Basco housing bubbles; origins and consequences (2018)

Housing Bubbles Origins and Consequences Sergi Basco Housing Bubbles Sergi Basco Housing Bubbles Origins and Consequences Sergi Basco Universitat Autònoma Barcelona Barcelona, Spain ISBN 978-3-030-00586-3 ISBN 978-3-030-00587-0  (eBook) https://doi.org/10.1007/978-3-030-00587-0 Library of Congress Control Number: 2018956582 © The Editor(s) (if applicable) and The Author(s) 2018 This work is subject to copyright All rights are solely and exclusively licensed by the Publisher, whether the whole or part of the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilms or in any other physical way, and transmission or information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed The use of general descriptive names, registered names, trademarks, service marks, etc in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use The publisher, the authors and the editors are safe to assume that the advice and information in this book are believed to be true and accurate at the date of publication Neither the publisher nor the authors or the editors give a warranty, express or implied, with respect to the material contained herein or for any errors or omissions that may have been made The publisher remains neutral with regard to jurisdictional claims in published maps and institutional affiliations Cover illustration: © Melisa Hasan This Palgrave Pivot imprint is published by the registered company Springer Nature Switzerland AG The registered company address is: Gewerbestrasse 11, 6330 Cham, Switzerland Per la Maria Acknowledgements This book represents a short summary of my research on housing bubbles My interest in asset price bubbles started in a classroom of Universitat Pompeu Fabra during a lecture taught by Jaume Ventura This initial interest gained momentum during my graduate studies in the Economics Department of the Massachusetts Institute of Technology I am very grateful to my Ph.D advisors Daron Acemoglu, Pol Antràs and Ricardo Caballero for their guidance and help during my graduate studies and beyond Part of this book is based on my Ph.D dissertation During these years I have learnt and talked about bubbles in several international seminars and conferences In particular, I would like to mention Óscar Arce, Oriol Aspachs, Klaus Desmet, Juanjo Dolado, Jordi Domènech, Jordi Galí, Ịscar Jordà, Pablo Kurlat, Jennifer La’o, Guido Lorenzoni, Martí Mestieri, Jair Ojeda, Alp Simsek, John Tang, Jean Tirole, Ernesto Villanueva, Joachim Voth and Iván Werning for their comments and discussions I also want to mention David López, with whom I started to work and learn about the Spanish bubble two years ago I am also grateful to the Bank of Spain for their financial support and opportunity to use its impressive database On a personal note, this book would not have been possible, as many other things, without the encouragement and love of my wife, Maria vii Contents 1 Introduction Reference A Brief History of Bubbles 2.1 Definition of Bubbles 2.2 A Review of Famous Bubbles 2.3 Housing Bubble Indicator 11 References 14 Origin of Asset Price Bubbles 17 3.1 Behavioral Explanation 18 3.2 Theory of Rational Bubbles 23 3.3 A Model of Rational Housing Bubbles 29 References 34 Globalization and Housing Bubbles 37 4.1 A First Look at the Data 38 4.2 Model of Globalization and Housing Bubbles 47 4.3 An Application: The US Housing Bubble 57 References 63 ix x    Contents Consequences of Housing Bubbles 65 5.1 Oops…the Housing Bubble Burst 73 5.2 An Application: Misallocation in Spain 76 References 82 Regulating Housing Bubbles 85 6.1 What Have We Learnt from Past Episodes? 85 6.2 Macroprudential Regulation 90 References 95 Index 97 List of Figures Fig. 2.1 Fig. 2.2 Fig. 3.1 Fig. 3.2 Fig. 4.1 Fig. 4.2 Fig. 4.3 Fig. 5.1 Fig. 6.1 Fig. 6.2 Fig. 6.3 The housing bubble in the United States 12 World housing bubble indicator 13 A model of rational bubbles 27 Capital market—equilibrium 32 Housing price and current account 41 Globalization and housing bubbles 51 House prices and housing supply elasticity 62 Misallocation and Housing Bubble: Spain 80 Credit bubble—average mortgage in Spain 88 Residential mortgage credit in Spain-high and low education towns 89 Macroprudential regulation 91 xi CHAPTER 1 Introduction Abstract   Booms and busts of asset price bubbles are recurrent throughout history In this book, we focus on one specific type of bubble: housing As the recent financial crisis illustrated, it is important to better understand the origin and consequences of housing bubbles This is the purpose of the book First, we define and briefly summarize three famous bubbles Then, we provide different explanations on the origin of asset price bubbles Next, we describe the economic consequences of housing bubbles Finally, we conclude with some lessons on how to minimize the emergence of new bubbles Keywords  Asset price bubble Housing bubble · Origins · Economic consequences Sharp increases in asset prices are frequently followed by large and sudden collapses of its price Some of these boom-bust episodes are so huge, quick and unexpected that they are popularly known as asset price bubbles Bubble episodes are frequent throughout history, from the Dutch Tulipmania in 1636 (the earliest documented asset price bubble) to the housing bubble episodes in the mid-2000s in several developed economies The bust of asset price bubbles tends to mark the end of economic expansions and it is not unusual that it coincides with the onset of a financial crisis It is thus important to have a good understanding of the origin and economic consequences of asset price bubbles © The Author(s) 2018 S Basco, Housing Bubbles, https://doi.org/10.1007/978-3-030-00587-0_1 5  CONSEQUENCES OF HOUSING BUBBLES  81 of the housing bubble, the difference between both lines shrinks and the variance of the two sets of municipalities starts to converge Therefore, by comparing the evolution of the variance of capital-labor ratio between these two groups of municipalities, we may conclude that the housing bubble distorted the Spanish economy and reduced TFP Basco et al (2018) directly estimate the effect of house prices on the investment of Spanish firms They find that the house price elasticity is − 7 + 2.7*(share real estate) This house price elasticity implies industry misallocation For a given municipality, the effect of house prices on investment is increasing with the share of real estate assets That is, imagine that house prices increase by 10% in a given municipality If the firm has no real estate assets, the price elasticity implies that investment fall by 7% In contrast, if the firm only has real estate assets, the investment will increase by 20% Similarly, the house price elasticity implies geographical misallocation According to Basco et al (2018), the average manufacturing firm has a share of real estate assets equal to 30%, which implies that the average house price elasticity is 11% This elasticity implies that there was overinvestment in Spain Note that this elasticity is higher than the one documented by Chaney et al (2012) for the United States (6%) This is not surprising given that Spanish firms rely more on banks to fund their investment and banks require collateral (mainly, real estate assets) In addition, the Spanish sample is more representative and includes a larger share of small firms, which are, arguably, more dependent on banks and more likely to be borrowing constrained Basco et al (2018) also document the channel through which the housing bubble distorted the allocation of capital In the model described above, firms with a larger share of real estate assets could invest more during the housing bubble because they were able to borrow more Basco et al test this channel by analyzing the effect of house prices on the amount of credit received by Spanish firms They find the same type of misallocation on credit They document that the borrowing of a firm without real estate assets falls when house prices increase However, if the firm had only real estate assets, it would borrow more This industry misallocation has also the geographical counterpart If this firm were located in a bubbly municipality, it would borrow more than if it were located in a non-bubbly municipality Basco et al have also information on the outcome of loan applications They document that loan applications of firms with real estate assets were 82  S BASCO more likely to be accepted Moreover, this effect was exacerbated in bubbly municipalities (low housing supply elasticity) The summary of this evidence for Spain is that a housing bubble may distort the optimal allocation of the economy and reduce aggregate productivity The mechanism through which the housing bubble generates misallocation is the collateral channel The housing bubble induces an increase in the price of houses above their fundamental value This increase in house prices translates into an increase in the value of the collateral of firms with a large share of real estate assets Since the borrowing capacity depends on the value of the collateral, firms with a larger share of real estate assets can borrow relatively more This additional borrowing generates overinvestment At this point, it is important to emphasize that this distortion is specific to the housing bubble If the bubble were attached to other assets, we would not observe that firms with a larger share of real estate assets borrowed and invested more For example, if the bubble were attached to asset A, we would expect that firms with a larger share of asset A were able to borrow and invest more References Basco, S., & Lopez-Rodriguez, D (2018) Credit Supply, Education and Mortgage Debt: The BNP Securitization Shock in Spain Madrid: Mimeo, Banco de España Basco, S., Lopez-Rodriguez, D., & Moral-Benito, E (2018) Housing Bubbles and Misallocation: Evidence from Spain Madrid: Mimeo, Banco de España Case, K., Quigley John, J., & Shiller Robert, R (2005) Comparing Wealth Effects: The Stock Market Versus the Housing Market The B.E Journal of Macroeconomics, 5(1), 1–34 De Gruyter Chaney, T., Sraer, D., & Thesmar, D (2012) The Collateral Channel: How Real Estate Shocks Affect Corporate Investment American Economic Review, 102(6), 2381–2409 Gopinath, G., Kalemli-Ozcan, S., Karabarbounis, L., & Villegas-Sanchez, C (2017) Capital Allocation and Productivity in South Europe Quarterly Journal of Economics, 132(4), 1915–1967 Jones, C I (2016) The Facts of Economic Growth (Working Paper) Stanford: Stanford GSB Jordà, Ò., Schularick, M., & Taylor, A M (2013) When Credit Bites Back Journal of Money, Credit and Banking, 45, 3–28 Jordà, Ò., Schularick, M., & Taylor, A M (2015) Leveraged Bubbles Journal of Monetary Economics, 76, S1–S20 5  CONSEQUENCES OF HOUSING BUBBLES  83 Kiyotaki, N., & Moore, J (1997) Credit Cycles Journal of Political Economy, 105(211), 248 Mian, A., Rao, K., & Sufi, M (2013) Household Balance Sheets, Consumption, and the Economic Slump Quarterly Journal of Economics, 128(4), 1687–1726 Mian, A., & Sufi, A (2011) House Prices, Home Equity-Based Borrowing, and the US Household Leverage Crisis American Economic Review, 101(5), 2132–2156 Saiz, A (2010) The Geographic Determinants of Housing Supply The Quarterly Journal of Economics, 125(3), 1253–1296 CHAPTER 6 Regulating Housing Bubbles Abstract  In this final chapter, we explain how regulation could mitigate the emergence of credit and housing bubbles We start with a discussion on the origin of the recent credit boom Then, we use the Spanish mortgage bubble to describe how macroprudential tools would have affected the lending behavior of banks Finally, we discuss how the recent financial crisis has changed the consensus on the determinants of potential vulnerabilities of countries Keywords  Housing bubble · Financial crisis Macroprudential tools · LTV · Mortgage · 6.1  What Have We Learnt from Past Episodes? We have arrived at the end of our journey through the origins and economic consequences of housing bubbles As we have seen in the last chapter, the crash of housing bubbles has large economic costs Thus, it seems reasonable to ask what could be done to mitigate the emergence of bubbles There exists a large academic literature that relates the probability of having financial crises to excessive credit (see, for example, Jordà et al 2013) Moreover, we have discussed that asset price bubbles are funded by credit Therefore, it is important to understand the causes of credit growth © The Author(s) 2018 S Basco, Housing Bubbles, https://doi.org/10.1007/978-3-030-00587-0_6 85 86  S BASCO As in all markets, the equilibrium amount of credit is determined by the supply and demand of credit For example, credit could increase because households feel that they will become richer in the future and, thus, they want to borrow today to smooth consumption This is the standard argument for why young agents should be borrowing and middle-aged agents should be saving for retirement Another demand-driven explanation is expected house price appreciation That is, households may want to participate in the housing bubble and apply for a mortgage We already saw in Chapter that this effect was empirically relevant Finally, it could also be the case that households are borrowing because the banks are expanding their supply That is, imagine that banks are obtaining money at a lower interest rate than before and, thus, they are happy to fund more investment projects than before Which of the three explanations was driving the recent mortgage credit boom in the developed economies? This question has sparked a huge academic debate in the United States Mian and Sufi (2009) represents the first attempt to answer this question They proceed as follows First, they rank municipalities according to the share of subprime borrowers.1 Then, they compare the increase in credit growth with this ranking of municipalities If credit growth was higher in municipalities with more subprime borrowers, this is a signal that supply was driving the credit boom The reason is that credit would be growing more in riskier municipalities They find that this is indeed the case They also provide evidence against the demand hypothesis They document that in these municipalities income was falling Moreover, they show that this supply effect also applies in housing supply elastic municipalities (where there was no housing bubble) Thus, Mian and Sufi (2009) conclude that the credit boom was supply driven because credit increased more in riskier municipalities, which also experienced lower income growth This established narrative has been recently challenged Adelino et al (2016) argue that the credit boom in the United States was driven by demand Their argument follows from directly comparing the evolution of mortgage debt of households with different income levels That is, whereas Mian and Sufi (2009) focus 1 Mian and Sufi (2009) define a borrower as subprime if the credit score is below 660 Credit scores are used in the United States to determine the probability of default of the borrower Borrowers with a score above 660 were considered (in 1996, which is the year used in their work) lower-risk borrowers 6  REGULATING HOUSING BUBBLES  87 on comparing municipalities, Adelino et al (2016) compare households The main finding of Adelino et al (2016) is that borrowing was not concentrated among poor and subprime borrowers, but mortgage debt increased in all income levels Their findings are consistent with the view that the increase in mortgage demand, through rising house prices, explains the credit boom in the United States To summarize, from this debate, there is a general consensus that increasing house prices explains the mortgage debt boom However, it is not clear if beyond the increase in house prices, an increase in supply affected debt growth From a policy perspective, it is important to know whether credit supply had a significant effect on mortgage debt growth That is, if we conclude that all the increase in debt is driven by house price expectations, the policy response should be focused on managing the expectations of households In contrast, if it was the supply, policymakers should better control how financial institutions provide this credit Basco and Lopez-Rodriguez (2018) contribute to this debate by analyzing the boom-bust of mortgage debt in Spain The Spanish credit bubble in the mid-2000s was very large by international standards For example, credit growth reached annual rates of 25% In comparison, the peak in the United States was 10% The increase in mortgage debt (mostly) explains the credit growth Mortgage debt even surpassed the 100% of GDP in 2008 Thus, along the same lines as in the studies on the US debt, they focus on the evolution of mortgage credit The empirical strategy in Basco and Lopez-Rodriguez (2018) to identify the driver of debt growth is based on an exogenous supply shock to the Spanish banking system The supply shock was the statement of BNP Paribas the 9th of August of 2007.2 In this statement, BNP Paribas announced that, due to problems in US subprime mortgage market, it was freezing two funds Why was it a supply shock for Spanish banks? It is well established that Spanish banks participated in international securitization markets to obtain funds (see discussion on Basco and LopezRodriguez 2018) After the statement of BNP Paribas, the securitization markets froze and this represented a liquidity shock for Spanish banks It is also important to emphasize that the Spanish economy was still growing at the time of the announcement Indeed, real GDP growth at the 2 The statement of BNP Paribas can be found in the next link https://group.bnpparibas/en/press-release/bnp-paribas-investment-partners-temporaly-suspends-calculation-net-asset-funds-parvest-dynamic-abs-bnp-paribas-abs-euribor-bnp-paribas-abs-eonia 88  S BASCO Fig. 6.1  Credit bubble—average mortgage in Spain Notes Each point represents the monthly average mortgage in Spain The vertical line is August 2007 (the month of the BNP shock) The interest reader is refereed to Basco and Lopez-Rodriguez (2018) end of 2007 was 3% Thus, we can argue that the announcement of BNP Paribas represented a supply but not a demand shock Figure 6.1 reports suggestive evidence on the aggregate effect of this shock It represents the monthly evolution of the average mortgage in Spain between 2004 and 2010 Note that it has an inverse-U shape with the peak in August 2007 (the BNP Paribas announcement) During the boom, it increased from around 120 to 170 thousand euros The rising trend suddenly stopped in August 2007 and the average mortgage declined up to 140 during 2008 The idea in Basco and Lopez-Rodriguez (2018) is the following: If the mortgage credit boom was driven by supply, aggregate credit growth should fall after the BNP shock In addition, the change in credit growth should be higher in municipalities with higher credit risk Note that this exercise is similar, in spirit, to Mian and Sufi (2009) However, by using the BNP shock, they can directly compare the relative fall in mortgage 6  REGULATING HOUSING BUBBLES  89 Fig. 6.2  Residential mortgage credit in Spain-high and low education towns Notes The blue (red) line represents the cumulative growth rate of residential mortgage credit in high (low) education municipalities Low (high) education municipalities are municipalities in the top (bottom) quartile of the of share of population with basic education distribution The vertical line is 2007 (the year of the BNP shock) See Basco and Lopez-Rodriguez (2018) for further information debt due to the supply shock As a measure of credit risk, Basco and Lopez-Rodriguez use the share of the population with (at most) basic education One advantage of this measure with respect to using credit scores is that it does not depend on previous credit activity Moreover, it seems a more fundamental predictor of credit and income risk For example, the data show that people with basic education are more likely to become unemployed in recessions Figure 6.2 reports the evolution of residential mortgage credit between low-educated (red line) and high-educated (blue line) municipalities First, note that during the boom period, the increase in credit growth was much larger in low education municipalities This is consistent with the finding of Mian and 90  S BASCO Sufi (2009) The largest increase in mortgage debt was in credit riskier municipalities Second, if we compare the change in the trend post-BNP, we can see that it was also larger in municipalities with low education That is, during the period in which Spanish banks had plenty of funds to grant mortgages, municipalities with higher credit risk experienced a larger increase in mortgage debt However, after the liquidity shock, it was precisely these same municipalities the ones who suffered the largest decline It is worth mentioning again that the real economy was still growing in Spain after the BNP shock Similar to Mian and Sufi (2009), they also obtain the same result when considering municipalities with high housing supply elasticity (no housing bubble) Therefore, given this evidence, we can conclude that the credit boom in Spain was driven by supply Once the supply froze, the credit boom collapsed Basco and Lopez-Rodriguez (2018) also provide evidence consistent with the view that the increase in mortgage debt was also larger among households with lower assets To summarize, even though there still exists a debate on the origin of the mortgage debt bubble, it seems clear that credit supply played an important role in the buildup and crash of the housing bubble 6.2  Macroprudential Regulation After the burst of the recent housing bubble, a consensus emerged on the need to have macroprudential regulation The formal definition of macroprudential regulation (according to the IMF) is the use of prudential tools to limit systemic risk In a nutshell, it means that it may be a good idea to control the financial sector before its vulnerabilities extend to the whole economy (systemic risk) Since it became clear that there was a feedback between house prices and debt, some of these macroprudential tools are focused on the mortgage market One of the preferred macroprudential tools is to put a limit on the loan to value ratio (henceforth, LTV) of mortgage contracts The idea is straightforward If borrowers need to make a larger down payment to purchase a house, the size of the loan will be lower, which will limit housing demand and the overall debt of the economy This policy has been encouraged by the IMF and several countries have introduced it (see, e.g., IMF 2013) Basco and Lopez-Rodriguez (2018) investigate the effect of ­ loanto-value on the Spanish mortgage debt growth As we discussed above, mortgage debt increased more in municipalities with low education 6  REGULATING HOUSING BUBBLES  Loan-to-Value 91 Value-to-Price Fig. 6.3  Macroprudential regulation Notes The left-hand side panel represents the evolution of the monthly mean of the loan-to-value ratios in high (blue dots) and low education (red dots) municipalities The right-hand side panel represents the evolution of the monthly mean of value-to-price ratios for the same municipalities Low (high) education municipalities are municipalities in the top (bottom) quartile of the of share of population with basic education distribution The vertical line is August 2007 (the month of the BNP shock) See Basco and Lopez-Rodriguez (2018) for further information Therefore, one possibility is that banks in these municipalities were offering softer terms to their borrowers That is, given the same value of the house, the loan that these borrowers obtained were larger If this were the case, we should observe that LTV was significantly larger in municipalities with lower education Moreover, if LTV were driving the increase in debt, we should also observe that the ratio was increasing during the boom The left-hand side of Fig 6.3 reports the monthly evolution of the LTV ratio in low education (red) and high education (blue) municipalities Note that none of the two predictions is borne out by the data On the one hand, the LTV ratios are almost identical in the two groups of municipalities That is, we cannot explain the difference in mortgage debt growth based on the differences in the LTV ratios On the other hand, note that during the boom, the LTV ratio was stable around 73 Therefore, the (aggregate) boom in mortgage debt was not driven by a relaxation of the LTV ratio Finally, note also that these average LTV ratios are way below the 80 threshold, which marks credit 92  S BASCO risk.3 It seems therefore that this macroprudential tool would not have helped to mitigate the housing and mortgage debt bubble The value of a house does not need to coincide with its price The price of the house is the transaction price However, the value of the house is certified by an independent appraisal company before the mortgage is signed The bank chooses this private company An important difference between the two is that the value of the house is the expected market price in the near future In general, the two variables should be very similar However, notice that the value of the house can depend on future price expectations Basco and Lopez-Rodriguez (2018) analyze the behavior of the value-to-price ratio during the boom-bust of house prices The right-hand side of Fig 6.3 reports the monthly evolution of the value-to-price ratio in municipalities with low (red) and high (blue) education We want to remark three facts from this figure First, the ­value-to-price ratio was above one during the whole boom-bust period Second, the value-to-price ratio was higher in low educated municipalities Third, the difference in the ratio between low and high-educated municipalities was exacerbated during the boom and it declined after the collapse of the housing bubble The three facts are consistent with the view that in low-educated municipalities there were high house price expectations, which enabled banks to grant larger loans to borrowers and contributed to inflate the housing bubble Once the housing bubble burst, this difference in future house price expectations disappeared and the value-to-price ratio converged Banking regulation seemed to favor this feedback between overvaluation, loans and the housing bubble With an overvalued house, the bank could lend a higher loan without being penalized for having an LTV too large (.80) Moreover, by having a mortgage with an LTV below 80, the bank could securitize the loan By securitizing these mortgages, the banks obtained liquidity to fund additional mortgages These additional mortgages were used to inflate house prices and house price expectations were fulfilled Note that this feedback loop breaks when the international securitization market freezes According to this view, a better macroprudential tool would be 3 Basco and Lopez-Rodriguez (2018) also discuss the distribution of these LTV ratios That is, one could be concerned for the average if the distribution were skewed to the left, so, that the average underrepresents the median value of LTV They document that this is not the case Indeed, only around 20% of new mortgage loans exceeded the 80 threshold 6  REGULATING HOUSING BUBBLES  93 to reduce this feedback mechanism between future house price expectations and loans Finally, in the model discussed in Chapter 3, we emphasized the role of financial globalization In the model, a financially developed country in autarky would be immune to rational bubbles In contrast, if the country opens up to trade with a financially underdeveloped economy, rational bubbles can emerge Therefore, financial globalization is conducive to rational bubbles Note that this does not imply that a financially developed economy cannot have behavioral bubbles in autarky Behavioral bubbles can always arise because they are driven by differences in beliefs However, it could be argued that behavioral bubbles are also more likely to emerge with globalization One reason is that financial globalization implies that more investors can participate in asset markets The dispersion of beliefs is likely to rise with the number of participants Therefore, it can be that financial globalization, by increasing the number of market participants, is conducive to behavioral bubbles too Another reason why financial globalization may be related to behavioral bubbles is through its effect on the interest rate As we saw in the model discussed in Chapter 3, financial globalization puts downward pressure on the interest rate It may be argued that, faced with a low safe return, some investors may enter into riskier asset markets Again, as more investors participate in an asset market, the more likely it is that disagreement on the price of the asset occurs and, thus, an asset price bubble arises Finally, financial globalization affects also the size of the bubble This applies to all types of asset price bubbles The larger the amount of money that investors are willing to invest, the larger will be its effect on the price of the asset This channel is intuitive For example, we documented that the current account deficit (over GDP) increased 5% in Spain between 2000 and 2007 This extra money helped to fund the housing bubble If the increase in deficit would have been just 5%, the rise on house prices would have been much lower Thus, we can conclude that keeping financial globalization in check may be a good macroprudential tool From this argument, we not mean to imply that financial globalization is bad by definition On the contrary, the fact that individual investors may invest in different countries reduces the risk faced by these individuals (i.e., they not need to put all eggs in the same basket) Similarly, firms not depend on the liquidity of their country to fund their investment projects More generally, the free movement of capital should, theoretically, achieve the most efficient 94  S BASCO allocation of resources That is, capital would go where it is more needed Our argument is that financial globalization may have negative side effects To have a sense of how recent financial crises have changed the policy consensus on the role of international macroeconomics, it is illustrative to consider the steps taken in the European Union Before the financial crisis, the only variables taken into account for the macroeconomic stability of European countries (the Maastricht Criteria) were inflation and government finances (debt and deficit) This consensus changed after the financial crisis Indeed, the European Union introduced in 2011 a “Macroeconomic Imbalance Procedure” Under this procedure, 14 indicators are selected to capture the internal and external imbalances of the countries.4 It is remarkable that the first indicator is the current account (over GDP) It is considered an imbalance when the 3-year backward moving average is either too high (+6%) or too low (−4%) Other indicators include net international investment position, private debt, financial sector liabilities and house prices This new set of indicators is more comprehensive and provides a better picture of the vulnerabilities of the countries than the narrow set of variables considered in the Maastricht Criteria It is therefore a good starting point to detect the emergence of future problems However, it is not clear how, after the monitoring of these vulnerabilities, the European Commission will be able to force the countries to revert these bad indicators Formally, if a country is found to have an excessive imbalance, the Commission requires that the country proposes a plan to correct the balance or it will impose penalties to the country In the best-case scenario that the country proposes a reasonable plan, the plan may fail to address the source of the problem or even create new imbalances To conclude, we have seen that the recent financial crisis has spurred a debate on the origin of financial crisis and credit bubbles Even though the evidence in the United States is mixed, it seems clear that the credit supply played an important role in the buildup of the recent mortgage debt bubble In this sense, some macroprudential tools have been 4 The interest reader may find more exhaustive information on the Macroeconomic Imbalance Procedure in the following website and links within https://ec.europa.eu/ info/business-economy-euro/economic-and-fiscal-policy-coordination/eu-economic-governance-monitoring-prevention-correction/macroeconomic-imbalance-procedure/ scoreboard_en 6  REGULATING HOUSING BUBBLES  95 proposed to mitigate the emergence of housing bubbles (or mortgage debt bubble) like limits to LTV ratio This macroprudential tool may have a merit and work in some countries, but as the Spanish bubble illustrates, it is not a universal solution One lesson that policymakers have learnt from the crisis is the need to consider the external imbalances of the countries, because they are an important source of vulnerability On this front, it is still unclear how international organizations will be able to coordinate and correct these global imbalances Moreover, as we have seen, asset price bubbles are recurrent throughout history and it would be a big surprise if we had witnessed the last housing bubble In this sense, policymakers should have learnt from the past and be ready for when the next housing bubble emerges References Adelino, M., Schoar, A., & Severino, F (2016) Loan Originations and Defaults in the Mortgage Crisis: The Role of the Middle Class Review of Financial Studies, 29(7), 1635–1670 Basco, S., & Lopez-Rodriguez, D (2018) Credit Supply, Education and Mortgage Debt: The BNP Securitization Shock in Spain Madrid: Mimeo, Banco de España IMF (2013) Key Aspects of Macroprudential Policy (IMF Working Paper) Jordà, Ò., Schularick, M., & Taylor, A M (2013) When Credit Bites Back Journal of Money, Credit and Banking, 45, 3–28 Mian, A., & Sufi, A (2009) The Consequences of Mortgage Credit Expansion: Evidence from the U.S Mortgage Default Crisis Quarterly Journal of Economics, 124(4), 1449–1496 Index A Aliber, R., 2, 7–10 asset price bubbles, 1–3, 6, 9, 17, 18, 23, 40, 65, 76, 85 B behavioral, 2, 17, 18, 20–23, 47, 57, 70, 93 BNP Paribas, 87 borrow/borrowing, 3, 9, 20, 29–31, 39, 40, 47–49, 56, 65–74, 81, 82, 86, 87 C Chaney, T., 61, 70, 81 collateral, 3, 67, 69–71, 73, 81, 82 consumption, 3, 23, 24, 28, 30, 38, 47–49, 65, 68, 74, 86 credit, 3, 24, 48, 68, 70, 71, 75, 81, 85–88 current account, 3, 37–40, 42–46, 52, 57–59, 61, 62 D distortion, 3, 66, 67, 70, 71, 77, 82 Dot-Com Bubble, 2, 5, 7, 9, 19, 57–59, 61, 74 E education, 89, 90, 92 expectations, 19–21, 87 F financial crisis, 1, 4, 59, 74–76, 85, 94 financial globalization, 3, 37, 48, 55, 58–61, 93 financially developed country, 3, 49, 50, 55, 57 financially underdeveloped, 3, 17, 31, 48–58 fundamental value, 2, 6, 7, 19, 20, 23, 25, 82 © The Editor(s) (if applicable) and The Author(s) 2018 S Basco, Housing Bubbles, https://doi.org/10.1007/978-3-030-00587-0 97 .. .Housing Bubbles Sergi? ?Basco Housing Bubbles Origins and Consequences Sergi Basco Universitat Autònoma Barcelona Barcelona, Spain ISBN... Canada, Denmark, Finland, France, Germany, Hong Kong, Ireland, Italy, Japan, Korea, Malaysia, Netherlands, New Zealand, Norway, South Africa, Spain, Sweden, Switzerland, Thailand, UK and the USA 2 ... The housing bubble in the United States 12 World housing bubble indicator 13 A model of rational bubbles 27 Capital market—equilibrium 32 Housing price and current account 41 Globalization and housing