Selected Issues

Abstract

Selected Issues

Macroprudential Policy in Panama: Implications for the Real Estate Market1

A. Introduction

1. Residential real estate prices have been rising in Panama City, against the backdrop of strong economic growth and interests from foreign buyers. Anecdotal evidence and private sector surveys feature a continued increase in property prices in Panama’s capital city during the first half of 2019, despite a brief moderation during the same period in 2018. At the same time, commercial banks’ residential mortgage lending grew by an average annual rate of 11.7 percent over the last 7 years. As a result, household indebtedness is on the rise. In Panama City, households’ exposure to mortgage debt accounted for around 96 percent of income in mid-2019 compared to 69.6 percent of income in 2012.2

2. This note presents an analysis of the residential real estate market—with focus on Panama City—and its implications for macroprudential policies. The evolution of real estate prices is of particular interest to policy makers but assessing the sustainability of property prices is a challenging task given the difficulty in assessing ex ante the presence of property price “bubbles” (i.e., a prolonged rapid growth in prices followed by a sudden crash). This paper addresses the following questions:

  • How have residential property prices, mortgage lending, and household debt evolved in recent years?

  • Are residential real estate prices showing signs of “overheating”?

  • Have there been changes in bank mortgage lending practices?

  • What macroprudential measures could be used to mitigate property price risks?

3. A thorough analysis of the housing market in Panama is challenging in view of limited availability of data and time series. At present, formal price indices for nationwide residential and commercial real estates are not available. Consumer and business sentiment indicators which are often used to gauge the private sector outlook on the real estate market, the volume of residential dwellings that provides information on housing supply, and transactions by foreign buyers are also unavailable. To work around these data limitations, this analysis uses the residential property price-to-income ratio in Panama City published by Numbeo based on market surveys as a proxy for residential property prices. In addition, internet search activity extracted from Google Trends is used as a proxy for foreigners’ interest in Panama City’s real estate. Given these challenges, the caveats underpinning this analysis must, in particular, be considered to ensure accurate interpretation of the findings. The results presented herein should be interpreted as early signals of possible risks, and as a guide to the areas where further data collection would be useful to support surveillance and further studies.

B. How Have Property Prices, Mortgage Lending, and Household Debt Evolved Recently?

4. Residential property prices in Panama City have been rising, precipitated by strong economic growth and interest from foreign buyers. While Panama does not have a formal real estate price index, private sources—such as Numbeo—which derive their data based on market surveys suggest continued optimism in the capital Panama City. For example, the ratios of residential real estate price-to-income and price-to-rent had risen by 52 percent and 34 percent, respectively, from 2012 to 2019, although they appeared relatively low compared to major cities in neighboring countries, based on data from Numbeo data.3 According to Global Property Guide, foreign buyers were pushing up property prices in Panama, as average dwelling sales price surged by 21 percent from 2015 to 2017 in Panama´s metropolitan area. Major foreign buyers originated from the United States, Europe, Canada, and Latin America. It is worth noting that over the last five years, Panama’s economy grew at an average annual rate of 5 percent, more than eight times the average annual growth rate of Latin American countries (at 0.6 percent).4

Figure 1.
Figure 1.

Panama: Household Mortgage Debt Indicators

Citation: IMF Staff Country Reports 2020, 125; 10.5089/9781513541679.002.A004

Source: Numbeo.

5. Strong demand for residential properties had led to an increase in mortgage lending by commercial banks and higher household mortgage debt. In line with the increase in property prices in Panama City, commercial banks’ residential mortgage loans grew at an average annual rate of 11.7 percent, from 2012 to 2019. During this period, the share of residential mortgage loans to total loans increased gradually, from 28 percent to 34.5 percent. As a result, household exposure to mortgage debt rose significantly in Panama City, amounting to 96 percent of income in 2019, based on data from Numbeo.5 While this ratio pales in comparison with neighboring capital cities, it accounts for close to the full amount of disposable income.

Banking Sector: Total Residential Mortgage Loans

(In percent of total loans)

Citation: IMF Staff Country Reports 2020, 125; 10.5089/9781513541679.002.A004

Source: Superintendency of Banks.
Figure 2.
Figure 2.

Panama: Real Estate Indicators

Citation: IMF Staff Country Reports 2020, 125; 10.5089/9781513541679.002.A004

Source: Numbeo.

C. Are Residential Real Estate Prices Showing Signs of “Overheating”?

6. It is difficult to know if house prices have deviated significantly from “fundamentals”. The definition of “fundamental” is model-specific and thus, the assumptions that underpin the model would need to be taken into consideration in interpreting the results (Annex I). In addition, data limitations are caveats that must be borne in mind:

  • Absence of a publicly available nationwide real estate price index.6 The residential property price-to-income ratio computed by Numbeo, based on market surveys, is used as a proxy for the trend in real estate prices. The annual series for this ratio begins in 2011. From 2014 onwards, Numbeo provide semi-annual data.

  • Unavailability of certain real estate market-related indicators. Panama does not produce statistics on the volume of houses and dwelling, detailed volume of real estate loans, real estate purchases by foreigners (volume and value), and consumer and business sentiment indices which are, among others, important determinants of the demand and supply of housing.

7. A residential property price model could be derived based on available economic variables and noting the caveats above (Annex II). A baseline predictor for residential property price-to-income ratio in Panama City is constructed using quarterly data from Q4–2013 to Q4–2018. It is used to predict the residential property price-to-income ratio from Q1–2019 to Q2–2019 (out-of-sample) which are then compared with the actual observed ratios.

8. The results indicate a significant deviation between the actual real estate prices and the predictor in the “out-of-sample” period. Panama City’s real estate price-to-income ratio continued to increase stably since from Q2–2018 to Q2–2019, departing from the predictor which suggests a reversal starting from end-2018. From Q1–2019 to Q2–2019, the average difference between the actual and predicted ratios is around 1.5 percentage points, or 2.4 standard deviations. Hypotheses tests based on t-distribution suggest that the deviations between the observed price-to-income ratios and the predictor are statistically significantly in Q1–2019 and Q2–2019, at the 5 percent level (Annex II).

Property Price-to-Income Ratio: Actual vs. Predicted

Citation: IMF Staff Country Reports 2020, 125; 10.5089/9781513541679.002.A004

9. The decline in the predictor in the “out-of-sample” period could be attributable to moderating economic growth and rising financing costs. A decomposition of the explanatory variables suggests that real GDP growth and interest rate jointly explained 56 percent of the variations in the property price-to-income ratio. While economic growth remained resilient, real GDP growth moderated by 1.1 percentage points from Q1–2018 to Q1–2019. During the same period, banks’ weighted average lending rate rose 51 basis points. A confluence of these two factors precipitated a decline in the predicted price-to-income ratio, by 2 points, from Q4–2018 to Q1–2019, departing from the observed actual price-to-income ratio which remained on a stable appreciation trajectory, rising by 0.2 points during the period.

Contribution of Explanatory Variables 1/

(In percent)

Citation: IMF Staff Country Reports 2020, 125; 10.5089/9781513541679.002.A004

Source: IMF staff calculations.1/ Computed based on partial R-squared and assuming that the four variables are the only explanatory variables.
Figure 3.
Figure 3.

Panama: Economic Growth and Commercial Banks’ Lending Rates

Citation: IMF Staff Country Reports 2020, 125; 10.5089/9781513541679.002.A004

Sources: Superintendency of Banks; National Statistics and Censuses Institute (INEC); Haver Analystics.

10. Continuous surveillance of developments in the real estate market is important. The statistically significant divergence in the structural relationship between the observed price-to-income ratio and the predictor could indicate three possibilities: (i) positive expectations of a future rebound in economic growth and lower mortgage rates7, underpinning the resilience in housing market; (ii) disequilibrium between current property prices and fundaments which could potentially lead to a correction in prices in the future; and (iii) “missing variables” in the model. Given that the R-squared of the regression is high, there is a fair chance that possibility (i) or (ii) could occur. If possibility (ii) materializes and residential property prices continue to increase—thus exacerbating the disequilibrium—then a destabilizing “bubble” could build up over time. To mitigate such risks, a prudent approach would be to strengthen the monitoring of developments in the real estate market, household debt and bank lending practices, and at the same time, fortify the macroprudential policy toolkits to stand ready to tighten them when necessary.

11. It is also important to identify the “missing variables” that could explain the divergence between the two indicators. Unexplained increases in real estate prices may not necessarily be a source of concern if they are driven by expected improvements in economic conditions in the future (i.e., strong growth that leads to higher property prices), lower interest rates, or improved liquidity of the housing market (Annex I). On the contrary, if the increase in prices is related to moral hazard in lending practices, then macroeconomic and financial stability could be at risk. This study should ideally be extended with alternative econometric models that include the following explanatory variables:

  • Variations in property taxes.

  • Changes in the supply of houses. Useful indicators include construction and residential permits.

  • Distribution of income and household debt across different population groups.

  • Business and household sentiment.

  • Real estate purchases by foreigners (volume and value).

Extending this study would require further efforts by the authorities to collect these data, which would also contribute towards enhancing surveillance.

D. Have There Been Changes in Bank Mortgage Lending Policies?

12. Over the last seven years, residential mortgage loans grew at a faster pace compared to total commercial bank loans. From 2012 to 2019, commercial banks’ residential mortgage loans grew by an average annual rate of 11.7 percent, higher compared to the average annual rate of total loans, at 9.4 percent. The rapid growth in housing loans led to an increase in the share of commercial banks’ residential mortgage loans to total loans from 28.9 percent in 2012 to 34.5 percent in 2019.

Growth in Commercial Banks’ Total Loans and Residential Mortgage Loans

(In percent change, y/y)

Citation: IMF Staff Country Reports 2020, 125; 10.5089/9781513541679.002.A004

Source: Superintendency of Banks.

13. A benchmark for the growth in residential mortgage lending was constructed following the methodology developed in Section C. A predictor was constructed from Q4–2008 to Q4–2018. It was then contrasted with the actual growth of mortgage loans for the period from Q1–2019 to Q2–2019 to ascertain whether there were any structural breaks between the two series (Annex III).

14. The results indicate that growth in residential mortgage loans was slightly stronger than the predictor in the first half of 2019. Residential mortgage loans grew 5.3 percent (Y/Y) in Q2–2019, slightly higher compared to the predictor, at 3.4 percent (Y/Y). The average divergence between the actual and the predicted mortgage growth rates during the first half of 2019 is 1.8 percentage points, accounting for around 0.9 standard deviations above the mean. Hypotheses tests based on t-distribution suggest that the deviations in Q1–2019 and Q2–2019 are not statistically significantly, at the 5 percent level (Annex III). This suggests that it is too early to conclude that banks’ lending practices have changed as more observations would be needed. The resilience in banks’ mortgage lending could be driven by expectations of a recovery in economic activities as in the case of the price-to-income ratio before. The deviation could also possibly reflect “missing variables”. That said, the divergence particularly in Q2–2019 reinforces the need for continuous monitoring and surveillance of the real estate market, including banks’ underwriting standards, to safeguard financial and macroeconomic stability.

Growth in Housing Loans: Actual vs. Predicted

(In percent change,y/y)

Citation: IMF Staff Country Reports 2020, 125; 10.5089/9781513541679.002.A004

Source: IMF staff calculations.

15. The predictor showed relatively slower growth rates during the first half of 2019 due to a moderation in economic growth and higher mortgage rates. A decomposition of the explanatory variables suggests that nominal GDP growth (as a proxy for income levels) and mortgage rates jointly explain 55 percent of the variations in residential mortgage loan growth. During the first half of 2019, nominal GDP grew by an average 2.8 percent (Y/Y), slower compared to 4.6 percent (Y/Y) during the same period in 2018. Banks’ mortgage rate increased to 5.81 percent in 2019, from 5.56 percent in 2018.

Figure 4.
Figure 4.

Panama: Contribution of Explanatory Variables and Commercial Banks’ Mortgage Lending Rates

Citation: IMF Staff Country Reports 2020, 125; 10.5089/9781513541679.002.A004

Sources: Superintency of Banks (SBP), and the National Statistics and Censuses Institute (INE); Haver Analystics.1/ Computed based on partial R-squared and assuming that the four variables are the only explanatory variables.

E. What Are the Macroprudential Measures to Mitigate Housing Risks?

16. At present, Panama has put in place various macroprudential policies. The Superintendency of Banks (SBP) implemented dynamic provisions rule (DPR) since June 30, 2014 as part of general rule for provisioning and credit risk management. The DPR is part of regulatory capital but cannot be included in the calculation of capital to meet the regulatory minimum (i.e., banks need to maintain the DPR in addition to regulatory minimum of 8 percent). In addition, capital requirements are imposed on lending to household and corporate sectors.

Sectoral Capital Requirements with Effect from July 1, 2016

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Source: IMF Macroprudential Policy Survey

17. The macroprudential toolkit could be expanded to include limits on loan-to-value (LTV) ratios, and caps on debt-service-to-income (DSTI) or loan-to-income (LTI) ratios.8 These measures would complement the existing sectoral capital requirements. Box 2 shows examples of various macroprudential instruments and useful indicators to identify when to tighten the macroprudential measures.

18. A wide range of indicators should be used to assess the need for policy action, especially the growth of mortgage loans and house prices. These two indicators are core indicators for vulnerabilities in housing markets, since they jointly provide powerful signals of a procyclical build-up of systemic risk.9 Deviations of house prices from long-term trends have proved useful in predicting financial stress (Borio and Drehmann, 2009); and house price-to-rent and house price-to-income ratio are often used as measures of over- or under-valuation of house prices. In addition, other indicators should be closely monitored, such as: (i) the average and the distribution of LTV, DSTI, and LTI ratios across new loans over a period and outstanding loans at a given point in time; (ii) the share of foreign currency denominated mortgage loans or interest-only mortgage loans; and (iii) housing price growth by regions and types of properties.

Macroprudential Instruments to Mitigate Risks in the Household Sector and Indicators to Identify When to Tighten

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Sources: IMF, 2014a and IMF, 2014b

19. Sectoral tools should be activated or tightened when multiple indicators point to rising systemic risk. A single signal, or mixed signals from multiple indicators, may not be sufficient for action. For example, strong growth in mortgage loans without house price growth may simply indicate improving housing penetration rather than an increase in risk. Conversely, a sharp increase in house prices, without strong mortgage loan growth, may reflect a shortage of house supply requiring structural policies to improve supply rather than a macroprudential response.

20. Policymakers should take a gradual approach when tightening or introducing sectoral tools. When several indicators show signs of a gradual build-up of risk in the housing sector, policymakers should first intensify supervisory scrutiny and step up communication. As a next step, less distortionary sectoral capital requirements may be tightened to build additional buffers.10 Tighter limits on LTV and/or DSTI ratios can follow if these defenses are not expected to meet policy objectives (Figure 5 below provides country examples). LTV and DSTI caps should always be imposed on the flow of new household loans. Otherwise, it would force some existing high LTV or DSTI borrowers to provide more collateral or repay part of their loans, leading to a possible distress.

Figure 5.
Figure 5.

Panama: Limits on LTV and DSTI Ratios and Number of Countries at Each Range, 2014

Citation: IMF Staff Country Reports 2020, 125; 10.5089/9781513541679.002.A004

Sources: IMF, 2014a and IMF, 2014bNote: Observed limits on LTV ratios are below 80 percent in more than half of 51 sample countries, and Most countries with caps on DSTI ratios have imposed 40–45 percent as the limit (seven out of 13 countries), and four countries restrict it to be below 35 percent.

21. As sectoral tools work via a range of transmission channels, combining them can reinforce their effectiveness and mitigate the shortcomings of any single tool. A higher risk weight forces lender to hold extra capital to buffer unexpected losses and restrains credit growth as lending rates increase due to higher funding costs. Limits on LTV ratios cap the size of a mortgage loan relative to the appraised value of a house, while caps on DSTI and LTI ratios restrict the size of debt service payments to a fixed share of household incomes. They can complement each other, for example when house prices increase, LTV limits may become less effective but DSTI or LTI caps continue to restrict credit to household income. The DSTI caps also enhance the effectiveness of LTV limits by containing the use of unsecured loans to meet the minimum down payment. In a low interest rate environment, DSTI caps complement LTV limits in containing increases in household leverage, thus help mitigate defaults when interest rates eventually rise. These caps can break the procyclical feedback between credit and house prices, and can also reduce speculative demand by containing expectations of future house prices. DSTI caps work as an automatic stabilizer— becoming more binding when house prices grow faster than disposable income, thereby helping to smooth credit booms (Figure 6).

Figure 6.
Figure 6.

Panama: Transmission Mechanism of Three Sectoral Macroprudential Instruments

Citation: IMF Staff Country Reports 2020, 125; 10.5089/9781513541679.002.A004

Sources: IMF, 2014a and IMF, 2014b

22. Expanding the regulatory perimeter would contain leakages. An increase in credit by domestic nonbank financial institutions—such as credit cooperatives—may render the sectoral tools ineffective if they are applied only to the domestic banking sector. Policymakers would need to expand the regulatory perimeter to these nonbank financial institutions through inter-agency cooperation.11

When to Loosen Macroprudential Policies?

23. Sectoral tools can be loosened to contain feedback loops between falls in credit and house prices during housing busts. A housing bust can result in a credit crunch that puts further downward pressure on house prices. Strategic default, fire sales and contraction in the supply of credit can create negative externalities beyond the parties involved in financial contracts (IMF, 2011b; Geanakopolos, 2009; and Shleifer and Vishny, 2011).

24. Indicators that inform the tightening phase could be used for informing decisions to loosen macroprudential policies when they turn in the opposite direction. Fast-moving indicators that could guide such decisions include house transaction volumes and spreads on household loans. A softening housing market alone is not sufficient to justify a loosening of macroprudential measures. Evidence of a systemic stress is vital, such as simultaneous decline in prices and credit, and an increase in non-performing loans or defaults. In such circumstances, loosening macroprudential policies would reduce stress in the housing market.

25. The loosening of macroprudential policies needs to respect certain prudential minima that could safeguard an appropriate degree of resilience against future shocks. If large additional buffers have been built during the tightening phase, they can be released to avoid a credit crunch without jeopardizing banks. However, the relaxation should not go beyond a “permanent floor”, i.e. level considered safe in downturns. Policymakers should also communicate clearly that a tightening can be followed by a relaxation so that market participants do not take an adverse view of the relaxation during downturn (BIS, 2012).

26. A loosening of these tools can be effective but may have limited effects when it is “pushing on a string.” Even if policymakers loosen sectoral instruments, banks may be reluctant to provide credit due to increased risk aversion or capital constraints, and may apply more stringent lending standards than the regulatory thresholds. Potential borrowers may be reluctant to enter the housing market while prices are still falling. Nonetheless, the relaxation would still be useful in containing the spillback from falling prices and credit.

27. Housing demand and house price growth financed directly by foreigners may thwart the effectiveness of macroprudential tools. In these cases, higher stamp duty or capital gains tax may be useful (e.g. Hong Kong SAR and Singapore).

F. Conclusion and Policy Implications

28. Residential real estate prices in Panama City and more generally, commercial banks’ mortgage lending, appear to be growing at a faster pace compared to their suggested econometric models during the first half of 2019. These results are suggestive of potentially important shifts in the dynamics of property prices, and therefore, should be further investigated. It would be useful to identify the “missing variables” that could explain the relatively stronger residential property price-to-income ratios since early 2019 as unexplained increases in real estate prices might not necessarily be a worrying concern, particularly if the price increase was driven by improved liquidity of the housing market. On the contrary, if the growth in prices were related to heightened moral hazard in lending practices, then risks to financial stability could emerge. This analysis also finds that it is too early to conclude that banks’ lending practices have changed although some signs of a departure between mortgage loan growth and the model appeared in Q2 2019.

29. It would be prudent to treat the findings as early signals of possible risks as well as a guide the areas where further data collection would help support surveillance and additional analysis. The divergences—in Panama City’s residential real estate price-to-income ratio and its predictor and in the growth between commercial banks’ mortgage loan and its predictor—could indicate early signs of risks. As such, it would be prudent to strengthen the monitoring of the housing market, household debt and bank lending practices. While Panama has put in place various macroprudential policies at present, the country’s overall macroprudential framework could be further fortified, including with more effective toolkits that the SBP could deploy, for early intervention, to mitigate risks to macroeconomic and financial stability. If lax underwriting standards are detected, intrusive and tighter prudential supervision, including appropriate enforcement actions, would be needed. It is also important to for the authorities to establish new data—including formal real property price indices (residential and commercial) and real estate transactions by foreigners (value and volume)—to help enhance surveillance and improve the accuracy of forecasts.

30. An extension of this study to include commercial real estate prices would strengthen early warning signals. Panama’s optimistic economic growth and continued foreigners’ interests in the property sector could spill over to the commercial real estate, potentially leading to a property price bubble. Extending this analysis to the commercial real estate would require fresh efforts by the authorities to gather new data, including establishing a commercial property price index. In the same vein, continued efforts by the authorities to monitor and supervise banks, particularly in underwriting standards, would help ensure no lax in the quality of real estate financing.

Annex I. When Do Real Estate Prices Depart from “Fundamentals”?

1. The question of when and whether real estate prices are disconnected with “fundamentals” is a difficult question to answer.

2. Any definition of a “fundamental” is model-specific. A simple asset pricing model would define the “fundamental” price of a house as the present discounted value of all future rents that an investor receives (or avoids to pay) from owning the house. Expanding this model, in a hypothetical country where individuals migrate inter-state to search for better jobs, the value attached to the purchase of a house depends on how liquid the housing market is. In such a scenario, liquidity is another determinant of “fundamentals” that influence house prices.

3. In another version of the pricing model, if house buyers are credit constrained, then house prices might be lower than the present discounted value of rents. In this case, the extent to which credit constraints are present and binding would also constitute a “fundamental” determinant of house prices.

4. A somewhat more subtle scenario is when moral hazard is present in the market for real estate lending. Moral hazard arises when the government provides “guarantees”—implicit or explicit—to the banking sector. When these “guarantees” are present, the value of real estate would be higher than in the case where there is no guarantee.

5. This leads to an important caveat: the definition of “fundamental” depends on the choice of a model used and the variables that define the model. House prices may differ from levels predicted using a certain definition of “fundamental”, but that same level of prices may be perfectly explained when the definition of “fundamental” is expanded to include one or more variables. Thus, to avoid confusion, this paper uses the expression “predictor” to be consistent with the econometric nature of the analysis instead of “fundamental”.

Annex II. Model: Residential Real Estate Price-to-Income

A. Model

1. Two models are considered, represented by the following equations:

  • Model 1(a): Yi = αi + β1 X1,i + β2 X2,i + β3X3,i + εi

  • Model 1(b): Yi = αi + β1 X1,i + β2 X2,i + β3X3,i + β4X4,i + εi

where:

Yi = Residential property price-to-income ratio

X1,i = Real GDP Growth (in percent, Y/Y)

X2,i = Population (in thousands )

X3,i = Interest rate, based on commercial banks’ weighted average lending rate (in percent per annum)

X4,i = Foreigners’ interest, based on internet searches using key words “Panama City apartments” and “Panama City houses”, based on Google Trends data (in percent change, Y/Y)

αi = Constant

εi = Residuals

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2. Model 1(a) is derived mainly on fundamental variables. The explanatory variables comprise real GDP growth (proxy for income), population (proxy for demand for housing), and weighted average lending rate (proxy for price of credit).1 Quarterly data for price-to-income ratio and population are derived using spline interpolation.2

3. Model 1(b) augments Model 1(a) with online internet search activity. This model expands Model 1(a) by including an additional explanatory on internet search activity as a proxy for foreigners’ interest in real estate in Panama City, given that data on residential property purchases by foreigners is not available. Specifically, data on online internet searches in the U.S. using keywords “Panama City apartments” and “Panama City houses” are extracted from Google Trends, based on Internet Protocol (IP) address in the US.3 The explanatory variable is derived by computing year-over-year (Y/Y) change for each quarter to remove seasonal effects. Augmenting Model 1(a) with data from internet searches improves the overall fit of the model, in line with recent findings which show that online search queries can be extremely useful when information is fragmented or missing.4

Results

4. The predictor derived from the regression provides a good in-sample fit. The estimated coefficients are all in the expected signs. Increases in real GDP growth and population lead to increasing real estate prices while rising interest rates increases financing costs which reduce the demand for housing—these are all in line with empirical observations. In Model 1(b), increasing foreigners’ interest on real estate in Panama City—captured by “Google Trends”—also contributes to higher real estate price-to-income ratios, reinforcing the suggestions that foreign buyers were influencing property prices. Augmenting Model 1(a) with internet search activity also improves the overall fit, as the adjusted R-squared increases from 83 percent to 86 percent. The coefficients of all the explanatory variables are statistically significant. The actual real estate price-to-income ratio and the predictor are cointegrated, and no significant autocorrelation is detected.5

Regression Analysis with Panama City Real Estate Price-to-Income Ratio as Dependent Variable 1/

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Source: IMF staff calculations.Note: Standard errors are in brackets. “Interest rate” refers to weighted aveage bank lending rates, in percent per annum. Foreigners’ interest is derived from “Google Trends”, computed as quarterly (Y/Y) change in internet searches using keywords “Panama City apartments” and “Panama City houses”.

All coefficients are significant at 1%, except for “Foreigners’ Interest” which is significant at 5%.

B. Hypothesis Test: Are the Observed Price-to-Income Ratios Significantly Different from the Predictor in Q1 2019 and Q2 2019?

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Hypothesis:

  • Ho: (A)-(B) = 0

  • H1: (A)-(B) ≠ 0

Mean=0.1278; Standard deviation=0.5659; N=23

For Q1 2019:

  • Test statistic, Zi = 2.32071

  • Critical value, tcrit,i =1.717, based on t-distribution with 5 percent significance level and n=23

Results: Reject Ho,s since Zi > tcrit,i. Therefore, the observed price-to-income ratio is significantly different from the predictor in Q1 2019.

For Q2 2019:

  • Test statistic, Zj = 2.4745

  • Critical value, tcrit,i =1.717, based on t-distribution with 5 percent significance level and n=23

Results: Reject Ho since Zj > tcrit,i. Therefore, the observed price-to-income ratio is significantly different from the predictor in Q2 2019.

Annex III. Model: Growth in Residential Mortgage Lending

A. Model

1. The model is represented by the following equation:

Zj = αj + γ1 V1,j + γ2 V2,j + γ3 V3,j + γ4 V4,j + εj where:

Zj = Growth in commercial banks’ residential mortgage loans (in percent, Y/Y)

V1,j = Mortgage rate (in percent)

V2,j = Growth in construction loans (in percent, Y/Y)

V3,j = Population growth (in percent, Y/Y)

V4,j = Nominal GDP growth (in percent, Y/Y)

αj = Constant

εj = Residuals

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Results

2. The predictor derived from the regression provides a good in-sample fit. The model is constructed using four explanatory variables: mortgage rate (as proxy for the cost of mortgage financing); growth in commercial banks’ lending to the construction sector (as proxy for expected future real estate volume); population growth (as proxy for demand for housing), and nominal GDP growth (as a proxy for income levels). The estimated coefficients are all in the expected signs and are statistically significant. Positive growths in lending to the construction sector, population, and nominal GDP lead to an increase in residential mortgage loan growth, in line with empirical findings. Conversely, higher mortgage rates increase financing costs to borrowers, thus reduce the demand for mortgage loans. The observed residential mortgage loan growth and the predictor are cointegrated, based on Engel-Granger test, and no significant autocorrelation is detected.

Regression Analysis with Growth in Commercial Bank’s Residential Mortgage Loans (percent change, y/y) as Dependent Variable 1/

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Source: IMF staff calculations.Note: Standard errors are in brackets.

All coefficients are significant at 1%, except for mortgage rate which is significant at 10%.

B. Hypothesis Test: Are the Observed Growths in Banks’ Mortgage Loans Significantly Different from the Predictor in Q1 2019 and Q2 2019?

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Hypothesis:

  • Ho: (A)-(B) = 0

  • H1: (A)-(B) ≠ 0

Mean= 0.0818; Standard deviation= 1.7762; N=43

For Q1 2019:

  • Test statistic, Zi = 0.8593

  • Critical value, tcrit,i, =1.681, based on t-distribution with 5 percent significance level and n=43

Results: Accept Ho since Zi < tcrit,i,. Therefore, the observed growth in banks’ mortgage loans is not significantly different from the predictor in Q1–2019.

For Q2 2019:

  • Test statistic, Zj = 1.0292

  • Critical value, tcrit,j =1.681, based on t-distribution with 5 percent significance level and n=43

Results: Accept Ho since Zj < tcrit,j. Therefore, the observed growth in banks’ mortgage loans is not significantly different from the predictor in Q2–2019.

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1

Prepared by Julian Chow (WHD).

2

Based on data from Numbeo which defines mortgage as percentage of income as a ratio of the actual monthly cost of the mortgage to take-home family income.

3

Numbeo defines price to income ratio as the ratio of median apartment prices to median familial disposable income, expressed as years of income (lower is less risky). Price to rent ratio is computed as the average cost of ownership divided by rent (lower values suggest that it is better to buy rather than rent, and vice versa).

4

Latin American countries comprise Argentina, Bolivia, Brazil, Chile, Colombia, Costa Rica, Mexico, Peru and Venezuela.

5

Numbeo defines mortgage-to-income as a ratio of the actual monthly cost of the mortgage to take-home family income.

6

While the authorities have created a price index of new housing (Indice de Precios de Vivienda Nueva (VNPI)), which includes Panama City and San Miguelito, it is used for internal monitoring and is not publicly available.

7

First time buyers of new homes receive preferential mortgage interest rate (two percentage points off the market rate) for residential real estate valued at US$180,000 and below.

8

For further details, refer to IMF, 2014a and IMF, 2014b

9

Research shows these indicators together can predict a crisis as early as two to four years in advance (IMF, 2011a).

10

See the BCBS consultative document (http://www.bis.org/bcbs/publ/d307.pdf) proposing a range of risk weights (from 25 to 100 percent) driven by LTV and DSTI ratios.

11

In Panama, commercial banks are regulated by the Superintendency of Banks; insurance companies are regulated by the Superintendence of Insurance and Reinsurance; and cooperatives are supervised by the Panamanian Autonomous Institute for Cooperatives.

1

An alternative specification for the econometric model has also been explored with filtered real GDP growth, using the Hodrick-Prescott (HP) filter to remove short-term fluctuations associated with business cycles. However, the coefficient of this variable is not statistically significant.

2

The frequency of the source data for price to income ratio and population are semi-annual and annual, respectively.

3

Google is the largest global internet search engine, with a share of over 90 percent of search activity. The Google Trends data—available since January 2004 on a monthly basis—aggregate individual search queries on G according to terms, time, category and location based on the IP address from which the search is conducted. Stephens-Davidowitz and Varian (2014) provide further details on the construction of the Google Trends data.

See Cevik (2020, forthcoming), Narita and Yin (2018), Carrieré-Swallow and Labbé (2013).

Based on Engel-Granger test for cointegration and Breusch-Godfrey serial correlation LM.oogle according to terms, time, category and location based on the IP address from which the search is conducted. Stephens-Davidowitz and Varian (2014) provide further details on the construction of the Google Trends data.

4

See Cevik (2020, forthcoming), Narita and Yin (2018), Carrieré-Swallow and Labbé (2013)

5

Based on Engel-Granger test for cointegration and Breusch-Godfrey serial correlation LM.

Panama: Selected Issues
Author: International Monetary Fund. Western Hemisphere Dept.
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    Panama: Household Mortgage Debt Indicators

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    Banking Sector: Total Residential Mortgage Loans

    (In percent of total loans)

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    Panama: Real Estate Indicators

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    Property Price-to-Income Ratio: Actual vs. Predicted

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    Contribution of Explanatory Variables 1/

    (In percent)

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    Panama: Economic Growth and Commercial Banks’ Lending Rates

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    Growth in Commercial Banks’ Total Loans and Residential Mortgage Loans

    (In percent change, y/y)

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    Growth in Housing Loans: Actual vs. Predicted

    (In percent change,y/y)

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    Panama: Contribution of Explanatory Variables and Commercial Banks’ Mortgage Lending Rates

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    Panama: Limits on LTV and DSTI Ratios and Number of Countries at Each Range, 2014

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    Panama: Transmission Mechanism of Three Sectoral Macroprudential Instruments