scholarly journals The Effect of Hybrid Attributes on Property Prices

2021 ◽  
Vol 29 (4) ◽  
pp. 36-52
Author(s):  
Sergio Copiello ◽  
Filippo Cecchinato ◽  
Mohammed Haj Salih
Keyword(s):  
Linear Models ◽  
Unit Area ◽  
Explanatory Power ◽  
Methodological Foundation ◽  
Property Prices ◽  
Surrounding Environment ◽  
Marginal Prices ◽  
Market Approach ◽  
Log Linear

Abstract This study focuses on the sales comparison and the adjustment grid methods, under the framework of the market approach to valuation. The literature mostly focuses on the structural qualities of the properties, referring to the buildings and their locational characteristics, which pertain to their positioning within the urban fabric. Here we focus on a particular set of attributes which depend on how a building is placed in a specific land plot and relate to the surrounding environment. The term «hybrid» is used to identify this class of attributes. It is shown that hybrid attributes affect property prices and should not be disregarded by practitioners and appraisers. The role played by hybrid characteristics is analyzed based on the methodological foundation of the hedonic and the adjustment grid methods. The derived linear and log-linear models are tested by means of a case study. The models prove to have high explanatory power. The weight of each hybrid attribute is estimated to vary between 1% and 10%. Overall, these attributes contribute to 17-23% of the property prices. The validation performed using the grid adjustment method shows that the estimated marginal prices are effective in adjusting the prices per unit area of the comparables.

The impact of corruption on sport demand

2020 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Carlo Amenta ◽  
Paolo Di Betta
Keyword(s):  
Design Methodology ◽  
Linear Models ◽  
Negative Externalities ◽  
Data Set ◽  
Content Type ◽  
Long Run ◽  
Log Linear ◽  
Systemic Corruption ◽  
The Impact

PurposeThe article presents an empirical analysis that evaluates the effects of a systemic corruption scandal on the demand in the short and the long run. In 2006, the Calciopoli scandal uncovered the match rigging in the Italian soccer first division. The exemplary sportive sanction of relegating the primary culprit to the second division imposed further negative externalities on the other clubs. Should we prefer the sportive sanction on the team or the monetary fines for the club?Design/methodology/approachWe estimated two log-linear models of the demand side (stadium attendance) using a fixed effect estimator, on two panel data set made of all the Italian soccer clubs in the first and second division (Serie A and Serie B) for the seasons 2004/2005 to 2009/2010, considering the relegation of the Juventus as the event which impacted the demand for soccer.FindingsRelegating Juventus to Serie B caused an immediate decrease of 18.4% in the attendance for all the teams, both in Serie A and in Serie B, for the three seasons considered, and 1% decrease when all the seasons are considered to measure the fallout of the scandal on the fans' disaffection.Originality/valueThe effect of corruption in sport on demand is an important issue, and there are few studies already published. As for sports economics and management, our results are of interest for sport-governing bodies – as a case study that can help in designing a more effective sanctioning system to prevent corruption episodes.

When and why are log-linear models self-normalizing?

2015 ◽  
Author(s):  
Jacob Andreas ◽  
Dan Klein
Keyword(s):  
Linear Models ◽  
Log Linear

Log-Linear Models with Dependent Spatial Data

1983 ◽  
Vol 15 (6) ◽  
pp. 801-813 ◽  
Author(s):  
B Fingleton
Keyword(s):  
Spatial Data ◽  
Statistical Models ◽  
Linear Models ◽  
Square Lattice ◽  
Categorical Variables ◽  
Systematic Sampling ◽  
Pairwise Correlations ◽  
Independent Observations ◽  
Intersection Points ◽  
Log Linear

Log-linear models are an appropriate means of determining the magnitude and direction of interactions between categorical variables that in common with other statistical models assume independent observations. Spatial data are often dependent rather than independent and thus the analysis of spatial data by log-linear models may erroneously detect interactions between variables that are spurious and are the consequence of pairwise correlations between observations. A procedure is described in this paper to accommodate these effects that requires only very minimal assumptions about the nature of the autocorrelation process given systematic sampling at intersection points on a square lattice.

Educational Homogamy Among Married and Unmarried Couples in Europe

2008 ◽  
Vol 30 (1) ◽  
pp. 28-52 ◽  
Author(s):  
Dana Hamplova
Keyword(s):  
Linear Models ◽  
Married Couples ◽  
European Social Survey ◽  
Social Survey ◽  
Educational Homogamy ◽  
Educational Barriers ◽  
Educational Groups ◽  
Institutional Contexts ◽  
Using Data ◽  
Log Linear

In this article, educational homogamy among married and cohabiting couples in selected European countries is examined. Using data from two waves (2002 and 2004) of the European Social Survey, this article compares three cultural and institutional contexts that differ in terms of institutionalization of cohabitation. Evidence from log-linear models yields two main conclusions. First, as cohabitation becomes more common in society, marriage and cohabitation become more similar with respect to partner selection. Second, where married and unmarried unions differ in terms of educational homogamy, married couples have higher odds of overcoming educational barriers (i.e., intermarrying with other educational groups).

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