A Geographically Temporal Weighted Regression Approach with Travel Distance for House Price Estimation

Liu, Jiping; Yang, Yi; Xu, Shengyuan; Zhao, Yangyang; Wang, Yong; Zhang, Fuhao

doi:10.3390/e18080303

Cited by 21 publications

(19 citation statements)

References 37 publications

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“…[15] developed a GTWR model focusing on spatiotemporal kernel function definition and spatiotemporal bandwidth optimization. [16] developed a GTWR model based on non-Euclidean travel distance. The GTWR model can be expressed as follows: where ( u i , v i , t i ) is the coordinate of the observation i in space ( u i , v i ) at time t i , β 0 ( u i , v i , t i ) indicates the intercept value, β k ( u i , v i , t i ) indicates the slope for each variable k and each space-time point i , and ϵ i represents the random error with no correlation between different points.…”

Section: Gtwar Modelmentioning

confidence: 99%

“…To model the effects of temporal heterogeneity, [14] and [15] proposed a geographically and temporally weighted regression (GTWR) to simultaneously capture both spatial and temporal nonstationarity by integrating temporal effects into the traditional GWR model. [16] developed a GTWR model based on travel time distance metrics. But these GTWR models do not consider spatial autocorrelation.…”

Section: Introductionmentioning

confidence: 99%

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Kernel-based geographically and temporally weighted autoregressive model for house price estimation

Shim

Hwang

2018

PLoS ONE

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Spatiotemporal nonstationarity and autocorrelation are two crucial points in modeling geographical data. Previous studies have demonstrated that geographically and temporally weighted autoregressive (GTWAR) model accounts for both spatiotemporal nonstationarity and autocorrelation simultaneously to estimate house prices. Therefore, this paper proposes a kernel-based GTWAR (KBGTWAR) model by incorporating the basic principle of support vector machine regression into spatially and temporally varying coefficients model. The efficacy of KBGTWAR model is demonstrated through a case study on housing prices in the city of Shenzhen, China, from year 2004 to 2008. Comparing the existing models, KBGTWAR model obtains the lowest value for the residual sum of squares (RSS) and the highest value for the coefficient of determination R2. Moreover, KBGTWAR model improves the goodness of fit of the existing GTWAR model from 12.0 to 4.5 in terms of RSS, from 0.914 to 0.968 in terms of R2 and from 3.84 to 4.45 in terms of F-statistic. The results show that KBGTWAR model provides a comparatively high goodness of fit and sufficient explanatory power for both spatiotemporal nonstationarity and autocorrelation. The results of this study demonstrate that the proposed KBGTWAR model can be used to effectively formulate polices for real estate management.

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Section: Gtwar Modelmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Kernel-based geographically and temporally weighted autoregressive model for house price estimation

Shim

Hwang

2018

PLoS ONE

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“…The neighborhood attributes include the influences of supermarkets, shopping centers, primary schools, gas stations and other factors. We obtained 1961 samples with attributes such as house price, house area, residential plot ratio, residential greening ratio, property management fee, the distance to the nearest primary school, the distance to the nearest shopping mall, age of construction and geographical coordinates [34]. The housing commodity data were provided by the National Bureau of Statistics, and Figure 10 illustrates the distribution of the housing commodity samples.…”

Section: The Real Data Experimentsmentioning

confidence: 99%

“…other [34]. The housing commodity data were provided by the National Bureau of Statistics, and Figure 10 illustrates the distribution of the housing commodity samples.…”

Section: The Real Data Experimentsmentioning

confidence: 99%

A Mixed Geographically and Temporally Weighted Regression: Exploring Spatial-Temporal Variations from Global and Local Perspectives

Liu

Zhao

Yang

et al. 2017

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Abstract:To capture both global stationarity and spatiotemporal non-stationarity, a novel mixed geographically and temporally weighted regression (MGTWR) model accounting for global and local effects in both space and time is presented. Since the constant and spatial-temporal varying coefficients could not be estimated in one step, a two-stage least squares estimation is introduced to calibrate the model. Both simulations and real-world datasets are used to test and verify the performance of the proposed MGTWR model. Additionally, an Akaike Information Criterion (AIC) is adopted as a key model fitting diagnostic. The experiments demonstrate that the MGTWR model yields more accurate results than do traditional spatially weighted regression models. For instance, the MGTWR model decreased AIC value by 2.7066, 36.368 and 112.812 with respect to those of the mixed geographically weighted regression (MGWR) model and by 45.5628, −38.774 and 35.656 with respect to those of the geographical and temporal weighted regression (GTWR) model for the three simulation datasets. Moreover, compared to the MGWR and GTWR models, the MGTWR model obtained the lowest AIC value and mean square error (MSE) and the highest coefficient of determination (R 2 ) and adjusted coefficient of determination (R 2 adj ). In addition, our experiments proved the existence of both global stationarity and spatiotemporal non-stationarity, as well as the practical ability of the proposed method.

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“…Subsequently, many scholars applied GTWR in empirical studies on urban housing prices, showing that GTWR can not only analyze the spatial-temporal differentiation law of influencing factors, but also greatly improve the explanatory power of the model [32,33]. While GTWR is quite popular in research on the real-estate market [34][35][36], most empirical research has largely studied the spatio-temporal non-stationarity and determinants of housing prices in monocentric cities [9,37,38]. There has only been a limited amount of original research into the spatio-temporal variation in the house prices of polycentric cities.…”

Section: Introductionmentioning

confidence: 99%

Spatial-Temporal Variation in the Impacts of Urban Infrastructure on Housing Prices in Wuhan, China

et al. 2020

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This study aims to investigate the spatial and temporal dynamics of housing prices associated with the urban infrastructure in Wuhan, China. The relationship between urban infrastructure and housing prices during rapid urbanization has drawn popular concerns. This article takes 619 residential communities during the period 2010 to 2018 in Wuhan’s main urban area as research units, and uses the geographically and temporally weighted regression (GTWR) model to study the spatial-temporal differentiation in the effects of urban infrastructure on housing prices. The results show that: 1) From 2010 to 2018, housing prices in Wuhan’s main urban area were generally on the rise, but the increment speed has shown an obvious periodic characteristic, the spatial distribution of housing prices has shown an obvious core and periphery distribution and the peak value area shifted from Hankou to Wuchang. 2) The influential factors of housing prices have significant spatiotemporal non-stationarity, while the impact, direction and intensity of the influential factors varies in time and space. Spatially, the influence factors show different differentiation rules for spatial distribution, and the influencing direction and strength of the urban infrastructure on housing prices are closely related to the spatial location, distribution density and the type of urban infrastructure. Temporally, the influencing strength of various urban facilities varies. This research will benefit both urban planners for optimizing urban facilities and policy-makers for formulating more specific housing policies, which ultimately contributes to urban sustainability.

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A Geographically Temporal Weighted Regression Approach with Travel Distance for House Price Estimation

Cited by 21 publications

References 37 publications

Kernel-based geographically and temporally weighted autoregressive model for house price estimation

Kernel-based geographically and temporally weighted autoregressive model for house price estimation

A Mixed Geographically and Temporally Weighted Regression: Exploring Spatial-Temporal Variations from Global and Local Perspectives

Spatial-Temporal Variation in the Impacts of Urban Infrastructure on Housing Prices in Wuhan, China

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