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The Effect of Listing Price Changes on the Selling Price of Single-Family Residential Homes

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Abstract

Sellers of unsold residential real estate usually have difficulty deciding whether to change the listing price and, if so, by how much. The purpose of this study is to determine what factors lead to listing price changes and the effect of listing price changes on the net selling price received by sellers. This study uses a sample of 13,461 single-family home sales in which 4308 had a selling price reduction during the listing period. The average original listing price for properties with one or more listing price changes is 18 % larger compared to properties where the listing price is unchanged; this difference narrows to 9.7 % when comparing final listing prices. The results indicate that the probability of a listing price reduction and the percent reduction are positively associated with house size, vacant property status, and a weak economy. A sample selection bias appears to exist for list price reduction properties, and while overpricing of properties often leads to a listing price reduction, the effect of a listing price change on the net selling price is estimated to be two to three times the given reduction in the listing price.

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Notes

  1. Salter et al. (2010) also studied the listing prices set by financially constrained sellers and determined that when homes were listed by agents that specialized in listings, as opposed to sales, that the listing price was more accurate when compared to the final selling price.

  2. In addition to the commission structure, another popular stream of research has examined contract duration and the role of buyer brokers on broker effort and competition.

  3. Source: Realtor.com Home Seller’s Guide http://marketing.realtor.com/star/emails/images/Home_Sellers_Guide.pdf

  4. The Dictionary of Real Estate Appraisal, 5th edition, The Appraisal Institute.

  5. Source: Bankrate.com Five Tips to Start a Bidding War on Your Home

    http://www.bankrate.com/finance/real-estate/tips-start-bidding-war-for-home.aspx#slide=2

  6. Former foreclosure properties are referred to hereafter as foreclosure or foreclosed properties for the sake of simplicity.

  7. Knight also notes other important findings. Results differ whether or not there is knowledge of price changes occurring. When purchasers are ignorant of price changes including the original list price and listing date, the time-on-market coefficient is negative and significant. However, if buyers have knowledge of a price change the time-on-the-market coefficient is positive and significant. Knight suggests that the inconsistent sign of the time-on-market variable throughout the real estate literature may be a result of not including listing price change data in the models used. Consistent with other literature, he found that the most significant causes of listing price changes were the total length of time that a home had been on the market, the amount by which the home had been overpriced initially, and whether the house was vacant. Atypical homes were less likely to undergo price changes since a failure to sell does not communicate as much information as it would for a more typical home.

  8. While the models for predicting the net selling price and the original listing price versus predicted selling price spread are based on 140 census tracts, the other 2SLS and 2SLS sample selection regressions use postal Zone Improvement Plan (ZIP) codes as location variables to conserve degrees of freedom because the sample size drops from the full sample size of 13,461 observations to 4,308 observations for those properties with a decreased listing price. The predicted net selling price regression has an adjusted R-squared of 88.5 % with almost all non-interaction regression coefficients statistically significant at the 0.01 level, and one-third of interactions reaching statistical significance levels of 0.10 or better.

  9. The number of observations is 13,461 with the same parameters as Eq. (2). The adjusted R-squared is substantially lower at 8.5 %, However, the residuals from this regression are robust for use as an overpricing variable. While the ratio of the original listing price to the predicted net selling price (from Eq. 2) could be used as a measure of overpricing, it does not provide a mechanism for incorporating the variance of the estimate whereas the standardized residuals from Eq. (3) provide it, and also adjust for the other variables associated with matrix Z, with the standardized residuals reflecting the unexplained deviation from an expected deviation of zero.

  10. The standardized residuals are calculated as \( u{(i)}_i={\left[\frac{\frac{\frac{e_i}{1-{h}_{it}}}{\left[{\mathbf{e}}^{\prime}\mathbf{e}-\frac{e_i^2}{1-{h}_{it}}\right)}}{n-K-1}\right]}^{1/2} \), where \( {h}_{it}={\mathbf{x}}_i^{\prime }{\left({\mathbf{X}}^{\mathbf{\prime}}\mathbf{X}\right)}^{-1}{\mathbf{x}}_i \) for n observations with K parameters.

  11. In this case, matrix X consists of the same variables as matrix W.

  12. Another interesting research question is the effect of multiple listing price changes and when they occurred. This information would might permit us to further examine the determinants of listing price change and the potential effect on the net selling price. Unfortunately information is not available in the data set at this level of detail.

  13. See Sirmans et al. (1995); Harding et al. (2003); Turnbull et al. (1990).

  14. In the 2SLS models in Eqs. (7) and (8), OCCUPIED, SAMEAGT, TRANSBRK and OVRPRR serve as instruments in the regression. Therefore, the first stage includes all variables in the system. The 2SLS and instrumental variable empirical results are identical using this method.

  15. These models are estimated using a cluster specification based on census tract location. This approach is frequently used when observations occur in groups that may be correlated. The parameters are unchanged but asymptotic covariance matrix is adjusted.

  16. There are an insufficient number of properties with listing price increases to conduct a robust empirical analysis.

  17. These estimates are consistent with the mean change in listing price of 4.4 and 8.2 %, respectively, for foreclosed and short sale properties.

  18. Unfortunately, because the OVRPRR variable is estimated as a standardized residual from the overpricing regression, it does yield easily interpretable coefficients.

  19. Adjusted R2 are not reported for the 2SLS models because the variance of the dependent variable cannot be properly decomposed so that the R2 has no natural interpretation.

  20. The regression dummy coefficients for the natural logarithm of net selling price regressions provide reasonably accurate estimates of the percentage change. The precise percentage change for the dummy variable coefficients can be more accurately estimated using \( y=\Big({e}^x-1 \))*100, where x is the coefficient and y is the percentage change.

  21. The FORCL coefficient of −1.8383 appears unjustifiably large, however, evaluating the LDOM and PRCCHG interactions with FORCL at the listing price decreased sample means for these variables, the net effect is −0.07.

  22. The estimated loss is determined by multiplying the mean percentage change in the listing price (7.45 %) by the M1 coefficient in Table 6 (1.16 %) as the estimated effect on the net selling price is 1.16 % for each percentage reduction in the listing price. Therefore, the estimated loss based on the mean net selling price is y = $159,344 × (7.45 % × 1.16), and y = $13,771 compared to a loss of $11,871 at the 7.45 % average.

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Authors and Affiliations

  1. UAB Collat School of Business, University of Alabama at Birmingham, BEC 306D, 1720 2nd Ave. S., Birmingham, AL, 35294-4460, USA

    Bruce L. Gordon

  2. Joseph M. Bryan School of Business and Economics, University of North Carolina at Greensboro, Greensboro, NC, 27412-5001, USA

    Daniel T. Winkler

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Correspondence to Daniel T. Winkler.

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Gordon, B.L., Winkler, D.T. The Effect of Listing Price Changes on the Selling Price of Single-Family Residential Homes. J Real Estate Finan Econ 55, 185–215 (2017). https://doi.org/10.1007/s11146-016-9558-z

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