The Effect of Relisting on House Selling Price

Abstract

When house sellers reach the end of a listing contract without a sale they are faced with several decisions. A seller who wants to continue to market the property can leave it on the market, relist the property immediately, or take it off the market for a period of time before relisting it. Research has shown that properties with longer time-on-market may carry a stigma and sell for less. In an attempt to mitigate the negative perception of a house that other buyers appear to have passed by, a seller can have the agent relist the property so it appears as a new listing. If a seller decides to relist the property, the owner also has to decide how long to wait before relisting. We use a hedonic approach to investigate the choices sellers have when deciding whether to relist their property and the impact those decisions have on the property’s selling price. We find that relisting a property results in a higher selling price and that to maximize price, the seller should relist the property with the same agent within 30 days.

Appendix

Predicted time on the market ($\widehat{TOM}$) is estimated using the following equation:

$$ TOM=c+\varpi \mathbf{DOP}+\kappa \mathbf{R}+\upsilon \mathbf{A}+\mu $$

(2)

where predicted time-on-market is a function of degree of overpricing, DOP; list price reduction, R; and the atypicality of the house, A. Predicted selling price is used to estimate DOP (see Eq. 3). The results for our intermediate steps are available in Table 5.

Table 5 Intermediate results

The degree of overpricing variable is calculated using the property’s original list price in the first listing contract and predicted selling price. The predicted selling price is obtained using a version of Eq. 1 that is comprised of the physical characteristics of the house, X; distressed conditions surrounding the sale, D; neighborhood characteristics, N; and location and time trend variables representing fixed effects for the exact geographic location and year and season of sale, T.

$$ \mathbf{DOP} = \mathrm{Log}\left(\mathrm{Original}\ \mathrm{List}\ \mathrm{Price}\right)\hbox{--} \mathrm{Log}\left(\mathrm{Predicted}\ \mathrm{Selling}\ \mathrm{Price}\right) $$

(3)

Previous research finds houses with unusual attributes sell for less and take longer to sell (Haurin 1988; Jud et al. 1996). To capture the atypicality effect we use a model similar to the one presented by Turnbull et al. (2006). They measure the extent to which a given house is either larger or smaller than the average living area in the surrounding neighborhood. We index all houses within a one half mile radius of house i by J. The standardized measure of the relative house size is:

$$ Localsiz{e}_i=\frac{Livingare{a}_i-{\displaystyle {\sum}_{j\in J} Livingare{a}_j/{N}_j}}{{\displaystyle {\sum}_{j\in J} Livingare{a}_j/{N}_j}} $$

(4)

where N _j is the number of surrounding houses in the neighborhood J. In order to allow for asymmetric relative house size effects on selling price, we define the relative size variables Larger _i and Smaller _i as the absolute values of the positive and negative values of Localsize _i respectively:

$$ Large{r}_i = {\left\{{}_{\left| Localsize\right|}^0\right.}_{, if\kern0.5em Localsiz{e}_i\kern0.5em >\kern0.5em 0}^{, if\kern0.5em Localsiz{e}_i\kern0.5em \le \kern0.5em 0} $$

(5)

$$ Smalle{r}_i = {\left\{{}_{\left| Localsize\right|}^0\right.}_{, if\kern0.5em Localsiz{e}_i\kern0.5em <\kern0.5em 0}^{, if\kern0.5em Localsiz{e}_i\kern0.5em \ge \kern0.5em 0} $$

(6)

List price reduction, R, is a dummy variable that takes on the value of 1 if the property’s list price was reduced during the final listing period. If the property’s list price at the beginning of the final listing period equals the list price when the property sold, R takes on the value of 0. Knight (2002) reports that a dummy variable representing whether the list price is changed during the listing period is significantly related to time-on-market.

We also estimate a probit model, where the dependent variable is the probability of a property being relisted, to produce the Inverse Mills Ratio (IMR) used in Eq. 1. The probit estimation is calculated using the following equation:

$$ \Pr \left(\mathbf{L}\right)=\varOmega \left(\mathbf{T}\mathbf{O}{\mathbf{M}}_{\mathbf{L}}\right)+\theta \left(\mathbf{DOP}\right)+\mu $$

(7)

The independent variables in the probit model are degree of overpricing (DOP) as described above and relisted time-on-market (TOM _L ). The time-on-market variable used in the probit model is calculated as the number of days the property was actively listed on the MLS. It represents the sum of all the listing contract periods from the starting date of the original listing contract to the time of sale or to the date it was relisted, whichever comes first. In this way, we avoid Benefield and Hardin’s (2014) criticism of using an MLS calculated time-on-market and correctly count the entire initial listing period plus all subsequent relisting periods. We calculate TOM _L as follows:

$$ \mathbf{T}\mathbf{O}{\mathbf{M}}_{\mathbf{L}} = \mathrm{M}\mathrm{I}\mathrm{N}\left(\left[\mathrm{Sale}\ \mathrm{Date}\right],\left[\mathrm{Relist}\ \mathrm{Date}\right]\right)-\left[\mathrm{Initial}\ \mathrm{L}\mathrm{isting}\ \mathrm{Date}\right]-\left[\mathrm{Days}\ \mathrm{not}\ \mathrm{on}\ \mathrm{M}\mathrm{L}\mathrm{S}\right] $$

(8)