Exploring Ames Housing Prices: A Data-Driven Analysis

Introductory

Welcome to our interactive project page on Ames Housing Prices. Using cutting-edge data analysis techniques, we delve into key factors driving home values. Explore the relationships between property features, neighborhood characteristics, and sale prices to uncover actionable insights for buyers, sellers, and industry professionals.

Figure 1: The histogram showing the distribution of SalePrice, labeled to showcase skewness and the need for transformations.

Exploratory Insights: Understanding the Data

This section offers an overview of the data we explored. The Ames dataset includes 79 variables detailing physical property features, construction quality, and more. By visualizing patterns, we aim to extract meaningful insights that support predictive modeling.

Figure 2: Correlation heatmaps (Spearman correlations) for numeric variables against SalePrice.

Data Cleaning and Preprocessing: Preparing the Data for Analysis

Accurate results require clean data. In this phase, we handled missing values, normalized skewed distributions, and removed significant outliers. These steps enhanced the dataset’s reliability for model training and prediction.

Figure 3: MDS plot illustrating multivariate distribution before and after outlier removal.

Key Findings: What Influences House Prices the Most?

Our analysis identifies several critical factors influencing house prices in Ames. Variables like construction quality (OverallQual), above-ground living area (GrLivArea), and garage capacity (GarageCars) are top predictors, alongside categorical variables like Neighborhood and KitchenQual.”

Table 1: Top Predictors (categorical variables) by Effect Size and Correlation

Feature	Eta_squared	Description
Neighborhood	0.57	Physical locations within Ames city limits
ExterQual	0.49	Evaluates the quality of the material on the exterior
BsmtQual	0.48	Evaluates the height of the basement
KitchenQual	0.46	Kitchen quality
GarageFinish	0.33	Interior finish of the garage
Foundation	0.30	Type of foundation
HeatingQC	0.21	Heating quality and condition

Table 2: Top Predictors (numeric variables) by Spearman Correlation Estimate

Feature	Correlation	Description
OverallQual	0.81	Rates the overall material and finish of the house
GrLivArea	0.72	Above grade (ground) living area square feet
GarageCars	0.68	Size of garage in car capacity
YearBuilt	0.67	Original construction date
GarageArea	0.64	Size of garage in square feet
FullBath	0.63	Full bathrooms above grade
TotalBsmtSF	0.59	Total square feet of basement area
YearRemodAdd	0.58	Remodel date (same as construction date if no remodeling)
GarageYrBlt	0.57	Year garage was built
1stFlrSF	0.56	First Floor square feet
TotRmsAbvGrd	0.51	Total rooms above grade (does not include bathrooms)
Fireplaces	0.51	Number of fireplaces

Figure 4: Scatter plot showing the relationship between SalePrice and GrLivArea before and after transformation

Applications: Real-World Impacts

This project showcases how data-driven insights can guide property investments, urban development, and market strategies. By understanding what drives house prices, stakeholders can make better decisions that align with buyer preferences and market demands.

Figure 5: Box plot showing that neighborhood predicts house sale price

Next Steps: Where Do We Go from Here?

Building on these exploratory insights, the next phase involves predictive modeling. By leveraging identified patterns and preprocessing strategies, we aim to develop models that accurately forecast house prices.

Figure 6: Exploratory data analysis workflow chart for house price prediction