Modeling the Hidden Handyman: A Data‑Driven Principal–Agent Analysis of Homeownership

Thomas M. Menino Convention & Exhibition Center 

We develop a principal–agent model to analyze the renting-versus-owning decision, where maintenance effort is costly and unobservable under renting but internalized under ownership. Effort reduces depreciation, increasing long run property quality and neighborhood stability. The model yields closed-form solutions for optimal effort and ownership thresholds:
e_r=β/c_e,
e_o=(β+αP)/c_e

The model also provides conditions under which households prefer owning based on property value and financing terms. To complement the theory, we implement simulation based sensitivity analysis using R, incorporating uncertainty in behavioral and economic parameters. Visual analytics (such as ΔU(P) curves, rate-threshold heatmaps, and affordability trajectories) quantify the joint effects of incentives, prices, and product design (e.g., FRM vs ARM).
This work illustrates how statistical modeling and simulation can inform housing policy, mortgage product design, and credit risk analytics by linking behavioral incentives to measurable outcomes. Planned empirical validation integrates loan-level data and predictive modeling. By combining optimization, simulation, and statistical visualization, this

Principal–Agent

Housing

Mortgage

Credit Risk

Finance

Simulated Sensitivity Analysis 

Business and Economic Statistics Section