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The purpose of this paper is to assess the long-run and short-run drivers of real house prices in Nigeria from 1991Q1 to 2020Q4.

Vector autoregression and cointegration tests were used to assess the key drivers of Nigeria’s real house prices in the long run and short run.

The empirical findings revealed that household disposable income is the most important determinant of house prices in Nigeria. House prices increased by 1.6% and 60.8% in response to a 1% increase in disposable income in the long run and short run, respectively, while real mortgage credits pushed up house prices by 5% and have no long-run effects, suggesting that most Nigerians depend on their money income rather than credits in securing a home. In addition, prices of oil sector products and real interest rates had negative and significant relationship with house prices, while positive correlations were found for real effective exchange rate and real housing investments regardless of the time horizon. The impact of construction costs and cement prices was also documented.

This is likely a pioneering study of its kind to focus on the determinants of real house prices in Nigeria. It is probably the first study, the best of the author’s knowledge, to empirically examine the impact of the oil sector on house prices in the country.

This study aims to examine the impact of some real variables such as real effective exchange rates, real mortgage rates, real money supply, real construction cost index and housing sales on the real housing prices.

This study uses a nonlinear autoregressive distributed lag (NARDL) model in the monthly period of 2010:1–2021:10.

The real effective exchange rate has a positive and symmetric effect. The decreasing effect of negative changes in real money supply on real housing prices is higher than the increasing effect of positive changes. Only positive changes in the real construction cost index have an increasing and statistically significant effect on real house prices, while only negative changes in housing sales have a small negative sign and a small increasing effect on housing prices. The fact that the positive and negative changes in real mortgage rates are negative and positive, respectively, indicates that both have a reducing effect on real housing prices.

This study suggests the first NARDL model that investigates the asymmetric effects on real housing prices instead of nominal housing prices for Turkey. In addition, the study is the first, to the best of the authors’ knowledge, to examine the effects of the five real variables on real housing prices.

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.

Swedish house prices have risen rapidly since the mid‐1990s. How can this be explained? Are houses overpriced? In this paper the author tries to answer these questions.

The author estimates an error correction model (ECM), and sees if the model can explain the house price developments.

The model suggests that increasing household disposable income and falling mortgage rates are the most important factors behind the upswing in prices. There is no evidence of overpricing.

Compared to earlier Swedish studies, this study is based on new data and new variables. Furthermore, the estimation period is restricted to the more recent period when Swedish credit markets have been unregulated.

This paper aims to investigate the presence of a housing bubble using Swedish data from 1986Q1-2016Q4 by using various methods.

First, the authors use affordability indicators and asset-pricing approaches, including the price-to-income ratio, price-to-rent ratio and user cost, supplemented by a qualitative discussion of other factors affecting house prices. Second, the authors use cointegration techniques to compute the fundamental (or long-run) price, which is then compared with the actual price to test the degree of Sweden’s housing price bubble during the studied period. Third, they apply the univariate right-tailed unit root test procedure to capture bursting bubbles and to date-stamp bubbles.

The authors find evidence for rational housing bubbles with explosive behavioral components beginning in 2004. These bubbles do not continuously diverge but instead periodically revert to their fundamental value. However, the deviation is persistent, and without any policy correction, it takes decades for real house prices to return to equilibrium.

The policy implication is that monetary policy designed to contain mortgage demand and thereby prevent burst episodes in the housing market must address external imbalances, as revealed in real exchange rate undervaluation. It is unlikely that current policies will stop the rise of house prices, as the growth of mortgage credit, improvement in Sweden’s international competitiveness and the path of interest rates are much more important factors.

The purpose of this paper is to use data on new apartment offerings in the municipality of Sao Paulo, Brazil to illustrate its main claim that the hedonic direct method using time dummies as well as the simple average method include cyclical behavior of observables and non‐observables in a house price index that may overestimate or underestimate the actual change in house prices, well beyond the composition effects.

The paper proposes the use of alternative characteristics hedonic functions to compute alternative Laspeyres house price indexes that differentiate the sources of observable shocks in the index. The decomposition allows for the inclusion of level and cyclical behavior of sets of aggregate variables into the index.

The appropriate house price index should filter out real shocks that potentially affect the real estate sector. An index should capture nominal variation and incorporating real variation biases the measurement. Thus, the index is intended and able to buffer the bias spillover into the rest of economy. In the limited sample from the city of Sao Paulo, Brazil, the main finding is that real shocks and US foreign shocks give an upward bias in the house price index, while nominal shocks give mostly a downward bias. Real shocks make the index incorporate gains that should not be incorporated into the index, thus providing a noisy picture of the nominal variation in house prices.

The key contribution of this paper is to provide a framework for the construction of a house price index that filters out real shocks that potentially affect the real estate sector. An index should capture nominal variation and incorporating real variation biases the measurement. Those biases can spillover to the rest of the economy is a detrimental way. Thus, the index is intended and able to buffer the bias spillover into the rest of economy.

This paper aims to assess the effects of housing market shocks on real output in South Africa, by focusing on the real private consumption channel.

It measures housing market shocks as non-monetary housing shocks, uses a data set covering the period 1969Q4-2014Q4 and uses the agnostic identification procedure.

The paper finds that 20 per cent of the variation in house prices is explained by these shocks. The paper also finds that the effects of housing demand shocks on real private consumption are short-lived and generate a transitory real output response. Overall, housing demand shocks have managed to explain nearly 13 per cent and 14 per cent of the variation in real private consumption and real output respectively, over 20-quarters ahead forecast revision.

This finding suggests that shocks emanating from the housing market in the country are essential and should be considered when making macroeconomic policy decisions.

None of the existing studies, to our knowledge, have empirically assessed the effects of housing market shocks on real output directly. This paper attempts to contribute to the literature by assessing the direct impact of housing market shocks on the real output, using South Africa as a case study.

This paper aims to assess the long-run drivers and short-term dynamics of real house prices in Sweden for 1986Q1 to 2016Q4. More specifically, the author examines the extent to which real house prices are determined by affordability, demographics and asset price factors.

The author conducts a cointegration analysis and applies a vector autoregression model to examine the long- and short-run responsiveness of Swedish real house prices to a number of key categories of fundamental variables.

The empirical results indicate that house prices will increase in the long run by 1.04 per cent in response to a 1 per cent increase in household real disposable income, whereas real after-tax mortgage interest and real effective exchange rates show average long-term effects of approximately – 8 and – 0.7 per cent, respectively. In addition, the results show that the growth of real house prices is affected by growth in mortgage credit, real after-tax mortgage interest rates and disposable incomes in the short run, whereas the real effective exchange rate is the most significant determinant of Swedish real house appreciation.

The impact of the two lending restrictions been implemented after the financial crisis – the mortgage cap in October 2010 and the amortization requirement in June 2016 – are ineffective to stabilize the housing market. This suggests that macroprudential measures designed to ease pressure on housing prices and reduce risks to financial stability need to focus on these fundamentals and address the issues of tax deductibility on mortgage rates and the gradual implementation of debt-to-income limits to contain mortgage demand and improve households’ resilience to shocks.

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.