The COVID-19 (also called “SARS‑CoV‑2”) pandemic is causing a dramatic reduction in consumption, with a further drop in prices and a decrease in workers’ per capita income. To this will be added an increase in unemployment, which will further depress consumption. The real estate market, as for other productive and commercial sectors, in the short and mid-run, will not tend to move independently from the context of the aforementioned economic variables. The effect of pandemics or health emergencies on housing markets is an unexplored topic in international literature. For this reason, firstly, the few specific studies found are reported and, by analogy, studies on the effects of terrorism attacks and natural disasters on real estate prices are examined too. Subsequently, beginning from the real estate dynamics and economic indicators of the Campania region before the COVID-19 emergency, the current COVID-19 scenario is defined (focusing on unemployment, personal and household income, real estate judicial execution, real estate dynamics). Finally, a real estate pricing model is developed, evaluating the short and mid-run COVID-19 effects on housing prices. To predict possible changes in the mid-run of real estate judicial execution and real estate dynamics, the economic model of Lotka–Volterra (also known as the “prey–predator” model) was applied. Results of the model indicate a housing prices drop of 4.16% in the short-run and 6.49% in the mid-run (late 2020–early 2021).

This paper proposes a theoretical model of evaluation of environmental externalities based on the analysis of real estate prices. This issue is included in regional planning policies which include activities and interventions that produce economic and non-economic effects. The monetary assessment of economic and non-economic effects can be expressed as a forecast (ex ante) and/or following (ex post) such activities and interventions. The assessment of the economic impact, with particular reference to interventions and infrastructure work, is widely based on procedures which make use of market prices. The proposed model was applied to an actual case, considering the effects of noise pollution, produced by traffic from the Naples Beltway, on residential property.

The main aim of this paper is the interpretation of the existing relationship between real estate rental prices and geographical location of housing units in a central urban area of Naples (Santa Lucia and Riviera of Chiaia neighborhoods). Genetic algorithms (GA) are used for this purpose. Also, to verify the reliability of genetic algorithms for real estate appraisals and, at the same time, to show the forecasting potentialities of these techniques in the analysis of housing markets, a multiple regression analysis (MRA) was applied comparing results of GA and MRA.

The evaluation of real estate assets is currently one of the main focal points addressed by territorial marketing strategies, with the view of developing high-performing or competitive cities. Given the complexity of the driving forces that determine the behavior of actors in a real estate market, it is necessary to identify a priori the factors that determine the competitive capacity of a city, to attract investments. The decision support system allows taking into account the key factors that determine the “attractiveness” of real estate investments in competitive urban contexts. This study proposes an integrated complex evaluation model that is able to map out and encapsulate the multidimensional spectrum of factors that shape the attractiveness of alternative real estate options. The conceptual–methodological approach is illustrated by an application of the model to a real-world case study of investment choice in the residential sector of Naples.

This paper aims to outline the application of Fuzzy Logic in real estate investment. In literature, there is a wide theoretical background on real estate investment decisions, but there has been a lack of empirical support in this regard. For this reason, the paper would fill the gap between theory and practice. The fuzzy logic system is adopted to evaluate the situations of a real estate market with imprecise and vague information. To highlight the applicability of the Possibility Theory, we proceeded to reconsider an example of property investment evaluation through fuzzy logic. The case study concerns the purchase of an office building. The results obtained with Fuzzy Logic have been also compared with those arising from a deterministic approach through the use of crisp numbers.

This paper experiments an artificial neural networks model with Bayesian approach on a small real estate sample. The output distribution has been calculated operating a numerical integration on the weights space with the Markov Chain Hybrid Monte Carlo Method (MCHMCM). On the same real estate sample, MCHMCM has been compared with a neural networks model (NNs), traditional multiple regression analysis (MRA) and the Penalized Spline Semiparametric Method (PSSM). All four methods have been developed for testing the forecasting capacity and reliability of MCHMCM in the real estate field. The Markov Chain Hybrid Monte Carlo Method has proved to be the best model with an absolute average percentage error of 6.61%.