Forecasting housing investment

Abstract

This study applies a model averaging approach to conditionally forecast housing investment in the largest Euro area countries and the Euro area. To account for substantial modeling uncertainty, it estimates a large and diverse number of vector error correction models using a wide set of long‐ and short‐run determinants and applies subset selection based on in‐sample and out‐of‐sample criteria. First, a pseudo out‐of‐sample forecast exercise shows that our model averaging approach consistently beats a battery of distinguished benchmark models, including BVARs, FAVARs, LASSO, and Ridge regressions. This evidences that model averaging provides more accurate forecasts also in the case of housing investment. Second, we find remarkable cross‐country heterogeneity in the drivers of housing investment. Overall, these findings guide forecasters and modelers on improving housing investment models and policymakers on implementing country‐specific housing market policies.

Predicting Housing Price Trends in Poland: Online Social Engagement - Google Trends

“…Therefore, accurate and reliable housing market forecasts are essential for all involved parties, including property buyers, owners, real estate brokers, investors, financial institutions, and decision-makers. The importance of housing market forecasts has been emphasized in numerous research studies (Billah, Hyndman, and Koehler 2005;Chatfield 2001;Chen et al 2014;Kishor and Marfatia 2017;Cañizares Martínez et al 2023;Wilson et al 2002).…”

“…Predictions of housing market trends are a fundamental issue for political decision-makers, investors, as well as consumers who are faced with the challenge of purchasing housing. Housing market forecasts have been extensively researched (Banerjee & Ghosal, 2020;Bełej, 2021;Chen et al, 2014;Kishor & Marfatia, 2018;Cañizares Martínez et al, 2023) based on historical data and predictive analytics that provide information about housing price dynamics in the future. In addition to classical factors that impact housing prices, the rapid development of an information society (Göhring, 2004;Hilty, 2002) and the rising popularity of online platforms that shape social behaviors should be also considered in such analyses.…”