scholarly journals Spatial weight matrix impact on real estate hierarchical clustering in the process of mass valuation

2019 ◽  
Vol 10 (1) ◽  
pp. 131-151
Author(s):  
Sebastian Gnat
Keyword(s):  
Real Estate ◽  
Hierarchical Clustering ◽  
Information Entropy ◽  
Value Added ◽  
Weight Matrix ◽  
Spatial Constraints ◽  
Spatial Weight Matrix ◽  
Real Estate Valuation ◽  
Spatial Weight ◽  
The Impact

Research background: The value of the property can be determined on an individual or mass basis. There are a number of situations in which uniform and relatively fast results obtained by means of mass valuation undoubtedly outweigh the advantages of the individual approach. In literature and practice there are a number of different types of models of mass valuation of real estate. For some of them it is postulated or required to group the valued properties into homogeneous subset due to various criteria. One such model is Szczecin Algorithm of Real Estate Mass Appraisal (SAREMA). When using this algorithm, the area to be valued should be divided into the so-called location attractiveness areas (LAZ). Such division can be made in many ways. Regardless of the method of clustering, its result should be assessed, depending on the degree of implementation of the adopted criterion of division quality. A better division of real estate will translate into more accurate valuation results. Purpose of the article: The aim of the article is to present an application of hierarchical clustering with a spatial constraints algorithm for the creation of LAZ. This method requires the specification of spatial weight matrix to carry out the clustering process. Due to the fact that such a matrix can be specified in a number of ways, the impact of the proposed types of matrices on the clustering process will be described. A modified measure of information entropy will be used to assess the clustering results. Methods: The article utilises the algorithm of agglomerative clustering, which takes into account spatial constraints, which is particularly important in the context of real estate valuation. Homogeneity of clusters will be determined with the means of information entropy. Findings & Value added: The main achievements of the study will be to assess whether the type of the distance matrix has a significant impact on the clustering of properties under valuation.

scholarly journals Application of economic distance for the purposes of a spatial analysis of the unemployment rate for Poland

2010 ◽  
Vol 1 (1) ◽  
pp. 79-98 ◽  
Author(s):  
Michał Bernard Pietrzak
Keyword(s):  
Spatial Analysis ◽  
Physical Properties ◽  
Unemployment Rate ◽  
Spatial Econometrics ◽  
Weight Matrix ◽  
Spatial Weight Matrix ◽  
Economic Distance ◽  
Spatial Weight ◽  
The Matrix ◽  
The Impact

The article presents the problem of the application of the spatial weigh matrix based on economic distance in spatial analysis of the unemployment rate. The spatial weight matrix expresses potential spatial interactions between the researched areas and forms a basis for the instruments applied in spatial econometrics. While identifying the neighbourhood, the following criteria are used: a common border, distance, and the k number of the nearest neighbours. The potential force of impact is identified by means of the standardisation of the matrix by rows to unity, or by means of the distance based on the physical properties of the areas. The disadvantage of the matrix standardisation is the fact of accepting the same force of impact for all the areas. It seems natural is the differentiation of the force of the impact dependent on the selected areas which should result from the differences and similarities of the areas in the scope of the researched phenomenon and its determinants. The use of the distance based on physical properties of the areas allows considering the diverse force of impact of neighbouring areas, which, in turn, allows to obtain a more precise outcome of analyses. Unfortunately, physical properties do not constitute the determinants of economic phenomena covered by a spatial analysis which means that they are not related directly to the scrutinised phenomenon. The application of economic distance for building spatial weight matrix shown in the present paper constitutes a way of determining of the force of impact for the economic spatial processes that is alternative to the distance based on physical properties of the researched areas and to the proposal of the standardisation by rows to unity.

scholarly journals Analysis of the Impact of the Type of Sampling of Representative Properties on the Results of Mass Appraisal

2020 ◽  
Vol 20 (2) ◽  
pp. 152-167
Author(s):  
Sebastian Gnat
Keyword(s):  
Real Estate ◽  
Random Sampling ◽  
Simple Random Sampling ◽  
Multiple Regression Model ◽  
Stratified Sampling ◽  
Sampling Errors ◽  
Real Estate Valuation ◽  
Simple Sampling ◽  
The Impact ◽  
Mean Square Errors

Abstract Research background: Mass valuation is a process in which many properties are valued simultaneously with a uniform approach. An example of a procedure used for mass real estate valuation is the Szczecin Algorithm of Real Estate Mass Appraisal (SAREMA), which can be developed into a multiple regression model. The algorithm is based on a set of drawn representative properties. This set determines, inter alia, the quality of obtained valuations. Purpose: The objective of the study is to verify the hypothesis whether changing the method of sampling representative properties from the originally used simple random sampling to stratified sampling improves the results of the SAREMA econometric variant. Research methodology: The article presents a study that uses two methods of representative properties sampling – simple random sampling and stratified sampling. Errors of the models of valuation created taking into account both methods of sampling and different number of representative properties are compared. A key aspect of the survey is the choice of a better sampling method. Results: The study has shown that stratified sampling improves valuation results and, more specifically, allows for lower root mean square errors. Stratified sampling yielded better results in the initial phase of the study with more observations, but reducing the percentage of strata participating in the draws, despite the increase in RMSE, guaranteed lower errors than the corresponding results based on simple sampling in all variants of the study. Novelty: The article confirms the possibility of improving the results of mass property valuation by changing the scheme of representative properties sampling. The results allowed for the conclusion that stratified sampling is a better way of creating a set of representative properties.

Mechanism Analysis of the Impact on Chinese Urban Rail Transit Construction on the Surrounding Real Estate Prices

2012 ◽  
Vol 178-181 ◽  
pp. 1866-1869
Author(s):  
Jian Liang Lv ◽  
Ying Jiang
Keyword(s):  
Real Estate ◽  
Land Development ◽  
Value Added ◽  
Urban Rail Transit ◽  
Rail Transit ◽  
Mechanism Analysis ◽  
The Real Estate ◽  
Urban Rail ◽  
Real Estate Prices ◽  
The Impact

This paper first studies the real estate prices and and its effecting factors, and point out the construction of urban rail transit effects large on its surrounding real estate prices. Then, the paper clarifies the theoretical basis of its effects, and finally analyzes mechanism that construction of rail transit can make its surrounding real estate value-added. The urban rail transit construction can improve surrounding property accessibility, the residents travel convenient, increasing the intense of land development, changing nature of land use, adjusting industrial layout, accelerating expanded urbanized areas, raising employment opportunities, promoting socio-economic prosperity and development. It provides reference for the reasonable allocation of late-stage value-added benefits, and people can get a comprehensive and systematic understanding of the impact of urban rail transit construction and its surrounding real estate prices.

scholarly journals Research on the Spatial Correlation and Spatial Lag of COVID-19 Infection Based on Spatial Analysis

2021 ◽  
Vol 13 (21) ◽  
pp. 12013
Author(s):  
Keqiang Dong ◽  
Liao Guo
Keyword(s):  
Spatial Analysis ◽  
Spatial Correlation ◽  
Lagrange Multiplier ◽  
Weight Matrix ◽  
Primary Data ◽  
Lagrange Multiplier Test ◽  
Spatial Lag ◽  
Spatial Weight Matrix ◽  
The World ◽  
Spatial Weight

COVID-19 has spread throughout the world since the virus was discovered in 2019. Thus, this study aimed to identify the global transmission trend of the COVID-19 from the perspective of the spatial correlation and spatial lag. The research used primary data collected of daily increases in the amount of COVID-19 in 14 countries, confirmed diagnosis, recovered numbers, and deaths. Findings of the Moran index showed that the propagation of infection was aggregated between 9 May and 21 May based on the composite spatial weight matrix. The results from the Lagrange multiplier test indicated the COVID-19 patients can infect others with a lag.

scholarly journals Added value of sites suitable for sustainable office development

2021 ◽  
Vol 13 (2) ◽  
pp. 2465-2471
Author(s):  
Josef Kupec
Keyword(s):  
Real Estate ◽  
Free Market ◽  
Market Value ◽  
Commercial Real Estate ◽  
Added Value ◽  
The Real Estate ◽  
Real Estate Valuation ◽  
Valuation Methodology ◽  
The Impact ◽  
Office Development

Abstract Valuations of real estate are widely used for various purposes and it relied always upon the financial and other markets. Valuation methodology is based on the operation of the free market economy and the real estate properties. The issue of certified properties is relatively new in the field of real estate valuation and is not sufficiently explored. Certified buildings are preferred by major corporate tenants with international field of activity who often have ethical rules for sustainable development. Therefore, certified properties are attractive to international commercial real estate investors who have higher purchasing power and are willing to pay a higher purchase price. Sustainable property certification is an element affecting the market value of the property. The purpose of this presented research is to quantify the impact of property certification on the value of office properties in Prague and subsequently to determine the impact of sustainability certificates on the market value of the land by using basic valuation techniques. The outcome of the project could be used by real estate valuation experts as a guideline to consider the future project certification and its impact on the land market value.

scholarly journals Spatial weight matrix in dimensionality reduction reconstruction for micro-electromechanical system-based photoacoustic microscopy

2020 ◽  
Vol 3 (1) ◽  
Author(s):  
Yuanzheng Ma ◽  
Chang Lu ◽  
Kedi Xiong ◽  
Wuyu Zhang ◽  
Sihua Yang
Keyword(s):  
Image Reconstruction ◽  
Dimensionality Reduction ◽  
Optical Resolution ◽  
Distortion Correction ◽  
Electromechanical System ◽  
Weight Matrix ◽  
Photoacoustic Microscopy ◽  
Spatial Weight Matrix ◽  
Spatial Weight ◽  
Micro Electromechanical System

AbstractA micro-electromechanical system (MEMS) scanning mirror accelerates the raster scanning of optical-resolution photoacoustic microscopy (OR-PAM). However, the nonlinear tilt angular-voltage characteristic of a MEMS mirror introduces distortion into the maximum back-projection image. Moreover, the size of the airy disk, ultrasonic sensor properties, and thermal effects decrease the resolution. Thus, in this study, we proposed a spatial weight matrix (SWM) with a dimensionality reduction for image reconstruction. The three-layer SWM contains the invariable information of the system, which includes a spatial dependent distortion correction and 3D deconvolution. We employed an ordinal-valued Markov random field and the Harris Stephen algorithm, as well as a modified delay-and-sum method during a time reversal. The results from the experiments and a quantitative analysis demonstrate that images can be effectively reconstructed using an SWM; this is also true for severely distorted images. The index of the mutual information between the reference images and registered images was 70.33 times higher than the initial index, on average. Moreover, the peak signal-to-noise ratio was increased by 17.08% after 3D deconvolution. This accomplishment offers a practical approach to image reconstruction and a promising method to achieve a real-time distortion correction for MEMS-based OR-PAM.

scholarly journals On Misspecification of Spatial Weight Matrix for Small Area Estimation in Longitudinal Analysis

2013 ◽  
Vol 15 (4) ◽  
pp. 305-318
Author(s):  
Tomasz Żądło
Keyword(s):  
Sample Size ◽  
Mixed Model ◽  
Linear Mixed Model ◽  
Weight Matrix ◽  
Random Sample Size ◽  
Monte Carlo Simulation Study ◽  
Spatial Weight Matrix ◽  
First Case ◽  
Spatial Weight ◽  
Random Components

The problem of prediction of subpopulation (domain) total is studied as in Rao (2003). Considerations are based on spatially correlated longitudinal data. The domain of interest can be defined after sample selection what implies its random sample size. The special case of the General Linear Mixed Model is proposed where two random components obey assumptions of spatial and temporal moving average process respectively. Moreover, it is assumed that the population may change in time and elements’ affiliations to subpopulation may change in time as well. The proposed model is a generalization of longitudinal models studied by e.g. Verbeke, Molenberghs (2000) and Hedeker, Gibbons (2006). The best linear unbiased predictor (BLUP) is derived. It may be used even if the sample size in the subpopulation of interest in the period of interest is zero. In the Monte Carlo simulation study the accuracy of the empirical version of the BLUP will be studied in the case of correct and incorrect specification of the spatial weight matrix. Two cases of model misspecification are studied. In the first case the misspecified spatial weight is used. In the second case independence of random components is assumed but the variable which is used to compute elements of spatial weight matrix in the correct case will be used as auxiliary variable in the model.

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