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.

Accurate and interpretable prediction of survey response rates is important from an operational standpoint. The US Census Bureau's well-known ROAM application uses principled statistical models trained on the US Census Planning Database data to identify hard-to-survey areas. An earlier crowdsourcing competition revealed that an ensemble of regression trees led to the best performance in predicting survey response rates; however, the corresponding models could not be adopted for the intended application due to limited interpretability. In this paper, we present new interpretable statistical methods to predict, with high accuracy, response rates in surveys. We study sparse nonparametric additive models with pairwise interactions via $\ell_0$-regularization, as well as hierarchically structured variants that provide enhanced interpretability. Despite strong methodological underpinnings, such models can be computationally challenging -- we present new scalable algorithms for learning these models. We also establish novel non-asymptotic error bounds for the proposed estimators. Experiments based on the US Census Planning Database demonstrate that our methods lead to high-quality predictive models that permit actionable interpretability for different segments of the population. Interestingly, our methods provide significant gains in interpretability without losing in predictive performance to state-of-the-art black-box machine learning methods based on gradient boosting and feedforward neural networks. Our code implementation in python is available at https://github.com/ShibalIbrahim/Additive-Models-with-Structured-Interactions.

Artificial Intelligence, Machine Learning, and Deep Learning are revolutionizing the real estate industry. Here's what you need to know. Real estate includes both the parcel of land and the structure built upon it. It also includes all-natural resources found on the property such as water, minerals, and crops. Real estate refers to immovable property, particularly housing units and buildings.

An architect hoping to rebuild war-torn Mosul, Iraq, has proposed a series of stunning 3D-printed bridges that would transform city using its own building debris into construction materials. Architect Vincent Callebaut is the brainchild behind'The 5 Farming Bridges', which features 3D-printed housing units in the form of articulated spiders over the Tigris River. Five 3D printers could construct 30 houses per day, or nearly 55,000 housing units in five years spread over the five bridges. The concept was a winning project of the Rifat Chadirji Prize Competition, 'Rebuilding Iraq's Liberated Areas: Mosul's Housing'. Architect Vincent Callebaut is the brainchild behind'The 5 Farming Bridges', which features 3D-printed housing units in the form of articulated spiders over the Tigris River in Mosul, Iraq The concept was a winning project of the Rifat Chadirji Prize Competition, 'Rebuilding Iraq's Liberated Areas: Mosul's Housing' Mosul, Iraq's second city, was retaken from IS in July after a massive months-long offensive.

Behavioral scientists contend that individuals, and organizations rarely make decisions solely on the basis of economic factors. Decisions are also shaped by perceived risk, social interactions, currency and salience of information, and other value propositions. Social diffusion of information on consumer experiences, entrance of new business models better aligned with customers’ concerns when evaluating investments, and perceived improving economic conditions are all factors in consumers’ decisions to adopt a new technology, such as solar photovoltaics (PV). We describe a new conceptual agent-based model, BE-Solar, that incorporates a social and behavioral decision framework for technology adoption decisions. We demonstrate the feasibility of including heterogeneity and behavioral factors into an agent-based model of the solar PV market, which is being applied to the Southern California market.