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School Quality and Property Values

By David Wang LR_WangDavid


School Quality and Property Values

The American education system spans communities of extremely diverse populations, across many socio-economic and ethnic lines.  It is likely that the successes of students in these communities hinge on the combination of the students’ innate intelligence, living environment, and school quality.  Standard models suggest and many have provided empirical evidence that housing prices have a correlation to this school quality.  However, since these factors could be interrelated, to isolate and identify the actual effects of school quality on property values requires careful consideration of student, house, and neighborhood-specific attributes.  It is also important to note how school quality is measured.  With the passage of the No Child Left Behind Act of 2001 and the shift away from using per pupil expenditures as a proxy for the quality of education, attention has been drawn to the use of standardized test scores as the marker of education quality.

Recent literature reconfirms the positive correlation of school performance on house prices, using basic hedonic models with added controls for test scores of local schools.  However, in older papers, the authors have difficulties controlling for neighborhood characteristics that are correlated with the test scores and house prices.  Black (1999) develops a new method for assessing school quality by using attendance district boundaries to account for neighborhood characteristics.  This method allows her to compare school to school differences in test scores with house prices.  Crone (2006) uses a model on a full unrestricted sample that allows for testing of house price and test score relationships on both a school and district level.  In addition, he adapts Black’s boundary model to allow for this district level analysis.  In contrast to Black, Crone argues that it is a district-wide educational quality, not individual school quality that affects house prices.  Finally, Clapp, Nanda, and Ross (2007) also consider Black’s model, but instead use a time-based fixed effects model over the period from 1994 to 2004 to control for the neighborhood characteristics.  Despite using different methods, all three papers agree that a positive correlation exists between school test scores and housing prices.

Black’s (1999) measurement of differences across attendance district boundaries enables the use of fixed effects in her model.  This district boundary is the line that separates the respective attendance areas of schools.  This line provides a discrete point at which standardized test scores should change.  However, the line may run through continuous neighborhoods, allowing Black to compare any sudden jump in test scores with houses that are situated in similar neighborhoods.  By using dummy variables to account specifically for the districts, Black avoids the omitted variable biases of property taxes, public goods, and neighborhood characteristics.  Using MEAP testing data from Massachusetts elementary schools, Black focuses on the fourth grade level.  Under the basic, unrestricted model, she must control separately for house level characteristics, distance from the CBD, in addition to other school quality characteristics, such as per-pupil expenditures.  She finds that per-pupil expenditure is positively correlated with house prices while higher pupil/teachers ratio is negatively correlated with house prices.  Nevertheless, the crux of the problem involves the unobservable characteristics of a neighborhood.  Black examines different subsets of her data, restricting the samples to houses nearer and nearer the boundary and increasing the probability that the houses on opposite sides of the boundary differ in only the elementary school quality.  Her study reveals that if neighborhood characteristics are not carefully controlled, the marginal value of school quality as measured by test scores on housing prices will be overestimated. Black concludes that parents will pay higher house prices for better schools, but does not examine whether there exists a district level effect of school quality on prices.

Newer researchers incorporate Black’s boundary model and conclusions as supplements to their models.  However, unlike Black, Crone (2006) argues that home buyers actually value local public education at the district level rather than the neighborhood school level.  Using fifth and eleventh grade Pennsylvania System of School Assessment (PSSA) data from Montgomery County, Crone makes findings that differ from Black’s.  While Black argues that differences on an individual school basis affect home prices, Crone claims otherwise.  Crone argues that for fifth grade test scores, differences are only significant on the district-level.  In fact, he finds that fifth grade test scores are better predictors of house prices than eleventh grade scores.  Perhaps this discrepancy could be attributed to the location of families with young children and the subsequent lack of relocation as the children grow up.  Crone’s differing results could also be due to his use of the full sample rather than a boundary restricted sample.  Crone’s more comprehensive dataset allows him to make district level regressions, while Black’s dataset is restricted to individual schools.

Crone’s study also provides additional factors that may affect school quality and thus house prices.  For example, class size is not significant at the elementary school level, but it makes a significant difference at the high school level.  By incorporating this measurement into the main model, Crone reduces the chance of omitted variable bias from Black’s neighborhood fixed effects model.  The neighborhood fixed effects do not account for differences in the schools, such as per-pupil expenditure or class size, of which the latter was not included in Black’s model, which only seeks to explain the impact of school quality differences.  As an additional test, Crone uses Black’s boundary method to estimate the effect of both school and district test scores on housing prices.  He finds that with this smaller sample, there is no significant coefficient on fifth grade scores, further conflicting with the results given by Black.  However, on the high school level, the results become more significant with the smaller sample with boundary dummies.  This result differs from the result when controlling for detailed characteristics in the model with a full sample.  Finally, Crone’s study also finds that per-pupil expenditures do not affect the house prices above their effect on student test scores or achievement. Overall, Crone brings the conclusion that school district quality should be considered over the quality of individual schools when determining the effect on house prices.

Clapp, Nanda, Ross (2007) introduce a twist on the examination of test scores and housing prices by suggesting that the quality of school districts is a function of both test scores and demographic composition.  Since people tend to use the most accessible signals to judge school quality, they often rely on the demographics of a school, which are very visible.  Thus, Clapp examines the significance of the test score and demographic composition effects on house prices.  Like Black and Crone, Clapp also finds a statistically significant, though very small, effect of test scores on house values.  Clapp also agrees with Black’s finding that failing to control for unobservable characteristics in the neighborhood leads to overstatement of the test score effect.  However, Clapp extends this argument by also including the effects of race percentages on home prices.  He finds that an increase in percent African-American and percent Hispanic leads to a decline in property values.  Nevertheless, over this time period, people appear to be placing more importance on test scores and less on demographics when evaluating school quality.

Of the three papers, Clapp’s is the only one to use a time-based fixed effects model.  While the studies of Black and Crone use averages of a single three-year period and district boundaries as fixed effects, Clapp instead exploits the cross time variation in the 1994-2004 panel data to separate school attributes from neighborhood quality.  Clapp also incorporates additional neighborhood fixed effects by comparing sales occurring in different neighborhoods, but the same school district.  This combination of time variation based identification strategy and also neighborhood fixed effects should yield more accurate estimates than either strategy alone.  However, one downside to Clapp’s method that does not appear with Black’s or Crone’s is the possibility of unobservable changes over the sample’s long time period.

Through these three papers, we see a wide variety of techniques used to analyze public school test scores and house prices, yet arrive at the conclusion that standardized test scores do impact housing prices.  In these papers, however, we assume that district boundaries are fixed and that students must attend schools in their attendance zone.  With the rise of charter schools, students no longer are limited to the public schools near their homes.  As the population of students going to charter schools increases, we may begin to see a declining importance of neighborhoods under the models discussed in the three papers reviewed here.  It may be worthwhile to examine the effects of charter schools on home prices, both in the area of the school and of the students.



Works Cited


Black, Sandra E. “Do Better Schools Matter? Parental Valuation of Elementary Education*.” Quarterly Journal of Economics 114.2 (1999): 577-99. Web. 7 Feb. 2013.

Clapp, John M., Anupam Nanda, and Stephen L. Ross. “Which School Attributes Matter? The Influence of School District Performance and Demographic Composition on Property Values.” Journal of Urban Economics 63.2 (2008): 451-66. Web. 7 Feb. 2013.

Crone, Theodore M. “Capitalization of the Quality of Public Schools: What Do Home Buyers Value?” Working Paper Series, Federal Reserve Bank of Philadelphia (2006): n. pag. Statistical Insight [ProQuest]. Web. 7 Feb. 2013.

Literature Survey: The Effect of School Quality on Housing Prices

by Lauren Taylor Lauren_Taylor_Literature_Survey (1)

There are many factors that go into an individual’s or a family’s decision to purchase a home.  Such factors include structural characteristics of the house such as square footage and number of baths, property tax levels, proximity to amenities, neighborhood quality, and school quality, all of which are reflected in a house’s retail price.  Of particular interest to homeowners, economists, and policy makers is the effect of school quality on housing prices in any given area.  Numerous individuals have performed studies attempting to quantify how much individuals value school quality by analyzing housing prices in school districts of different quality schools.  Some studies have placed emphasis on “output-based” means of measurement such as standardized test scores and school ranking while others have used “input-based” measurements such as teacher-pupil ratio and per-pupil spending.  Most recently, researchers have focused on the use of standardized test scores as a measure of school quality and have compared these to the prices of housing in corresponding school districts.


Much of the research on the impact of school quality on housing prices can relate back to the model developed by Charles Tiebout in his 1956 paper, “A pure theory of local expenditures”.  In conducting his research, Tiebout’s model predicts that consumers pick a community to reside in based on which community best satisfies their preference patterns for local public goods (LPGs), which include schools, parks, and other amenities.  Furthermore, these individuals will move to the community whose local government best satisfies their sets of preferences, resulting in individuals self-sorting into homogenous communities with residents who demand equal levels of quality of LPGs (Tiebout, 1956).  This model can be applied to the analysis of school quality on housing prices.  According to such a model, individuals with similar preferences will populate a community.  Thus, individuals who prefer better quality schools may be willing to move to a community in which they pay higher housing prices because their desired LPG quality exists there.


Many studies have been conducted in the past two decades to further explore the effect of school quality on housing prices in various cities across the United States.  The Reinvestment Fund (TRF) conducted such a study which measured school quality in Philadelphia and quantified its impact on the value of Philadelphia real estate.  In this study, residential sales between 2006 and 2007 are geocoded and combined with data on the elementary zone  in which each sale lay as well as the percent of elementary school students scoring proficient or above on the combined Pennsylvania System of School Assessment (PSSA) for Reading and Math at the schools in that zone.  TRF also used a multilevel modeling analysis that accounts for correlation at various levels and among several factors, in its study in order to more accurately assess the relationship between school quality and housing prices.  This model takes into account the fact that other neighborhood characteristics are correlated with school quality and may affect the prices of housing in a given school district as well.  This model attempts to get rid of some bias, but some bias still remains due to omitted variables that haven’t been controlled for.   This study concluded that point increases in Structural Decline scores, which measure the impact of new construction and neighborhood disinvestment on housing prices, reduce sale prices of homes by $1.50 per square foot.  Similarly, point increases in Crime scores, which measure the impact of crime on housing prices, reduce the sale prices of homes by $1.00 per square foot.  Of even greater interest to this paper, TRF’s study found that for each percentage point increase in school district PSSA score of students who scored proficient or above, the prices of housing in that area increase by $0.52 per square foot. The correlation between housing prices and school quality can be seen in the Figure 1 below.  The two maps in Figure 1 show that central Philadelphia districts tend to be of lower housing price and lower school quality, and higher quality school districts and districts with higher housing prices tend to be clustered in the top right and top left regions of the Philadelphia area.  Thus, this study shows that overall school quality, as measured by test scores, is positively related to the price of housing in that school district.


Figure 1: Median sales price by school catchment (Map 1) and Percent of elementary students scoring proficient or above on PSSA (Map 2)


In another study, Kwame Owusu-Edusei and Molley Espey used data on housing transactions between the years of 1994 and 2000 to estimate the effect of K-12 school rankings on housing prices in Greenville, South Carolina.  These two researchers used a relative measure of school quality – school rankings – rather than an absolute measure.  Like many of the other studies that will be discussed later in this paper, this study applied a hedonic pricing model to estimate the quality of schools on housing prices.  Owusu-Edusei and Espey made two important conclusions from their data: 1) high-ranked schools have values embedded in single-family housing prices and 2) greater commuting distances to schools has a negative impact on the value of property.  In this specific hedonic housing pricing technique, the price of a house in Greenville, SC was modeled as a function of the following characteristics of a house: structural characteristics including condition, number of baths, square footage, air conditioning, lot size, and garage; block characteristics; proximity to parks, golf course and schools; and school rank categories.  The study then used ordinary least square estimations of a semi-log model and regressions to interpret the data collected.  As will be seen in many other studies documented, Owusu-Edusei and Espey found that proximity to and quality of a school does affect the prices of housing in its respective school district.  Of the houses studied in Greenville, SC, houses with elementary schools within 2640 feet (a half of a mile) of their properties have prices 18% higher than those of houses located further than 10560 feet (2 miles) from an elementary school.  Similarly, houses with middle schools within 10560 feet of their properties have prices 16% higher than those of houses located further than 10560 feet from a middle school and houses with high schools within 10560 feet of their properties have prices 12% higher than those of houses located further than 10560 feet from a high school.  Furthermore, if an elementary school rated Good, houses in that school district sell for 12% higher than those in districts with schools with a worse rating.  If a middle school rated Average, houses sell for 31% higher than houses in a district with a school of a worse rating.  Lastly, if all K-12 schools in an area rated Average and Above, the value of homes is 19% higher in that area than those in areas with Below Average schools.  Thus, it can be concluded that both greater proximity to and better quality of schools does positively affect the prices of housing located in their attendance zone.


Sandra Black conducted a study of great importance, which also used housing prices – this time in suburbs of Boston, Massachusetts – to infer the value homeowners place on school quality (1999).  Black used a sample of single-family residences within 39 school districts across 3 counties outside of Boston from 1993 to 1995 and used test scores on a statewide 4th grade assessment called the Massachusetts Educational Assessment Program as her measurement of school quality.  Black set out to calculate how much more people are willing to pay for houses located in areas with better schools.  However, Black made an extremely important observation that such a calculation can be complicated by the fact that better schools tend to be located in better neighborhoods, a characteristic which also influences the price of housing.  Black concluded that estimates of the effect of school quality on housing prices that do not adequately control for neighborhood characteristics may overestimate the value of better schools.  Thus in order to control for variation in neighborhood characteristics, property taxes, and school spending, Black used a hedonic housing price regression – which again describes house sale price as a function of the characteristics of the house and its location – that includes boundary fixed effects which restrict the sample of houses studied to those close to and on opposite sides of school attendance district boundaries.  Instead of the traditional hedonic price function, Black used the formula ln(priceiab) = α + X’iabβ + K’bφ + γtesta + εiab in which boundary dummies (the K term) account for unobserved characteristics shared by houses on either side of the attendance district boundary.  In this way, Black was able to eliminate bias caused by omitted variables such as neighborhood characteristics and property taxes.


Black conducted her calculations twice: once using a simple hedonic housing price regression and once using a hedonic housing price regression which incorporated boundary dummies.  Results from the simple hedonic regression indicate that a 5% increase in the average elementary school test score is associated with a 4.9% increase in house prices in that school district, as shown in Table 1 below.  However, results from the hedonic regression which controlled for omitted variable bias such as neighborhood characteristics show that when houses observed are restricted to those within only 0.15 miles from the boundary of a school attendance zone, a 5% increase in the average elementary school test score is associated with a 2.1% increase in house prices in that school district (Table 1).  This second calculation is roughly half of the estimated effect calculated using the simple hedonic housing price regression.  Thus, in her paper, Black has demonstrated that it is very important to control for neighborhood characteristics by restricting the housing sample used to houses on school attendance boundaries.  Otherwise, one may greatly overestimate the value of school quality as shown by test scores on the prices of housing in the respective district.  Yet, even after controlling for omitted variables, it can be seen that better school quality, as shown by an increase in test scores, has a positive effect on housing prices.

Table 1: Magnitude of Results



Using both the work of Tiebout and Black as background research, John Wulsin more recently conducted a study on the effects of school quality on housing prices in Durham, North Carolina.  In his paper “An Analysis of the Effects of Public School Quality on House Prices in Durham, North Carolina” (2009), Wulsin stated that when families buy a home, they also buy the right for their kids to attend the local public school in that district and that the price of that right is incorporated into the price of the house they purchase.  Like Black, Wulsin used school performance composite test scores, which is the North Carolina Department of Education’s standardized metric for measuring school quality.  Building on Black’s work, Wulsin, too, recognized the importance of using boundary fixed effects to control for neighborhood characteristics in order to ensure that the effects of school quality on housing prices are not overstated.  Wulsin gathered the following data on houses in Durham County: the fair market value of the house, observable characteristics that affect house prices, which school attendance zone the house is in, and the distance the house is to the border of the school attendance zone.  He gathered data from sources such as the Durham Tax Assessors Office and Durham Public Schools.  Wulsin then worked with GIS shapefiles, computer software programs such as ArcMap, StatTransfer, and Stata, and an OLS regression to organize and decipher the data.  After collecting and interpreting his data, Wulsin concluded that “parents do pay more to live in areas with better schools”.  Wulsin’s study found that in Durham Country school districts, a 10% increase in elementary school scores leads to an 11% increase in housing prices, a 10% increase in middle school scores leads to an 11% increase in housing prices, and a 10% increase in high school scores leads to a 5% increase in housing prices.  Thus, it can be seen that an increase in school quality, as measured by test scores, once again leads to an increase in housing prices in that school attendance zone.


As with many other attractive neighborhood qualities, it has been observed that people value the quality of the schools they send their children to.  In fact, a 2000 survey by the Philadelphia City Planning Commission found that the third most important neighborhood characteristic for buyers and sellers with children was the presence of a good school in the area (TRF, 2007).  Many researchers have attempted to determine the exact value consumers place on school quality and have used housing prices as a means of measurement.  As shown by the studies analyzed above, better school quality is correlated to higher housing prices.  Furthermore, this trend has been observed across the United States.  However, there are still several issues that need to be explored further.  Firstly, measuring school quality is often difficult and very subjective.  Many have argued for the use of input-based metrics such as per-pupil spending while others believe that output-based measurements such as test scores are a better indication of school quality.  Future studies should attempt to collect and use a greater range of data on each school observed in order to gain a clearer picture of what makes a school “good quality”.  Furthermore, researchers need to continue to develop better ways to isolate the effects of school quality on housing prices and reduce, with an ultimate goal to eliminate omitted variable bias in which variables such as neighborhood characteristics and property taxes cause an overestimate of the impact of school quality on housing prices.  In conclusion, as each of these papers has shown, the quality of a school has a positive impact on the prices of houses located within that school attendance zone.  This finding is not only important to homeowners and parents, but also to economists and policy makers.  Because it has been found that better quality schools increase the real estate value of houses in their areas, improving schools can be a method for improving neighborhoods and stimulating economic growth.





Works Cited:


Black, Sandra. 1999. “Do Better Schools Matter? Parental Variation of Elementary Education”. Quarterly Journal of Economics, Vol. 114. 4 February 2013.


Owusu-Edusei, Kwame and Molley Espey.  2003. “School Quality and Property Values in Greenville, South Carolina.” Department of Agricultural and Applied Economics, Clemson University. 30 January 2013.


The Reinvestment Fund.  2007. “Schools in the Neighborhood: Are Housing Prices Affected by School Quality?”  Reinvestment Brief: Issue 6. 30 January 2013.


Tiebout, Charles. 1956. “A Pure Theory of Local Expenditures”. Journal of Political Economy. 5 February 2013.


Wulsin, John.  2009. “An Analysis of the Effects of Public School Quality on House Prices in Durham, North Carolina.” Economics Department, University of North Carolina at Chapel Hill.  2 February 2013.