Longer term housing market effects of a major US airport closure

Using a unique dataset, we examine various effects of closing Denver's Stapleton Airport, and subsequent redevelopment, on nearby housing markets. We find immediate anticipatory price effects upon announcement, but no price changes at closing and little evidence of upward trending prices between announcement and closing. Post-closure, more higher income and fewer Black households moved in, and developers built larger houses on larger lots. Increases in the price of pre-existing housing are also found. Finally, we find that post-closing price increases were largest in areas that were closest to the center of new commercial development and that had greater exposure to new housing construction.     

Embodying the past,designing the future: technological determinism reconsidered in technology education

International Journal of Technology and Design Education - From a philosophical viewpoint, technological design is about connecting what is desirable with what is technically possible. Technology...     

THE WELFARE EFFECTS OF MANDATORY REEMPLOYMENT PROGRAMS: COMBINING A STRUCTURAL MODEL AND EXPERIMENTAL DATA

This article estimates a model of job search with utility costs and benefits triggered by mandatory reemployment programs. I use data from an experiment that generates exogenous variation in the threat of program participation and an increase in the job finding rate. I calculate the impact of the experiment on worker welfare, and find that participants would be willing to give up 1.5–1.7 weeks of unemployment benefits to avoid participation. These welfare costs vary across workers and are larger for workers with weaker employment prospects. Overall, welfare costs are substantial and therefore important to take into account.     

Interpretable machine learning for real estate market analysis

Machine Learning (ML) excels at most predictive tasks but its complex nonparametric structure renders it less useful for inference and out-of sample predictions. This article aims to elucidate and enhance the analytical capabilities of ML in real estate through Interpretable ML (IML). Specifically, we compare a hedonic ML approach to a set of model-agnostic interpretation methods. Our results suggest that IML methods permit a peek into the black box of algorithmic decision making by showing the web of associative relationships between variables in greater resolution. In our empirical applications, we confirm that size and age are the most important rent drivers. Further analysis reveals that certain bundles of hedonic characteristics, such as large apartments in historic buildings with balconies located in affluent neighborhoods, attract higher rents than adding up the contributions of each hedonic characteristic. Building age is shown to exhibit a U-shaped pattern in that both the youngest and oldest buildings attract the highest rents. Besides revealing valuable distance decay functions for spatial variables, IML methods are also able to visualise how the strength and interactions of hedonic characteristics change over time, which investors could use to determine the types of assets that perform best at any given stage of the real estate investment cycle.     

Towards a three-part heuristic framework for technology education

International Journal of Technology and Design Education - There is not one single global version of technology education; curricula and standards have different forms and content. This sometimes...