“How Should We Measure Shocks To Housing Return: Total versus Appreciation Return?” (Job Market Paper)

The total return to housing is properly measured as the sum of its rental and appreciation returns. This paper discusses specific properties of the relation between the two components of total return using the estimates of these rates across US MSAs. Capitalization rates are constructed using pooled-tenure hedonic estimation. Persistence in these return rates are demonstrated using Im-Pesaran-Shin panel unit root tests and augmented Dickey-Fuller regressions. Empirical tests following the Case-Quigley-Shiller framework examine the housing wealth effect on consumption, using total versus appreciation returns. The principle finding is that the effect of a housing return shock on consumption is much larger when it is measured as an innovation in the total return to housing, implying that the CAP rate component of return is important.

“Shifting House Price Gradients: Evidence Using Both Rental and Asset Prices”

Alonso (1964), Mills (1967) and Muth (1969) formalize the monocentric standard urban model and advocate the gradient approach to analyze the urban structure. Subsequent literature concentrated on changes in the population density or the housing asset price gradients. This paper adds to the literature by providing direct evidence documenting shifts of the house price gradients for both the owner-occupied market and the rental market. Rental and asset price indices are constructed based on simple hedonic estimation, repeat value method, and hybrid model. The slope of the rental price gradient largely increased before the early 1990s, and has stayed stable since then. For the owner-occupied market, there is an overall steepening of the asset price gradient over 1985-2013. Possible reasons for the steepening of house price gradient are then developed using the standard urban model.


“Study on the Skill Intensity Ratio and Labor Market Dynamics among International Immigrations”

“Measuring Total Return to Single Family Rentals Using Census Data”


“Mapping Crime Data Using Spatial Analysis Tools in R”

Spatial objects from shapefiles of large crime datasets from the National Institute of Justice are loaded in R. Data is manipulated by clipped the crime points outside target space to produced cross polygon comparisons. Crime heat maps are plotted with Leaflet based on a own-created evenly spaced and finer grid, and an open source layer of city map is added to create interactive crime maps.

“Estimating the Capitalization Rate of Multifamily Housing in Northern Virginia in Python”

A Python wrapper around the Zillow API is used to scrape information of multifamily housing in Northern Virginia based on zip code and address. The capitalization rate is estimated based on the pooled-tenure hedonic model, using housing units located in buildings where owning and renting are observed simultaneously to control for the neighborhood amenity effect. Visualization of the capitalization rates in forms of cross-sectional comparison and time series trend are produced using plotting tools in Python.