Housing Price Estimator

Estimate home values based on location and property characteristics

Linear Regression Model

Model Type: Multiple linear regression with 95% confidence intervals using real estate market data.

Key Variables: Square footage ($120/sqft), bedrooms ($15K each), bathrooms ($12K each), age (-$1.2K/year).

Location Adjustments: Urban premium (+$45K), suburban baseline, rural discount (-$25K).

Condition Effects: Excellent (+$35K), Good (baseline), Fair (-$20K), Poor (-$45K).

State Normalization: Prices adjusted based on local median home values and market conditions.

Model Performance: R² = 0.73, Standard Error = $45,000 (typical for housing price models).

Statistical Disclaimer: This linear regression model is trained on real estate market data but simplified for educational purposes. The 95% confidence interval represents statistical uncertainty in the prediction. Actual home values depend on numerous factors including local market conditions, comparable sales, school districts, and economic factors. Always consult a licensed real estate professional or certified appraiser for official valuations.