How to Use EDA to Investigate the Relationship Between Housing Prices and Local School Quality

Exploratory Data Analysis (EDA) is a powerful tool for understanding the relationships between variables in a dataset, especially when investigating complex relationships like the one between housing prices and local school quality. In this case, the goal is to explore how the quality of schools in a particular area impacts the prices of houses nearby.

Here’s a step-by-step guide on how to use EDA to investigate this relationship:

1. Data Collection and Preparation

Before starting with EDA, it’s essential to gather the relevant data. In this case, the two main variables you’ll focus on are:

• Housing Prices: This could include variables like the price of homes, the size of homes (square footage), and other factors like the number of bedrooms or bathrooms.

• School Quality: This could be represented by various indicators such as test scores, school ratings, student-teacher ratios, or school funding.

You will need to obtain data on both of these aspects. There are several public datasets available for housing prices (such as from Zillow, Redfin, or government agencies) and for school quality (e.g., GreatSchools or government databases on education).

After obtaining the data, you may need to clean it:

• Handle Missing Data: Missing values in either housing prices or school quality indicators need to be managed. You can either fill in missing values using imputation techniques or drop the rows with missing data.

• Standardization/Normalization: School quality ratings and housing prices might be on different scales. It’s helpful to normalize or standardize the data to make comparison easier.

• Feature Engineering: Create new variables that might provide more insights, such as distance from the nearest school or average school rating in the neighborhood.

2. Data Exploration and Visualization

Once the data is cleaned and prepped, the next step is to begin exploring the data with visualizations and summary statistics. This will give you a better understanding of potential relationships and anomalies.

Univariate Analysis

• Summary Statistics: Use measures like mean, median, standard deviation, and percentiles to understand the basic properties of the housing prices and school quality variables. For instance, do the housing prices have a large spread, or are they concentrated in a specific range?

• Distribution Plots: Visualize the distribution of housing prices and school ratings separately to see if either of them follows a skewed or normal distribution. Histograms, box plots, or kernel density plots can help you with this.

Bivariate Analysis

• Scatter Plot: Plot a scatter plot with housing prices on the y-axis and school quality on the x-axis. This is a simple and direct way to visualize if there’s a potential relationship between the two variables. Look for patterns such as a positive correlation (higher school quality = higher house prices) or any outliers.

• Correlation Matrix: Compute the correlation coefficient between different features, especially between housing prices and school quality indicators. If the correlation is strong (positive or negative), this suggests a potentially strong relationship.

• Pair Plot: A pair plot (or scatterplot matrix) can be helpful when dealing with multiple features related to housing and schools. It lets you compare all variables against each other and see if any pair of variables exhibit a strong relationship.

Geospatial Analysis

Since housing prices and schools are geographically located, you may want to include some geospatial analysis:

• Map Visualizations: Plot housing prices and school quality on a map. This helps to observe regional patterns—whether certain neighborhoods with better schools also have higher housing prices.

• Heat Maps: Create heat maps to visualize areas with high housing prices and high-quality schools. This will help in identifying any spatial clustering of these variables.

3. Identifying Outliers and Anomalies

While performing EDA, you will likely encounter outliers or anomalies in both housing prices and school quality indicators. It’s important to:

• Examine Outliers: Outliers can provide useful insights, but they can also skew the analysis. Determine whether these outliers are data errors or represent a real phenomenon (e.g., a luxury property near a top-rated school).

• Handling Outliers: Decide whether to remove outliers, cap extreme values, or transform the data to lessen their impact.

4. Statistical Testing

Once you have explored the data visually, it is helpful to perform some statistical tests to validate any hypotheses about the relationship between housing prices and school quality.

• T-tests/ANOVA: If you are comparing different groups (e.g., areas with high vs. low school quality), you can use t-tests or ANOVA to determine if the differences in housing prices are statistically significant.

• Regression Analysis: You can perform a linear regression to model the relationship between housing prices and school quality. The regression model will give you a quantitative understanding of how much of the variation in housing prices can be explained by school quality.

5. Multivariable Analysis

Often, the relationship between housing prices and school quality isn’t linear or one-dimensional. There may be other variables (e.g., income levels, crime rates, public amenities) that affect housing prices.

• Multivariate Regression: You can expand the simple linear regression model to include additional variables. This will help you understand the effect of school quality on housing prices while controlling for other factors.

• Principal Component Analysis (PCA): If there are many variables involved (e.g., multiple school quality indicators), PCA can help reduce the dimensionality of the dataset and highlight the most important features driving the relationship between housing prices and school quality.

6. Insights and Conclusions

At the end of the EDA process, you should be able to make some data-driven conclusions about how school quality impacts housing prices. For example:

• Positive Correlation: If the analysis shows that areas with higher school ratings tend to have higher housing prices, this could suggest that buyers value proximity to high-quality schools, driving up demand in these areas.

• Spatial Patterns: If the map reveals clear clusters of higher housing prices near top-rated schools, this could suggest that school quality is a significant factor influencing local property values.

• Policy Implications: The findings from this analysis could have broader implications for local governments and urban planners. For example, improving local schools could indirectly boost the local housing market.

Conclusion

EDA provides a robust framework for investigating complex relationships like the one between housing prices and school quality. By using a variety of visualization techniques, statistical analyses, and geospatial tools, you can uncover valuable insights that help explain how these two variables interact. Keep in mind that while EDA can help identify patterns and trends, further analysis, such as predictive modeling, may be needed to make concrete predictions.