residential location modeling

This study addresses the complex trade-offs households face when choosing residential locations, particularly between commuting costs and relocation decisions. It develops an algebraic utility maximization model that integrates housing costs, commuting expenditures, income affordability (including the 30% rule), social ties, and locational amenities. By uniquely synthesizing classical urban economic theory with contemporary considerations of housing affordability, social networks, and quality of life, the approach yields a concise, interpretable, and readily extensible mathematical decision rule—distinct from more complex discrete choice or agent-based models. The resulting analytical framework offers both theoretical innovation and practical utility, supporting household migration decisions and informing urban policy design, thereby advancing interdisciplinary research at the intersection of urban economics and housing studies.

This study addresses the limitations of traditional urban integration models in capturing household trade-offs under dual constraints of time and money. Building on household production theory, the authors develop a microeconomic framework that jointly models transportation and land-use choices, innovatively incorporating a parallel-constrained multiple discrete-continuous extreme value (PC-MDCEV) structure to accommodate multi-person households. The model endogenously accounts for minimum travel time required for activities as a measure of accessibility, deducting it directly from the household time budget. Empirical analysis using data from the Greater Toronto Area reveals significant economies of scale in household production with respect to time and demonstrates that housing type profoundly influences both time allocation and consumption patterns.

This study addresses the challenge of quantifying residents’ subjective preferences in large-scale planning communities, which hinders democratic strategic decision-making. We propose a community-level preference quantification framework grounded in utility theory and an exponential marginal diminishing utility model. Preferences are elicited via brief online surveys on public facility and infrastructure investment priorities, then integrated with usage forecasts, cost/risk sensitivity, and willingness-to-pay to enable group-level preference aggregation and automated multi-alternative ranking. To our knowledge, this is the first application of an exponential marginal diminishing utility model at the community scale for strategic planning, enabling end-to-end translation from subjective preferences to actionable investment options. Empirical evaluation demonstrates that the framework significantly improves ranking consistency and public acceptability, while accurately predicting resident support levels and actual contribution willingness.

This paper addresses imbalanced matching markets—such as school choice, ride-hailing, and spatial markets—where supply and demand are misaligned. Methodologically, it establishes a rigorous equivalence between stable matching and optimal transport theory by modeling preference-aligned markets as parametric optimal transport problems. This formulation reveals that stability inherently induces welfare inequality and characterizes the co-evolution of efficiency, fairness, and inequality with societal preference heterogeneity. The paper proves, for the first time, that large-scale heterogeneous-preference markets admit uniform approximation by homogeneous-preference models; further, it shows that structural properties of stable matchings and their multi-objective trade-offs can be precisely captured via convex optimization and asymptotic analysis. These results unify diverse real-world matching settings, quantify the fundamental tension between stability and fairness, and yield a new matching design framework that is computationally tractable, interpretable, and scalable.

This paper addresses the inefficiency and fragility of conventional stated-preference experiments for probabilistic choices, where ex ante expected returns and willingness-to-pay (WTP) estimates rely on multiple choice rounds and strong parametric assumptions—leading to lengthy surveys and low feasibility for ex ante policy evaluation. We propose a nonparametric identification method requiring at most two probabilistic choices per respondent. It imposes no functional-form assumptions on utility and, for the first time, fully identifies both the population distribution of ex ante expected returns and WTP for structured preference objects (e.g., multidimensional job attributes). Theoretical foundations integrate nonparametric identification theory with structured discrete choice modeling. Applied to elite student employment preferences in Côte d’Ivoire, the method robustly identifies a significant upward effect of public-sector jobs on private-sector hiring costs—demonstrating both empirical validity and direct policy relevance.

This study investigates commuters’ joint choice behavior regarding travel mode and departure time under congestion pricing policies, with a focus on spatial heterogeneity. Using stated preference data from Calgary commuters, the authors estimate and compare multinomial logit, nested logit, and cross-nested logit models. The cross-nested logit model proves superior in capturing cross-substitution effects between mode and time dimensions, significantly improving behavioral fit. Findings reveal higher price elasticity among travelers in central urban areas and during peak periods. The results suggest that dynamic, differentiated pricing schemes—such as zone- and time-based tolling (e.g., cordon pricing combined with time-of-day rates)—can effectively alleviate congestion. The study further underscores the critical importance of complementary public transit enhancements and equity-oriented measures for the successful implementation of congestion pricing policies.

Accurately estimating the direct, unmediated effect of environmental amenities on housing prices (DUET) is crucial for welfare analysis, yet existing methodologies face significant limitations. This study leverages over one million property transactions in New York State from 1990 to 2024 to construct an empirical “ground truth” that preserves the true data-generating process. Using Monte Carlo simulations, it systematically evaluates the estimation accuracy of generalized difference-in-differences (DID), two-way fixed effects models, and causal machine learning methods—including causal forest DID—across varying sample sizes. The results demonstrate that generalized DID consistently outperforms benchmark models across all scenarios, while causal machine learning approaches exhibit strong performance when sample sizes exceed 3,000 observations, with causal forest DID achieving accuracy nearly on par with generalized DID, thereby offering a reliable methodological alternative for DUET estimation.