How many listings are too many? Agent inventory externalities and the residential housing market

Bian, Xun; Waller, Bennie D.; Turnbull, Geoffrey K.; Wentland, Scott

doi:10.1016/j.jhe.2015.04.002

Cited by 20 publications

(12 citation statements)

References 44 publications

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“…The negative and statistical significant estimates associated with T otalSale suggest the sale price of a house decreases by 0.2% with one more count of total homes sold by the listing agent. This finding is consistent with results of Bian et al (2015), suggesting the amount of effort devoted to the selling of each house decreases as the selling agents' experiences increase. The estimated effect of T otalSale × U nique i in Columns (1)-(2) is positive and statistically significant, implying agents learn from past transactions and become more experienced in selecting value enhancing house features to advertise in descriptions.…”

Section: Unique Description and Realtor Experiencessupporting

confidence: 90%

Information Value of Property Description: A Machine Learning Approach

Shen

Ross

2018

SSRN Journal

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This paper employs a ML-Hedonic approach to quantify the value of uniqueness, a type of "soft" information embedded in real estate advertisements. We first propose an unsupervised learning algorithm to quantify levels of semantic deviation ("uniqueness") in descriptions, the textual portions of real estate advertisements. We then estimated the impact of description uniqueness on real estate transaction outcomes using linear hedonic pricing models. The results indicate textual data disseminate information that numerical data cannot capture, and property descriptions effectively narrow the information gap between structured real estate data and the houses by conveying "soft" information about unique house features. A one standard deviation (0.08) increase in description uniqueness compared to neighboring properties leads to a 5.6% increase in property sale prices and a 2.3-day delay in the closing time, controlling for house characteristics, transaction circumstances, and agent unobservables. This paper provides theoretical and empirical insights on how to utilize the emerging Machine Learning tools in economic research.

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Section: Unique Description and Realtor Experiencessupporting

confidence: 90%

Information Value of Property Description: A Machine Learning Approach

Shen

Ross

2018

SSRN Journal

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Section: Results With Diffused Effortmentioning

confidence: 99%

“…The relevant studies in the literature on the agent's network size are Brastow, Springer, and Waller (2012) and Bian, Waller, Turnbull, and Wentland (2015). Bian et al (2015) show that taking on additional inventory results in diluting an agent's selling effort. Furthermore, they document empirical evidence that a larger inventory tends to lead to longer TOM, which is equivalent to a lower likelihood of sale.…”

Section: Results With Diffused Effortmentioning

confidence: 99%

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Diffused effort, asset heterogeneity, and real estate brokerage

Sun

et al. 2021

Real Estate Economics

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We generalize the classic Williams (1998, Review of Financial Studies, 11, 239–280) brokerage model by introducing diffused effort and asset heterogeneity. The term “diffused effort” refers to the fact that an agent can cross‐utilize effort spending on one listing to another . One counterintuitive finding in Williams' paper is the absence of the agency problem . As a special case in our model, we recover the agency problem. We demonstrate the positive externality due to the diffused effort and show it depends on the agent's inventory size. Hence, there is a trade‐off between agents' effort committed to existing listings and expanding network size by soliciting new listings.

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“…For example, Rutherford et al (2005) shows that agents contribute more effort to sell their own properties. Bian et al (2015) find that greater agent listing inventory dilutes efforts contributed to each listing. Our view is that the listing agent takes into account all relevant factors and first determines the expected effort level, and the expected effort level then serves as the basis for the agent to further choose the level of commission split.…”

Section: Pr Salementioning

confidence: 89%

Commission Splits in Real Estate Transactions

Bian

Waller

Yavaş

2015

J Real Estate Finan Econ

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We examine commission splits between listing and selling agents in real estate transactions. We construct a theoretical model to show that agency problems arise when a listing agent attempts to maximize his or her payoff while setting the commission split. Mitigation to these agency problems can be achieved through the imposition of a limited duration on listing contracts. Our model produces several testable hypotheses, which are supported by empirical evidence. We find property listings with higher list prices and quick sales are associated with lower commission splits. Commission split is more likely to be higher when the listed property has a high degree of atypicality and/or is overpriced. Additionally, agent-owned properties pay higher commission splits.

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How many listings are too many? Agent inventory externalities and the residential housing market

Cited by 20 publications

References 44 publications

Information Value of Property Description: A Machine Learning Approach

Information Value of Property Description: A Machine Learning Approach

Diffused effort, asset heterogeneity, and real estate brokerage

Commission Splits in Real Estate Transactions

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