Collaborative Research on Models of Bargaining and Price Determination of Residential Real Estate, with and without Real Estate Agents

项目名称：Merlo， antonio题目：有和没有房地产中介的住宅房地产议价与价格决定模型的合作研究。住宅房地产在现代经济中占有很大的财富和GDP份额。此外，大多数家庭拥有自己的住房，出售和/或购买住房往往是一个家庭从事的最大的金融交易。令人惊讶的是，很少有模型可以用来分析住房交易过程。本研究将研究房地产市场的交易过程。它将开发可计算的住房市场中买方、卖方和中介行为模型，并使用个人住房交易的高频微观数据来估计模型，其中包括标价如何随时间修改，买家每次访问的信息，以及pi将在美国和英国收集的长时间内大量房屋样本的议价结果。该模型有3个代理人——卖方、买方和房地产经纪人，他们在几轮讨价还价中相互作用。房地产经纪人被建模为拥有一种技术和数据（MLS），可以提高买家的到达率，并将潜在买家与卖家匹配。本研究将不完全信息下的动态决策和议价理论扩展、应用并实证地应用于房地产市场，以描述这些大型市场的运行和效率。这是第一次尝试将动态决策应用于房地产市场，对现有文献的重大贡献。了解房地产市场的交易过程本身就很重要。此外，更好的住宅房地产市场分析模型还会带来潜在的公共政策利益。例如，美国司法部目前正在调查美国全国房地产经纪人协会（U.S. National Association of Realtors），以确定该协会是否设置了不公平的进入门槛，特别是在限制进入MLS方面，以维持高额的房地产佣金。这项研究的结果在此类诉讼中可能非常有用。该分析将扩展到包括其他房地产中介，以及是通过房地产中介出售还是由业主出售的内生选择。这将允许研究人员与房地产经纪人相关的产业组织问题，包括房地产合同和佣金的内生决定。使用来自美国和英国的数据，可以让pi揭示影响房地产市场不同形式组织的相对效率的制度、法律和习俗。这项研究的结果也应该帮助家庭和房地产经纪人在制定房屋销售或购买策略时了解在发挥的权衡。

Proposal No: 0635955Institution: University ofPennsylvaniaNSF Program: ECONOMICSPrincipal Investigator: Merlo, AntonioTitle: Collaborative Research on Models of Bargaining and Price Determination of Residential RealEstate, with and without Real Estate Agents. Residential real estate accounts for a large share of wealth and GDP in modern economies. In addition, the majority of households own their homes and the sale and/or the purchase of a home is often the largest financial transaction a household engages in. Surprisingly, there are few models available to analyze the housing transaction process. This research will study the transaction process in the housing market. It will develop computationally tractable models of the behavior of buyers, sellers, and intermediaries in the housing market, and estimate the models using a high frequency micro data on individual housing transactions, which include how list prices are revised over time, information on each visit by buyers, and outcomes of bargaining for a large sample of homes over a long period of time the PIs will collect in both the US and the UK. The model has 3 agents---seller, buyers, and real estate agents who interact in several bargaining rounds. Real estate agents are modeled as having access to a technology and data (the MLS) that can increase the arrival rate of buyers as well as match potential buyers to sellers.This research extends, applies, and empirically implements theories of dynamic decision making and bargaining under incomplete information to the housing market, in order to describe the operation and efficiency of these large markets. This is the first attempt to apply dynamic decision making to the housing market, a significant contribution to the existing literature. Understanding the transaction process in the housing market is important in itself. In addition, there are potential public policy benefits resulting from better analytical models of the residential real estate market. For example, the U.S. Department of Justice is currently investigating the U.S. National Association of Realtors to determine whether it has created unfair barriers to entry, particularly in restricting access to the MLS, in order to maintain large real estate commissions. The result of this research could be very useful in such litigation.The analysis will be extended to include other real estate intermediaries and an endogenous choice of whether to sell via a real estate agency, or to sell by owner. This will allow researchers industrial organizational issues connected with real estate agents, including endogenous determination of real estate contracts and commissions. Using data from both the US and the UK allows the PIs to shed light on institutions, laws, and customs affect the relative efficiency of different forms of organization of the housing market. The results from this research should also help households and real estate agents understand the trade-offs at play when formulating home selling or buying strategies.