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The purpose of this paper is to explore how the difference between the sale price and list price of houses varies across the market cycle.

The paper utilises quarterly transaction‐based information on house prices from the Belfast Metropolitan Area. The information is structured on a time series basis from 2002 to 2008. The analysis is concerned with the mean differences between list price and sale price, the standard deviation of the differences, the skewness and kurtosis of the distributions.

The results show that under normal market conditions the mean deviation between list price and sale price is small circa 1 per cent. However, the departure between list price and sale price becomes substantial on both the up‐ and down‐cycles of the market. The analysis shows that the highest mean positive deviation of 12.1 per cent occurred in the first quarter of 2007 and two quarters before sale prices peaked, suggesting that buyer bidding behaviour was changing prior to the market peak. The extent of market change is highlighted by the mean negative deviation of 8.6 per cent for the fourth quarter of 2008. The results demonstrate that volatility increases over the cycle and distributions of price differences are lower and flatter.

This paper breaks new ground through the analysis of differences between list and sale price in a period of high volatility in the housing market. The analysis shows how list price lags sale price on the up‐cycle but leads on the down‐cycle.

The purpose of this article is based on the unit patent license fee model in the closed-loop supply chain.

This paper analyzes the impact of the bundling strategy of the retailer selling new products and remanufactured products on the closed-loop supply chain under the condition that the original manufacturer produces new products and the remanufacturer produces remanufacturing products.

The results show that alternative products can be bundled, and in many cases, the bundling of remanufactured products and new products is better than selling alone.

If the retailer chooses bundling, for the remanufacturer, when certain conditions are met, the benefits of bundling are greater than the separate sales at that time; for the original manufacturer, when the recycling price sensitivity coefficient is high, the bundling is better than separate sales.

This paper aims to study how price and sales level information influence consumer product perceptions and choices in online settings. It, in particular, tests whether displaying sales level information increases consumer price sensitivity, which is a potential strategic risk to retailers.

Study 1 uses eBay data to investigate whether the interaction effects between price and sales level can be observed in an existing market. Study 2 involves online experiments across three product categories. Participants choose from product pairs that are shown with either the same or different prices and with no, the same or different sales levels.

Study 1 shows strong effects of a product’s displayed sales and price level on its daily sales but finds no interaction effect. Study 2 shows strong effects of price and sales levels on product choice but similarly finds no evidence that sales level information influences consumer price sensitivity, although it reveals an effect on quality perceptions. The results show how perceptions of quality, sacrifice and popularity mediate the effects of price and sales level information on product choice.

Study 1 has limited control over prices and sales levels. Study 2 involves only hypothetical choices.

These findings indicate that businesses can use sales level information to manage consumer product quality perceptions and choices without having to be concerned that this will make consumers more price-sensitive.

To the best of the authors’ knowledge, this paper is the first to investigate how sales level information affects consumer responses to price differences in online contexts.

Many studies have examined the short‐term and long‐term effects of price promotions. This study adds to previous research by examining, in some depth, the effects of a massively successful price promotion in a consumer goods category. This study sought to determine if this large price promotion had any: longer‐term effect on brand volume; short‐term effect on total category volume for the retailer; short‐term effect on competing retailers; and longer‐term effect on category sales for the retailer that ran the promotion. The results showed that this promotion did not have any longer‐term (positive or negative) effect on the brand, but it did expand the total category for the retailer, albeit temporarily. Sales dropped slightly for one competing retailer at the time of the promotion, but not for the other two retailers in the market. Finally, the study found that the promotion was followed by a decline in total category volume for the retailer, suggesting some degree of purchase acceleration or stockpiling by consumers. The results suggest that the longer‐term negative effect on category volume cancelled out approximately two thirds of the gains of the price promotion to the retailer.

The role of list price is often discussed in a narrative describing sellers’ preferences or sellers’ price expectations. This paper aims to investigate a set of list price strategies that real estate brokers have available to influence the outcome of the sale, which may be many times self-serving.

By analyzing real estate brokers’ arguments on the choice of the list price level, a couple of hypotheses are formulated with regard to different expected outcomes that depend on the list price. This study empirically tests two hypotheses for the underlying incentives in the choice of list price from the real estate broker’s perspective: lower list price compared to market value leads to the higher sales price, lower list price compared to market value leads to a quicker sale. To investigate the two hypotheses, this paper adopts different methodological frameworks: H1 is tested by running a classical hedonic model, while H2 is tested through a duration model. This study further tests the hypotheses by splitting the full sample into two different price segments: above and below the median list price.

The results show that H1 is rejected for the full sample and for the two sub-samples. That is, contrary to the common narrative among brokers that underpricing leads to a higher sales price, underpricing lower sales price. H2, however, receives support for the full sample and for the two sub-samples. The latter result points to that brokers may be tempted to recommend a list price significantly below the expected selling price to minimize their effort while showing a high turnover of apartments.

Although there are a large number of previous studies analyzing list price strategies in the housing market, this paper is one of the few empirical studies that address the effect of list price choice level on auction outcomes of non-distressed housing sales.

This study aims to examine previously identified hotel automated valuation models (AVMs) using a more extensive and updated sample of hotel sale transaction data, introduce economic conditions as a new determinant for hotel market value and test the moderating role of economic conditions on the relationship between the previously identified indicators and market value, operationalized as sale transaction price.

This study performs simple and multiple regression analyses to examine the proposed relationships. The sample period is from 2000 to 2012, because the economy prior to 2000 is likely to have been a less-relevant economic environment for the contemporary business world.

As hypothesized, there is an insignificant main effect of economic conditions on hotel sale prices. A moderating role of economic conditions on the relationship between average daily rate (and net operating income) and market value was found, while no moderating effect was found for occupancy rate and hotel size. Results also find certain support for AVMs previously presented in the literature, as described herein.

This study attempts to make contributions to the existing lodging and real estate literature by investigating a main effect of economic conditions on hotel sale prices and a moderating role of economic conditions on the relationship between various hotel performance indicators and hotel sale prices. In addition, this study operationalizes a relatively extensive (large base sample size of 2,441 hotel sale transactions) and recent (since 2000) database compared to previous studies.

The purpose of this paper is to analyze price decision-making through a practice-based approach. The paper investigates the micro-level practices used to arrive at sales price decisions.

In this study, a qualitative study approach is used to develop findings abductively. The data are gathered through an in-depth case study at two firms: semi-structured interviews, meeting observations, shadowing and pricing documents.

This paper finds that pricing is a collective decision-making process involving multiple actors across the organization. The case firms work on solving information, coordination and control problems to arrive at sales prices by enacting interlinked practices. Pricing is therefore neither a structure nor a single decision but a process consisting of multiple micro-level practices that enable firms to make pricing decisions.

This paper develops a practice-based approach to pricing that conceptualize the micro-level practices used to to make pricing decisions in the face of information, coordination and control problems. The paper is interdisciplinary and adds to the accounting literature and the market literature, which have tended to study pricing as a decision made by one decision maker, and not as an organizational process where multiple actors share, evaluate, interpret and coordinate information and decisions.

Oklahoma has seen rapid growth in the development of wind energy over the past decade. Residents are concerned about the negative impacts of turbines such as noise or their appearance. This has raised concerns about property values. Thus, this paper aims to examine and quantify the overall impact of wind turbines upon real estate prices in Western Oklahoma.

Sales prices and the history of approximately 23,000 residential real estate records for both platted and unplatted properties in five counties were examined prior to the announcement of construction, after announcement and after construction. A hedonic analysis was undertaken to examine the real estate prices of the properties near wind farms.

While there may be isolated instances of lower property values for homes near wind turbines, results show no significant decreases in property values over homes near wind farms in the study area. Similar results are found for the unplatted properties.

This paper highlights that in spite of mixed attitudes toward wind farms and misconceptions regarding the link between turbines and property values, Oklahoma’s growing wind industry can continue to thrive without negatively impacting nearby home and land values and prices.

Although there have been numerous studies examining the relationship between wind turbine locations and real estate prices, no study has combined the large quantity of records (over 23,000) as well as both platted and unplatted locations.

The purpose of this paper is to evaluate the interaction between inventory availability (scarcity) and pricing levels (price-leadership (PL)), and its effect on product returns in the internet retail supply chain. Specifically, this paper investigates how supply chain managers can use inventory (seller-induced scarcity) and pricing (PL) levers to control product returns.

Empirical data of sales and product returns from an internet retailer is analyzed to identify the scale of the effect that product scarcity and PL has on product returns. These factors are considered in developing a sales-return process model which is used with empirical data in a simulation study. The study evaluates changes in product returns for different policy settings related to PL and inventory levels. Findings of the simulation study are validated using statistical analysis of empirical data.

PL and seller-induced product scarcity affect the rate of product returns; however, the scale of this effect depends on inventory and pricing decisions. The results identify an inflection boundary based on scarcity and PL levels which reverses this effect. This reversal is explained by underlying principles at play regarding buyers’ valuation of the sale and corresponding product attributes.

Supply chain managers in internet retail can leverage lower inventory under the seller-induced scarcity approach to improve revenues. However, reducing inventory levels beyond a threshold is counterproductive, due to an associated increase in product returns. Similarly, setting market competitive prices (PL) can help reduce product returns. Under the seller-induced scarcity condition, this effect is reversed for inventory levels below a threshold. Retailers can implement the methodology developed in this paper to identify the inventory-price threshold that can help increase revenues while keeping the rate of product returns at a manageable level.

This research extends prior work regarding the role of product scarcity and pricing on product returns and develops a deeper understanding of how these factors can be managed to control product returns in the internet retail setting.

The purpose of this paper is to capture the dynamic variations in sales of a product based upon the dynamic estimation of the time series data and propose a model that imitates the price discounting and promotion strategy for a product category in a retail organization.

Time series data relating to sales has been used to model the sales estimates using moving average and proportional and derivative control; thereafter a sales forecast is generated to estimate the sales of a particular product category. This provides valuable inputs for taking lot sizing decisions regarding procurement of the products and selection of suppliers. A hybrid model has been proposed and explained with a hypothetical case, which considerably impacts the sales promotion and intelligent pricing decisions.

A conceptual framework is developed for modeling the dynamic price discounting strategy in retail using fuzzy logic. The model imitates sales promotion and price discounting strategy. This has helped minimize the inventory cost thereby keeping the profitability of the retail organization intact.

There is no appropriate empirical data to verify the models. In light of the research approach (modeling based upon historical time series data of a particular product category) that was undertaken, there is a possibility that the research results may be valid for the product category that was selected. Therefore, the researchers are advised to test the proposed propositions further for other product categories.

The study provides valuable insight on how to use the real-time sales data for designing a dynamic automated model for product sales promotion and price discounting strategy using fuzzy logic for a retail organization.