Accounting for Local Spatial Heterogeneities in Housing Market Studies

Osland, Liv; Thorsen, Ingrid Sandvig; Thorsen, Inge

doi:10.1111/jors.12281

Cited by 14 publications

(17 citation statements)

References 43 publications

(73 reference statements)

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“…Indeks Moran digunakan untuk mempelajari sejauh mana ukuran interaksi spasial dan kualitas lingkungan berkontribusi untuk spatial smoothing (Osland, Sandvig, & Thorsen, 2016). Indeks Moran (𝐼) merupakan parameter autokorelasi spasial yang dinyatakan sebagai:…”

Section: Indeks Moranunclassified

Penerapan Spatial Error Model (SEM) untuk Mengetahui FaktorFaktor yang Memengaruhi Kriminalitas

Nabilah¹,

Zidni²,

Humairoh³

et al. 2021

semnasoffstat

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Masyarakat berisiko menjadi korban tindak kriminalitas. Semakin tinggi risiko maka semakin tidak aman suatu daerah. Berdasarkan data Badan Pusat Statistik (BPS) dari tahun 2009-2020 kriminalitas di Jawa Tengah selalu menempati posisi sepuluh besar tertinggi di Indonesia. Walaupun pada tahun 2020 kasus kriminalitas di Jawa Tengah menurun pada kasus curat dan curanmor, namun kasus seperti narkoba, curas, dan uang palsu bertambah. Hal ini dapat merugikan psikologis dan ekonomis sehingga masyarakat menentangnya. Penelitian ini bertujuan untuk mengetahui faktor-faktor yang memengaruhi kriminalitas di Jawa Tengah dengan menggunakan regresi spasial. Hasil analisis yang diperoleh Tingkat Pengangguran Terbuka, Upah Minimum Kabupaten/Kota, Harapan Lama Sekolah, dan proporsi penduduk laki-laki memengaruhi tingkat kriminalitas di Jawa Tengah. Kemudian terdapat interaksi spasial antar 35 kabupaten/kota di Jawa Tengah dengan Spatial Error Model (SEM).

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Section: Indeks Moranunclassified

Penerapan Spatial Error Model (SEM) untuk Mengetahui FaktorFaktor yang Memengaruhi Kriminalitas

Nabilah¹,

Zidni²,

Humairoh³

et al. 2021

semnasoffstat

View full text Add to dashboard Cite

show abstract

“…The scalar s reflects the general variance of the random effects. As pointed out by Osland et al (2016), the model is 'improper' because the covariance matrix of the simultaneous distribution is not positive definite. However, it is still being used (Gschl€ oßl and Czado, 2007).…”

Section: A Conditional Autoregressive Spatial Model Formulationmentioning

confidence: 99%

“…First, we account for local spatial interdependencies in a rich and flexible way using a spatial conditional autoregressive (CAR) modelling framework, which includes spatially correlated random effects. Osland et al (2016) have demonstrated that this modelling approach largely adjusts for missing information on spatial characteristics, and contributes considerably to more accurate predictions of house prices. This is particularly so for heterogeneous urban areas, where controlling for omitted spatial variables is highly important.…”

Section: Introductionmentioning

confidence: 99%

Capitalization of neighbourhood diversity and segregation

et al. 2019

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In this paper, we study how concentrations and diversity of different groups of households were reflected in the housing prices of neighbourhoods in the Oslo urban area, Norway. The focus is primarily on the settlement pattern of immigrants, but the analysis controls for socioeconomic and demographic characteristics. Based on a hedonic conditional autoregressive spatial model formulation, we find that households on average prefer neighbourhoods with a high concentration of natives, many immigrants from Western countries, and, at the same time, a diverse, thin representation of neighbours from a wide range of countries. We do not find that immigrants from specific countries or continents have a substantial negative impact on housing prices in a neighbourhood.

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“…Of course, the model can also be estimated without this component. In such case, the Wy element can be omitted and the model obtained in this situation would be equal to the HLM model (Hierarchical Linear Model) [60], although HSAR fits a simultaneous autoregressive (SAR) spatial random effect rather than a conditional autoregressive (CAR) spatial random effect.…”

Section: Theoretical Basic Of Conducted Researchmentioning

confidence: 99%

Application of Hierarchical Spatial Autoregressive Models to Develop Land Value Maps in Urbanized Areas

Cellmer

Kobylińska

Bełej

2019

IJGI

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This article aims at testing the possibilities of applying hierarchical spatial autoregressive models to create land value maps in urbanized areas. The use of HSAR (Hierarchical Spatial Autoregressive) models for spatial differentiation of prices in the property market supports the multilevel diagnosis of the structure of this phenomenon, taking into account the effect of spatial interactions. The article applies a two-level hierarchical spatial autoregressive model, which will permit the evaluation of interactions and control spatial heterogeneity at two levels of spatial aggregation (general and detailed). The results of the research include both the evaluation of the impact of location on prices (taking into account non-spatial factors) and the creation of the average land price map, taking into consideration the spatial structure of the city. In empirical studies, the HSAR model was compared with classic LM (Linear Model), HLM (Hierarchical Linear Model), and SAR (Spatial Autoregressive) models to perform comparative analyses of the results.

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Accounting for Local Spatial Heterogeneities in Housing Market Studies

Cited by 14 publications

References 43 publications

Penerapan Spatial Error Model (SEM) untuk Mengetahui FaktorFaktor yang Memengaruhi Kriminalitas

Penerapan Spatial Error Model (SEM) untuk Mengetahui FaktorFaktor yang Memengaruhi Kriminalitas

Capitalization of neighbourhood diversity and segregation

Application of Hierarchical Spatial Autoregressive Models to Develop Land Value Maps in Urbanized Areas

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