More or less openness? The credit cycle, housing, and policy

Farias, Maria Elisa; Godoy, David R. Article More or less openness? The credit cycle, housing, and policy Economies Provided in Cooperation with: MDPI – Multidisciplinary Digital Publishing Institute, Basel Suggested Citation: Farias, Maria Elisa; Godoy, David R. (2025) : More or less openness? The credit cycle, housing, and policy, Economies, ISSN 2227-7099, MDPI, Basel, Vol. 13, Iss. 7, pp. 1-46, https://doi.org/10.3390/economies13070207 This Version is available at: https://hdl.handle.net/10419/329487 Standard-Nutzungsbedingungen: Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden. Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen. Sofern die Verfasser die Dokumente unter Open-Content-Lizenzen (insbesondere CC-Lizenzen) zur Verfügung gestellt haben sollten, gelten abweichend von diesen Nutzungsbedingungen die in der dort genannten Lizenz gewährten Nutzungsrechte. Terms of use: Documents in EconStor may be saved and copied for your personal and scholarly purposes. You are not to copy documents for public or commercial purposes, to exhibit the documents publicly, to make them publicly available on the internet, or to distribute or otherwise use the documents in public. If the documents have been made available under an Open Content Licence (especially Creative Commons Licences), you may exercise further usage rights as specified in the indicated licence. https://creativecommons.org/licenses/by/4.0/ Academic Editor: Robert Czudaj Received: 13 May 2025 Revised: 13 June 2025 Accepted: 15 June 2025 Published: 18 July 2025 Citation: Farias, M. E., & Godoy, D. R. (2025). More or Less Openness? The Credit Cycle, Housing, and Policy. Economies,13(7), 207. https://doi.org/ 10.3390/economies13070207 Copyright: © 2025 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/ licenses/by/4.0/). Article More or Less Openness? The Credit Cycle, Housing, and Policy Maria Elisa Farias * and David R. Godoy Department of Industrial Engineering, Universidad Técnica Federico Santa María, Avenida Santa María 6400, Santiago 7630000, Chile; [email protected] *Correspondence: [email protected] Abstract Housing prices have recently risen sharply in many countries, primarily linked to the global credit cycle. Although various factors play a role, the ability of developing countries to navigate this cycle and maintain autonomous monetary policies is crucial. This paper introduces a dynamic macroeconomic model featuring a housing production sector within an imperfect banking framework. It captures key housing and economic dynamics in advanced and emerging economies. The analysis shows domestic liquidity policies, such as bank capital requirements, reserve ratios, and currency devaluation, can stabilize investment and production. However, their effectiveness depends on foreign interest rates and liquidity. Stabilizing housing prices and risk-free bonds is more effective in high-interest environments, while foreign liquidity shocks have asymmetric impacts. They can boost or lower the effectiveness of domestic policy, depending on the country’s level of financial development. These findings have several policy implications. For example, foreign capital controls would be adequate in the short term but not in the long term. Instead, governments would try to promote the development of local financial markets. Controlling debt should be a target for macroprudential policy as well as promoting saving instruments other than real estate, especially during low interest rates. Keywords: credit cycle; housing shocks; small open economy; investment decisions; monetary policy JEL Classification: E1; E4; E5; E6 1. Introduction This research addresses three major concerns that have affected global economies in recent years: housing prices, financial and macroeconomic stability, and policy responses. As shown in Figures 1and 2, representing developed and developing countries, respectively, housing prices have generally increased since 2014, with few exceptions. This trend aligns with periods of low global interest rates (federal fund rates). After sharp declines during the 2009 global financial crisis, housing prices 1 increased by up to 14 percent in developed countries between 2015 and 2022, and by approximately 32 percent on average in developing countries. With ongoing urbanization in developing countries and waves of immigration, excess demand has led to housing shortages in many areas, 2 sometimes accompanied by land speculation. What mechanisms drive these dynamics? Are there notable differences between developed and developing countries? Based on evidence from developed countries, authors such as Iacoviello (2005), Iacoviello and Neri (2010), and Quadrini (2020) link housing price dynamics to the credit cycle, where housing prices tend to increase as borrowing costs Economies 2025,13, 207 https://doi.org/10.3390/economies13070207 Economies 2025,13, 207 2 of 46 decrease. Although higher prices increase debt servicing costs, they also enhance debtors’ borrowing capacity, especially when debt is collateralized in nominal terms. Figure 1. Housing costs and interest rates in developed countries. Figure 2. Housing costs and interest rates in developing countries. Several periods of housing price booms can be explained by similar mechanisms, where shifts in agents’ expectations, collateral constraints, and uncertainty exacerbate market distress (Gete,2020;Eichenbaum et al.,2022). The Asian financial crisis of 1998–1999 and the 2009 global financial crisis exemplify the strong correlation between credit availabil- Economies 2025,13, 207 3 of 46 ity and housing market collapses, with varying degrees of spillover across global economies (see Cox & Ludvigson,2019). A second perspective, highly relevant for developing countries, attributes the volatility of housing prices to debt and financial integration (Mian et al.,2017). Table A1 shows that the credit financing ratios for housing investment are similar between developed and developing countries. However, in open economies, financial markets are more susceptible to distress when global credit conditions tighten—particularly when debt is denominated in foreign currency. 3 As emerging markets become more financially integrated with global markets—especially with major economies—these vulnerabilities are intensified. Data from 45 countries (both developed and developing) 4 for the main statistics between 2001 and 2022 reveal a strong correlation between changes in credit-to-GDP ratios and corresponding changes in the U.S. ratio. When comparing the periods before the crisis (2001–2014) and after the crisis (2015–2022), this correlation increased for both groups (see Table A3). On the other hand, Table A4 shows a strong correlation between household debt and housing prices, especially for developing countries after 2015. A third explanation considers the dual nature of housing: as a fundamental human need for shelter and as a source of wealth accumulation (including precautionary savings). Shocks can spread between the financial and housing markets, influencing individual decisions and broader economic outcomes (Ng & Feng,2016). Although this phenomenon exists in all economies, developing countries are often more vulnerable, especially in the face of increasing financial integration. Dong et al. (2021) provide evidence from China, where underdeveloped financial markets and a scarcity of safe assets have led households to view real estate as a primary savings vehicle, driving prices upward—particularly during periods of uncertainty.5 Other sources of distress include inflation pressures, as depicted in Figure 3a–c. Although a positive correlation between inflation and housing prices is evident between countries, the behavior differs between developed and developing economies. According to Table A5, in developing countries, the interest rate gap between the national and US economies is highly correlated with inflation. In developed countries, interest rate gaps would be more correlated with the devaluation of the currency. What role does policy play in this context? Financial integration can deepen credit markets and benefit developing countries by expanding investment and savings opportunities. However, it also introduces higher risks, especially for smaller countries (Rey,2016, 2018), which are more exposed to global credit cycles when monetary autonomy is limited 6 . In such cases, policy shocks, such as interest rate changes or liquidity adjustments, can be transmitted from large economies7to developing countries (Ilzetzki & Jin,2021). Governments have sought ways to mitigate financial and macroeconomic shocks, especially since the 2008 crisis revealed the limitations of traditional tools such as inflation targeting, fiscal discipline, and flexible exchange rates. Since 2008, macroprudential measures—such as capital controls, reserve accumulation, and nontraditional monetary tools— have gained prominence, as seen in countries like China, which has implemented restrictions on housing purchases to stabilize prices (Reinhart et al.,2015;Dong et al.,2021; Pateiro-Rodriguez et al.,2025). Despite these efforts, debates persist about the effectiveness of policies during financial distress. Low global interest rates, particularly in major economies, do not always benefit developing countries equally (Quadrini,2020). Evidence indicates that, with some exceptions (for example, India and Vietnam), their economic performance has been relatively weak recently. Between 2001 and 2022, developing countries increased on average by 3.6 percent, with lending rates around 13 percent. From 2015 to 2022, growth slowed to 2.6 percent, despite lower interest rates. Economies 2025,13, 207 4 of 46 (a) (b) (c) Figure 3. Inflation and housing prices: (a) all countries; (b) developed countries; (c) developing countries. Economies 2025,13, 207 5 of 46 The question remains: What policies are most effective for developing countries with flexible exchange rates and which strategies best ensure financial stability while achieving national objectives? To address these questions, we develop a general equilibrium model representing an open economy exposed to the credit cycle, with heterogeneous agents, market frictions, and perfect and imperfect competition in the banking sector. We examine policy implications when the central bank’s autonomy is constrained by analyzing outcomes under lowand high-interest rates. Recent theoretical models have advanced by including different dissemination mechanisms in developed and developing countries. However, they fail to consider more realistic environments, such as market imperfections in the banking sector and the role of national liquidity in open economies. Our model fills these gaps. The main contributions of the present work are summarized as follows: (1) the paper develops a dynamic macroeconomic model with an embedded housing production sector, in which investment returns are subject to uncertainty and demand is endogenously determined by households’ preferences, relative prices, interest rates, and income. This structure allows the model to account for stylized housing and aggregate dynamics observed in both advanced and emerging economies. (2) The analysis identifies the conditions under which domestic liquidity enhancing policies—such as banks’ capitalization, reserve ratios, and currency devaluation—succeed or fail in stabilizing investment and output. In particular, the effectiveness of these tools depends crucially on the level of foreign interest rates, where alternative measures such as housing prices stabilization and risk-free bonds are preferable in high-interest rates scenarios. (3) The results demonstrate strong robustness across model calibrations and highlight the asymmetric influence of foreign liquidity shocks. Depending on financial development, foreign liquidity can reinforce or override domestic instruments. These findings imply that capital control measures, especially those that target inflow restrictions, should be designed with awareness of the external rate environment and the depth of the financial market. The rest of the paper proceeds as follows: Section 2discusses the theoretical framework, and Section 3develops a model to examine the transmission mechanisms of the global credit cycle in an open economy. Section 4analyzes monetary policy, Section 5presents a numerical exercise, and Section 6concludes. 2. Related Literature The theoretical framework integrates the approach of Bernanke et al. (1999) with more recent literature that explains asset market behavior (see for example, Iacoviello & Neri,2010;Gete,2020;Quadrini,2020;Geanakoplos & Wang,2020) such as the housing market, within the context of the global credit cycle. Although some of these models are primarily based on the experiences of developed countries, they are also applicable for analyzing developing countries. Beyond classifying countries according to a per capita GDP frontier, developing nations today face challenges common to developed and less developed economies. Economic stability remains a significant concern for these groups. The primary differences lie in the degree of global market power, the magnitude of economic problems, and the capacity of these economies to address them. According to Rey (2016,2018), the global credit cycle can be understood through comovements in risky asset prices, credit growth, leverage, and financial aggregates across countries around the world. Building on the early ideas of Minsky (1986), Kiyotaki and Moore (1997) argue that credit cycles emerge in an economy due to two main factors: (i) credit constraints affecting firms, and (ii) the use of productive assets, such as collateral. The interaction of these factors transmits fluctuations in asset prices to the credit market and subsequently to the real economy. From a national economic perspective, Geanakoplos Economies 2025,13, 207 6 of 46 (2016) and Bora and Zhong (2023) suggest that the credit cycle is related to the business cycle of market economies, which market frictions and imperfections can amplify. Bruno and Shin (2015) highlight the role of cross-border monetary policy spillovers channeled through global banks in open economies. In this context, Rey (2018) links credit cycles with monetary policies in the central country and changes in risk aversion and uncertainty. Concerning the transmission mechanisms of shocks, such as monetary policy shocks, theories identify interest rates and credit channels, uncertainty and expectations, the collateral channel, the supply side and global spillover, among others. Regarding interest rates and credit, the literature emphasizes the role of banks and financial intermediaries that can increase financial distress when financial market frictions occur (Ramey,2016; Gertler et al.,2016). Failures in common knowledge and imperfect information have been included in the literature to explain phenomena of asset bubbles and adverse shock dissemination (Caballero & Krishnamurthy,2008;Kamara & Koraila,2023;Caballero & Simsek,2022). In the case of collateral, since Kiyotaki and Moore (1997), the theories have evolved by introducing asset price dynamics in response to monetary policy, such as quantitative easing (Geanakoplos & Wang,2020). Adopting a different perspective, Baqaee et al. (2024) propose a supply-side channel in which expansionary monetary policy can stimulate productivity at the aggregate level. In terms of global spillover, extensive research links policy decisions in central countries with capital flows, current accounts, and exchange rates in open economies (Calvo et al.,2006). However, scholars differ about the more appropriate instruments of monetary policy. During financial distress, for example, the traditional perspective primarily focused on addressing inflation and reducing output gaps would be challenged by the facts (Walsh, 2010;Christiano et al.,2010). Thus, interest rate cuts, such as lowering rates during economic booms driven by optimistic expectations, may contribute to asset market booms (e.g., the Japanese stock market boom of the 1980s). Instead, these authors propose that credit growth could be a proxy for the natural interest rate within the interest rate rule, thereby improving the economy’s response to monetary policy. Similarly, Geanakoplos (2016) and Clarida (2019) find evidence that the effectiveness of monetary policy diminishes when financial markets experience turbulence. Research on open economies emphasizes the choice of exchange rate regimes for stability and competitiveness (Gali & Monacelli,2005). After the global financial crisis, the focus shifted to financial stability and new intervention tools, including financial regulation (Rey,2018;Carillo et al.,2021). Market imperfections, such as rigid prices and informational problems, limit traditional monetary policy, especially in open and developing countries with less policy space. Consequently, Rey (2018) suggests replacing the “trilemma” with a “dilemma”. Independent monetary policy is only feasible if the capital account is managed, regardless of exchange rate regimes.8 Recent international shocks have challenged traditional theories, particularly in developing countries. An expansionary global credit cycle can boost liquidity and growth, but due to market imperfections, it often causes turbulence or limited benefits. The literature on the housing sector evolved after the last financial crisis, highlighting spillover mechanisms, such as interest rates and credits, and the collateral channel (Piazzesi & Schenider,2016). Thus, Iacoviello and Neri (2010) found that U.S. housing prices and investment were highly procyclical and sensitive to shocks. Ng and Feng (2016) emphasize the role of external shocks in small open economies, while Gete (2020) found a strong link between housing prices and the current account, where heterogeneous agents react differently to shocks. From a different perspective Beraja et al. (2019) and Eichenbaum et al. (2022) focused on the effectiveness of monetary policy and mortgage refinancing 9 , finding that such policies lost impact in the medium term. Duca et al. (2021) discuss the impact Economies 2025,13, 207 7 of 46 of housing price cycles in the economy and how macroprudential policy can affect this market and access to credit. Similarly, using the mechanisms of the financial accelerator in the housing sector, Muellbauer (2024) stresses the necessity of applying macroprudential policy to safeguard financial stability linked to housing markets. 3. The Model The model represents the case of an open economy with heterogeneous households, firms, and financial intermediaries (banks). Figure A2 in Appendix Aprovides a simplified definition of this environment. Households consume goods and housing services, 10 offer labor services to firms, save in the form of physical assets (housing shares) and bank deposits, and borrow from banks.Firms produce final goods and housing that are sold to households, and intermediate goods (reproducible capital) that are sold to firms. Banks collect deposits from households, borrow from foreign banks, and provide credit to households and firms. Households own firms and financial intermediaries (banks). To examine the economic fluctuations driven by different sources, we consider two types of shocks: domestic (national) and external (foreign). Among national shocks, we include a productivity shock, a shock to capital quality, and an idiosyncratic housing shock, which affects the value 11 of the housing investment. Foreign shocks arrive as part of the global credit cycle, affecting interest rates and domestic and foreign liquidity. 3.1. Households There is a large mass of households with infinite lives, differentiated by discount rates β∈( 0, 1 ) . Thus, among households, there are patient ones, with β close to 1, and impatient ones, with β close to 0. Households consume final goods and housing services ( m ), supply labor for final production (lf), and housing (lm). To save, households can acquire housing shares ( m ) and bank deposits ( d ). Thus, housing shares provide housing services and can be accumulated for saving. Bank deposits can only be accumulated for saving purposes (see Quadrini,2020). In addition, without the risk of default, bank deposits are considered risk-free investments. By contrast, the returns of household’ shares are exposed to shocks; thus, saving in physical assets becomes a risky investment.12 Households can finance housing shares with their own resources and bank credit bh . The latter includes any type of financial agreement with intermediaries. 13 By using their own resources, households become savers ( s ) and by using bank credit, households are borrowers ( b ). Although any type of household can become a net saver or a net borrower, evidence shows that impatient agents are more prone to be borrowers than the patient ones (Tobin,1980). Thus, we assume that patient agents are savers and impatient agents are borrowers, with βs>βb . Notice here that because housing shares can be considered precautionary saving, agents can accumulate shares either to prevent economic downturns or to compensate for the lack of other saving options in incomplete credit markets, which is the case for several developing countries’ economies.14 Households receive income from labor, rent, 15 and non-distributed profits from firms and intermediaries. Saver households receive rent from their housing shares and bank deposits. Borrower agents receive rent from housing shares and have to repay their debts at time t . Given the utility function ui=u(Ci,t , mit , lf i,t , lm i,t) , the optimization problem for a household of type i={s,b}follows max {Ci,t,lf i,t,lh i,t,di,t+1,mi,t+1} E0 ∞ ∑ t=0 βt iu(Ci,t,mit,lf i,t,lm i,t) Economies 2025,13, 207 8 of 46 subject to budget constraints: Ci,t+St+1mi,t+1+di,t+1=wf tlf i,t+wm tlm i,t+εi,tmi,t+Rd tdi,t+Πi,t(1) bit+1≤Φ(mit+1)(2) where Ci,t is the consumption of the household i at time t , St+1mi,t+1 is the expenditure on housing goods, where St+1 represents the prices of housing in t+ 1, and mi,t+1=viht+1 denotes the housing shares 16 . On the right-hand side of the equation, lf i is the supply of labor in the sector of final production, lm i is the supply of labor in the housing sector, wf tlf i,t+wm tlm i,t denotes labor income coming from these two sectors, and εi,tmi,t is the value of the housing share at time t , where εi,t> 0 is an exogenous shock that affects this value. 17 The expression Πi,t is the amount of profits coming from firms and intermediaries at this time. In the case of saver agents, di,t+1 are bank deposits at t+ 1, and Rd t= ( 1 +id t−1)/πt denotes the rate of return of these deposits over time t , with (1 +πt ) being the inflation rate in this economy. In the case of borrower households, bh i,t+1=−di,t+1 is the debt acquired for these agents at t+ 1, and Rl tbh i,t=−Rd tdi,t is debt repayment. The lending interest rate is denoted by Rl t= ( 1 +il t−1)/πt . Equation (2) denotes a limit constraint for debt bt+1=−dt+1 at time t+ 1, where Φ(mt+1) depends on the share of housing mt+1 at that time. By solving the optimization problem, the first-order conditions for a household of type iare uCi,tSt+1=Et{βi[uCi,t+1εi,t+1+umi,t+1] + λ2t+1Φmi,t+1}(3) uCi,t=Et[βiuCi,t+1Rbank t+1+λ2t+1](4) ulf i,t ulm i,t =wf t wm t (5) St+1=Et{βi[uCi,t+1εi,t+1+umi,t+1]/uCi,t+λ2tΦmi,t+1/uCi,t}(6) where Equation (4) is the Euler equation with a constraint parameter λ2t+1 . The interest rate, Rbank t+1 , becomes Rd t+1 for savers and Rl t+1 for borrowers. In Equation (5), ulf i,t /ulm i,t represents the marginal rate of substitution between labor in final production and housing, and wf t/wm t is the ratio of wages between these two sectors. Note that with equal preferences for labor, wages should be identical in the two types of production at equilibrium. Otherwise, workers would always be willing to leave by moving from one sector to another. Equation (6) shows that housing prices evolve with marginal rates of utility for consumption of goods, uCi,t+1 , and housing, umi,t+1 , and with the marginal value of collateral, Φmi,t+1 . Whereas the first term suggests that prices are driven by saving motivations, the second term ( umi,t+1 ) indicates that housing preferences drive these prices. For patient households, Equation (6) remains St+1=Et{[(umi,t+1/uCi,t+1+εi,t+1)( 1 /Rd t+1)]} , which shows a negative relationship between housing prices and interest rates. This result is in line with Woodford (2003) for asset prices and Quadrini (2020). It also coincides with the evidence observed in several developing countries in the last years, where low-interest-rate scenarios have accompanied increases in housing prices. 3.2. Firms There are a large number of firms that produce consumption goods, housing, and intermediary goods in the form of reproducible capital. Firms belong to households that receive any type of profit that firms can obtain. The distribution rule for the profits of Economies 2025,13, 207 15 of 46 duce liquidity and deposit rates. This effect is stronger under Cournot competition (see Equation (30)), where deposit rates move inversely of banks’ market power. Unlike what is discussed in the literature, results show that more capitalized banks can obtain higher rents but also be protected in cases of default. 3.4. Market-Clearing Conditions 3.4.1. Labor Market The labor market clears when at any time t , the demand for labor in the production of final goods lf tand housing lm tequals the supply lf t+lm t=ls,t+lb,t(32) where ls,t=lf s,t+lm s,t represents the labor supply of saver households and lb,t=lf b,t+lm b,t denotes the labor supply of borrowers. 3.4.2. Physical Capital Market The physical capital market clears when at any time t , the supply of capital kt equals the demand. kt=kf t+km t(33) where, by combining Equations (17) and (27), we obtain qtkt=nt+hk + (bk t+bm t) . The dynamics of the capital then remains kt+1= (1−δ)kt+e It where δcorresponds to the rate of depreciation of reproducible capital. 3.4.3. Housing Market The housing market clears when at any time t, demand equals supply with N ∑ j=1 vjHj,t=Ht(34) 3.4.4. Lending The lending market clears when the supply of lending is equal to the demand. bt=bb t+bm t+bm t(35) where supply satisfies bS t=nwt+ ( 1 −ζ)dt+ϑtetb∗ t , and demand is equal to bD t=bh t+ qtkt−(nt+hk). 3.4.5. Final Output The market of the final output clears when aggregate demand equals aggregate supply, with Ct+HIt+e It+k 2(πt−1)2+µG=Yt where Ct is aggregate consumption, with Ct=Cs,t+Cb,t , HIt=St+1Ht+1−( 1 +ε)StHt denotes housing investment, e It=Φ(It/Kt)Kt is investment in reproducible capital, k 2(πt−1)2represents adjustment costs, and µGis production lost due to firm default. Economies 2025,13, 207 16 of 46 3.5. The Credit Cycle According to the theoretical model, the global credit cycle can occur in the domestic economy through two mechanisms: the liquidity shock θ that determines the supply of foreign funds, b∗ , and changes in the foreign interest rate R∗ ; see Equations (26)–(29). In this context, we analyze two extreme cases, credit constraint and credit expansion, in which the effect on the local economy can be exacerbated or smoothened by the behavior of domestic shocks. 3.5.1. Credit Constraint A credit constraint can be caused by an adverse shock ( θ→ 0) that occurs in the economy at time t , reducing the supply of foreign liquidity ( de b∗< 0) and the supply of domestic credits, such that db <0 (see Equation (27)). In addition, it can be caused by a rise in the foreign interest rate ( dR∗> 0 ) , which leads to an increase in the cost of foreign debt, affecting prices and agent decisions in the domestic economy. The magnitude of the credit constraint depends in general on the severity of the liquidity shock θ and the capacity of the domestic economy to compensate for foreign liquidity, which depends on banking capital nw and net households’ saving on bank deposits (1−ς)d. According to Equations (1), (17), (20), and (24), a decrease in credit supply reduces the possibility of households and firms to finance investment projects, which has a direct impact on intermediate and final production. At the aggregate level, output can decrease. Regarding increases in foreign interest R∗ , Equations (28) and (29) show that the lending interest rate in the domestic economy fluctuates directly with this rate, at equilibrium, where the effect can be magnified by an adverse liquidity shock dθ< 0. Equilibrium conditions (3), (4), (21), and (25) show that a rise in interest rates affects housing prices and risk, modifying households’ choices for consumption and investment. However, these effects differ among households. For patient agents (savers), higher interest rates lead to higher bank returns. For impatient households, borrowing becomes more expensive. Consequently, although the substitution effect between current consumption and saving can be negative for both types of agents, the income effect would be positive for saver agents and negative for the borrower ones. Therefore, while saving and investing can increase for saver agents, investment in housing shares can decrease for borrower agents. In the housing market, because the amount of nonreproducible capital hk is constant, credit reduction leads to a fall in housing supply, increasing prices in the short term. However, Equation (6) suggests a reduction in housing prices following the current contraction of demand. The net effect on prices depends on which of these two effects is more significant. In terms of financial stability, the rise in interest rates leads to a higher marginal cost of lending for capital-renting firms, increasing the probability of firm default. Conversely, as external financing is more expensive, firms choose to finance investment with internal resources, decreasing leverage (see Equations (21) and (22)). Which of these two effects prevails depends on the firms’ capacity to finance investment with internal funds 22 and the magnitude of credit constraints. 3.5.2. Credit Expansion We assume here that the expansionary cycle can be driven by a positive shock dθ/dt > 0 (with θ→ 1) and/or by a fall in the foreign interest rate dR∗/dt < 0. Whereas a positive shock θ leads to an expansion in the domestic supply of credit, a fall in interest rate R∗ Economies 2025,13, 207 17 of 46 reduces the cost of foreign lending. 23 The more financially integrated an economy is with the rest of the world, the more it can benefit from both types of shocks. With greater liquidity, households and firms would have more opportunities to finance their consumption and investments. Consequently, the demand for housing shares can increase for borrower households. However, supply can be restricted by the limited capacity of firms to build new structures. 24 Accordingly, prices can increase following excess demand. For firms, cheaper capital increases their opportunities to finance investment projects. Thus, greater liquidity should be accompanied by an expansion of output at the aggregate level. However, the reduction in foreign interest rates has different effects among households. For saver households, it would be more attractive to save in housing shares than in bank deposits, leading then to a reduction in the supply of domestic liquidity. Although the substitution effect leads both saver and borrower households to increase their consumption and investment, the income effect differs between them. Whereas income can increase for borrower agents following cheaper debt, it would decrease or remain constant for saver agents because of the fall in the return on bank deposits. Therefore, the net effect on housing demand and income depends on how the investment and consumption decisions of agents respond to changes in interest rates and on the returns of housing investment. In the case of firms, a decrease in interest rates leads rental capital firms to finance projects with external funds instead of internal funds. Consequently, leverage can increase even though the probability of default decreases (see Equations (21) and (22)). 4. Monetary Policy and the Credit Cycle Using the framework of the theoretical model, we analyze here the tools of monetary policy for an economy exposed to the credit cycle and domestic shocks. We assume for this purpose that there is a government (a Central Bank) that can intervene by applying conventional and nonconventional tools, such as monetary easing, interest rates, exchange rates, and reserve ratios. To evaluate results, we consider two cases: perfect competition (as a benchmark) and Cournot competition. In each case, we examine the behavior of the economy under perfect foresight when parity purchasing power (PPP) is maintained and imperfect foresight when PPP is not (Obstfeld & Rogoff,1995). Thus, defining the real risk-free interest rate at time t25 such as Rt= ( 1 +it−1)/πt , we have 1+it=Rt+1πt+1(36) where it is the nominal interest rate in the small economy, and (1 +πt+1) is the inflation at time t+ 1 (see Section 3.2). Therefore, assuming that the lending interest rate Rl approximates the risk-free interest rate at time t , we have Rl t≃Rt= ( 1 +it−1)/πt , with il t≃it . Analogously, in the foreign economy, we have 1+i∗ t=R∗ t+1π∗ t+1(37) where i∗ t is the nominal foreign interest rate, and (1 +π∗ t+1) denotes foreign inflation. In perfect competition, the relationship between the two rates ( R and R∗ ) is given by Equation (28). When banks compete under Cournot competition, the relationship follows Equation (29). 4.1. Perfect Competition In a perfectly competitive market, the lending rate Rl is equal to the foreign interest rate R∗times 1/θ, at equilibrium. Therefore, from Equations (36) and (37), we have: Economies 2025,13, 207 18 of 46 1+it πt+1 =1+i∗ t π∗ t+1 1 θ Taking the logs of both sides of this equation and reorganizing, we obtain26 e it=e i∗ t+e πt+1−e π∗ t+1+Υ(38) where Equation (38) tells us that local and foreign interest rates are related to inflation rates and the degree of market openness, given by Υ=log(1 θ) , for any variable x , e x=log(x). To analyze policy in the presence of the credit cycle and shocks, we compare equilibrium when there is perfect foresight (and the law of one price holds) and when there is imperfect foresight. 4.1.1. Perfect Foresight When there is perfect foresight and the law of one price holds, the real exchange rate at time tremains the same as et=EtP∗ t/Pt=1. In this case, Equation (38) becomes e it=e i∗t+e γEt+1+Υt(39) where γEt=dlog Et/dt denotes the nominal depreciation at time t and e γEt=log[ 1 + γEt/π∗ t+1] . Thus, capital mobility between the economy and the rest of the world is related to nominal depreciation γEt , foreign inflation, and shock θ . As γEt+1 goes to zero and θ→ 1, e it approaches e i∗ t and the economy becomes perfectly integrated with the rest of the world (perfect capital mobility). In that case, the benefits of financial integration can be offset by a reduction in autonomy for the domestic central bank. When γEt+1> 0 and/or θ< 1, the degree of financial integration depends on the magnitude of currency depreciation and shock θ. 4.1.2. Imperfect Foresight When there is imperfect foresight and the law of one price does not hold, the relationship between interest rates under perfect competition becomes E0e it=E0{e i∗ t+ (e γE′ t+1) + Υ}(40) where E0 denotes expectations at time t= 0, and γE′ t denotes the gap between nominal and real expected devaluation, γE′ t=γEt−γet ; see Appendix A.3 for a proof. In this case, there is uncertainty about the behavior of foreign interest rates, devaluation, and liquidity shock θ. 4.2. Cournot Competition When banks compete under a Cournot model, the relationship between the domestic and the foreign interest rate is driven by Equation (30), where the lending rate follows Rl t=R∗ t(1/θt)Ξ, with Ξ= 1 /[ 1 −1 NηD l ] . Then, assuming that the lending rate approaches the risk-free interest rate, the relationship between the domestic and the foreign interest rate is given by 1+it πt+1 =1+i∗ t π∗ t+1 1 θΞ where the term Ξ varies with the degree of competition of banks in the market. As ηD l goes to infinity, the banking industry approaches perfect competition and the relationship between interest rates is given by Equations (36) and (37). Otherwise, for small values of Economies 2025,13, 207 19 of 46 ηD l , Ξ< 1, acting as a buffer on the interest rate gap. Thus, taking the logs and reorganizing, the relationship between the two rates iand i∗remains e it=e i∗ t+e πt+1−e π∗ t+1+Υt+e Ξ where e Ξ=log(Ξ) . Then, under Cournot competition, in addition to the inflation gaps and shock θ , the relationship between the two interest rates ( i and i∗ ) depends on the degree of market power of the banks, given by Ξ . As in Section 4.1, to analyze policy in the presence of the credit cycle and shocks, we compare the interest-rate equilibrium under perfect and imperfect foresight. 4.2.1. Perfect Foresight When there is perfect foresight and the law of one price holds, the relationship between the two interest rates under Cournot competition becomes e it=e i∗t+e γEt+1+Υt+e Ξ(41) Consequently, if e Ξ→ 0, either because there is a large number of banks in the markets or the demand elasticity goes to infinity, Equation (40) approaches the case of perfect competition. Otherwise, for e Ξ> 0, the risk-free interest rate in the domestic economy increases with the degree of market power in the banking industry. 4.2.2. Imperfect Foresight When there is imperfect foresight and the law of one price does not hold, Equation (40) becomes E0e it=E0{e i∗ t+e γE′ t+1+Υt+e Ξ}(42) Then, as we can see in Equations (41) and (42), in both cases (perfect and imperfect foresight), in addition to the expected devaluation and shock θ , under Cournot competition, domestic interest rates deviate from world rates in the magnitude of the banks’ market power of the small economy. Therefore, financial integration (measured by the interest rate gaps) between this economy and the rest of the world is affected by credit market imperfections, which have implications for policy. 4.3. Monetary Policy We analyze here conventional and nonconventional policy tools used by a central bank. For this purpose, we assume that conventional monetary policy in the domestic economy and the central country (rest of the world) follows simple Taylor rules (see Gertler et al.,2012), such as it=ilt +aπ(πt−πlt ) + ay(yt−ylt ) + ϵt i∗ t=i∗ lt +a∗ π(π∗ t−π∗ lt ) + a∗ y(y∗ t−y∗ lt ) + ϵ∗ t where ilt ( i∗ lt ) is the long-term domestic (foreign) interest rate, (1 +πt) ( ( 1 +π∗ t) ) denotes the current domestic (foreign) inflation, (1 +πlt ) ( ( 1 +π∗ lt ) ) represents long-term domestic (foreign) inflation, and (yt−ylt ) ( (y∗ t−y∗ lt ) ) is the output gap in the domestic (foreign) economy, with yt=log(Yt) . The terms ϵt and ϵ∗ t denote monetary shocks in the domestic and foreign economy, respectively, with E(ϵt) = 0, E(ϵ∗ t) = 0, and cov(ϵ∗ t,ϵt) = 0. Nonconventional monetary policy in the domestic economy would imply quantitative easing (or contractions), exchange rate interventions, and other tools meant to affect domestic credit markets, such as changes in reserve requirements ζ . In addition to directly influencing the amount of credits b , quantitative easing (or contraction) can modify interest Economies 2025,13, 207 20 of 46 rates and exchange rates. The shock θ would also react to these movements. In all of these cases, the effectiveness of monetary policy in the domestic economy would be limited by capital mobility. For example, if a positive monetary shock hit the domestic economy ( ∆ϵ> 0 ) , the capacity for central bank intervention would be limited by foreign conditions. When the central country (the foreign economy) does not receive shock ( ∆ϵ∗= 0 ) , from the Taylor rule we see that the domestic interest rate i must increase. Depending on the degree of capital mobility, this intervention would cause the domestic currency to appreciate. However, if the expansive domestic shock is accompanied by a contractive foreign shock ( ∆ϵ∗< 0 ) , the effectiveness of monetary policy would be diminished by a reduction in the foreign interest rate. Which of these two effects prevails depends on the degree of capital mobility, given by Equations (39)–(42). 5. Numerical Analysis To evaluate the primary analytical properties of the model, we utilized the tools of Christiano et al. (2014) and Bora and Zhong (2023). Specifically, we examined agents’ investment and consumption decisions in an economy exposed to domestic and external shocks (credit cycles). To calibrate the theoretical model, we adopted log-linear household preferences as in Iacoviello (2005). In addition, we considered two types of households, patient and impatient, represented by two agents. The patient (saver) agent can invest in bank deposits and housing shares, whereas the impatient (borrower) agent can only invest in housing shares. The patient agent uses their own funds to finance investments, while the impatient agent borrows from banks. In any period, households received an idiosyncratic shock ε that represented the value of their housing shares. In addition, they were exposed to productivity shocks, intermediate production shocks (capital), and the foreign credit cycle. According to Bora and Zhong (2023), we assumed that this cycle entered the economy through a liquidity shock θ and the foreign interest rate R∗, which modified the domestic rate R27. In terms of the credit cycle and its aggregate effects, we considered four scenarios: a closed economy ( θ= 0) with low interest rates (Case 1); a partially open economy ( θ< 1) with low interest rates (Case 2); an open economy ( θ= 1) with high interest rates (Case 3); and a partially open economy, with high interest rates (Case 4). For comparison, we used a baseline basic case, which assumed no adjustment costs, an open economy, and low interest rates. Housing investment decisions depended on two types of shocks: (i) those affecting interest rates (originating from the rest of the world), and (ii) idiosyncratic shocks ( ε ) that determined the value and return of housing investments. For each scenario, we examined three policy interventions: bank capitalization ( nw ), exchange rate devaluation ( ∆e> 0), and changes in reserve ratios ( ζ ). Furthermore, we evaluated households’ welfare by implementing borrowing limits, a mechanism 28 to smooth housing prices, and introducing alternative financial investments for impatient households (a risk-free bond). We used Matlab 2023(b), Stata 18, and R Studio 2024 for the computation and data analysis. Table 1presents the values of the main parameters derived from our calculations and the existing literature (Gete,2020;Quadrini,2020;Acoling et al.,2022). In the first case, a panel dataset of 45 developed and developing countries was used from 2001 to 2022. Table A2 summarizes the key statistics. We used this dataset’s mean values and standard deviations to generate interest rates, housing prices, and default rates. We performed a regression analysis to support the indicators discussed in the Introduction. As seen in Tables A3–A5, the results were very robust, suggesting strong correlations between the main endogenous variables and the set of explanatory variables. For example, we obtained Economies 2025,13, 207 21 of 46 an error of − 0.5 on average between the observed and predicted interest rate gaps for a selection of developing countries (see Table A6). To evaluate changes in income distribution, we assumed that initially both agents received an equal proportion of income and held the same share of housing investments, thus τ= 0.5 and vs=vb= 0.5. For simplicity, the model was expressed in per capita terms and units of consumption goods. At the aggregate level, it behaved similarly to a real business cycle model but with two sectors of final production: consumption goods and housing. Agents’ welfare derived from consumption in both sectors. Table 1. Parameter values. Parameter Value Source Capital Share in Final Production α0.35 Quadrini (2020) Capital Share in Housing Production γ0.40 Assumption Constant Productivity in Final Production A 10 Assumption Constant Productivity in Housing Production Z 10 Assumption Patient Household Discount Factor βs0.97 Gete (2020) Impatient Household Discount Factor βb0.85 Gete (2020) Housing Preferences in Utility Function ψ0.15 Acoling et al. (2022) Housing Shares vs0.50 Assumption Capital Utilization in Final Production ksh 0.70 Data Bank Reserve Ratio ζ0.10 Data Adjustment Costs ϕ0.20 Data Mean of Housing Shock (Impatient Agent) µb0.55 Data Standard Deviation Housing Shock (Impatient Agent) σb0.05 Data Mean of Default Shock µx−0.10 Data Standard Deviation of Default Shock σx0.10 Data Coefficient of Full Liquidity θ1.00 Assumption Note: The main parameter values were obtained from 45 developed and developing countries between 2001 and 2022. Other parameters were obtained from Gete (2020), Quadrini (2020), and Acoling et al. (2022). Idiosyncratic shocks to housing and firms, ε and κ , were generated according to Bora and Zhong (2023), in which mean values and standard deviations coincided with the data (see Table A2). In particular, the shock κ influenced the default rate of the rental firms, following a log-normal distribution (see Figure A1). The parameters µκ and σκ characterized that distribution. The initial domestic liquidity was established with nw = 0, and foreign credit was fixed at b∗= 10. Two productivity growth rates were considered: high ( gA = 0.03) and low (gA =0.01). The results are summarized in Tables 2–6, and Figures 4–14. For clarity, we summarized the results of the tables in Figures 15–17. In Table 2(basic case), we compare the agents’ allocations under adverse ( εs< 1) and favorable idiosyncratic shocks ( εs> 1), see panels A and B, respectively, where the data indicate that, although housing prices ( S ) tend to increase under low-interest-rate scenarios, adverse idiosyncratic shocks ( εs< 1) cause the patient households to save more in bank deposits than in housing shares, thereby increasing domestic liquidity. Higher domestic liquidity provides more resources to impatient agents for housing investments, resulting in higher income levels for these agents compared to patient households, especially when productivity growth is faster (panel A). In contrast, favorable shocks reduce domestic liquidity, constraining credit availability for impatient households and firms (panel B). This contraction redistributes wealth, increasing income for savers and decreasing it for borrowers. Economies 2025,13, 207 22 of 46 Table 2. Basic case (full liquidity). Interest Rates Prices Housing Shocks Income Investment Liquidity Wealth Deposits Lending Housing Saver Borrower Saver Borrower Saver Borrower Deposits Credits Panel A W Rd Rl S esebWs Wb msmbd b θ=1;e=1;nw=0;gA=0.01 Mean 159.71 1.02 1.13 1.35 0.88 1.23 66.63 53.63 2.95 31.30 38.10 45.29 Std Deviation 30.26 0.07 0.08 0.10 0.04 0.05 18.39 20.43 10.98 14.85 16.33 14.69 Rate of Growth 1.2% 0.4% 0.4% 0.6% W Rd Rl S esebWs Wb msmbd b θ=1;e=1;nw=0;gA=0.03 Mean 168.81 1.04 1.16 1.32 0.88 1.23 53.07 62.59 0.83 31.10 20.67 29.60 Std Deviation 48.07 0.07 0.08 0.09 0.03 0.05 34.73 48.68 4.72 26.07 17.80 13.38 Rate of Growth 4.3% 0.4% 0.3% 0.7% Panel B W Rd Rl S esebWs Wb msmbd b θ= 1; e = 1; nw = 10; gA = 0.01 Mean 169.78 1.02 1.14 1.56 1.11 1.23 70.90 53.39 35.46 24.33 5.64 16.08 Std Deviation 32.05 0.07 0.07 0.11 0.05 0.05 18.14 16.63 17.40 10.96 15.09 13.58 Rate of Growth 1.2% 0.5% 0.5% 0.8% W Rd Rl S esebWs Wb msmbd b θ= 1; e = 1; nw = 10; gA = 0.03 Mean 173.82 1.01 1.13 1.57 1.11 1.23 74.66 40.77 39.45 13.60 3.12 12.81 Std Deviation 36.78 0.06 0.07 0.10 0.04 0.05 30.39 23.22 19.73 9.19 11.77 10.59 Rate of Growth 2.9% 0.4% 0.4% 0.6% Note: Table 2shows the results we obtained by solving the model with discount factors 0.97 and 0.85, for the patient and the impatient households, respectively. In addition, agents differed in the values of the housing shocks. In panel A, we assumed zero banks’ capitalization (nw = 0) and no intervention. In panel B, banks’ capitalization was larger than zero (nw = 10). In both cases, we compared a productivity growth of 1% with 3%. Economies 2025,13, 207 23 of 46 Table 3. Case 1 (closed economy). Interest Rates Prices Housing Shocks Agents’ Income Agents’ Investment Liquidity Wealth Deposits Lending Housing Saver Borrower Saver Borrower Saver Borrower Deposits Credits Panel A W Rd Rl S esebWs Wb msmbd b θ= 0.51; e = 1; nw = 0; gA = 0.01 Mean 152.02 1.02 1.13 1.56 1.11 1.23 60.04 47.64 29.03 20.88 4.63 9.82 Std Deviation 29.15 0.06 0.07 0.11 0.05 0.05 16.46 14.59 15.36 9.73 12.38 11.12 Rate of Growth 1.0% 0.3% 0.3% 0.7% Panel B W Rd Rl S esebWs Wb msmbd b θ= 0.51, e = 2; nw = 0; z = 0.07; gA = 0.01 Mean 153.55 1.03 1.11 1.58 1.14 1.23 58.47 50.56 28.97 24.03 2.08 13.03 Std Deviation 29.87 0.06 0.07 0.11 0.05 0.05 15.80 14.50 12.08 8.75 8.16 10.30 Rate of Growth 1.1% 0.4% 0.4% 0.5% Panel C W Rd Rl S esebWs Wb msmbd b θ= 0.51, e = 2; nw = 0; z = 0.07; gA = 0.03 Mean 157.48 1.02 1.09 1.60 1.14 1.23 62.90 54.46 32.43 26.20 1.14 12.06 Std Deviation 34.13 0.07 0.07 0.12 0.05 0.05 24.89 21.08 15.36 10.64 7.09 9.47 Rate of Growth 1.9% 0.5% 0.5% 0.5% Note: Table 3shows the results we obtained by solving the model with discount factors 0.97 and 0.85, for the patient and the impatient households, respectively. In addition, agents differed in the values of the housing shocks. In panel A, we assumed zero banks’ capitalization (nw = 0) and no intervention. In panel B, we assumed an exchange rate devaluation and a reserve ratio z = 0.07. Panel C differs from panel B by considering a rate of productivity growth of 0.03. Economies 2025,13, 207 24 of 46 Table 4. Case 2 (shock on foreign liquidity). Interest Rates Prices Housing Shocks Agents’ Income Agents’ Investment Liquidity Wealth Deposits Lending Housing Saver Borrower Saver Borrower Saver Borrower Deposits Credits Panel A W Rd Rl S esebWs Wb msmbd b q = 0.51; e = 1; nw = 0; gA = 0.01 Mean 152.02 1.02 1.13 1.56 1.11 1.23 60.04 47.64 29.03 20.88 4.63 9.82 Std Deviation 29.15 0.06 0.07 0.11 0.05 0.05 16.46 14.59 15.36 9.73 12.38 11.12 Rate of Growth 1.0% 0.3% 0.3% 0.7% Panel B W Rd Rl S esebWs Wb msmbd b q = 0.51, e = 2; nw = 0; z = 0.07; gA = 0.01 Mean 153.55 1.03 1.11 1.58 1.14 1.23 58.47 50.56 28.97 24.03 2.08 13.03 Std Deviation 29.87 0.06 0.07 0.11 0.05 0.05 15.80 14.50 12.08 8.75 8.16 10.30 Rate of Growth 1.1% 0.4% 0.4% 0.5% Panel C W Rd Rl S esebWs Wb msmbd b q = 0.51, e = 2; nw = 0; z = 0.07; gA = 0.03 Mean 157.48 1.02 1.09 1.60 1.14 1.23 62.90 54.46 32.43 26.20 1.14 12.06 Std Deviation 34.13 0.07 0.07 0.12 0.05 0.05 24.89 21.08 15.36 10.64 7.09 9.47 Rate of Growth 1.9% 0.5% 0.5% 0.5% Note: Table 4shows the results we obtained by solving the model with discount factors 0.97 and 0.85, for the patient and the impatient households, respectively. In addition, the economy faced a foreign liquidity shock and agents differed in the values of the housing shocks. In panel A, we assumed zero banks’ capitalization (nw = 0) and no intervention. In panel B, we assumed an exchange rate devaluation and a reserve ratio z = 0.07. Panel C differs from panel B by considering a rate of productivity growth of 0.03. Economies 2025,13, 207 31 of 46 (a) (b) (c) Figure 10. Household decisions under imperfect liquidity: (a) consumption; (b) investment; (c) housing investment. Economies 2025,13, 207 32 of 46 (a) (b) Figure 11. Agents’ welfare with imperfect foreign liquidity: (a) low interest rates; (b) high interest rates. Figure 12. Agents’ welfare with high interest rates and policy. Economies 2025,13, 207 33 of 46 Figure 13. Agents’ welfare with high interest rates and policy. Figure 14. Agents’ welfare with high interest rates and policy. In Case 2 (Table 4), the effects of the exchange rate devaluation (from e= 1 to e= 2) and adjustments to the reserve ratio were analyzed. The results showed that, although the improvements were modest, both the level and the growth rate of aggregate wealth increased, and the income inequality between households decreased. Higher productivity growth amplified these benefits. Figure 6a,b compare agents’ welfare with full foreign liquidity, under lowand highinterest-rate scenarios. In addition, Figure 7a–c illustrate that optimal investment decisions for housing, savings, and consumption differ between agents. The impatient household tends to consume more in the short term, while the patient household achieves higher long-term consumption and investment paths. Housing shocks induce significant volatility in housing investment for both types of agents. During adverse shocks to the foreign interest rate ( R∗ ), Cases 3 and 4 (Tables 5and 6) show that domestic interest rates spiked, ranging between 1.23 and 1.33.29 Economies 2025,13, 207 34 of 46 Higher deposit and lending rates depress housing prices: for savers, investing in bank deposits becomes more attractive than housing; for borrowers, increased borrowing costs make housing investment less affordable. Although domestic credit remains abundant, high interest rates limit borrowing for the impatient household and firms, reducing housing investment, output, and aggregate wealth (see panels A in Tables 5and 6). Figure 15. Basic case—Table 2. Figure 16. Cases 1 and 2, Tables 3and 4: low interest rates. Economies 2025,13, 207 35 of 46 Figure 17. Cases 3 and 4, Tables 5and 6: high interest rates. Macroprudential policies become more challenging under high interest rates, especially if rates are exogenous. Measures to increase domestic liquidity or limit foreign capital are less effective, and strategies aimed at reducing borrowing costs, such as those implemented during the COVID-19 pandemic (see Adrian et al.,2023), may be more appropriate when the central bank has limited autonomy or the economy is experiencing an inflationary phase. Among the measures, we reduced the reserve ratio from 0.07 to 0.03 and implemented a more aggressive devaluation (from e= 1 to e= 3), as shown in panels B of Tables 5and 6, respectively. As a result, we observed a reduction in lending rates by nearly 0.8 points compared to the results in panels A, along with increases in both the levels and growth rates of aggregate wealth. Similarly to previous cases, the policy intervention led to a more egalitarian income distribution, increasing the income of the impatient agent relative to the patient agent. Furthermore, higher productivity rates contributed to higher levels and growth rates of wealth (see panel C in Tables 3–5). Comparing the four cases analyzed here, the results indicate that periods of high interest rates are more adverse for the economy than periods of low interest rates, especially when interest rates are exogenous and not aligned with macrofundamentals. Figure 6a,b demonstrate that low interest rates enable agents to achieve higher and more stable levels of welfare than high interest rates, particularly when there is full foreign liquidity. Although housing prices tend to increase during periods of low interest rates—causing volatility—the scope for macroprudential policy is greater than under high-rate scenarios. For instance, measures such as banks’ capitalization and policies aimed at increasing domestic liquidity can be useful to offset credit constraints caused by adverse foreign shocks and the diversion of financial resources to the real estate sector. In contrast, during periods of high interest rates, policies focused on reducing debt costs become more critical. Regarding volatility, Figure 7a–c illustrate short-term fluctuations in consumption and investment decisions at the household level, assuming full foreign liquidity ( θ= 1). This volatility is exacerbated in housing investment, as shown in Figure 7c. Economies 2025,13, 207 36 of 46 When foreign liquidity is restricted ( θ< 1), not only does efficiency decrease due to shocks, but aggregate wealth also becomes more volatile as interest rates increase (see Figures 8and 9a,b. Optimal decision-making by agents also changes in the presence of imperfect foreign liquidity, as examined in Tables 3–5. Figure 10a–c show that the impatient household tends to over-consume in the long term, whereas the patient household invests more in housing. Despite increased volatility at high interest rates, the patient household generally enjoys higher welfare than the impatient household (see Figure 11a,b). In Figures 12–14, we examined the welfare of the agents by applying different measures: a debt limit for the impatient household, a smoothing mechanism for housing prices, and a risk-free bond for the impatient household. We considered full foreign liquidity and high interest rates in the three cases. As illustrated in the figures, limiting borrowing improved welfare for the impatient household during downturns or adverse shocks; however, without intervention, agents were better off in all other periods (see Figure 12). A similar improvement was observed when housing prices were smoothed (see Figure 13). When the impatient agent could save on a risk-free asset as an alternative to housing, as shown in Figure 14, the welfare improved, and both agents, the patient and the impatient, benefited from this intervention. These results can reinforce those previously found. As Figures 15–17 summarize, agents’ welfare increased with domestic liquidity in the three scenarios: basic case (Table 2), low interest rates (Tables 3and 4), and high interest rates (Tables 5and 6). 6. Conclusions Departing from the existing business cycle literature on financial market frictions, we developed a general equilibrium model to analyze the dynamics of housing and the macroeconomy of an open economy exposed to the global credit cycle. Considering heterogeneous agents (patient and impatient) and two types of savings instruments, bank deposits, and real estate, this framework can be suitable for economies with different degrees of domestic financial development and a variety of integration status with international markets. Among other contributions, this article developed a dynamic macroeconomic model incorporating an embedded housing production sector with imperfect competition in the banking sector. In the model, investment returns were uncertain, and demand was endogenously determined by households’ preferences, relative prices, interest rates, and income. This structure enabled the model to capture stylized housing and aggregate dynamics observed in advanced and emerging economies. Second, the analysis identified the conditions under which domestic liquidityenhancing policies—such as bank capitalization, reserve ratios, and currency devaluation— succeeded or failed in stabilizing investment and output. In particular, the effectiveness of these tools is critically dependent on the level of foreign interest rates and liquidity. In high-interest-rate scenarios, alternative measures, such as stabilizing the price of housing and risk-free bonds, are preferable. Third, the results demonstrated robust findings across various model calibrations and highlighted the asymmetric effects of foreign liquidity shocks. Foreign liquidity can reinforce or override domestic policy instruments, depending on the level of financial development. These findings imply that capital control measures should be designed by considering the external interest rate environment and the depth of the domestic market, particularly those that target inflow restrictions. Combining microand macro-level interventions would be preferable, especially for developing countries with less developed financial markets. Consequently, facing multiple challenges, these countries must attenuate external shocks and develop local financial markets. They must also control debt and guarantee ac- Economies 2025,13, 207 37 of 46 cess to market housing for middleand low-income groups. Promoting domestic savings in other instruments than real estate would help households and entrepreneurs attenuate price fluctuations. For example, developing a market for bonds, such as green bonds, fintech, and microfinance, would complement other measures such as government subsidies. Some of these measures were adopted during the COVID-19 pandemic, although significant levels of government and private debt accompanied them. Today, the challenge is to guarantee macroeconomic and financial stability with an affordable market. These novel insights help explain the housing dynamics and poor growth performance observed in several developing countries during low foreign interest rates. This is particularly relevant in evolving policy shocks in major economies. The policy design would be limited because we did not include a secondary financial market in our model, and other characteristics that affect the dissemination of shocks (for example, geographical aspects, climate change, and technological shocks). Future research should address these aspects and a wider variety of transmission mechanisms. Author Contributions: Conceptualization, M.E.F.; Methodology, M.E.F.; Validation, D.R.G. and M.E.F.; Formal analysis, M.E.F.; Investigation, M.E.F. and D.R.G.; Writing—original draft preparation, M.E.F. and D.R.G.; Writing—review and editing, D.R.G. and M.E.F.; Supervision, M.E.F. All authors have read and agreed to the published version of the manuscript. Funding: This research received no external funding. Data Availability Statement: Data is contained within the article. Acknowledgments: We appreciate the comments and suggestions of the conference participants at the World Finance Conference, Malta 2020, and the seminar participants at the Chilean Central Bank, the Universidad de Chile, and the Universidad Alberto Hurtado. The authors thank Eloy Alvarado for computational solutions and Alexis Alfaro for research assistance, Universidad Técnica Federico Santa María. Conflicts of Interest: The authors declare no conflict of interest. Appendix A. Proofs Appendix A.1. The Phillips Curve From Section 3.2.1, we have the problem: max E0{ ∞ ∑ t=0 Λi,0,t[( Pj,t Pt −mct)yj,t−λ 2(Pj,t Pj,t−1 −1)2Yt} and the first-order conditions (FOCs), πt(1+πt) = 1 λ(1−v+vmct) + βEt(1+πt+1)πt+1Yt+1/Yt(A1) where Pj,t Pt=πt and λ is the adjustment costs’ parameter. In the steady state, we have the following: (1) 1 +πt= 1, then πt= 0, which implies Pit =Pt=Pss , for all t and i = 1. ..n. This implies that prices are constant in the steady state. (2) With constant prices, we have the following: πt=πt+1=πss = 0 in the steady state, and from FOCs, the marginal cost of production follows mcss =v−1 v(A2) Economies 2025,13, 207 38 of 46 Performing c mct= (mct−mcss )/mcss and κ= (v− 1 )/λ , with a little algebra, we obtain κb mct= (1−v+vmct)/λ Linearizing the FOCs around the steady state and doing a little algebra, the Phillips curve becomes (see Aoki et al.,2018) b πt=κc mct+βEtb πt+1(A3) Proof. with bκ= (x−xss)/xss. Appendix A.2. Rental Capital From Section 3.2.2, the problem of a rental capital firm jfollows: max {kf t,bf t,κj,t+1} EtΛi,t,t+1{(1−Γ(κj,t+1))Rk t+1qtkj,t} subject to qtkf j,t=nj,t+bk j,t(A4) and the lender’s participation constraint Et{G(κj,t+1)Rk t+1qtkf j,t} ≥ Rl tbk j,t(A5) Using G(κj,t+1) = Γ(κj,t+1)−µΓu(κj,t+1) and solving through a Lagrangian function, we obtain L=EtΛi,t,t+1{(1−Γ(κj,t+1))Rk t+1qtkj,t}+λ1t[nj,t+bk j,t−qtkf j,t]−λ2t[Rl tbk j,t−G(κj,t+1)Rk t+1qtkf j,t] Consider the following: (i) ∂L/∂kf j,t=0; (ii) ∂L/∂bk j,t=0; (iii) ∂L/∂κj,t+1=0. Then, the FOCs become Rk t+1=Θ(κj,t+1)Rl t(A6) Proof. with Θ(κj,t+1) = Γ′(κj,t+1) [Γ′(κj,t+1)G(κj,t+1)+G′(κj,t+1)(1−Γ(κj,t+1))] > 0, which represents the risk related to rental-capital investment. Appendix A.3. Interest Rates From the definitions of Sections 3.2 and 3.3, we see that the nominal lending interest rate follows (1+il t) = Rl t+1πt+1(A7) with b πt+1=κc mct+1+βEtb πt+2 . Taking the logs of both sides of Equation (46), we obtain log (1+il t) = log Rl t+1+log πt+1. Under perfect competition, we have Rl t=Rl t/θ ; thus, ( 1 +il t)/πt+1= ( 1 /θ)( 1 + i∗ t)/π∗ t+1 , where i∗ t is the nominal foreign interest rate, and π∗ t+1 is foreign inflation. Taking the logs of both sides of this expression, we obtain the following: e it=e i∗t+e γEt+1+Υt(A8) Economies 2025,13, 207 39 of 46 where e it=log( 1 +il t) , e i∗t=log( 1 +i∗ t) , and γEt+1=γet+1+ (π∗ t+1−πt+1) , which denotes the expected devaluation at time t+ 1, with γet+1 being the real devaluation at this time. Υt=log(1/θt). In the case of Cournot competition, when there is perfect foresight, local interest rates behave as e it=e i∗t+e γEt+1+Υt+e Ξ Proof. where e Ξ= 1 /[ 1 −1 NηD l ] denotes the degree of market power in the banking sector. Table A1. Housing deficits in selected countries. Developed Countries Deficit Population Financing (Millions) (%) Finland 0.6 5.6 70 Germany 0.7 83.3 90 Greece 0.2 10.4 70–75 Portugal 0.1 10.9 60–70 United Kingdom 4.3 68.4 80–95 Developing Countries Deficit Population Financing (Millions) (%) Argentina 4.0 46.2 50–70 Brazil 5.8 215.3 80 Chile 0.6 19.6 75 Colombia 5.2 51.9 70 Mexico 2.2 127.5 80–90 India 47.0 1417 85 Note: Housing deficit can include units in poor conditions or of poor quality. Source: Bank of Mexico (2024); Central Bank of Argentina (2024); Casen (2022); Central Bank of Brazil (2024); Central Bank of Chile (2024); Central Bank of Colombia (2024); DANE (2024); Instituto Brasileiro de Geografia e Estatistica (2024); Mordor Intelligence (2024); OECD (2023); World Bank (2021). Table A2. Main basic statistics. Variable Observations Mean Std. Dev. Min Max Central Bank Interest Rate 946 5.7 12.3 0.0 316.0 Credit to GDP (Percentage) 926 107.0 69.5 8.0 288.5 Current Account to GDP (Percentage) 985 −0.5 5.8 −30.9 28.0 Deposit Interest Rate (Percentage) 862 4.7 6.4 0.0 74.7 Devaluation Rate (Percentage) 944 1.3 15.0 −272.2 120.0 Fed Fund Interest Rate (Percentage) 990 1.4 1.5 0.1 5.0 Foreign Reserves (USD Millions) 986 108,160 392,610 195 3,900,000 Gap Between Interest Rates 946 4.3 12.3 −2.2 314.3 Household Debt to GDP (Percentage) 771 50.1 31.0 2.5 137.4 Index of Housing Cost (Base Year = 2015) 667 100.7 24.3 12.5 178.5 Inflation Rate (Percentage) 914 4.1 5.3 −3.4 93.6 Lending Interest Rate (Percentage) 830 8.9 10.5 0.0 118.4 Money Market Interest Rate (Percentage) 745 4.4 6.2 −0.6 86.1 Nominal Exchange Rate (Domestic Currency to USD) 989 536.2 1929.4 0.5 15,731 Nonperforming Loans (Percentage of Debt) 543 4.1 5.3 0.2 45.6 Openness Index 2001–2022 940 69.7 28.7 0.0 100.0 Developed Countries 396 87 10 29 100 Developing Countries 544 57 32 0 100 Per Capita GDP (Constant USD of 2015) 987 24,031 29,724 263 288,275 Private debt to GDP (Percentage) 771 80.0 40.4 12.5 185.3 Economies 2025,13, 207 40 of 46 Table A2. Cont. Variable Observations Mean Std. Dev. Min Max Real GDP growth 989 2.8 3.6 −21.4 14.0 Total Factor Productivity (Rate of Growth) 665 1.0 0.1 0.8 1.2 Unemployment Rate 871 8.0 5.0 0.0 33.0 U.S. Central bank Rate 990 1.5 1.6 0.1 5.3 VIX 990 20.2 6.2 11.1 31.8 Year 990 2012 6 2001 2022 Note: Index of housing cost is an index number calculated by the OECD that measures the prices of residential properties over time. The gap between interest rates is obtained as the difference between each country’s central bank interest rate and the U.S. policy rate (Federal Fund Rates). Openness index (overall openness index) provides information on the state of openness of the capital account based on 12 types of asset categories, where one implies a fully liberalized economy, and zero implies a closed economy. Source of data: BIS (2023); IMF (2023); OECD (2023). Table A3. Financial integration. yit = ai+ bXit + vit (A): All Countries 2001–2022 2001–2014 2015–2022 Dependent Variable: D log Credit/GDPi D log Credit/GDPus 0.5196 *** 0.4549 *** 0.3810 ** 0.3474 ** 1.0388 *** 1.1067 *** (0.1066) (0.0880) (0.1427) (0.1244) (0.1394) (0.0927) D log GDPpc −0.2445 *** −0.2677 *** −0.2963 *** −0.3221 *** −0.2145 *** −0.2044 *** (0.0248) (0.0257) (0.0355) (0.0367) (0.0309) (0.0356) Adj R211% 11% 28% Chi2142 80 314 Observations 881 881 543 543 338 338 (B): Developed Countries 2001–2022 2001–2014 2015–2022 Dependent Variable: D log Credit/GDPi D log Credit/GDPus 0.7553 *** 0.6814 *** 0.5978 *** 0.5412 *** 1.2064 *** 1.1491 *** (0.0757) (0.0577) (0.0826) (0.0679) (0.1371) (0.0909) D log GDPpc −0.0793 *** −0.0732 ** −0.0922 *** −0.0839 ** −0.1164 *** −0.2652 *** (0.0223) (0.0236) (0.0274) (0.0284) (0.0344) (0.0495) Adj R221% 18% 39% Chi2141 64 260 Observations 377 377 233 233 144 144 (C): Developing Countries 2001–2022 2001–2014 2015–2022 Dependent Variable: D log Credit/GDPi D log Credit/GDPus 0.2298 0.1773 0.0573 0.0381 0.8373 *** 1.0595 *** (0.1745) (0.1468) (0.2351) (0.2064) (0.2214) (0.1502) D log GDPpc −0.3202 *** −0.3479 *** −0.3757 *** −0.4088 *** −0.2798 *** −0.1947 *** (0.0361) (0.0372) (0.0515) (0.0533) (0.0455) (0.0491) Adj R213% 14% 27% Chi295 59 145 Observations 504 504 310 310 194 194 Note: Table A3 shows the correlation between variations in the ratio of credit to GDP for each country and corresponding changes in the U.S. ratio. We estimated the regression utilizing changes in per capita GDP as a control variable. In panel A, the table shows the correlations for the full sample of data, and panel B shows the correlations for the group of developing countries. The coefficients indicate the direction and the importance of the correlation. For example, 0.5196 in column 1 of panel A (2001–2022) indicates that each country’s credit-to-GDP ratio increases 0.52 percent (approximately) when the U.S. ratio increases 1 percent. In column 5, the country ´ s ratio on the right increases by 1.03 percent. In columns 2, 4, and 6, Chi2 is the value of the Wald test. **: significance at 5%; ***: significance at 1%.