Submissions Accepted for Presentation at the World Bank Land Conference 2024

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Session Overview
Session
03-10: New ways of land valuation for effective property taxation
Time:
Thursday, 16/May/2024:
10:30am - 12:30pm

Session Chair: Rajul Awasthi, World Bank, Ethiopia
Location: MC 8-100


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Presentations

Land value capture: guidance for practitioners

Olga Kaganova, Jon Kher Kaw, Gabor Peteri

World Bank, United States of America

The paper analyses all 16 LVC instruments discussed in the literature and used internationally, classified into three groups, according to their three sources of government authority: (a) government ownership of land; (b) the power to regulate land uses / land–use parameters on both private and public land; and (c) the power to impose taxes and fees (fiscal instruments) on private land/property. The instruments are analyzed within a unified framework that is comprised of multiple characteristics (e.g., potential public benefit, breadth of the payers’ base, scope of global usage, critical pre-requisites and implementation requirements, etc.).

03-10-Kaganova-253_paper.pdf


The Netherlands study into discrepancy between assessed values and market prices

Luc Hermans2, Jacob Zevenbergen1, Ruud Kathmann2, Marija Bockarjova1

1University of Twente, Netherlands, The; 2Netherlands Council for Real Estate Assessment

The Dutch system for real estate assessment for government purposes, such as taxation, operates on an ad valorem system, based on the market value of the property with full legal ownership on the valuation date. The assessed value serves as the tax base for several local and national taxes for a total of 11 billion euros yearly. The assessment process is the responsibility of (joint services centers of) municipalities with oversight on the quality by the Netherlands Council for Real Estate Assessment (NCREA). Every five years a thematic research is done on the discrepancy between the assessed values and the observed market values, the sales price for all residential properties in the Netherlands. Based on the sales prices and assessed values in the period 2018 up to 2022, this research is current done together with the University of Twente, and the findings are presented and explained.

03-10-Hermans-547_paper.pdf
03-10-Hermans-547_ppt.pptx


Land value chain for property tax administration

Wolaganathan Govender

Terra Vital, South Africa

The Land Value chain cuts across national, state and provincial governments that normally administer the titling, registration and cadaster, and local governments that must service the land and realize tax revenues where applicable. Integration between these tiers of government, or rather the lack thereof, hinders the holistic management of the land base of countries, which often leads to poor land use and spatial planning. Property tax dates to over 2000 years ago, so it is inconceivable that some of the fastest growing cities in the world have land information systems that are ineffective and outdated. Cities require finances to operate effectively, and land taxes do not contribute to the city financing, economy or GDP. This paper examines the entire land value chain, look at the interoperability of data and systems and suggest an approach to overcome the issues faced, using modern technologies and practical approaches.

03-10-Govender-506_paper.docx
03-10-Govender-506_ppt.pptx


Analyzing factors affecting land prices in urbanized areas using machine learning: A basis for future 3D property valuations

Peyman Jafary1,2, Davood Shojaei2, Abbas Rajabifard2, Tuan Ngo1,3

1Building 4.0 CRC, Caulfield East, Victoria, Australia; 2Centre for Spatial Data Infrastructures and Land Administration, Department of Infrastructure Engineering, The University of Melbourne, Melbourne, Victoria, Australia; 3Department of Infrastructure Engineering, The University of Melbourne, Melbourne, Victoria, Australia

Accurate land valuation promotes fairness and efficiency in the property market, aiding property development and land use planning. Land price is also a key component for property valuation using cost approach, particularly with 3D models like Building Information Modeling (BIM). Identifying and creating a database on value-related features is essential to establishing a robust land valuation system. Advanced Machine Learning techniques can handle non-linear relationships between land value and the driving factors. This study analyzes factors affecting land prices in Melbourne Metropolitan, considering a wide range of features. The importance of each factor is calculated through an ensemble method based on four techniques: Random Forest, XGBoost, Recursive Feature Elimination (RFE) and Mutual Information (MI). The results show that land area, longitude, land use, latitude, distance to the Central Business District (CBD), elevation, mortgage rate and primary school zone have the highest impact on the land value in the study area.

03-10-Jafary-164_paper.pdf
03-10-Jafary-164_ppt.pptx


 
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