This paper studies behavior patterns among theater attendees in the process of ticket purchasing. Since the theater attempts to balance between a high occupancy and affordable prices, the purpose of the study is to reveal the effects of changes in prices on attendance. This project is conducted conjointly with the Perm Tchaikovsky Opera and Ballet Theater. Data are taken from the sales information system of the theater for four seasons 2011-2012/2014-2015. The data are disaggregated to the level of the seating area and performance and consist of the attendance rate, the set of prices and the performance characteristics. The research explores the determinants of demand using a censored quantile regression which accounts for the heterogeneity of effects on different levels of attendance rates and censoring. We estimate the parameters of the demand function and show that the aggregated demand is elastic by price, at the same time the elasticity varies across different seating areas. Moreover, demand for the more popular seats and performances is less elastic.

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PurposeA common approach to predicting the price of residential properties uses the hedonic price model and its spatial extensions. Within the hedonic approach, real estate prices are decomposed into internal characteristics of an apartment, apartment characteristics and external characteristics. To account for the unobserved quality of the surrounding environment, price models include spatial price correlation factors, where the distance is usually measured as the distance in geographic space. In determining the price, a seller focuses not only on the observed and unobserved factors of the apartment and its environment but also on the prices of similar marketed objects that can be selected both by geographic proximity and by characteristics similarity. The purpose of this study is to show the latter point empirically. Design/methodology/approachThis study uses an ensemble clustering approach to measure objects' proximity and test whether the proximity of objects in the property characteristics space along with spatial correlation explain the significant variation in prices. FindingsIn this paper, the pricing behaviour of sellers in a reselling market in Perm, Russia is studied. This study shows that the price transmission mechanism includes both geographic and characteristics spaces. Practical implicationsAfter testing on market data, the proposed framework for the distance construct could be used to obtain higher predictive power for price predictive models and construction of automated valuation services. Originality/valueThis study tests the higher explanatory power of the model that includes both the distance measured in geographic and property characteristics spaces.

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