چانه زنی بر سر املاک مسکونی واقعی: شواهد از انگلستان

This paper presents a new data set of individual residential property transactions in England. The main novelty of the data is the record of all listing price changes and all offers made between initial listing and sale agreement. We establish a number of stylized facts pertaining to the sequence of events that occur within individual property transaction histories. We assess the limitations of existing theories in explaining the data and discuss alternative theoretical frameworks for the study of the strategic interactions between buyers and sellers.

The sale of a house is a typical example of a situation that entails strategic interactions between a seller and a set of potential buyers.When a house is put on the market, the seller posts a listing price and waits for potential buyers to make offers. When a match between the seller and a potential buyer occurs, bargaining takes place, leading possibly to a sale agreement. At any point in time while a house is still on the market, the seller has the option of revising the listing price.This paper presents and investigates a new data set of individual residential property transactions in England. The main novel features of our data are the record of all listing price changes and all offers ever made on a property since initial listing. In addition, we have a complete record of visits by potential buyers, called viewings, for a subset of the transactions in our sample. We are therefore in a unique position to analyze the behavior of buyers and sellers within individual transaction histories and the extent to which the sequence of events leading to a transaction affects the sale price.1 The picture of the house transaction process that emerges from the data can be summarized as follows. The listing price influences the arrival of offers, which ultimately determines the timing of the sale. As time on the market increases, the arrival rate of potential buyers decreases and the probability of a listing price revision increases. The longer the time the property remains on the market, the lower the level of offers relative to the listing price, the higher the probability a match is successful, and the lower the sale price relative to the listing price. A relatively high initial listing price results in a higher sale price but also a longer time on the market. Listing price reductions concern primarily properties that have not received any offer while being on the market for a substantial period of time (in fact, a period equal to the average time to sale). Proportionally, decreases in listing price are also substantial (in fact, greater than the average percentage difference between the sale price and the initial listing price). Almost 40 percent of sales occur at the first offer ever received. One third of the potential buyers whose first offer is turned down walk away from the negotiation. The remaining two thirds continue bargaining with the seller and are observed to make up to four consecutive, increasing offers before either they succeed in purchasing the property or the negotiation terminates without an agreement. One-third of all matches between a seller and a potential buyer are unsuccessful. The vast majority of sellers whose first match is unsuccessful end up selling at a higher price; a few end up accepting a lower offer. The higher the number of matches in a transaction history, the higher the sale price. These are just a few of the salient features observed in the data. To date, the lack of adequate data has limited the scope of empirical research on housing transactions.2 Existing data sets typically include property characteristics, time to sale, initial listing price, and sale price. They do not contain information on the buyer’s side of the transaction (e.g., the timing and terms of offers made by potential buyers), or on the seller’s behavior between the listing and the sale of a property (e.g., the seller’s decision to reject an offer or to revise the listing price). This explains why most of the empirical literature on housing transactions has either focused on the determinants of the sale price or on the role of the initial listing price and its effect on the time to sale.3Recent attempts to overcome some of the data limitations by supplementing conventional data sets with additional information have generated valuable insights. For example, Genesove and Mayer [12] build a data set for the Boston condominium market where they are able to uncover the financial position of each seller. They find that sellers with high loan-to-value ratio tend to set a higher initial listing price, have a lower probability of sale but, if and when they sell, obtain a higher price. Glower et al. [14] conduct a phone survey to obtain information on each seller’s motivation (e.g., whether or not they have a planned moving date), for a real estate transactions data set for Columbus, Ohio. The evidence suggests that sellers convey information about their willingness to sell (i.e., their reservation value), through the listing price.4In addition to providing a valuable resource for empirical research on housing transactions, our data raises new challenges for theoretical research on the strategic interactions between buyers and sellers.5 We assess the limitations of existing theories in explaining the data and discuss alternative frameworks that are consistent with the empirical evidence. Our analysis highlights the importance of accounting for incomplete information in the matching and bargaining environment where buyers and sellers interact in order to explain the sequence of events in the housing transaction process as well as its outcomes. The remainder of the paper is organized as follows. Section 2 describes our data set and provides institutional details of the residential real estate market in England. Section 3 reports the results of our descriptive empirical analysis of the process leading to the sale of a property, from its initial listing to a sale agreement. In Section 4, we summarize our main findings, discuss the limitations of existing theories, and suggest alternative theoretical frameworks that are consistent with the data. We conclude with Section 5.

In this paper, we have analyzed a new data set of housing transactions in England. The main novelty of the data is the record of all listing price changes and all offers ever made on a property. This data has enabled us to provide a more accurate picture of the process by which residential properties are transacted and to discuss the implications of our empirical findings for theoretical research on the strategic interaction between buyers and sellers in the housing market. Ourmain findings can be summarized as follows. First, listing price reductions are fairly infrequent; when they occur they are typically large. Listing price revisions appear to be triggered by a lack of offers. The size of the reduction in the listing price is larger the longer a property has been on the market.Second, the level of a first offer relative to the listing price at the time the offer is made is lower the longer the property has been on the market, the more the property is currently overpriced, and if there has been no revision of the listing price. Negotiations typically entail several offers. About a third of all negotiations are unsuccessful (i.e., they end in a separation rather than a sale). The probability of success of a negotiation decreases with the number of previous unsuccessful negotiations. Third, in the vast majority of cases, a property is sold to the first potential buyer who makes an offer on the property (i.e., within the first negotiation). The vastmajority of sellers whose first negotiation is unsuccessful end up selling at a higher price, but a few end up accepting a lower offer. The higher the number of negotiations between initial listing and sale agreement, the higher the sale price. In an attempt to further improve our understanding of the interactions between buyers and sellers in the housingmarket, our future research will focus on developing a theoretical framework capable of explaining all of the qualitative features of the data and estimating this model using our data set. We will then use the estimated structural model to quantify the effects of various policies on the behavior of buyers and sellers in the housing market, and to study the potential effects of institutional reforms of the housing market.