A Joint Synthetic Housing-Household Inventory

TL;DR

This paper introduces a comprehensive framework for creating a detailed, realistic joint housing and household inventory by integrating synthetic population generation, deep learning for compatibility, and hierarchical allocation, enabling diverse urban analysis applications.

Contribution

It presents a novel integrated approach combining synthetic population modeling, deep contrastive learning, and optimization to produce a high-fidelity joint housing-household dataset.

Findings

The synthetic population closely matches census microdata statistics.

The deep contrastive learning model accurately predicts housing-household compatibility.

The generated inventory reproduces spatial population patterns without bias.

Abstract

Accurately understanding the interactions between humans and the built environment requires integrated representations of both the buildings and the populations that occupy them. However, high-fidelity datasets that jointly capture detailed housing structures and demographic characteristics at the household level do not currently exist. This paper presents a framework for constructing a joint housing-household inventory that explicitly links individuals and households to compatible housing units from the National Structure Inventory (NSI), while preserving realistic population densities and demographic distributions. The framework integrates three components: (i) synthetic population generation from American Community Survey (ACS) Public Use Microdata Sample (PUMS) records that preserve complex intra-household relationships; (ii) a deep contrastive learning model that quantifies housing-household compatibility; and (iii) a hierarchical optimization-based allocation procedure that enforces building-level capacity and block-group-level demographic constraints. The generated synthetic population attains high statistical realism relative to the census microdata, and the contrastive learning model identifies compatible housing-household pairs with high predictive accuracy. Applied to coastal North Carolina, evaluations at building, neighborhood, and regional scales show that the joint inventory matches block-group-level demographic distributions, reproduces observed spatial population patterns without systematic bias, and maintains consistent allocation quality across urban, suburban, and rural contexts. By enabling coupled household- and building-level analyses, the resulting inventory supports a broad range of applications, including disaster resilience planning, housing and affordability analysis, energy-use assessment, and public health research.