Home Articles Reader Opinion Editorial Book Reviews Discussion Writers Guide About TCRecord
transparent 13

Teacher Quality Data Releases Have Unintended Consequences on Neighborhood and School Demographics

by Elizabeth I. Rivera Rodas - February 08, 2019

Using New York City as an example, the author of this commentary argues that the public release of teacher quality data impacts neighborhood and school demographics.

In the past decade, there has been a lot of debate over teacher evaluations, from what should be included to who should have access to this information. Two of the most contentious debates have centered around whether teachers should be evaluated based on their student test scores and whether or not this data should be readily available to the public. As of 2017, 38 states and the District of Columbia include teachers’ effectiveness regarding test scores in their teacher evaluations, and 22 of these states explicitly state that districts may fire teachers for ineffectiveness. In most cases, districts in these states provide information about how many teachers are rated effective in each school (National Council on Teacher Quality, 2017). While some of the discussion around teacher quality data has focused on issues of teacher privacy and whether or not test scores are a good measure of teacher quality, it has not focused on the how parents and stakeholders would respond to this information and the potential effects of providing this information on residential and school demographic shifts.

The New York City Department of Education (NYC DOE) has been among the districts that have been in the news over the past few years because of the release of their teacher quality data in 2012 and their recent changes to their evaluations. The NYC DOE has publicly released individual teacher effectiveness rankings in some form since 2012. In February 2012, after years of litigation, the NYC DOE publicly released a list of individual value-added scores for thousands of its fourth through eighth grade public school teachers for the 2007-2008, 2008-2009, and 2009-2010 school years. Simply put, value-added scores look at the impact that each teacher had on student test score growth based on student and school demographics.

Prior to this release, parents and stakeholders in New York City obtained school quality information from NYC DOE-produced School Progress Reports, but they never had access to information measuring the impact of individual teachers on their students. This is important because these new data on school quality could impact consumer choices. This kind of release has never occurred since. Instead, the New York State Education Department (NYSED) has released evaluation ratings without identifying teachers since the 2012-2013 school year. So, while it is no longer possible to identify which teacher has been rated highly effective, effective, developing or ineffective, it is possible to determine how many teachers in a school are in each category.

The February 2012 NYC DOE teacher quality data release was not the first of its kind. In fact, many districts began supplementing traditional observation-based teacher evaluations with test-based measures of teacher quality because of Race to the Top (RTTT) funding, which focused more attention on the use of student test scores to measure teacher quality. However, not all districts in the 38 states and the District of Columbia that use test scores as a part of teacher evaluation data make these data public. Like the NYSED, the majority of states and/or districts that do make these data available make the teacher data unidentifiable. However, this still provides parents and stakeholders with the percentage of effective teachers in each school and therefore allows them to choose schools based on this information.


Studies have found that housing prices are influenced by neighborhood school quality. For instance, the initial release of school report card data in Florida did impact housing prices at first, but the impact on housing prices each year was not as strong as it was in the first year (Figlio & Lucas, 2004). In addition, school quality data releases began neighborhood re-sorting (Bayer, Ferreira, & McMillian, 2005) and increased housing prices (Bayer, Ferreira, & McMillian, 2007). This research suggests that teacher quality data would also impact housing prices. However, Imberman and Lovenheim (2015) found that the release of teacher quality data over time was not always impactful, especially when data was conflicting; they found this to be the case in Los Angeles, where data was provided from several sources.

New research counters this conclusion, showing that home prices increased 3.7% with every 10 percentage point increase in the weighted multi-year teacher quality average for elementary schools. These data suggest that the housing market responds significantly to the release of teacher quality information, even when considering the school grades and other variables that may influence teacher quality measures. As such, policymakers should keep this in mind and be cautious when releasing this information (Rivera Rodas, 2019a).

Housing prices increase in areas with higher teacher quality. In particular, homebuyers value teacher quality in elementary schools where a higher percentage of the teachers have been rated. Teachers who have not been in their school for three years or more do not receive a teacher quality score, and as the literature states, teacher retention is low in schools that serve predominantly underrepresented populations. Therefore, these schools have fewer of their teachers included in teacher quality data releases than higher performing schools where teachers remain for longer periods of time (Rivera Rodas, 2019a).

This is especially problematic when looking at Title I schools. NYC Title I elementary schools have lower quality teachers than non-Title I elementary schools. Therefore, students that attend Title I schools are not receiving the same quality of education as their peers. Since Title I focuses on providing funding to school districts and schools with a high concentration of students who are typically from poor families, the gap in teacher quality based on Title I status indicates that students from lower socioeconomic status backgrounds have lower quality teachers (Rivera Rodas, 2019b). Since homebuyers value teacher quality and show a preference for elementary schools with a higher percentage of teachers who have been rated, areas zoned to Title I schools are not able to attract homebuyers as effectively.


The degree to which housing prices increase is greatly impacted by certain neighborhood demographics. Latino neighborhoods with a high proportion of free and reduced price lunch students are among the neighborhoods that had the highest increase in housing prices due to the teacher quality release. Not only that, but the areas that had the highest increase in housing prices due to the release of teacher quality scores have experienced increases in the proportion of white students (Rivera Rodas, 2019a).

The effects of the 2012 teacher quality scores release in New York City show that the release of this information might in fact hurt the very people it was designed to help. While neighborhoods and schools with higher teacher quality have become more diverse, it is clear that some of the original residents and students who benefitted from high-quality teachers are being priced out due to these information releases. Paradoxically, the lowest income families are those most likely in need of such data because they lack the time and resources to acquire this information by other means.

As other districts and states continue to release teacher quality ratings, these unintended consequences must be considered. Education policy is not independent of the real estate market; if urban education policies are designed to improve educational outcomes for low-income residents, then housing affordability across neighborhoods needs to be part of the policy equation. How can school choice work if low-income families find they can no longer afford to live in a neighborhood with high-quality schools?


Bayer, P., Ferreira, F., & McMillian, R. (2005). Tiebout sorting, social multipliers and the demand for school quality (Working Paper No. 11087). Cambridge, MA: National Bureau of Economic Research.

Bayer, P., Ferreira, F., & McMillian, R. (2007). A unified framework for measuring preferences for schools and neighborhoods. Journal of Political Economy, 15(4), 588–638.

Figlio, D., & Lucas, M. (2004). What's in a grade? School report cards and the housing market. American Economic Review, 94(3), 591–604.

National Council on Teacher Quality (2017). State teacher policy yearbook: National summary. Washington, DC: Author.

Rivera Rodas, Elizabeth, I. (2019a). Separate and unequal: Title I and teacher quality. Education Policy Analysis Archives 27(14).

Rivera Rodas, Elizabeth, I. (2019b). Which New Yorkers vote with their wallets? The impact of teacher quality data on household sorting, and residential and school demographics. Economics of Education Review 68. 104–121.

Cite This Article as: Teachers College Record, Date Published: February 08, 2019
http://www.tcrecord.org ID Number: 22668, Date Accessed: 2/19/2019 2:27:27 PM

Purchase Reprint Rights for this article or review
Article Tools
Related Articles

Related Discussion
Post a Comment | Read All

About the Author
  • Elizabeth Rivera Rodas
    Montclair State University
    E-mail Author
    ELIZABETH IRIS RIVERA RODAS holds a joint PhD in Economics and Urban Educational Policy from Rutgers University, a MS in Social Research from Hunter College at the City University of New York and a BA in Economics from Barnard College, Columbia University. Dr. Rivera Rodas is an Assistant Professor of Quantitative Methods and Sociology of Education in the Department of Educational Foundations in the College of Education and Human Services at Montclair State University. Her research uses advanced econometrics to study the impact of public policies on racial and socioeconomic disparities in educational access, quality, and achievement.
Member Center
In Print
This Month's Issue