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«Does the Market Value Value-Added? Evidence from Housing Prices After a Public Release of School and Teacher Value-Added Scott A. Imberman∗ Michigan ...»

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Nonetheless, previous work examining how property values respond to researcher-calculated school value-added or changes in school test scores have findings similar to our own (Black and Machin, 2011), but those studies are distinct from ours as they implicitly assume that home buyers make the same calculations from available data. Thus, the fact that property values do not respond to these school quality measures could be due to a lack of awareness of this information.

The previous analysis most similar to this paper is Figlio and Lucas (2004), which examines the effect of property values from the public release of “school report cards.” They find releasing this information leads to large property value increases in the higher-performing districts. There are several potential explanations for why our results differ from theirs. First, the school report cards in their study are based on test score levels, which are highly correlated with other aspects of schools, such as demographic composition. Even though demographic data was already available to the public, property values may be responding to the repackaging of that information into a simple and intuitive form rather than what the public perceived to be the school’s quality, per se. Second, the type of information contained in the Florida school report cards already was available to LAUSD residents in the form of API scores. The value-added information releases we study provide school quality data on top of this pre-existing information.

Property values may respond less to these measures because the information shock about school quality is smaller or because the computational complexity of the value-added models, as well as the associated statistical noise in the estimates, render them uninformative for the marginal home buyer. One other explanation is that the release of three, often conflicting, value-added measures in LAUSD may have generated confusion amongst home buyers and thus led them to ignore all three measures. While this is possible, we note that there was a period of seven months (September 2010 - March 2011) during which the only value-added data available was that from the initial LA Times release. We find no evidence of increased property values during this period, nor do we see more capitalization when the value-added estimates agree.

That we find no effect of school or teacher value-added information on home prices suggests these school quality measures are not valued by local residents, at least on the margin. This is a surprising result, given the strong relationship found in other studies between these measures and student academic and future labor market success (Rivkin, Hanushek and Kain, 2005;

Chetty, Friedman and Rockoff, 2011) as well as the contentiousness that tends to accompany the release of value-added data. In some sense, however, the heightened controversy could have driven the public to ignore the value-added. Not only did the public debate and the widespread coverage of the LA Times’ release in the media likely increase awareness of these methods, it also probably made the public more aware of the flaws in these measures. Thus the public may be rationally waiting for the research community to decide on what value-added measures are accurate before changing behavior in response to them. As a result, while value-added scores will undoubtedly be generated by more and more school districts and will be disseminated to the public in the near future, the evidence presented here suggests that in the current environment homeowners and parents do not value value-added as a relevant measure of school quality.

References [1] Bayer, Patrick, Fernando Ferreira and Robert McMillan. 2007. “A Unified Framework for Measuring Preferences for Schools and Neighborhoods.” Journal of Political Economy 115(4): 588-638.

[2] Black, Sandra. 1999. “Do Better Schools Matter? Parental Valuation of Elementary Education.” Quarterly Journal of Economics 114(2): 577-599.

[3] Black, Sandra E. and Stephen Machin. 2011. “Housing Valuations of School Performance” in Eric A. Hanushek, Stephen Machin and Ludger Woessmann (Eds.) Handbook of the Economics of Education, Volume 3. North-Holland: Amsterdam.

[4] Brasington, David M. 1999. “Which Measures of School Quality Does the Housing Market Value?” Journal of Real Estate Research 118(3): 395-413.

[5] Brasington, David and Donald R. Haurin. 2006. “Educational Outcomes and House Values:

A Test of the Value Added Approach.” Journal of Regional Science 46(2): 245268.

[6] Buddin, Richard. 2010. “How Effective are Los Angeles Elementary Teachers and Schools?” MPRA Working Paper No. 27366: http://mpra.ub.uni-muenchen.de/27366/.

[7] Buddin, Richard. 2011. “Measuring Teacher and School Effectiveness at Improving Student Achievement in Los Anageles Elementary Schools.” Available at http://documents.latimes.com/buddin-white-paper-20110507/.

[8] Chetty, Raj, John N. Friedman and Jonah E. Rockoff. 2011. “The Long-Term Impacts of Teachers: Teacher Value-Added and Student Outcomes in Adulthood.” NBER Working Paper No. 17699.

[9] Cellini, Stephanie Riegg, Fernando Ferreira and Jesse Rothstein. 2010. “The Value of School Facility Investments: Evidence from a Dynamic Regression Discontinuity Design.” The Quarterly Journal of Economics 125(1): 215-261.

[10] Davidoff, Ian and Andrew Leigh. 2008. “How Much Do Public Schools Really Cost? Estimating the Relationship Between House Prices and School Quality.” The Economic Record 84(265): 193-206.





[11] Dills, Angela K. 2004. “Do Parents Value Changes in Test Scores? High Stakes Testing in Texas.” Contributions to Economic Analysis and Policy 3(1): Article 10.

[12] Downes, Thomas A. and Jeffrey E. Zabel. 2002. “The Impact of School Characteristics on House Prices: Chicago 1987-1991.” Journal of Urban Economics 52(1): 1-25.

[13] Fack, Gabrielle and Julien Grenet. 2010. “When do Better Schools Raise Housing Prices?

Evidence from Paris Public and Private Schools.” Journal of Public Economics 94(1-2):

59-77.

[14] Figlio, David N. and Maurice E. Lucas. 2004. “What’s in a Grade? School Report Cards and the Housing Market.” American Economic Review 94(3): 591-604.

[15] Gibbons, Stephen, Stephen Machin and Olmo Silva. 2013. “Valuing School Quality Using Boundary Discontinuities.” Journal of Urban Economics 75(1): 15-28.

[16] Gibbons, Steve and Stephen Machin. 2003. “Valuing English Primary Schools.” Journal of Urban Economics 53(2): 197219.

[17] Gibbons, Steve and Stephen Machin. 2006. “Paying for Primary Schools: Admissions Constraints, School Popularity or Congestion.” Economic Journal 116(510): 7792.

[18] Guarino, Cassandra M., Mark D. Reckase, and Jeffrey M. Wooldridge. 2012. “Can ValueAdded Measures of Teacher Performance Be Trusted?” IZA Discussion Paper No. 6602.

[19] Jacob, Brian and Lars Lefgren. 2007. ”What Do Parents Value in Education? An Empirical Investigation of Parents’ Revealed Preferences for Teachers.” Quarterly Journal of Economics 122(4): 1603-37.

[20] Kane, Thomas J. and Douglas O. Staiger. 2008. “Estimating Teacher Impacts on Student Achievement: An Experimental Evaluation.” NBER Working Paper No. 14607.

[21] Kane, Thomas J., Stephanie K. Riegg and Douglas O. Staiger. 2006. “School Quality, Neighborhoods, and Housing Prices.” American Law and Economic Review 8(2): 183-212.

[22] Rivkin, Steven G., Eric A. Hanushek and John F. Kain. 2005. “Teachers, Schools, and Academic Achievement.” Econometrica 73(2): 417-458.

[23] Rockoff, Jonah E. 2004. “The Impact of Individual Teachers on Student Achievement:

Evidence from Panel Data.” American Economic Review Papers and Proceedings 94(2):

247-252.

[24] Rothstein, Jesse. 2010. “Teacher Quality in Educational Production: Tracking, Decay, and Student Achievement.” Quarterly Journal of Economics 125(1): 175-214.

[25] Song, Jason. 2010. “Teachers Blast L.A. Times for Releasing Effectiveness Rankings.” Los Angeles Times. August, 30.

Figure 1: Example of Information Displayed in LATimes Database

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Percentile ranking amongst LAUSD elementary schools using the three value-added scores. Each dot is a single elementary school.

Figure 3: API, Free/Reduced-Price Lunch, and Value-Added by Elementary School

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Percentile ranking amongst LAUSD elementary schools using 2009-10 API versus percentile rankings and the three value-added scores. Each dot is a single elementary school.

Figure 5: Effect of Value-Added Information on Log Sales Price by Month of Sale

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.002.003.002.001

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The estimates in all panels come from a single regression and show the impact of an increase in value-added percentile on log sale price by month, using each quality measure. The second LA Times value-added ranking replaces the first in month 8. Controls include school fixed-effects, month of sale indicators, API percentile, API, two years of lagged API, the California DOE similar school rank and the following: Housing characteristic controls - the number of bedrooms, bathrooms and units in the home, square footage, and year built; School characteristic controls - percent of students of each race, percent free lunch, percent gifted, percent English language learners, percent disabled, and parent education levels; Neighborhood characteristic controls at the census tract level - percents of the population who are adult, minor, senior, foreign born, of each race, speak a language other than English, and who lived in the same house one year prior, the percent of adults who are married, institutionalized, veterans, of each education level, in the labor force, and unemployed, percent of households vacant and owner-occupied, average household size, family size, commute time and household income, the percent of households with children, single-parent families, receiving social security, receiving cash public assistance, and receiving food stamps and the poverty rate.

Housing characteristics are also interacted with a linear time trend. The dotted lines are the bounds of the 95% confidence intervals that are calculated using standard errors clustered at the school level.

Figure 6: Heterogeneity in the Estimated Effect of LA Times Value-Added on Log Sale Price

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.002.001.002.0005.001.001 −.0005

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.002.001.001.0005.001 −.001

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−.0005 −.001 −.002 −.001 −.002 −.003

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.004.002.002.001 −.002 −.001 −.004 −.002

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The value-added variable uses the LA Times value-added percentile from the August 2010 release until May 2011, at which point the variable is replaced with the value-added percentile from the May 2011 (2nd ) release. Similar measures for the LAUSD value-added are included in the regressions but are not shown.

Controls include school fixed-effects, month of sale indicators, API, two years of lagged API, the California DOE similar school rank and the following: Housing characteristic controls - the number of bedrooms, bathrooms and units in the home, square footage, and year built; School characteristic controls - percent of students of each race, percent free lunch, percent gifted, percent English language learners, percent disabled, and parent education levels; Neighborhood characteristic controls at the census tract level - percents of the population who are adult, minor, senior, foreign born, of each race, speak a language other than English, and who lived in the same house one year prior, the percent of adults who are married, institutionalized, veterans, of each education level, in the labor force, and unemployed, percent of households vacant and owner-occupied, average household size, family size, commute time and household income, the percent of households with children, single-parent families, receiving social security, receiving cash public assistance, and receiving food stamps and the poverty rate. Housing characteristics are also interacted with a linear time trend. The dotted lines are the bounds of the 95% confidence intervals that are calculated using standard errors clustered at the school level.

Table 1: Summary Statistics of Main Analysis Variables

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Month of Sale N Y Y Y Y Y Y Housing Characteristics N N N Y Y Y Y School Characteristics N N N N Y Y Y Neighborhood Characteristics N N N N N Y Y School Fixed-Effects N N Y Y Y Y Y Boundary Fixed-Effects (0.1 mi) N N N N N N Y The data cover April 2009 through September 2011 and are at the property sale level. The pooled LA Times value-added variable uses the value-added percentile from the August 2010 release until May 2011 at which point the variable is replaced with the VA percentile from the May 2011 (2nd ) release.

All regressions include school and month fixed-effects along with controls for API, two years of lagged API, API percentile, and the school’s rank relative to comparison schools defined by the California DOE.

School characteristics include, percent of students of each race, percent free lunch, percent gifted, percent English language learners, percent disabled, and parent education levels. Neighborhood characteristic controls are at the census tract level and include percents of the population who are adult, minor, senior, foreign born, of each race, speak a language other than English, and who lived in the same house one year prior, the percent of adults who are married, institutionalized, veterans, of each education level, in the labor force, and unemployed, percent of households vacant and owner-occupied, average household size, family size, commute time and household income, the percent of households with children, single-parent families, receiving social security, receiving cash public assistance, and receiving food stamps and the poverty rate. Housing characteristic controls include the number of bedrooms, bathrooms and units in the home, square footage, and year built. Housing characteristics are also interacted with a linear time trend. School-average value added measures are included as controls in column (1). Standard errors clustered at the school level are in parentheses. ***,** and * indicate significance at the 1%, 5% and 10% levels, respectively.

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population who are adult, minor, senior, foreign born, of each race, speak a language other than English, and who lived in the same house one year prior, the percent of adults who are married, institutionalized, veterans, of each education level, in the labor force, and unemployed, percent of households vacant and owner-occupied, average household size, family size, commute time and household income, the percent of households with children, single-parent families, receiving social security, receiving cash public assistance, and receiving food stamps and the poverty rate. Housing characteristic controls include the number of bedrooms, bathrooms and units in the home, square footage, and year built. Housing characteristics are also interacted with a linear time trend. Standard errors clustered

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