Addressing Zillow’s Pricing Challenges

Accurate home price predictions are critical for Zillow’s business model, influencing the Zestimate and the reliability of market insights.

However, traditional Ordinary Least Squares (OLS) regression does not account for spatial dependencies, leading to potential inaccuracies.

Why Spatial Autocorrelation Matters for Zillow

Real estate prices are inherently influenced by their geographical surroundings. Spatial autocorrelation occurs when neighboring home values exhibit clustering, violating OLS’s assumption of independent residuals. A significant Global Moran’s I statistic confirms this issue in Philadelphia, highlighting a key limitation in Zillow’s current OLS-based models.

Testing for Spatial Dependence in Zillow’s Data

To quantify spatial relationships, we constructed a Queen contiguity spatial weights matrix, defining neighborhoods based on shared boundaries. Using this, we computed Moran’s I for home prices, with permutation tests confirming significant spatial clustering. Ignoring this dependence in regression models could result in misleading inferences, an issue Zillow must address to maintain Zestimate accuracy.

Initial Findings and Limitations

We first estimated an OLS model using key predictors relevant to Zillow’s pricing models:

• Percentage of vacant housing units

• Percentage of single-unit homes

• Percentage of individuals with a bachelor's degree

• Log-transformed number of households in poverty

The OLS model achieved an R² of 0.662, suggesting reasonable explanatory power. However, diagnostics revealed spatial autocorrelation, heteroscedasticity, and non-normality in residuals. These violations suggest that spatial regression models could provide more robust predictions for Zillow.

Accounting for Neighborhood Effects

To address spatial dependencies, we applied two spatial regression models:

1. Spatial Lag Model (SLM): This model includes a spatially lagged dependent variable, meaning a home’s price is influenced by neighboring prices.

2. Spatial Error Model (SEM): Instead of modifying the dependent variable, SEM accounts for spatial dependence in the error term, capturing unobserved local influences on pricing.

Both models outperformed OLS, with SLM providing superior predictive power.

Geographically Weighted Regression (GWR): Localized Precision for Zillow

Unlike global models, Geographically Weighted Regression (GWR) allows predictor effects to vary across space, making it ideal for Zillow’s localized home price predictions. Key findings from the GWR model:

• Best model fit (AIC = 308.7, lowest among all models)

• Minimal residual spatial autocorrelation (Moran’s I = 0.033)

• Revealed spatial heterogeneity—predictors like poverty rates and education levels influence home prices differently across Philadelphia neighborhoods.

This spatial variability suggests that Zillow’s current models may benefit from location-specific adjustments rather than a one-size-fits-all approach.

Comparing Model Performance for Zillow

GWR provided the best performance, demonstrating that Zillow can enhance Zestimate precision by adopting spatial regression techniques. Spatial Lag also showed promise, reinforcing the need to account for neighborhood spillover effects.

Key Takeaways and Zillow-Specific Implications

1. OLS is insufficient for urban home price forecasting—Zillow’s Zestimate can be improved by integrating spatial econometric techniques.

2. Neighborhood effects matter—Spatial Lag performed better than Spatial Error, highlighting the importance of considering local price interactions.

Zillow’s Next Steps

• Incorporate hybrid models—combining machine learning with spatial econometrics for enhanced predictive performance.

• Develop real-time spatial weight updates—ensuring models capture evolving neighborhood trends.

• Expand to multiscale models—adjusting prediction granularity based on local market conditions.

UI Recommendations for Zillow

1. Heatmap Visualizations – Allowing users to see how home prices are influenced by neighborhood factors.

2. Localized Zestimate Confidence Scores – Displaying confidence intervals based on spatial dependencies to inform users of potential price volatility in different areas.

3. Spatial Trends Dashboard – A dedicated section showcasing how home prices have changed over time with respect to spatial dependencies, helping users make data-driven decisions..

4. Neighborhood Influence Score – A metric indicating how much surrounding home values impact a specific listing, providing better price transparency.