Agenda

Lorenzo Tonon

Abstract:

Precise evaluation of real estate prices represents a complex task that holds significant relevance for the majority of the actors involved in a society, as home-buyers/sellers, investors, and policymakers. Over the last decades, in parallel with technological advancements, scholars and practitioners proposed several models and methodologies, spanning from simple hedonic regression models to sophisticated machine learning algorithms, in the attempt to improve the accuracy of price forecasts. Nonetheless, the interest towards the development of more precise and performing methodologies remains high, not only for static valuation but also, more recently, for dynamic approaches. Focusing on dynamic valuation, we turn the attention towards Recurrent Neural Network (RNN) models: while widely tested for stock prices forecast, RNNs have seen limited to no application in the domain of real estate valuation. In this study, using official data of prices of Italian residential properties, we propose and implement two different architectures for RNN models to perform both price prediction and significance analysis with respect to the explanatory variables. Differently from other studies, we utilize data for the whole Italian territory, focusing, rather than on individual properties' valuation, on a range of prices for groups of properties identified by house type, geographical position and state of conservation. Specifically, the output variables of our analysis are represented by the minimum and maximum prices that determine this range. The results of our analysis indicate that a Standard Recurrent Neural Network forecasts well the prices in small municipalities (with less than 15 000 inhabitants), while a Double-Input Layer RNN, with an input layer dedicated to static features and a separate input layer for dynamic features, performs better in medium-sized (with a number of inhabitants between 15 000 and 50 000 inhabitants) and large municipalities (with more than 50 000 inhabitants).

The seminar can be attended also remotely, connecting to ZOOM.

Link Zoom: bit.ly/insem-2425
ID riunione:  880 2639 9452
Passcode: InSem-2425