Multicriteria analysis and genetic algorithms for mass appraisals in the Italian property market

Abstract

Purpose This paper aims to test and compare two innovative methodologies (utility additive and evolutionary polynomial regression) for mass appraisal of residential properties. The aim is to deepen their characteristics, by exploring the potentialities and the operating limits. Design/methodology/approach With reference to the same case studies, concerning samples of residential properties recently sold in three Italian cities, the two procedures are tested and the results are compared. The first method is the utility additive, which interprets the process of the property price formation as a multi-criteria selection of multi-objective typology, where the selection criteria are the property characteristics that are decisive in the real estate market; the second method is a hybrid data-driven technique, called evolutionary polynomial regression, that uses multi-objective genetic algorithms to search those models expressions that simultaneously maximize accuracy of data and parsimony of mathematical functions. Findings The outputs obtained from the experimentation highlight the potentialities and the limits of the two methodologies, as well as the possibility of jointly applying them to interpret and predict the real estate phenomena in a more realistic representation. Originality value In all countries, mass appraisal techniques have become strategic for the definition of management and enhancement policies of public and private property assets, in the case of investments of technical and economic refunctionalization (energy, environment, etc.), and for the alienation of buildings no longer suitable for public needs (military barracks, hospitals, areas in disuse, etc.). In this context, the use of mass appraisal techniques for residential properties assumes a leading role for sector operators (buyers, sellers, institutions, insurance companies, banks, real estate funds, etc.). Therefore, the results of the applications outline the potentialities of the two methodologies implemented and the opportunity of further insights of the topics that have been dealt with in this research.

Contextualized Property Market Models vs. Generalized Mass Appraisals: An Innovative Approach

“…Initially applied to hydraulic systems [94][95][96], the methodology has been borrowed and used to elicit the correlations between the property market and socio-economic variables [97,98], energetic factors [99], and for property tax purposes [100]. Other studies have allowed researchers to highlight the advantages of the methodology compared to orthodox [101] and innovative mass appraisal approaches [102]. There are no other applications to the property market so far.…”

Monitoring and Analysis of the Real Estate Market in a Social Perspective: Results from the Turin’s (Italy) Experience

“…In Reference [2], the impact of house characteristics on the selling process is explored. Assuming the Turin's residential market, and by mean of hedonic analysis [3][4][5][6], the impact of building characteristics, apartment characteristics and location on the bargaining outcome are compared, in order to quantify the overall contribution of characteristics and location to prices and bargaining outcome [7]. Then, the association between each characteristic and listing price, and between each characteristic and transaction price, is analyzed (according to the methodology introduced by [8].…”

Mass Appraisal Models of Real Estate in the 21st Century: A Systematic Literature Review

“…Genetic algorithm (GA) is a computational model simulating the natural selection and genetic mechanism of Darwinian biological evolution, and it is a method to search the optimal solution by simulating natural evolution. Morano et al (2018) couple evolutionary polynomial regression with genetic algorithms to search those models with maximization accuracy of data and parsimony of mathematical functions [73]. Ahn et al (2012) use ridge regression combined with a genetic algorithm (GA-Ridge) to test performance in the Korean real estate market [74].…”

“…The relevant models are based on the traditional models or existing models, combined with AI and GIS methods, and then highlight the application and analysis. Furthermore, some models can combine with each other into a better one, such as the fuzzy clustering [58], geostatistical model and clustering [59,106], multi criteria analysis and genetic algorithms [73], ANN and GIS [36], support vector machine and decision support system [34] and so forth. The third type is to mix with innovative ideas and unique perspectives.…”