Machine Learning Techniques Applied to Residential Home Price Estimations

Music City Center 

Automated valuation models (AVMs) have gained popularity with the rise of online platforms like Zillow. However, the noisy nature of residential home sales data poses challenges for estimation and prediction methods that assume normality. As a result, some AVMs exhibit significant bias and imprecision during validations. To address this issue, we employed a robust regression method (MM-estimation) combined with a bootstrapping procedure (Stine 1985) to downweigh outliers. This approach yielded unbiased and precise regression estimated residential prices, as demonstrated through k-fold validation. Additionally, this method provides a confidence interval for each residential property, enabling property-level hypothesis testing, which is uncommon for AVMs. The k-fold validation confirms that these confidence intervals exhibit the required level of statistical confidence.

Automated valuation models

robust regression method

bootstrapping