The Effect of School Quality on Residential Sales Price

Seo, Youngme; Simons, Robert A.

doi:10.1080/10835547.2009.12091255

Cited by 37 publications

(8 citation statements)

References 12 publications

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“…Baltagi & Bresson (2010) underline how much the location influences the price of a dwelling. Ioannides & Zabel (2003), Figlio & Lucas (2004), Bono et al (2007) or Seo & Simons (2009) emphasize the importance of the quality and density of the neighborhood, the reputation of nearby schools and the level of security. Following Tiebout (1956), these authors point out that the decision of where to live is based on families' preferences for the quality of public services and amenities, particularly education.…”

Section: Introductionmentioning

confidence: 99%

Modeling urban housing market dynamics: Can the socio-spatial segregation preserve some social diversity?

Gauvin¹,

Vignes²,

Nadal³

2013

Journal of Economic Dynamics and Control

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Addressing issues of social diversity, we introduce a model of housing transactions between agents who are heterogeneous in their willingness to pay. A key assumption is that agents' preferences for a location depend on both an intrinsic attractiveness and on the social characteristics of the neighborhood. The stationary space distribution of income is analytically and numerically characterized. The main results are that socio-spatial segregation occurs if -and only if -the social influence is strong enough, but even so, some social diversity is preserved at most locations. Comparison with data on the Paris housing market shows that the results reproduce general trends of price distribution and spatial income segregation.

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Section: Introductionmentioning

confidence: 99%

Modeling urban housing market dynamics: Can the socio-spatial segregation preserve some social diversity?

Gauvin¹,

Vignes²,

Nadal³

2013

Journal of Economic Dynamics and Control

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“…Define the effective t-statistic as the t value reported in the study when coefficients have expected signs and minus one times the t-value when reported coefficients have signs opposite expected sign. Crone (2006), Kane, Riegg and Staiger (2006), Clapp, Nanda and Ross (2008), Sedgley, Williams and Derrick (2008), Zahirovic-Herbert and Turnbull (2008, 2009, Seo and Simons (2009), Dhar and Ross (2012) and Turnbull, Zahirovic-Herbert and Zheng (2018) in Table 1. Among these studies, Jud and Watts (1981) find strong positive estimates for elementary school test scores but not the minority student ratio; Hayes and Taylor (1996) and Downes and Zabel (2002) show that school test scores yield more intuitively appealing results than expenditure per pupil; similarly, Crone (2006) and Seo and Simons (2009) argue that output-based variables perform better.…”

Section: Notesmentioning

confidence: 99%

“…Crone (2006), Kane, Riegg and Staiger (2006), Clapp, Nanda and Ross (2008), Sedgley, Williams and Derrick (2008), Zahirovic-Herbert and Turnbull (2008, 2009, Seo and Simons (2009), Dhar and Ross (2012) and Turnbull, Zahirovic-Herbert and Zheng (2018) in Table 1. Among these studies, Jud and Watts (1981) find strong positive estimates for elementary school test scores but not the minority student ratio; Hayes and Taylor (1996) and Downes and Zabel (2002) show that school test scores yield more intuitively appealing results than expenditure per pupil; similarly, Crone (2006) and Seo and Simons (2009) argue that output-based variables perform better. In contrast, Clauretie and Neill (2000) conclude that test scores are not significant among the various school quality measures they use; Clark and Herrin (2000) argue that inputbased measures outperform output-based measures; and Clapp, Nanda and Ross (2008) show that the percentage of Hispanic students has consistently strong effects on house price, while middle school average math exam scores price effects are mixed.…”

Section: Notesmentioning

confidence: 99%

A Meta‐Analysis of School Quality Capitalization in U.S. House Prices

Turnbull

Zheng

2019

Real Estate Economics

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School quality capitalization studies report widely mixed results. This article conducts meta-analysis to identify the data characteristics and research methods that explain this variation. The way the school quality is measured matters but no single measure clearly dominates. Both boundary and neighborhood fixed effects significantly control for the influence of neighborhood amenities. Increasing the number of quality variables reduces the capitalization significance of each. Regional differences also appear to matter; studies using data drawn from the South exhibit weaker capitalization. Also, it is surprising that econometric methods do not appear to be driving results.

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“…Because this result is somewhat counter-intuitive and possibly controversial, several alternative model specifications to the four presented earlier were estimated to determine whether or not the results were robust. These alternative specifications included: (1) interacting the study-area fixed effects variables with the home and site characteristics to mimic the estimation of separate regressions for each study area; (2) replacing the study-area fixed effects variables with alternative location measures (specifically, census tract and school district delineations, the importance of which is discussed in Seo and Simons (2009)); (3) including additional microspatial variables in the models (specifically, distance to nearest highway ramp and proximity to a major road); (4) omitting either view or distance (to turbines) measurements from the model to explore potential collinearity between these variables; (5) removing the variable for the spatially weighted sales price of the five nearest neighbours (Spatial Control -Post Con) ; (6) including five outlier and influential observations that had previously been removed from the dataset (as discussed in Hoen at al., 2009); ( 7) including a quantitative measurement of VIEW (pct_vis) constructed from the total number of turbines visible and the distance of the home to the nearest wind turbine 8 rather than using the qualitative VIEW categories; and (8) adding fixed effects variables for the year in which the home sold.…”

Section: Robustness Testsmentioning

confidence: 99%

Wind Farms in North America

Hoen

2013

Towers, Turbines and Transmission Lines

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For the last decade, the United States has increasing relied on wind energy to combat both depleting oil and coal reserves and to mitigate the effects of climate change through a reduction in CO2 production. Consequently, wind power development has expanded dramatically in recent years (GWEC, 2010) and that expansion is expected to continue (GWEC, 2008;Wiser and Hand, 2010).The U.S. Department of Energy, for example, published a report that analysed the feasibility of meeting 20% of U.S. electricity demand with wind energy by 2030, which equates to approximately 300,000 MW (US DOE, 2008). Currently the U.S. generating capacity from installed wind farms is approximately 40 GW up from approximately 4 GW in 2000 (Wiser andBolinger, 2011). To achieve a 20% wind electricity target in the United States of 300 GW, about 3,000 new wind facilities would need to be sited, permitted, and constructed.Not only is the number of wind farms sited in the U.S. increasing and will continue to increase, but the size and number of turbines in each development is increasing too. In 1998-1999 turbines averaged 180 feet (55 meters) in height at the hub, with blades of almost 165 feet (50 meters), in 2010 land based turbines averaged approximately 295 feet (90 meters) with 278 foot (85 meter) blades.Similarly, in 1998-1999 the average project consisted of 28 turbines; in 2010 this average had risen to 40 turbines after peaking at 60 turbines in 2007 (Wiser and Bolinger, 2011). Industry expectations for siting in areas with lower overall wind speeds (where larger rotor diameters will allow for increased power output, all else being equal) is expected to further increase the scaling.Though surveys show that public acceptance in the U.S. is high in general for wind energy (e.g., Firestone and Kempton, 2006), a variety of local concerns exist that can impact the length and outcome of the siting and permitting process. One such concern is related to the views of and proximity to wind facilities, which, in turn, might impact surrounding property values. To that end, surveys of local communities considering wind facilities have frequently found that adverse impacts on aesthetics and property values are in the top tier of concerns relative to other matters such as impacts on wildlife habitat and mortality, radar and communications systems, ground transportation,

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The Effect of School Quality on Residential Sales Price

Cited by 37 publications

References 12 publications

Modeling urban housing market dynamics: Can the socio-spatial segregation preserve some social diversity?

Modeling urban housing market dynamics: Can the socio-spatial segregation preserve some social diversity?

A Meta‐Analysis of School Quality Capitalization in U.S. House Prices

Wind Farms in North America

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