Second-hand housing batch evaluation model of zhengzhou city based on big data and MGWR model

2021 ◽  
pp. 1-20
Author(s):  
Chaojie Liu ◽  
Jie Lu ◽  
Wenjing Fu ◽  
Zhuoyi Zhou
Keyword(s):  
Real Estate ◽  
Geographically Weighted Regression ◽  
Euclidean Distance ◽  
Evaluation Model ◽  
Housing Prices ◽  
Information Criterion ◽  
Access Point ◽  
Least Square ◽  
Weighted Regression ◽  
Spatial Error

How to better evaluate the value of urban real estate is a major issue in the reform of real estate tax system. So the establishment of an accurate and efficient housing batch evaluation model is crucial in evaluating the value of housing. In this paper the second-hand housing transaction data of Zhengzhou City from 2010 to 2019 was used to model housing prices and explanatory variables by using models of Ordinary Least Square (OLS), Spatial Error Model (SEM), Geographically Weighted Regression (GWR), Geographically and Temporally Weighted Regression (GTWR), and Multiscale Geographically Weighted Regression (MGWR). And a correction method of Barrier Line and Access Point (BLAAP) was constructed, and compared with three correction methods previously studied: Buffer Area (BA), Euclidean Distance (ED), and Non-Euclidean Distance, Travel Distance (ND, TT). The results showed: The fitting degree of GWR, MGWR and GTWR by BLAAP was 0.03–0.07 higher than by ND. The fitting degree of MGWR was the highest (0.883) by BLAAP but the smallest by Akaike Information Criterion (AIC), and 88.3% of second-hand housing data could be well interpreted by the model.

scholarly journals Geographically varying relationships of COVID-19 mortality with different factors in India

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Asif Iqbal Middya ◽  
Sarbani Roy
Keyword(s):  
Geographically Weighted Regression ◽  
Information Criterion ◽  
Driving Factors ◽  
Least Square ◽  
Weighted Regression ◽  
Related Factors ◽  
Local Method ◽  
Ordinary Least Square ◽  
Local Methods ◽  
Global And Local

AbstractCOVID-19 is a global crisis where India is going to be one of the most heavily affected countries. The variability in the distribution of COVID-19-related health outcomes might be related to many underlying variables, including demographic, socioeconomic, or environmental pollution related factors. The global and local models can be utilized to explore such relations. In this study, ordinary least square (global) and geographically weighted regression (local) methods are employed to explore the geographical relationships between COVID-19 deaths and different driving factors. It is also investigated whether geographical heterogeneity exists in the relationships. More specifically, in this paper, the geographical pattern of COVID-19 deaths and its relationships with different potential driving factors in India are investigated and analysed. Here, better knowledge and insights into geographical targeting of intervention against the COVID-19 pandemic can be generated by investigating the heterogeneity of spatial relationships. The results show that the local method (geographically weighted regression) generates better performance ($$R^{2}=0.97$$ R 2 = 0.97 ) with smaller Akaike Information Criterion (AICc $$=-66.42$$ = - 66.42 ) as compared to the global method (ordinary least square). The GWR method also comes up with lower spatial autocorrelation (Moran’s $$I=-0.0395$$ I = - 0.0395 and $$p < 0.01$$ p < 0.01 ) in the residuals. It is found that more than 86% of local $$R^{2}$$ R 2 values are larger than 0.60 and almost 68% of $$R^{2}$$ R 2 values are within the range 0.80–0.97. Moreover, some interesting local variations in the relationships are also found.

scholarly journals Geographically varying relationships of COVID-19 mortality with different factors in India

2020 ◽  
Author(s):  
Asif Iqbal Middya ◽  
Sarbani Roy
Keyword(s):  
Geographically Weighted Regression ◽  
Information Criterion ◽  
Driving Factors ◽  
Least Square ◽  
Weighted Regression ◽  
Related Factors ◽  
Local Method ◽  
Ordinary Least Square ◽  
Local Methods ◽  
Global And Local

Abstract COVID-19 is a global crisis where India is going to be one of the most heavily affected countries. The variability in the distribution of COVID-19-related health outcomes might be related to many underlying variables, including demographic, socioeconomic, or environmental pollution related factors. The global and local models can be utilized to explore such relations. In this study, ordinary least square (global) and geographically weighted regression (local) methods are employed to explore the geographical relationships between COVID-19 deaths and different driving factors. It is also investigated whether geographical heterogeneity exists in the relationships. More specifically, in this paper, the geographical pattern of COVID-19 deaths and its relationships with different potential driving factors in India are investigated and analysed. Here, better knowledge and insights into geographical targeting of intervention against the COVID-19 pandemic can be generated by investigating the heterogeneity of spatial relationships. The results show that the local method (geographically weighted regression) generates better performance (R2 = 0:973) with smaller Akaike Information Criterion (AICc = -77:93) as compared to the global method (ordinary least square). The GWR method also comes up with lower spatial autocorrelation (Moran’s I = -0.0436 and p < 0:01) in the residuals. It is found that more than 87.5% of local R2 values are larger than 0.60 and almost 60% of R2 values are within the range 0:80 - 0:97. Moreover, some interesting local variations in the relationships are also found.

scholarly journals Parameter estimation of geographically weighted regression (GWR) model using weighted least square and its application

2018 ◽  
Author(s):  
Saskya Mary Soemartojo ◽  
Rima Dini Ghaisani ◽  
Titin Siswantining ◽  
Mariam Rahmania Shahab ◽  
Moch. Muchid Ariyanto
Keyword(s):  
Parameter Estimation ◽  
Geographically Weighted Regression ◽  
Least Square ◽  
Weighted Regression ◽  
Weighted Least Square ◽  
Gwr Model

scholarly journals Spatial Analysis of Housing Prices and Market Activity with the Geographically Weighted Regression

2020 ◽  
Vol 9 (6) ◽  
pp. 380
Author(s):  
Radosław Cellmer ◽  
Aneta Cichulska ◽  
Mirosław Bełej
Keyword(s):  
Spatial Heterogeneity ◽  
Housing Market ◽  
Spatial Data ◽  
Geographically Weighted Regression ◽  
Housing Prices ◽  
Spatial Diversity ◽  
Spatial Data Analysis ◽  
Weighted Regression ◽  
Market Activity ◽  
Administrative Units

The main part of the study will be to demonstrate that models taking into account spatial heterogeneity (Geographically Weighted Regression and Mixed Geographically Weighted Regression) which reproduce housing market determinants better reflect market relationships than conventional regression models. The spatial heterogeneity of the housing market determinants results in the spatial diversity of the market activity, as well as of real estate prices and values. The main aim of the study was to analyse an effect of these socio-demographic and environmental factors on average housing property prices and on the number of transactions in a spatial approach. In previous research conducted on a national scale, usually all variables were treated in a similar way, i.e., as global or local variables. During the research, an attempt was also made to answer the question of which of the variables adopted for analysis have a local impact on prices and market activity, and which are global. The study was conducted in Poland and used data from the year 2018 on 380 counties (Local Administrative Units). The study showed that determinants both for average prices and for the housing market activity show spatial autocorrelation with high–high and low–low cluster groups. Owing to these models, it was possible to draw specific conclusions on local determinants of flat prices and the market activity in Poland. The study findings have confirmed that they are an extremely effective tool for spatial data analysis.

scholarly journals Exploring Housing Rent by Mixed Geographically Weighted Regression: A Case Study in Nanjing

2019 ◽  
Vol 8 (10) ◽  
pp. 431 ◽  
Author(s):  
Shiwei Zhang ◽  
Lin Wang ◽  
Feng Lu
Keyword(s):  
Real Estate ◽  
Spatial Variability ◽  
Geographically Weighted Regression ◽  
Spatial Differentiation ◽  
Scientific Basis ◽  
Weighted Regression ◽  
Utility Value ◽  
Real Estate Management ◽  
Group Pattern

In China, the housing rent can clearly reveal the actual utility value of a house due to its low capital premium. However, few studies have examined the spatial variability of housing rent. Accordingly, this study attempted to determine the utility value of houses based on housing rent data. In this study, we applied mixed geographically weighted regression (MGWR) to explore the residential rent in Nanjing, the largest city in Jiangsu Province. The results show that the distribution of residential rent has a multi-center group pattern. Commercial centers, primary and middle schools, campuses, subways, expressways, and railways are the most significant influencing factors of residential rent in Nanjing, and each factor has its own unique characteristics of spatial differentiation. In addition, the MGWR has a better fit with housing rent than geographically weighted regression (GWR). These research results provide a scientific basis for local real estate management and urban planning departments.

scholarly journals Estimation of Error Variance-Covariance Parameters Using Multivariate Geographically Weighted Regression Model

2020 ◽  
Vol 2020 ◽  
pp. 1-5 ◽  
Author(s):  
Sri Harini
Keyword(s):  
Statistical Inference ◽  
Geographically Weighted Regression ◽  
Weighting Function ◽  
Likelihood Estimation ◽  
Error Variance ◽  
Least Square ◽  
Weighted Regression ◽  
Inference Procedure ◽  
Geographically Weighted Regression Model ◽  
Estimated Error

The Multivariate Geographically Weighted Regression (MGWR) model is a development of the Geographically Weighted Regression (GWR) model that takes into account spatial heterogeneity and autocorrelation error factors that are localized at each observation location. The MGWR model is assumed to be an error vector ε that distributed as a multivariate normally with zero vector mean and variance-covariance matrix Σ at each location ui,vi, which Σ is sized qxq for samples at the i-location. In this study, the estimated error variance-covariance parameters is obtained from the MGWR model using Maximum Likelihood Estimation (MLE) and Weighted Least Square (WLS) methods. The selection of the WLS method is based on the weighting function measured from the standard deviation of the distance vector between one observation location and another observation location. This test uses a statistical inference procedure by reducing the MGWR model equation so that the estimated error variance-covariance parameters meet the characteristics of unbiased. This study also provides researchers with an understanding of statistical inference procedures.

scholarly journals Measuring Bandwidth Uncertainty in Multiscale Geographically Weighted Regression Using Akaike Weights

2019 ◽  
Author(s):  
Ziqi Li ◽  
Alexander Stewart Fotheringham ◽  
Taylor M. Oshan ◽  
Levi John Wolf
Keyword(s):  
Geographically Weighted Regression ◽  
Regression Models ◽  
Bandwidth Selection ◽  
Information Criterion ◽  
Weighted Regression ◽  
Parameter Estimates ◽  
Multi Scale ◽  
Single Scale ◽  
Heterogeneous Processes ◽  
Key Parameter

Bandwidth, a key parameter in geographically weighted regression models, is closely related to the spatial scale at which the underlying spatially heterogeneous processes being examined take place. Generally, a single optimal bandwidth (geographically weighted regression) or a set of covariate-specific optimal bandwidths (multiscale geographically weighted regression) is chosen based on some criterion such as the Akaike Information Criterion (AIC) and then parameter estimation and inference are conditional on the choice of this bandwidth. In this paper, we find that bandwidth selection is subject to uncertainty in both single-scale and multi-scale geographically weighted regression models and demonstrate that this uncertainty can be measured and accounted for. Based on simulation studies and an empirical example of obesity rates in Phoenix, we show that bandwidth uncertainties can be quantitatively measured by Akaike weights, and confidence intervals for bandwidths can be obtained. Understanding bandwidth uncertainty offers important insights about the scales over which different processes operate, especially when comparing covariate-specific bandwidths. Additionally, unconditional parameter estimates can be computed based on Akaike weights accounts for bandwidth selection uncertainty.

scholarly journals Augmenting Geostatistics with Matrix Factorization: A Case Study for House Price Estimation

2020 ◽  
Vol 9 (5) ◽  
pp. 288
Author(s):  
Aisha Sikder ◽  
Andreas Züfle
Keyword(s):  
Matrix Factorization ◽  
Geographically Weighted Regression ◽  
House Prices ◽  
Housing Prices ◽  
House Price ◽  
Weighted Regression ◽  
Regression Kriging ◽  
Truncated Svd ◽  
Latent Features ◽  
Kriging Approach

Singular value decomposition (SVD) is ubiquitously used in recommendation systems to estimate and predict values based on latent features obtained through matrix factorization. But, oblivious of location information, SVD has limitations in predicting variables that have strong spatial autocorrelation, such as housing prices which strongly depend on spatial properties such as the neighborhood and school districts. In this work, we build an algorithm that integrates the latent feature learning capabilities of truncated SVD with kriging, which is called SVD-Regression Kriging (SVD-RK). In doing so, we address the problem of modeling and predicting spatially autocorrelated data for recommender engines using real estate housing prices by integrating spatial statistics. We also show that SVD-RK outperforms purely latent features based solutions as well as purely spatial approaches like Geographically Weighted Regression (GWR). Our proposed algorithm, SVD-RK, integrates the results of truncated SVD as an independent variable into a regression kriging approach. We show experimentally, that latent house price patterns learned using SVD are able to improve house price predictions of ordinary kriging in areas where house prices fluctuate locally. For areas where house prices are strongly spatially autocorrelated, evident by a house pricing variogram showing that the data can be mostly explained by spatial information only, we propose to feed the results of SVD into a geographically weighted regression model to outperform the orginary kriging approach.

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