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

提案编号：0635955机构：宾夕法尼亚大学NSF项目：经济问题主要调查员：Merlo，Antonio标题：在有和没有房地产经纪人的情况下，住宅房地产的讨价还价和定价模型的合作研究。在现代经济中，住宅房地产在财富和GDP中占有很大份额。此外，大多数家庭拥有自己的住房，出售和/或购买住房往往是家庭从事的最大的金融交易。令人惊讶的是，几乎没有可用的模型来分析住房交易过程。本研究将研究住宅市场的交易过程。它将开发房地产市场中买家、卖家和中间商行为的易于计算的模型，并使用单个住房交易的高频微观数据来估计这些模型，这些数据包括标价如何随着时间的推移进行修订，买家每次访问的信息，以及PPI将在美国和英国收集的长时间内对大样本房屋的讨价还价结果。该模型有3个经纪人-卖家、买家和房地产经纪人，他们在几轮讨价还价中进行互动。本研究将不完全信息下的动态决策和讨价还价理论扩展、应用到房地产市场，以描述这些大市场的运作和效率。这是首次尝试将动态决策应用于住房市场，这是对现有文献的重大贡献。了解住房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可能带来公共政策方面的好处。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，以确定它是否为了维持巨额房地产佣金而制造了不公平的进入壁垒，特别是在限制进入MLS方面。这项研究的结果可能对这类诉讼非常有用。分析将扩大到包括其他房地产中介机构，以及是通过房地产中介销售还是由业主销售的内生选择。这将使研究人员能够解决与房地产中介有关的行业组织问题，包括房地产合同和佣金的内生决定。通过使用来自美国和英国的数据，采购经理人指数可以揭示影响住房市场不同组织形式的相对效率的制度、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起作用的权衡。

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.