Model Citizens Consulting

Improving Property Tax Assessments

Ming Cao, Mark Deng, Jun Luu, Josh Rigsby, Alex Stauffer, Tess Vu

Philadelphia Housing Price Prediction

The Question: What data do we have on property, the Census, and built environment that can help predict housing prices?

The Motivation: Philadelphia is a city of more than 1.5 million people and growing, we need properties to be accurately assessed.

The Problem & The Goal

Why This Matters

Improving the Office of Property Assessment’s Automated Valuation Model (AVM) can potentially:

  • More stable values aligned with market rates for properties being sold.

  • Alleviate burden from gentrification in previously disinvested neighborhoods.

The Problem & The Goal

What We Want

  • Equitable taxation for residents.

  • Financial stability for Philadelphia.

  • Help set the future for more reliable open data.

Data Overview

Philadelphia Properties and Current Assessments from Open Data Philly (n = 559,322)

  • Property Coordinates, Sale Date (n = 34,559; 2023-2024), Sale Price (> $10,000; n = 26,344), Property Characteristics: bathrooms, basement type, central air, fireplaces, fuel type, garages, square feet, stories, year built (n = 18,221)

2023 5-Year American Community Survey (ACS) from the United States Census Bureau (n = 28,261; 2019-2023)

  • Median Household Income, Single-Family Detached Homes, Vacancies (n = 3,401)

Data Overview

Open Data Philly

  • Neighborhood Boundaries (n = 159; 2025), Parks (n = 15; 2025), Philadelphia 2020 Census Tract Boundaries (n = 3,446; 2020), SEPTA Transit Stations (n = 13,884; 2025)

Open Street Maps

  • Points of Interest: Convenience and Food-Related Amenities (n = 18,413; 2025)

Where Are Expensive Homes?

Findings

  • Higher Prices: Center City, University City, the riverfront, and affluent Northwest pockets.

  • Potentially due to easy access to transit and amenities.

  • Lower Prices: North of Broad Street into parts of West and North Philadelphia.

  • Potentially reflecting long-term disinvestment.

  • Sale price is place-dependent in Philadelphia, mostly due to neighborhood qualities.

What Drives Prices?

  • Larger homes = an increase in price, but only up to a certain point.

Model Comparison & Performance

  • RMSE = 138,279.40 → Predicted sale price differs by about ± $138,279 from actual market sale price.

  • R² = 0.746 → Explains 75% of variance in home prices.

Top Predictors

  • Neighborhoods (e.g. Fitler Square $431,911.65 > East Falls)

  • Livable Square Footage ($187.32 increase)

  • Median Household Income ($0.63 increase)

Hardest To Predict

Conclusions & Recommendations

Human review for over-valued neighborhoods that are being gentrified.

Limitations & Next Steps

Limitations / Ethical Concerns:

  • Undervalues some disinvested neighborhoods and overvalues wealthier ones.

  • Areas with residents depend more on personal vehicles, this model is predicting on public transit, not highways.

Future Potential:

  • Incorporate additional categorical variables after consideration.

  • Separate rural modeling can be helpful too.

Thank you, questions?