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Building Information Modelling (BIM) for property valuation: A new approach for Turkish Condominium Ownership / Nida Celik Simsek in Survey review, vol 54 n° 384 (May 2022)  

Public [article]  inSurvey review > vol 54 n° 384 (May 2022) . - pp 187 - 208 Titre : Building Information Modelling (BIM) for property valuation: A new approach for Turkish Condominium Ownership Type de document : Article/Communication Auteurs : Nida Celik Simsek, Auteur ; Bayram Uzun, Auteur Année de publication : 2022 Article en page(s) : pp 187 - 208 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Cadastre étranger [Termes IGN] cadastre étranger [Termes IGN] évaluation foncière [Termes IGN] lever cadastral [Termes IGN] modélisation 3D du bâti BIM [Termes IGN] propriété foncière [Termes IGN] Turquie [Termes IGN] valeur économique [Termes IGN] visualisation 3D Résumé : (auteur) In Turkey, calculation of the factors affecting the value of the condominium units of a building via 2D architectural project data leads to problems. One of the biggest problem is the land share calculation. The aim of this study was to establish a mechanism by which the properties of the factors affecting the value can be determined mathematically and to arrive at a value-based land share. For this purpose, the study utilized a 3D virtual Building Information Modelling (BIM) model. The value factors and weights were determined via a questionnaire, 3D BIM model of the structure was created, metric values of the factors were calculated and the nominal values of the condominium units were calculated. This study demonstrate that a building nonexistent in the real world can be represented in a virtual environment and comparable information source can be presented to the expert who will carry out the valuation process. Numéro de notice : A2022-354 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/00396265.2021.1905251 Date de publication en ligne : 02/04/2021 En ligne : https://doi.org/10.1080/00396265.2021.1905251 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100552 [article]

A geographically weighted artificial neural network / Julian Haguenauer in International journal of geographical information science IJGIS, vol 36 n° 2 (February 2022)  

Public [article]  inInternational journal of geographical information science IJGIS > vol 36 n° 2 (February 2022) . - pp 215 - 235 Titre : A geographically weighted artificial neural network Type de document : Article/Communication Auteurs : Julian Haguenauer, Auteur ; Marco Helbich, Auteur Année de publication : 2022 Article en page(s) : pp 215 - 235 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale [Termes IGN] analyse comparative [Termes IGN] analyse de sensibilité [Termes IGN] Autriche [Termes IGN] coût [Termes IGN] évaluation foncière [Termes IGN] hétérogénéité spatiale [Termes IGN] logement [Termes IGN] régression géographiquement pondérée [Termes IGN] relation spatiale [Termes IGN] réseau neuronal artificiel Résumé : (auteur) While recent developments have extended geographically weighted regression (GWR) in many directions, it is usually assumed that the relationships between the dependent and the independent variables are linear. In practice, however, it is often the case that variables are nonlinearly associated. To address this issue, we propose a geographically weighted artificial neural network (GWANN). GWANN combines geographical weighting with artificial neural networks, which are able to learn complex nonlinear relationships in a data-driven manner without assumptions. Using synthetic data with known spatial characteristics and a real-world case study, we compared GWANN with GWR. While the results for the synthetic data show that GWANN performs better than GWR when the relationships within the data are nonlinear and their spatial variance is high, the results based on the real-world data demonstrate that the performance of GWANN can also be superior in a practical setting. Numéro de notice : A2022-162 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/13658816.2021.1871618 Date de publication en ligne : 08/02/2021 En ligne : https://doi.org/10.1080/13658816.2021.1871618 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=99785 [article]

Decentralized markets and the emergence of housing wealth inequality / Omar A. Guerrero in Computers, Environment and Urban Systems, vol 84 (November 2020)  

Public [article]  inComputers, Environment and Urban Systems > vol 84 (November 2020) . - n° 101541 Titre : Decentralized markets and the emergence of housing wealth inequality Type de document : Article/Communication Auteurs : Omar A. Guerrero, Auteur Année de publication : 2020 Article en page(s) : n° 101541 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale [Termes IGN] analyse socio-économique [Termes IGN] bien immobilier [Termes IGN] coefficient de Gini [Termes IGN] évaluation foncière [Termes IGN] inégalité [Termes IGN] logement [Termes IGN] marché foncier [Termes IGN] modèle orienté agent [Termes IGN] patrimoine immobilier [Termes IGN] propriété foncière [Termes IGN] Royaume-Uni Résumé : (auteur) Recent studies suggest that the traditional determinants of housing wealth are insufficient to explain its current inequality levels. Thus, they argue that efforts should focus on understanding institutional factors. From the perspective of complex adaptive systems, institutions are more than the ‘the rules of the game’, they also consider the interaction protocols or the ‘algorithm’ through which agents engage in socioeconomic activities. By viewing markets as complex adaptive systems, I develop a model that allows estimating how much housing wealth inequality is attributable to the market institution. It combines virtues from two different modeling traditions: (1) the microeconomic foundations from overlapping-generation models and (2) the explicit interaction protocols of agent-based models. Overall, the model generates prices and housing inequality endogenously and from bottom-up; without needing to impose assumptions about the aggregate behavior of the market (such as market equilibrium). It accounts for economic and institutional factors that are important to housing consumption decisions (e.g., wages, consumption of goods, non-labor income, government transfers, taxes, etc.). I calibrate the model with the British Wealth and Assets Survey at the level of each individual household (i.e., ~25 million agents). By performing counter-factual simulations that control for data heterogeneity, I estimate that, in the United Kingdom, the decentralized protocol interaction of the housing market contributes with one to two thirds of the Gini coefficient. I perform policy experiments and compare the outcomes between an expansion in the housing stock, a sales tax, and an inheritance tax. The results raise concerns about the limitations of traditional policies and call for a careful re-examination of housing wealth inequality. Numéro de notice : A2020-711 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article DOI : 10.1016/j.compenvurbsys.2020.101541 Date de publication en ligne : 26/08/2020 En ligne : https://doi.org/10.1016/j.compenvurbsys.2020.101541 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96268 [article]

Predictive land value modelling in Guatemala City using a geostatistical approach and Space Syntax / Jose Morales in International journal of geographical information science IJGIS, vol 34 n° 7 (July 2020)  

Public [article]  inInternational journal of geographical information science IJGIS > vol 34 n° 7 (July 2020) . - pp 1451 - 1474 Titre : Predictive land value modelling in Guatemala City using a geostatistical approach and Space Syntax Type de document : Article/Communication Auteurs : Jose Morales, Auteur ; Alfred Stein, Auteur ; Johannes Flacke, Auteur ; Jaap Zevenbergen, Auteur Année de publication : 2020 Article en page(s) : pp 1451 - 1474 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale [Termes IGN] analyse de la valeur [Termes IGN] analyse syntaxique [Termes IGN] cartographie statistique [Termes IGN] estimation quantitative [Termes IGN] évaluation foncière [Termes IGN] géostatistique [Termes IGN] Guatemala [Termes IGN] krigeage [Termes IGN] méthode du maximum de vraisemblance (estimation) [Termes IGN] modèle conceptuel de données localisées [Termes IGN] modèle de simulation [Termes IGN] régression [Termes IGN] système d'information foncière Résumé : (auteur) Spatial information of land values is fundamental for planners and policy makers. Individual appraisals are costly, explaining the need for predictive modelling. Recent work has investigated using Space Syntax to analyse urban access and explain land values. However, the spatial dependence of urban land markets has not been addressed in such studies. Further, the selection of meaningful variables is commonly conducted under non-spatialized modelling conditions. The objective of this paper is to construct a land value map using a geostatistical approach using Space Syntax and a spatialized variable selection. The methodology is applied in Guatemala City. We used an existing dataset of residential land value appraisals and accessibility metrics. Regression-kriging was used to conduct variable selection and derive a model for spatial prediction. The prediction accuracy is compared with a multivariate regression. The results show that a spatialized variable selection yields a more parsimonious model with higher prediction accuracy. New insights were found on how Space Syntax explains land value variability when also modelling the spatial dependence. Space Syntax can contribute with relevant spatialized information for predictive land value modelling purposes. Finally, the spatial modelling framework facilitates the production of spatial information of land values that is relevant for planning practice. Numéro de notice : A2020-306 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/13658816.2020.1725014 Date de publication en ligne : 11/02/2020 En ligne : https://doi.org/10.1080/13658816.2020.1725014 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95148 [article]

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Modelling housing rents using spatial autoregressive geographically weighted regression: a case study in cracow, Poland / Mateusz Tomal in ISPRS International journal of geo-information, vol 9 n° 6 (June 2020)  

Public [article]  inISPRS International journal of geo-information > vol 9 n° 6 (June 2020) . - 20 p. Titre : Modelling housing rents using spatial autoregressive geographically weighted regression: a case study in cracow, Poland Type de document : Article/Communication Auteurs : Mateusz Tomal, Auteur Année de publication : 2020 Article en page(s) : 20 p. Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale [Termes IGN] auto-régression [Termes IGN] bien immobilier [Termes IGN] Cracovie (Pologne) [Termes IGN] distribution spatiale [Termes IGN] économétrie [Termes IGN] évaluation foncière [Termes IGN] gestion foncière [Termes IGN] hétérogénéité spatiale [Termes IGN] méthode de Monte-Carlo [Termes IGN] méthode des moindres carrés [Termes IGN] régression géographiquement pondérée Résumé : (auteur) The proportion of tenants will undoubtedly rise in Poland, where at present, the ownership housing model is very dominant. As a result, the rental housing market in Poland is currently under-researched in comparison with owner-occupancy. In order to narrow this research gap, this study attempts to identify the determinants affecting rental prices in Cracow. The latter were obtained from the internet platform otodom.pl using the web scraping technique. To identify rent determinants, ordinary least squares (OLS) regression and spatial econometric methods were used. In particular, traditional spatial autoregressive model (SAR) and spatial autoregressive geographically weighted regression (GWR-SAR) were employed, which made it possible to take into account the spatial heterogeneity of the parameters of determinants and the spatially changing spatial autocorrelation of housing rents. In-depth analysis of rent determinants using the GWR-SAR model exposed the complexity of the rental market in Cracow. Estimates of the above model revealed that many local markets can be identified in Cracow, with different factors shaping housing rents. However, one can identify some determinants that are ubiquitous for almost the entire city. This concerns mainly the variables describing the area of the flat and the age of the building. Moreover, the Monte Carlo test indicated that the spatial autoregressive parameter also changes significantly over space. Numéro de notice : A2020-314 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.3390/ijgi9060346 Date de publication en ligne : 26/05/2020 En ligne : https://doi.org/10.3390/ijgi9060346 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95169 [article]

Street-Frontage-Net: urban image classification using deep convolutional neural networks / Stephen Law in International journal of geographical information science IJGIS, vol 34 n° 4 (April 2020)  

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Précis de cadastre / Royal institute of navigation (2002)

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