Quantile Regression Caught the House Price OLS Missed by $55K

Ordinary Least Squares (OLS) regression models the average and can perform poorly on data with non-constant variance, a condition known as heteroscedasticity. This leads to inaccurate predictions where data variance is high because OLS assumptions are violated. Quantile regression offers a solution by modeling specific percentiles of the data distribution rather than just the mean. By fitting models for lower and upper quantiles, such as the 5th and 95th, it is possible to create a prediction interval that provides a more realistic range for individual outcomes. This technique is demonstrated with a Python example on house price data, showing how it captures market dynamics that OLS completely misses.