A multiparametric procedure to understand how the Covid-19 influenced the real estate market

Laura Gabrielli, Aurora Ruggeri and Massimiliano Scarpa

ERES from European Real Estate Society (ERES)

Abstract: This paper, part of a more comprehensive research line, aims to discuss how the covid-19 pandemic has affected the demand in the real estate market in Padua. Padua is a medium-sized city that represents the typical Italian town.The authors have been investigating the real estate market in Padua for a few years, collecting information on the buildings on sale from related selling websites. This data collection procedure has been accomplished with the help of an automated web crawler developed in Python language.For this reason, the authors are now able to compare the real estate market in Padua at different times. In particular, two databases are here put into a detailed comparison. Database A dates back to 2019 (II semester), capturing a pre-Covid-19 scenario, while database B is dated 2021 (II semester), representing the actual situation.First of all, two forecasting algorithms to predict the market value of the properties as a function of their characteristics are developed using an Artificial Neural Networks (ANNs) approach.ANNs are a multi-parametric statistical technique employed to forecast a property's market value. The input neurons of the network, i.e. the independent variables, are the buildings' descriptive features and characteristics, while the output neuron is the market value, the dependent variable.ANN(A) is developed on database A, and ANN(B) is created on B. The comparison of the two forecasting functions represents the differences in the demand after two years from the first Covid-19 alerts.Since ANNs are a multi-parametric procedure, this methodology isolates each attribute's singular influence on the forecasted price. It is, therefore, possible to understand how the preferences of the demand have changed during the pandemic. Some characteristics are now more appreciated than before, such as external spaces, like a terrace or a private garden. Also, systems and technologies seem more appealing now than before the pandemic, for example, the presence of optical fibre or mechanical ventilation. Moreover, wider building typologies are more appreciated now, like villas, detached and semi-detached houses, or farmhouses. But, on the contrary, other characteristics are less appreciated. The location, for instance, is less influential than before in price formation. These changes in preferences can be attributed to the new lifestyle since new habits have been produced after the lockdown experience and new smart working schedules that the pandemic has led to.

Keywords: Analytical Neural Network; COVID-19; Real Estate Valuation; Structural characteristics (search for similar items in EconPapers)
JEL-codes: R3 (search for similar items in EconPapers)
Date: 2022-01-01
New Economics Papers: this item is included in nep-big, nep-cmp and nep-ure
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