Elliot Anenberg

Information Frictions and Housing Market Dynamics

This paper examines the effects of seller uncertainty over their home value on the housing market. Using evidence from a new dataset on home listings and transactions, I first show that sellers do not have full information about current period demand conditions for their homes. I incorporate this type of uncertainty into a dynamic search model of the home selling problem with Bayesian learning. Simulations of the estimated model show that information frictions help explain short-run persistence in price appreciation rates and a positive (negative) correlation between price changes and sales volume (time on market).

INFORMATION FRICTIONS AND HOUSING MARKET DYNAMICS

I examine the effects of seller uncertainty over their home value on the housing market. Using evidence from home listings and transactions data, I first show that sellers do not have full information about current period demand conditions for their homes. I incorporate this type of uncertainty into a dynamic microsearch model of the home selling problem with Bayesian learning. The estimated model highlights how information frictions help to explain the microdecisions of sellers and how these microdecisions affect aggregate market dynamics. The model generates a significant microfounded momentum effect in short‐run aggregate price appreciation rates.

A More Timely House Price Index

Using listings data, we construct a new repeat-sales house price index that describes house values at the contract date when the price is determined rather than the closing date when the property is transferred. We showthat this difference in timing helps explain several puzzles about house prices, including their strong short-term serial correlation and their weak correlation with stock prices and macroeconomic news shocks. In addition, we showthat a variant of our index that relies exclusively on listings data for recent transactions accurately reveals trends in house prices several months before existing price indexes like Case-Shiller become available.

Information Technology and Product Variety in the City: The Case of Food Trucks

Using the food truck industry as the setting, we provide direct evidence for how information technology can complement consumption variety in cities by reducing spatial information frictions associated with locally produced goods. We document the following facts: (1) food trucks use technology to overcome a spatial information friction; (2) proliferation of technology is related to growth in food trucks; (3) food trucks use their mobility to respond to consumer taste-for-variety; and (4) growth in food trucks is positively correlated with growth in food expenditures away from home. Taken together, our results illustrate how information technology can provide a meaningful increase in variety for urban consumers.

Interest Rates and Housing Market Dynamics in a Housing Search Model

We introduce mortgages into a dynamic equilibrium, directed search model of the housing market. Mortgage rates play their natural role in our model by affecting the share of per-period income that a homeowner spends on mortgage payment rather than consumption. We estimate the model using microdata on home listings, exploiting the insights of Menzio and Shi (2010) to handle the significant heterogeneity that even basic mortgages introduce into a search model. The estimated model shows that, because of search frictions, housing market conditions are significantly more responsive to mortgage rates than suggested by reduced-form correlations of rates with house prices. Buyer willingness to pay for the typical home changes by more than twice as much as average house prices in response to an interest rate change. As in the data, home construction is more rate sensitive than prices in our model.