Econometric Estimates of Hedonic Price Indexes for Personal Computers (PC) in Russia

Redkina, Anastasiya; Parkhomenko, Alexander; Maslivets, Olga

doi:10.2139/ssrn.1008011

Cited by 2 publications

(5 citation statements)

References 5 publications

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“…However, the Box-Cox model has the disadvantage of not allowing for continuous variables with zero values (Hansen and Benson, 2013; Benson et al , 1998). Moreover, the Box-Cox test is likely to favor nonlinear models despite being the incorrect functional form in cases of omitted and mis-specified variables (Parkhomenko et al , 2007). Similar to Langrin (2008) and Li et al (2006), therefore, the preferred model is evaluated according to signs of coefficients and value of coefficients, as well as out-of-sample goodness-of-fit measures (see the subsection below).…”

Section: Methodological Frameworkmentioning

confidence: 99%

“…Sirmans et al (2005) and Herath, and Maier (2010) thoroughly reviewed the literature on the hedonic price method in the housing market context. Other works that have utilized the hedonic pricing framework include Kim et al (2015), Osland (2013), Mak et al (2010), Cebula (2009), Langrin (2008), Zietz et al (2008) and Parkhomenko et al (2007). In the hedonic house price literature, the discussion has been concentrated on two main practical issues: how should we specify the functional form and which house attributes should be included in the model (Ekeland et al , 2002; Osland, 2013)?…”

Section: Literature Review: Toward a Conceptual Frameworkmentioning

confidence: 99%

“…This study suggests that Box-Cox quantile regression provides a more comprehensive description of the determinants of house prices. The Box-Cox test is likely to favor nonlinear models, despite being the incorrect functional form in cases of omitted variables (Parkhomenko et al , 2007), which might be the case with the data used in this study[3]. This study, therefore, examines alternative linear and nonlinear (semi-log, log-log and Box-Cox) specifications, as well as quantile regression specifications, to address nonlinearity and heterogeneity in the relationship between house prices and attributes.…”

Section: Literature Review: Toward a Conceptual Frameworkmentioning

confidence: 99%

“…Many economic studies have used a hedonic regression framework to analyze the relationship between house prices and house attributes (Osland, 2013; Herath and Maier, 2010; Langrin, 2008; Parkhomenko et al , 2007 and Sirmans et al , 2005). The theoretical underpinning in this framework involves estimating the value of individual attributes of a house and measuring their effect on its price (Malpezzi, 2003).…”

Section: Introductionmentioning

confidence: 99%

“…However, the Box-Cox model has the disadvantage of not allowing for continuous variables with zero values (Hansen and Benson, 2013; Benson et al , 1998). Moreover, the Box-Cox test is likely to favor nonlinear models, despite this being the incorrect functional form in cases of omitted variables (Parkhomenko et al , 2007). This study examines alternative linear and nonlinear (semi-log, log-log and Box-Cox) specifications, as well as quantile regression specifications, to address nonlinearity and heterogeneity, including the recurring patterns in day-to-day housing data in the presence of an underlying time-trend (and a house price bubble) driving house sale prices.…”

Section: Introductionmentioning

confidence: 99%

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House price drivers in Dubai: nonlinearity and heterogeneity

Worku

2017

IJHMA

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Purpose This paper aims to examine house price drivers in Dubai, addressing nonlinearity and heterogeneity. Design/methodology/approach The study applies a combination of linear and nonlinear, as well as quantile regression, specifications to address these concerns and better explain the real-world phenomenon. Findings The study shows the double-log quantile regression approach is an overarching description of house price drivers, confirming that not only the price of housing and its determinants are non-linearly related but also that their relationship is heterogeneous across house price quantiles. The findings reveal the prevalence of sub-market differentials in house price sensitivity to house attributes such as size (in square meters), location and type of house, as well as government laws. The study also identifies the peaks and deflation, as well as the rebounding nature of the house price bubble in Dubai. Research limitations/implications The data used are limited, in that information on only a few house attributes was available. Future research should include data on other house attributes such as house quality, zip codes and composition. Practical implications The findings of this study are expected to suggest results with significant ramifications for researchers, practitioners and policy makers. From a policy perspective, there is an obvious interest in understanding whether the price of housing is affected by different attributes differently along its distribution. Social implications This study allows policy makers, developers and buyers of higher-priced houses to behave differently from buyers of lower-priced or medium-priced houses. Originality/value Methodologically, it demonstrates alternative linear and nonlinear, as well as quantile regression, specifications to address two increasing concerns in the house price literature: nonlinearity and heterogeneity. Unlike most other studies, this study used a rich data (140,039 day-to-day transactions of 10 years’ pooled data). The Dubai housing market presents an interesting case. UAE (Dubai, in particular) is named as the second-hottest marketplace for global residential property investors, ahead of Singapore, the UK and Hong Kong (Savills plc, 2015).

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Section: Methodological Frameworkmentioning

confidence: 99%

Section: Literature Review: Toward a Conceptual Frameworkmentioning

confidence: 99%

Section: Literature Review: Toward a Conceptual Frameworkmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

“…However, the Box-Cox model has the disadvantage of not allowing for continuous variables with zero values (Hansen and Benson, 2013; Benson et al , 1998). Moreover, the Box-Cox test is likely to favor nonlinear models, despite this being the incorrect functional form in cases of omitted variables (Parkhomenko et al , 2007). This study examines alternative linear and nonlinear (semi-log, log-log and Box-Cox) specifications, as well as quantile regression specifications, to address nonlinearity and heterogeneity, including the recurring patterns in day-to-day housing data in the presence of an underlying time-trend (and a house price bubble) driving house sale prices.…”

Section: Introductionmentioning

confidence: 99%

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House price drivers in Dubai: nonlinearity and heterogeneity

Worku

2017

IJHMA

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Factors affecting the housing prices in the metropolis of Tehran

Rajaei,

Mottaghi,

Elhaei Sahar

et al. 2024

IJHMA

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Purpose This study aims to investigate the spatial distribution of housing prices and identify the affecting factors (independent variable) on the cost of residential units (dependent variable). Design/methodology/approach The method of the present study is descriptive-analytical and has an applied purpose. The used statistical population in this study is the residential units’ price in Tehran in 2021. For this purpose, the average per square meter of residential units in the city neighborhoods was entered in the geographical information system. Two techniques of ordinary least squares regression and geographically weighted regression have been used to analyze housing prices and modeling. Then, the results of the ordinary least squares regression and geographically weighted regression models were compared by using the housing price interpolation map predicted in each model and the accurate housing price interpolation map. Findings Based on the results, the ordinary least squares regression model has poorly modeled housing prices in the study area. The results of the geographically weighted regression model show that the variables (access rate to sports fields, distance from gas station and water station) have a direct and significant effect. Still, the variable (distance from fault) has a non-significant impact on increasing housing prices at a city level. In addition, to identify the affecting variables of housing prices, the results confirm the desirability of the geographically weighted regression technique in terms of accuracy compared to the ordinary least squares regression technique in explaining housing prices. The results of this study indicate that the housing prices in Tehran are affected by the access level to urban services and facilities. Originality/value Identifying factors affecting housing prices helps create sustainable housing in Tehran. Building sustainable housing represents spending less energy during the construction process together with the utilization phase, which ultimately provides housing at an acceptable price for all income deciles. In housing construction, the more you consider the sustainable housing principles, the more sustainable housing you provide and you take a step toward sustainable development. Therefore, sustainable housing is an important planning factor for local authorities and developers. As a result, it is necessary to institutionalize an integrated vision based on the concepts of sustainable development in the field of housing in the Tehran metropolis.

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Econometric Estimates of Hedonic Price Indexes for Personal Computers (PC) in Russia

Cited by 2 publications

References 5 publications

House price drivers in Dubai: nonlinearity and heterogeneity

House price drivers in Dubai: nonlinearity and heterogeneity

Factors affecting the housing prices in the metropolis of Tehran

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