Modeling dynamic price dispersion of hotel rooms in a spatially agglomerated tourism city for weekend and midweek stays

2019 ◽  
Vol 25 (8) ◽  
pp. 1245-1264 ◽  
Author(s):  
Ibrahim Mohammed ◽  
Basak Denizci Guillet ◽  
Rob Law
Keyword(s):  
Spatial Competition ◽  
Price Dispersion ◽  
Hotel Industry ◽  
Spatial Models ◽  
Spatial Econometric ◽  
Spatial Agglomeration ◽  
Econometric Methods ◽  
Dynamic Price ◽  
Online Pricing ◽  
Dynamic Price Dispersion

In spite of the abundant evidence suggesting the existence of spatial agglomeration in the hotel industry and the potential for spatial econometric methods to contribute to the understanding on the effect of spatial competition on room pricing, limited research has been conducted in this regard. To contribute to this area of research, this study applied spatial models to examine online pricing data of hotels in Hong Kong to determine the effect of spatial agglomeration on dynamic or intertemporal price dispersion. The findings revealed that the magnitude of dynamic price dispersion is not only influenced by demand but also the pricing of neighboring hotels and hotel-specific attributes, such as number of rooms, star rating, chain affiliation, and scale. A major implication of this finding is that real-time tracking and analysis of neighboring hotels’ prices could be an effective strategy to stay competitive in a spatially agglomerated environment.

Beyond Policy Diffusion: Spatial Econometric Models of Public Administration

2018 ◽  
Vol 29 (4) ◽  
pp. 591-608 ◽  
Author(s):  
Scott J Cook ◽  
Seung-Ho An ◽  
Nathan Favero
Keyword(s):  
Public Administration ◽  
Spatial Models ◽  
State Level ◽  
Cross Sectional Study ◽  
Econometric Models ◽  
Education Spending ◽  
Spatial Econometric ◽  
Cross Sectional ◽  
Econometric Methods ◽  
Spatial Econometric Models

Abstract Interdependence in the decision-making or behaviors of various organizations and administrators is often neglected in the study of public administration. Failing to account for such interdependence risks an incomplete understanding of the choices made by these actors and agencies. As such, we show how researchers analyzing cross-sectional or time-series-cross-sectional (TSCS) data can utilize spatial econometric methods to improve inference on existing questions and, more interestingly, engage a new set of theoretical questions. Specifically, we articulate several general mechanisms for spatial dependence that are likely to appear in research on public administration (isomorphism, competition, benchmarking, and common exposure). We then demonstrate how these mechanisms can be tested using spatial econometric models in two applications: first, a cross-sectional study of district-level bilingual education spending and, second, a TSCS analysis on state-level healthcare administration. In our presentation, we also briefly discuss many of the practical challenges confronted in estimating spatial models (e.g., weights specification, model selection, effects calculation) and offer some guidance on each.

scholarly journals Modelling Housing Rents Using Spatial Autoregressive Geographically Weighted Regression: A Case Study in Cracow, Poland

2020 ◽  
Vol 9 (6) ◽  
pp. 346
Author(s):  
Mateusz Tomal
Keyword(s):  
Geographically Weighted Regression ◽  
Rental Housing ◽  
Ordinary Least Squares ◽  
Weighted Regression ◽  
Spatial Econometric ◽  
Econometric Methods ◽  
Depth Analysis ◽  
Sar Model ◽  
Internet Platform ◽  
Spatial Autoregressive

The proportion of tenants will undoubtedly rise in Poland, where at present, the ownership housing model is very dominant. As a result, the rental housing market in Poland is currently under-researched in comparison with owner-occupancy. In order to narrow this research gap, this study attempts to identify the determinants affecting rental prices in Cracow. The latter were obtained from the internet platform otodom.pl using the web scraping technique. To identify rent determinants, ordinary least squares (OLS) regression and spatial econometric methods were used. In particular, traditional spatial autoregressive model (SAR) and spatial autoregressive geographically weighted regression (GWR-SAR) were employed, which made it possible to take into account the spatial heterogeneity of the parameters of determinants and the spatially changing spatial autocorrelation of housing rents. In-depth analysis of rent determinants using the GWR-SAR model exposed the complexity of the rental market in Cracow. Estimates of the above model revealed that many local markets can be identified in Cracow, with different factors shaping housing rents. However, one can identify some determinants that are ubiquitous for almost the entire city. This concerns mainly the variables describing the area of the flat and the age of the building. Moreover, the Monte Carlo test indicated that the spatial autoregressive parameter also changes significantly over space.

scholarly journals Spatial Econometrics – Usage in Transportation Sciences: A Review Article

2019 ◽  
Vol 48 (2) ◽  
pp. 143-149 ◽  
Author(s):  
Zsombor Szabó ◽  
Árpád Török
Keyword(s):  
Spatial Econometrics ◽  
Review Article ◽  
Spatial Econometric ◽  
Econometric Methods ◽  
Review Articles

Nowadays the spatial econometrics is became widely used in transportation sciences. In order to know which method can be used or how they should be used, the review articles give answers. In this recent paper the goal is to collect all of the methods in one article which can be used in further researches. The improvement in this article is that beside the spatial econometric methods, other necessary techniques are also introduced. With this fact a whole analysis can be applied.

Predicting the direction of dynamic price adjustment in the Hong Kong hotel industry

2020 ◽  
pp. 135481662090390
Author(s):  
Ibrahim Mohammed ◽  
Basak Denizci Guillet ◽  
Rob Law ◽  
Wassiuw Abdul Rahaman
Keyword(s):  
Hong Kong ◽  
Dynamic Pricing ◽  
Contextual Factors ◽  
Hotel Industry ◽  
Price Adjustment ◽  
Probit Regression ◽  
Market Conditions ◽  
Negatively Associated ◽  
Dynamic Price ◽  
Price Increases

This study analysed dynamic pricing data of Hong Kong hotels within the last-minute 1-week booking window to determine patterns and direction of room rate changes and their association with hotel characteristics regarding tangible attributes, reputational variables and contextual factors. Findings show that room rates are more likely to increase than decrease or stay constant, and that, holding demand and market conditions constant, the likelihood of price increases (decreases), based on standard binomial probit regression, is positively (negatively) associated with size (tangible attribute), chain affiliation and star rating (reputational attributes), and seller density and location accessibility (contextual factors). These results confirm the importance of differentiation in pricing hotel rooms and indicate how hotel customers and revenue managers can combine these characteristics with predicted demand to anticipate the direction of room rate change in the last-minute booking window as the booking horizon approaches check-in.

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