Developing automated valuation models for estimating property values: a comparison of global and locally weighted approaches

Doumpos, Michael; Papastamos, Dimitrios; Andritsos, Dimitrios; Zopounidis, Constantin

doi:10.1007/s10479-020-03556-1

Cited by 23 publications

(8 citation statements)

References 38 publications

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“…Non-linear models perform better when applied in a global context. [55] Combine dimensionality reduction methods with ML techniques to obtain the estimated prices. Linear Regression (LR), KNN, support vector regression (SVR), Artificia Neural Networks (ANN) and Nonnegative Matrix Factorization (NMF), Recursive Feature Elimination (RFE), Feature Selection (FS) and Multilayer Perceptron (MLP).…”

Section: Goalmentioning

confidence: 99%

Applications, Opportunities and Challenges of AI in the Real Estate Sector

Antão,

Ribeiro,

Pereira

2024

Preprint

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The article presents a Multivocal Literature Review (MLR) on the use of Artificial Intelligence (AI) in the real estate sector, aiming to analyze existing applications, literature gaps, and current challenges. The methodology involved defining keywords, searching online repositories, and applying inclusion/exclusion criteria. In total, 185 documents were selected and reviewed. Findings underscore the significance of areas such as personalized strategies for clients, real estate recommendation systems, and process automation. A literature gap was identified regarding the analysis of performance and accuracy of these applications, indicating the need for further research and technological development to meet the demands of the real estate sector.

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Section: Goalmentioning

confidence: 99%

Applications, Opportunities and Challenges of AI in the Real Estate Sector

Antão,

Ribeiro,

Pereira

2024

Preprint

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“…This type of procedure is often applied in the literature on the issue of property mass valuation. Exponential models used for automatic valuation are better known as hedonic models [71][72][73].…”

Section: Stage Ii-the Regression Analysis Of the Prices Of Flatsmentioning

confidence: 99%

Spatial and Statistical Analysis of Urban Poverty for Sustainable City Development

Kisiała

Rącka

2021

Sustainability

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One of the main pillars of sustainable urban development at the local scale is to control the social aspect of urban equality of socio-economic systems. A number of studies confirm that poverty in urban space is accompanied by negative phenomena, such as high unemployment, social pathologies, increased crime rate, or the high level of the decapitalization of space, including the poor condition of housing and municipal infrastructure. However, there is a gap in defining the relation between urban poverty and city structure to control and preferably minimize social inequalities. The aim of the study was to empirically verify the impact of the location of residential properties in relation to poverty-stricken areas in the city. The research covered the housing market in one Polish city (Kalisz) in the years 2006–2018. By applying GIS technologies, we identified the location of each property in relation to poverty areas. The data was subjected to regression analysis, with the use of the hedonic approach based on exponential models. The analysis of data allowed us to conclude that location in a poorer area does affect the prices of new flats, which is not only a contribution to the development of science, but is also information that could be used by developers or property valuers to establish the prices of flats, as well as city managers to avoid pauperization of urban districts.

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“…At present, a number of automated systems are presented in the professional literature [6][7][8]. In most cases, however, these are systems for valuing apartments [9,10].…”

Section: Introductionmentioning

confidence: 99%

Possibilities of House Valuation Automation in the Czech Republic

2020

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Valuation of single-family detached houses is necessary when determining the amounts of some taxes. The current systems of the Czech Republic are outdated in this respect, and they are based on procedures used in the 1980s. The values found do not correspond to current market conditions very often. This article attempts to verify the applicability of a methodology where the value of a detached house is decomposed into the value of the land and the value of the object as such when considering wear. For verification, 122 sales of detached houses in Ostrava and its surroundings were analyzed, and the results show that the values determined by the verified methodology do not differ by more than 10% from the actual sales prices in most cases. The methodology is very simple and practically applicable for users without deep knowledge of construction or valuation principles. It can be applied, for example, when calculating the bases of certain taxes or as an indicative guide for the pricing of real estate for sellers, buyers, real estate agents, etc.

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Developing automated valuation models for estimating property values: a comparison of global and locally weighted approaches

Cited by 23 publications

References 38 publications

Applications, Opportunities and Challenges of AI in the Real Estate Sector

Applications, Opportunities and Challenges of AI in the Real Estate Sector

Spatial and Statistical Analysis of Urban Poverty for Sustainable City Development

Possibilities of House Valuation Automation in the Czech Republic

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