Market Strength and Brokerage Choice in Residential Housing

Xiangou Deng, Zhaohui Li, Michael J. Seiler, Hua Sun
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Abstract

This study develops a theoretical model examining the relation between housing market strength and brokerage choice. Our model shows that although internal transactions (where both buyer and seller agents are either the same or work for the same firm) have the potential side benefits of higher commission rates and lower search costs, in a strong housing market, brokerage firms are more likely to engage external transactions because of the greater demand for housing. However, when the market weakens, external demand for housing decreases, and brokerage firms become more willing to conduct internal transactions. Furthermore, while an internal transaction tends to occur at the expense of lowering the selling price, we show that it could also be chosen by brokerage firms with higher in-house searching-matching efficiency. This higher in-house efficiency generates a (second-order) counterforce of increasing the price. Hence, our model demonstrates that the housing market has a (partial) self-correction mechanism for the principal-agent incentive misalignment problem, especially when the market strengthens. Conversely, when the market weakens, internal transactions increase and prices decline, which can further weaken the market. Therefore, the equilibrium brokerage choice creates a self-reinforcing mechanism for generating more extreme market conditions. Using the Doubly Robust (DR) estimation method, we present empirical evidence consistent with the model with multiple listing service data from Hampton Roads, Virginia.

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住宅市场实力与中介选择
本研究建立了一个理论模型,探讨了住房市场强度与经纪选择之间的关系。我们的模型显示,尽管内部交易(买卖双方经纪人为同一人或为同一公司工作)具有佣金率较高和搜索成本较低的潜在副作用,但在住房市场强劲的情况下,经纪公司更倾向于进行外部交易,因为对住房的需求更大。然而,当市场疲软时,外部对住房的需求减少,经纪公司更愿意进行内部交易。此外,虽然内部交易往往以降低售价为代价,但我们发现,内部搜索匹配效率较高的经纪公司也会选择内部交易。这种较高的内部效率会产生一种(二阶)反作用力,即提高价格。因此,我们的模型证明,住房市场对委托代理激励错位问题具有(部分)自我纠正机制,尤其是在市场增强时。相反,当市场疲软时,内部交易增加,价格下降,这会进一步削弱市场。因此,均衡经纪商选择产生了一种自我强化机制,以产生更极端的市场条件。我们使用双稳健(Doubly Robust,DR)估计方法,利用弗吉尼亚州汉普顿路的多重上市服务数据,提出了与模型一致的经验证据。
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