|Title||Automated Valuation Models (AVMs): Machine Learning, namely Mass (Advanced) Valuation Methods and Algorithms|
Digitalisation is becoming increasingly common within the valuation sector. Thus, it is vital to understand how traditional valuation methods are being replaced by machine learning technology, namely mass (advanced) valuation methods.
According to Soni and Sadiq (2015: 100), real estate markets are popular with investors, who are keen to identify a fast way to play the market or to hedge against existing volatile portfolios. Therefore, an accurate prediction of house price is essential to prospective home owners, developers, investors, valuers, tax assessors, mortgage lenders and insurers.
Demirci, O (2021) stated that the fluctuation and the relationship between value, worth, and risk remain unchanged in the current market. This means that the increased use of Automated Valuation Models (AVMs) requires a discussion of the machine learning technology, namely mass (advanced) valuation methods, which are the fundamental basis of the algorithms used within the valuation sector.
As defined by Erdem (2017), valuation can be categorised into traditional, statistical and modern methods.
This Research Paper will investigate both the statistical and modern methods of valuation and their application to the real estate valuation.
In particular, it will look at the main limitations of the traditional valuation methods in respect to their accuracy, consistency and speed (Jahanshiri, 2011; Wang & Wolverton, 2012; Adetiloye & Eke, 2014). Moreover, these methods will be compared against mass (advanced) valuation methods, when there is a need to value a group of properties. Indeed, with the increasing volume of transactions and changing marketplace of real estate, mass (advanced) valuation has been widely adopted in many countries for different purposes, including assessment of property tax (Osborn, 2014).
|Keywords||Valuation, Machine Learning, Automated Valuation Models, AVMs, Algorithms, Market Volatility|
|Published||16 Feb 2021|
File Access Level
Open (open metadata and files)
|Web address (URL)||https://www.property-elite.co.uk/e-books-and-templates|
|Digital Object Identifier (DOI)||https://doi.org/10.13140/RG.2.2.12649.42080|