Deep Contrastive Learning for Feature Alignment: Insights from Housing-Household Relationship Inference

TL;DR

This paper introduces a deep contrastive learning model to infer housing-household relationships from survey data, overcoming lack of labels and demonstrating superior accuracy and transferability across regions.

Contribution

The study develops a novel dual-encoder contrastive learning approach with bisect K-means clustering for relationship inference without ground truth labels.

Findings

The model outperforms existing methods in Delaware.

It generalizes well to North Carolina.

SHAP analysis highlights tenure and mortgage as key factors.

Abstract

Housing and household characteristics are key determinants of social and economic well-being, yet our understanding of their interrelationships remains limited. This study addresses this knowledge gap by developing a deep contrastive learning (DCL) model to infer housing-household relationships using the American Community Survey (ACS) Public Use Microdata Sample (PUMS). More broadly, the proposed model is suitable for a class of problems where the goal is to learn joint relationships between two distinct entities without explicitly labeled ground truth data. Our proposed dual-encoder DCL approach leverages co-occurrence patterns in PUMS and introduces a bisect K-means clustering method to overcome the absence of ground truth labels. The dual-encoder DCL architecture is designed to handle the semantic differences between housing (building) and household (people) features while mitigating noise introduced by clustering. To validate the model, we generate a synthetic ground truth dataset and conduct comprehensive evaluations. The model further demonstrates its superior performance in capturing housing-household relationships in Delaware compared to state-of-the-art methods. A transferability test in North Carolina confirms its generalizability across diverse sociodemographic and geographic contexts. Finally, the post-hoc explainable AI analysis using SHAP values reveals that tenure status and mortgage information play a more significant role in housing-household matching than traditionally emphasized factors such as the number of persons and rooms.