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School Quality, House Prices and Liquidity

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Abstract

This paper develops an empirical framework for taking into account the effects of endogenous liquidity on price capitalization estimates. Changes in school attendance zones in the East Baton Rouge Parish public school district provide a natural experiment for studying how changes in school characteristics affect house prices and liquidity. House price and selling time, or liquidity, are simultaneously determined in search markets. The empirical model exploits variation in the surrounding neighborhood market conditions pertinent to each house to identify the system of price and liquidity equations. The estimates are consistent with search-market theory in that liquidity absorbs part of the capitalization of school quality.

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Notes

  1. For example, see Haurin and Brasington (1996), Hayes and Taylor (1996), and Black (1999). See Ross and Yinger (1999) and Fischel (2001) for reviews of the empirical literature on the capitalization of public service quality and property taxes into house prices.

  2. We use liquidity definition introduced by Lippman and McCall (1986). See Forgey et al. (1996) for a summary discussion of other liquidity definitions in the housing context.

  3. The extra waiting time is required to increase the cumulative likelihood of a (low frequency) idiosyncratic buyer who values the house more than the average buyer in the market.

  4. Krainer (2001) establishes an empirical link between days on the market and variables such as the interest rate, the slope of the term structure, and the job growth rate.

  5. We focus on elementary schools because only these schools allow for enough within-district variation to conduct empirical analysis. In any event, schools with grades 9–12 and 9–12 portions of K–12 schools (i.e., high school and combination schools) officially entered the Louisiana School Accountability System in 2001, making their school quality measures unavailable for most of our sample period.

  6. The subject area covers a contiguous region and excludes houses in the separate small cities of Baker and Zachary, the only parts of East Baton Rouge Parish that are not within the unified City-Parish jurisdiction.

  7. All schools also receive an annual growth target and are expected to reach a target score by the 2013–2014 school year.

  8. Private school enrollment data comes from National Center of Education Statistics’ (NCES) Common Core Data (CCD).

  9. These dummies capture the growth SPS or lack thereof over time in student achievement. These measures are consistent with the value added approach that education and labor economists argue is a better measure of school quality than just proficiency test results (Hanushek and Taylor 1990; Meyer 1997; Figlio 1999). We assume that it is difficult for parents to notice a small change in the level values of test scores; a school that is improving has a difficult time signaling that improvement to the buyers in the housing market. The Louisiana Department of Education started publishing for parents the School Report Card that presented the categorical ranking of a school and its growth target as well as its performance relative to other schools in the district. We believe that this information gives parents better understanding of how their local school performs.

  10. Norris (2002) data covers six parishes with large shares of ethnic minorities, blacks in particular, but does not include East Baton Rouge Parish.

  11. Recall that our focus on the impact of changes in school assignments (attendance zone boundaries) and changes in school categorical rankings on house prices requires that we use the full jurisdiction data, so we do not restrict our attention to boundary samples.

  12. These results are available from the authors.

  13. There are only a few studies that take into account the effect of the housing stock adjustment (Edel and Sclar 1974; Hilber and Mayer 2001, 2002; Brasington 2002). For example Brasington (2002) finds that public services are always capitalized into house values at a considerably stronger rate toward the interior of the urban area than toward the edge, where developers are more active and the housing supply is more elastic.

  14. Areas with active new housing construction normally would be areas with new school construction as well. In such cases, that fact, by itself, might yield higher school quality. However, this is not true in Baton Rouge. There was no new school construction for the time period we consider. The average age of a school in East Baton Rouge Parish is more than 40 years.

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Acknowledgement

Velma Zahirovic-Herbert acknowledges support from the Dan Sweat Fellowship and the Lincoln Institute of Land Policy. We would like to thank Brent Smith and participants of 2007 AREUEA Mid-Year Meeting for helpful comments and discussion. We also thank an anonymous referee for detailed suggestions. The authors are responsible for any errors.

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Authors and Affiliations

  1. Department of Housing & Consumer Economics, University of Georgia, 213 Dawson Hall, Athens, GA, 30602-3622, USA

    Velma Zahirovic-Herbert

  2. Fiscal Research Center, Georgia State University, P.O. Box 3992, Atlanta, GA, 30302-3992, USA

    Geoffrey K. Turnbull

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  1. Velma Zahirovic-Herbert
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  2. Geoffrey K. Turnbull
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Correspondence to Velma Zahirovic-Herbert.

Appendix

Appendix

Table 4 Complete parameter estimates endogenous variables: lnPrice and DOM
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Zahirovic-Herbert, V., Turnbull, G.K. School Quality, House Prices and Liquidity. J Real Estate Finance Econ 37, 113–130 (2008). https://doi.org/10.1007/s11146-007-9081-3

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