Data sources in isolation are insufficient to provide the decision support that school place planners require. Central to an effective PSS is a forecasting model that is driven by a robust supply of data, appropriately calibrated in line with historical trends, and which considers how cohort progression, migration and new housing may influence future demand for school places. A pupil forecasting model will typically produce a range of forecast scenarios, disaggregated by school year- group and forecast year. Outputs may be generated for the local authority as a whole, for each planning area (or zone) and for individual schools.
Although there is no statutory method for forecasting pupil numbers, the ESFA provides guidance to local authorities on ways of improving the accuracy of their pupil forecasts (ESFA, 2017). Therefore, whilst the sophistication and implementation of pupil forecasting models differs between local authorities, the general principles underpinning the forecasting process are relatively similar (Figure 5). The model description that follows is that employed in the edge-ucate™ technology, which is used by school place planners in a number of larger county councils in England, including Surrey, Kent, Hertfordshire and West Sussex. The model methodology encompasses seven distinct stages and whilst these councils manage schools with a mix of year- group ranges, the explanation that follows assumes that a primary school consists of seven year groups (years R-6) and that a secondary school consists of five year groups (years 7-11). Schools with ‘atypical’ year groups (e.g., middle schools, studio schools and infant/junior schools are handled as special cases within each system).
Figure 5 The edge-ucateTM school place forecasting methodology
In Stage 1, the number of resident pre-school children is quantified by age (0+ to 4+) and zone using pre-school population data relating to the base year and a ‘feed’ of estimated births. Migration factors, calibrated for each zone from the three-year history of pre-school population data, are applied in each forecast year, adjusting (up or down) the size of the cohorts as they approach school age.
In Stage 2, intake rates identify the proportion of the pre-school population aged 4+ in each zone that enters year R of each state-funded primary school in the local authority area. The intake rates are derived for each zone/school using a three-year history of pre-school population data and school census data for corresponding years. Additional year R pupils, resident in the buffer area, are accommodated at each school, based on trends in the school census data. The application of the intake rates removes resident children that enter year R of independent schools or schools located outside the local authority area.
In Stage 3, the number of pupils in each primary school is quantified by year group (R-6) using school census data relating to the base year and a ‘feed’ of year R pupils from Stage 2. Migration factors, calibrated for each primary school from the three-year history of school census data, are applied in each forecast year, adjusting (up or down) the size of the pupil cohorts as they age through the year groups. Where appropriate, the size of each pupil cohort is increased to account for pupil yield from new housing.
In Stage 4, intake rates identify the proportion of resident year 6 pupils in each zone (from Stage 3) that enters year 7 of each state-funded secondary school in the local authority area. The intake rates are derived for each zone/school using a three-year history of school census data. Additional year 7 pupils, joining from independent primary schools or primary schools located outside the local authority area, are accommodated, as are year 7 pupils resident in the buffer area.
In Stage 5, the number of pupils in each secondary school is quantified by year group (7-11) using school census data relating to the base year and a ‘feed’ of year 7 pupils from Stage 4. Migration factors, calibrated for each school from the three-year history of school census data, are applied in each forecast year, adjusting (up or down) the size of the pupil cohorts as they age through the year groups. Where appropriate, the size of each pupil cohort is increased to account for pupil yield from new housing.
To account for sixth-form provision, the model may optionally include a further stage, in which ‘stay-on rates’ (derived from the three-year history of school census data) identify the number of pupils that enter year 12 and progress to year 13 in each sixth-form.
The operation of the school place PSS, appropriately configured and calibrated using the suite of data sources enables a range of pupil forecasts to be derived at different spatial scales for different time-periods and underpinned by alternative forecasting assumptions. School place planners typically draw on three alternative forecast scenarios to inform their decision-making: (i) a ‘Migration’ scenario (to consider the potential pupil impact of pre-school and in-school migration); (ii) a ‘Housing’ scenario (to consider the potential pupil impact of planned new housing); and (iii) a ‘Base’ scenario (with the effects of migration and planned new housing excluded). A fourth ‘Combined’ scenario, based on the combination of the pupil impact of migration and planned new housing may also be considered. In the most sophisticated school place PSS, local authorities may have the option to consider bespoke scenario actions and assumptions. In edge-ucate™, users can open and close schools and generate forecasts underpinned by alternative migration/housing assumptions.