CAREER: An accessible, dynamic model of pricing applied to wage and housing purchases

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Employers across the United States are facing a crisis of labor supply, driven in part by changes in the working conditions employees desire and the wages they perceive as fair. Simultaneously, prospective homebuyers are faced with rising costs and fierce competition for housing – with aging adults, veterans, minorities, and low-income families hit hardest by rising home prices. This project seeks to understand how people assign value in these types of transactions (wages, homebuying), focusing on how individuals respond to competition and market changes. The project team is partnering with financial education programs on money management and first-time homebuyers to understand how differences between people – such as their tolerance for risks or willingness to delay purchases until a future date – predict their success on job and housing markets. The findings will allow us to better understand the psychology of value, create educational resources to help people adopt better strategies for finding jobs or housing, and identify public policy interventions that can alleviate housing and labor shortages.Across a series of experiments, this project tests computational models of pricing in multi-alternative, multi-attribute settings. These experiments manipulate simulated market competitiveness, delays, risks, and attributes of the choice alternatives in order to examine the dynamics of price-setting among individuals and test the cognitive mechanisms of pricing models. To improve the accessibility and efficiency of pricing model fitting and comparison, the project embeds them in neural networks trained to map observed pricing data onto generative model parameters. These will be added to online tools, teaching materials, and workshops to make them widely available. The pricing models will be used to estimate risk and delay aversion, bias, and pricing dynamics in multi-alternative choice experiments. The model parameters will also be used to predict the outcomes of job and home searches among participants in financial education programs.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.