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Research Assistants

Macro-Finance Links between Real Estate and Stock Market Boom-Bust Cycles

Boom-bust cycles throughout history exhibit an empirical regularity: during economic expansions, prices and sales volume in real estate markets peak many months before they do in the stock market. What drives this “twin bubbles” phenomenon, and how might policymakers use local real estate market conditions as a leading indicator to prevent macro-financial distress?

Building permits are good predictors of macroeconomic risk because they serve as proxies for investors’ beliefs about the strength of local economies. Real estate developers and investors use building permits as an option to build, exercising their rights when macroeconomic prospects are favorable. In bad economic times, agents choose not to exercise these options. This phenomenon is exemplified by the “skyscraper wave” in New York City around the Great Depression, when permits for remarkable buildings of the Manhattan skyline were delayed, built with a lower number of levels, or never built.

I am seeking a team of RAs to assist in the creation of a database of longitudinal records pertaining to building permit surveys covering all U.S. states and major metro areas. These records will then be merged with larger databases containing individual permit filings, corporate plant locations, and stock and bond market returns.

Requisite Skills and Qualifications:
The data acquisition portion of this project involves compiling records from PDF scans of historical government reports and periodicals such as newspapers and investor manuals. This may require a combination of using optical character recognition (OCR) techniques and software such as ABBYY, as well as recent Python packages designed to convert historical texts into a machine readable format. Much of the work will involve harmonizing tables from the historical records over different time periods and across changing definitions of geographic boundaries.

Prior experience with web/text scraping would be useful. Candidates who have experience applying ArcGIS, Google Maps API, and other software packages related to geo-locating data points are especially encouraged to apply.

Proficiency in statistical/econometric software packages such as Python/R is required for aspects of the project which involve cleaning the source data collected. Otherwise, a working knowledge of Excel and attention to detail are sufficient.

I welcome applicants with interests in other fields besides economics – including but not limited to – urban studies, computer science, mathematics/statistics, physics, and political science.