Philadelphia Housing Price Prediction

Improving Property Tax Assessments

Sixers

2025-10-26

Are current property tax assessments in Philadelphia fair and accurate?

  • Property taxes depend on OPA’s assessed values.

  • Assessments often deviate from actual sale prices.

  • Some areas are consistently over- or under-assessed.

  • Unequal assessments create unfair tax burdens.

Source: Reinvestment Fund, Examining the Accuracy, Uniformity & Equity of Philadelphia’s 2023 Real Estate Assessments (Apr 2024).

Motivations

Fairness: Ensure residents pay taxes aligned with true property values.

Transparency: Build trust through objective, data-based methods.

Efficiency: Improve city revenue stability and policy planning.

Traditional assessments rely on outdated, manual approaches.

Machine learning models can better capture local market dynamics.

Data Sources

  • Philadelphia Property Sales (n= 24023,2023-2024)

  • Census ACS (Income, Education, Poverty)

  • OpenDataPhilly (Number and Distance: Crime, Park&Recreation, Transportation, Hospital,School )

Spatial Distribution of Housing and Prices

Where Are the Houses?

Where Are the Expensive Houses?

Larger homes, higher neighborhood income, and more bathrooms increase prices — while older properties tend to sell for less.

Adding more real-world data to build a more fair and accurate housing price model

M1: Basic home features (size, age) → simple but limited

M2: + Census data → adds community context

M3: + Spatial data → captures location effects

M4: + Interactions → reflects real neighborhood differences

Home size and bathrooms remain important across all models, while neighborhood and location features gain influence after improving.

  • Bathrooms and Livable area stay top-ranked across all models
  • Income and Census tract rise in importance as they’re added
  • Final model shows Location Effects becoming dominant predictors of price

Model Performance: The predicted prices from our final model align strongly with actual sales.

> “Average Error($)” shows the average gap between predicted and actual prices (lower = more accurate).

The map highlights neighborhoods where predicted prices differ most from actual sales.

Interpretation

Blue areas: Homes undervalued by the model → may face under-assessment

Red areas: Homes overvalued → may face over-assessment

Central & southern zones: show the largest mismatches — indicating uneven market patterns

> The areas, shown in deep red or blue, are likely where property assessments are least accurate, and where tax fairness may be at greatest risk.

> These “hard-to-predict” areas should be prioritized for review in future assessment updates.

Three Evidence-Based Recommendations

Review Where the Model Shows the Largest Gaps → Our residual analysis pinpoints neighborhoods with the highest prediction errors — the same areas where assessments are likely least fair. Prioritize these zones for reassessment and data verification.

Use the Model as a Fairness Benchmark → Instead of replacing official assessments, use the model as a cross-check tool to flag properties with unusually high or low assessed-to-predicted ratios.

Institutionalize Annual Model Updates → Retrain the model each year using new sales and census data so assessments stay current with real market trends, preventing future inequities.

Limitations & Improvements

Data gaps: Some neighborhoods have limited or inconsistent sales data, and important local factors like school quality or public amenities are not fully represented.

Spatial variation: The model’s accuracy differs across regions — it performs very well in some areas but less so in others, suggesting that geography strongly influences results.

Spatial modeling: Next, we will apply models that explicitly account for spatial relationships and neighborhood effects to improve prediction accuracy.

Data enhancement: We plan to expand data coverage in underrepresented areas to reduce bias and strengthen fairness across communities.

Thank You

Turning Data into Fairer Assessments

Project Team

Xiaoqing Chen

Zicheng Xiang

Lingxuan Gao

Zhiyuan Zhao

Fan Yang

Zhe Fang

Contact

Sixers Consulting 6ers@upenn.edu|www.6ers.com

Prepared for the City of Philadelphia — Office of Property Assessment (OPA)