The Impact of Broker Vernacular in Residential Real Estate

Summary This paper incorporates text data from MLS listings into a hedonic pricing model. We show that the comments section of the MLS, which is populated by real estate agents who arguably have the most local market knowledge and know what homebuyers value, provides information that improves the performance of both in‐sample and out‐of‐sample pricing estimates. Text is found to decrease pricing error by more than 25%. Information from text is incorporated into a linear model using a tokenization approach. By doing so, the implicit prices for various words and phrases are estimated. The estimation focuses on simultaneous variable selection and estimation for linear models in the presence of a large number of variables using a penalized regression. The LASSO procedure and variants are shown to outperform least‐squares in out‐of‐sample testing. Copyright © 2016 John Wiley & Sons, Ltd.

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“…In a recent follow‐up study, Goodwin et al. () extend the Haag et al. () study by including additional keywords and categories.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Goodwin et al. () also cover a longer time period (2000–2009) that includes both an up and down real estate market. This is important because a study by Soyeh et al.…”

Section: Literature Reviewmentioning

confidence: 99%

Textual Analysis in Real Estate

Nowak

Smith

2016

J. Appl. Econ.

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“…The evidence on the price impacts of frequent keywords in real estate advertisements thus far has been mixed. On one hand, Levitt and Syverson (2008), Rutherford and Yavas (2005), and Nowak and Smith (2017) find the inclusion of indicator variables for positive/negative words and short phrases in real estate advertisements can reduce omitted variable biases; On the other hand, Goodwin (2014) and Pryce (2008) point out the effects of positive/negative words on real estate prices are not consistent across different word classes.…”

Section: Introductionmentioning

confidence: 99%

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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“…The effects of brokerage firm size on time on the market were confirmed by Jud et al (1996) and Larsen and Park (1989). Goodwin et al (2014) explored the words used by different brokers to market the property and found that brokers' description of the property could affect the listing's performance characteristics, such as the number of days on the market. Benefield et al (2011) and Wiley et al (2011) found that the limited services brokerages provided sped up the sale and increased the selling price.…”

Section: Literature Reviewmentioning

confidence: 85%

Understanding Sellers’ Agents in the Residential Property Market

Xiong

Cheung

2021

International Journal of Strategic Property Management

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Buyers in the property market often use an agent who is employed by the seller to assist their home searches. This unique and widely used agency arrangement in the property market is known as “sellers’ agents”. While principal-agent theory advocates that buyers should directly hire their agents (i.e., buyers’ agents) to do the home-hunting, search theory however implies that sellers would employ their agents (i.e., sellers’ agents) to increase the probability of the sale and selling prices. Although sellers’ agents and buyers’ agents are two very distinct institutions, many previous studies assume that their agency characteristics are identical and provide limited insights on how such a seemingly subtle but crucial agency arrangement affects transaction outcomes. Using transaction data from Wuhan China, this study disentangles the effects of the buyers’ and sellers’ agents on properties’ selling prices and their time on the market. The findings indicate that on average transactions conducted by sellers’ agents will be associated with a significant selling price premium of around 3.4%. As a critical test, we further show that the transactions completed by sellers’ agents with selling price premiums will have a shorter marketing time than those completed by buyers’ agents.

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The Impact of Broker Vernacular in Residential Real Estate

Cited by 38 publications

References 25 publications

Textual Analysis in Real Estate

Textual Analysis in Real Estate

Information Value of Property Description: A Machine Learning Approach

Understanding Sellers’ Agents in the Residential Property Market

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