to fall and housing construction to

increase.18




18
  Figure 3a suggests that the LIHTC program causes the entire supply function to shift out. This is a simplification
that captures the dominant impact of the LIHTC program that is relevant to this paper while streamlining the
discussion. An alternative and more accurate portrayal is that the LIHTC program flattens the slope of the lower
most section of the supply curve up to the maximum number of LIHTC units allocated to a given location. Beyond
that level of construction, the supply function steepens because additional investment is unsubsidized. Such a
specification would imply that developers choose to invest first in LIHTC developments before unsubsidized
construction. Moreover, to the extent that LIHTC development draws low-cost factor inputs away from the
unsubsidized sector, input costs in the unsubsidized sector will be higher than in the absence of the LIHTC program.
This would result in a further inward rotation of the unsubsidized segment of the supply function. See Olsen (2007)
for a further discussion of this issue.



                                                                                                                 11
         In Figure 3a, notice that aggregate rental housing construction increases by less than the level of

LIHTC construction. This is a direct result of the downward sloping nature of demand, the assumed

elastic nature of supply, and the corresponding reduction in rents following LIHTC development. In

particular, whereas H3-H1 equals the number of LIHTC units built, aggregate supply expands by only H2-

H1. The difference, H3-H2, represents unsubsidized private construction that is “crowded out” by the

LIHTC program. Only when housing demand is perfectly elastic or supply of newly constructed housing

is perfectly inelastic would H3-H2 equal zero and crowd out not occur.

         To put this figure in perspective, Mayer and Somerville (2000) estimate that the supply elasticity

of newly built housing of all types is roughly 6. This is close to DiPasquale and Wheaton (1992) who

estimate a supply elasticity for newly built multi-family rental housing of 6.8. Other estimates of the

elasticity of supply of newly built housing are smaller, but generally well above 1 (e.g. DiPasquale, 1999;

Rosenthal, 1999).19

         On the demand side, Hanushek and Quigley (1980) use data from the housing allowance

experiments of the 1970s to estimate the elasticity of demand for rental housing. For Pittsburgh and

Phoenix their estimates are -0.36 and -0.41, respectively. Higher price elasticities of demand are typically

obtained when using owner-occupied units. Rosen (1979), for example, estimates a price elasticity of -

0.99, but Rosenthal, Duca, and Gabriel (1991) obtain an estimate of -0.5 for FHA homebuyers, a group

that is much closer in income to that of the typical renter.

         Summarizing, only when housing demand is perfectly elastic or the supply of newly constructed

housing is perfectly inelastic would crowd out not occur. However, estimates in the literature strongly

suggest that the supply of newly built housing is very elastic and demand for housing is relatively




19
  As a further perspective, suppose that developable land is plentiful, either as raw land or as previously developed
land suitable for redevelopment. Suppose also that long run costs for building materials and labor are constant in
real terms. Then home building is approximately constant returns to scale and new housing supply should be quite
elastic (e.g. Rosenthal and Helsley, 1994). See also Glaeser and Gyourko (2005) for a discussion of the relative
elasticity of supply with respect to expansion versus contraction of the stock of housing.



                                                                                                                    12
inelastic. This suggests that on a qualitative basis, advocates and opponents of LIHTC development alike

should anticipate crowd out from the program. The question then is, how much?



3.2 Empirical Model

            Before developing our regression model it is useful to first highlight certain key features of the

data. We use decennial census data as the primary data source, aggregated to the census tract level.

These data were obtained from the Geolytics, Inc. Neighborhood Change Database file for 1970, 1990,

and 2000.20 Geolytics re-codes data from each of these years to year-2000 census tract boundaries. These

data were combined with information on LIHTC projects placed into service up through 2000. The

LIHTC data were obtained from HUD over the web.21 Information on the LIHTC database includes the

year placed in service and the year-2000 census tract. In all of the estimation to follow, we restrict our

sample to just those tracts located within MSAs. This leaves us with information on roughly 45,000 tracts

including 17,774 LIHTC projects containing 877,972 individual units. Tract-level summary measures of

the variables used in the regressions are provided in Table 2.

            Two points should be noted when working with the census tract data. First, tracts are often rather

small and for reasons outlined earlier, crowd out is more accurately identified over a larger geographic

area. Second, tract shapes and sizes differ from one tract to the next and that can complicate efforts to

assess the geographic scope of both crowd out and stigma/amenity effects. For both reasons, we

reorganize our data geographically into uniform circular units using Geographic Information Systems

(GIS) software (i.e. MapInfo and MapBasic). Specifically, data are organized into circles of radius d

miles drawn around the geographic centroid of the individual census tracts, i (i = 1, …, n). In all cases,




20
     See www.geolytics.com.
21
     See http://lihtc.huduser.org .



                                                                                                                 13
this is done based on year-2000 census tract geography.22 The circle-based measures are produced for all

of the dependent, independent, and instrumental variables. Summary statistics of circle-level data with

radii 0.5, 1, and 10 miles can be found in Tables 3a – 3c, respectively.

         Configuring the data in this manner allows us to examine the impact of different levels of

geography (d) while ensuring that each observation is measured over an area of the same size. In

addition, our empirical design ensures that the activity reflected in the dependent variable is based on the

same underlying geography as the activity of the independent variables and instruments. Bearing these

features in mind, we estimate the following model,

H id ,1990−rental = aH id ,1987 − 2000 +
   Private ,
             2000
                        LIHTC


                         b1 H id ,1985−1989 + b2 H id ,1980−1984 + b3 H id ,1970−1979 + b4 H id , pre −1970 +
                               Rental               Rental               Rental               Rental

                                                                                                                  .   (3.1)
                            b5 H id ,1985−1989 + b6 H id ,1980−1984 + b7 H iOwner −1979 + b8 H id , pre −1970 +
                                  Owner                Owner
                                                                            d ,1970
                                                                                                Owner


                              l1 DistCBDi + l2 LandAreaid + φ MSAi + ε id

         The dependent variable in this regression is the level of unsubsidized rental housing construction

between 1990 and 2000 within a radius of d miles drawn around the geographic centroid of the year-2000

census tract, i.23 This is constructed by subtracting LIHTC units built in the 1990s from the number of

rental units reported as under 10 years in age in the year-2000 census tract data. Control measures in

(3.1) are as follows.

         Variables in the last row control for location-specific elasticities of demand and supply for newly

built housing. In part, this is done by including MSA fixed effects for each of the 331 MSAs in the

United States. On the demand side, the fixed effects control for population, income, preferences, and

other broader determinants of demand. On the supply side, the fixed effects control for the MSA’s

distribution of land use restrictions (e.g. zoning) and natural features of the landscape that affect
22
  MapInfo and MapBasic were used to manipulate the geographic features of the data. When drawing rings around
the census tract centroids, proportional sum measures were used to calculate the various variables. This was done
for all of the variables used in the analysis including the instruments described later in the paper.
23
  Roughly 1,000 Public Housing units were also constructed in the 1990s. These units were also subtracted off from
newly built rental housing when forming the dependent variable. The public housing data were obtained from HUD
as noted earlier.



                                                                                                                              14
opportunities for development (e.g. mountains, lakes, rivers). Also included in the last row are measures

of the distance from the geographic centroid of a given circle i to the central business district (CBD) and

the amount of land within circle id not covered by water. Both of these measures proxy for the supply of

developable land: in the first case, outlying areas have more open land, while in the second case circle-

areas covered with more land also offer greater opportunities for development.

         The middle two rows include controls that approximate lagged levels of construction. This helps

to control for additional unobserved tract-specific demand and supply factors. This also soaks up serial

correlation in development decisions that would otherwise be embedded in the model’s error term. In

practice, we control for lagged construction by including separate controls for the age distribution of the

rental and owner-occupied housing stocks, where values are measured in numbers of housing units as

reported in the 1990 decennial census. As we do not control for demolitions these variables approximate,

but are not equal to, past levels of construction in the geographic area. An assumption throughout is that

our controls for lagged construction levels successfully remove serial correlation from the model error

term. Under that assumption, the model error term represents contemporaneous shocks and is orthogonal

to lagged housing stocks. As with the dependent variable, each control variable in the middle rows is

subscripted by the years (e.g. 1985 to 1989) of construction its variable represents.

         In the first row of (3.1), the number of LIHTC units built between 1987 to 2000 (H3 – H1 in

                                LIHTC
Figure 3a) is represented by H id ,1987 − 2000 . This is the primary variable of interest. If its coefficient a

equals 0 that would indicate that construction of LIHTC units has no effect on the level of new

unsubsidized private rental construction. Absent stigma/amenity effects, that would imply zero crowd out

and the presence of a perfectly elastic demand function (given strong priors that long-run expansion of

supply is elastic). If instead, a equals -1 that would imply a perfectly inelastic demand function and

complete crowd out.

         For reasons described in the Introduction, equation (3.1) is estimated multiple times for different

values of d, where d is the radius of the circles used to measure the dependent and independent variables.




                                                                                                                  15
Specifically, we estimate the models for ½-mile circles, 1-mile circles, 2-mile circles, …, 5-mile circles,

and 10-mile circles. In addition, each model is estimated twice, first by OLS and then again by two-step

GMM. In the latter case, we instrument for LIHTC construction using 1970 housing stock structure type

(e.g. the distribution of single-family versus various sizes of multi-family) and the distribution of

bedrooms, both measured separately for the renter and owner-occupied housing stock in location id.

Recall that these instruments proxy for a past willingness to allow high-density development and possibly

local zoning ordinances that may restrict or enhance further opportunities to build multi-family LIHTC

housing. For reasons also described in the Introduction, our prior is that as the level of geography

expands, LIHTC development should become increasingly exogenous and crowd out estimates from the

OLS and IV models should become similar. That is because endogenous substitution of capital and

households across neighborhoods in response to LIHTC development nets out.

        A final empirical issue concerns the overlapping nature of the circles used to measure the

dependent variables. As noted above, the root data source is tract-level census data and census tracts are

non-uniform in shape and size. The circle measures ensure a common size for each sampled area and are

drawn around the geographic centroids of the underlying tracts. This implies that many circles overlap.

When measuring the independent variables such overlap presents no special problems, but for the

dependent variables underlying census tract information is repeated across observations. Failing to

address that issue would cause the standard errors from the model to be biased downwards but would not

bias the coefficient estimates.

        A convenient solution to this problem is available. Specifically, we cluster the standard errors by

MSA (e.g. Woolridge, 2003). Because non-MSA census tracts are excluded from the analysis, any

overlap across dependent variables is fully addressed. At the same time, because many circles within

individual MSAs do not overlap, clustering by MSA overcompensates for the overlapping ring problem.

For that reason, our estimation procedure is inefficient and provides an upper bound on the true standard

errors, or equivalently, a lower bound on the true t-ratios. In most models that is sufficient to identify the

patterns of interest.


                                                                                                            16
3.3 Results

         Table 4 presents OLS and GMM estimates of (3.1) for various levels of geography from one-half

up to 10-mile radius circle areas. To conserve space and maintain focus on the LIHTC program we report

just the coefficients on the LIHTC variables.24 Values in parentheses below the coefficients are t-ratios

based on standard errors clustered at the MSA level. As indicated in the table and earlier in the

discussion, all of the models include MSA fixed effects.

         Consistent with our priors, OLS and GMM coefficient estimates differ markedly when the

geographic scope of the analysis is narrow – as with the one-half mile circle samples – but are very

similar once the circle radius is expanded to ten miles. That pattern is plotted in Figure 4 to facilitate

review. The pattern suggests that sitting of LIHTC projects at the neighborhood level is endogenous, but

that at the ten mile level, LIHTC development is exogenous. This also suggests that our ten-mile circle

sample estimates are especially robust, benefiting both from the IV controls and the broader level of

geography. For narrower geographic levels of analysis (e.g. one-half mile radius circles), we are reliant

on the instrumental variables used in the GMM models for identification.

         Observe also that for each level of geography, the OLS estimates of crowd out are always larger

in magnitude than the GMM estimates. This indicates that, on average, developers tend to locate LIHTC

projects in neighborhoods that receive relatively little rental housing construction, resulting in a more

negative coefficient. As noted in the Introduction, that finding is in some respects, consistent with the

spirit of the LIHTC program – to extend housing opportunities for lower income families.

         Consider next the diagnostic tests of the validity of the instruments. These are reported at the

bottom of the table. The F-statistic for the joint significance of the excluded instruments clearly support




24
 GMM results for all of the estimated coefficients are provided in the appendix for circle radii of 0.5, 1.0, 5.0, and
10.0 miles.




                                                                                                                     17
the “strength” or relevance of the instruments in all instances.25 Hansen-J (1982) tests also fail to reject

the overidentifying restrictions for each of the models.26 Moreover, the Hansen-J tests appear especially

compelling for the ten-mile level of geography. These diagnostic tests provide some confidence that the

IV estimates for the narrower levels of geography are credible while offering further support for the idea

that our crowd-out estimates based on ten-mile geography are robust.

         Finally, consider the magnitude of the ten-mile circle estimates of crowd out. The GMM estimate

implies that roughly 33 percent of LIHTC housing units built are offset by crowd out of private,

unsubsidized rental housing construction; the corresponding OLS estimate is 35 percent. Thus, one-third

of LIHTC development is offset by crowd out. On the surface, this strikes us as a large number, but how

one perceives that estimate depends on perspective. At a minimum, it is important for proponents of the

LIHTC program to be aware that the impact of LIHTC development on the total number of rental housing

units is well below the number of LIHTC units built. However, as noted earlier in the paper, roughly 28

percent of LIHTC units are built in communities in the middle third of their MSA income distribution,

while another 16 percent are built in neighborhoods in the upper third of their MSA income distribution.27

This suggests that the LIHTC program potentially expands low-income housing opportunities in higher

income communities. To explore this feature of the program further, we next stratify the sample to

consider the impact of LIHTC development on low- and high-income areas. This includes consideration

of the degree to which LIHTC housing may be viewed as an amenity or may carry with it a stigma that

could have adverse local spillover effects.



4. Stigma/Amenity Effects
25
  This is important because weak instruments bias estimates from IV models. See Murray (2006) for a discussion of
this issue. The F-statistic reported in each table accounts for the clustering of standard errors by MSA.
26
  Hansen-J tests evaluate the joint null that the instruments are exogenous and that they are appropriately excluded
from the structural model. We note that these tests are potentially sensitive to model specification and controversial
for that reason (Davidson and McKinnon, 2004; Cameron and Trivedi, 2006).
27
  In addition, as noted earlier, other research has emphasized that LIHTC housing is high quality relative to most
other low-income homes.



                                                                                                                     18
            Suppose that local residents do not want to live in close proximity to LIHTC housing because of

either an aversion to the housing structures or to the lower-income occupants of the projects themselves.

Such NIMBY-type responses would reduce demand for the locale and cause market rents and

unsubsidized construction to decline. 28 Stigma, therefore, would exacerbate the negative crowd out

effects of LIHTC development. This is a likely characterization of how LIHTC housing is viewed in

higher-income communities. This situation is portrayed in Figure 3b.29

            On the other hand, suppose that LIHTC housing is perceived as a positive amenity that enhances

the appeal of a neighborhood. Cummings and DiPasquale (1999) and DiPasquale, Fricke, and Garcia-

Diaz (1999), for example, report that LIHTC housing is high quality relative to most low-income rental

housing. In addition, Stegman (1991) and Wallace (1995) suggest that the average resident of a LIHTC

unit has a much higher income than the typical resident of traditional public housing and that LIHTC

housing is often welcome in lower income areas. Under these conditions, LIHTC development increases

demand for the local community, causing rents and unsubsidized construction to increase. A positive

perception of LIHTC housing, therefore, would mitigate crowd out effects. This is a likely

characterization of LIHTC development in lower-income communities. This situation is portrayed in

Figure 3c.

            Together, Figures 3a to 3c indicate that the influence of LIHTC development on neighborhoods

likely differs with the economic status of that community. To help identify these differences we

reorganize our data geographically. To be precise, for this portion of the analysis, our independent

variables including LIHTC construction and our instruments are measured exactly as in Section 3.

However, the dependent variable is modified in two important ways. First, the estimating sample of

circle observations is stratified into three groups based on the economic status of the core census tract

around which a given circle is drawn. Based on 1990 tract average income, low-income circles are


28
     See Jacob (2004) for discussion of the impact of public housing on perceptions of neighborhood quality.
29
 For convenience, Figure 3b is drawn such that the crowd out and stigma effects exactly offset resulting in no
change in the level of construction. That need not be the case, of course.


                                                                                                                 19
defined as those whose core tracts are in the bottom third of their MSA tract income distribution.

Analogous definitions are used to identify middle- and high-income circles for core tracts in the middle

and upper thirds of their MSA income distribution.

        It is tempting to estimate our crowd out regressions separately over each of these three samples

with no further adjustments. But such an approach would bias the crowd out estimates to be similar

across the stratified samples. To clarify why, consider the following illustration. Suppose all census

tracts were of the same size and dimension, and all tracts are organized along a line. Further, census

tracts are organized along the line in an alternating sequence of low-, middle-, and high-income tracts.

Then as the circle size increases, the economic distribution of the underlying tracts contained in the circle

would be alike regardless of whether the circle was centered on a low-, middle-, or high-income tract. As

a result, regressions based on such stratified samples would tend to yield similar results even though the

marginal impact of LIHTC development on unsubsidized construction might differ in low-, middle-, and

high-income areas.

        To address this issue, for each stratified sample of core census tracts (i.e., low-, middle-, and

high-income), we measure the dependent variable, construction of unsubsidized rental housing, only in

areas within the circle belonging to underlying census tracts that are of the same economic status as the

core tract. Organizing the data in this fashion allows us to estimate the marginal impact of LIHTC

development on unsubsidized nearby low-, middle-, and high-income areas. It should also be noted that

the estimated crowd-out effects across these stratified samples will typically not add up to the crowd out

estimate from Table 4 based on the full sample. That is because the values of the model covariates differ,

on average, for the low-, middle-, and high-income sub-samples, as illustrated in Tables 3a – 3c. Those

differences affect the weights embodied in the regression algebra used to obtain the crowd out estimates

in a complicated and non-linear fashion.30


30
  This is especially relevant for smaller circle sizes (e.g. one-half mile) for which the covariate values differ
markedly across sub-groups (see Tables 3a-3c, for example). For larger circle sizes (e.g. 10 miles), the covariate
values become more similar on average, for the low-, middle-, and high-income circle samples. As a result, and for
reasons outlined above, estimates from large-circle stratified sample models should add up to a value that


                                                                                                                20
           Tables 5a-5c present results from this exercise for the low-, middle-, and high-income circle

samples. In each case, the structure of the tables is the same as in Table 4. This includes reporting only

the coefficients on the LIHTC variable to maintain focus and conserve space.31 In addition, Figure 5 plots

the GMM LIHTC coefficients for the low- (Table 5a), middle- (Table 5b), and high-income (Table 5c)

circle samples for the different values of d. Notice that the diagnostic tests at the bottom of Tables 5a-5c

once again generally support the validity of the instruments, although in some models they are less

compelling than in Table 4. In particular, the instruments are always very strongly correlated with

LIHTC construction (F-statistic for the significance of excluded intruments). In addition, the Hansen-J

tests generally fail to reject the overidentifying restrictions. The primary exceptions are for the high-

income sample for models based on circle radii two to five miles; for these models the Hansen-J P-values

are low.

           Focusing on the GMM estimates in Tables 5a-5c, and also in Figure 5, for the one-half mile

samples these estimates indicate that LIHTC development has a sharp negative impact on unsubsidized

construction in the high-income circle sample, but a small positive effect in the low-income circle

sample.32 To be precise, construction of 100 LIHTC units in a half-mile circle reduces private

unsubsidized construction in the high-income neighborhoods within one-mile by just over 100 rental

units, indicative of full crowd out. Conversely for the low-income sample, the analogous estimate is an

increase of roughly 26 units. Expanding the geographic scope of the analysis beyond one-half mile,

differences in the impact of LIHTC development on rental construction in low- and high-income circle

samples largely disappear. This is anticipated because of the adding up issues note earlier. In that regard,

note that at the 10 mile circle level of geography, our estimates indicate that development of 100 LIHTC


approximates that of the full-sample regression. This is the case for the 10-mile circle samples as can be seen when
comparing results from Tables 5a-5c to Table 4.
31
  Complete regression output for the income-stratified samples is provided in the appendix (Tables A-2 to A-4,
respectively) for circle samples of radii 0.5, 1, 5, and 10 miles.
32
  In Table 5b, for the middle-income sample, note that the coefficient on LIHTC development within one-half mile
is -0.39, roughly halfway between the corresponding estimates for the low- and high-income samples.



                                                                                                                  21
units would reduce unsubsidized rental housing construction by 12, 14, and 8 units in low-, middle-, and

high-income neighborhoods, respectively.

        To interpret these patterns, it is important to recall two principles that facilitate identification.

First, stigma/amenity effects attenuate with distance and will be most prominent for narrow levels of

geography. Second, crowd out effects are most accurately identified for larger levels of geography.

Bearing these principles in mind, several implications follow from the patterns observed in Figure 5. In

high-income areas, the sharp negative impact of LIHTC development within one-half mile on

unsubsidized construction is indicative of either negative stigma effects, crowd out, or both. On the other

hand, LIHTC effects on high-income areas attenuate sharply with an expansion of the geographic scope

of the analysis; this almost certainly reflects attenuation of stigma effects rather than a dissipation of

crowd out. Overall, the pattern of results is strongly suggestive that LIHTC development has a negative

stigma effect on nearby high-income neighborhoods. This differs markedly from the response to LIHTC

development in low-income communities. For low-income neighborhoods within one-half mile, the

positive impact of LIHTC development on unsubsidized construction can only arise from positive

amenity effects that more than offset crowd out.



5. Conclusion

        The recent dramatic growth of the Low-Income Housing Tax Credit (LIHTC) program has

caused an old question to gain new importance. Should government provide low-income housing support

through tenant- or placed-based programs? In that context, the LIHTC program has ballooned since its

inception in 1987, and is now the largest subsidized rental housing construction program in U.S. history.

The program subsidizes 30 to 91 percent of construction costs for eligible projects, 44 percent of which

are located in middle- and higher-income neighborhoods, and has accounted for one-third of all multi-

family rental housing construction in recent years. Moreover, recent proposals in Congress have sought

to double the size of the program. Nevertheless, little is known about the efficacy of this increasingly

important and expensive program. This paper has sought to fill part of that gap.


                                                                                                                22
        Our most important finding concerns the level of crowd out associated with the LIHTC program.

Over a ten mile area, OLS and GMM estimates both indicate that one-third of LIHTC development is

offset by crowd out of private unsubsidized construction. Moreover, based on conceptual arguments and

also diagnostic tests, we argue this estimate of crowd out is robust. It is clear, therefore, that the impact of

LIHTC development on the total stock of rental housing units is well below the number of LIHTC units

built. This implies a windfall gain for developers and raises questions about the viability of the LIHTC

program. How then might the LIHTC program be justified?

        One possibility is the spatial distribution of LIHTC development. Summary measures document

that 16 percent of LIHTC projects have been built in neighborhoods in the upper third of their MSA’s

income distribution, and another 28 percent in communities in the middle third of the income distribution.

This is a radical departure from the historic public housing program in which the great majority of units

were built in low-income communities. The LIHTC program does, therefore, appear to expand housing

opportunities for low-income families in traditionally higher income neighborhoods. To the extent that

mixed-income development at the community level is desirable, the LIHTC program appears successful

in this regard.

        On the other hand, it is also important to recognize that not everyone gains from such a

development strategy. Although we find that LIHTC development has a positive amenity effect on

nearby low-income communities, our results are strongly suggestive that LIHTC development has a

negative stigma effect on nearby high-income areas. This latter result implies that LIHTC development

imposes windfall loses on existing residents and landowners in higher income communities. It is beyond

the scope of this paper to offer a full accounting of the costs and benefits of the LIHTC program. But

stigma effects in high-income communities along with crowd out greatly increase the cost of the LIHTC

program. These issues warrant close attention in any further discussion about possible expansion of the

LIHTC program, as well as on-going debates about place- versus tenant-based (e.g. voucher) housing

assistance.




                                                                                                             23
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                                                                                                    26
                                                                                      Total Units Constructed (in Thousands)




                                                                            -75
                                                                                  0
                                                                                         75
                                                                                                 150
                                                                                                         225
                                                                                                                300
                                                                                                                        375
                                                                                                                               450
                                                                                                                                     525
                                                       1 93 6
                                                                -19 4
                                                                        0



                                                       1 94 1
                                                                -19 4
                                                                        5



                                                       1 94 6
                                                                -19 5
                                                                        0



                                                       1 95 1
                                                                -19 5
                                                                        5




     LIHTC Construction
                                                       1 95 6
                                                                -19 6
                                                                        0



                                                       1 96 1
                                                                -19 6
                                                                        5



                                                       1 96 6
                                                                -19 7
                                                                        0



                                                       1 97 1
                                                                -19 7
                                                                        5



                                                       1 97 6
                                                                -19 8
                                                                        0



                                                       1 98 1
                                                                -19 8
                                                                        5
     Public Housing Construction (minus demolitions)




                                                       1 98 6
                                                                -19 9
                                                                        0
                                                                                                                                           Figure 1. Place-Based Subsidized Construction and Demolitions of Housing Units




                                                       1 99 1
                                                                -19 9
                                                                        5



                                                       1 99 6
                                                                -20 0
                                                                        0
27
  Figure 2a. Location of Low-Income
  Housing T ax Credit Units by 2000
     Neighborhood Income Status




                           Middle
             Low
                           196,554
            401,861
                            28%
             56%

                        High
                       111,724
                        16%




Figure 2b. Location of T raditional Public
 Housing Units by 2000 Neighborhood
             Income Status




                            Middle      High
                            125,994    55,597
              Low            16%        7%
             618,107
              77%




                                                28
                  Figure 3a: Crowd Out of Rental Housing

      Market
      House
       Rent
                                                               Supply without
                                                               LIHTC
                         Demand

                                     LIHTC Construction
                                                                        Supply with
                                                                        LIHTC



   RNo LIHTC


       RLIHTC




                                            H1       H2   H3            Quantity of newly built
                                                                        rental housing


                                   Increase in new
                                                           Crowd out effect on
                                   construction
                                                           private stock



                  Figure 3b: Crowd Out of Rental Housing With Stigma

      Market
      House
       Rent
                                                               Supply without
                         Demand without                        LIHTC
                         LIHTC
                                     LIHTC Construction
                                                                        Supply with
                  Demand with                                           LIHTC
                  LIHTC


   RNo LIHTC


       RLIHTC


RLIHTC & Stigma




                                            H1       H2   H3            Quantity of newly built
                                                                        rental housing

                      Stigma effect on
                      equilibrium stock                    Crowd out effect on
                                                           private stock

                                                                                                  29
                   Figure 3c: Crowd Out of Rental Housing with Amenity Effects

       Market
       House
        Rent
                                                             Supply without
                        Demand without    Demand with        LIHTC
                        LIHTC             LIHTC

                                   LIHTC                              Supply with
                                   Construction                       LIHTC



RNo LIHTC =
RLIHTC & Amenity

        RLIHTC




                                           H1     H2    H3           Quantity of newly built
                                                                     rental housing


                                                         Crowd out effect on
                                                         equilibrium stock equal
                                                         to amenity effect




                                                                                               30
                                           Figure 4: GMM and OLS Estimates of Crowd Out (Table 4)
                         0.1

                          0
 Coefficient Estimate




                        -0.1

                        -0.2


                        -0.3

                        -0.4


                        -0.5
                               0.5             1             2             3              4         5            10
                                                                 Circle Radius in Miles

                                                                 GMM-IV                   OLS




                               Figure 5: GMM Estimates of Crowd Out By Neighborhood Income (Tables 5a-5c)
                        0.4
Coefficient Estimate




                          0



                        -0.4



                        -0.8



                        -1.2
                               0.5             1             2             3              4         5            10
                                                                 Circle Radius in Miles

                                     Low-Income (Table 5a)        Middle-Income (Table 5b)      High-Income (Table 5c)




                                                                                                                         31
Table 1. Federal Government Low-Income Housing Expenditures (2005-2006)1
(in Millions of Dollars)

                                                                       2005            2006
                            2
Internal Revenue Service
Low-Income Housing Tax Credit                                          4,700           4,900
Preferential Depreciation Allowance                                    3,800           4,200
State-issued Tax Exempt Financing for Rental Housing                    300             300


Department of Housing and Urban Development3
Housing Choice Vouchers (HCV)                                         20,064           20,917
Public Housing                                                         5,017           5,734
Other HUD Programs                                                     8,734           4,559


Department of Agriculture3
Rural Housing Administration                                           1,369           1,029

Total                                                                 43,984           41,639
1
 LIHTC costs reflect foregone tax revenues associated with the 10-years of tax credit allocations.
Those costs are expected to increase sharply in the next several years given an increase in
LIHTC allocations since 2001. Public housing operating costs are likely to decline in coming
years as increasing numbers of public housing units are demolished.
2
Tax Expenditures as Estimated by Joint Committee on Taxation (2005).
3
    Budget of the United States, Office of Management and Budget (2006).




                                                                                                     32
Table 2. Sample Means at the Census Tract Level
                                                                                        Values in ___ Income Census Tractsa
Attributes in the Year 2000                                                      All            Low          Middle           High
Private Construction of Rental Housing 1990 - 2000                              58.57           46.53        58.05            72.16
LIHTC Construction 1987 - 2000                                                  13.80           22.03        11.52             7.33
# of Rental Units Constructed 1985-1989                                         56.53           52.73        58.91            58.03
# of Rental Units Constructed 1980-1984                                         55.73           56.83        58.22            51.91
# of Rental Units Constructed 1970-1979                                     117.73             131.27        123.42           97.39
# of Rental Units Constructed Prior to 1970                                 310.40             463.22        270.45           191.07
# of Owner-Occupied Units Constructed 1985-1989                                 96.50           39.09        103.69           149.59
# of Owner-Occupied Constructed 1980-1984                                       77.54           37.58        86.15            110.69
# of Owner-Occupied Units Constructed 1970-1979                             183.24              94.00        205.99           253.54
# of Owner-Occupied Units Constructed Prior to 1970                         539.76             475.19        593.36           551.28
Surface Area not Covered by Water (Square Miles)                                14.38           12.61        20.22            10.12
Distance to Central Business District of MSA                                    12.66           11.01        14.40            12.62
               b
Observations                                                                46,930             16,165        15,826           14,939
a
    Neighborhood income status is defined by ranking 1990 census tract average incomes by terciles for each MSA.
b
    Sample restricted to census tracts located in an MSA as of the year 2000.




                                                                                                                              33
Table 3. Sample Means in Circles Drawn Around Census Tract Centroids

                                                                                   Values in Circles Drawn Around
                                                                                 Census Tracts That are ____ Incomea
Panel A: 0.5 Mile Circle Radius                                           All            Low          Middle           High
Private Construction of Rental Housing 1990 - 2000                        43              53             29             29
LIHTC Construction 1987 - 2000                                            16              36             7               5
# of Rental Units Constructed 1985-1989                                   49              70             42             33
# of Rental Units Constructed 1980-1984                                   49              75             40             30
# of Rental Units Constructed 1970-1979                                   118            183            100             67
# of Rental Units Constructed Prior to 1970                               616            1037           473             311
# of Owner-Occupied Units Constructed 1985-1989                           35              21             34             50
# of Owner-Occupied Constructed 1980-1984                                 31              21             32             40
# of Owner-Occupied Units Constructed 1970-1979                           78              54             83             98
# of Owner-Occupied Units Constructed Prior to 1970                       529            651            533             391
Surface Area not Covered by Water (square miles)                         0.74            0.74           0.75            0.73
Panel B: 1.0 Mile Circle Radius
Private Construction of Rental Housing 1990 - 2000                        156             168            82             95
LIHTC Construction 1987 - 2000                                            59             122             31             21
# of Rental Units Constructed 1985-1989                                   175            241            155             127
# of Rental Units Constructed 1980-1984                                   175            256            146             118
# of Rental Units Constructed 1970-1979                                   423            624            362             270
# of Rental Units Constructed Prior to 1970                              2179            3592           1712           1144
# of Owner-Occupied Units Constructed 1985-1989                           128             85            126             176
# of Owner-Occupied Constructed 1980-1984                                 114             81            117             145
# of Owner-Occupied Units Constructed 1970-1979                           286            209            303             349
# of Owner-Occupied Units Constructed Prior to 1970                      1927            2429           1907           1406
Surface Area not Covered by Water (square miles)                         2.94            2.92           2.98            2.91
Panel C: 10 Mile Circle Radius
Private Construction of Rental Housing 1990 - 2000                       8,443           4,142         2,418           3,274
LIHTC Construction 1987 - 2000                                           2,476           3,287         2,144           1,950
# of Rental Units Constructed 1985-1989                                  9,054          10,402         8,306           8,387
# of Rental Units Constructed 1980-1984                                  8,444           9,690         7,754           7,826
# of Rental Units Constructed 1970-1979                                 20,691          24,563         19,002          18,292
# of Rental Units Constructed Prior to 1970                             95,493          125,945        87,726          70,771
# of Owner-Occupied Units Constructed 1985-1989                          8,352           7,718         8,097           9,308
# of Owner-Occupied Constructed 1980-1984                                7,068           6,684         6,821           7,745
# of Owner-Occupied Units Constructed 1970-1979                         17,911          17,653         17,322          18,813
# of Owner-Occupied Units Constructed Prior to 1970                     102,911         125,936        95,380          85,975
Surface Area not Covered by Water (square miles)                        276.25          271.80         278.92          278.22

Distance to Central Business District of MSA (miles)                      12.66         11.01            14.40          12.62
Observations                                                             46,930        16,165           15,826         14,939
 a
   Neighborhood income status is defined by ranking 1990 census tract average incomes by terciles for each MSA.




                                                                                                                       34
                                     Table 4. LIHTC Crowd Out of Unsubsidized Rental Housing Construction 1990 to 2000a

                                                                                     Circle Radius in miles Around Each Census Tract Centroid
                                                            0.5               1.0               2.0               3.0             4.0                5.0              10.0
                   b
GMM Estimates
                                                          0.0354            0.0102           -0.2424           -0.2695          -0.2545           -0.2774           -0.3327
LIHTC construction 1987-2000 (t-statistic)c
                                                          (0.65)            (0.18)            (4.85)            (5.53)           (5.52)            (6.17)            (5.39)
OLS Estimates
                                                         -0.3495           -0.3737           -0.3962           -0.3946          -0.3762           -0.3692           -0.3502
LIHTC construction 1987-2000 (t-statistic)c
                                                          (3.83)            (3.98)            (3.83)            (3.39)           (2.94)            (2.76)            (2.92)
Summary Measures
Observations                                              46,930            46,930            46,930            46,930           46,930            46,930            46,930
MSA Fixed Effects                                          331               331               331               331              331               331                331
                                                  d
F-stat for Significance of Excluded Instruments           127.93            153.75            75.76             69.04            83.23             144.79             49.56

                                      e                    20.37             22.71             17.26             18.65            18.60             18.13             16.55
Hansen (1982) J-Statistic (P-value)
                                                         (0.2556)          (0.1590)          (0.4367)          (0.3491)         (0.3522)          (0.3809)          (0.4851)
a
  Each of the models includes additional controls for the age distribution of owner-occupied and rental housing stocks as of 1990, distance to the CBD, and the area within
the circle not covered by water. All variables (dependent, independent, and instrumental) were measured over circle areas of radii indicated above. GMM results for all of
the control variables in the model are provided in the appendix for circle radii of 0.5, 1.0, 5.0, and 10.0 miles.
b
 LIHTC Construction 1987 – 2000 treated endogenously. Instruments include 1970 distribution of housing structure type (single family and various sizes of multi-family)
and bedroom counts (0, 1, 2, 3, and 4+ bedrooms), separately for owner-occupied and rental housing stock.
c
    Corresponding t-ratios are based on MSA-clustered standard errors.
d
    The F-statistic for the joint significance of excluded instruments accounts the clustering of standard errors by MSA.
e
 The Hansen (1982) J-Statistic is a test of the overidentifying restrictions with a joint null hypothesis that the excluded instruments are uncorrelated with the error term and
correctly excluded from the estimated equation. Rejection of the null hypothesis calls into question the validity of the instruments (for more details see Davidson and
MacKinnon (2004)).




                                                                                                                                                                          35
                       Table 5a. LIHTC Crowd Out of Unsubsidized Rental Housing Construction 1990 to 2000 in Low-Income Areasa

                                                                                     Circle Radius in miles Around Each Census Tract Centroid
                                                            0.5               1.0               2.0               3.0             4.0                5.0              10.0
                   b
GMM Estimates
                                                          0.2621            0.1382           -0.0563           -0.0475          -0.0460            0.0577           -0.1162
LIHTC construction 1987-2000 (t-statistic)c
                                                          (7.15)            (2.48)            (1.07)            (0.95)           (1.13)            (1.38)            (2.19)
OLS Estimates
                                                         -0.3004           -0.2967           -0.2730           -0.1799          -0.0883           -0.0221           -0.0702
LIHTC construction 1987-2000 (t-statistic)c
                                                          (2.66)            (2.45)            (2.00)            (1.13)           (0.52)            (0.13)            (0.61)
Summary Measures
Observations                                              16,165            16,165            16,165            16,165           16,165            16,165            16,165
MSA Fixed Effects                                          331               331               331               331              331               331                331
                                                  d
F-stat for Significance of Excluded Instruments            37.58            113.87            87.81             69.81            67.16             108.27             39.18

                                      e                    20.21             21.05             25.93             24.89            19.00             26.60             13.48
Hansen (1982) J-Statistic (P-value)
                                                         (0.2632)          (0.2241)          (0.0758)          (0.0971)         (0.3287)          (0.0643)          (0.7032 )
a
  Each of the models includes additional controls for the age distribution of owner-occupied and rental housing stocks as of 1990, distance to the CBD, and the area within
the circle not covered by water. All variables (dependent, independent, and instrumental) were measured over circle areas of radii as indicated above. Low-Income Areas
defined by a tract’s average income located in the bottom third of its respective MSA distribution.
b
 LIHTC Construction 1987 – 2000 treated endogenously. Instruments include 1970 distribution of housing structure type (single family and various sizes of multi-family)
and bedroom counts (0, 1, 2, 3, and 4+ bedrooms), separately for owner-occupied and rental housing stock.
c
    Corresponding t-ratios are based on MSA-clustered standard errors.
d
    The F-statistic for the joint significance of excluded instruments accounts the clustering of standard errors by MSA.
e
 The Hansen (1982) J-Statistic is a test of the overidentifying restrictions with a joint null hypothesis that the excluded instruments are uncorrelated with the error term and
correctly excluded from the estimated equation. Rejection of the null hypothesis calls into question the validity of the instruments (for more details see Davidson and
MacKinnon (2004)).




                                                                                                                                                                          36
                          Table 5b. LIHTC Crowd Out of Unsubsidized Rental Housing Construction 1990 to 2000 in Middle-Income Areasa

                                                                                     Circle Radius in miles Around Each Census Tract Centroid
                                                            0.5               1.0               2.0               3.0             4.0                5.0              10.0
                   b
GMM Estimates
                                                         -0.3999           -0.5703           -0.4192           -0.3586          -0.3192           -0.2590           -0.1360
LIHTC construction 1987-2000 (t-statistic)c
                                                          (6.02)           (15.88)           (18.94)           (12.55)           (9.82)            (8.95)            (4.69)
OLS Estimates
                                                         -0.4087           -0.3035           -0.2240           -0.2030          -0.2017           -0.1949           -0.1113
LIHTC construction 1987-2000 (t-statistic)c
                                                          (9.09)            (6.28)            (3.32)            (3.09)           (3.40)            (0.34)            (1.66)
Summary Measures
Observations                                              15,826            15,826            15,826            15,826           15,826            15,826            15,826
MSA Fixed Effects                                          331               331               331               331              331               331                331
                                                  d
F-stat for Significance of Excluded Instruments            10.57            27.59             48.86             29.40            30.52             47.26              38.50

                                      e                    20.91             24.94             25.08             21.85            20.44             18.82             17.83
Hansen (1982) J-Statistic (P-value)
                                                         (0.2305)          (0.0960)          (0.0929)          (0.1907)         (0.2525)          (0.3389)          (0.3998)
a
  Each of the models includes additional controls for the age distribution of owner-occupied and rental housing stocks as of 1990, distance to the CBD, and the area within
the circle not covered by water. All variables (dependent, independent, and instrumental) were measured over circle areas of radii as indicated above. Low-Income Areas
defined by a tract’s average income located in the middle third of its respective MSA distribution.
b
 LIHTC Construction 1987 – 2000 treated endogenously. Instruments include 1970 distribution of housing structure type (single family and various sizes of multi-family)
and bedroom counts (0, 1, 2, 3, and 4+ bedrooms), separately for owner-occupied and rental housing stock.
c
    Corresponding t-ratios are based on MSA-clustered standard errors.
d
    The F-statistic for the joint significance of excluded instruments accounts the clustering of standard errors by MSA.
e
 The Hansen (1982) J-Statistic is a test of the overidentifying restrictions with a joint null hypothesis that the excluded instruments are uncorrelated with the error term and
correctly excluded from the estimated equation. Rejection of the null hypothesis calls into question the validity of the instruments (for more details see Davidson and
MacKinnon (2004)).




                                                                                                                                                                          37
                       Table 5c. LIHTC Crowd Out of Unsubsidized Rental Housing Construction 1990 to 2000 in High-Income Areasa

                                                                                     Circle Radius in miles Around Each Census Tract Centroid
                                                            0.5               1.0               2.0               3.0             4.0                5.0              10.0
                   b
GMM Estimates
                                                         -1.0754           -0.4383           -0.2457           -0.1755          -0.1593           -0.1628           -0.0766
LIHTC construction 1987-2000 (t-statistic)c
                                                         (17.30)            (8.92)            (8.51)            (8.72)           (6.00)            (4.75)            (1.43)
OLS Estimates
                                                         -0.4037           -0.2382           -0.1471           -0.1585          -0.1582           -0.1544           -0.1293
LIHTC construction 1987-2000 (t-statistic)c
                                                          (5.03)            (3.63)            (2.01)            (2.14)           (2.14)            (2.05)            (1.88)
Summary Measures
Observations                                              14,939            14,939            14,939            14,939           14,939            14,939            14,939
MSA Fixed Effects                                          331               331               331               331              331               331                331
                                                  d
F-stat for Significance of Excluded Instruments            72.20            49.70             63.32             46.78            69.19             136.89             40.84

                                      e                    22.95             20.94             28.25             31.00            32.85             34.84             27.45
Hansen (1982) J-Statistic (P-value)
                                                         (0.1508)          (0.2288)          (0.0421)          (0.0200)         (0.0118)          (0.0065)          (0.0517)
a
  Each of the models includes additional controls for the age distribution of owner-occupied and rental housing stocks as of 1990, distance to the CBD, and the area within
the circle not covered by water. All variables (dependent, independent, and instrumental) were measured over circle areas of radii as indicated above. Low-Income Areas
defined by a tract’s average income located in the upper third of its respective MSA distribution.
b
 LIHTC Construction 1987 – 2000 treated endogenously. Instruments include 1970 distribution of housing structure type (single family and various sizes of multi-family)
and bedroom counts (0, 1, 2, 3, and 4+ bedrooms), separately for owner-occupied and rental housing stock.
c
    Corresponding t-ratios are based on MSA-clustered standard errors.
d
    The F-statistic for the joint significance of excluded instruments accounts the clustering of standard errors by MSA.
e
 The Hansen (1982) J-Statistic is a test of the overidentifying restrictions with a joint null hypothesis that the excluded instruments are uncorrelated with the error term and
correctly excluded from the estimated equation. Rejection of the null hypothesis calls into question the validity of the instruments (for more details see Davidson and
MacKinnon (2004)).




                                                                                                                                                                          38
                                                       Appendix: Supplemental Tables



Table A-1. Impact on Private Rental Construction in All Neighborhoods 1990 to 2000
in Response to LIHTC Construction from 1987 to 2000

                                                                                             Radius of Circles in Miles
GMM Estimatesa (t-statistics)b
                                                                                   0.5            1.0            5.0          10.0
LIHTC Construction 1987-2000                                                     0.0354         0.0102         -0.2545      -0.3327
                                                                                 (0.65)         (0.18)          (5.52)       (5.39)

# of Rental Units Constructed 1985-1989                                         0.3461          0.4341         0.5397       0.3777
                                                                                (25.67)         (16.81)        (25.89)      (10.55)

# of Rental Units Constructed 1980-1984                                         0.1547          0.1837         0.2168       0.3943
                                                                                (12.72)         (9.74)         (5.56)       (8.24)

# of Rental Units Constructed 1970-1979                                          0.0135         0.0041         0.0205       0.0428
                                                                                 (2.31)         (0.54)         (1.69)       (2.03)

# of Rental Units Constructed 1970 or earlier                                   0.0083          0.0095         0.0106       0.0138
                                                                                (13.06)         (15.75)        (10.40)      (10.28)

# of Owner-Occupied Units Constructed 1985-1989                                  0.1317         0.1316         0.2705       0.2353
                                                                                 (7.86)         (5.47)         (6.22)       (3.66)

# of Owner-Occupied Units Constructed 1980-1984                                 -0.0518         -0.0173        -0.0810      -0.0556
                                                                                 (2.53)          (0.54)         (1.40)       (0.62)

# of Owner-Occupied Units Constructed 1970-1979                                 -0.0153         -0.0158        -0.0416      -0.0432
                                                                                 (2.83)          (1.53)         (2.57)       (2.19)

# of Owner-Occupied Units Constructed 1970 or earlier                           -0.0167         -0.0133        -0.0089      -0.0078
                                                                                 (8.59)          (8.81)         (7.48)       (7.17)

Distance to Central Business District of MSA                                    -0.4818         -0.7859       -10.8006      -11.9175
                                                                                 (4.61)          (2.58)         (3.18)        (1.09)

Surface Area not Covered by Water (Square Miles)                                23.1592        13.2437         1.5941       0.2646
                                                                                 (6.14)         (4.63)         (1.03)       (0.26)

Observations                                                                     46,930         46,930         46,930       46,930
MSA Fixed Effects                                                                 331             331            331          331
F-stat for Significance of Excluded Instrumentsc                                 127.93         153.75         144.79        49.56
                                       d
Hansen (1982) J-Statistic (P-value)                                               20.37          22.71           18.13        16.55
                                                                                (0.2556)       (0.1590)        (0.3809)     (0.4851)
a
  LIHTC Construction 1987 – 2000 treated endogenously. Instruments include 1970 distribution of housing structure type (single
family and various sizes of multi-family) and bedroom counts (0, 1, 2, 3, and 4+ bedrooms), separately for owner-occupied and rental
housing stock. All variables (dependent, independent, and instrumental) were measured over circle areas of radii indicated above.
b
    Corresponding t-ratios are based on MSA-clustered standard errors as described in the text.
c
    The F-statistic for the joint significance of excluded instruments accounts the clustering of standard errors by MSA.
d
 Hansen (1982) J-test is a test of overidentifying restrictions with a joint null hypothesis that the excluded instruments are
uncorrelated with the error term and correctly excluded from the estimated equation. Rejection of the null hypothesis calls into
question the validity of the instruments (for more details see Davidson and MacKinnon (2004).




                                                                                                                             39
Table A-2. Impact on Private Rental Construction in Low-Income Neighborhoods 1990 to 2000
in Response to LIHTC Construction from 1987 to 2000

                                                                                             Radius of Circles in Miles
GMM Estimatesa (t-statistics)b
                                                                                   0.5            1.0            5.0         10.0
LIHTC Construction 1987-2000                                                     0.2621         0.1382         -0.0460      -0.1162
                                                                                 (7.15)         (2.48)          (1.13)       (2.19)

# of Rental Units Constructed 1985-1989                                         0.2751          0.3100         0.2582       0.3074
                                                                                (18.22)         (17.03)        (14.01)      (22.79)

# of Rental Units Constructed 1980-1984                                          0.1138         0.1660         0.1880       0.1009
                                                                                 (6.10)         (6.08)         (5.05)       (2.35)

# of Rental Units Constructed 1970-1979                                         -0.0185         -0.0068        0.0108       -0.0052
                                                                                 (1.87)          (0.75)        (0.66)        (0.30)

# of Rental Units Constructed 1970 or earlier                                    0.0008         0.0034         0.0160       0.0154
                                                                                 (0.96)         (2.89)         (13.65)      (13.13)

# of Owner-Occupied Units Constructed 1985-1989                                  0.1268         0.1408         0.0271       0.0364
                                                                                 (3.26)         (3.92)         (0.84)       (1.07)

# of Owner-Occupied Units Constructed 1980-1984                                 -0.0043         -0.0291        -0.1482      -0.2215
                                                                                 (0.07)          (0.45)         (2.71)       (2.98)

# of Owner-Occupied Units Constructed 1970-1979                                 -0.0164         -0.0481        -0.0345      0.0238
                                                                                 (1.05)          (2.61)         (2.44)      (1.48)

# of Owner-Occupied Units Constructed 1970 or earlier                           -0.0166         -0.0171        -0.0157      -0.0088
                                                                                 (4.49)          (4.78)         (8.94)       (6.72)

Distance to Central Business District of MSA                                    -0.6042         -1.3503        -5.7270      -8.7532
                                                                                 (5.40)          (4.13)         (2.37)       (1.56)

Surface Area not Covered by Water (Square Miles)                                34.4016        18.8986         11.1282      1.9005
                                                                                 (2.37)         (2.08)          (3.69)      (1.77)

Observations                                                                     16,165         16,165         16,165       16,165
MSA Fixed Effects                                                                 331             331            331          331
F-stat for Significance of Excluded Instrumentsc                                 37.58          113.87         108.27        39.18
Hansen (1982) J-Statistic (P-value)d                                              20.22          21.05           26.60        13.48
                                                                                (0.2632)       (0.2241)        (0.0643)     (0.7032)
a
  LIHTC Construction 1987 – 2000 treated endogenously. Instruments include 1970 distribution of housing structure type (single
family and various sizes of multi-family) and bedroom counts (0, 1, 2, 3, and 4+ bedrooms), separately for owner-occupied and rental
housing stock. All variables (dependent, independent, and instrumental) were measured over circle areas of radii indicated above.
b
    Corresponding t-ratios are based on MSA-clustered standard errors as described in the text.
c
    The F-statistic for the joint significance of excluded instruments accounts the clustering of standard errors by MSA.
d
 Hansen (1982) J-test is a test of overidentifying restrictions with a joint null hypothesis that the excluded instruments are
uncorrelated with the error term and correctly excluded from the estimated equation. Rejection of the null hypothesis calls into
question the validity of the instruments (for more details see Davidson and MacKinnon (2004).




                                                                                                                             40
Table A-3. Impact on Private Rental Construction in Middle-Income Neighborhoods 1990 to 2000
in Response to LIHTC Construction from 1987 to 2000

                                                                                             Radius of Circles in Miles
GMM Estimatesa (t-statistics)b
                                                                                   0.5            1.0            5.0         10.0
LIHTC Construction 1987-2000                                                    -0.3999         -0.5703        -0.3192      -0.1360
                                                                                 (6.02)         (15.88)         (9.82)       (4.69)

# of Rental Units Constructed 1985-1989                                         0.3714          0.3156         0.1936       0.1313
                                                                                (13.84)         (14.81)        (16.07)      (9.27)

# of Rental Units Constructed 1980-1984                                          0.0986         0.0866         0.0383       0.0428
                                                                                 (7.22)         (6.38)         (1.94)       (1.50)

# of Rental Units Constructed 1970-1979                                          0.0103         -0.0020        -0.0118      0.0035
                                                                                 (1.99)          (0.40)         (1.80)      (0.28)

# of Rental Units Constructed 1970 or earlier                                   -0.0014         0.0013         0.0008       0.0031
                                                                                 (4.02)         (5.60)         (2.07)       (4.18)

# of Owner-Occupied Units Constructed 1985-1989                                  0.0989         0.1223         0.0647       0.0352
                                                                                 (7.61)         (6.37)         (3.59)       (2.34)

# of Owner-Occupied Units Constructed 1980-1984                                 -0.0283         -0.0290        0.0332       0.0793
                                                                                 (1.75)          (1.85)        (1.53)       (2.56)

# of Owner-Occupied Units Constructed 1970-1979                                 -0.0004         0.0086         -0.0031      -0.0073
                                                                                 (0.05)         (1.36)          (0.38)       (0.97)

# of Owner-Occupied Units Constructed 1970 or earlier                           -0.0028         -0.0008        0.0033       0.0006
                                                                                 (4.45)          (1.55)        (6.46)       (1.11)

Distance to Central Business District of MSA                                    -0.3448         -0.7870        -2.9349      -0.8227
                                                                                 (5.41)          (4.31)         (2.24)       (0.33)

Surface Area not Covered by Water (Square Miles)                                14.3984         5.6086         3.1669       0.5299
                                                                                 (5.02)         (3.16)         (3.15)       (1.10)

Observations                                                                     15,826         15,826         15,826       15,826
MSA Fixed Effects                                                                 331             331            331          331
F-stat for Significance of Excluded Instrumentsc                                 10.57           27.59          47.26        38.50
Hansen (1982) J-Statistic (P-value)d                                              20.91          24.94           18.82        17.83
                                                                                (0.2305)       (0.0960)        (0.3389)     (0.3998)
a
  LIHTC Construction 1987 – 2000 treated endogenously. Instruments include 1970 distribution of housing structure type (single
family and various sizes of multi-family) and bedroom counts (0, 1, 2, 3, and 4+ bedrooms), separately for owner-occupied and rental
housing stock. All variables (dependent, independent, and instrumental) were measured over circle areas of radii indicated above.
b
    Corresponding t-ratios are based on MSA-clustered standard errors as described in the text.
c
    The F-statistic for the joint significance of excluded instruments accounts the clustering of standard errors by MSA.
d
 Hansen (1982) J-test is a test of overidentifying restrictions with a joint null hypothesis that the excluded instruments are
uncorrelated with the error term and correctly excluded from the estimated equation. Rejection of the null hypothesis calls into
question the validity of the instruments (for more details see Davidson and MacKinnon (2004).




                                                                                                                             41
Table A-4. Impact on Private Rental Construction in High-Income Neighborhoods 1990 to 2000
in Response to LIHTC Construction from 1987 to 2000

                                                                                             Radius of Circles in Miles
GMM Estimatesa (t-statistics)b
                                                                                   0.5            1.0            5.0          10.0
LIHTC Construction 1987-2000                                                    -1.0754         -0.4383        -0.1593      -0.0766
                                                                                (17.30)          (8.92)         (6.00)       (1.43)

# of Rental Units Constructed 1985-1989                                         0.4363          0.4309         0.1681       0.0807
                                                                                (15.52)         (16.41)        (8.09)       (4.56)

# of Rental Units Constructed 1980-1984                                          0.1531         0.1200         0.0906       0.2725
                                                                                 (7.03)         (6.45)         (2.79)       (7.42)

# of Rental Units Constructed 1970-1979                                          0.0479         0.0119         0.0378       0.0049
                                                                                 (6.25)         (1.95)         (3.45)       (0.19)

# of Rental Units Constructed 1970 or earlier                                    0.0012         -0.0024        -0.0045      -0.0061
                                                                                 (2.47)          (4.94)         (5.88)       (3.14)

# of Owner-Occupied Units Constructed 1985-1989                                  0.0773         0.1123         0.1239       0.1855
                                                                                 (3.88)         (5.02)         (4.01)       (4.93)

# of Owner-Occupied Units Constructed 1980-1984                                 -0.0553         -0.0272        0.1366       0.0720
                                                                                 (3.07)          (1.74)        (3.46)       (1.13)

# of Owner-Occupied Units Constructed 1970-1979                                 -0.0182         -0.0227        -0.0538      -0.0443
                                                                                 (2.16)          (2.71)         (4.18)       (3.10)

# of Owner-Occupied Units Constructed 1970 or earlier                           -0.0089         -0.0068        -0.0055      -0.0041
                                                                                 (7.54)          (8.32)         (5.76)       (3.85)

Distance to Central Business District of MSA                                    -0.4549         -1.1769        -8.6142      -14.4049
                                                                                 (5.34)          (4.36)         (3.33)        (1.93)

Surface Area not Covered by Water (Square Miles)                                19.5199         9.4554         -1.1215      -2.4112
                                                                                 (5.61)         (4.41)          (0.56)       (2.22)

Observations                                                                     14,939         14,939         14,939       14,939
MSA Fixed Effects                                                                 331             331            331          331
F-stat for Significance of Excluded Instrumentsc                                 72.20           49.70         136.89        40.84
Hansen (1982) J-Statistic (P-value)d                                              22.95          20.94           34.84        27.46
                                                                                (0.1508)       (0.2288)        (0.0065)     (0.0517)
a
  LIHTC Construction 1987 – 2000 treated endogenously. Instruments include 1970 distribution of housing structure type (single
family and various sizes of multi-family) and bedroom counts (0, 1, 2, 3, and 4+ bedrooms), separately for owner-occupied and rental
housing stock. All variables (dependent, independent, and instrumental) were measured over circle areas of radii indicated above.
b
    Corresponding t-ratios are based on MSA-clustered standard errors as described in the text.
c
    The F-statistic for the joint significance of excluded instruments accounts the clustering of standard errors by MSA.
d
 Hansen (1982) J-test is a test of overidentifying restrictions with a joint null hypothesis that the excluded instruments are
uncorrelated with the error term and correctly excluded from the estimated equation. Rejection of the null hypothesis calls into
question the validity of the instruments (for more details see Davidson and MacKinnon (2004)




                                                                                                                             42

				
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