Do Social Conditions Within Schools Affect Local Home Values?

Crespin (2025) examines whether publicly releasing school social climate ratings affects housing prices and the socioeconomic sorting of homebuyers. He studies a plausibly exogenous information shock in Chicago in 2011, when school climate reports were first made public. He links parcel-level housing transaction data from Zillow (ZTRAX) with homebuyer income data from HMDA and school administrative records. He finds that homes zoned to the highest-rated schools saw price increases of about 9% to 21% in the first quarter after release. He also finds suggestive evidence that buyer incomes rose by roughly 6% to 16%, though effects faded within a year.

Why This Article Was Selected for The Policy Scientist

Crespin’s article addresses a policy issue with broad implications: whether non-academic measures of school quality meaningfully shape household behavior and neighborhood stratification. As school systems increasingly publish climate, safety, and socioemotional indicators, understanding whether families respond to this information is central to debates about transparency, equity, and local public finance. This question is especially timely as districts expand accountability systems beyond test scores. Crespin has contributed to this emerging line of research on informational shocks and revealed preferences. The study uses unusually rich parcel-level housing transactions linked to mortgage income data, a clear empirical strength. Its border discontinuity design with event studies and difference-in-differences provides credible causal identification, aligning with strong causal inference standards.

Full Citation and Link to Article

Crespin, R. A. (Forthcoming 2026). The value of school social climate information: Evidence from Chicago housing transactions. American Economic Journal: Economic Policy. https://doi.org/10.1257/pol.20230136 

Central Research Question

This article examines whether publicly releasing school social climate information affects housing prices and the socioeconomic sorting of homebuyers. Specifically, Crespin asks whether families value school climate—measured through survey-based indicators of social and organizational conditions—and whether making this information salient and accessible leads to measurable capitalization into property values. A related question is whether higher-income families respond differently to this information than lower-income families, thereby altering neighborhood composition. The study leverages the first public release of school climate ratings in Chicago Public Schools (CPS) in 2011 as a plausibly exogenous information shock. By isolating the release of new, non-test-based school quality information, the article identifies whether climate—distinct from proficiency or value-added—enters household decision-making in the housing market.

Previous Literature

The study builds on a substantial literature examining how school quality is capitalized into housing prices. Foundational work, such as Black (1999), demonstrated that test-based school quality influences property values using boundary discontinuity designs. Subsequent research has examined informational shocks from school grades, accountability ratings, and performance report cards, often finding short-run capitalization effects that dissipate over time. Much of this literature focuses on academic proficiency levels rather than broader school attributes.

More recent work explores parental preferences for non-test-based characteristics, including value-added, safety, peer composition, and long-run student outcomes. Evidence is mixed. Some studies find strong reactions to easily interpretable letter grades, while responses to continuous value-added measures are weaker. Research using experimental information interventions suggests that parents can respond to novel school quality information, though preferences vary by socioeconomic status. Crespin contributes by examining a multidimensional, survey-based measure of school climate that captures supportive environments, instructional ambition, family involvement, teacher collaboration, and leadership effectiveness. Unlike cross-sectional studies of happiness or disorder, this article employs a quasi-experimental design tied to a discrete information shock, strengthening causal inference.

Data

The study combines several unusually rich administrative datasets. First, Crespin uses parcel-level housing transaction data from Zillow’s ZTRAX database, covering single-family homes, townhouses, condominiums, and rowhouses sold between September 2009 and August 2012. This period spans two years before and one year after the public release of climate ratings. These data provide sale prices, dates, physical characteristics, and geocoded property locations.

Second, the author links housing transactions to Home Mortgage Disclosure Act (HMDA) data, allowing identification of buyer self-reported income for approximately 82 percent of transactions. This linkage enables analysis of socioeconomic sorting rather than price effects alone.

Third, school-level administrative data from CPS and the Illinois State Board of Education provide climate ratings, proficiency rates, value-added measures, and demographic composition. Climate ratings are derived from the University of Chicago Consortium’s Five Essentials survey framework and summarized into five color-coded categories.

Finally, neighborhood-level demographic and crime data from the American Community Survey and Chicago Police Department allow the author to control for local characteristics. The resulting dataset includes over 13,000 transactions located within 0.2 miles of school attendance boundaries, enhancing comparability across adjacent neighborhoods.

Methods

The empirical strategy centers on a difference-in-differences framework embedded within a boundary discontinuity design. The identification strategy compares properties located on opposite sides of school attendance boundaries before and after the September 2011 public release of climate ratings. Boundary fixed effects ensure that comparisons are made between geographically proximate properties sharing similar neighborhood amenities. Month-by-year fixed effects account for seasonal and citywide housing trends.

The analysis also employs event-study specifications to test for differential pre-trends and to trace dynamic responses across quarters. Constrained regressions ensure that pre-shock coefficients average to zero, improving interpretability. Standard errors are clustered at the school zone level.

This design constitutes a strong quasi-experimental approach. While not a randomized controlled trial, the study leverages a plausibly exogenous information shock that changed salience but not underlying school characteristics. The absence of pre-trend differences strengthens the causal interpretation. Compared to cross-sectional multivariate regression, this design provides more credible identification of the impact of information disclosure.

Findings/Size Effects

The results indicate that public release of climate ratings produced immediate capitalization into housing prices. In the first quarter following the information shock, homes zoned to the highest-rated schools experienced price premiums of approximately 21 percent relative to homes zoned to the lowest-rated schools. Homes assigned to intermediate ratings (“B,” “C,” and “D”) saw premiums between roughly 9 percent and 13 percent. These effects are economically large and comparable to, though somewhat smaller than, some estimates from school letter-grade information shocks.

However, these capitalization effects dissipated within two to three quarters. By the end of the first post-shock year, price differences returned to pre-release levels. Event-study estimates reveal no differential pre-trends, supporting the validity of the identification strategy.

The study also finds suggestive evidence of socioeconomic sorting. In the first quarter after release, buyer incomes in the highest-rated zones increased by approximately 16 percent relative to the lowest-rated zones. The income effects are less precisely estimated than price effects and fade more quickly. Nonetheless, the direction and magnitude of the initial response indicate that higher-income families were more responsive to the newly released climate information.

Additional analyses show limited evidence that supply adjustments explain the fadeout. Transaction counts do not significantly increase in higher-rated zones, suggesting that demand responses rather than supply expansion drove initial price premiums. The author instead documents declining Google search interest in school climate over time, consistent with a salience or search-cost mechanism. A second, less-publicized release of updated ratings produced muted capitalization effects, further supporting the role of information visibility.

Conclusion

Crespin concludes that families value school climate as a dimension of school quality and that making this information salient can produce immediate housing market responses. The study provides revealed-preference evidence that non-test-based measures influence residential choice, at least in the short run. At the same time, the rapid dissipation of price effects indicates that information salience and search costs play a critical role in sustaining capitalization.

The article contributes to the literature by extending the informational-shock framework beyond academic performance measures to a multidimensional climate index. The linked administrative datasets are a major empirical strength, permitting analysis of both prices and buyer income. The boundary-based difference-in-differences design constitutes a credible causal inference strategy, though future research could strengthen external validity through replication in other districts or through experimental information provision.

Overall, the findings suggest that transparency about school social conditions can influence household behavior, but that the durability of these effects depends on continued visibility and access to information.