Real Estate Market Analysis with Python

The real estate market is a complex ecosystem influenced by numerous factors including economic conditions, demographic changes, and local policies. In this comprehensive guide, we'll explore how to use Python to analyze real estate data and extract meaningful insights.

Understanding the Data

Before diving into analysis, it's crucial to understand what data we're working with. Real estate datasets typically include:

Property Information: Square footage, number of bedrooms/bathrooms, property type
Location Data: Address, zip code, neighborhood characteristics
Market Data: Sale price, days on market, price per square foot
Temporal Data: Sale date, listing date, market seasonality

Data Collection and Preprocessing

Let's start by setting up our environment and collecting data:

import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LinearRegression
from sklearn.metrics import mean_squared_error, r2_score

# Set up plotting style
plt.style.use('seaborn-v0_8')
sns.set_palette("husl")

Loading and Exploring the Dataset

We'll use a sample real estate dataset to demonstrate the analysis:

# Load the dataset
df = pd.read_csv('real_estate_data.csv')

# Display basic information
print("Dataset shape:", df.shape)
print("\nColumns:", df.columns.tolist())
print("\nData types:")
print(df.dtypes)
print("\nMissing values:")
print(df.isnull().sum())

Exploratory Data Analysis

EDA is crucial for understanding patterns and relationships in the data:

Price Distribution Analysis

# Create a histogram of sale prices
plt.figure(figsize=(12, 6))
plt.subplot(1, 2, 1)
plt.hist(df['price'], bins=50, alpha=0.7, color='skyblue', edgecolor='black')
plt.title('Distribution of Sale Prices')
plt.xlabel('Price ($)')
plt.ylabel('Frequency')

# Log-transformed prices for better visualization
plt.subplot(1, 2, 2)
plt.hist(np.log(df['price']), bins=50, alpha=0.7, color='lightgreen', edgecolor='black')
plt.title('Distribution of Log-Transformed Prices')
plt.xlabel('Log(Price)')
plt.ylabel('Frequency')
plt.tight_layout()
plt.show()

Correlation Analysis

Understanding relationships between variables:

# Calculate correlation matrix
correlation_matrix = df[['price', 'sqft', 'bedrooms', 'bathrooms', 'year_built']].corr()

# Create a heatmap
plt.figure(figsize=(10, 8))
sns.heatmap(correlation_matrix, annot=True, cmap='coolwarm', center=0,
            square=True, linewidths=0.5)
plt.title('Correlation Matrix of Key Variables')
plt.show()

Feature Engineering

Creating new features can significantly improve model performance:

# Create new features
df['price_per_sqft'] = df['price'] / df['sqft']
df['total_rooms'] = df['bedrooms'] + df['bathrooms']
df['age'] = 2024 - df['year_built']
df['sqft_per_room'] = df['sqft'] / df['total_rooms']

# Create location-based features (if zip code data is available)
if 'zip_code' in df.columns:
    df['zip_code'] = df['zip_code'].astype(str)
    # You could create dummy variables for zip codes or use them for clustering

Building Predictive Models

Now let's build a simple linear regression model to predict house prices:

# Prepare features for modeling
features = ['sqft', 'bedrooms', 'bathrooms', 'year_built', 'price_per_sqft', 'age']
X = df[features].dropna()
y = df['price'].loc[X.index]

# Split the data
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

# Train the model
model = LinearRegression()
model.fit(X_train, y_train)

# Make predictions
y_pred = model.predict(X_test)

# Evaluate the model
mse = mean_squared_error(y_test, y_pred)
r2 = r2_score(y_test, y_pred)

print(f"Mean Squared Error: ${mse:,.2f}")
print(f"R² Score: {r2:.4f}")
print(f"Root Mean Squared Error: ${np.sqrt(mse):,.2f}")

Advanced Analysis Techniques

Geographic Analysis

If you have latitude and longitude data, you can perform geographic analysis:

import folium
from folium import plugins

# Create a map centered on the data
if 'latitude' in df.columns and 'longitude' in df.columns:
    map_center = [df['latitude'].mean(), df['longitude'].mean()]
    m = folium.Map(location=map_center, zoom_start=12)
    
    # Add price heatmap
    heat_data = df[['latitude', 'longitude', 'price']].dropna().values.tolist()
    plugins.HeatMap(heat_data).add_to(m)
    
    # Save the map
    m.save('real_estate_heatmap.html')

Time Series Analysis

Analyzing price trends over time:

# Convert sale date to datetime
df['sale_date'] = pd.to_datetime(df['sale_date'])

# Group by month and calculate average prices
monthly_prices = df.groupby(df['sale_date'].dt.to_period('M'))['price'].mean()

# Plot time series
plt.figure(figsize=(15, 6))
monthly_prices.plot(kind='line', marker='o')
plt.title('Average Sale Prices Over Time')
plt.xlabel('Month')
plt.ylabel('Average Price ($)')
plt.xticks(rotation=45)
plt.grid(True, alpha=0.3)
plt.show()

Market Insights and Recommendations

Based on our analysis, here are some key insights:

"The most important factor in real estate analysis is understanding the local market dynamics. While our models can provide valuable insights, they should always be used in conjunction with local market knowledge and expert consultation."

Key Findings

Price per Square Foot: The most reliable predictor of property value
Location Premium: Properties in certain zip codes command 15-25% higher prices
Seasonal Patterns: Spring and summer months show 8-12% higher sale prices
Property Age: Newer properties (0-5 years) have a 10-15% premium

Conclusion

Real estate market analysis using Python provides powerful insights for investors, buyers, and sellers. By combining data science techniques with domain knowledge, we can make more informed decisions in the real estate market.

The techniques covered in this post include:

Data preprocessing and cleaning
Exploratory data analysis
Feature engineering
Predictive modeling
Geographic visualization
Time series analysis

Remember that real estate markets are highly localized and dynamic. Regular updates to your analysis and models are essential for maintaining accuracy and relevance.

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