CAPITALIZATION OF CHARTER SCHOOLS

INTO RESIDENTIAL PROPERTY VALUES

Abstract
Although prior research has found clear impacts of schools and
school quality on property values, little is known about whether
charter schools have similar effects. Using sale price data for
residential properties in Los Angeles County from 2008 to 2011
we estimate the neighborhood level impact of charter schools
on housing prices. Using an identiﬁcation strategy that relies
on census-block ﬁxed effects and variation in charter penetration
over time, we ﬁnd little evidence that the availability of a charter
school affects housing prices on average. We do ﬁnd, however,
that when restricting to districts other than Los Angeles Uniﬁed
and counting only charter schools located in the same school
district as the household, housing prices fall in response to an
increase in nearby charter penetration.

Margaret Brehm

Department of Economics

Michigan State University

East Lansing, MI 48824

orourk49@msu.edu

Scott A. Imberman

(corresponding author)

Department of Economics

Michigan State University

and NBER

East Lansing, MI 48824

imberman@msu.edu

Michael Naretta

Department of Economics

Michigan State University

East Lansing, MI 48824

manaretta@gmail.com

doi:10.1162/EDFP_a_00192
C(cid:2) 2017 Association for Education Finance and Policy

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Capitalization of Charter Schools

I N T R O D U C T I O N

1 .
The charter school movement began about twenty years ago and was driven by the
belief that privately run and publicly ﬁnanced schools could be superior to traditional
public schools. Proponents argue that charters can adapt more smoothly in times
of ﬁnancial hardship than traditional schools (e.g., by reducing nonunionized labor
force or changing administrative policies). They also argue that charters are leaders
in methodological innovations in education. On the other hand, opponents argue that
charters are able to restrict admission to make them look better than they are and
they divert necessary resources from public schools. Existing research has generally
shown charter effectiveness to be mixed (e.g., Bettinger 2005; Bifulco and Ladd 2006;
Sass 2006; Hoxby and Murarka 2009; Abdulkadiro˘glu et al. 2011; Dobbie and Fryer
2011; Imberman 2011b; Angrist et al. 2012; Angrist, Pathak, and Walters 2013), yet
the impacts of these schools on the wider economy is not well known. In this paper,
using data from Los Angeles County (LA County), California, we attempt to establish
the extent to which charter schools impact residential property markets by examining
how charter penetration rates in a community are capitalized into surrounding home
prices. Understanding whether housing markets are responsive to charter availability
is important given the increasing prevalence of charter schools across the country.
Indeed, California has seen signiﬁcant growth in the number of charter schools since
they were authorized in 1992; the overall number of charters has increased from 299
in 2000 to 912 in 2010, with 242 of those in LA County alone. This is the highest
number of charter schools in any county in the United States.1

There is also a substantial literature relating housing values and school characteris-
tics (e.g., Black 1999; Gibbons and Machin 2003; Figlio and Lucas 2004; Kane, Riegg,
and Staiger 2006; Bayer, Ferreira, and McMillan 2007; Gibbons, Machin, and Silva
2013; Imberman and Lovenheim 2016), but only Buerger (2014), in an unpublished
working paper, speciﬁcally considers homeowners’ valuation of charter schools. To
identify the impact of charters on housing prices, we use data on single-family home
sales from 2008 to 2011, obtained from the LA County Assessor’s Ofﬁce. We estimate
the impacts of both the number of charters and the share of public enrollment in char-
ters within various distances of a property, up to 2 miles. To account for endogenous
charter locations and changes in the geographic distribution of sales we include census-
block2 ﬁxed effects along with a set of housing and school characteristics to account
for the nonrandom location of charter schools. Month-by-year ﬁxed-effects account for
any general changes to the education and housing markets over time in LA County.3
Thus, our identiﬁcation comes from houses sold in the same census block at different
times as charters open, close, expand, and shrink. As a result, we note that our study
does not identify how existing charter enrollment affects housing prices but rather how
contemporaneous changes in charter enrollment and the number of charters affect
housing prices in localized areas, speciﬁcally within census blocks.

1. See California Charter Schools Association Web site at www.ccsa.org/.
2. Census blocks are the smallest geographic area used by the U.S. Census Bureau and typically cover a single

city block in urban areas.

3. We acknowledge, nonetheless, that because we do not have neighborhood controls that vary over time, our
model does not account for changes in neighborhoods independent of changes in local schools that may affect
charter penetration. We discuss this issue in more detail in section 6.

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Margaret Brehm, Scott A. Imberman, and Michael Naretta

Overall, our results suggest that neither the increase in the number of char-
ter schools nor the expansion in charter enrollment relative to public school
enrollment—our proxy for the availability of charter school slots to local residents—is
capitalized into housing prices on average. This holds both for Los Angeles Uniﬁed
School District (LAUSD) and other parts of LA County. It also holds for both startup
charters (new schools that begin as charters) and conversion charters (public schools
that convert to charter status), though we caution that very few schools convert dur-
ing our sample period. Further, we ﬁnd no evidence that capitalization varies with
income level, minority population, or achievement levels of the local public elementary
school.

We do ﬁnd, however, that when we count charters located only within the house-
hold’s school district’s boundaries and exclude LAUSD there is a signiﬁcant negative
effect of additional nearby charter schools on housing prices. This restriction is rea-
sonable as students who reside within the charter’s authorizing school district (which
is almost always the district in which they are located) have admissions priority, thus
generating a link between these schooling options and local district boundaries. A po-
tential explanation for this ﬁnding is that opening a nearby charter school reduces the
value of a local community school, thus weakening the link between the availability of
local schooling as a public good and house prices.

2 . C H A R T E R S C H O O L S B A C K G R O U N D
Charter schools are public schools that are tuition-free and managed by an independent
operator. Typically, they are open to any student wishing to attend, regardless of where
they live, though some schools give preference to students who reside nearby. Many
schools require an application, and those in high demand will often have a waitlist.
Charters are typically governed by parents, teachers, members of the local community,
or a private company, and are reviewed for renewal every few years by an authorizer,
usually the state or a local school district. In California, charters are funded through
a mix of block grants and a state-based funding formula that provides funding at the
same per-pupil rate to all charters of a given grade level across the state (see CDE 2016).
There is substantial heterogeneity across schools in the way they are managed, their
goals, their targeted student population, and level of autonomy from the local school
system.

An important distinction to recognize among charter schools is they are either
brand new schools (startup charters) or were previously a traditional public school that
switched to a charter model (conversion charter). According to the California Charter
Schools Association, there are many reasons why traditional schools decide to convert
to charter status, but above all is the appeal of increased ﬂexibility and autonomy.
Conversion charters must satisfy the same legal requirements and processes as startup
charter schools. This involves submitting a charter petition establishing features, such
as the school’s goals, ﬁnances, and governance plan, as well as obtaining signatures
of at least 50 percent of the permanent teachers currently employed at the school.
California law does require that conversions give priority to students in the school’s
district, and many districts (including LAUSD), give priority to students in a local
catchment area. Typically, startup charters do not have catchment areas, but if they
are oversubscribed they are also required to give priority to students who reside in the

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Capitalization of Charter Schools

authorizing school district and may choose to give priority to those in the local school
zone if the neighborhood school has high rates of economic disadvantage.

As of the 2010–11 school year, conversion charters represented 16 percent of Califor-
nia’s charter schools, enrolling about 25 percent of all charter school students (CCSA
2012). Charter school facilities vary with type of charter, with some building brand new
structures, renting available spaces in churches, community centers, or commercial
buildings, or occupying a previously traditionally run public school campus.4 When a
school converts to charter status, it usually remains in the same building and retains
teachers, staff, and students. In contrast, startup charters need to recruit a student body
because parents have the option to enroll their child in the charter or in the assigned
public school.

Another important distinction between types of charter schools that has drawn
interest recently is the role of larger charter management organizations (CMOs). CMOs
are nonproﬁts that operate multiple charter schools and charters within an organization
and are able to pool management and resources in order to gain economies of scale—a
beneﬁt often shared by schools within a traditional public school district. Evidence of
the impacts of these types of charters on student outcomes suggests that effectiveness
varies substantially across CMOs and students (Angrist et al. 2012; Furgeson et al. 2012).
Another heterogeneous distinction between charter schools is whether a charter has
a waiting list. Recent work using oversubscription lotteries has indicated that waitlist
charters perform better than local public schools but are unable to assess the impacts
of non-waitlist charters (Hoxby and Murarka 2009; Abdulkadiro˘glu et al. 2011; Dobbie
and Fryer 2011; Angrist et al. 2012; Angrist, Pathak, and Walters 2013). Unfortunately,
although it would be interesting to see whether housing prices respond differently to
these two ways charters vary, we do not have data on whether charters are operated by
CMOs or have waitlists.

3 . T H E O R Y O F C H A R T E R I M P A C T S O N H O U S I N G P R I C E S
The theory behind the relationship between housing prices and local school quality pre-
dicts that, because of the close link between residential location and the school attended
via attendance zones, higher quality schooling will generally lead to an increase in hous-
ing prices, though the extent of this increase depends on a number of factors (Rosen
1974; Black and Machin 2011). This relationship has been well established through em-
pirical analyses (Black 1999; Downes and Zabel 2002; Figlio and Lucas 2004; Kane,
Riegg, and Staiger 2006; Bayer, Ferreira, and McMillan 2007; Gibbons, Machin, and
Silva 2013). Because charter schools do not typically have attendance zones and be-
cause typically students may attend a charter regardless of their location of residence,
the theoretical link between charter schools and housing prices is ambiguous.

Despite a less obvious link between charter schools and housing prices, economic
theory suggests homeowners may respond to charters in a neighborhood for a few
reasons. First, charters provide an option value. Even if a child does not attend a charter
school, the availability of charters nearby may make a location more attractive for
parents. Charters rarely offer busing, therefore travel distance is especially important if
transport costs are expensive, as is the case in LA County where there is limited public

4. See California Charter Schools Association’s Web site at www.ccsa.org/.

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Margaret Brehm, Scott A. Imberman, and Michael Naretta

transportation, heavy trafﬁc congestion, and high gas prices. Further, as previously
mentioned, in California oversubscribed charters give priority to students who reside
in the school district containing the charter, and this could increase the option value to
living in the district.

Second, charters may have an indirect effect on housing prices if they affect the
performance of local public schools. Evidence on how charters affect local public schools
is mixed. Whereas Booker et al. (2008), Bifulco and Ladd (2006), and Sass (2006) ﬁnd
positive effects of charters on nearby public schools, Imberman (2011a) ﬁnds negative
effects. Thus it is unclear how this mechanism might inﬂuence housing prices.

Third, the public may value the direct infrastructure and community improvements
charters sometimes provide. Indeed, Cellini, Ferreira, and Rothstein (2010) show that
housing prices respond to non-charter public school facility investments. Many charters
rent or use donated space, whereas some build their own facilities or convert abandoned
properties for use as schools. Even those that rent will often ﬁll up vacant properties
in locations like strip malls (Imberman 2011a). Thus, the additional economic activity
generated by the charters may inﬂuence local housing prices.

Another theory is that charter schools may serve to break the connection between
local public schools and housing prices. In so doing we might expect additional char-
ters (and more school choice options more broadly) to lead to increased housing
prices where existing schools are low-performing because these locations would have
artiﬁcially low housing values due to the poor school quality. Alternatively, in high-
performing areas, additional charters may actually reduce housing prices as the avail-
ability of nearby charters weakens a key beneﬁt of being zoned to a high-performing
school if, through attending charters, high-quality schools become available to house-
holds outside the attendance zone (Nechyba 2003). Another possibility, however, is that
by severing this link, the availability of having a public school option at all, irrespective
of school quality, is less valuable. The public good of a local school provides less utility
and, thus, without a commensurate reduction in property taxes, lowers the value of
residing near that school.

These theories indicate that it is unclear how charter schools may affect housing
prices because some economic effects may be positive and some may be negative.
As such, understanding the overall effect on local property markets is necessarily an
empirical question. We should also note that although it may be tempting to interpret
housing price responses as measures of how much people value charters, the complexity
of the underlying processes makes it difﬁcult to do this. In fact, the theories described
above of how charter schools may sever the link between local public schools and
property values highlight that the effects could be showing something entirely different
from valuation.

4 . P R E V I O U S L I T E R A T U R E
Most of the existing literature on charter schools focuses on the effect of charters
on student achievement. Early research relying on panel data methods found mixed
results, with some researchers ﬁnding insigniﬁcant or signiﬁcant negative impacts on
student test scores of attending a charter school (Bifulco and Ladd 2006; Sass 2006;
Zimmer and Buddin 2006; Hanushek et al. 2007; Imberman 2011b), and others

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Capitalization of Charter Schools

ﬁnding positive impacts (Hoxby and Rockoff 2004; Booker et al. 2008). More recent
research using random lotteries (Hoxby and Murarka 2009; Abdulkadiro˘glu et al.
2011; Dobbie and Fryer 2011; Angrist et al. 2012; Angrist, Pathak, and Walters 2013)
and natural experiments (Abdulkadiro˘glu et al. 2014) have found large positive effects.
Some research has also recognized the distinction between conversion and startup
charters and suggests there is a differential impact on performance across the two
types (Buddin and Zimmer 2005; Sass 2006; Zimmer and Buddin 2009).

There are two studies in particular that are similar to ours. First, Chakrabarti and
Roy (2010) try to use the impact of charter schools on enrollment in private schools
as a proxy for how much parents prefer charters to other schooling options. They ﬁnd
modest declines in private school enrollment when charters locate nearby. Second, in
an unpublished working paper Buerger (2014) looks at differences in housing prices
across school districts in New York due to charter penetration and ﬁnds positive effects.
His identiﬁcation relies on differences in charter penetration across school districts and
census-tract ﬁxed effects.

Nonetheless, our paper is distinct from Buerger’s in a few key ways. First, the focus
on differences across districts, although useful in areas with many school districts,
is less relevant to areas like Los Angeles that are dominated by a large central core
district. Indeed, most charter schools tend to locate in urban core areas dominated
by large urban districts. Thus, our analysis allows for identiﬁcation of charter impacts
within these urbanized areas. Second, Buerger looks at the impacts on housing prices
from the entry of the ﬁrst charter school into the district. In our analysis, we look
at capitalization of marginal changes in charter penetration using multiple charter
penetration measures. Third, our inclusion of census-block ﬁxed effects instead of the
geographically larger census-tract ﬁxed effects allows us to account for more potential
sources of time-invariant unobserved characteristics.

A separate branch of literature focuses on the relationship between housing prices
and school characteristics. There is ample evidence from previous work that housing
prices are responsive to test score differences across schools.5 Both Black (1999) and
Bayer, Ferreira, and McMillan (2007) estimate regression discontinuity models across
school zone boundaries to identify how school-average test scores are capitalized into
housing prices. Figlio and Lucas (2004) examine the effect of the release of “school
report card” data in Florida on property values. These report cards rated schools from A
to F based on average performance on statewide exams. All three studies ﬁnd sizable,
positive impacts of higher school test scores on home values, suggesting parents place
signiﬁcant value on this school-quality measure. Gibbons, Machin, and Silva (2013)
ﬁnd similar results in England using boundary discontinuities using test score gains.
On the other hand, Imberman and Lovenheim (2016) ﬁnd little impact of the release
of teacher and school value-added information on housing prices in Los Angeles.

Several studies have considered the effects of other school characteristics, such as
student demographics, per-pupil spending, and pupil-teacher ratio, on housing prices.
In the footsteps of Oates’ (1969) seminal paper, which uses per-pupil spending and
pupil-teacher ratio as measures of school quality, much of this research has found posi-
tive relationships between similar measures and housing prices (Bogart and Cromwell

5. For a comprehensive review see Black and Machin (2011).

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Margaret Brehm, Scott A. Imberman, and Michael Naretta

1997; Bradbury, Mayer, and Case 2001; Weimer and Wolkoff 2001). Clapp, Nanda,
and Ross (2008), using panel data from Connecticut, ﬁnd that an increase in the per-
centage of Hispanic students has a negative effect on housing prices. Using data from
Chicago, Downes and Zabel (2002) ﬁnd that households do not capitalize per-pupil
expenditures.

Bogart and Cromwell (2000) exploit school redistricting in Ohio and ﬁnd that
disruption of neighborhood schools—in terms of student demographics, changes in
transportation services, and geographic location within the neighborhood—reduces
house values by nearly 10 percent. Reback (2005) analyzes the effect of adoption of
a public school choice program in Minnesota to estimate the capitalization effects
related to changes in school district revenues, because districts’ state revenues depend
on enrollment. He ﬁnds that a 1 percentage point increase in outgoing transfer rates is
associated with an increase in house prices of about 1.7 percent.

Our analysis builds off the approaches of these studies by estimating the impact
of charter schools on local housing prices while carefully accounting for selection of
charters into neighborhoods. In particular, our baseline speciﬁcation includes census-
block ﬁxed effects to account for unobserved heterogeneity across local neighborhoods
in the propensity for charters to open or close nearby.

5 . D A T A
Our home price data come from the Los Angeles County Assessor’s Ofﬁce (LACAO).
The data contain the most recent sale price of every home in LA County as of Octo-
ber 2011. In addition to LAUSD, the second largest district in the country, the data
encompass 75 other school districts. Because our data are based on most recent sales,
to avoid endogenous selection into the sample and small sample sizes in early years,
we restrict our data to include only residential sales that occurred between 1 September
2008 and 30 September 2011. From LACAO, we also obtained parcel-speciﬁc property
maps, which we overlay with school zone maps from 2002 (the most recent year such
data are available for the whole county).6 The data also include home and property
characteristics, such as the number of bedrooms, the number of bathrooms, units on
the property, square footage, and the year the structure was built.

We drop all properties with sale prices above $1.5 million in order to avoid results being driven by home-price outliers. Further, about 25 percent of the residential prop- erties in the dataset do not have a sale price listed. Usually, these are property transfers between relatives or inheritances. Hence, we limit our sample to those sales that have “document reason codes” of “A,” which denotes that it is a “good transfer” of property. We also drop all properties with more than either eight bedrooms or eight bathrooms. The charter school data are from the California Department of Education. We rely on two measures of charter school penetration: the counts of the number of charter schools within a speciﬁed distance from a home and the percentage of total enrollment in the public sector attributable to charter schools within a speciﬁed distance from a home. For the former measure, we calculate the distance between each charter and 6. The 2002 LA County maps come from the Los Angeles County GIS portal at http://egis3.lacounty .gov/dataportal/. The maps were created using a variety of sources and thus may not match precisely to actual school zones. l D o w n o a d e d f r o m h t t p : / / d i r e c t . m i t . / / f e d u e d p a r t i c e – p d l f / / / / / 1 2 1 1 1 6 9 1 0 9 5 e d p _ a _ 0 0 1 9 2 p d . f f b y g u e s t t o n 0 8 S e p e m b e r 2 0 2 3 7 Capitalization of Charter Schools Table 1. Schools in LA County Panel A. Schools by Grade Level Elementary Middle High Multiple Levels Non-charter public schools Total charter schools % charter schools Conversion charters Startup charters 1,196 113 8.6 21 92 243 390 48 16.5 1 47 88 18.4 10 78 68 35 34.0 3 32 Panel B. Schools by Years of Operation Non-charter Public Conversion Startup % Charter Schools 1,743 1,758 1,777 1,809 19 23 24 26 127 147 181 213 7.7 8.8 10.3 11.7 2008 2009 2010 2011 Notes: Schools included in panel A are those open and active at any point September 2008 through September 2011. Data obtained from California Department of Education. the home, and count the number of charters falling within a speciﬁed distance. For the latter measure, we use enrollment ﬁgures for all public schools in LA County from the Common Core of Data, managed by the Institute of Education Sciences at the U.S. Department of Education. An explanation for why we choose these variables and our speciﬁed distances is provided in section 6. We combine these data with school-by-academic year data on Academic Perfor- mance Index (API) scores, API rank, school average racial composition, percent on free- and reduced-price lunch, percent disabled, percent gifted and talented, average parental education levels, and enrollment. The API score is California’s summary in- dex of school test score performance. These covariates, which are available through the California Department of Education, control for the differences in charter school penetration that are correlated with underlying demographic trends in each school. Our main analytic sample consists of 158,211 house sales occurring from September 2008 through September 2011. Of these, 65,170 are sales of homes zoned to an elemen- tary school in LAUSD and 93,041 are sales of homes zoned to an elementary school in another school district in LA County. Table 1 provides information on the types of charter and public schools that operate in LA County over our sample period. Panel A provides schools by grade level. Charters are more common for middle and high schools but still account for a substantial portion of elementary schools, at 9 percent. Conversion charters in particular are common for elementary schools but not middle and high schools. Panel B shows that over the time period of our study, the percent of schools that are charters grows from 7.7 percent in 2008 to 11.7 percent in 2011. Tables 2 and 3 provide sample means and standard deviations at the property level for several of the variables we include in our regressions. In table 2 we see that properties in LA County have an average sale price of $383,546 and tend to be of modest size, averaging
around three bedrooms, two bathrooms, and 1600 square feet. We also have a ranking
of the quality of the structures on the property, which will be useful for conducting
validity tests. The property is given a rating on a scale of 1 to 12.5 by LACAO assessors,

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Table 2.
Sale Prices

Summary Statistics of Properties with

Property characteristics

Sale price, $ Number of bedrooms Number of bathrooms Square footage Quality Number of charters 0–0.5 miles 0.5–1 mile 1–1.5 miles 1.5–2 miles Charters as percentage of enrollment 0–0.5 miles 0.5–1 mile 1–1.5 miles 1.5–2 miles Observations 383,546 (247,685) 2.98 (1.05) 2.11 (0.92) 1,573 (718) 6.45 (1.25) 0.16 (0.54) 0.47 (1.11) 0.78 (1.59) 1.06 (2.04) 0.05 (0.18) 0.06 (0.15) 0.06 (0.13) 0.06 (0.12) 158,211 Notes: Summary statistics are means for sales from September 2008 through September 2011. Property sample excludes homes with a sale price exceeding $1.5 million, and a bedroom or bathroom count in
excess of eight. Homes are divided into the “LAUSD”
or “Rest of LA County” samples via the location of
the elementary school to which the property is zoned.
Standard deviations in parentheses.

where a rating of 12.5 is the highest assessed quality. Not surprisingly, the average
quality of a property in LA County is close to the midway point on this scale, at 6.45.
For charter penetration, the number of charters in each distance ring increases as we
go further out, primarily because of the larger amount of land area in larger distance
rings. When we look at charters as a percentage of total public school enrollments, the
rates are relatively constant across distance rings at 5 to 6 percent.

We note that our data cover some periods of abnormal rigidity in the Los Angeles
housing market due to the housing collapse of 2008 and the Great Recession. Figure 1
shows the Case-Shiller House Price Index for the Greater Los Angeles area from 2008
through 2011.7 Even though housing prices in Los Angeles fell dramatically until May

7. Acquired from http://us.spindices.com/indices/real-estate/sp-case-shiller-ca-los-angeles-home-price-index.

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Capitalization of Charter Schools

Table 3.

Summary Statistics: Schools Near Properties with Sale Prices

Panel A: Characteristics of Zoned
School

Panel B: Characteristics of Charters
Within 1 Mile (Enrollment Weighted)

Elementary

Middle

High

Elementary

Middle

High

440.5
(165.6)
805.6
(73.6)
10.9
(14.4)
58.2
(28.5)
7.1
(12.6)
11.4
(4.5)
8.4
(7.3)
64.7
(30.0)
28.1
(17.2)
158,211

1,197.4
(488.0)
746.1
(90.3)
10.1
(11.9)
62.9
(24.7)
7.0
(12.0)
11.4
(2.8)
13.8
(9.7)
66.9
(25.7)
19.8
(11.5)
127,558

2,002.6
(680.6)
707.0
(88.2)
11.2
(13.3)
60.1
(24.8)
7.6
(12.3)
10.3
(3.1)
11.4
(8.8)
55.9
(26.9)
18.0
(10.5)
141,212

443.0
(138.0)
800.1
(64.5)
10.6
(13.2)
62.0
(25.7)
7.2
(12.0)
11.8
(4.1)
7.5
(5.4)
68.7
(26.6)
30.3
(15.2)
136,546

1,121.3
(435.7)
744.7
(93.0)
10.3
(11.6)
63.6
(25.2)
7.7
(13.2)
11.1
(2.7)
12.6
(8.6)
67.5
(26.6)
20.2
(11.7)
81,204

1,140.5
(814.3)
663.7
(112.4)
11.1
(12.6)
65.2
(23.9)
5.9
(11.5)
10.6
(12.2)
7.3
(7.0)
61.2
(24.1)
20.8
(12.2)
83,079

158,211

127,174

140,866

136,536

80,686

80,979

Enrollment

API score

% Black

% Hispanic

% Asian

% Disabled

% Gifted

% Free or reduced price lunch

% English language learner

Observations

for API score:

Notes: Summary statistics are means for sales from September 2008 through September 2011. Sample excludes
homes with a sale price exceeding $1.5 million, and a bedroom or bathroom count in excess of eight. School
zones are based on 2002 zoning. See text for details on how to access school zone maps. Standard deviations
in parentheses.

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Figure 1. Case-Shiller House Price Index for Greater Los Angeles.

Margaret Brehm, Scott A. Imberman, and Michael Naretta

2009, afterwards they had begun to rebound, increasing by 11 percent through July
2010. The prices fell slightly thereafter until the end of our data in September 2011.
Thus, the housing market had been in recovery for most of our sample period. Even
so, we may be worried that market rigidities would continue to limit capitalization.
To address this we provide results in a separate online appendix (accessible on Ed-
ucation Finance and Policy’s Web site at www.mitpressjournals.org/doi/suppl/10.1162
/EDFP_a_00192) that vary by year of sale and show that our estimates are similar to
baseline in later years of the sample when the market had more fully recovered.

In panel A of table 3 we provide information on the characteristics for the elemen-
tary, middle, and high schools to which each property is zoned. Panel B provides a
comparison with charters at each grade level within one mile of the property. For ele-
mentary and middle schools, the characteristics of charters are pretty similar to those
of the zoned school in terms of enrollment, API score, and demographics. For high
schools, however, there are some differences. Charter high schools tend to be substan-
tially smaller (1,140 students versus 2,002) but lower-performing as measured by API
score. Zoned and charter high schools are demographically similar, though high school
charters tend to have fewer gifted students.

6 . E M P I R I C A L S T R A T E G Y
Our identiﬁcation strategy relies on variation across households and over time within
a census block in the number of charters within various distance radii. To achieve this,
in addition to controls for characteristics of the local elementary school and property
characteristics, we include census-block ﬁxed effects along with month-of-sale ﬁxed
effects. Including census-block ﬁxed effects allows us to compare the sale prices of
properties that are geographically very close; the mean land area for census blocks in
LA County is 108,322 square feet with a median of 19,283 square feet. Although it
may be preferable to use repeated sales on the same property, this is not possible with
our data because we only have sale price information for the most recent sale. Even
if we did have repeated sales, given the short timeframe, restricting to those types of
households would create a selected sample since a disproportionate number of those
properties may be distressed, in fast changing neighborhoods, or houses that are often
“ﬂipped.”

We believe that multiple sales within census blocks provide a reasonably small
geographic area to closely mimic repeated sales for speciﬁc properties while avoiding
the potential selection issues generated by using repeated sales. For example, in our
ﬁnal estimation sample the median census block in LA County has three sales during
the study period with a mean of 3.9. Figure 2 provides a histogram of the distribution
of sales within census blocks, conditional on having any sales, over the study period.
While our econometric strategy identiﬁes the effect of charter penetration only from
blocks with more than one sale, a substantial number of census blocks provide this
identiﬁcation. There are 29,512 blocks with at least two sales and of those, 14,494 blocks
have at least four sales and 7,387 blocks have at least six sales. Further, of all blocks with
at least one sale, 73 percent have multiple sales, providing wide geographic variation
in blocks that contribute to identiﬁcation. Finally, we conduct an analysis of variance
of property characteristics to assess the within- and between-census-block variance. In

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Figure 2. Distribution of House Sales by Census Block During Sample Period Conditional on Census Block Having any Sales.

our estimation sample, only 39 percent of the variance in house size and 20 percent of
the variance in housing quality are within a census block, along with less than half of
the variation in bedrooms and bathrooms.8 These results suggest that different houses
within a block have largely similar characteristics.

By including census-block ﬁxed effects, our identiﬁcation strategy assumes there are
no changes in neighborhood conditions over time that are correlated both with housing
prices and charter penetration. Of course, housing prices are increasing in general in
Los Angeles during our analysis period, as is the number of charter schools. Hence,
to account for general changes in house prices related to overall market conditions, we
include year-by-month indicators in all of our regression models.

Even with census-block ﬁxed effect and year-by-month ﬁxed effects, it is possible
that there are factors changing locally that could bias our estimates. Of primary concern
is the possibility that charters select into neighborhoods where the local public school is
under-performing and the poor quality of the school is reﬂected in lower housing prices.
Ideally, we would be able to at least control for changes in neighborhood characteristics
as we do for school characteristics and housing supply. Unfortunately, the data available
to us are very limited. To our knowledge, only the American Communities Survey (ACS)
provides neighborhood data at a small enough geographic level (e.g., census tract) to be
relevant for this analysis. The ACS only provides ﬁve-year estimates at the census-tract
level, however, because estimates based on smaller periods of time are too imprecise. As
a result, the ACS data do not provide temporal variation in neighborhood characteristics
over our three-year time period and any data on neighborhood characteristics would be
absorbed by the census-block ﬁxed effects. Thus, we assume that selection of charter
location is unrelated to time-varying neighborhood characteristics that are themselves
not captured in our housing and school characteristics controls. We cannot test this

8. An analysis of variance using the residuals from regressions of the characteristics on month-by-year indicators

provides similar results.

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assumption directly, but we do attempt to address it indirectly by testing whether our
observable measures of housing characteristics change when more charters move in,
and by testing whether charter penetration can be explained by prior changes in house
prices. If time-varying neighborhood characteristics are correlated with prior house
prices and the types of houses put on the market then we should expect to see some
impact on these observables, and indeed we do not ﬁnd evidence for this. Nonetheless,
although we do not have temporal variation in neighborhood variables, we do have
such variation for local elementary school characteristics. Thus in table A.1 of the online
appendix, we look at how charter entry relates to public school characteristics when
we condition on school ﬁxed effects. Without school ﬁxed effects, the estimates show
that charters tend to locate in the zones of elementary schools with fewer minorities,
more gifted students, more English language learners, and more disabled students.
When school ﬁxed effects are added some characteristics are statistically signiﬁcant,
but importantly they are all economically small. The largest statistically signiﬁcant
coefﬁcient is on percent of black residents in the public school zone, but this coefﬁcient
is still rather small. For a one charter increase in the school zone, there would need to
be a percent black student increase of 84 percentage points. Given this pattern and the
general shift in the coefﬁcients toward zero as the school ﬁxed effects are added, these
results suggest that lower levels of geographic ﬁxed effects, speciﬁcally census-block
effects, should reduce these correlations further to the point where they are negligible.
Another difﬁculty in this analysis is deciding how to measure charter penetration.
There are two key factors here. First, there is the question of whether the important
factor is the existence of a charter school as a whole or the relative size of a charter
school. Arguably, whereas the former is the most visible aspect of the school to the wider
public (people in the neighborhood know that a school exists but may be uncertain as
to how large it is), the latter is a potentially better indicator of the supply constraints on
a family that wishes to send a child to the charter. The second issue is that it is unclear
how far from the charter a household must be before we can be conﬁdent the household
should not care about the charter’s existence. To deal with both of these issues we follow
the prior literature on the effects of charter schools on public schools (Bifulco and Ladd
2006; Sass 2006; Booker et al. 2008; Imberman 2011a). The analyses in these studies
estimate the effects of charter schools on traditional public schools within concentric
rings of various distances. Because it is not obvious whether what matters is relative
enrollment in charters or the number of charters, they estimate the effects of both
charter counts and enrollment in the charters as a share of total enrollment.

We use measures of charter penetration equal to (1) the number of charters and
(2) the share of all public school enrollments in charters in concentric rings between 0
and 0.5 miles, 0.5 and 1 mile, 1 and 1.5 miles, and 1.5 and 2 miles from a property. We
focus our attention on charters within relatively short distances of properties because
of the urbanicity and size of school zones in LA County. The mean elementary school
zone in LA County has an area of 3.2 square miles. With this area, if school zones were
circular, the radius of the average zone would be 1.0 mile. The median school zone has
an area of 0.8 square miles, translating into a radius of 0.5 miles. Hence, given the size
of school zones in LA County, these are reasonable distances within which to measure
the effect of charters. Indeed, in a large southwestern city that is less densely populated
than Los Angeles, Imberman (2011a) shows charters only impact enrollment of public

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schools within 2 miles of the charter. Further, in an analysis of charter applicants in
Boston, Walters (2014) ﬁnds that 40 percent of applicants apply to the closest charter
school and a further 22 percent apply to the second closest. Although we do not have
data on who actually applies to or attends charters, we note that in LA County the
median property is 1.35 miles from the nearest charter and the second closest charter
is 2.18 miles away. Because these measures include all properties, it is likely that the
average distances for charter attendees are substantially smaller. Based on these factors,
we believe 2 miles is a reasonable maximum distance, though we also report distances
between 2 and 5 miles in the online appendix.

Our baseline model estimates the impact of charter penetration on the log of the

sales price of property i in census block s at time t as

Ln(SalePriceist) = α + Charteritβ + Xit(cid:3) + Hi(cid:4) + λt + γs + εit,

(1)

where Charter is a vector of charter penetration variables calculated as the number
of charters or the share of public school enrollment in charters between 0 and 0.5
miles, 0.5 and 1 mile, 1 and 1.5 miles, and 1.5 and 2 miles from the property. The β
coefﬁcients can be interpreted as jointly identifying a house price gradient that captures
the differential valuation of charter penetration by homeowners over distance. X is a
vector of school-by-year observables, where the school is the elementary school to which
the property is zoned. H is a vector of house-speciﬁc characteristics, such as the number
of bedrooms, the number of bathrooms, age, quality, and square footage. The model
also includes month-by-year ﬁxed effects (λt) to control for common time trends and
census-block ﬁxed effects (γ s) to control for time-invariant neighborhood quality and
quality of the locally zoned school.9 We cluster standard errors at the school-zone
level to account for correlation between prices of properties in the same census block.
An adjustment to this model also restricts to charter schools within school-district
boundaries. This is relevant because, as previously mentioned, California requires
oversubscribed charters to give admissions priority to within-district students.

We expand the baseline model to account for heterogeneous effects on housing
price by disaggregating our charter penetration variables by type of charter: conversion
or startup. In this model, the charter penetration vector is split into two:

Ln(Pist) = α + StartupCharterit

β1 + ConversionCharteritβ

+ Xit(cid:3) + Hi(cid:4) + λt + γs + εit.

(2)

In this setup, the β 1 coefﬁcients will provide a gradient for startup charters and the
β
2 coefﬁcients will provide a gradient for conversion charters. We include the same
controls as in equation 1. As mentioned earlier, we would expect to ﬁnd differing
valuation of these two types of charters if homeowners place different weights on the
inputs of each type—conversion charters often remain in the same building, with
the same student body and staff, and adopting new operating styles, whereas startup
charters are often in rental spaces, tend to be smaller than conversions and traditional

9. The baseline model excludes school-zone ﬁxed effects because most census blocks do not straddle school

zones. Nonetheless, inclusion of school-zone ﬁxed effects has a negligible impact on the results.

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Table 4.

Effect of Charters on Log Sale Prices for Los Angeles County

Los Angeles County

Number of Charters

Charter Seats as Percentage of Enrollment

(i)

(ii)

(iii)

(iv)

(v)

(vi)

A. Distance Gradient

0–0.5 miles

0.5–1 mile

1–1.5 miles

1.5–2 miles

B. Condensed 0–2 Miles

0–2 miles

−0.00725
(0.0131)
0.00858
(0.00748)
0.0252∗∗∗
(0.00578)
0.0239∗∗∗
(0.00494)

−0.0353∗∗∗
(0.00800)
−0.0253∗∗∗
(0.00609)
−0.0149∗∗∗
(0.00387)
−0.00460
(0.00309)

−0.00543
(0.00827)
0.000950
(0.00476)
0.00223
(0.00313)
−0.00110
(0.00279)

0.0741∗
(0.0438)
0.117∗∗
(0.0564)
0.140∗∗
(0.0616)
0.120
(0.0770)

0.00648
(0.0249)
−0.0166
(0.0270)
−0.0442∗
(0.0268)
−0.0217
(0.0340)

−0.00134
(0.0194)
−0.0128
(0.0195)
−0.0123
(0.0239)
−0.00470
(0.0255)

0.0193∗∗∗
(0.00321)

−0.0101∗∗∗
(0.00255)

−0.00010
(0.00207)

0.328∗∗∗
(0.112)

−0.0301
(0.0609)

−0.00750
(0.0544)

Observations

158,211

Housing characteristics

School characteristics

School ﬁxed effects

Census-block ﬁxed effects

Notes: Sample includes property sales from April 2009 through September 2011. The independent variable denotes either the
number of charters in operation or the share of enrollment in operating charters as of the sale date in various distance rings from
the property. Housing characteristics include number of bedrooms, bathrooms, square footage, and quality. School characteristics
include API levels overall, lags and second lags of overall API scores, percent of students of each race, percent free lunch,
percent gifted, percent English language learners, percent disabled, and parent education levels for elementary school zoned to
the property in 2002. All regressions include month-by-year ﬁxed effects. Robust standard errors clustered by elementary school
zone in 2002 in parentheses.
∗Signiﬁcant at the 10% level; ∗∗signiﬁcant at the 5% level; ∗∗∗signiﬁcant at the 1% level.

public schools, and need to recruit students and staff in addition to operating under a
new management style.10

7 . R E S U L T S
Effect of Charter Penetration on Housing Prices
Table 4 provides the baseline results of our analysis using variations of equation 1 and
the sample of homes sold across all of LA County. The table includes two panels, one
for each charter measure, overall numbers of charters, and percentage of total enroll-
ment attributed to charters. Each speciﬁcation in the table includes month-by-year time
dummies, housing controls (square footage, number of bedrooms, number of bath-
rooms, and quality) and controls for the locally zoned elementary school (enrollment,
API score, school demographics, percentage disabled, gifted, free- or reduced-price
lunch eligible, and English language learners). All standard errors are clustered at the
school-zone level, where the school is the elementary school to which a property was
zoned in 2002.

10. The fact that conversions usually maintain the same attendance zone after converting suggests the potential
for using a difference-in-differences approach to assessing the impacts of these schools on housing prices.
Unfortunately, only ﬁve schools in LA County convert to charter status during our study period, making the
estimates from this type of analysis too imprecise.

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In columns (i) and (iv), respectively, of table 4, we regress the log of the house
price on charter counts and the share of public school enrollment in charters within
half-mile diameter rings without geographic ﬁxed effects. The estimates suggest there
is a positive relationship that strengthens as the distance from the property increases.
In columns (ii) and (v), we include elementary school-zone ﬁxed effects to account
for characteristics of the locally zoned school. In these models the patterns differ
depending on how we measure charter penetration. When using charter counts, the
results indicate charters negatively impact housing prices, becoming more negative the
closer charters are to the property. The coefﬁcient on the zero to half-mile radius charter
measure indicates that an additional charter is associated with a statistically signiﬁcant
3.5 log point decrease in the sale price. When using enrollment share, however, only 1
to 1.5 miles is signiﬁcant.

We may still be concerned that there are endogenous differences within school
zones, but across neighborhoods, that affect both housing prices and charter penetra-
tion. Thus, in columns (iii) and (vi) we provide our preferred estimates that replace
school-zone ﬁxed effects with census-block ﬁxed effects. In this model, estimates are
all statistically insigniﬁcant and small. The largest estimate in column (iii) suggests,
when taken at face value, an additional charter school increases housing prices between
1 and 1.5 miles away by 0.2 percent, with smaller values for other distances. For the
enrollment share measure, all of the values are negative, insigniﬁcant, and economi-
cally small with a 10 percentage point (pp) increase in charter share reducing housing
prices by less than 0.2 percent at all distance levels. To provide additional context, if we
focus on charter penetration within 0.5 miles of the property, the 95 percent conﬁdence
interval for the impact of an additional charter is [–2%, 1%] and for a 10 pp increase in
charter enrollment share it is [0.4%, –0.4%].

One potentially important issue in interpreting the estimated effect of charter pen-
etration is that as the distance increases, the area in which the charter could locate
increases. This is not a substantial concern when focusing on share of enrollment
but it does indicate there may be more variation in the number of charters in farther
rings, making comparison of the estimated effects of charter penetration at different
distances difﬁcult. To address this we also provide estimates using charter penetration
within the full 2-mile radius around the property in panel B. The results are similar to
those in panel A and show no impact of charters on housing prices when we include
census-block ﬁxed effects. It is also interesting to note that the standard errors decrease
when we add census-block (or school) ﬁxed effects. This is another indicator that there is
substantial identifying power within blocks and that including between-block variation
adds uninformative noise to the analysis.

Table 5 provides results for our preferred model that includes census-block ﬁxed
effects when we split the sample by whether the properties are within the boundaries
of LAUSD, which is the largest district in LA County, or all other school districts in the
county. We may suspect that there are different property effects for the two samples
because LAUSD covers the main urban core of the county, and recent evidence suggests
urban charters are more effective than suburban charters (Angrist, Pathak, and Walters
2013). Our results, however, provide little evidence that house price effects vary via this
location difference. Only one estimate—for charter counts in LAUSD from 1 to 1.5
miles—is statistically signiﬁcant.

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Margaret Brehm, Scott A. Imberman, and Michael Naretta

Table 5.

Effect of Charters on Log Sale Prices for Los Angeles County by School District

LAUSD

Rest of LA County

Number
of
Charters

Charter Seats
as Percentage
of Enrollment

Number
of
Charters

Charter Seats
as Percentage
of Enrollment

0.000504
(0.00970)
0.00591
(0.00559)
0.00712∗∗
(0.00349)
0.00233
(0.00342)

65,170
0.83
Y

0.00923
(0.0236)
−0.00620
(0.0278)
−0.00607
(0.0314)
−0.0130
(0.0408)

65,170
0.83
Y

−0.0143
(0.0169)
−0.00447
(0.00869)
−0.00225
(0.00632)
−0.00375
(0.00470)

93,041
0.91
Y

−0.0220
(0.0373)
−0.0114
(0.0241)
0.000579
(0.0351)
0.0259
(0.0206)

93,041
0.91
Y

0–0.5 miles

0.5–1 mile

1–1.5 miles

1.5–2 miles

Observations
R2

Housing characteristics

School characteristics

School ﬁxed-effects

Census-block ﬁxed effects

Notes: See table 4 for a description of baseline sample and controls. Robust standard errors
clustered by elementary school zone in 2002 in parentheses.
∗∗Signiﬁcant at the 5% level.

Table 6 provides the results for equation 2, splitting the charter penetration variable
by charter type (conversion and startup) for homes in all of LA County. As in our
regression split by school district, we focus on our preferred model with census-block
ﬁxed effects, zoned elementary school controls, and housing controls. As in the pooled
model, none of the coefﬁcients is statistically signiﬁcant and the magnitudes and signs
of the estimates do not reveal a consistent relationship between charter counts or
charter enrollment rates and sale price for either charter type.

In table 7 we provide estimates that look at how charters affect house prices when
we restrict the charters included in the count and enrollment share variables to those
located in the same school district as the household. In California, within-district
students get priority for charter enrollment and so there may be a stronger link with
housing prices for these charters than those outside the district. Because LAUSD is
especially large with most properties located far from district boundaries, when we
estimate this model for LAUSD the estimates are little changed from baseline. Hence,
in table 7 we only provide estimates using the districts in LA County outside LAUSD.
These estimates are the only ones in this paper that provide a consistent indicator of
a charter impact on housing prices. Intriguingly, this estimated effect is negative. An
additional charter school within 2 miles reduces house prices by 1.9 percent and a 10 pp
increase in charter share of enrollment within 0.5 miles reduces prices by 1.2 percent.
This analysis provides some evidence that charters weaken the link between public
schools and housing prices.

We build on this analysis further by testing whether we see larger effects in areas
with higher quality schools. Tables A.2 and A.3 in the online appendix provide estimates,
respectively, that are allowed to differ by terciles of income and school API score
counting all charters, and counting only within-district charters. Although there are

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Capitalization of Charter Schools

Table 6.

Effect of Charters on Log Sale Prices by Charter Type

Los Angeles County

Number of
Charters

Charter Seats as
Percentage of Enrollment

Startup charters

0–0.5 miles

0.5–1 mile

1–1.5 miles

1.5–2 miles

Conversion charters

0–0.5 miles

0.5–1 mile

1–1.5 miles

1.5–2 miles

Observations
R2

Housing characteristics

School characteristics

School ﬁxed effects

Census-block ﬁxed effects

−0.00450
(0.00952)
0.00253
(0.00537)
0.00341
(0.00357)
−0.00110
(0.00299)

−0.0137
(0.0133)
−0.0110
(0.0103)
−0.00664
(0.00854)
−0.00225
(0.00788)

158,211
0.881
Y

0.00389
(0.0226)
0.000917
(0.0241)
0.00269
(0.0240)
0.0273
(0.0234)

−0.0226
(0.0362)
−0.0432
(0.0311)
−0.0397
(0.0456)
−0.0506
(0.0513)

158,211
0.881
Y

Notes: See table 4 for a description of baseline sample and controls.
Robust standard errors clustered by elementary school zone in 2002
in parentheses.

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some marginally signiﬁcant estimates in the low income schools in table A.3, overall
the evidence for a pattern across school types is weak. One possibility is that the district
quality is what matters. In table A.4 in the online appendix we investigate this by
extending the model in table 7 to allow for different estimates by tercile of district API
score. Here a clearer pattern emerges. In fact, the estimates suggest that there is a
small increase in property values for high-performing districts but a reduction for low-
performing districts. The relationship remains weak, however, with only one estimate
for bottom tercile schools signiﬁcant at the 5 percent level. It is unclear why such a
pattern emerges but one possibility is that the low-achieving districts competing with
LAUSD, which is also low-performing (12th percentile API), beneﬁt from a premium
over LAUSD that is weakened by charters. Or it could be the relationship between
housing prices and school quality is more sensitive to charters in low-performing
districts. Nonetheless, it is unclear to what extent this restriction to within-district
charters should matter. Although district students get priority, this is only relevant if
charters are oversubscribed. Hence, given the null results when we do not make this
restriction we think it is best to consider these estimates to be a bound on the potential
negative effect of charters.

Margaret Brehm, Scott A. Imberman, and Michael Naretta

Table 7.
Sale Prices for Los Angeles County, Excluding LAUSD

Effect of Charters Within the Home’s School District on Log

Panel A. Half-mile Increments

0–0.5 miles

0.5–1 mile

1–1.5 miles

1.5–2 miles

Number of
Charters

Charter Seats as
Percentage of Enrollment

−0.0387
(0.0284)
−0.0123
(0.0149)
−0.0178∗
(0.0098)
−0.0186
(0.0156)

−0.118∗∗∗
(0.0431)
−0.0786∗
(0.0409)
−0.0758∗
(0.0433)
−0.0670
(0.0541)

Panel B. Condensed 0–2 Miles

0–2 miles

−0.0192∗∗
(0.0090)

−0.0292
(0.1540)

Observations

93,041

Housing characteristics

School characteristics

School ﬁxed effects

Census-block ﬁxed effects

Notes: Sample includes property sales from April 2009 through Septem-
ber 2011. The independent variable denotes either the number of charters
within the home’s zoned school district in operation or the share of enroll-
ment in operating charters as of the sale date in various distance rings
from the property. Housing characteristics include number of bedrooms,
bathrooms, square footage, and quality. School characteristics include
API levels overall, lags and second lags of overall API scores, percent
of students of each race, percent free lunch, percent gifted, percent En-
glish language learners, percent disabled, and parent education levels
for elementary school zoned to the property in 2002. All regressions in-
clude month-by-year ﬁxed effects. Robust standard errors clustered by
elementary school zone in 2002 in parentheses.
∗Signiﬁcant at the 10% level; ∗∗signiﬁcant at the 5% level; ∗∗∗signiﬁcant
at the 1% level.

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Testing for Endogenous Charter Location
A consistent estimate of the relationship between charter penetration and housing
prices rests on the assumption that the variation in charter penetration is exogenous,
conditional on the included controls and, most importantly, the census-block ﬁxed
effects. As a test of this, we regress our limited set of housing characteristics on charter
penetration variables and census-block ﬁxed effects. Ideally, we would like to test
the relationship between charter penetration and local neighborhood characteristics.
Including census-block ﬁxed effects precludes such an analysis, however, as we do
not have access to time-varying neighborhood characteristics. Thus, we must rely on
characteristics of the speciﬁc households that can be acquired from the property sales
data.

Table 8 presents results that estimate whether charter penetration is related to
square footage, the number of bedrooms, the number of bathrooms, and the quality
of the structures on the property, as measured by the county assessor. We ﬁnd no
statistically signiﬁcant relationship between the numbers of charters in any radius ring
and square footage, the number of bedrooms, or the number of bathrooms. For quality,

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Capitalization of Charter Schools

Table 8.

Impacts of Charters on Exogenous Observables

Los Angeles County

Number of Charters

Square Footage No. of Beds No. of Baths

Quality

0–0.5 miles

0.5–1 mile

1–1.5 miles

1.5–2 miles

3.899

(14.0)
−6.593
(7.4)
4.954
(5.0)
−2.357
(4.8)

0.0147
(0.0247)
−0.0112
(0.0147)
0.0116
(0.0098)
−0.0015
(0.0097)

0.0186
(0.0192)
−0.0183
(0.0112)
0.0045
(0.0074)
0.0001
(0.007)

0.0124
(0.0172)
−0.0096
(0.0098)
−0.0086
(0.0066)
−0.0120∗
(0.0065)

Observations

158,211

0.5–1 mile

0–0.5 miles

Charter seats as percentage of enrollment
−22.710
(37.3)
25.590
(40.6)
28.490
(39.5)
−23.360
(43.2)

1–1.5 miles

1.5–2 miles

Observations
R2

School ﬁxed effects

158,211
0.67
N

Census-block ﬁxed effects

−0.005
(0.048)
0.011
(0.062)
0.011
(0.057)
−0.044
(0.052)

158,211
0.55
N

0.023
(0.040)
0.023
(0.050)
−0.009
(0.051)
−0.046
(0.052)

158,211
0.59
N

0.025
(0.046)
−0.037
(0.05)
−0.021
(0.050)
0.000
(0.049)

158,211
0.80
N

Notes: See table 4 for a description of baseline sample. Robust standard errors clustered
by elementary school zone in 2002 in parentheses.
∗Signiﬁcant at the 10% level.

only the estimate for charters between 1.5 and 2 miles is statistically signiﬁcant and
only at the 10 percent level. For charter seats as a percentage of all public education
seats, no estimate is statistically signiﬁcant.

In a second analysis, we regress the log of house price on charter penetration within
a half-mile of the home in 12-month lag and lead intervals up to 3 years before the home
was sold and 3 years after the home was sold. For example, the 12-month lag measure
corresponds to charters within a half-mile of the property that were in operation 12
months prior to the home’s sale. The purpose of this analysis is to test for pre-existing
trends and to see if there are any anticipatory or delayed impacts of charter openings.
Thus, a clear pattern of higher prices from charters in operation after the house sale
would be evidence of either anticipatory effects or pre-existing trends in housing prices,
the latter of which would invalidate the identiﬁcation strategy. A pattern of higher prices
from charters in operation prior to the home sale would indicate that housing prices
are affected by charters but with a delay, potentially due to short-term price stickiness.
Table 9 provides the impacts of lags and leads, which show little evidence of
responses to charter penetration. Of the two signiﬁcant coefﬁcients, one is for the 12-
month lead in charter enrollment percentage. This estimate suggests that an increase
in enrollment rates of 10 pp within a half-mile of a home 2 months following its
sale increases the sale price by 0.3 percent. This could be indicative of a pre-existing

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Margaret Brehm, Scott A. Imberman, and Michael Naretta

Table 9.

Effect of Lags and Leads of Charter Penetration

Los Angeles County

36 months prior to sale

24 months prior to sale

12 months prior to sale

Time of sale

12 months after sale

24 months after sale

36 months after sale

Observations
R2

Number of
Charters

−0.0005
(0.0100)
−0.0039
(0.0093)
−0.0055
(0.0089)
−0.0010
(0.0088)
0.0038
(0.0054)
−0.0082
(0.0080)
0.0190∗
(0.0102)

158,211
0.88

Charter Seats as
Percentage of Enrollment

−0.001
(0.027)
−0.017
(0.031)
−0.008
(0.030)
0.006
(0.031)
0.033∗∗
(0.015)
−0.019
(0.025)
0.013
(0.037)

158,211
0.88

Notes: See table 4 for a description of baseline sample and controls.
Robust standard errors clustered by elementary school zone in 2002 in
parentheses.
∗Signiﬁcant at the 10% level; ∗∗signiﬁcant at the 5% level.

trend, although the other estimates suggest this is not likely to be the case. First,
estimates for charter penetration 24 and 36 months after the sale show no impact.
Second, there is no similar impact when measuring penetration using the number
of charters. The other signiﬁcant coefﬁcient is for the 36-month lead in number of
charters, suggesting an additional charter school within a half-mile of the property 36
months following the sale of the home increases the sale price by 1.9 percent. If this
were indicative of an anticipatory response or pre-existing trend, however, we would
expect to ﬁnd signiﬁcant impacts from charter penetration 24 months and 12 months
after the sale, as well. Thus, although there are a couple of estimates that suggest
anticipatory responses or pre-trends, the bulk of the evidence in table 7 argues against
such a pattern. Further, we note that the results in the table also provide little evidence
of a delayed response since there is no signiﬁcant impact from the number of charters
open or the charter enrollment rates 12, 24, or 36 months prior to the sale.

Finally, in table 10, we test the concern that the addition (or closure) of charter
schools may generate sample selection by inducing some people to enter or stay out
of the housing market. To do this we regress the number of annual sales in a census
block on charter penetration near the block centroid. Further, even though we only have
price data for the most recent sale of a property, we can see the dates for the three most
recent sales. Thus, in the second column we repeat the analysis using the three most
recent sales of properties in the sales counts. The results show little impact of charter
share of enrollment on housing sales. There is also no signiﬁcant relationship between
charter counts and sale counts within one mile of the centroid. Nonetheless, there is
a statistically signiﬁcant but economically small relationship between sales counts and

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Capitalization of Charter Schools

Table 10.
Census Block

Relationship Between Charter Penetration and the Number of Annual House Sales in

Quantity of House Sales within Census Block

Counting Most Recent House
Sale

Counting Three Most Recent
House Sales

Number of
Charters

0.0828
(0.168)
0.119
(0.110)
0.139∗
(0.0776)
0.0972∗
(0.0547)

Charter Seats
as Percentage
of Enrollment

0.354
(0.414)
0.346
(0.533)
0.581
(0.647)
−0.0104
(0.607)

Number of
Charters

0.0901
(0.174)
0.130
(0.110)
0.171∗∗
(0.0794)
0.121∗∗
(0.0554)

Charter Seats
as Percentage
of Enrollment

0.346
(0.417)
0.391
(0.537)
0.801
(0.721)
0.120
(0.659)

0–0.5 miles

0.5–1 mile

1–1.5 miles

1.5–2 miles

Observations

87,683

Census-tract ﬁxed effects

Census-block ﬁxed effects

Notes: Sample includes property sales from September 2008 through September 2011. The
independent variable denotes either the number of charters in operation or the share of enrollment
in operating charters as of the sale date in various distance rings from the property. Robust standard
errors clustered by elementary school zone in 2002 in parentheses.
∗Signiﬁcant at the 10% level; ∗∗signiﬁcant at the 5% level.

the number of charters 1 to 2 miles from the centroid. The estimates suggest, after
conditioning on census-block ﬁxed effects, that a new charter opening 1 to 2 miles from
the block centroid is related to an increase of 0.1 to 0.2 sales in a year. To put this
in perspective, it would take ﬁve to ten new charter openings in a year to generate an
additional sale. Given that the average number of charters in that distance range from
properties is 1.9, we believe this impact is too small to substantially affect our estimates.

Effect of Charter Penetration on Housing Prices: Heterogeneity and Speciﬁcation Checks
In the online appendix we provide a series of analyses to look at impacts of charters
when we allow the characteristics of the charters, local neighborhoods, and local public
schools to vary. First, in table A.5 we provide different estimates by the grade level of
the charter. We split charter penetration measures into four categories—elementary,
middle, high, and multi-level schools. We see little evidence of differential impacts on
housing prices by the level of the charter school at any distance up to 2 miles from the
property. Only one estimate out of thirty-two is statistically signiﬁcant at the 5 percent
level. Three more are signiﬁcant at the 10 percent level, but show no clear pattern and
differ in sign.

In table A.6 we interact the charter penetration measures with the year of the
property sale. Because the housing market in Los Angeles had undergone substantial
declines just prior to our study, we may be concerned that the lack of capitalization
is due to abnormal rigidities in the market, although we note the signiﬁcant effects
when we restrict to within-district charters suggest this is not the case. Nonetheless, to
address this, we focus on the estimates for 2010 and 2011, well after the market had

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Margaret Brehm, Scott A. Imberman, and Michael Naretta

started its recovery. As with our main results, we ﬁnd no statistically signiﬁcant impacts
of charter penetration at any distance within 2 miles of a property in 2010 or 2011. In
fact only one estimate out of the thirty-two shown is statistically signiﬁcant—that is, 1
to 1.5 miles in 2008.

In table A.7 we provide evidence on whether the overall mean charter impacts may
be hiding heterogeneous effects between neighborhoods with high-performing and
low-performing schools by interacting the charter penetration variables with both the
distance from the property and quartiles of household income (across all properties
in the data) in the census tract, the zoned elementary school’s API score, percent
minority enrollment in the zoned elementary school, and minority enrollment in the
census tract. Only 5 estimates out of 128 are statistically signiﬁcant at the 10 percent
level (one estimate at the 1 percent level) and do not show a clear pattern. Thus we
see little indication of our pooled estimates hiding heterogeneous impacts among
these characteristics. This further indicates that the overall null results are not due to
differential impacts from weak and strong schools canceling each other out.

Finally, in table A.8 we provide estimates under different speciﬁcations and sample
restrictions. Through all of these speciﬁcation and sample checks, no estimates are
statistically signiﬁcant. These checks include using sale price levels rather than log
sale prices, splitting the sample by the number of bedrooms, keeping properties with
more than eight bedrooms in the regression, dropping large (5,000 square feet or
larger) properties, dropping multi-unit properties, and limiting to the summer months
of June, July, and August as families with children are more likely to move during this
period between school years. Further, we show that adding a ﬁfth distance ring of 2 to 5
miles does not change the estimates, nor is the estimate on the added ring signiﬁcant,
and adding in school ﬁxed effects (in addition to census-block ﬁxed effects) has little
impact on the baseline estimates.

8 . C O N C L U S I O N S
Research has previously shown close links between school quality and property prices.
This has been explained as a capitalization of both the quality and capital stock of
schooling into local property values, given that students typically are required to attend
a speciﬁc local public school. Hence, properties zoned to schools and districts with
higher performance and more resources have seen higher values, all else equal. Charter
schools have the potential to weaken this relationship. Students can typically attend a
charter regardless of where they reside, thus making the local school potentially less
important to residency decisions. Given that enrollment in charter schools has been
increasing across the country over the past twenty years and, if present trends continue,
is likely to increase further, the breaking of the link between housing prices and school
quality can have implications for local public ﬁnance as well as socioeconomic diversity
across schools.

In this study we directly estimate how charter schools affect local property values in
Los Angeles County. We also expand our analysis to separate our measures of charter
penetration by urbanicity, charter type, and grade level of the school, along with wealth
of the local neighborhood and the achievement levels of the local elementary school. Our
approach follows the work of other researchers relating school characteristics to housing
prices, and carefully accounts for the correlation between neighborhood characteristics

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Capitalization of Charter Schools

and housing prices, by including census-block ﬁxed effects. This method allows us to
estimate the impacts of charters net of any time-invariant differences between local
neighborhoods and, by extension, local public schools.

Using data from the Los Angeles County Assessor’s Ofﬁce on property sale prices
from 2008 through 2011, our estimates show there is very little impact of charters on
home prices on average. The results are not sensitive to sample selection or model
speciﬁcation, nor do we ﬁnd differential impacts by whether a charter is a startup or
conversion, whether the property is in the primary urban school district in the area, Los
Angeles Uniﬁed School District, by the grade level of the charter, by the income level
of the neighborhood, or by test scores in the zoned elementary school. However, given
that in California oversubscribed charters must provide priority enrollment to students
within the local school district, we also estimate a model that restricts to charters located
in the same school district as the property. In this case, which we consider a negative
lower-bound impact as it is not clear whether such a restriction is appropriate, we ﬁnd
some evidence that housing prices actually fall by 2 percent for each additional charter
within 2 miles. Because evidence of differential impacts by school quality is weak and, at
best, negatively related to income and performance, this suggests that perhaps charter
schools weaken the capitalization of schooling as a public good into property values
rather than the capitalization of school quality in particular.

ACKNOWLEDGMENTS
The research reported here was supported by the Institute of Education Sciences, U.S. De-
partment of Education, through grant R305B090011 to Michigan State University (MSU). This
grant supports MSU’s doctoral training program in the economics of education. The opinions
expressed are those of the authors and do not represent views of the Institute or the U.S. Depart-
ment of Education. We are grateful for the support. We would also like to thank Robert Bifulco,
Eric Brunner, and seminar participants at Michigan State University and the Association for
Education Finance and Policy for helpful comments and suggestions. All errors and omissions
are our own.

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