Machine Learning Fairness in House Price Prediction: A Case Study of America's Expanding Metropolises

TL;DR

This study evaluates racial and ethnic biases in ML-based house price prediction models and compares bias mitigation techniques to promote fairness and equity in housing market predictions.

Contribution

It introduces a comprehensive fairness assessment of ML house price models and compares the effectiveness of bias mitigation strategies in this context.

Findings

ML models exhibit bias towards protected attributes like race and ethnicity.

Bias mitigation methods vary in effectiveness across models.

In-processing mitigation generally outperforms pre-processing approaches.

Abstract

As a basic human need, housing plays a key role in enhancing health, well-being, and educational outcome in society, and the housing market is a major factor for promoting quality of life and ensuring social equity. To improve the housing conditions, there has been extensive research on building Machine Learning (ML)-driven house price prediction solutions to accurately forecast the future conditions, and help inform actions and policies in the field. In spite of their success in developing high-accuracy models, there is a gap in our understanding of the extent to which various ML-driven house price prediction approaches show ethnic and/or racial bias, which in turn is essential for the responsible use of ML, and ensuring that the ML-driven solutions do not exacerbate inequity. To fill this gap, this paper develops several ML models from a combination of structural and neighborhood-level attributes, and conducts comprehensive assessments on the fairness of ML models under various definitions of privileged groups. As a result, it finds that the ML-driven house price prediction models show various levels of bias towards protected attributes (i.e., race and ethnicity in this study). Then, it investigates the performance of different bias mitigation solutions, and the experimental results show their various levels of effectiveness on different ML-driven methods. However, in general, the in-processing bias mitigation approach tends to be more effective than the pre-processing one in this problem domain. Our code is available at https://github.com/wahab1412/housing_fairness.