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Investigating the effect of good schools on surrounding housing prices in London

Liu, Shengnan LU and Sampson, Nicholas Kofi Gyasie LU (2018) NEKN02 20181
Department of Economics
Abstract
In this paper we have looked at the effect good schools have on surrounding house prices, focusing on London. We consider both the effect of “% of students that meet expected standard in math, reading, and writing”, and the new “progression score” that the UK government has introduced. These two scores are used as the basis of how a school is defined as ‘good’. We used two different propensity score matching methods to estimate the effect that good schools have on surrounding house prices. We got different results depending on which matching method we used, and which score to define good schools. “% of students that meet expected standard in math, reading and writing” showed a positive effect for both the Nearest Neighbor method (8.42%)... (More)
In this paper we have looked at the effect good schools have on surrounding house prices, focusing on London. We consider both the effect of “% of students that meet expected standard in math, reading, and writing”, and the new “progression score” that the UK government has introduced. These two scores are used as the basis of how a school is defined as ‘good’. We used two different propensity score matching methods to estimate the effect that good schools have on surrounding house prices. We got different results depending on which matching method we used, and which score to define good schools. “% of students that meet expected standard in math, reading and writing” showed a positive effect for both the Nearest Neighbor method (8.42%) and the Kernel method (3.74%) whereas “progression scores” showed a negative effect for the nearest neighbor method (-9.95%) and positive for the Kernel method (0.59%). (Less)
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author
Liu, Shengnan LU and Sampson, Nicholas Kofi Gyasie LU
supervisor
organization
course
NEKN02 20181
year
type
H1 - Master's Degree (One Year)
subject
keywords
Propensity score, Kernel Matching Method, Greater London areas, housing prices, School Quality
language
English
id
8949147
date added to LUP
2018-07-02 15:40:41
date last changed
2018-07-02 15:40:41
@misc{8949147,
  abstract     = {In this paper we have looked at the effect good schools have on surrounding house prices, focusing on London. We consider both the effect of “% of students that meet expected standard in math, reading, and writing”, and the new “progression score” that the UK government has introduced. These two scores are used as the basis of how a school is defined as ‘good’. We used two different propensity score matching methods to estimate the effect that good schools have on surrounding house prices. We got different results depending on which matching method we used, and which score to define good schools. “% of students that meet expected standard in math, reading and writing” showed a positive effect for both the Nearest Neighbor method (8.42%) and the Kernel method (3.74%) whereas “progression scores” showed a negative effect for the nearest neighbor method (-9.95%) and positive for the Kernel method (0.59%).},
  author       = {Liu, Shengnan and Sampson, Nicholas Kofi Gyasie},
  keyword      = {Propensity score,Kernel Matching Method,Greater London areas,housing prices,School Quality},
  language     = {eng},
  note         = {Student Paper},
  title        = {Investigating the effect of good schools on surrounding housing prices in London},
  year         = {2018},
}