Bellevue School District
Funding Formula Breakdown for 2024-25
What is the Prototypical School Funding Model?
Following the McCleary decision in 2012, Washington State adopted the Prototypical School Funding Model to fully fund "Basic Education" as required by the state constitution.
"Prototypical School"
Based on Enrollment
= Allocation Amount
District Enrollment
1 Prototypical School Sizes
Theoretical enrollment for each school type:
| School Type | Grade Levels | Prototypical Enrollment (AAFTE) |
|---|---|---|
| Elementary School | Grades K-6 | 400 students |
| Middle School | Grades 7-8 | 432 students |
| High School | Grades 9-12 | 600 students |
AAFTE = Annual Average Full-Time Equivalent Students
2 Class Size Ratios (Students per Teacher)
Number of students allocated per certificated instructional staff (teacher):
| Grade Level | Class Size | Notes |
|---|---|---|
| Kindergarten | 17.00 | K-3 Class Size Reduction |
| Grade 1 | 17.00 | |
| Grade 2 | 17.00 | |
| Grade 3 | 17.00 | |
| Grade 4 | 27.00 | General |
| Grades 5-6 | 27.00 | General |
| Grades 7-8 | 28.53 | General |
| Grades 9-12 | 28.74 | General |
| Lab Science | 19.98 | Specialized |
| CTE / Skill Center | 19.00 | Career & Technical Education |
Example: 200 K-3 students → 200 ÷ 17 = 11.76 FTE teachers
3 Salary Allocations - Bellevue School District
2024-25 Base Salary Allocations
| Staff Type | State Base | × Regional (1.18) | = District Allocation | FTE Count | Total Allocation |
|---|---|---|---|---|---|
| Certificated Instructional Staff (CIS) | $78,134 | ×1.18 | $92,198 | 1,411.4 | $130.1M |
| Certificated Administrative Staff (CAS) | $116,012 | ×1.18 | $136,894 | 91.0 | $12.5M |
| Classified Staff (CLS) | $56,105 | ×1.18 | $66,204 | 848.3 | $56.2M |
| Total Staff Salary Allocation: | $198.8M | ||||
4 MSOC - Bellevue School District
Non-personnel operating cost allocations (2024-25):
| Category | Per-Student | × 19,509 Students | = District Total |
|---|---|---|---|
| Technology | $201.02 | × | $3.9M |
| Utilities & Maintenance | $412.89 | × | $8.1M |
| Curriculum & Textbooks | $173.81 | × | $3.4M |
| Instructional Supplies | $277.59 | × | $5.4M |
| Other (Library, Office, Insurance) | $449.28 | × | $8.8M |
| Total MSOC | $1,514.59 | $29.5M |
5 Total Funding Summary: Bellevue School District
| Component | Amount |
|---|---|
| Staff Salaries (CIS + CAS + CLS) | $198.8M |
| Employee Benefits (SEBB) | $37.0M |
| MSOC (Materials, Supplies, Operating Costs) | $29.5M |
| Special Education | $38.8M |
| Transportation | $12.6M |
| Total State Funding | $260.2M |
| Per Student | $13,338 |
Key Takeaways
Funding is based on theoretical "prototypical" schools, not actual district needs or costs.
Districts receive funding based on the formula but have discretion in how to deploy resources.
Special education, transportation, and MSOC are chronically underfunded compared to actual costs.
Regionalization factors only partially account for cost-of-living differences across the state.
Many districts rely on local levies to cover gaps, creating inequities between property-rich and property-poor districts.
References & Resources
- RCW 28A.150.260 - Prototypical School Funding Statute
- Citizen's Guide to WA K-12 Finance 2024
- K-12 Regionalization Factors
- OSPI School Apportionment
- District Allocation of State Resources Portal
Understanding School Funding
Washington schools face a $2+ billion funding gap. Here's why your district doesn't have enough money, and what you can do about it.
Where Does School Money Come From?
Basic ed, special ed, transportation, and more
Extracurricular programs, student support services, classroom materials, and staff positions not fully funded by the state
Services for low-income students, multilingual/English learners, educator training, and more
Investment earnings, fees, donations, and miscellaneous revenue sources
Data Source: OSPI SAFS Data Files
Revenue Source Trend
Year-over-year revenue source breakdown for the selected district. Data source: OSPI F-196 Financial Reports.
Your District at a Glance
Funding Gap Breakdown
Revenue vs. Expenditure by program (in millions)
State Funding by District Demographics
How does state revenue per student vary with district characteristics?
Simulator
Adjust policy parameters to see how different reforms would impact your district's funding.
Funding Simulator
Adjust parameters to see the impact on your district
State Revenue Predictor
XGBoost ML model: Predict your district's state revenue per pupil using 10 features from OSPI data. Select bills to see their impact on future funding.
Forecast Settings
Select a District - State Revenue Forecast
Two-Stage Prediction Model
transport_cost, av_per_pupil, lea_per_pupil,
msoc_per_pupil, is_rural
E2SSB 5263, HB 1956, ESSB 5192, etc.
Feature Contributions
Financial Risk Assessment
RISK SCORE
Higher score = Higher financial vulnerability
XGBoost Feature Importance
Relative importance of 10 features (based on actual data)
Feature Importance (|β| × σ)
Key Drivers for This District
About This Prediction
This forecast uses an XGBoost (Gradient Boosting) model with 10 features from OSPI data (2019-2025).
Y = Total Expenditure / Enrollment (적정 펀딩)
학군이 "필요하다고 판단한" 학생당 지출액
R² = 99.2%, RMSE = $660
- enrollment - Total Students (FTE)
- pct_frl - Low-Income Rate
- pct_ell - ELL Rate
- pct_sped - SpEd Rate
- region_factor - Regionalization Factor
- transport_cost - Transport Cost/Pupil
- av_per_pupil - Assessed Value/Pupil
- lea_per_pupil - LEA/Pupil
- msoc_per_pupil - MSOC/Pupil
- is_rural - Rural Status
- OSPI F-196 Financial Reports
- OSPI Enrollment Data
- OSPI Levy/LEA Data
- OSPI Apportionment Data
All features are calculated from actual OSPI data. N/A shown when data is unavailable.
District Clustering Analysis
All WA districts plotted by Fiscal Health (X) vs Vulnerability (Y). Find similar districts to yours.
Similar Districts
Districts closest to your district in the clustering space:
Your District's Position
XGBoost ML Model
XGBoost model using 10 features to predict operating balance per pupil (Y = (Revenue - Expenditure) / Enrollment). Positive = surplus, Negative = deficit.
XGBoost Model Overview
Y Target Variable
Model Info
X (13 Features) - Input Variables
All features are calculated from actual OSPI data for each district. If data is unavailable, N/A is displayed.
Model Interpretation
Feature Importance (Relative Weight)
SHAP Values (Feature Contribution to Y)
양수(+) = 더 많은 펀딩 필요, 음수(-) = 더 적은 펀딩 필요 (규모의 경제 등)
Data Sources
OSPI F-196
- rev, revState, revLocal, revFederal
- exp, salaryExp, transExp, msocExp
- fundBalance (Item 442)
OSPI Enrollment
- t (Total Enrollment)
- li (Low-Income %)
- el (ELL %), sp (SpEd %)
OSPI Levy/LEA
- av (Assessed Value)
- enrichmentLevy
- leaPerPupil
OSPI Apportionment
- regional (A33r factor)
- msocFunding
- cisFte, casFte, clsFte
Funding Gap Analysis
Y = Funding Gap Overview
Negative Y = Underfunded (district spends more than state provides)
Positive Y = Surplus (state provides more than district spends)
X (9 Features) - Input Variables
Model Evaluation
XGBoost Feature Importance (Gain-based)
Feature Importance Summary
About This Model
What is XGBoost?
XGBoost (eXtreme Gradient Boosting) is a machine learning algorithm that builds many decision trees sequentially, where each tree learns from the errors of previous trees. It's widely used for prediction tasks due to its accuracy and efficiency.
What We're Predicting (Y)
This is the Funding Balance per pupil. A negative value indicates underfunding (district spends more than state provides), while a positive value indicates a surplus.
Input Features (X)
Data Filtering
To ensure meaningful comparisons, the model excludes:
- Small districts with fewer than 100 students
- Special/charter schools (tribal, detention, virtual, academy, etc.)
Feature Importance (Gain-based)
Feature importance shows how much each variable contributes to the model's predictions. Higher importance means the feature has more influence on determining a district's funding balance.
How to Interpret Results
Underfunded — The district spends $2,500 more per pupil than what the state provides. This gap must be covered by local levies or other sources.
Surplus — The state provides $500 more per pupil than what the district spends. This is rare and usually indicates lower-cost operations.
District Clustering: Funding Gap vs Profile
About District Clustering
What is District Clustering?
District clustering groups Washington's school districts into 4 categories based on two dimensions: Funding Balance (Y-axis) and District Profile Score (X-axis). This helps identify which districts face similar challenges and funding situations.
Understanding the Axes
Formula: (State Revenue − Expenditure) ÷ Enrollment
Negative values = Underfunded (spending exceeds state funding)
Positive values = Surplus (rare)
Composite score (0-100%) based on:
• 40% Low-Income %
• 20% Enrollment Decline
• 20% ELL % • 20% SpEd %
The Four Clusters
Bottom 2.5% of funding balance. These districts face the most severe funding shortfalls regardless of demographics. Priority for policy intervention.
Below-median funding with challenging demographics (high poverty, ELL, SpEd). These districts need both more funding and targeted support programs.
Below-median funding but with favorable demographics. Often suburban districts that rely heavily on local levies to cover the gap.
Above-median funding balance. State revenue covers most expenditures. Often smaller rural districts with lower operating costs.
Key Insights
- High-need districts are not always the most underfunded — some receive adequate state support through categorical funding
- Suburban districts often show larger gaps — higher costs but limited categorical funding eligibility
- Enrollment decline amplifies funding challenges — fixed costs spread over fewer students
- Local levy capacity varies dramatically — property-rich districts can compensate, others cannot
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