Housing market investment in peripheral areas: Evidence from Italy

Abstract

The causal relationship between living in a peripheral area and real estate investment decisions is currently unexplored in empirical literature. Using survey data on 2711 Italian respondents collected between 2022 and 2023, we show that living in a peripheral area has a negative impact on the probability of having invested or investing in real estate in the future. OLS estimates suggest that being in a peripheral area reduces the likelihood of having invested in real estate in the last 24 months by about 3–4 percent and the likelihood of investing in real estate in the next 24 months by about 4–6 percent. These results are robust, having tried different models (OLS, probit, bivariate probit, and rare events logistic regression), different control variables, and also an instrumental variable approach to control for the potential endogeneity of homeownership. In this regard, our work introduces a new instrument to tackle the endogeneity problem of homeownership: hereditary motivation to purchase a home. The instrument has all the necessary characteristics to address this problem, such as a very strong first stage. Finally, we adopt a new machine learning algorithm, ABESS, to show the importance of including residence in a peripheral area as an explanatory variable and to select the best subset. This approach could also be exploited in further real estate and regional studies.