Forecasting and Assessing Euro Area House Prices through the Lens of Key Fundamentals

Gattini, Luca; Hiebert, Paul

doi:10.2139/ssrn.1679733

Cited by 16 publications

(5 citation statements)

References 23 publications

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“…A parsimonious set of the determinants of real residential property prices is used in the longterm equation, as in Anundsen (2019) and Gattini & Hiebert (2010). We use the variables conventionally employed in the literature as the main fundamental factors of the house prices, as was described in Section 2.…”

Section: Methodsmentioning

confidence: 99%

“…Among the studies that use a panel of countries, Gattini & Hiebert (2010) estimate long-term equilibrium house prices for the euro area countries using data from 1970-2009. They use a VECM model to analyse the response of house prices to shocks that originate from changes in the fundamental factors.…”

Section: Literature On the Long-term Determinants Of House Pricesmentioning

confidence: 99%

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Misalignments in house prices and economic growth in Europe

Cuestas¹,

Kukk²,

Levenko³

2022

Applied Economics

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In this paper we investigate house price misalignments and how they affect the real economy. We estimate the long-term relationship between house prices and the fundamentals that determine long-term house prices for a panel of European countries with dynamic OLS, using data from 2005-2018. We find that income has been the main driver of fundamental house prices in all countries, while the supply of dwellings has calmed the rise in house prices in some of them. We calculate house price misalignments, which are deviations of house prices from the fundamental value, and we employ them in the growth model. The results of the growth regression indicate that house price imbalances amplify business cycles in the short term, but in the long term house price overvaluations slow economic growth down. The findings imply that it is crucial to take measures to stabilise housing cycles.

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Section: Methodsmentioning

confidence: 99%

Section: Literature On the Long-term Determinants Of House Pricesmentioning

confidence: 99%

Misalignments in house prices and economic growth in Europe

Cuestas¹,

Kukk²,

Levenko³

2022

Applied Economics

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show abstract

“…Moreover, Gattini and Hiebert (2010) used a quarterly vector correction model to estimate supply and demand forces central to the determination of house prices in the European countries using data ranging from 1970 to 2009. The variables used in the study included the housing investment, real disposable income per capita and the mixed maturity measure of the real interest rate.…”

Section: Literature Reviewmentioning

confidence: 99%

“…The persistent increase in house prices since the end of the 1990s globally has made the housing market a key economic topic (Hott and Monnin, 2008). The dynamics of residential house prices have displayed considerable volatility in recent years, especially in European countries (Gattini and Hiebert, 2010). Additionally, the market is very complex with many variables defining its dynamic behavior (Malmir, 2015).…”

Section: Introductionmentioning

confidence: 99%

Comparing simple and complex regression models in forecasting housing price: case study from Kenya

Okuta

Kivaa

Kieti

et al. 2023

IJHMA

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Purpose The housing market in Kenya continues to experience an excessive imbalance between supply and demand. This imbalance renders the housing market volatile, and stakeholders lose repeatedly. The purpose of the study was to forecast housing prices (HPs) in Kenya using simple and complex regression models to assess the best model for projecting the HPs in Kenya. Design/methodology/approach The study used time series data from 1975 to 2020 of the selected macroeconomic factors sourced from Kenya National Bureau of Statistics, Central Bank of Kenya and Hass Consult Limited. Linear regression, multiple regression, autoregressive integrated moving average (ARIMA) and autoregressive distributed lag (ARDL) models regression techniques were used to model HPs. Findings The study concludes that the performance of the housing market is very sensitive to changes in the economic indicators, and therefore, the key players in the housing market should consider the performance of the economy during the project feasibility studies and appraisals. From the results, it can be deduced that complex models outperform simple models in forecasting HPs in Kenya. The vector autoregressive (VAR) model performs the best in forecasting HPs considering its lowest root mean squared error (RMSE), mean absolute error (MAE), mean absolute percentage error (MAPE) and bias proportion coefficient. ARIMA models perform dismally in forecasting HPs, and therefore, we conclude that HP is not a self-projecting variable. Practical implications A model for projecting HPs could be a game changer if applied during the project appraisal stage by the developers and project managers. The study thoroughly compared the various regression models to ascertain the best model for forecasting the prices and revealed that complex models perform better than simple models in forecasting HPs. The study recommends a VAR model in forecasting HPs considering its lowest RMSE, MAE, MAPE and bias proportion coefficient compared to other models. The model, if used in collaboration with the already existing hedonic models, will ensure that the investments in the housing markets are well-informed, and hence, a reduction in economic losses arising from poor market forecasting techniques. However, these study findings are only applicable to the commercial housing market i.e. houses for sale and rent. Originality/value While more research has been done on HP projections, this study was based on a comparison of simple and complex regression models of projecting HPs. A total of five models were compared in the study: the simple regression model, multiple regression model, ARIMA model, ARDL model and VAR model. The findings reveal that complex models outperform simple models in projecting HPs. Nonetheless, the study also used nine macroeconomic indicators in the model-building process. Granger causality test reveals that only household income (HHI), gross domestic product, interest rate, exchange rates (EXCR) and private capital inflows have a significant effect on the changes in HPs. Nonetheless, the study adds two little-known indicators in the projection of HPs, which are the EXCR and HHI.

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“…30 30 VAR studies seem to confirm that, although house price shocks are important for some countries in the euro area, house price spillovers are not significantly large across European countries. For instance, Gattini and Hiebert (2010) find that housing demand and financing cost shocks appear to have contributed strongly to the dynamism in euro area house prices over the sample period they analyze. However, Nocera and Roma (2017) find that the effects of housing demand and monetary policy shocks differ widely across countries.…”

mentioning

confidence: 99%

Macroprudential policies and Brexit: A welfare analysis

Rubio

2024

Economic Inquiry

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Brexit will have implications on financial stability and the implementation of macroprudential policies. The United Kingdom (UK) will no longer be subject to the jurisdiction of the European Systemic Risk Board. This paper studies the welfare implications of this change of regime. By means of a dynamic stochastic general equilibrium model, I compare the pre‐Brexit scenario with the new one, in which the UK sets macroprudential policy independently. I find that, after Brexit, the UK is better off by setting its own macroprudential policy without taking into account Europe's welfare as a whole.

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Forecasting and Assessing Euro Area House Prices through the Lens of Key Fundamentals

Abstract: We would like to give a special thanks to Anders Warne for providing several MATLAB programs (i.e. SVAR suite) as well as insightful discussions.

Cited by 16 publications

References 23 publications

Misalignments in house prices and economic growth in Europe

Misalignments in house prices and economic growth in Europe

Comparing simple and complex regression models in forecasting housing price: case study from Kenya

Macroprudential policies and Brexit: A welfare analysis

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