Drivers of European housing prices in the new millennium: demand, financial, and supply determinants

Vol.:(0123456789) Empirica (2024) 51:731–753 https://doi.org/10.1007/s10663-024-09611-5 1 3 ORIGINAL PAPER Drivers ofEuropean housing prices inthenew millennium: demand, financial, andsupply determinants AlesMelecky1 · DanielPaksi1 Accepted: 28 March 2024 / Published online: 15 April 2024 © The Author(s) 2024 Abstract Many countries in Europe have experienced a steady increase in housing prices over the past decade, which continued even during the recent crisis. We analyze a panel of 15 European countries over the period 2000–2020. We find that demandside determinants, such as GDP, unemployment, wage and population, strongly influence housing prices. Nevertheless, we suggest that construction costs, access to finance (credit to GDP), and financing costs (long-term interest rate) should be included to avoid biased results. We find that financial development can significantly affect housing prices in the long run. We confirm the robustness of our results by conducting a lag sensitivity analysis of selected determinants. In addition, we find a negative effect of the GFC and a positive effect of the Covid crisis on housing prices. Furthermore, we find that countries with a mild reaction to or a quick recovery from the GFC experienced significantly higher housing price growth. Keywords Housing price determinants· EU· Euro area· Housing prices· Panel data JEL Classification C33· E31· R21· R31 Responsible Editor: Julia Wörz. * Ales Melecky ales.melec[email protected] Daniel Paksi [email protected] 1 Department ofEconomics, VSB-Technical University ofOstrava, 17. Listopadu St. 2172/15, 70800Ostrava-Poruba, CzechRepublic 732 Empirica (2024) 51:731–753 1 3 1 Introduction In recent years, many countries have experienced seemingly endless housing price growth. During the Covid crisis, housing prices did not fall as they did during the Global Financial Crisis (GFC); on the contrary, housing price growth actually accelerated in the Euro Area. Geng (2018) adds that housing prices have been rising faster than incomes in many economies. Many central banks have tightened monetary policy to combat rising inflation. The growth in housing prices, combined with rising financing costs, reduces the purchasing power of households, which have to allocate a larger share of their income to housing. In addition, rapid growth in housing prices can jeopardize financial stability and real economic activity (Vogiazas and Alexiou 2017). We find a fairly significant gap in the literature in terms of underresearched supply-side effects, missing comparison of the effects of the GFC and Covid crises, and missing updates of older works that would reflect varying effects of housing price determinants during ZLB and zero to negative inflation periods. Moreover, we add a lag sensitivity analysis, which is also missing in the literature. In our research, based on the literature review and identified gaps, we focus on three main questions connected to the two sides of the housing market and the means of financing: 1. Do demand-side determinants have statistically significant effects on housing prices? 2. Do supply-side determinants have statistically significant effects on housing prices? 3. Do financial determinants affect housing prices in any way? We control for the effects of global crises, carefully specify the number of lags, and examine common characteristics across groups of countries. We analyze the factors influencing housing prices in European countries over the last two decades. We choose to examine housing price determinants using panel data regression. Our approach differs from the majority of the literature, which focuses on employing VAR models. These models are often limited to single-country studies and include only a limited number of determinants and lags to preserve degrees of freedom. We capture supply-side determinants by available proxies–construction costs, production in the construction sector, and construction permits. We aim to determine the impact of supply-side factors on housing prices. Can their effects compete with the effects of demand-side factors? Should we keep them in our models, or has their time passed and for now, their effects are negligible? Our results suggest that demand-side and financial determinants have mostly significant effects on housing prices. Positive effects come from GDP growth, wage growth, population change, financial development, and availability of household credit. However, financial system development and availability of financing should go hand in hand with a well-designed macroprudential policy to overcome the possible negative effects of the formation of housing market bubbles and exclusion of 733 1 3 Empirica (2024) 51:731–753 lower-income groups from the housing market. Conversely, rising unemployment, inflation, and higher long-term interest rates reduce housing price growth. We find that, except for the positive effect of construction costs, which partly motivated the recent housing price boom, supply-side proxies are mostly insignificant. Supply-side factors cannot compete with demand-side factors, which have been boosted by a period of low interest rates and QE, in terms of their impact on housing prices, but their exclusion from the model can lead to biased results. We find significant effects of crisis variables, with opposite effects of the GFC and the Covid crisis on housing prices. Subsequent lag sensitivity analysis of selected determinants confirms the validity of our initial model specification. Our findings update and extend our knowledge of the effects of housing price determinants. Heterogeneity in housing price growth and the impact of its determinants have been documented in both the spatial and time dimensions. Some recent studies confirm our results, suggesting heterogeneity even across Euro Area countries, finding only smaller groups of countries with similar dynamics (see Maynou etal. 2021, and Miles 2020, among others). Regarding heterogeneity in effects over time, Dröes and van de Minne (2017) employ a large historical database and find changes in the significance and effect sizes of housing price drivers over the long run. They note that in Europe, housing accounts for 40–60% of total household wealth. Therefore, there is a need to revise our knowledge on the effects of housing price drivers. The remainder of the paper is organized as follows: Sect.2 presents a literature review focusing on the determinants of housing prices and the impact of crises. Section3 describes the data and empirical methodology employed. Section4 discusses the results of the panel data analysis, and Sect.5 concludes. 2 Literature review Over time, several topics have emerged in housing price research. One branch of the literature focuses on trends, correlations, co-movements, and gaps in housing prices (Knoll etal. 2017; Eichholtz etal. 2015; Abott and De Vita 2013, etc.). Knoll etal. (2017) conclude that housing prices in most developed countries were constant in real terms until the mid-twentieth century, but have risen sharply in recent decades. Eichholtz etal. (2015) uses a long time series of housing prices in Amsterdam to show that agent expectations are driven by fundamentals during economic downturns, and by momentum and recent trends during booms. Abott and De Vita (2013) find no long-run convergence among regional housing prices in the UK. Several papers examine convergence in the EU (see, e.g., Tsai 2018; Miles 2020; Maynou etal. 2021; Álvarez etal. 2010). Miles (2020) states that the euro, as a common currency, was expected to trigger convergence of various financial and economic variables across the continent, but finds only marginal evidence of housing price convergence. Tsai (2018) finds that housing prices across EA countries are more correlated than across non-EA countries. Maynou etal. (2021) confirm convergence only within five smaller “clubs” of EU countries over the period 2004–2016. Álvarez etal. (2010) find significant co-movement of GDP cycles, but 734 Empirica (2024) 51:731–753 1 3 rather weak co-movement of housing prices for four large EU economies. They state that country-specific variables largely determine housing prices. Our research contributes to this branch of the literature by investigating the effects of country groups in the panel data model using more recent data. A branch of the literature that is closely related to our analysis focuses on housing price determinants. Regarding economic factors, various studies consider the output of the economy, as well as labor market conditions represented by the unemployment rate and average wages (see, e.g., Cunha and Lobão 2021; Maynou etal. 2021; Geng 2018; Vogiazas and Alexiou 2017; Égert and Mihaljek 2007; Abelson etal. 2005; Baffoe-Bonnie 1998). Housing prices are closely linked to monetary policy and financial stability. Therefore, some authors consider interest rates, credit conditions, and macroprudential policy as drivers of housing prices (Robstad 2018; Nocera and Roma 2018; Hanck and Prüser 2020; Iacoviello 2005). Moreover, systemic crises, such as the GFC, influence housing prices and should be included in the models (see, e.g., Maynou etal. 2021; Kang and Liu 2014; Agnello and Schuknecht 2011). These issues are discussed in more detail in the following sections. 2.1 Demand‑side determinants Various studies consider economic output, mostly represented by GDP growth, and labor market conditions, represented by the unemployment rate and average wage, to be the traditional demand-side determinants of housing prices (see, e.g., Maynou etal. 2021; Cunha and Lobão 2021; Geng 2018; Vogiazas and Alexiou 2017; Égert and Mihaljek 2007; Abelson etal. 2005; Baffoe-Bonnie 1998). In general, faster GDP growth, higher wages, and a lower unemployment rate support demand for housing, which increases housing prices. Most of these papers only use demandside determinants, which are relatively easy to obtain data for, while only a few add supply-side determinants, as discussed below in Sect. 2.2. Demand-side models therefore dominate housing price research. Some authors add financial determinants to their models to capture how access to and cost of finance affect buyer demand (Maynou etal. 2021; Robstad 2018; Nocera and Roma 2018; Vogiazas and Alexiou 2017; Bouchouicha and Ftiti 2012; Beltratti and Morana 2010; Égert and Mihaljek 2007; Otrok and Terrones 2005; Baffoe-Bonnie 1998). Maynou etal. (2021) find that housing prices are driven by fiscal factors and unemployment, specifically the consolidated private debt to GDP ratio, unemployment rate, and the crisis period (2008–2012). Furthermore, they identify the property tax to GDP ratio, the long-term government bond interest rate, and inflation as significant factors influencing housing prices. They confirm similar patterns across smaller groups of countries, but not within the overall EA or non-EA country groups. Several authors employed Bayesian structural VAR models. For Norway, Robstad (2018) finds significant reactions of housing prices, but no effect on household credit. Nocera and Roma (2018) find that the effects of housing demand and monetary policy shocks differ considerably across the seven analyzed European countries. On average, monetary policy shocks account for 25–30 percent of the 735 1 3 Empirica (2024) 51:731–753 forecast error variance of housing price growth. However, the contribution of monetary policy shocks to housing price dynamics is historically highly heterogeneous. Using a panel of advanced economies, Vogiazas and Alexiou (2017) find positive effects of GDP growth, the real effective exchange rate, and credit to the private non-financial sector on housing prices. Bouchouicha and Ftiti (2012) employ a dynamic coherence function and find a common trend driving the housing markets in the US and UK, which becomes stronger in the long run. During crises, housing expenditure and wealth channels are important in the real estate market in the US, whereas the wealth effect is significant in the UK. Beltratti and Morana (2010) find that the US is an important source of global economic fluctuations for real economic activity, as well as for nominal variables and stock prices in G7 countries. Furthermore, they identify global supply-side shocks as an important determinant of real housing prices. Using a VAR model, Otrok and Terrones (2005) confirms a strong but lagged impact of US monetary policy shocks on housing price growth both in the US and internationally. Égert and Mihaljek (2007) find that increase in GDP per capita and housing or private sector credit significantly increase housing prices. Nevertheless, the size of the reaction varies across countries. Housing prices increase twice as much in response to an equivalent decline in the real interest rate in CEE compared with other OECD countries. Conversely, housing prices in other OECD countries react much stronger to credit growth compared to CEE economies. They also identify heterogeneous effects in housing price reactions to demographic and labor market factors. Housing prices respond more strongly to real wage increases in CEE countries due to initially lower average housing quality compared to non-CEE OECD countries. Development of housing markets and financial institutions, which is proxied by EBRD indicators, significantly affects housing prices in CEE. Using a VAR model, Baffoe-Bonnie (1998) finds that housing prices and number of homes sold respond significantly to regional economic conditions (i.e., national interest rate, money supply, employment growth, and inflation). Moreover, he notes that economic variables alone cannot explain the extreme fluctuations that occurred in some countries. 2.1.1 Macroprudential factors andcredit conditions Some authors consider macroprudential factors and credit conditions as determinants of housing prices (Kuttner and Shim 2016; Cerutti etal. 2017; Vandenbussche etal. 2015; Cronin and McQuinn 2016; Kelly etal. 2018; Shi etal. 2014). According to Cerutti etal. (2017), use of macroprudential policies is more commonly associated with lower credit growth, most notably in household credit, and these policies are less effective during busts than during booms. Shi etal. (2014) examine the impact of real fixed interest rates on housing prices in New Zealand. They find that higher interest rates do not have the expected negative effect on real housing prices, once household mortgage choice and other economic conditions are controlled for. Banti and Phylaktis (2019) investigate the impact of global liquidity on the world’s housing prices proxied by the availability of funding to global banks located in global financial centers. They find a significant impact of liquidity shocks on housing prices in advanced and emerging economies. Nevertheless, developed 736 Empirica (2024) 51:731–753 1 3 countries can use their macroprudential policy and other policy tools to shield their economies more effectively than developing countries. Using loan-level data on Irish mortgages in a property-level housing price model, Kelly etal. (2018) show that a 10% increase in available credit leads to a 1.5% increase in the value of purchased property. However, the decline in housing prices is sensitive to the choice of LTV and LTI. Kuttner and Shim (2016) examine the impact of nine non-interest rate policies on housing credit and housing prices in 57 countries over 30years. Introducing or increasing a maximum DSTI ratio and increasing housing-related taxes have significant negative effects on housing credit. Overall, the literature emphasizes the importance of demand-side factors in determining housing prices and suggests that the effects may be similar for certain smaller groups of countries. However, evidence on the latter remains very limited. We fill this gap in the literature by providing evidence on recent developments in a panel model framework. 2.2 Supply‑side determinants Use of supply-side factors is rather scarce in the literature compared with demandside factors, and is often limited to single-country models and regional analyses due to data availability–see Cunha and Lobão (2021), Geng (2018), Sivitanides (2018), Belke and Keil (2018), Dröes and van de Minne (2017), Hanck and Prüser (2020), Hlaváček et al. (2016), Adams and Füss (2010), Duca et al. (2011), Borowiecki (2009), and Janet Ge (2009). Cunha and Lobão (2021) use a four-level analysis of housing prices—the EU as a whole, the 28 EU countries, Portugal, and the 25 administrative regions of Portugal—considering construction costs and construction permits as supply-side determinants. They find that GDP, interest rates, tourism, and the number of residential properties under construction are significant drivers of real estate prices; however, their significance varies across the geographic levels. Geng (2018) uses crosscountry analysis and finds a significantly negative effect of housing stock per capita on housing prices. He concludes that tax relief on housing finance and the strictness of rent controls also drive housing prices and may cause different dynamics across countries. Sivitanides (2018) uses supply-side determinants as control variables only, and finds a long-term relationship of housing prices in London with UK GDP, London population, and housing completions. Belke and Keil (2018) employ a panel data model for German regions covering nearly a hundred German cities. They find that construction activity and housing stocks are significant supply-side determinants of housing prices. Dröes and van de Minne (2017) examine housing prices over a 200-year period. They find that the relative importance of determinants changes over time and reflects the current economic environment. Supply-side determinants were dominant before 1900 and again after WW2, especially construction costs and new housing supply. In the post-WW2 period, reconstruction and a baby boom greatly contributed to housing price growth. Hanck and Prüser (2020) examine housing prices in Germany using Bayesian VAR models and find that interest rates significantly influence housing prices. 737 1 3 Empirica (2024) 51:731–753 Nevertheless, a permanent increase of interest rates to 4% may stop housing price growth. Borowiecki (2009) analyzes the situation in Switzerland using a VAR model and a self-constructed housing quality index. He finds that construction prices have a significantly positive effect on housing prices, whereas housing construction has a significantly negative effect. Hlaváček etal. (2016) analyze commercial property prices in Central Europe. Apart from the significantly positive effect of traditional demand-side determinants (GDP, credit to GDP ratio, and inflation), they find a significantly negative effect of available office space. Adams and Füss (2010) examine the impact of macroeconomic variables on housing prices in 15 countries using panel cointegration analysis. They find that in the long run, a 1% increase in both economic activity and construction costs leads to a similar increase in housing prices (0.6%). Conversely, a 1% increase of interest rates decreases housing prices by 0.3% in the long run. Duca etal. (2011) emphasize that housing supply and user costs help explain housing prices only if credit conditions remain stable. However, changes in the degree of financial liberalization, credit standards, and the responsiveness of housing supply may cause fluctuations in construction and housing prices across economies and over time. Substantial swings in housing construction led to macroeconomic effects in the US, Ireland and Spain. Janet Ge (2009) analyzes housing prices in New Zealand using quarterly real housing prices while experimenting with different lags of explanatory variables. She finds that the different variables show their effects with different lags, and therefore that appropriate lag setting is important in housing price modeling. We reflect this finding in our analysis by empirically testing the optimal number of lags in our model and by conducting a lag sensitivity test. In general, the literature suggests that supply-side factors may be important for housing price developments. However, the results vary across studies and evidence from international samples remains scarce. We fill this gap in the literature by analyzing the effects of three supply-side factors (construction costs, construction output, and building permits) on housing prices in a panel of 15 European countries. Moreover, the supply-side factors may partly reflect different institutional conditions, a domain for which data are limited. We provide more details on institutional conditions in terms of key business indicators related to the processing of construction permits and the registration of properties in Table6 in the Appendix.1 2.3 Effects ofcrises onhousing prices Since the beginning of the millennium, two global crises have hit European economies. The first was the Global Financial Crisis (GFC), with its roots in the US housing market, and the second was the Covid crisis, which quickly transformed from a public health to an economic crisis as a result of the restrictions imposed, the extraordinary spending, the increase in debt, and the disruption of supply chains, among other things. Surprisingly, even recent studies tend to neglect the effects 1 We cannot include these indicators in the model because they are only available with short time series and low frequency. 738 Empirica (2024) 51:731–753 1 3 of the GFC in their models. The effects of the GFC on housing are documented in several studies (see, e.g. Maynou etal. 2021; Kang and Liu 2014; Agnello and Schuknecht 2011). In addition, Dröes and van de Minne (2017) demonstrate on their large dataset that global crises in the past had significant effects on local housing prices in the Netherlands. Furthermore, the Covid crisis and its impacts differ significantly from the GFC. Therefore, it is necessary to update our knowledge on the effects of global crises on housing prices. Maynou etal. (2021) explicitly capture the effects of the GFC and its aftermath with a crisis dummy covering the period 2008–2012. They find a significant negative effect of the crisis on housing prices due to the bursting of a housing price bubble and strong adjustment of housing prices. Kang and Liu (2014) employ quantile regression to analyze the impact of the GFC on housing prices in China and Taiwan. In Taiwan, the impact of the GFC on housing prices appears to be higher where real estate prices were already high. Conversely, a lesser impact of the GFC on housing prices was found where property prices had been high in China. Using a multinominal probit model, Agnello and Schuknecht (2011) find significant influence of domestic credit and interest rates on the probability that booms and busts in housing markets will occur. In conjunction with banking crises, international liquidity plays a significant role in the occurrence of housing booms and busts. Zhao (2020) employs a structural break model and zip code level data to analyze the impact of Covid on the US housing market. Monetary easing and consequent lower mortgage rates increased housing demand and created a structural break in that demand, which led to a sharp increase in housing prices. He concludes that for the period April–August 2020, median housing prices rose faster than in any four-month period before the GFC. Inspired by the literature, we incorporate the effects of global crises in our model and provide new evidence on the effects of the GFC and the Covid crisis on European housing prices. 3 Data andmethodology 3.1 Data The available data are limited either in terms of series length or cross-sectional coverage. The longer series are available only for approximately half of all European countries, while the time series of the other half only start around the GFC. In this paper, we prefer longer time series to cover more than one business cycle and to capture the impact of the two global crises (GFC and Covid crisis). The GFC had severe impacts on the economy and housing market. Geng (2018) notes that housing prices remained below their pre-crisis level for a long time in some countries, for instance, Denmark, Ireland, and Spain. Our dataset covers the period 2000–2020 and includes quarterly data for 15 European countries from Eurostat, the BIS, and the IMF. This leaves us with more than 1100 observations even after we transform the data and consider the lags in the model. We analyze mostly old EU member states and only 739 1 3 Empirica (2024) 51:731–753 a few non-EU countries (Great Britain and Norway).2 The housing price index data limits our ability to construct a broader panel, as it is only available for new EU member states from 2008 onwards (Table1). 3.2 Methodology In line with our research questions, we formulate several assumptions regarding the effects of the explanatory variables that are reflected in the regression model (1). To test these assumptions, we employ panel data models that account for both individual-specific and time-specific effects and are able to capture the interdependence between observations within the same individual unit. Overall, panel data regression offers several advantages over cross-sectional or time series analysis alone, including increased efficiency, controlling for unobserved heterogeneity, and the ability to analyze both individual-level and aggregate-level effects simultaneously. We use two types of panel data models, which we briefly describe. The fixed effects model accounts for individual-specific effects by including dummy variables for each individual unit in the regression equation, which capture the unobserved heterogeneity across individual units that remains constant over time. This type of model is useful when there are time-invariant characteristics that vary across individual identities. In contrast, the random effects model assumes that the individual-specific effects are random and uncorrelated with the independent variables. In the random effects model, individual-specific effects are treated as random variables with a specific distribution. Random effects models are more efficient than fixed effects models when the individual-specific effects are uncorrelated with the independent variables. In our analysis, we use a panel data regression with fixed effects. The FE model is estimated as follows: where d denotes differences and gr is growth, μi represents unobserved country fixed effects, 𝜏t denotes an unobserved common time effect across countries, and 𝜀it is the idiosyncratic disturbance term (residual). For interpretation purposes, we standardize all variables and present them in separate tables. We calculate z-scores using Stata data options. That is, for each observed value of the variable we subtract the mean and divide by the standard deviation. This standardization enables us to compare effect sizes across variables. We make several assumptions derived from economic theory that motivate the inclusion of variables in the model and inform sign expectations for their effects on housing prices. The GDP growth variable serves as a proxy for the business cycle (1) d_HPI i,t= 𝛽 0+ 𝛽 1 GDP_gr i,t−1+ 𝛽 2 d_U i,t−6+ 𝛽 3 gr_W i,t−6+ 𝛽 4 Pop_gr i,t−1 +𝛽5_Inf i,t−1+𝛽6d_LTIRi,t−6+𝛽7d_FDi,t−8+𝛽8d_CH_GDPi,t− 6 +𝛽9d_CCi,t−6+𝛽10d_PCi,t−6+𝛽11d_CPi,t−6+𝛽12GFCi,t +𝛽13COVID i,t +𝜇 i +τ t +𝜀 i , t 2 Austria, Belgium, Denmark, Finland, France, Germany, Greece, Ireland, Italy, the Netherlands, Portugal, Spain, Sweden, Norway, Great Britain. 746 Empirica (2024) 51:731–753 1 3 the two groups. This requires three dummies; the results are provided in Table3 (models 5–7). The only relevant result which is significant at the 1% level is that group A + C, i.e., countries with minor GFC impact or rather quick recovery, experienced significantly larger housing price growth. Finally, we evaluate whether the expected effects formulated in the theoretical model are consistent with our empirical results. The results summarized in Table4 show that the effects of the demand-side variables are in line with theoretical expectations. The results for the supply-side variables are mixed. The effect of construction costs is consistent with our expectations, but the other two supply-side factors are insignificant. The GFC reduced housing prices as predicted by the theory, but the specificity of the Covid crisis caused its positive effect on housing prices. 5 Conclusion This paper analyzes housing price drivers using a sample of 15 European countries over the period 2000–2020. Compared to existing studies, we employ a longer data series with a larger set of determinants, including traditional demand-side variables, determinants capturing cost and availability of financing, supply-side variables, and crisis dummies. This enables us to study the impact of two global crises (the GFC and Covid crisis) in addition to the effects of the determinants. Furthermore, we analyze the effects of sorting countries into smaller groups. Our results suggest that income stability and economic prospects, including population changes, drive housing prices more than the macroeconomic aggregate of wages. We confirm a significant role of financial factors. Higher interest rates have a negative impact on housing prices, whereas financial sector development and the ratio of household credit to GDP have a positive effect on housing prices. The effects of supply-side factors remain inconsistent, with the exception of the positive impact of construction costs on housing prices. However, supply-side determinants help properly identify the effects of other variables and should be considered when modelling housing prices. We find different impacts of the two global crises on housing prices—negative for the GFC and positive for the Covid crisis—that stem from their different nature, adopted measures, and effects on the economy. Supply chain problems, rising costs of construction, and immigration in some countries, in combination with higher cost of capital due to growing policy rates and risks, create pressure on housing price growth. On the other hand, higher cost of loanable funds and economic slowdown work in the opposite direction, slowing down housing price growth. Moreover, we confirm that the Euro Area group is heterogeneous. Instead, smaller groups of countries with similar development paths should be considered when analyzing housing prices. Subsequent lag sensitivity tests show a stable effect of GDP growth, a diminishing effect of population growth after three lags and two opposite effects of inflation—negative in the short run and positive in the long run. This paper contributes to our understanding of housing price development in European countries and provides a comparison of the relative effects of demand, financing, and supply-side conditions on the housing market. It should be noted 747 1 3 Empirica (2024) 51:731–753 Table 3 Country groups as explanatory variables (RE model, standardized variables) Variables (1) (2) (3) (4) (5) (6) (7) M_RE_A M_RE_B M_RE_C M_RE_D M_RE_AB M_RE_AC M_RE_AD GDP growth t-1 0.178*** 0.184*** 0.180*** 0.178*** 0.179*** 0.184*** 0.177*** (0.049) (0.046) (0.051) (0.051) (0.051) (0.048) (0.049) Unemployment t-6 − 0.200*** –0.194*** –0.199*** –0.200*** − 0.198*** − 0.198*** − 0.199*** (0.041) (0.039) (0.041) (0.041) (0.041) (0.040) (0.040) Wage growth t-6 0.139** 0.140** 0.140** 0.141** 0.142** 0.137** 0.142** (0.065) (0.065) (0.065) (0.065) (0.064) (0.066) (0.065) Population change t-1 0.118 0.142** 0.130 0.120 0.127 0.136* 0.118 (0.080) (0.070) (0.080) (0.082) (0.079) (0.074) (0.081) Inflation t-1 − 0.083 − 0.081 − 0.079 − 0.079 − 0.077 − 0.085 − 0.079 (0.057) (0.057) (0.059) (0.059) (0.058) (0.058) (0.056) Long-term interest rate t-6 − 0.103*** –0.103*** –0.103*** –0.104*** − 0.104*** − 0.102*** − 0.103*** (0.037) (0.036) (0.037) (0.037) (0.037) (0.037) (0.037) Financial development index t-8 0.067*** 0.064*** 0.067*** 0.067*** 0.066*** 0.066*** 0.066*** (0.018) (0.018) (0.018) (0.018) (0.018) (0.017) (0.018) Credit to household GDP t-6 0.026* 0.023* 0.022 0.023 0.021 0.025* 0.024* (0.014) (0.014) (0.015) (0.015) (0.015) (0.014) (0.014) Construction costs t-6 0.126** 0.120** 0.124** 0.126** 0.123** 0.124** 0.124** (0.062) (0.059) (0.061) (0.061) (0.060) (0.061) (0.061) Production in construction t-6 0.026 0.022 0.027 0.027 0.026 0.025 0.025 (0.073) (0.071) (0.074) (0.074) (0.074) (0.073) (0.072) Housing permits index t-6 0.010 0.010 0.011 0.011 0.011 0.010 0.010 (0.010) (0.009) (0.010) (0.010) (0.010) (0.009) (0.010) GFC − 1.047*** –1.040*** –1.047*** –1.050*** − 1.049*** − 1.040*** − 1.049*** (0.269) (0.263) (0.270) (0.271) (0.269) (0.267) (0.268) 748 Empirica (2024) 51:731–753 1 3 Table 3 (continued) Variables (1) (2) (3) (4) (5) (6) (7) M_RE_A M_RE_B M_RE_C M_RE_D M_RE_AB M_RE_AC M_RE_AD COVID 0.661*** 0.684*** 0.671*** 0.662*** 0.669*** 0.677*** 0.662*** (0.195) (0.184) (0.198) (0.199) (0.196) (0.192) (0.194) EA − 0.107 − 0.007 − 0.100 − 0.112 − 0.074 − 0.102 − 0.067 (0.084) (0.057) (0.075) (0.072) (0.072) (0.080) (0.078) Country group dummy 0.123* − 0.236** 0.090 − 0.022 − 0.067 0.169*** 0.097 (0.070) (0.097) (0.072) (0.043) (0.073) (0.063) (0.076) Constant 0.049 0.075* 0.055* 0.091* 0.096* − 0.010 0.009 (0.053) (0.045) (0.033) (0.048) (0.054) (0.041) (0.088) Observations overall 1110 1110 1110 1110 1110 1110 1110 R-squared 0.3248 0.3301 0.3235 0.3222 0.323 0.3283 0.324 Number of ID 15 15 15 15 15 15 15 Note: Robust standard errors in parentheses, ***p < 0.01, **p < 0.05, *p < 0.1 749 1 3 Empirica (2024) 51:731–753 that the effect of crises on housing prices varies depending on the nature of the crisis and the measures adopted. Appendix See Tables 5, 6 and 7. Table 4 Coefficients Hypotheses and their evaluation Variable Expected impact Empirical result Compliance with expectations GDP + + Yes Unemployment − − Yes Average wages + + Yes Population + + Yes Inflation − − Yes Long-term interest rates − − Yes Financial development + + Yes Credit to households + + Yes Construction costs index + + Yes Production in construction index − 0 No Construction permits index − 0 No Global financial crisis − − Yes Covid crisis − + No Table 5 Descriptive statistic Note: Indices tend to vary due to the baseline year (2010 or 2015). Therefore, outliers occur in states severely hit by the GFC and subsequent debt crises Variable N Mean Std. Dev Min Max Housing price index 1344 96.09 24.64 38.36 169.16 GDP growth 1344 1.37 3.48 − 21.60 29.08 Unemployment 1303 7.97 4.46 2.20 27.60 Wage growth 1279 1.03 0.05 0.79 1.20 gr_pop 1330 0.13 0.14 − 0.92 1.04 Inflation 1344 1.63 1.35 − 6.13 6.57 Long-term interest rate 1344 3.25 2.42 − 0.78 26.40 Financial development index 1280 0.73 0.10 0.44 1.00 Credit to household GDP 1344 70.87 26.95 1.00 137.90 Construction costs 1343 91.75 12.74 55.90 121.10 Production in construction 1343 123.30 75.69 56.60 679.80 Housing permits index 1259 258.77 423.46 26.90 6217.30 750 Empirica (2024) 51:731–753 1 3 Table 6 Indicators Related to the Processing of Building Permits and the Registration of Properties. Source: Self processing based on Doing Business 2021 dataset (data for 2020) Economy Dealing with construction permits Registering property Scoredealing with construction permits Procedures (number) Time (days) Cost (% of warehouse value) Building quality control index (0–15) Scoreregistering property Procedures (number) Time (days) Cost (% of property value) Quality of land administration index (0–30) Austria 75.3 11 220.5 1.1 13 80.3 3 17.5 4.6 23 Belgium 76.5 9 211 0.9 12 51.0 8 56 12.7 22 Denmark 87.9 7 64 0.6 11 89.9 3 4 0.6 24.5 Finland 73.6 17 98 0.7 10 79.0 3 61.5 4.0 26.5 France 73.3 10 213 3.9 13 63.3 8 42 7.3 24 Germany 78.2 9 126 1.1 9.5 66.5 6 52 6.7 23 Greece 69.5 17 180 1.9 12 47.7 11 26 4.8 5.5 Ireland 76.3 10 164 4.3 13 71.7 5 31.5 6.5 23.5 Italy 68.3 14 189.5 3.4 11 81.7 4 16 4.4 26.5 Netherlands 66.9 13 189 4.0 10 80.0 5 3 6.1 28.5 Norway 80.6 11 109.5 0.6 11 87.3 1 3 2.5 20 Portugal 73.2 14 160 1.2 11 78.4 1 10 7.3 20 Spain 70.8 13 147 4.7 11 71.7 6 13 6.1 22.5 Sweden 78.0 8 117 1.9 9 90.5 1 7 4.3 28 United Kingdom 80.3 9 86 1.1 9 75.7 6 21.5 4.8 26 751 1 3 Empirica (2024) 51:731–753 Acknowledgements We gratefully acknowledge financial support from the SGS project no. SP2022/38 at VSB–Technical University of Ostrava. Funding Open access publishing supported by the National Technical Library in Prague. Declarations Conflict of interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. 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