Massachusetts Institute of Technology
75 Amherst Street, E14-374C
Cambridge MA 02139
Institutional Affiliation: Massachusetts Institute of Technology
NBER Working Papers and Publications
|October 2018||Computer Vision and Real Estate: Do Looks Matter and Do Incentives Determine Looks|
with Edward L. Glaeser, Michael Scott Kincaid: w25174
How much does the appearance of a house, or its neighbors, impact its price? Do events that impact the incentives facing homeowners, like foreclosure, impact the maintenance and appearance of a home? Using computer vision techniques, we find that a one standard deviation improvement in the appearance of a home in Boston is associated with a .16 log point increase in the home’s value, or about $68,000 at the sample mean. The additional predictive power created by images is small relative to location and basic home variables, but external images do outperform variables collected by in-person home assessors. A home’s value increases by .4 log points, when its neighbor’s visually predicted value increases by one log point, and more visible neighbors have a larger price impact than less visible...
|December 2015||Big Data and Big Cities: The Promises and Limitations of Improved Measures of Urban Life|
with Edward L. Glaeser, Scott Duke Kominers, Michael Luca: w21778
New, “big” data sources allow measurement of city characteristics and outcome variables higher frequencies and finer geographic scales than ever before. However, big data will not solve large urban social science questions on its own. Big data has the most value for the study of cities when it allows measurement of the previously opaque, or when it can be coupled with exogenous shocks to people or place. We describe a number of new urban data sources and illustrate how they can be used to improve the study and function of cities. We first show how Google Street View images can be used to predict income in New York City, suggesting that similar image data can be used to map wealth and poverty in previously unmeasured areas of the developing world. We then discuss how survey techniques can b...
Published: Edward L. Glaeser & Scott Duke Kominers & Michael Luca & Nikhil Naik, 2016. "BIG DATA AND BIG CITIES: THE PROMISES AND LIMITATIONS OF IMPROVED MEASURES OF URBAN LIFE," Economic Inquiry, . citation courtesy of
|October 2015||Do People Shape Cities, or Do Cities Shape People? The Co-evolution of Physical, Social, and Economic Change in Five Major U.S. Cities|
with Scott Duke Kominers, Ramesh Raskar, Edward L. Glaeser, César A. Hidalgo: w21620
Urban change involves transformations in the physical appearance and the social composition of neighborhoods. Yet, the relationship between the physical and social components of urban change is not well understood due to the lack of comprehensive measures of neighborhood appearance. Here, we introduce a computer vision method to quantify change in physical appearance of streetscapes and generate a dataset of physical change for five large American cities. We combine this dataset with socioeconomic indicators to explore whether demographic and economic changes precede, follow, or co-occur with changes in physical appearance. We find that the strongest predictors of improvement in a neighborhood’s physical appearance are population density and share of college-educated adults. Other socioeco...