Zobrazeno 1 - 8
of 8
pro vyhledávání: '"Paul Bidanset"'
Publikováno v:
Applied Sciences, Vol 12, Iss 20, p 10660 (2022)
Prediction accuracy for mass appraisal purposes has evolved substantially over the last few decades, facilitated by the evolution in big data, data availability and open source software. Accompanying these advances, newer forms of geo-spatial approac
Externí odkaz:
https://doaj.org/article/a3d04c73dd824b0d8840a749f8f3ff8d
Publikováno v:
International Journal of Housing Markets and Analysis. 13:845-867
PurposeNumerous geo-statistical methods have been developed to analyse the spatial dimension and composition of house prices. Despite these advances, spatial filtering remains an under-researched approach within house price studies. This paper aims t
Publikováno v:
Journal of Housing Economics. 58:101880
Publikováno v:
Journal of Financial Management of Property and Construction. 24:231-250
PurposeThe purpose of this study is to enhance the estimation of vertical and horizontal inequity within property valuation. Property taxation is a crucial source of finance for local government around the world – based on a presumptive tax base un
Publikováno v:
Journal of European Real Estate Research. 11:353-398
PurposeAir quality, noise and proximity to urban infrastructure can arguably have an important impact on the quality of life. Environmental quality (the price of good health) has become a central tenet for consumer choice in urban locales when decidi
Autor:
Sean MacIntyre, Martin Haran, Peadar Davis, John McCord, Paul Bidanset, Michael McCord, William McCluskey
Publikováno v:
International Journal of Housing Markets and Analysis. 11:263-289
Purpose Understanding the key locational and neighbourhood determinants and their accessibility is a topic of great interest to policymakers, planners and property valuers. In Northern Ireland, the high level of market segregation means that it is pr
Publikováno v:
Advances in Automated Valuation Modeling ISBN: 9783319497440
Research has consistently demonstrated that geographically weighted regression (GWR) models significantly improve upon accuracy of ordinary least squares (OLS)-based computer-assisted mass appraisal (CAMA) models by more accurately accounting for the
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::ac1303db9fbd00449a1d12a2af780d7e
https://doi.org/10.1007/978-3-319-49746-4_11
https://doi.org/10.1007/978-3-319-49746-4_11