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Properties that Sell at or above Listing Price: Strategic Pricing, Better Broker or Just Dumb Luck?

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Abstract

A surprisingly large number of houses sell above listing prices in a wide range of markets and in all market conditions. The question is: why do some houses sell above listing price while neighboring similar houses do not? Is it that sellers misprice the property at the outset, work with real estate brokers who are particularly skilled at bringing in high value buyers, or are simply lucky to have high value buyers show up during the marketing period? This paper makes two contributions. It offers an empirical framework to isolate seller and agent influences on the likelihood of selling above listing price. It also offers empirical evidence about the seller, agent and market determinants of sales above list across all market phases. Sellers who do not follow their agent’s guidance and under-price their property increase the likelihood of selling above list. Agent experience also increases the likelihood. We also identify specific marketing strategies and agent incentives that do and do not appear influence the likelihood of selling above listing price once the other seller behavior, agent characteristics, and market conditions are taken into account.

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Notes

  1. Following convention, we use the terms broker and agent interchangeably since the differences in their qualifications and legal duties are not relevant to the questions addressed here. The National Association of Realtors estimates that over 90% of sellers use real estate agents (NAR 2017).

  2. Following other housing market studies using MLS data, we omit properties with selling prices in the lower and upper 5 percentiles. We also omit properties with living areas in the lower and upper 5 percentiles and with less than 1 or greater than 5 bedrooms. In addition, we delete new construction since developers typically do not discount prices but instead add upgrades in this market segment.

  3. Using the initial list price allows us to account for the potential effects of list price reductions and property relistings that are found to impact final sales outcomes. (Knight 2002; Smith et al. 2016)

  4. For agents selling their own properties, DOPSeller captures any difference in listing price strategies they adopt relative to client sellers that are not correlated with the set of agent characteristics used to remove agent influences on DOPSeller in the second stage estimation.

  5. The results are the same for alternative definitions of sales price when determining sales above listing price: transaction price, transaction price less stated seller concessions, and transaction price less hypothetical seller concessions of 3%.

  6. We also estimate these models on reduced sample that excludes all agent-owned properties. Removal of the filters or the inclusion does not change our findings. These results are available upon request.

  7. The correlation between DOPAgent and the vector of agent characteristics makes it inappropriate to include both as separate variables in the probit model.

  8. Spatial fixed effects provide a nonparametric approach to spatial dependence when estimated with clustered errors. In particular, we rely on error-clustering at the census tract level following, among others, Davis (2004). This approach essentially uses a simplified version of the spatial weighting matrix where we allow for spatial dependence at the scale of the fixed effects. Similarly, we allow error terms to be correlated for observations within tracts, but assume zero correlation of the error terms for observations in different tracts. This clustering also implicitly adjusts the standard error estimates for heteroscedasticity. The conclusions are unchanged when estimating with clustered errors, which suggests that spatial interdependence or endogeneity arising from either seller or agent behavior is not driving the results reported below.

  9. Note that the analysis focuses on the probability of selling above listing price, not whether the ultimate selling price is higher or lower than houses selling below listing price. Nonetheless, a simple hedonic price regression for sold properties shows that houses sold above listing price sell at a 1.6% premium on average in this market. This result is, however, at best suggestive as the standard hedonic approach does not control for possible endogeneity in the sold-above-list dummy variable that is the focus of this study.

  10. Because our data does not include information about unsuccessful buyer offers we cannot determine the extent to which agents’ strategies, including setting listing price, lead to the type of buyer bidding wars discussed by Han and Strange (2016).

  11. Estimating the probit models with errors clustered on MLS areas yields the same conclusions as discussed in the text. These results are available upon request.

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Authors and Affiliations

  1. University of Georgia, Athens, GA, USA

    Velma Zahirovic-Herbert

  2. Longwood University, Farmville, VA, USA

    Bennie D. Waller

  3. University of Central Florida, Orlando, FL, USA

    Geoffrey K. Turnbull

Authors
  1. Velma Zahirovic-Herbert
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  2. Bennie D. Waller
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  3. Geoffrey K. Turnbull
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Correspondence to Velma Zahirovic-Herbert.

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Appendix

Appendix

Table 6 Descriptive statistics for variables used in the first stage of empirical analysis
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Zahirovic-Herbert, V., Waller, B.D. & Turnbull, G.K. Properties that Sell at or above Listing Price: Strategic Pricing, Better Broker or Just Dumb Luck?. J Real Estate Finan Econ 60, 53–76 (2020). https://doi.org/10.1007/s11146-019-09714-y

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