Exploring the value of education within local

housing markets: Sheffield, a case study

Sue EastonTown & Regional PlanningUniversity of Sheffield

Intro to the project

Background

◦ The value of schools

◦ Accessibility and socioeconomic status

Research questions

Methods

Findings

s.easton@sheffield.ac.uk

Overview / structure

ESRC Secondary Data Analysis Initiative (18 months)

Pupil census and parental preference data shared by local authority under strict data-sharing contract

www.traveltoschoolproject.org.uk

Sheffield Travel to School Project

Leech and Campos (2001) average additional increase for good secondary school catchment in Coventry: £10,000 - £20,000 (16-20%) on the average house in July 2000.

Gibbons and Machin (2003) 1% increase in primary school performance (Key Stage 2) associated with a £90 increase in mortgage fees per child.

Cheshire and Sheppard (2004) relationship non-linear, differed between primary & secondary schools. £42,541 (33.5%) between best and worst primary schools in Reading (2000)

Background – Hedonic Models

Burgess et al. (2011) – only 37% of schools within 3km of a child’s house actually accessible to that child. Lowest Socioeconomic Status quintile in metropolitan areas effectively excluded from over 70% of schools within 3km of their home.

Hamnett and Butler (2013) showed that the most popular schools in East London had the shortest distances to school within the tightest de facto catchment areas. They argue that places at the best state schools now rationed through geography via “distance to school” criteria.

Accessibility and Socioeconomic Status

How do local education markets in Sheffield interact with local housing markets?

What are the socioeconomic and spatial differences in de facto catchment areas for different types of schools (faith/secular, high and low-performing)?

Do the best (highest-performing) state schools have tighter, more homogeneous, higher socioeconimic local catchment areas?

Research Questions

Property Price index – HPI-adjusted, weighted◦ Based on Land Registry data 2007-2011 (4 years)

“De facto” catchment areas for schools◦ Based on network analysis of drive and walk times◦ From point data on pupil residential location

Geodemographic classification of Sheffield using census output areas

◦ Based on census 2011 data, property price index and urban form variables (building density etc)

Parental Preference Data – too “fluid” for analysis (administrative data).

Methods and Data

Property Sales 2010-11 (£)

Primary 65% “de facto” Catchment Areas

Legend

Buffered Smoothed 65% de facto Catchment Areas

Sheffield Local Authority District Boundary 2011

Neighbourhood Classification - 2011 Census

Legend

65% de facto Catchment Areas

No data

Working Class - lower income

Wealthy Rural

City Centre Living

Multicultural

Prosperous Suburbs

Pockets of Poverty

Middle Income Families

Correlation of 0.51 between mean 65% catchment property price and school performance on Key Stage 2 results (at age 11) for best state primary schools (p=0.000, N = 52)

Correlation of 0.88 mean 65% catchment property price (2010-11 data) and school performance (using Key Stage 4 – GCSEs) excluding the Catholic High School at top-performing secondary schools (p = 0.01, N=7).

Positive correlation between “core” standard distance and best-performing secondary schools on key stage results (0.75 at 5% significance). Weaker for primaries.

Inverse relationship between property price and residential density?

Results so far …

65% De Facto Catchments for Best Primaries

Stannington

Stocksbridge and Upper Don

Fulwood

Darnall

Dore and Totley

Ecclesall

Birley

Mosborough

Beighton

Burngreave

Woodhouse

Southey

Walkley

Crookes

Firth Park

Richmond

West Ecclesfield

Central

East Ecclesfield

Graves Park

Manor Castle

Hillsborough

Arbourthorne

Broomhill

Nether Edge

Gleadless Valley

Beauchief and Greenhill

Shiregreen and Brightside

Legend

65% de facto Catchment Areas

No data

Working Class - lower income

Wealthy Rural

City Centre Living

Multicultural

Prosperous Suburbs

Pockets of Poverty

Middle Income Families

Best-performing Primary Schools and Local “Neighbourhood” Classification

Legend

65% de facto Catchment Areas

No data

Working Class - lower income

Wealthy Rural

City Centre Living

Multicultural

Prosperous Suburbs

Pockets of Poverty

Middle Income Families

Endogeneity – children of high-performing professionals advantaged from birth >> perform better academically >> cluster together and attend local schools in/near wealthier residential areas.

Definitely a relationship between property price and school performance on KS results – strongest at secondary level.

Distance-based over-subscription criteria exclude children from other residential areas from accessing the best state schools and associated peer group (thinking social capital). Also religious selection criteria.

Inverse relationship between residential population density and school performance based on Key Stage results.

Next steps – to test relationships between pupils in residential areas and schools in a cross-classified multilevel model.

Conclusions

Atypical Primary CatchmentsLegend

65% de facto Catchment Areas

No data

Working Class - lower income

Wealthy Rural

City Centre Living

Multicultural

Prosperous Suburbs

Pockets of Poverty

Middle Income Families

Tinsley Junior

91.5% BME-12 below Average for England KS2

Netherthorpe Primary – city central

Legend

65% de facto Catchment Areas

No data

Working Class - lower income

Wealthy Rural

City Centre Living

Multicultural

Prosperous Suburbs

Pockets of Poverty

Middle Income Families

-26 below mean for England (KS2); 34% FSM, 88% BME pupils

s.easton@sheffield.ac.uk

Department of Town & Regional PlanningUniversity of Sheffield

Contact Details:

Correlation between Mean Weighted Property Price for 65% Catchments & KS2 Primary Schools performing above mean for England

Correlation between Mean 2010-11 Property Price for 65% Catchments & KS4 for Secondary Schools (performing above mean for England)

Correlation = 0.88 (p=0.01)