A 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.
This 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.
In 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.
After 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.
This study tests the higher explanatory power of the model that includes both the distance measured in geographic and property characteristics spaces.
This paper discusses the effect of conformism on the demand for products that differ in quality and studies its implications for firm selection, entry, average quality, and trade pat- terns. Demand for each variety is shown to fall when consumers have a lower degree of conformism or when the distribution of conformism becomes more concentrated. This in- duces firms facing lower demand and of lower quality to exit the market, which raises average quality and diminishes product diversity. In an international trade context, home consumption bias is amplified when there is a lower degree of conformism. Home con- sumption bias is mitigated by the presence of global conformism, in which individuals tend to conform to people across the world rather than within their own country.
Business‐like approaches are applied more and more widely in nonprofit organization contexts, and theaters are no exception. Revenue generation, customer segmentation, and personalized marketing are becoming the key managerial concerns. Our study focuses on two relevant aspects of theater attendees' behavior. We examine visitors' willingness‐to‐pay (WTP) for theater seats (to derive revenue drivers), and its difference between two segments – single and couple visitors (to uncover the social motivation effect). These aspects taken together have never been previously studied in the nonprofit marketing context. We model WTP using the actual purchase data from Perm Opera and Ballet Theatre in Russia. Unlike most marketing studies which use stated preference for WTP evaluation, we employ the revealed preference approach. The results verify that single and couple visitors may be treated as separate segments, allowing for personalized promotion and other marketing decisions.
We consider a model of monopolistic competition with several heterogeneous sectors and endogenous labor supply. For low (high) values of the labor supply elasticity, we show that there is always a unique equilibrium. For medium values of the labor supply elasticity, there are either zero or two equilibria.
This study examines the effects of individual health shocks on labour market outcomes in the Russian Federation during the period 2000–2018. Employing data from the Russia Longitudinal Monitoring Survey—Higher School of Economics, we demonstrate that adverse health shocks have negative consequences for employment, wages, and income. We find that the effects are strongest for males, the less educated, those on lower incomes, those in middle-ranking and professional occupations, and for those experiencing the most severe health shocks. However, consistent with our knowledge of the Russian labour market, we also observe that the wage and income elasticities are considerably higher than the employment elasticities and above those reported for other countries. Understanding how to attenuate the negative labour market consequences associated with health shocks is paramount and we, therefore, consider the potential role that labour, health and social policies can play in mitigating risk.
We investigate whether plants inside and outside geographic clusters differ in their resilience to adverse economic shocks. To this end, we develop a bottom-up procedure to delimit clusters using Canadian geo-coded plant-level data. Focusing on the textile and clothing (T&C) sector and exploiting the series of dramatic changes faced by that sector between 2001 and 2013, we find little evidence that plants in T&C clusters are more resilient than plants outside clusters. Over the whole period, plants inside clusters are neither less likely to die nor more likely to adapt by switching their main line of business. However, in the industries the most exposed to the surge of Chinese imports after 2005, plants inside clusters die and exit less than others in the following 2 years.
In this paper, we address several aspects of applying classical machine learning algorithms to a regression problem. We compare the predictive power to validate our approach on a data about revenue of a large Russian restaurant chain. We pay special attention to solve two problems: data heterogeneity and a high number of correlated features. We describe methods for considering heterogeneity — observations weighting and estimating models on subsamples. We define a weighting function via Mahalanobis distance in the space of features and show its predictive properties on following methods: ordinary least squares regression, elastic net, support vector regression, and random forest.
Larger cities typically give rise to two opposite effects: tougher competition among firms and higher production costs. Using an urban model with substitutability of production factors and pro-competitive effects, I study product market responses to an increase in city population, land-use regulations, and commuting costs. I show that those responses depend on the land intensity in production. If the input share of land is low, a larger city attracts more firms setting lower prices, whereas for an intermediate land share, city expansion increases both the mass of firms and product prices. For a high land share, the mass of firms decreases with city size while product price increases. Softer land-use regulations and/or lower commuting costs reinforce pro-competitive effects, making city residents better-off via lower product prices and broader diversity.
The common approach to predict the price of residential property is the hedonic price model and its extension to the case of spatial autoregression. The hedonic approach models the dependence between the price and internal characteristics of an apartment, house characteristics and external characteristics. To account for the unobserved quality of the surrounding environment price model includes factors of spatial price correlation, where the distance is usually measured as the distance in geographic space. Determining the price the seller focuses not only on the observed and unobserved factors of the apartment, house and its environment but also on the prices of similar marketed objects which can be selected both by geographic proximity and by characteristics similarity. In this paper, we use ensemble clustering approach to measure objects proximity and test that the proximity of objects in the characteristics space along with spatial correlation explains the significant variation in prices that in turn leads to an improvement of predictive ability of the model.
By deriving a ‘housing market Hosios condition’, we show that the main conclusions of the seminal paper on housing search frictions by Wheaton (1990) were erroneously derived. We show that households may search too much, optimally, or too little, which is in contrast to the paper’s conclusion that they always search too little. Furthermore, we show that exogenous increases in housing vacancies always have a negative effect on house prices, given standard assumptions.
We document the geographic concentration patterns of Russian manufacturing using detailed microgeographic data. About 80% of three‐digit industries are significantly agglomerated, and a similar share of three‐digit industry pairs is significantly coagglomerated. Industry pairs with stronger buyer–supplier links—as measured using Russian input–output tables—tend to be slightly more coagglomerated. This result is robust to instrumental variable estimation using either Canadian or US instruments. Using Canadian ad valorem transport costs as a proxy for transport costs in Russia, we further find that industries with higher transport costs are more dispersed, and industry pairs with higher transport costs are less coagglomerated.
Study is aimed to estimate the relation between producer effect from employing a downsizing strategy and the number of such producers. For this purpose we train sales prediction model on the actual sales data from a large retail grocery chain and estimate the average effect from implementing downsizing strategy under different market conditions
The main purpose of the study is to estimate the relation between producer effect from employing a downsizing strategy and the number of such producers. For this purpose we train sales prediction model on the actual sales data from a large retail grocery chain and estimate the average effect from implementing downsizing strategy under different market conditions.
Equilibria and optima generally differ in imperfectly competitive markets. Although this is well understood theoretically, it is unclear how large the welfare distortions are in the aggregate economy. Do they matter quantitatively? To answer this question, we develop a multisector monopolistic competition model with endogenous firm entry and selection, productivity, and markups. Using French and UK data, we quantify the gap between the equilibrium and optimal allocations. We find that inefficiencies in the labor allocation and entry between sectors, as well as inefficient selection and output per firm within sectors, generate welfare losses of about 6%–10% of GDP.
Theatrical productions are supposed to be perishable good, since the tickets for a particular play cannot be inventoried and sold after a time of play. In the revenue management of a perishable good price discrimination is widely used. Since the theatre audience is heterogeneous in terms of visit purpose, ability to perceive quality, willingness-to-pay, the strategy of price discrimination is developed in the context of theatre segments. In this paper, we segment consumers of Perm Opera and Ballet Theatre and propose marketing and pricing instruments to manage theatre revenue. Since development of detailed price discrimination strategy requires data on consumer’s purchase history, her behavioral and socio-demographic characteristics, we collect and combine two data sources: data on ticket purchases and data obtained from discrete choice experiment. We modify latent class logit model for joint revealed and stated preferences data where data on consumer characteristics is only partially observed and employ the model to segment the audience. We identify four segments of the theater's audience. The study reveals theatregoers segments with different willingness-to-pay for performance and seat location characteristics. Segmenting allows to develop detailed recommendations on the pricing strategy for various theater audiences.