Spatially filtered unconditional quantile regression: Application to a hedonic analysis

Murakami, Daisuke; Seya, Hajime

doi:10.1002/env.2556

Cited by 9 publications

(6 citation statements)

References 45 publications

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“…This technique is highly valuable for analyzing data with spatial dependence and gaining insights into the varied effects of explanatory variables on different quantiles of the response variable. The methodology employed combines Murakami and Seya's (2019) approach with the methodology developed by Firpo et al (2009), incorporating Moran's eigenvectors (Griffith, 2003) to account for spatial autocorrelation and implement spatial filtering.…”

Section: Methodsmentioning

confidence: 99%

“…The data utilized in this study are derived from the 2017 Rural Census survey and municipal accounting values, which were provided by the Brazilian Institute of Geography and Statistics (IBGE). In order to analyze the age distribution patterns within rural areas of Brazilian municipalities, a combination of conditional and unconditional quantile regressions, drawing from the works of Koenker andBassett (1978) andFirpo et al (2009), as well as spatially filtered unconditional quantile regression as proposed by Murakami (2019), are employed. These methodologies allow for a comprehensive examination of the relationships between variables and the distribution of age groups in rural settings, offering insights into the complex dynamics at play.…”

Section: Introductionmentioning

confidence: 99%

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Factors associated with rural aging in Brazilian municipalities: an analysis using quantile regressions

De Moura Vogt,

Fochezatto

2023

BGSS

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In rural Brazil, aging is an important phenomenon for the future of the country’s agricultural production. This process is associated with both increased life expectancy and rural-urban migration. According to studies on migration, young people are more likely to change their place of residence, being attracted by the existence of work opportunities and, consequently, repulsed by their scarcity. This study assumes the rate of rural aging tends to be higher in places where the ratio of urban to rural work opportunities is high. Thus, using spatial dependence models and quantile analysis of data from the 2017 Agricultural Census, the effects of factors associated with income, agrarian structure, and location on rural aging in Brazilian municipalities were assessed. The results suggest rural areas with low attractiveness situated close to urban centers are the most susceptible to population aging and, consequently, rural depopulation.

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Factors associated with rural aging in Brazilian municipalities: an analysis using quantile regressions

De Moura Vogt,

Fochezatto

2023

BGSS

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“…Also, there are analyses of the impact of typhoons on housing prices in East Asia, i.e. Murakami and Seya (2019). River floods are the main focus of studies on European regions such as Votsis and Perrels (2016) and Daniel et al (2009).…”

Section: Literature Reviewmentioning

confidence: 99%

“…Atreya et al (2013) decide on a combined spatial lag and spatial error model, Votsis and Perrels (2016) apply a Spatial Durbin model while Steininger (2016, 2020) implement a Spatial Durbin Error model (SDEM). 11 Investigating heterogeneity over the distribution of sales prices, Murakami and Seya (2019) use a spatial filtering approach to control for spatial dependence within their analysis.…”

Section: Spatial Dependencementioning

confidence: 99%

Die Auswirkungen von unerwarteten Hochwasserereignissen und Anpassungsmaßnahmen auf Grundstückspreise

Häse

Hirte

2023

Rev Reg Res

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Associated with climate change are the rising frequency, probability, and intensity of natural hazards, as stated by IPCC (2014). To contribute to the evaluation of climate change consequences, we study the impact of river floods caused by torrential rain periods, which are supposed to become and already are one of the major future challenges. Since subsequent flood and adaptation measures are usually interdependent, identifying causal effects is the main challenge. Therefore we investigate a sequence of river floods and subsequent adaption measures in a natural experiment setting. Our study area is the city of Dresden, Germany in the time period from 2000 until 2017. Remarkable in this setting is the exogeneity of the first flood event in 2002 that hit the city unexpectedly after 60 years without considerable flood events. We use a complete set of lot transactions from 2000 until 2017 to study the effect of these events on lot prices. The basis of our identification strategy is a Difference-in-Differences design in which we control for an unstable assignment to treatment and control group. Additionally we consider the heterogeneity of the treatment, which is caused by the varying intensity levels during a flood. It shows that flood risk is incorporated into lot prices only after awareness is risen due to a sudden flood event and that a higher expected intensity increases discounts. A long-term effect can be verified once we control for adaption, such as public protection measures.

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“…Two distinct spatial adaptations were considered: (i) a regression model that accounts for spatial autocorrelation effects in the error term [16]; and (ii) a regression model that accounts for spatial heterogeneity effects in the response to predictor variable relationships [17][18][19]. The use of spatially-explicit hedonic models is common in the house and land price literature [20][21][22][23][24][25][26][27][28][29], from which this current study transfers concepts and ideas. Thus, three hedonic regressions were specified, a non-spatial model (mimicking previous work) and two spatial adaptations, to provide a novel and spatially-informed comparison of hedonic price models for the Uruguayan cattle markets.…”

Section: Introductionmentioning

confidence: 99%

Influence of Geographical Effects in Hedonic Pricing Models for Grass-Fed Cattle in Uruguay

Harris

Lanfranco

et al. 2020

Agriculture

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A series of non-spatial and spatial hedonic models of feeding and replacement cattle prices at video auctions in Uruguay (2002 to 2009) were specified with predictors measuring marketing conditions (e.g., steer price), cattle characteristics (e.g., breed) and agro-ecological factors (e.g., soil productivity, water characteristics, pasture condition, season). Results indicated that cattle prices produced under extensive production systems were influenced by all of predictor categories, confirming that found previously. Although many of the agro-ecological predictors were inherently spatial in nature, the incorporation of spatial effects into the estimation of the hedonic model itself, through either a spatially-autocorrelated error term or allowing the regression coefficients to vary spatially and at different scales, was able to provide greater insight into the cattle price process. Through the latter extension, using a multiscale geographically weighted regression, which was the most informative and most accurate model, relationships between cattle price and predictors operated at a mixture of global, regional, local and highly local spatial scales. This result is considered a key advance, where uncovering, interpreting, and utilizing such rich spatial information can help improve the geographical provenance of Uruguayan beef and is critically important for maintaining Uruguay’s status as a key exporter of beef with respect to the health and safety benefits of natural, open-sky, grass-fed production systems.

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Spatially filtered unconditional quantile regression: Application to a hedonic analysis

Cited by 9 publications

References 45 publications

Factors associated with rural aging in Brazilian municipalities: an analysis using quantile regressions

Factors associated with rural aging in Brazilian municipalities: an analysis using quantile regressions

Die Auswirkungen von unerwarteten Hochwasserereignissen und Anpassungsmaßnahmen auf Grundstückspreise

Influence of Geographical Effects in Hedonic Pricing Models for Grass-Fed Cattle in Uruguay

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