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Unpacking the impacts of the Low-Income Housing Tax Credit program on nearby property values

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Most existing research on the impacts of the Low-Income Housing Tax Credit (LIHTC) on neighbouring property values is limited in terms of providing causal attribution and uncovering nuances in the role of housing market and neighbourhood composition. This article addresses these shortcomings by investigating the impacts of the LIHTC program in Charlotte, North Carolina and Cleveland, Ohio. Levels and trends in housing prices before and after LIHTC developments in neighbourhoods are examined based on parcel-level housing sales data from 1996 to 2007. The Adjusted Interrupted Time Series-Difference in Differences (AITS-DID) model is used to clarify the causal direction of impacts of LIHTC developments. The results show that LIHTC developments have negative impacts in Charlotte, while having upgrading effects in Cleveland. Also, these impacts vary across neighbourhoods’ income heterogeneity. Thus, care should be taken when siting LIHTC developments to minimise negative impacts and enhance its use for community revitalisation across different housing market conditions.
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Urban Studies
2016, Vol. 53(12) 2488–2510
ÓUrban Studies Journal Limited 2015
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DOI: 10.1177/0042098015593448
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Unpacking the impacts of the
Low-Income Housing Tax Credit
program on nearby property values
Ayoung Woo
Texas A&M University, USA
Kenneth Joh
Texas A&M University, USA
Shannon Van Zandt
Texas A&M University, USA
Abstract
Most existing research on the impacts of the Low-Income Housing Tax Credit (LIHTC) on neigh-
bouring property values is limited in terms of providing causal attribution and uncovering nuances
in the role of housing market and neighbourhood composition. This article addresses these
shortcomings by investigating the impacts of the LIHTC program in Charlotte, North Carolina
and Cleveland, Ohio. Levels and trends in housing prices before and after LIHTC developments
in neighbourhoods are examined based on parcel-level housing sales data from 1996 to 2007.
The Adjusted Interrupted Time Series-Difference in Differences (AITS-DID) model is used to
clarify the causal direction of impacts of LIHTC developments. The results show that LIHTC
developments have negative impacts in Charlotte, while having upgrading effects in Cleveland.
Also, these impacts vary across neighbourhoods’ income heterogeneity. Thus, care should be
taken when siting LIHTC developments to minimise negative impacts and enhance its use for
community revitalisation across different housing market conditions.
Keywords
housing prices, Low-Income Housing Tax Credit, neighbourhood, neighbourhood heterogeneity,
planning, spillover effects, subsidised housing
Received December 2014; accepted May 2015
Introduction
Policymakers have long faced a dilemma
with respect to the implementation of federal
housing assistance programs. While the
Corresponding author:
Ayoung Woo, Landscape Architecture and Urban Planning,
Texas A&M University, MS 3137 TAMU, College Station,
TX 77843, USA.
Email: awoo@arch.tamu.edu
at Texas A&M University - Medical Sciences Library on July 31, 2016usj.sagepub.comDownloaded from
least-advantaged populations need afford-
able housing and suitable living environ-
ments, affluent residents frequently oppose
the introduction of subsidised housing into
their communities. Community opposition
has been rooted in a negative perception of
subsidised households, which fundamentally
stems from attitudes toward tenant charac-
teristics such as race/ethnicity and poverty
(Freeman and Botein, 2002). The core issue
of ‘not in my back yard’ (NIMBY) attitudes
has been fear of the deterioration of property
values due to the influx of ‘undesirable’
households. These attitudes have resulted in
the exclusion of low-income residents from
affluent neighbourhoods, and have exacer-
bated social, racial and housing inequalities
in the United States. This is of paramount
concern for planners and policymakers,
especially in desirable neighbourhoods where
affordable housing is scarce.
Prior studies examining the impacts of
subsidised housing programs on nearby prop-
erty values have produced conflicting results.
Some researchers have found a negative
impact (Cummings and Landis, 1993; Lee
et al., 1999), while others have found a posi-
tive (Santiago et al., 2001; Schwartz et al.,
2006) or even no impact (Castells, 2010;
Sedway and Associates, 1983). These incon-
sistent findings may be due to two reasons: 1)
the causal direction of impacts of subsidised
housing on nearby property values is not
clarified in the analysis, thereby confounding
results; and 2) the impact of subsidised hous-
ing may vary across local housing market
and submarket conditions. We address these
gaps by examining how the spatial distribu-
tion of subsidised housing developments
influences neighbouring property values. This
article addresses a simple question: Do Low
Income Housing Tax Credit (LIHTC) subsi-
dised housing developments significantly
affect nearby property values?
We examine the impacts of LIHTC devel-
opments on nearby property values from
1996 to 2007 in two US cities: Charlotte,
North Carolina and Cleveland, Ohio. These
cities are selected to compare how LIHTC
impacts vary across different housing mar-
kets. Further, our analysis compares hous-
ing submarkets stratified by median family
income to determine how impacts differ
across low-, middle- and high-income neigh-
bourhoods (Freeman and Botein, 2002). We
examine factors associated with LIHTC
developments and property values using a
methodology that accounts for the levels
and trends in housing prices over time. Our
research may help policymakers better
understand how LIHTC developments
affect neighbourhoods and can help pro-
mote policies to enhance positive impacts
and mitigate negative impacts.
Literature review
How subsidised housing developments
may affect nearby property values
The impact of subsidised housing develop-
ments in neighbourhoods can be capitalised
into housing prices, reflecting physical and
socioeconomic changes (Baum-Snow and
Marion, 2009). Literature from the past
decade is replete with studies assessing
whether subsidised housing has negative
impacts on neighbouring housing prices.
However, the findings have been inconsis-
tent. Some researchers who found negative
spillover effects of subsidised housing pro-
grams point to the influx of ‘undesirables’ as
the cause of neighbourhood decline
(Cummings and Landis, 1993; Lee et al.,
1999), while others suggested that subsidised
housing developments lead to neighbour-
hood revitalisation by eliminating disame-
nities in communities (Baum-Snow and
Marion, 2009; Koschinsky, 2009; Schwartz
et al., 2006). A critical review of the litera-
ture provides support for both perspectives.
Disparities between residents of subsidised
housing and neighbourhood residents may
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result in dissonance among neighbours
(Freeman and Botein, 2002; Nguyen, 2005).
Such dissonance may induce a drop in hous-
ing prices if white residents flee or potential
purchasers begin to view the neighbourhood
as undesirable based on tenant characteristics
or perceived disamenities (e.g. crime). In
contrast, if subsidised households and
non-subsidised households share similar
socioeconomic characteristics, especially in
lower-income neighbourhoods, the impact
on neighbouring housing prices may be negli-
gible (Ellen et al., 2005; Freeman and Botein,
2002). This underlines the significance of
neighbourhood heterogeneity in assessing
impacts of subsidised housing that have been
overlooked in many previous studies. This
article addresses this gap by classifying neigh-
bourhood heterogeneity based on income
level to examine the impacts of subsidised
housing.
1
Subsidised housing developments may
also affect surrounding housing prices due to
the removal of amenities or disamenities
(Ellen et al., 2005; Freedman and Owens,
2011). The loss of historic buildings, parks
and open space and other neighbourhood
amenities may have a negative effect (Ellen
et al., 2005); in contrast, the removal of
undesirable land uses such as deteriorated
buildings and abandoned lots may result in a
positive impact (Castells, 2010; Freedman
and Owens, 2011; Schwartz et al., 2006).
Traditional US public housing programs
have often been criticised for depressing
housing prices and increasing crime within
neighbourhoods, triggering neighbourhood
decline (Lee et al., 1999; McNulty and
Holloway, 2000; Roncek et al., 1981). In
response to these concerns, the LIHTC pro-
gram was established in 1986 to subsidise the
development of low-income housing through
an equity contribution by private developers,
considered to be a more effective strategy for
creating higher quality housing units and
maintaining neighbourhood vitality (Deng,
2009). Additionally, many state agencies
have promoted LIHTC developments to
improve urban neighbourhoods by removing
disamenities (Deng, 2007; National Council
of State Housing Agencies, 2002). This may
suggest that LIHTC developments, by revi-
talising distressed neighbourhoods, may
have positive impacts on neighbouring prop-
erty values. However, further investigation is
needed to assess LIHTC’s impacts on neigh-
bourhoods, which is the aim of this research.
Project-based subsidised housing develop-
ments may also yield spillover effects due to
new residential investment. Studies on hous-
ing investments typically found a positive
impact of residential investment in new con-
struction and rehabilitation on nearby prop-
erty values (DeSalvo, 1974; Ding et al.,
2000; Simons et al., 1998). Compared to
other project-based subsidised housing pro-
grams, the LIHTC program is a market-
based approach to providing affordable
housing as well as market rate units (Deng,
2007; Van Zandt and Mhatre, 2009). Thus,
LIHTC housing investments can reap the
benefit of collective action in large-scale
investments through partnerships between
government and housing developers (Ellen
et al., 2005; Schwartz et al., 2006). Further,
new LIHTC developments that demonstrate
success may attract additional residential
investment into an area (Caplin and Leahy,
1998; Schwartz et al., 2006). In this way,
LIHTC housing investments may have posi-
tive spillover effects on neighbouring hous-
ing prices by stimulating local development,
which is directly investigated in this article.
Methodological limitations undercut
previous evidence on the impacts of
subsidised housing
This research builds on previous literature
examining spillover effects of subsidised
housing developments on neighbouring
housing prices. Earlier studies used the test
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versus control area methodology which
compares housing prices in neighbourhoods
that contained subsidised housing develop-
ments to neighbourhoods without subsidised
housing. For instance, MaRous (1996)
found no evidence that low-income housing
units have a negative impact on market-rate
housing units in Chicago. Additionally, the
majority of previous studies found that there
were positive (Nourse, 1963; Rabiega et al.,
1984) or insignificant impacts of subsidised
housing developments (Schafer, 1972;
Sedway and Associates, 1983). However, a
methodological limitation of earlier studies
is the difficulty of knowing whether the test
neighbourhoods were identical to the con-
trol neighbourhoods except for the existence
of subsidised housing (Freeman and Botein,
2002). Although previous studies selected
comparable neighbourhoods, there might be
subtle differences that were not easily cap-
tured by the test versus control area
approach. Further, this methodology cannot
control for other factors such as locational,
environmental and neighbourhood charac-
teristics that can affect property values
(Nguyen, 2005).
To overcome previous limitations and
examine spillover effects of subsidised hous-
ing developments, many recent studies have
employed a hedonic price methodology.
However, these studies have typically used a
cross-sectional analysis to examine the
impact of subsidised housing on nearby
property values, while not accounting for
the direction of causality (Cummings and
Landis, 1993; Goetz et al., 1996; Lee et al.,
1999; Lyons and Loveridge, 1993).
Subsidised housing tends to be developed in
lower priced neighbourhoods due to
NIMBY attitudes (Rohe and Freeman,
2001). Methodologically, this suggests that
the causal direction of subsidised housing
impacts should be accounted for in the anal-
ysis, to determine whether subsidised hous-
ing complexes are the cause of property
value depreciation, or whether these units
are placed only in neighbourhoods with
lower prices to avoid controversy. The
majority of previous studies do not account
for these directions of causality.
Additionally, as Galster (2004) notes, these
studies are also prone to selection bias since
they do not identify the preexisting price lev-
els of neighbourhoods where subsidised
housing was developed. However, a few
researchers have accounted for causal direc-
tion by employing a quasi-experimental
research design to consider preexisting price
levels and trends (Ellen et al., 2005; Galster
et al., 1999b; Koschinsky, 2009; Schwartz
et al., 2006). For example, after accounting
for causal direction, Schwartz et al. (2006)
found that place-based subsidised housing
had positive impacts in New York City, and
this spillover effect increased with the size of
subsidised developments.
Limitations of many previous studies
examining the relationship between subsi-
dised housing programs and nearby property
values stem from the difficulty in employing
a robust analytic methodology at the indi-
vidual parcel level. This article attempts to
overcome some of these limitations by apply-
ing the AITS-DID (Adjusted Interrupted
Time Series-Difference in Differences) model
to parcel-level sales transaction data. This
approach helps to better understand the
impacts of LIHTC developments on prop-
erty values because the AITS-DID model
identifies the direction of causality based on
a difference-in-differences specification on
levels and trends in housing prices. In addi-
tion to a more robust methodology, our
study employs a comparative analysis by
expanding the scope of our research area to
two cities with contrasting market conditions
to determine whether impacts of LIHTC
developments vary across housing markets
and by income level. The results from this
study may help policymakers better under-
stand the differential impacts of LIHTC
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developments on property values under vari-
ous market and submarket conditions.
Study area and data
Defining proximity to LIHTC developments
We examined impacts on housing prices
before and after the introduction of LIHTC
subsidised housing using a concentric ring
buffer around each property, known as a
microneighbourhood (Castells, 2010; Galster
et al., 1999b; Koschinsky, 2009; Lee, 2008;
Schwartz et al., 2006). The microneighbour-
hood of each property, based on Euclidean
distance rings, may include other properties
within its radius. We classified properties
based on their proximity to LIHTC develop-
ments; when the boundaries of LIHTC devel-
opments are contained within the property’s
microneighbourhood, the property was con-
sidered to be within the sphere of influence of
LIHTC developments (Galster et al., 1999a).
Boundaries of LIHTC complexes rather than
the centroid were used, based on the notion
that neighbours would be able to identify
LIHTC units from the edge of the complexes.
While buffer distances vary across research
and are somewhat arbitrary, previous studies
have used distances of 500 feet (Galster et al.,
1999b; Santiago et al., 2001), 1000 feet
(Galster et al., 1999b; Koschinsky, 2009;
Santiago et al., 2001) or 2000 feet (Castells,
2010; Ellen et al., 2005; Galster et al., 1999b;
Santiago et al., 2001; Schwartz et al., 2006).
2
For this study, a buffer distance of 2000 feet
(600 meters) was used to define the micro-
neighbourhood of each property. This buffer
distance was selected based on a sensitivity
test using various distance thresholds, and
also to make results comparable to the exist-
ing literature.
Study areas
Charlotte, North Carolina and Cleveland,
Ohio were selected as study areas,
representing different regions of the U.S.
(South and Midwest, respectively). Both cit-
ies are notably outside of the urban
Northeast, the focus of previous studies
given its history of public housing programs
(Freeman and Botein, 2002; Nguyen, 2005).
Thus, our study extends the existing litera-
ture on subsidised housing beyond the
Northeast region.
These two cities show contrasting eco-
nomic and housing market conditions
(Table 1). Charlotte exemplifies a growing
Southern city (population 542,131 in 2000)
and has experienced steady population
growth during the past few decades (Delmelle
et al., 2013). Despite the recent economic
downturn, Charlotte remains one of the fast-
est growing cities in the U.S. (Rohe et al.,
2012). In response to pressures from rapid
growth, Charlotte has actively promoted resi-
dential revitalisation and investments in both
downtown and its suburbs (Delmelle et al.,
2014). In contrast, Cleveland has experienced
post-industrial decline resulting in severe pop-
ulation loss and neighbourhood destabilisa-
tion (Koschinsky, 2009). To offset this
substantial decline, Cleveland has focused on
neighbourhood revitalisation and new resi-
dential investments as the top priority of the
city development administrators’ agenda
(Ding et al., 2000). Despite civic and commu-
nity efforts, the housing market in Cleveland
remains stagnant: vacancy rates are twice as
high as Charlotte and annual average number
of sales transactions and housing permits are
substantially lower than Charlotte during the
study period. The annual average sales prices
from 1996 to 2007 in Cleveland were around
$52,000, lower than its median housing value,
indicating that Cleveland’s housing market
consists largely of lower price housing trans-
actions. In contrast, the average sales prices
in Charlotte ($313,000) was two times higher
than its median housing value, indicating that
higher price housing is substantially trans-
acted in Charlotte’s housing market.
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Trends in housing sales transactions and
housing permits per person in the two cities
from 1996 to 2007 are shown in Figure 1. In
Charlotte, the number of housing sales
transactions per person increased steadily
from 0.05 to 0.10. In Cleveland, housing
sales transactions were lower than Charlotte,
and remained stagnant for most of this
Table 1. City profile for Charlotte and Cleveland.
City profile Charlotte Cleveland
Number Percentage Number Percentage
Population 542,131 - 478,393 -
Racial Composition
White 298,501 55.06 186,368 38.96
African-American 175,563 32.38 240,362 50.24
Hispanic 40,008 7.38 34,554 7.22
Others 28,059 5.18 17,109 3.58
Median Family Income ($) 56,517 - 30,286 -
Median Housing Value ($) 134,300 - 72,100 -
Total Housing Units 230,556 - 215,844 -
Vacancy Rate 14,811 6.42 25,211 11.68
Annual Average Number of Sales Transactions 44,454 - 18,402 -
Annual Average Sales Transaction Prices ($) 313,306 - 52,604 -
Annual Average Number of Housing Permits 7636 - 407 -
Source: Average sales prices and number of sales transactions (1996–2007) came from historical housing sales data from
the Mecklenburg County Assessor’s Office and the Northeast Ohio Community and Neighborhood Data for Organizing
(NEO CANDO); Average number of housing permits (1996–2007) was obtained from the Mecklenburg County IDS
Public Reports and HUD’s State of the Cities Data Systems; all the other data were tabulated based on the 2000 census
data (U.S. Bureau of the Census 2000).
Figure 1. Housing market trends in the cities of Charlotte and Cleveland.
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period. In terms of annual average number
of housing sales transactions per person,
Charlotte was twice that of Cleveland (0.8
versus 0.4). Additionally, although the num-
ber of housing permits per person in
Charlotte gradually decreased since its peak
in 1999, Charlotte was substantially higher
than Cleveland (0.013 versus 0.001). Hence,
there is a clear contrast in housing market
conditions between Charlotte and Cleveland;
by comparing these two cities, we can exam-
ine the varying impacts of subsidised housing
developments.
Data sources and descriptions
This study relies primarily on historic hous-
ing sales data for Charlotte and Cleveland
from 1996 to 2007, obtained with permission
from the Mecklenburg County Assessor’s
Office for Charlotte and from the Northeast
Ohio Community and Neighborhood Data
for Organizing (NEO CANDO) for
Cleveland. The unit of analysis for this study
was a single-family housing unit, and all
forced sales transactions were excluded.
Additionally, only housing sales at the latest
transfer date between 1996 and 2007 were
considered because structure characteristics
data only included attributes at the latest
transfer date and not historical records. The
top and bottom 1 percent of the sample in
sales prices was excluded to remove outliers.
Also, census tracts with fewer than 10 prop-
erty sales were excluded. After these condi-
tions were applied, the final sample consisted
of 114,471 housing sales transactions in
Charlotte and 27,636 housing sales transac-
tions in Cleveland during the 1996 to 2007
period. The summary statistics of variables
included in our analysis are presented in
Table 2.
Data obtained from the U.S. Department
of Housing and Urban Development’s
(HUD) Picture of Subsidised Households
were used to identify characteristics of
LIHTC developments such as their size and
spatial locations. However, due to incom-
plete data, the HUD data was supplemented
with additional data from the Mecklenburg
County Integrated Data Store (IDS) Public
Reports, the Mecklenburg County
GeoPortal and the Ohio Housing Finance
Agency. The locations of LIHTC develop-
ments were triangulated using the
Mecklenburg County GeoPortal (Charlotte),
the Ohio Housing Finance Agency
(Cleveland), Google satellite imagery and
FindTheData. Project completion dates were
obtained from the Mecklenburg County IDS
Public Reports and the Ohio Housing
Finance Agency. After cleaning the data, we
determined the locations of 75 developments
(4718 units) in Charlotte and 123 develop-
ments (8603 units) in Cleveland (see
Figure 2).
Figure 2 shows the spatial distribution of
neighbourhoods stratified by median family
income for Charlotte and Cleveland.
3
For
Charlotte, high-income neighbourhoods fan
out from the centre towards the south, while
low- and middle-income neighbourhoods
are clustered in a crescent shape near the city
centre. In Cleveland, low-income neighbour-
hoods comprise most of the centre while
high-income neighbourhoods occupy the
outskirts of the city.
The distribution of LIHTC developments
in both cities across income submarkets is
shown in Table 3. The majority of LIHTC
developments are located in low- and
middle-income neighbourhoods: 79 percent
of LIHTC developments and 73 percent of
units were located in low-income neighbour-
hoods in Charlotte; for Cleveland, 48 per-
cent of LIHTC developments and 59 percent
of units were located in low-income
neighbourhoods.
Unobserved and time-invariant neigh-
bourhood characteristics were also captured
using 2000 census data. A total of 125 cen-
sus tracts in Charlotte and 192 census tracts
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Table 2. Citywide descriptive statistics.
Variable definition and unit Charlotte Cleveland
Mean Std. Dev. Min Max Mean Std. Dev. Min Max
Dependent Variable
Sales Price ($1000) 194.95 142.71 23.50 1039.50 73.74 37.41 3.50 239.90
Independent Variables
Structural Characteristics
Heated Areas (sq ft) 2055.27 904.84 414.00 13,580.00 1268.10 344.21 390.00 8023.00
Property Sizes (sq ft) - - - - 5.16 2774.76 340.00 102,000.00
Building Age (years) 17.29 20.59 0.00 107.00 74.64 26.03 0.00 207.00
Number of Bedrooms (#) 3.31 0.67 1.00 44.00 2.97 0.78 1.00 10.00
Number of Full Bathrooms (#) 2.03 0.63 1.00 8.00 1.10 0.31 1.00 4.00
Number of Half Bathrooms (#) 0.57 0.52 0.00 11.00 0.14 0.36 0.00 10.00
Number of Fireplaces (#) 0.84 0.39 0.00 11.00 - - - -
Binary: 1 = Electric Heating Source 0.11 0.31 0.00 1.00 - - - -
Binary: 1 = Oil Heating Source 0.01 0.09 0.00 1.00 - - - -
Binary: 1 = Brick/Stone Exterior 0.26 0.44 0.00 1.00 0.09 0.29 0.00 1.00
Binary: 1 = High-Housing Quality 0.02 0.13 0.00 1.00 0.00 0.06 0.00 1.00
Binary: 1 = Low-Housing Quality 0.01 0.09 0.00 1.00 0.07 0.26 0.00 1.00
Binary: 1 = Bungalow Housing Style - - - - 0.07 0.25 0.00 1.00
Binary: 1 = Colonial Housing Style - - - - 0.49 0.50 0.00 1.00
Locational Characteristics
Binary: 1 = Parks 250 ft. (75 m) 0.07 0.25 0.00 1.00 0.05 0.21 0.00 1.00
Binary: 1 = Rivers/Lakes 500 ft. (150 m) - - - - 0.04 0.19 0.00 1.00
Year/Quarter Characteristics
1997 (Sales Year) 0.04 0.20 0.00 1.00 0.06 0.24 0.00 1.00
1998 (Sales Year) 0.05 0.22 0.00 1.00 0.07 0.25 0.00 1.00
1999 (Sales Year) 0.06 0.24 0.00 1.00 0.07 0.26 0.00 1.00
2000 (Sales Year) 0.07 0.25 0.00 1.00 0.07 0.26 0.00 1.00
2001 (Sales Year) 0.07 0.26 0.00 1.00 0.08 0.27 0.00 1.00
2002 (Sales Year) 0.08 0.27 0.00 1.00 0.06 0.24 0.00 1.00
2003 (Sales Year) 0.09 0.28 0.00 1.00 0.09 0.29 0.00 1.00
2004 (Sales Year) 0.10 0.30 0.00 1.00 0.10 0.30 0.00 1.00
2005 (Sales Year) 0.12 0.32 0.00 1.00 0.13 0.33 0.00 1.00
2006 (Sales Year) 0.14 0.35 0.00 1.00 0.12 0.32 0.00 1.00
2007 (Sales Year) 0.14 0.35 0.00 1.00 0.10 0.30 0.00 1.00
April–June (Sales Quarter) 0.28 0.45 0.00 1.00 0.29 0.45 0.00 1.00
July–Sept (Sales Quarter) 0.28 0.45 0.00 1.00 0.27 0.45 0.00 1.00
Oct–Dec (Sales Quarter) 0.23 0.42 0.00 1.00 0.23 0.42 0.00 1.00
LIHTC Developments
Within 2000 ft. (600 m) of LIHTC 0.07 0.26 0.00 1.00 0.29 0.45 0.00 1.00
LIHTC Units (#) 3.21 22.61 0.00 504.00 12.21 38.18 0.00 811.00
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in Cleveland were analysed in our sample.
Geographic coordinates of each property
normalised by the distance to the Central
Business District (CBD) were derived from
parcel data from both counties, 2000 census
data and the Census Transportation
Planning Products (CTPP) 2000 Home-to-
work Flows data. We also used data
obtained from each county’s GIS Center
data to calculate proximity to parks, rivers
and lakes.
Methodology
We used the AITS-DID model to specify the
impact of subsidised housing on nearby
property values. This approach compares
the levels and trends of housing prices in
neighbourhoods with LIHTC housing before
and after it was developed with those in
nearby neighbourhoods where LIHTC hous-
ing was not developed during the study
period (Galster, 2004; Koschinsky, 2009).
Figure 2. Charlotte and Cleveland LIHTC developments by neighbourhood heterogeneity.
Source: Cities of Charlotte and Cleveland GIS data sets.
Table 3. Distribution of LIHTC developments by submarkets.
Submarkets
(by income)
Charlotte Cleveland
No. of projects (%) No. of units (%) No. of projects (%) No. of units (%)
Low-income 59 (78.67) 3447 (73.06) 59 (47.97) 5105 (59.34)
Middle-income 12 (16.00) 830 (17.59) 46 (37.40) 2645 (30.75)
High-income 4 (5.33) 441 (9.35) 18 (14.63) 853 (9.91)
Citywide 75 (100.00) 4718 (100.00) 123 (100.00) 8603 (100.00)
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The fundamental concept of the AITS-DID
model is based on the hedonic price model,
which postulates that housing goods are
traded as a bundle of inherent attributes
(Chin and Chau, 2003; Koschinsky, 2009;
Rosen, 1974).
The centrepiece of the AITS-DID model
is estimating housing prices after taking into
account neighbourhood characteristics in
terms of LIHTC developments. This model
controls for the locational and neighbour-
hood characteristics of properties by using
spatial fixed effects, and clarifies the direc-
tion of causality to capture the differentials
in levels and trends of pre- and post-housing
prices associated with LIHTC housing devel-
opments by comparing control and impact
sales. Thus, the AITS-DID framework in
this study is specified as:
ln Pint =a+bSi+gTit +dLi
+zNn+hRit +mMit +eint,ð1Þ
where lnPint is the log of housing sales price
of property iin neighbourhood nat time t,
that is transformed to a natural logarithmic
functional form to reduce skewness and pull
in outliers. Siis a vector of property-related
structural characteristics such as heated
areas, building age, number of bedrooms,
number of bathrooms, number of half bath-
rooms, number of fireplaces, heating
sources, exterior types, housing styles and
housing quality.
4
The vector Tit is a set of
time indicators for each property indicating
the year and quarter in which the sale
occurred in order to account for seasonal
differences. These variables consist of 11
indicators for the year of sale (with 1996 as
the reference category) and three indicators
for the quarter (with the first quarter as the
reference category) in which the sale
occurred. The variable Liincludes the indi-
cators of locational characteristics for each
property such as proximity to parks (within
250 feet or 75 meters), rivers and lakes
(within 500 feet or 150 meters) and the geo-
graphic coordinates of each property (nor-
malised by distance to the CBD) to capture
most remaining locational attributes
(Koschinsky, 2009).
5
Nnis a set of census
tract fixed effects capturing the unobserved
and time-invariant neighbourhood charac-
teristics, which is census tract dummy vari-
ables signifying the year 2000 census tract
for each neighbourhood to control for their
distinct characteristics. Rit is a vector of ring
variables that captures housing price differ-
entials and trends before and after LIHTC
developments were located within a micro-
neighbourhood, described in more detail in
the section describing key variables. Mit is a
vector of size variables that explores the size
effects of newly developed subsidised hous-
ing, which is the total number of subsidised
housing units within a microneighbourhood
after subsidised housing was developed, and
eit is an error term of the model. The coeffi-
cients a,b,g,d,z,hand mare estimated
employing ordinary least squares (OLS)
regression with a robust standard error
to correct for heteroskedasticity which
might violate the assumption that the var-
iance of the error term is the same across
the housing submarket segmentations or
space (Koschinsky, 2009; Wooldridge,
2009). Finally, for each city, models were
estimated separately for three types of neigh-
bourhoods stratified by family income, to
test whether impacts of subsidised housing
vary based on income heterogeneity.
Key variables
The key variables comprise the vector of
ring variables (Rit), which account for the
differentials in levels and trends of pre- and
post-housing prices related to LIHTC devel-
opments by comparing control and impact
sales (Galster, 2004; Koschinsky, 2009; Lee,
2008). The underlying concept of these vari-
ables could be explained in terms of: 1)
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control/impact sales; and 2) pre/post differ-
entials in levels and trends of housing prices.
First, all sales can be categorised into two
groups: control sales and impact sales.
Impact sales are properties where LIHTC
housing is located within the property’s
microneighbourhood. Control sales are
defined as properties where LIHTC housing
is not within the property’s microneighbour-
hood but located in the same census tract
with impact sales (Koschinsky, 2009).
Second, the ring variables capture the differ-
entials in the levels and trends of housing
prices in microneighbourhoods including
LIHTC developments before and after its
completion. Impact sales can be further
divided into two categories according to the
completion dates of LIHTC housing: pre-
impact sales and post-impact sales. Pre-
impact sales are transactions that took place
prior to LIHTC developments while post-
impact sales are sales that occurred after
LIHTC housing was developed within their
microneighbourhoods.
The ring variables include two dummy
variables for the microneighbourhood of
each property to capture the differences in
housing price levels. Pre-impact sales for
housing price levels (pre-price level) take on
a value of one when there is or will be
LIHTC developments within the micro-
neighbourhood of the property (Galster
et al., 1999b; Schwartz et al., 2006). This
explains the existing average price levels in
microneighbourhoods and reflects the inher-
ent neighbourhood price levels before
LIHTC housing is developed. Post-impact
sales for housing price levels (post-price
level) take on a value of one when the prop-
erty has a completed LIHTC development
within the property’s microneighbourhood.
This measures the housing price levels in
microneighbourhoods after LIHTC housing
is developed. By specifying these two indica-
tors, we can compare the differentials in
housing price levels with control sales before
and after LIHTC was developed within
microneighbourhoods.
The ring variables also include two more
indicators for the microneighbourhood to
estimate the break in housing price trends.
One variable signifies the distance in days
between the date of sale and the beginning of
the research period (pre-price trend) (Galster
et al., 1999b; Koschinsky, 2009). Another
variable measures the distance in days
between the date of sale and the completion
date of subsidised housing (post-price trend)
(Galster et al., 1999b; Koschinsky, 2009;
Schwartz et al., 2006). In sum, the vector of
ring variables allows us to compare the dif-
ferentials in levels and trends of housing
prices between impact sales and control sales
before and after LIHTC housing was devel-
oped within microneighbourhoods.
Results
Citywide results
Table 4 shows the results for the citywide
models for Charlotte and Cleveland. We first
present the results for Charlotte.
All variables explain around 76.2 percent
of the variance in property values. For sales
price, the model showed expected coefficient
signs for all structural variables. Thus, for
the sake of brevity, we will focus on inter-
preting key coefficients for LIHTC develop-
ments. The main finding in the AITS-DID
model for Charlotte was that the completion
of LIHTC developments in a microneigh-
bourhood had a significant negative effect
on nearby property values (see Table 4 and
Panel A in Figure 3). The coefficient of the
pre-impact variable is negative, indicating
that the housing price level is lower com-
pared to the control area (i.e. outside the
impact area but in the same census tract)
before the siting of LIHTC housing. The
housing price level for impact sales was
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Table 4. Citywide results.
Variables Citywide results, Charlotte Citywide results, Cleveland
Coef. t-value Adj.BetayRobust
Std. Err
Coef. t-value Adj.BetayRobust
Std. Err
LIHTC Developments
Pre-price level 2000ft. –0.055 *** –5.19 –5.383 0.011 –0.085 *** –3.18 –8.104 0.027
Post-price level 2000ft. –0.068 *** –5.33 –6.550 0.013 0.070 ** 2.32 7.299 0.030
Pre-price trend 2000ft. 0.005 *** 3.18 0.474 0.001 0.012 *** 2.71 1.174 0.004
Post-price trend 2000ft. 0.008 *** 5.28 0.848 0.002 –0.008 *–1.75 –0.785 0.005
# of LIHTC Units –1.08e–04 –1.50 –0.011 7.20e–05 –6.41e–05 –0.33 –0.006 1.92e–04
Structural Characteristics
Log Heated Areas 0.641 *** 125.43 89.806 0.005 0.380 *** 19.03 46.178 0.020
Log Property Sizes - - - 0.092 *** 8.13 9.636 0.011
Building Age –0.009 *** –43.99 –0.917 2.09e–04 –0.013 *** –19.33 –1.303 0.001
Building Age
2
1.01e–04*** 30.39 0.010 3.33e–06 4.57e–05*** 8.38 0.005 5.46e–06
Log Number of Bedrooms 0.023 *** 3.02 2.324 0.008 0.072 *** 4.28 7.423 0.017
# of Full Bathrooms 0.065 *** 21.95 6.765 0.003 0.017 1.32 1.759 0.013
# of Half Bathrooms 0.004 1.60 0.360 0.002 0.037 *** 2.79 3.751 0.013
Number of Fireplaces 0.048 *** 15.27 4.950 0.003 - - - -
Electric Heating Source 0.009 *** 3.24 0.885 0.003 - - - -
Oil Heating Source 0.011 0.75 1.075 0.014 - - - -
Brick/Stone Exterior Types 0.108 *** 36.10 11.451 0.003 0.052 *** 5.53 5.285 0.009
High-Housing Quality 0.211 *** 17.59 23.477 0.012 0.067 1.24 6.921 0.054
Low-Housing Quality –0.072 *** –3.25 –6.935 0.022 –0.211 *** –10.16 –19.002 0.021
Bungalow Housing Style - - - - 0.059 *** 3.96 6.120 0.015
Colonial Housing Style - - - - 0.055 *** 6.69 5.631 0.008
Locational Characteristics
Parks 250 ft. –0.016 *** –4.30 –1.572 0.004 0.003 0.20 0.310 0.016
River/Lake 500 ft. - - - - 0.001 0.05 0.089 0.017
X, Y Coordinates (CBD) Included Included
Census Tract Indicators Included Included
Seasonal Indicators Included Included
Number of Observations 114,471 27,636
R20.7623 0.3999
Notes: ***denotes a 1% significance level; **denotes a 5% significance level; *denotes a 10% significance level; yAdjustment = 100(eb–1), except Log Heated Areas, Log
Property Sizes, and Log Number of Bedrooms; The full table including all variables (X-Y coordinates, seasonal indicators, and census tract indicators) is available from the
lead author upon request.
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Figure 3. Citywide and submarket results: Pre- and post-sales price levels and trends.
Notes: ***denotes a 1% significance level; **denotes a 5% significance level; *denotes a 10% significance level; L = Low-
income submarkets; M = Middle-income submarkets; H = High-income submarkets; Left-lines indicate the levels and
trends of housing prices before LIHTC was developed while right-lines indicate the levels and trends after LIHTC was
developed.
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5.4 percent lower in the microneighbour-
hood compared to control properties located
outside of the microneighbourhood before
LIHTC was developed, ceteris paribus.
However, the gap in housing price level
between impact and control sales increased
after the introduction of LIHTC units within
a microneighbourhood. After the LIHTC
complexes were developed within a micro-
neighbourhood, the housing price level for
impact sales was 6.6 percent lower compared
to control sales; hence, the gap in housing
price levels widened by 1.2 percent. Price
trend changes show a 0.5 percent incline on
average before LIHTC developments while
averaging a 0.8 percent incline after the
introduction of LIHTC.
Cleveland’s citywide model explained
around 40.0 percent of the variance in prop-
erty values.
6
In contrast to Charlotte,
LIHTC developments in a microneighbour-
hood had a positive impact on surrounding
housing prices (see Table 4 and Panel A in
Figure 3). The coefficient for the pre-impact
variable showed a negative sign similar to
Charlotte, and housing price level for impact
sales was 8.1 percent lower in the micro-
neighbourhood compared to control sales
before LIHTC developments were sited.
However, after LIHTC developments were
located in a microneighbourhood, the hous-
ing price level was 7.3 percent higher than
for control sales. The completion of the
LIHTC developments significantly increased
the housing price level in neighbourhoods,
from –8.1 percent to 7.3 percent. Positive
pre-price trends, averaging a 1.2 percent
incline within a microneighbourhood, con-
trast with declining post-price trends by
around 0.8 percent.
The citywide results for Charlotte showed
that the introduction of the LIHTC develop-
ments had a negative impact on nearby
property values. In contrast, the LIHTC
developments for Cleveland positively
affected surrounding housing prices.
Neighbourhood heterogeneity results
Next we consider how the impact of LIHTC
developments varies across housing submar-
ket heterogeneity, in terms of low-, middle-
and high-income neighbourhoods in
Charlotte and Cleveland. For brevity,
Table 5 reports only the results of key vari-
ables for each housing submarket.
Model 1 shows the results for low-income
neighbourhoods in Charlotte and Cleveland.
The pre-impact variable in Charlotte showed
a negative coefficient indicating that the
housing price level for impact sales was 10.0
percent lower compared to control sales
before LIHTC developments. However, the
post-impact variable was not statistically sig-
nificant. Similar to the citywide models in
terms of post-trend variables, post-price
trend changes were far less substantial,
averaging a 0.7 percent incline before the
development of LIHTC, and a 0.6 percent
incline after the development. Size effects of
LIHTC developments were statistically sig-
nificant; a one-unit increase in the number
of LIHTC units at the time of sale decreased
the housing price by 0.03 percent, ceteris
paribus, in the low-income neighbourhoods
of Charlotte.
The results for Cleveland showed that the
LIHTC developments in a microneighbour-
hood had a positive impact on neighbouring
housing prices for low-income neighbour-
hoods (see Panel B in Figure 3). The post-
impact variable showed a positive coefficient
indicating that the housing price level was
higher compared to the control area after the
LIHTC developments were sited. However,
other variables related to LIHTC develop-
ments were not statistically significant.
Model 2 presents the results for middle-
income neighbourhoods in both cities. The
key finding for middle-income neighbour-
hoods in Charlotte was that LIHTC devel-
opments in a microneighbourhood had a
negative impact on neighbouring housing
prices (see Panel B in Figure 3). The housing
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Table 5. Results for low-, middle- and high-income submarkets.
Model 1 Low-income submarkets, Charlotte Low-income submarkets, Cleveland
Coef. t-value Adj.BetayRobust
Std. Err
Coef. t-value Adj.BetayRobust
Std. Err
Pre-price level 2000ft. –0.105 *** –4.49 –9.954 0.023 –0.082 –1.32 –7.914 0.063
Post-price level 2000ft. 0.011 0.43 1.075 0.025 0.124 ** 2.05 13.168 0.060
Pre-price trend 2000ft. 0.007 ** 2.00 0.682 0.003 0.008 0.85 0.801 0.009
Post-price trend 2000ft. 0.006 *1.87 0.633 0.003 0.001 0.06 0.052 0.008
# of LIHTC Units –2.81e–04 *** –2.96 –0.028 9.48e–05 –2.48e–04 –0.72 –0.025 –3.44e–04
Structural Characteristics Included Included
Locational Characteristics Included Included
X, Y Coordinates (CBD) Included Included
Census Tract Indicators Included Included
Seasonal Indicators Included Included
Number of Observations 17,853 4549
R20.5770 0.3434
Model 2 Middle-income submarkets, Charlotte Middle-income submarkets, Cleveland
Coef. t-value Adj.BetayRobust
Std. Err
Coef. t-value Adj.BetayRobust
Std. Err
Pre-price level 2000ft. –0.036 *** –2.73 –3.536 0.013 –0.034 –0.94 –3.362 0.036
Post-price level 2000ft. –0.153 *** –7.20 –14.209 0.021 0.022 0.58 2.233 0.038
Pre-price trend 2000ft. 0.004 ** 1.88 0.386 0.002 0.011 *1.73 1.075 0.006
Post-price trend 2000ft. 0.007 *** 2.96 0.668 0.002 –0.010 –1.58 –0.961 0.006
# of LIHTC Units 0.001 *** 4.75 0.106 2.23e–04 3.82e–05 0.16 0.004 2.35e–04
Structural Characteristics Included Included
Locational Characteristics Included Included
X, Y Coordinates (CBD) Included Included
Census Tract Indicators Included Included
Seasonal Indicators Included Included
Number of Observations 51,728 10,598
R20.5863 0.2870
(continued)
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Table 5. (Continued)
Model 3 High-income submarkets, Charlotte High-income submarkets, Cleveland
Coef. t-value Adj.BetayRobust
Std. Err
Coef. t-value Adj.BetayRobust
Std. Err
Pre-price level 2000ft. 0.040 ** 2.44 4.094 0.016 –0.113 *–1.81 –10.673 0.062
Post-price level 2000ft. –0.034 *–1.69 –3.377 0.020 0.095 0.94 9.948 0.100
Pre-price trend 2000ft. –0.008 *** –3.94 –0.825 0.002 0.011 1.10 1.137 0.010
Post-price trend 2000ft. 0.007 *** 3.26 0.704 0.002 –0.012 –1.07 –1.199 0.011
# of LIHTC Units –4.81e–04 *** –3.14 –0.048 1.53e–04 –2.13e–04 –0.15 –0.021 0.001
Structural Characteristics Included Included
Locational Characteristics Included Included
X, Y Coordinates (CBD) Included Included
Census Tract Indicators Included Included
Seasonal Indicators Included Included
Number of Observations 44,890 12,489
R20.6988 0.4318
Notes: ***denotes a 1% significance level; **denotes a 5% significance level; *denotes a 10% significance level; yAdjustment = 100(eb–1); The full table including all variables
(structural and locational characteristics, X-Y coordinates, seasonal indicators and census tract indicators) is available from the lead author upon request.
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price level for impact sales was 3.5 percent
lower than control sales before LIHTC was
developed, and 14.2 percent lower after
LIHTC was introduced. Post-price trend
change was statistically significant, aver-
aging a 0.4 percent incline before the LIHTC
development, and a 0.7 percent incline after
the development. Interestingly, building
more units in LIHTC developments appears
to mitigate the negative effects of LIHTC
developments; a one-unit increase in the
number of LIHTC units at the time of sale
increased housing price by 0.1 percent,
ceteris paribus.
For Cleveland, the R
2
for the middle-
income neighbourhoods was lower than that
for other models such as models for low-
and high-income neighbourhoods; the model
accounted for around 28.7 percent of the
variance in the property values. Compared
to other models, small numbers of structural
variables were only statistically significant.
Furthermore, the pre- and post-impact vari-
ables were not statistically significant,
although the coefficients maintained the
same signs as other submarket models.
7
For high-income neighbourhoods in
Charlotte, the pre-price level variable
showed a positive coefficient (see Model 3).
The pre-existing housing price level of
impact areas prior to LIHTC developments
was 4.1 percent higher compared to control
areas. The pre-price level in high-income
submarkets contrasts with that in other sub-
markets such as low- and middle-income
neighbourhoods in Charlotte. The post-
impact variable for high-income neighbour-
hoods showed a negative sign indicating that
the housing price level for impact sales was
lower compared to control sales after the
LIHTC units were developed, although sta-
tistically significant only at the 10 percent
level.
8
Negative pre-price trends, averaging a
0.8 percent decline within a microneighbour-
hood, contrast with inclining post-price
trends around 0.7 percent. The development
size effect was also statistically significant; a
one-unit increase in the number of LIHTC
units at the time of sale decreased housing
price by 0.05 percent, ceteris paribus.
For Cleveland, the post-price level, pre-
and post-trend variables were not statisti-
cally significant, although the coefficients
maintained the same signs as other models.
Conclusion
Our citywide results suggest that LIHTC
developments have a differential impact
across local housing markets. LIHTC devel-
opments had a negative impact on surround-
ing housing prices in Charlotte, while having
a positive impact in Cleveland.
Charlotte experienced active residential
development, mirroring its rapid population
growth. Furthermore, transactions of higher
priced housing were more prevalent in the
Charlotte housing market than in Cleveland.
Given the relatively higher property values
of non-subsidised housing compared to sub-
sidised housing, LIHTC developments
appear to have been perceived as undesirable
development, rather than an instrument for
stimulating residential investment. In con-
trast, Cleveland’s housing market has expe-
rienced stagnation shown by the lack of new
housing construction and fewer sales trans-
actions, juxtaposed with severe population
loss and neighbourhood destabilisation. In
an already stagnant housing market, any
residential investment (including LIHTC
development) may have a stimulating effect.
Indeed, many state agencies have utilised
LIHTC developments to revitalise distressed
communities, and the positive impacts of
such developments may be related to the
removal of disamenities such as dilapidated
and abandoned buildings that damage resi-
dential property values and promote crime
(Schwartz et al., 2006). Therefore, subsidised
housing developments in stagnant housing
markets may serve as tools for new
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residential investment and community revi-
talisation rather than a locally undesirable
land use.
Our findings also showed that the impacts
of LIHTC developments varied across dif-
ferent housing submarkets. It is notable that
spillover effects in low-income neighbour-
hoods were not statistically significant in
Charlotte. This finding might be interpreted
in several ways. First, differences in tenant
characteristics between LIHTC housing and
neighbours in low-income neighbourhoods
of Charlotte may not be as pronounced
compared to middle- or high-income neigh-
bourhoods. Thus, the response to the intro-
duction of new low-income households
might not be as sensitive in low-income
neighbourhoods. Second, low-income sub-
markets in higher density (i.e. multifamily)
residential areas may be less sensitive to new
subsidised housing developments than mid-
dle- or high-income neighbourhoods domi-
nated by low density single family housing.
Third, realtors may not want to exert the
same level of effort to provide this informa-
tion to low-income clients compared to those
of higher income due to lower commission
(Galster, 1987; Kobie and Lee, 2011). In
contrast, LIHTC developments in low-
income neighbourhoods of Cleveland had
positive impacts on neighbouring housing
prices. These upgrading effects imply that
LIHTC developments might be deemed as
the tool for new residential investment and
community revitalisation by neighbours in
deteriorated neighbourhoods in cities with
depressed housing market conditions like
Cleveland.
Our results for middle-income submarkets
showed that the influx of LIHTC subsidised
households negatively affects surrounding
housing prices in Charlotte. Interestingly,
the size effects of LIHTC developments were
positive for middle-income neighbourhoods
in Charlotte. This finding suggests that
larger developments might mitigate the
negative effects of LIHTC units. The influx
of subsidised households into neighbour-
hoods contributes to housing price
decreases, but larger developments may alle-
viate housing price decreases. However, the
impacts of LIHTC developments in middle
and high-income neighbourhoods were not
statistically significant in Cleveland
(although the coefficient maintained the
same positive sign as the Cleveland citywide
results). Thus, LIHTC developments used as
a tool for residential investment and revitali-
sation may have a minimal impact on hous-
ing prices for middle- and high-income
neighbourhoods in a depressed housing mar-
ket. The results for high-income submarkets
in Charlotte suggested that LIHTC develop-
ments had negative impacts on neighbouring
property values. Size effects of LIHTC devel-
opments were also significant indicating that
a larger number of LIHTC units for high-
income neighbourhoods had larger spillover
effects on neighbouring property values in
Charlotte.
Our results suggest that the common per-
ception that subsidised housing develop-
ments negatively affect neighbourhoods
needs to be viewed in a more nuanced way.
Subsidised housing developments in
depressed housing market conditions may
generate positive externalities by enhancing
neighbourhood housing prices. Low-income
neighbourhoods in depressed housing mar-
kets such as Cleveland may benefit from
increased property values as LIHTC devel-
opments contribute to community revitalisa-
tion and residential investment, stimulating
stagnant market conditions. Further, even in
more robust market conditions, subsidised
housing developments may have a positive
impact, as shown by the impact of larger
developments in middle-income neighbour-
hoods in Charlotte. Hence, housing market
and submarket conditions should be consid-
ered when siting LIHTC developments.
Cities with stagnant market conditions may
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be able to use LIHTC investments to pro-
vide affordable housing for low-income fam-
ilies by stimulating neighbours’ perception
of the demonstration effect of residential
investments and community revitalisation
for communities. Furthermore, policy-
makers may encounter less community
opposition to LIHTC developments in these
markets and neighbourhoods.
However, our findings should not be
interpreted as implying that subsidised hous-
ing units should not be placed in affluent
neighbourhoods and robust markets.
LIHTC developments in high-income sub-
markets and stimulated market conditions
may not necessarily lead to housing price
decreases in the long run because trends in
housing prices increased after LIHTC devel-
opments in the citywide and high-income
submarket results. The mixing of income
groups via the integration of subsidised
housing developments in affluent neighbour-
hoods is a worthy aim and should be facili-
tated to achieve poverty deconcentration
and more equitable access to socioeconomic
opportunities for subsidised households
(Van Zandt and Mhatre, 2009). The neigh-
bourhoods where subsidised households live
largely affect their socioeconomic opportu-
nities for improved life outcomes and
upward mobility (Crowley, 2003; Ellen and
Turner, 1997; Massey and Denton, 1993).
Our intent here is to help identify the con-
ditions under which undesirable outcomes of
LIHTC developments are more or less likely.
Developers of affordable housing in ‘hot’ or
rapidly appreciating markets face a special
kind of challenge, as is evident by the dearth
of affordable units in the upper end of the
market. At the very least, our findings sug-
gest that policymakers should be prepared
to defuse strong community opposition to
LIHTC developments to achieve poverty
deconcentration and income mixing in afflu-
ent neighbourhoods. They are not intended
to suggest that affordable housing
developers should avoid these areas, but
rather highlight the difficulties they may face
in placing affordable housing in burgeoning
or affluent markets.
However, creative design approaches may
help affordable housing units blend in with
existing buildings in the community. For
instance, the visibility of physical and design
structure in LIHTC developments may be
improved to enhance the tool for commu-
nity revitalisation and mitigate community
opposition because physical design of new
developments may affect nearby property
values. Schill et al. (2002) have suggested
that the design of developments may matter
to the extent that the physical design is con-
sistent with the neighbourhood’s visual char-
acter. HUD’s partnership with the American
Institute for Architects (AIA) in developing
the Affordable Housing Design Advisor
(www.designadvisor.org) also offers recom-
mendations for maintaining massing, setback
and other land development regulations to
ensure consistency.
Further, statewide Qualified Allocation
Plans (QAPs), which guide requirements for
LIHTC development within each state,
including guidelines for the siting of devel-
opments, may also address income mixing in
LIHTC developments. While the LIHTC
program was conceived of as a mixed-
income approach, it has largely been
implemented with very high proportions of
low-income units (HUD, 2005). Our results
suggest that in higher-income neighbour-
hoods in rapidly appreciating markets, such
as Charlotte’s, LIHTC guidelines might limit
the proportion of low-income units, and off-
set the loss of volume of units available for
low-income households by encouraging
developments with higher numbers of units.
Limitations
We examined the impacts of subsidised
housing in Charlotte and Cleveland,
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choosing these study areas to provide con-
trasting market conditions; these cities may
not, however, represent housing market con-
ditions in other cities by reason of the
uniqueness of each city. Further, we were
unable to control for all alternative explana-
tions for the observed differences, given data
limitations. For instance, the differential
impacts of LIHTC developments might
reflect differences in racial and ethnic com-
position between the two cities, with
Charlotte being majority white and
Cleveland being majority African-American.
If LIHTC residents are people of a race/eth-
nicity different than surrounding residents,
the influx of these residents may have been
more strongly opposed by more affluent and
white residents in Charlotte. While this sug-
gests a racial and ethnic bias that might
account for differences in the impacts of
LIHTC development, we are unable to
determine the magnitude or extent of this
bias due to the unavailability of tenant level
data on race and ethnicity for LIHTC resi-
dents. We suggest that future studies extend
the scope of impacts of LIHTC develop-
ments across other cities and other housing
market conditions with tenant level data
about LIHTC units.
Additionally, although many state agencies
aim to utilise LIHTC developments to revita-
lise deteriorated communities, there is a lack
of empirical evidence as to whether increases
in housing prices are due to the effort of com-
munity revitalisation. Thus, additional future
studies examining the change of land use due
to subsidised housing developments (i.e. the
replacement of a desirable or undesirable use)
may empirically look beyond the current role
of LIHTC developments as the tools for com-
munity revitalisation.
Funding
This research received no specific grant from any
funding agency in the public, commercial, or not-
for-profit sectors.
Notes
1. Neighbourhood heterogeneity could be
defined by ethnicity and income level.
However, we focused on the income levels of
neighbourhoods to stratify the neighbour-
hood’s heterogeneity. Because LIHTC subsi-
dized units are occupied by households with
income below 50 or 60 percent of the Area
Median Income (AMI), the discrepancies in
income levels of tenant characteristics
between subsidized households and neigh-
bours would play a key role in allowing differ-
ent impacts across neighborhoods (Freeman,
2004; McClure, 2006).
2. In some studies, buffer distances are classified
as three distance bands: 500, 1000, and 2000
feet (Galster et al., 1999b; Santiago et al., 2001).
3. Neighbourhoods in both cities were stratified
into three categories based on median family
income in 2000 at the census tract level: low-
income (less than 80 percent of the city’s med-
ian family income), middle-income (80 to 120
percent) and high-income (greater than 120
percent).
4. After checking the normality of each variable
using tests for kurtosis and skewness, two
variables (heated areas and number of bed-
rooms) for Charlotte and three variables
(property sizes, heated areas and number of
bedrooms) for Cleveland were transformed
using a natural log transformation. Also,
the independent variable ‘age of a structure’
consists of quadratic functions to capture the
marginal effect.
5. Incorporating the geographic coordinates of
each property in the empirical model explains
or reduces spatial heterogeneity and spatial
correlation (Koschinsky, 2009). Additionally,
a CBD was defined as the centroid of the cen-
sus tract including the highest job density in
the city. Job density was calculated as the
number of jobs per square metre of land use
in each census tract.
6. Due to the different sources of data for struc-
tural characteristics, the models for Charlotte
and Cleveland are not identical. There are cer-
tain structural variables included in the model
for Charlotte (e.g. number of fireplaces and
heating sources) that are not included in the
model for Cleveland, and vice versa.
Woo et al. 2507
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7. Cleveland consists mostly of poorer neigh-
borhoods compared to its suburbs. If the
Cleveland submarkets were not classified by
the city’s median family income but by
Cuyahoga County’s median family income,
Cleveland would have a higher proportion of
low-income submarkets (86.9 percent) com-
pared to the suburbs. The greater homogene-
ity of neighbourhood characteristics in terms
of poorer housing and neighbourhood condi-
tions in Cleveland may explain the insignifi-
cance of many independent variables in the
results (Kobie and Lee, 2011). Lack of varia-
tion in the explanatory variables may not
account for the effects of LIHTC develop-
ments, especially in middle-income neigh-
bourhoods of Cleveland.
8. For the analysis of high-income neighbor-
hoods, the small variation of impact sales
might be an issue. In Charlotte high-income
submarkets, out of 44,890 sales, 2.9 percent
(1290 sales) are within 2000 feet of LIHTC
developments. In Cleveland high-income sub-
markets, out of 12,489 sales, 10.7 percent
(1334 sales) are within 2000 feet of LIHTC
projects.
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