Um modelo espacial de demanda habitacional para a cidade do Recife

Dantas, Rubens Alves; Magalhães, André Matos; Vergolino, José Raimundo Oliveira

doi:10.1590/s0101-41612010000400006

Cited by 5 publications

(7 citation statements)

References 12 publications

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“…Hence, we deal with cross-sectional dependence with a balanced data of 141 cross-sectional units ( i = municipalities). ɛ is the disturbance vector of random errors from the regression model, which considers an autoregressive spatial process in the error term for the spatial model, where λ the autoregressive coefficient of the error terms; while u is a vector of independent and identically distributed error terms 78 – 80 . As ordinary least-squares regression (OLS) is unsuitable for spatial regression models because it assumes independence among observations 60 , 81 , the proposed SDEM relies on the maximum likelihood estimation 77 , suitable to estimate the significance and magnitude of spatial lags 59 , 81 .…”

Section: Methodsmentioning

confidence: 99%

Section: Moving Window Approachmentioning

confidence: 99%

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Socioeconomic and environmental effects of soybean production in metacoupled systems

Silva

Viña

Moran

et al. 2021

Sci Rep

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Human–environment interactions within and across borders are now more influential than ever, posing unprecedented sustainability challenges. The framework of metacoupling (interactions within and across adjacent and distant coupled human–environment systems) provides a useful tool to evaluate them at diverse temporal and spatial scales. While most metacoupling studies have so far addressed the impacts of distant interactions (telecouplings), few have addressed the complementary and interdependent effects of the interactions within coupled systems (intracouplings) and between adjacent systems (pericouplings). Using the production and trade of a major commodity (soybean) as a demonstration, this paper empirically evaluates the complex effects on deforestation and economic growth across a globally important soybean producing region (Mato Grosso in Brazil). Although this region is influenced by a strong telecoupling process (i.e., soybean trade with national and international markets), intracouplings pose significant effects on deforestation and economic growth within focal municipalities. Furthermore, it generates pericoupling effects (e.g., deforestation) on adjacent municipalities, which precede economic benefits on adjacent systems, and may occur during and after the soybean production takes place. These results show that while economic benefits of the production of agricultural commodities for global markets tend to be localized, their environmental costs tend to be spatially widespread. As deforestation also occurred in adjacent areas beyond focal areas with economic development, this study has significant implications for sustainability in an increasingly metacoupled world.

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Section: Methodsmentioning

confidence: 99%

Section: Moving Window Approachmentioning

confidence: 99%

Socioeconomic and environmental effects of soybean production in metacoupled systems

Silva

Viña

Moran

et al. 2021

Sci Rep

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“…Uma variável regionalizada é uma variável distribuída no espaço ou tempo cujos valores são considerados como realizados de uma função aleatória. Dantas (2003) diz que esta teoria identifica que a distribuição espacial de uma variável é expressa pela soma de três componentes:…”

Section: Técnicas De Krigagemunclassified

Avaliação Em Massa De Imóveis Usando Geoestatística E Krigagem Bayesiana (Um Estudo De Em Balneário Camboriú/Sc)

Hornburg¹,

Hochheim

2017

REEC

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RESUMO: Uma das grandes dificuldades que se tem na avaliação em massa de imóveis é encontrar um modelo que mostre a realidade do mercado de imóveis para que se possa construir uma Planta de Valores Genéricos (PVG), usada como base para a cobrança do Imposto Predial e Territorial Urbano (IPTU). Este artigo apresenta um método que combina o uso da econometria espacial com a geoestatística bayesiana visando estimar o valor dos imóveis levando em consideração as interações espaciais devidas às características da localização. Os métodos da regressão espacial e da krigagem bayesiana são usados com esta finalidade. A técnica da regressão espacial possibilita a modelagem da dependência espacial. A técnica da krigagem bayesiana permite estimar valores de variáveis espacialmente distribuídas a partir de valores adjacentes considerados como interdependentes. Dessa maneira, a krigagem é considerada um método de médias móveis. O semivariograma é a ferramenta básica de suporte às técnicas de krigagem, permitindo representar quantitativamente a variação de um fenômeno regionalizado no espaço. Uma aplicação do método proposto é realizada no bairro Centro da cidade de Balneário Camboriú (SC), usando-se uma amostra de dados de mercado para a avaliação em massa de imóveis do tipo apartamento. Destaca-se como contribuição do método apresentado uma possibilidade de melhora na determinação do valor justo dos imóveis numa avaliação em massa, amparando assim a equidade e consequente justiça fiscal quando aplicados pelos municípios. ABSTRACT: One of the greatest difficulties in bulk value appraisal of buildings is to find a model that shows the reality of the real estate market so you can build a Standard Ground Value (SGV), used as a basis for the collection of Land and Territorial Tax Urban (Land Tax). This paper presents a method that combines the use of spatial econometrics with Bayesian geostatistics in order to estimate the value of real estate taking into account the spatial interactions due to the characteristics of the location. The methods of spatial regression and Bayesian kriging are used for this purpose. The spatial regression technique allows the modeling of spatial dependence. The Bayesian kriging technique allows the estimation of values of spatially distributed variables from adjacent values considered as interdependent. In this way, kriging is considered a moving average method. The semivariogram is the basic tool to support kriging techniques, allowing quantitative representation of the variation of a regionalized phenomenon in space. An application of the proposed method is carried out in the downtown neighborhood of Balneário Camboriú (SC), using a sample of market data for the mass evaluation of apartment-type properties. As a contribution of the method presented, there is a possibility of improvement in the determination of the fair value of real estate in a mass valuation, thus supporting equity and consequent fiscal justice when applied by municipalities.

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“…Among the first group, there are studies that analysed the cities of São Paulo (Hermann & Haddad, 2005;Nadalin, 2010), Belo Horizonte (Furtado, 2009) and Recife (Dantas, Magalhães, & Vergolino, 2010). The second group applied different techniques to study different localities, such as instrumental variables to analyse the city of São Paulo (Biderman, 2001), two-stage least squares models to study São Paulo Metropolitan Region (Fávero, Belfiore, & Lima, 2008) and hierarchical models to analyse the city of Belo Horizonte (Aguiar & Simões, 2010).…”

Section: Brief Literature Reviewmentioning

confidence: 99%

Housing market analysis using a hierarchical–spatial approach: the case of Belo Horizonte, Minas Gerais, Brazil

Aguiar¹,

Simões²,

Golgher³

2014

Regional Studies, Regional Science

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The paper analyzes the determinants of apartments' prices in Belo Horizonte, MG, Brazil, with the use of hierarchical models, spatial models and a hierarchical-spatial approach. Besides the apartments' characteristics, such as area, age and building standard, prices were determined by local urban amenities. The hierarchical models indicated that local variables, such as urban violence, infrastructure and services, explained over 75% of prices' remaining variability. The spatial models analyzed if, after controlling for price variability with the explanatory variables in the second level of the hierarchical models, spatial correlations still existed in price determination. The positive and significant spatial coefficient in spatial autoregressive models (SAR) indicated spatial dependency. The hierarchical-spatial approach showed that over 70% of apartment prices' remaining variability could be explained by local variables and that the lagged urban services variable explained another 12% of this variability.

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Um modelo espacial de demanda habitacional para a cidade do Recife

Cited by 5 publications

References 12 publications

Socioeconomic and environmental effects of soybean production in metacoupled systems

Socioeconomic and environmental effects of soybean production in metacoupled systems

Avaliação Em Massa De Imóveis Usando Geoestatística E Krigagem Bayesiana (Um Estudo De Em Balneário Camboriú/Sc)

Housing market analysis using a hierarchical–spatial approach: the case of Belo Horizonte, Minas Gerais, Brazil

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