Market Microstructure and Behavior
Paper Session
Friday, Jan. 6, 2023 8:00 AM - 10:00 AM (CST)
- Chair: Yingchun Liu, University of North Texas
Seeing is Believing: The Impact of Buyers’ Onsite Viewing Activities on Housing Transactions
Abstract
Buyers’ onsite viewing is an important process of house transaction and is the direct measure of buyer search. Yet empirically we know little about the information revealed through a buyer’s onsite viewing, and neither do we know about the impacts of buyers’ onsite house viewings on transaction outcomes. Using a unique proprietary dataset which includes 4,397,652 onsite viewing records and 621,040 transaction outcomes from the largest real estate agency in China, we find that buyers who are more active in onsite viewings are associated with larger deal likelihoods, as well as higher purchase prices and greater chances of making over budget payment in completed deals. The findings suggest that a buyer’s onsite viewing is a reflection of his or her housing demand. Buyers achieve improved deal likelihood from active searching. However, as they reveal their stronger demand to sellers through active onsite house viewings, they lose bargaining power and end up paying higher prices. To establish causality, we perform instrument variable regressions exploiting the exogenous variations in onsite house viewings caused by national basketball games that increase the opportunity cost of searching, and find consistent results.The Good, the Bad and the Ordinary: Estimating Agency Value-Added Using Real Estate Transactions
Abstract
We document the rise in Flat-Fee listings, a new method for homeowners selling their own homes, to gain access to the MLS and use these sales as a benchmark to measure the efficacy of listing agents. Controlling for property and location characteristics, we find that, on average Flat-Fee listings obtain prices that are 1%-5% higher compared to those obtained by a traditional listing agent and take no longer to sell. We also estimate the value-added of employing a buyer's agent in the transaction process and find similar results relative to a benchmark of dual-agent transactions where the buyer shares the agent with the seller. In addition, to mean price and DOM effects, we also recover the distribution of realtor value-added as listing agent and buyer's agent. With these distributions, we estimate the trade-off between price and days on the market by realtors. Focusing on a sample of repeat transactions, agents who obtain higher prices do not take longer to sell, suggesting that they are not simply setting higher reservation prices. Finally, we show that agents who sell homes for more also appear to pay more for a home when serving as a buyer's agentInitial Property Offering: Underpricing and Learning in the Presale Housing Market
Abstract
Utilizing a transaction-level dataset of presale private properties in Singapore over 20 years (2000-2020), this paper investigates the property price dynamics following a project launch. We show that for a newly launched residential project, presale prices increase by approximately 1% every 100 days from the launch date, indicating an IPO underpricing price pattern. By matching transaction data with developer information, we demonstrate that developers tend to underprice their first two presale projects, and then adjust pricing strategies in subsequent projects by learning from experience and adjacent peers. Our study discloses developers’ underpricing and learning behavior in the presale housing market.Discussant(s)
Yichen Su
,
Federal Reserve Bank of Dallas
Walter D'Lima
,
Florida International University
Tingyu Zhou
,
Florida State University
Xi Yang
,
University of North Texas
JEL Classifications
- R3 - Real Estate Markets, Spatial Production Analysis, and Firm Location