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Manchester Grand Hyatt, Regatta C
Hosted By:
American Real Estate and Urban Economics Association
by auction, exhibit greater volatility than prices in the search and matching model with
Nash bargaining from the literature. This helps solve the puzzle of excess volatility of house
prices. The outcomes of the two models dier in hot markets when buyers' house values
are heterogenous. With Nash bargaining, a buyer who gets a house is chosen randomly
among interested buyers, so prices are determined by the average house values. In auctions,
competition among buyers drives up prices to the willingness to pay of the buyer with the
highest value. In hot markets, the highest values uctuate more than the average values,
making the auction prices more volatile than the negotiated prices. This high volatility is
constrained ecient in the sense that the equilibrium allocation decentralizes the solution
of the social planner problem constrained by the search frictions.
House Price Dynamics and Indexes
Paper Session
Saturday, Jan. 4, 2020 10:15 AM - 12:15 PM (PDT)
- Chair: Chris Redfearn, University of Southern California
Local Constant-Quality Housing Market Liquidity Indices
Abstract
The average time on market (TOM) of sold properties is frequently used by practitioners and policymakers as a market liquidity indicator. This figure might be misleading as the average TOM only considers properties that have been sold. Furthermore, traded properties are heterogeneous. Since these features differ over the cycle, the average TOM could provide wrong signals about market liquidity. These problems are more severe in markets where properties trade infrequently. In this paper, a methodology is provided that allows for the construction of constant-quality housing market liquidity indices in thin markets that can be estimated up to the end of the sample. The latter is particularly important since market watchers are generally interested in the most recent information regarding market liquidity and less in historical information. Using individual transactions data on three different types of Dutch municipalities (small, medium, and large) it is shown that the average TOM overestimates market liquidity in bad times and underestimates market liquidity in good times. The option to withdraw is the most important reason why the average TOM is misleading. Furthermore, constant-quality liquidity leads the average TOM and price changes. The indices not only show that illiquidity is higher during busts, but also that liquidity risk is higher. Additional results suggest that setting a high list price relative to the estimated value results in a higher TOM, but this effect differs over time. Both the list price premium and the effect on sale probability are higher during busts. Differences in housing quality over the cycle, however, also play a significant role. Finally, the method allows for the construction of indices that are more robust to revisions, especially in thinner markets.How Auctions Amplify House-Price Fluctuations
Abstract
I develop a dynamic search model of the housing market in which prices, determinedby auction, exhibit greater volatility than prices in the search and matching model with
Nash bargaining from the literature. This helps solve the puzzle of excess volatility of house
prices. The outcomes of the two models dier in hot markets when buyers' house values
are heterogenous. With Nash bargaining, a buyer who gets a house is chosen randomly
among interested buyers, so prices are determined by the average house values. In auctions,
competition among buyers drives up prices to the willingness to pay of the buyer with the
highest value. In hot markets, the highest values uctuate more than the average values,
making the auction prices more volatile than the negotiated prices. This high volatility is
constrained ecient in the sense that the equilibrium allocation decentralizes the solution
of the social planner problem constrained by the search frictions.
The Most Wonderful Time of the Year? Thin Markets, House Price Seasonality, and the December Discount
Abstract
In Norway, house prices tend to drop in December. This regularity could be caused by a composition effect, a seller effect, or a thin market effect. This article exploits a high-resolution transaction data set with exact sell dates to demonstrate the existence of a December discount. To show existence, we deal with unobserved unit and seller heterogeneity using unit fixed effects, ask prices, and appraisal values. We examine generating mechanisms and find that cross-sectional evidence supports a thin market effect. We find no evidence of stressed sellers. Examination of bidding behavior in a bid-by-bid micro auction data set indicates that sellers grow impatient as the holiday season nears. Scrutiny of seller behavior in advertisement data shows reduced activity on the supply side in December.Discussant(s)
Shawn Rohlin
,
Kent State University
Steven Bourassa
,
Florida Atlantic University
Katherine Kiel
,
College of the Holy Cross
Ronan Lyons
,
Trinity College Dublin
JEL Classifications
- R3 - Real Estate Markets, Spatial Production Analysis, and Firm Location
- C4 - Econometric and Statistical Methods: Special Topics