Data-Driven Regionalization of Housing Markets

Helbich, Marco; Brunauer, Wolfgang; Hagenauer, Julian; Leitner, Michael

doi:10.1080/00045608.2012.707587

Cited by 71 publications

(64 citation statements)

References 54 publications

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“…Thus, one faces the modifiable areal unit problem (MAUP; see Fischer and Wang, 2011). In order to incorporate more homogeneous region delineations, statistical methods, such as cluster analysis (e.g., Bourassa et al, 1999) or regionalization (e.g., Helbich et al, 2013a), have been utilized. While Orford (2000) emphasizes the usefulness of multilevel modeling, Fotheringham et al (2002) sharply criticize the discrete nature in which space is implemented, which further implies that the price function is discrete and homogeneous within an spatial unit.…”

Section: Multilevel Regressionmentioning

confidence: 99%

“…In real estate research, it is well established that hedonic prices may vary across space such as stratifications of metropolitan areas, regions, and counties (e.g., Bourassa et al, 1999;Goodman and Thibodeau, 2003;Bischoff and Maennig, 2011;Helbich et al, 2013a). However, this parametric modeling approach has some restrictions: Spatial units have to be defined exogenously, SH is modeled in a discrete fashion where continuous changes across space can be expected, and usually the same definition of spatial units is used for all spatially varying effects (e.g., Redfearn, 2009;McMillen and Redfearn, 2010).…”

Section: Introductionmentioning

confidence: 99%

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Spatial Heterogeneity in Hedonic House Price Models: The Case of Austria

Brunauer²,

et al. 2013

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Modelling spatial heterogeneity (SH) is a controversial subject in real estate economics. Single-family-home prices in Austria are explored to investigate the capability of global and locally weighted hedonic models. Even if regional indicators are not fully capable to model SH and technical amendments are required to account for unmodelled SH, the results emphasise their importance to achieve a well-specified model. Due to SH beyond the level of regional indicators, locally weighted regressions are proposed. Mixed geographically weighted regression (MGWR) prevents the limitations of fixed effects by exploring spatially stationary and non-stationary price effects. Besides reducing prediction errors, it is concluded that global model misspecifications arise from improper selected fixed effects. Reported findings provide evidence that the SH of implicit prices is more complex than can be modelled by regional indicators or purely local models. The existence of both stationary and non-stationary effects implies that the Austrian housing market is economically connected.

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Section: Multilevel Regressionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Spatial Heterogeneity in Hedonic House Price Models: The Case of Austria

Brunauer²,

et al. 2013

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“…While the original research paper [9] applied contextual neural gas [16,17] to account for spatial autocorrelation [18], we complement this spatially explicit approach with a non-spatial analysis using widely applied self-organizing maps (SOMs) [19,20] (other options can be found in [21]). A SOM is an unsupervised artificial neural network for data clustering and visualization [19].…”

Section: Data Usage and Applicationmentioning

confidence: 99%

“…Data 2017, 2, 7 5 of 10 that the supermarket location data can also be used to apply, for example, cluster detection algorithms [20,21]. To facilitate quick access and use, the first code snippet loads the required R packages [22][23][24][25][26], sets the workspace, and unzips the data.…”

Section: Data Usage and Applicationmentioning

confidence: 99%

Data on Healthy Food Accessibility in Amsterdam, The Netherlands

Helbich

Hagenauer

2017

Data

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Abstract:This data descriptor introduces data on healthy food supplied by supermarkets in the city of Amsterdam, The Netherlands. In addition to two neighborhood variables (i.e., share of autochthons and average housing values), the data comprises three street network-based accessibility measures derived from analyses using a geographic information system. Data are provided on a spatial micro-scale utilizing grid cells with a spatial resolution of 100 m. We explain how the data were collected and pre-processed, and how alternative analyses can be set up. To illustrate the use of the data, an example is provided using the R programming language.

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“…Such a coincidence of locational and attribute similarity is referred to as spatial autocorrelation, a well-known concept in geography (e.g. HELBICH et al 2013a). To receive unbiased and correct inference results, spatial autocorrelation must be explicitly considered in statistical analysis (TITA & RADIL 2011).…”

Section: Introductionmentioning

confidence: 99%

Driving Forces of Non-Violent Crime in Houston, TX: A Spatially Filtered Negative Binomial Model

Helbich

Arsanjani

Leitner³

2013

GI_Forum 2013 – Creating the GISociety

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The analysis and understanding of spatial crime patterns is crucial for law enforcements to improve strategic and tactical decision-making. In this context, generalized linear models, such as count regressions, are commonly applied. These non-spatial models are challenged by spatial autocorrelation effects, contradicting fundamental model assumptions. Therefore, the purpose of this research is to present a spatially explicit approach, which combines a negative binomial model and spatial filtering to explain the spatial distribution of nonviolent offences in Houston, TX, for the year 2010. The results provide evidence that the non-spatial negative binomial model is biased while the supplementary consideration of a spatial filter is capable to absorb these undesirable spatial effects and results in a wellspecified regression model. Moreover, besides the significant importance of space in the explanation of the non-violent crime patterns, only the percentage of renter-occupied housing units and the percentage of Asian population are significantly related to the crime. The former covariate has a stimulating effect while the latter has an inhibiting effect.

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Data-Driven Regionalization of Housing Markets

Cited by 71 publications

References 54 publications

Spatial Heterogeneity in Hedonic House Price Models: The Case of Austria

Spatial Heterogeneity in Hedonic House Price Models: The Case of Austria

Data on Healthy Food Accessibility in Amsterdam, The Netherlands

Driving Forces of Non-Violent Crime in Houston, TX: A Spatially Filtered Negative Binomial Model

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