Many goods are marketed after first stating a list price, with the expectation that the eventual sales price will differ. In this paper we first present a simple model of search behavior that includes the seller setting a list price. Holding constant the mean of the buyers' distribution of potential offers for a good, we assume that the greater the list price, the slower the arrival rate of offers but the greater is the maximal offer. This tradeoff determines the optimal list price, which is set simultaneously with the seller's reservation price. Comparative statics are derived through a set numerical sensitivity tests, where we show that the greater the variance of the distribution of buyers' potential offers, the greater is the ratio of the list price to expected sales price. Thus, sellers of atypical goods will tend to set a relatively high list price compared with standard goods.We test this hypothesis using data from the Columbus, Ohio housing market and find substantial support. We also find empirical support for another hypothesis of the model: atypical dwellings take longer to sell.3

To assess the quality of the fit in a multiple linear regression, the coefficient of determination or R 2 is a very simple tool, yet the most used by statistics users. It is well known that the classical (least-squares) fit and coefficient of determination can be arbitrary misleading in the presence of a single outlier. In many applied setting, the assumption of normality of the error and of absence of outliers are difficult to establish. In these cases, robust procedures for the estimation and the inference in linear regression are available and provide an excellent alternative.In this paper we present a companion robust coefficient of determination that has several desirable properties not shared by others: it is robust to deviations from the specified regression model (like in the presence of outliers), it is efficient if the errors are perfectly normal, and we show that it is a consistent estimator of the population coefficient of determination. A simulation study and two real datasets support the appropriateness of this estimator, compared with classical (least-squares) and existing robust R 2 .

To assess the quality of the fit in a multiple linear regression, the coefficient of determination or R 2 is a very simple tool, yet the most used by statistics users. It is well known that the classical (least-squares) fit and coefficient of determination can be arbitrary misleading in the presence of a single outlier. In many applied setting, the assumption of normality of the error and of absence of outliers are difficult to establish. In these cases, robust procedures for the estimation and the inference in linear regression are available and provide an excellent alternative.In this paper we present a companion robust coefficient of determination that has several desirable properties not shared by others: it is robust to deviations from the specified regression model (like in the presence of outliers), it is efficient if the errors are perfectly normal, and we show that it is a consistent estimator of the population coefficient of determination. A simulation study and two real datasets support the appropriateness of this estimator, compared with classical (least-squares) and existing robust R 2 .