Estimating Preferences for Neighborhood Amenities Under Imperfect Information
Abstract:
This paper presents a new framework for estimating preferences for neighborhood amenities in the presence of imperfect information. Since individuals observe different sets of amenities with different errors, this omitted variable bias is at the neighborhood-by-individual level and cannot be resolved with standard methods. To solve the bias from imperfect information we introduce a sufficient neighborhood-by-individual latent quality index, which is only feasible to estimate with a panel in which individuals choose neighborhoods under imperfect and perfect information about some amenities. Our data comes from a new neighborhood choice program to help students choose where to live upon graduation. The program provided information about market rents and same-school professional and social network, and collected neighborhood rankings for the same individual before and after receiving information. Even though many students have persistent rankings, we also observe switchers - who change their neighborhood rankings after the information intervention - increase network shares by 1.46 percentage points and decrease rents by $430 for their top-three neighborhoods. We estimate our general model using these data, and find more negative estimates of marginal utility of rents, and lower marginal willingness-to-pay for the network, relative to a model that does not account for imperfect information. Finally, we show that the neighborhood choice program also affected revealed preferences: Switchers responded to the information intervention by searching neighborhoods with lower rents and higher network shares, and also by choosing a neighborhood of residence with similar characteristics.
Bio:
Fernando Fernando is a C.F. Koo Professor, Professor of Real Estate, Professor of Business Economics and Public Policy. More info on his research see his research profile and CV
Maisy Wong is the James T. Riady Associate Professor of Real Estate at the Wharton School of the University of Pennsylvania. Her research interests include labor mobility, urbanization, and real estate finance. At Wharton, she has taught courses on Real Estate Investments (REAL/FNCE 209/721) and Global Real Estate markets (REAL 205/705) to undergraduate and MBA students, and has several Excellence in Teaching Awards.
Dr. Wong is a Research Associate at the NBER, on the editorial board of the Journal of Urban Economics, and a former board member of the American Real Estate and Urban Economics Association (AREUEA). Dr. Wong earned her undergraduate degree from U.C. Berkeley and her Ph.D. from the Massachusetts Institute of Technology (MIT).