GIS and Spatial Analysis of Housing and Mortgage Markets

Can, Ayşe

doi:10.1080/10835547.1998.12091927

Cited by 53 publications

(36 citation statements)

References 20 publications

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“…The geographic contiguity of census tracts in Seattle raises the possibility of spatial clustering in our dependent variables, and indeed research on the distribution of crime and lending has suggested spatially embedded processes for both outcomes (Baller et al, 2001; Can, 1998; Morenoff, Sampson, and Raudenbush, 2001; Wyly and Holloway, 1999). The spatial lags represent the average mortgage investment ( number/occupied , dollars/lending potential ) and log violence rates in geographically adjacent census tracts 3 .…”

Section: Methodsmentioning

confidence: 99%

Neighborhood Housing Investments and Violent Crime in Seattle, 1981–2007*

2012

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Despite significant advances in the study of neighborhoods and crime, criminologists have paid surprisingly less attention to the extralocal forces that shape violence. To address this issue, we draw on an emerging body of work that stresses the role of home mortgage lending-a resource secured via interaction with external actors-in reducing neighborhood violence and extend it by addressing concerns that the lendingviolence relationship is spurious and confounded by simultaneity. We explore the longitudinal relationship between residential mortgage lending and violence in Seattle with a pooled time series of 118 census tracts over 27 years, and we instrument our endogenous predictors (home mortgage lending and violent crime) with changes in their levels from prior periods. Employing Arellano-Bond difference models, we assess both the effect of mortgage lending on violent crime as well as the effect of violent crime levels on mortgage activity. We find that infusions of home mortgage lending yield reductions in subsequent violent crime;

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Section: Methodsmentioning

confidence: 99%

Neighborhood Housing Investments and Violent Crime in Seattle, 1981–2007*

2012

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“…Spatial autocorrelation entails determining the autocorrelation of specific spatial attribute variables to elucidate the distribution characteristics of spatial elements within a given space. Anselin and Getis [32] consolidated the theoretical spatial autocorrelation and spatial dependence models proposed by Cliff and Ord [33] to test various spatial autocorrelation methods and relevant applications, including spatial autocorrelation analysis, spatial lag price variables, expansion theory, geographically weighted regression, and the Kriging method [22,34,35]. The theoretical models were used to calculate the correction coefficient.…”

Section: Autocorrelationmentioning

confidence: 99%

“…Spatial statistics refers to the application of statistics in the analysis of spatial targets to obtain the rules or structures concerning the geospatial distribution of these targets [21]. Subsequently, advancements in theoretical applications and geographic information systems (GIS) have facilitated the flow of big spatial data, from input and storage, helping researchers quickly process vast amounts of spatial data and elucidate the phenomena and characteristics of spatial changes in the real world [22][23][24][25]. In this study, the global Moran's I was adopted as the measure of spatial autocorrelation to test whether aggregated characteristics were present in the real estate prices of Taitung City.…”

Section: Introductionmentioning

confidence: 99%

An Application of the Spatial Autocorrelation Method on the Change of Real Estate Prices in Taitung City

Wang

Chang

Wang

2019

IJGI

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The main purpose of this paper is to use regression models to explore the factors affecting housing prices as well as apply spatial aggregation to explore the changes of urban space prices. This study collected data in Taitung City from the year 2013 to 2017, including 3533 real estate transaction price records. The hedonic price method, spatial lag model and spatial error model were used to conduct global spatial self-correlation tests to explore the performance of house price variables and space price aggregation. We compare the three models by R² and Akaike Information Criterion (AIC) to determine the spatial self-correlation ability performance, and explore the spatial distribution of prices and the changes of price regions from the regional local indicators of spatial association spatial distribution map. Actual analysis results show an improvement in the ability to interpret real estate prices through the feature price mode from the R² value assessment, the spatial delay model and the spatial error model. Performance from the AIC values show that the difference of the spatial delay model is smaller than that of the feature price model and the spatial model, demonstrating a better performance from the space delay model and the spatial error model compared to the feature price model; improving upon the estimation bias caused by spatial self-correlation. For variables affecting house pricing, research results show that Moran’s I is more than 0 in real estate price analysis over the years, all of which show spatial positive correlation. From the LISA analysis of the spatial aggregation phenomenon, we see real estate prices rise in spaces surrounded by high-priced real estate contrast with the scope of space surrounded by low-cost real estate shifting in boundary over the years due to changes in the location and attributes of real estate trading transactions. Through the analysis of space price aggregation characteristics, we are able to observe the trajectory of urban development.

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“…Zhixiong Mei and Xia Li (2007), using the house price in Dongguan as a subject, plotted the prices of residential houses in a grid graph and superimposed major roads buffer zones on to the house price distribution graph, in order to discover and explain the spatial variance in house prices of Dongguan [6]. Can (1998) used GIS technology to analyze the spatial relativity between the housing market and the mortgage market [7].…”

Section: Introductionmentioning

confidence: 99%

Time and Space Analysis of House Price in Mainland China in the Last 10 Years

Guan¹,

Gao²

2018

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In this paper, time series analysis, geographic information, cluster analysis, causality test and other techniques and means are used to analyze the housing prices of mainland China in the past 10 years. Kmeans clusters after comparison of house prices between provinces and neighboring provinces, provinces of the same category are continuously distributed on the map; the highest price area and the lowest price area on the time series are also continuously distributed on the map; the time series-based causality test found that the growth rate of house prices in six provinces was affected by the surrounding provinces, and the growth rate of house prices in one province affected the growth rate of house prices in neighboring provinces.

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GIS and Spatial Analysis of Housing and Mortgage Markets

Cited by 53 publications

References 20 publications

Neighborhood Housing Investments and Violent Crime in Seattle, 1981–2007*

Neighborhood Housing Investments and Violent Crime in Seattle, 1981–2007*

An Application of the Spatial Autocorrelation Method on the Change of Real Estate Prices in Taitung City

Time and Space Analysis of House Price in Mainland China in the Last 10 Years

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