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01-08: Methodological approaches to urban property valuation
Time:
Tuesday, 26/Mar/2019:
8:30am - 10:00am
Session Chair: Ruud Kathmann, Netherlands Council for Real Estate Assessment, Netherlands, Netherlands, The
Location:MC 8-100
Presentations
ID: 470 / 01-08: 1 Individual Papers Topics: Urban expansion, land use, land value capture, and housing Keywords: Property tax, efficiency, administration, self-declaration
Self-declaration of value: an option for the urban property tax
William McCluskey1, Riel Franzsen1, Peadar Davis2
1African Tax Institute, University of Pretoria, Pretoria, South Africa; 2School of the Built Environment, University of Ulster, N. Ireland, UK
ID: 813 / 01-08: 2 Individual Papers Topics: New ways of land data capture & analysis (incl. machine learning) Keywords: valuation, property tax, appraisal, gis, spatial
Valuing property with bad data: utilizing GIS and spatial modeling to achieve equitable property tax valuations in the face of incomplete data
Paul Bidanset1, Jones Brent2
1IAAO; 2Esri
ID: 992 / 01-08: 3 Individual Papers Topics: New ways of land data capture & analysis (incl. machine learning) Keywords: mass appraisal, response surface analysis, sparse data, modelling
Response surface analysis (RSA): modeling values in geographically sparse markets
William Mccluskey1, Paul Bidanset2, Peadar Davis3, Michael McCord3
1African Tax Institute, University of Pretoria, Pretoria, South Africa; 2International Association of Assessing Officers, Kansas, United States; 3School of the Built Environment, University of Ulster, N. Ireland, UK
ID: 1012 / 01-08: 4 Individual Papers Topics: New ways of land data capture & analysis (incl. machine learning) Keywords: mass valuation, property taxation, land value, land markets, spatial interpolation
Standard price points in spatial interpolation. A case study
Risto Peltola, Mikko Korpela, Pauliina Krigsholm, Arthur Kreivi
National Land Survey, Finland
ID: 1329 / 01-08: 6 Individual Papers Topics: New ways of land data capture & analysis (incl. machine learning) Keywords: property valuation, machine learning, remote sensing
Using remote sensing data and machine learning to value property in Kigali, Rwanda
Felix Bachofer1, Jonathan Bower2, Andreas Braun3, Paul Brimble4, Patrick McSharry5
1German Aerospace Center; 2International Growth Centre, Rwanda; 3University of Tübingen; 4Ministry of Economic and Financial Planning, Rwanda; 5Carnegie Mellon University
