David Leather
David Leather

Assistant Professor of Real Estate and Finance

Chapman University

About Me

I’m a quantitative researcher who studies real estate markets.

I have a Ph.D. in Economics from the University of North Carolina at Chapel Hill where I specalized in empirical macroeconomics and finance.

I’m currently an Assistant Professor of Real Estate and Finance at Chapman University’s Argyros College of Business and Economics. I teach courses in Macroeconomics and Real Estate Economics to undergraduate and graduate business school students.

Recently I have investigated student housing markets in the United States, and the impact of height regulations on the supply of residential real estate in New York City.

Please reach out to collaborate at david.a.leather [at] gmail [dot] com.

Download Resume
Interests
  • Commercial Real Estate
  • Affordable Housing
  • Monetary Policy
  • Machine Learning
  • Simulation-based Inference
Education

  • PhD Economics

    University of North Carolina at Chapel Hill

  • MS Economics

    University of North Carolina at Chapel Hill

  • BBA Finance

    University of Massachusetts at Amherst

Featured Research
Student Housing and the Cost of Higher Education
Bunching in real-estate markets: Regulated building heights in New York City
What's The Use? Land Use Uncertainty, Real Estate Prices, and the Redevelopment Option
Research
David Leather, Jack Liebersohn (2025). Student Housing and the Cost of Higher Education. Working Paper.
PDF
Jan K. Brueckner, David Leather, Miguel Zerecero (2024). Bunching in real-estate markets: Regulated building heights in New York City. J. Urban Econ..
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David Leather (2023). What's The Use? Land Use Uncertainty, Real Estate Prices, and the Redevelopment Option. Working Paper.
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Andra C. Ghent, David Leather (2021). Is America’s Housing Affordability Problem a Housing Problem?. Working Paper.
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Projects
NYC Building Permits Dashboard
Python

NYC Building Permits Dashboard

Project Overview I developed a sophisticated web-based dashboard that transforms raw NYC building permit data into interactive, actionable visualizations. This project demonstrates my expertise in data processing, geospatial analysis, and full-stack development. View the live dashboard here. Key Technical Achievements Advanced Data Processing: Implemented efficient data pipelines using Pandas and GeoPandas to handle large-scale permit datasets Interactive Visualization: Created dynamic visualizations using Dash and Plotly, enabling real-time data exploration Geospatial Analysis: Developed innovative hexagonal mapping techniques to visualize permit density across NYC Scalable Architecture: Designed a modular codebase that separates concerns and enables easy maintenance Technical Implementation Data Processing Pipeline The dashboard’s backend leverages Python’s data science ecosystem to clean and transform raw permit data:

QuadraticKalman.jl