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This paper explores the real estate price determinants at four geographical levels: in the European Union as a whole, in the 28 European Union countries, in one European Union country (Portugal) and in 25 Portuguese metropolitan statistical areas (MSAs).

The authors run two time series regression models and two panel data regression models with observations of potential real estate price determinants and House Price Indices collected from Eurostat.

The results show that price determinants, such as gross domestic product (GDP), interest rates, housing starts and tourism, are statistically significant, but not in all the four geographical levels of analysis. The results also confirm the autoregressive characteristic of real estate prices, with the last period price change being the most important determinant of current period real estate price change.

Forecasting real estate prices can be made more effective by knowing that each geographical level of analysis implies different price determinants and that momentum is an important determinant in real estate returns.

To the best of the authors knowledge, this is the first study to develop and test a real estate price equilibrium model at several different geographical levels of the same political space.

The article aims to examine why residential property price indices (RPPI) are important, particularly in the European Union (EU) with its highly integrated financial system and examines the problems in developing a pan-European price index that aggregates the indices of different countries.

The reasons why RPPI are important is explored through a review of the literature on residential price bubbles and the issues with the indices through studies of individual examples.

Financial integration in the EU has taken place without adequate consideration having been given to diversity in residential property markets. The development of means of monitoring them has lagged behind integration with the national price indices using a variety of methods and approaches to data that limit the extent to which they can be aggregated.

The article shows the need for better quality data about house price trends in Europe if the consequences of future bubbles are to be avoided. Current initiatives are unlikely to satisfy this, as they leave too many choices about methodology and data in the hands of individual countries.

This paper studies the dynamics and elasticities of house prices in Spain and Portugal (Iberia) at the Metropolitan Statistical Area (MSA) level, addressing panel regression problems such as heterogeneity and cross-sectional dependence between MSA.

The authors develop a two steps study. First, five distinct estimation methodologies are applied to estimate the long-term house price equilibrium of the Iberian MSA house market: Mean Group (MG), Fully Modified Ordinary Least Square (FMOLS) MG (FMOLS-MG), FMOLS Augmented MG (FMOLS-AMG), Common Correlated Effects MG (CCEMG) and Dynamic CCEMG (DCCEMG). FMOLS-AMG is found to be the best estimator for the long-term model. Second, an additional five distinct estimation methodologies are applied to estimate the short-term house price dynamics using the long-term FMOLS-AMG estimated price in the error-correction term of the short-term dynamic house price model: OLS Fixed Effects (FE), OLS Random Effects (RE), MG, CCEMG and DCCEMG. DCCEMG is found to be the best estimator for the short-term model.

The results show that in the long run Iberian house prices are inelastic to aggregate income (0.227). This is a much lower elasticity than what was previously found in US MSA house price studies, suggesting that there are other factors explaining Iberian house prices. According to our study, coastal MSA presents an inelastic housing supply and a price to income elasticity close to one, whereas inland MSA are shown to have an elastic supply and a non-significant price to income elasticity. Spatial differences are important and cross-section dependence is prevalent, affecting estimates in conventional methodologies that do not account for these limitations, such as OLS-FE and OLS-RE. Momentum and mean reversion are the main determinants of short-term dynamics.

Recent econometric advances that account for slope heterogeneity and cross-section dependence produce more accurate estimates than conventional panel estimation methodologies. The results suggest that house markets should be analyzed at the metropolitan level, not at the national level and that there are significant differences between short-term and long-term house price determinants.

To the best of the authors' knowledge, this is the first study applying recent econometric advances to the Iberian MSA house market.

This paper aims to present the dynamics of housing prices in Italian cities based on unpublished data with regional details from the late 1960s, half-yearly base, for all main Italian cities measuring the average prices for three city dimensions: city centre, sub-centres and outskirts or suburbs. It estimates the Italian long-term house price index, city based in real terms, and shows a combination of methods to deal with large time-series data.

This paper builds long-term cycles based on the city (real) data by estimating the common components of cointegrated time series and extracting the unobservable signals to build real house price index for sub-regions in Italy. Three different econometric methodologies are used: Johansen cointegration test and VAR models to identify the long-term pattern of prices at the estimated aggregate level; principal components to obtain the common (permanent and transitory) components; and signal extraction in ARIMA time series–model-based approach method to extract the unobserved time signals.

Results show three long-term cycle-trends during the period and identify several one-direction causal non-permanent relationships among house prices from different Italian areas. There is no evidence of convergence among regional’s house prices suggesting that the Italian housing prices converge inside the local market with only short diffusion effects at larger regional level.

Data are measured as the average price in squared meters, and the resulting index is not quality controlled.

The long-term trends on housing prices serve to implement further research and know deeply the evolution of Italian housing prices.

This paper contains new and unknown information about the evolution of housing prices in Italian regions and cities.

The purpose of this paper is to examine the role of monetary liquidity in house price evolution through examining the Asset (housing) Inflation channel. It identifies the main channels of transmission affecting house prices from monetary supply channels to house price change, examining how the Asset Price channel transmits changes in M1 to housing prices in Spain and the UK.

The paper uses Vector Auto Regression (VAR) and Error Correction models to test the Asset Inflation channel in the UK and Spain from 1991 to 2013 in two steps. In the first step, the supply elasticity is estimated through the long-term relationship between house prices and stock supply. The second step estimates a Vector Error Correction (VEC) to explain house price dynamics conditioned on supply reactions. The latter is defined as a long-term inverse demand model where housing prices are controlled by fundamentals in each market. Models allow forecast testing using Choleski impulse responses methodology.

Several results are found. In the supply model, both countries show rapid convergence to equilibrium with a larger elasticity of supply in Spain than in the UK but with a short run effect of new supply on prices in the UK. Regarding the Asset Inflation Channel model, the paper finds evidence of the existence of a housing accelerator effect in Spain, but not in the UK where changes in liquidity fully impact house prices in one direction.

Implications of findings are mainly to forecast the effects of Monetary Policy measures in different economies.

The model supports the evaluation of different impacts of monetary policy in territories. It shows that the same policy will have different impacts in different housing markets and therefore highlights the importance of examining each market separately to identify the appropriate policy interventions.

This is the first paper that estimates the impact of the Asset Inflation Channel on house prices that endogenises housing market conditions and compares effects and interrelationships in two different economies.

The purpose of this paper is to present a framework for the assessment of the fundamental value of house prices in the largest Ukrainian cities, as well as to identify the thresholds, the breach of which would signal a bubble.

House price bubbles are detected using two approaches: ratios and regression analysis. Two variants of each method are considered. The authors calculate the price-to-rent and price-to-income ratios that can identify a possible overvaluation or undervaluation of house prices. Then, the authors perform regression analyses by considering individual multi-factor models for each city and by using a within regression model with one-way (individual) effects on panel data.

The only pronounced and prolonged period of a house price bubble is the one that coincides with the Global Financial Crisis. The bubble signals produced by these methods are, on average, simultaneous and in accordance with economic sense.

The framework described in this paper can serve as a model for the implementation of a tool for detecting house price bubbles in other countries with emerging, small and open economies, due to adjustments for high inflation and significant dependence on reserve currencies that it incorporates.

A tool for measuring fundamental house prices and a bubble indicator for housing markets will be used to monitor the systemic risks stemming from the real estate market. Thus, it will help the National Bank of Ukraine maintain financial stability.

The framework presented in this research will contribute to the enhancement of the systemic risk analysis toolkit of the National Bank of Ukraine. Therefore, it will help to prevent or mitigate risks that might originate in the real estate market.

The authors show how to implement an instrument for detecting house price bubbles in Ukraine. This will become important in the context of the after-war reconstruction of Ukraine, with mortgages potentially becoming the main tool for the financing of the rebuilding/renovation of the residential real estate stock.

Homeownership is a problem among younger working households (YWH). This is a more serious problem with YWH working in urban areas. New housing schemes introduced by the government show that measures are being taken. This paper aims to determine homeownership problems among YWH in order to assess the new housing schemes towards helping YWH. The questions arise are what are the homeownership problems among YWH and to what extent does YWH perceive the new housing schemes to help them. The objectives are to ascertain homeownership problems among YWH and to explore the perceptions of YWH on the new housing schemes introduced. The study was conducted qualitatively through in-depth interviews with YWH. The findings showed that the main homeownership problem highlighted by the YWH is housing prices are high in urban area which resulted to the location of affordable houses inconvenient. From the assessment, majority of the YWH agree with the new housing schemes. However there are weaknesses such as high land prices and absence of financial literacy. In conclusion, housing schemes should also consider financial education as part of their aims. Nevertheless, the introduction of housing schemes is beneficial to address homeownership problems among YWH.

The private rental market in Malta has expanded significantly in recent years, but as at 2020, no official rent index is yet published. This paper aims to construct such an index and explores the relative importance of structural, locational and neighbourhood factors to advertised rents.

The authors compile hedonic indices for advertised rents in Malta collected from publicly available sources using webscraping techniques. The database comprises more than 25,000 listings with information on various property attributes. Hedonic regressions are estimated using ordinary least squares and rent indices are computed using three alternative methods: the time dummy method, the rolling time dummy method and the average characteristics method. For the latter, indices are computed using the Laspeyres, Paasche and Fisher methods.

The results from the hedonic indices indicate that the annual growth rate in advertised rents was slowing down during 2019, albeit still remaining relatively high, while in 2020, advertised rents contracted sharply, amplified by the effects of COVID-19. The findings also reveal that advertised rental prices are significantly influenced by various structural, locational and neighbourhood factors.

This paper introduces the first rent index in Malta that will be used to monitor developments in the rental segment of the housing market and for financial stability purposes given the share of buy-to-let properties. It also provides various elasticities on the impact of property attributes on advertised rents in Malta. Finally, the study contributes to the literature on the effect of foreign-born residents on advertised rents.