Computing Student Yields at the School District Attendance Area by Length of Ownership

Department of Educational Administration and Foundations. College of Education, Illinois State University, Campus Box 5900, Normal, IL 61790-5900. Tel: 309-438-2399; Fax: 309-438-8683; Web site: http://education.illinoisstate.edu/planning/

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School Districts; Suburban Schools; Housing; Enrollment Projections; Real Estate; Geographic Information Systems; School Demography; Public Schools; Residential Patterns

0032-0684

As part of school district planning and projecting future enrollments, demographers need to factor the impact of new housing developments by using student yields. While resources are available that provide statewide student yields, they may not best reflect the demographic attributes of a school district's attendance area. In this case study of a large, suburban school district in central New Jersey, Geographic Information System (GIS) software was used to project student yields by joining student address records to parcel-level property records. Student yields were computed by length of home ownership and home assessment value for detached single-family homes and townhouses/condominiums as yields are typically higher for short-held homes as opposed to long-held homes. Student yields in long-held homes, which include empty nesters and senior citizens, are not likely to have children in the school district as they would have graduated. The results showed that computing student yields by length of ownership generates a much higher yield than if the entire housing database is utilized which includes long-held homes with low student yields. In addition, the results showed that local student yields were greater in magnitude than the statewide multipliers and were also greater in value for homes that were above the median assessment as compared to those that were below. If school demographers use statewide student yields when estimating the impact of future housing development, they may underestimate or overestimate its impact as these yields may not capture the demographic characteristics of the population moving into a community. Therefore, when time and resources permit, local data should be used to compute public school student yields.

As Provided

2021

EJ1284180