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An attempt is made in this paper to discriminate among four groups of transfer pricing methods namely: market price, cost, negotiation, and other methods, according to the transfer pricing objectives and other environmental issues (i.e. transfer pricing determinants). Discrimination is attempted on the data collected in the study of UK companies described in the earlier paper “A Survey of UK Transfer Pricing Practice — Some Preliminary Findings”. The aim of the discrimination is to evaluate the relationship between the transfer pricing method and transfer pricing determinants. This evaluation enables the prediction of transfer pricing method for new cases according to the company's perception of the relative importance of the transfer pricing determinants.

In most companies of any size internal transactions will be conducted between divisions which may be departments or separate legal entities. Even if a company has no subsidiary it may wish to formally apply to the transactions monetary values which customarily are known as transfer prices.

This paper explores the real estate price determinants at four geographical levels: in the European Union as a whole, in the 28 European Union countries, in one European Union country (Portugal) and in 25 Portuguese metropolitan statistical areas (MSAs).

The authors run two time series regression models and two panel data regression models with observations of potential real estate price determinants and House Price Indices collected from Eurostat.

The results show that price determinants, such as gross domestic product (GDP), interest rates, housing starts and tourism, are statistically significant, but not in all the four geographical levels of analysis. The results also confirm the autoregressive characteristic of real estate prices, with the last period price change being the most important determinant of current period real estate price change.

Forecasting real estate prices can be made more effective by knowing that each geographical level of analysis implies different price determinants and that momentum is an important determinant in real estate returns.

To the best of the authors knowledge, this is the first study to develop and test a real estate price equilibrium model at several different geographical levels of the same political space.

Many companies lack insights or fact-based support for the pricing decisions they make in an increasingly complex environment. In order to optimize their pricing process, managers need to identify key indicators that may influence the performance of their decisions. The purpose of this paper is to report an investigation of pricing determinants in large companies manufacturing capital goods in France. First a conceptual framework is proposed, in order to fill several gaps identified in the literature on pricing practices and more precisely by operating a distinction between environmental variables (determinants), decision making (pricing strategy and price and product-line structures) and its consequence in terms of price level.

The author conducted an empirical research on the determinants of the pricing process. This study consistedof a questionnaire survey addressed to pricing managers (or executives in charge of pricing) in 98 of the largest manufacturing companies in France about their new-product pricing decision-making process.

The author studies environmental determinants and their influence on the pricing and describes the structure of pricing determinants as a five dimensions construct: market-based, value-based, position-based, competition-based and production-based. The results show that firms rely on environmental determinants as indicators of their pricing flexibility. These indicators operate as pricing levers: a good position on these variables gives firms more pricing power. But in the vast majority of the cases, companies extensively relied on competitive conditions instead of taking advantage of a favorable position, described as pricing myopia.

This paper describes current pricing practices in leading companies with key informants (mainly pricing managers) highly involved in the pricing decision process, and contrasts two pricing orientations, pricing power vs pricing myopia.

Presents the results of an empirical study that investigates comparatively the mortgage‐pricing determinants of national, regional and local building societies. Considers and discusses the importance of the three main generic strategies (focus, differentiation and cost leadership) and building societies′ main pricing objectives – profit margins, market share, and mutuality. The findings suggest that building societies′ mortgage pricing is influenced primarily by internal industry determinants – such as “costs” and “competitors′ prices” – and to a lesser extent by market‐related factors (customers′ perception of value and elasticity of demand). A large majority of building societies view profit margins, rather than market share, as their primary pricing objective. In order to facilitate this pricing objective, societies select strategies that match their size and market characteristics. Local building societies employ primarily a focus strategy, while national and regional building societies aim at achieving their profit margins mainly through differentiation and cost leadership strategies.

This paper aims to examine real estate price volatility in Hong Kong. Monthly data on housing, offices, retail and factories in Hong Kong were analyzed from February 1993 to February 2019 to test whether volatility clusters are present in the real estate market. Real estate price determinants were also investigated.

Autoregressive conditional heteroscedasticity–Lagrange multiplier test is used to examine the volatility clustering effects in these four kinds of real estate. An autoregressive and moving average model–generalized auto regressive conditional heteroskedasticity (GARCH) model was used to identify real estate price volatility determinants in Hong Kong.

There was volatility clustering in all four kinds of real estate. Determinants of price volatility vary among different types of real estate. In general, housing volatility in Hong Kong is influenced primarily by the foreign exchange rate (both RMB and USD), whereas commercial real estate is largely influenced by unemployment. The results of the exponential GARCH model show that there were no asymmetric effects in the Hong Kong real estate market.

This volatility pattern has important implications for investors and policymakers. Residential and commercial real estate have different volatility determinants; investors may benefit from this when building a portfolio. The analysis and results are limited by the lack of data on real estate price determinants.

To the best of the authors’ knowledge, this paper is the first study that evaluates volatility in the Hong Kong real estate market using the GARCH class model. Also, this paper is the first to investigate commercial real estate price determinants.

This paper aims to explore two research questions. The first focuses on the main methodological characteristics of previous studies in the field of price and revenue research in terms of commercial peer-to-peer accommodation platforms (P2P APs). The second compares the 33 articles and identifies the convergent and divergent findings. The literature review outlines some future research avenues.

This paper is a literature review that explore the price and revenue determinants in the field of P2P APs (such as Airbnb). The methodology shows how the sample was defined and is based on three steps: selection of papers, sample selection, and coding. The research was carried out in March 2020 and generated an initial sample composed of roughly 3,000 articles. A two-stage inclusion/exclusion process was applied to select the final sample, which includes 33 articles.

The empirical findings report the main antecedents of price and revenue as well as the methodological domains of the analyzed papers. Focusing on determinants, six blocks of variables were identified. The listing variables (48%), host characteristics (18%), location (12%), guest review (11%), destination characteristics (11%) and external comparison (1%). For each block, convergent and divergent findings are illustrated.

The main limitations of this study relate to the criteria used in selecting the sample and the manual coding activity. This last decision (manual coding activity) was based on the limited number of papers available and the wide spectrum of variables used.

The practical implications are many, and they include a greater awareness of the variables that influence price and revenue as well as the impact that these variables could have on rates. Furthermore, managers interested in analyzing specific topics or variables can find analytical support in the information reported in the appendices.

The evidence provided is useful both for scholars considering further research in this field and for practitioners seeking to define pricing policies. Some future research avenues have been outlined, including new topics for development and methodological advancements.

Scholars note there are limited studies analyzing ticket price determinants. Using the common seat approach, the authors sought to advance this line of research by analyzing determinants of National Basketball Association (NBA) ticket prices in the secondary ticket market. The authors’ research seeks to ask two questions. The first is how ticket prices in the secondary market are associated with common determinants of consumer demand. The second question is what impact the COVID-19 pandemic has on ticket prices in the secondary market.

Ticket prices of NBA regular season games in the 2021–2022 season were collected a week before the game day from Ticketmaster.com. A regression model was estimated with a group of independent variables: income, population, consumer preference, quality of viewing, quality of contest and pandemic (the number of COVID-19 cases).

Results indicate income, population, consumer preferences (e.g. team quality and star players) and quality of viewing (e.g. arena age and weekend) impact prices. Further, the number of COVID-19 cases did reduce the ticket price.

The present study illuminates the theoretical significance of analyzing ticket prices as a proxy of demand in professional sport, while providing practical implications regarding the potential opportunity to increase revenue.

This paper aims to test the volatility and analyses the macroeconomic determinants of house price volatility in Namibia over the period 2007 Quarter 1 to 2017 Quarter 2. It further explores the causal relations between house price volatility and its determinants.

The study used autoregressive conditional heteroskedastic and generalized autoregressive conditional heteroskedastic models to test for volatility. The vector error correction model was used to analyse the determinants and causal relations.

The results support the hypothesis that house prices in Namibia exhibits persistent volatility. It was further established that past period volatility’ GDP and mortgage loans are the key determinants of house price volatility. Additionally’ there exists unidirectional causality from GDP and mortgage loans to house price volatility.

Policy implications emanating from the study implies that macroeconomic fundamentals should be monitored closely to mitigate the issues of house price volatility.

The study is the first of its kind in Namibia to address the pertinent issues of ever increasing housing prices.

Pricing studies have largely focused on sellers' pricing strategies and price determinants. To expand earlier work on sellers' pricing decisions, this study considers time as a major factor driving sellers' ticket prices in the secondary market. Specifically, because most secondary market transactions occur in the last moments before a game, this study considers how resellers adjust ticket prices within a few days prior to a game day including an actual game day.

To examine the impact of time on secondary market ticket prices for Major League Baseball (MLB), ticket prices were collected from StubHub (one of the largest secondary ticket markets) four times per game: from 3 days to 1 day prior to a game day and on the actual game day. Additionally, 10 control variables were obtained from previous research on price determinants (N = 19,155). A multiple regression model was created based on the extant literature regarding secondary market ticket prices.

Results indicate the number of days before a game negatively influenced ticket prices: resellers decreased ticket prices consistently during the last few days prior to a game's first inning. Specifically, secondary market ticket prices decreased relatively dramatically on an actual game day. Time had no significant effects on ticket prices 2 days prior to a game day. In addition to the role of time, league affiliation and the number of all-star players were identified as key price determinants in the secondary market. Moreover, changes in weather forecasts and the home team starting pitcher's ERA played significant roles in price changes.

Despite containing a relatively high number of data observations compared with prior pricing studies, this study's findings were limited to certain teams. Additionally, as only MLB secondary market ticket pricing was considered, different outcomes and implications may apply in other major sport ticket markets (e.g. NBA, NFL, NHL and MLS) featuring distinct league structures, policies and demand.

This study offers practical guidance for sellers' pricing decisions. Most secondary ticket market sellers lowered their ticket prices relatively dramatically on an actual game day. Reducing ticket prices prior to a game day can lead to greater chances to avoid unsold tickets that compromise revenue management. This study's results also afford professional sport organizations and secondary ticket market consumers a clearer understanding of the factors resellers consider when setting ticket prices.

Although previous studies have uncovered essential elements influencing ticket prices and consumer demand in the secondary ticket market, little work has examined how time affects sellers' pricing decisions within a few days prior to a game day. Little is known about the elements that significantly influence sellers’ decisions to adjust (i.e. increase or decrease) ticket prices in the secondary market as well. This topic deserves ongoing attention, as new outcomes can supplement previous studies' findings due to changing market environments.