## wp17190

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---

### I. Introduction — high-level findings
- House prices in the main three cities of Colombia (Bogota, Cali, and Medellin) rose by around 200 percent in nominal terms (110 percent in real terms) from 2005 to mid-2016.
- Some housing indicators reached historically high levels: housing affordability ratio, price-to-rent ratio, and mortgage debt.
- Model-based estimate: house prices are around 13 percent above equilibrium.
- Housing finance characteristics that mitigate risk:
  - ceiling on loan-to-value ratios of 70 percent for most loans (VIS loans have an 80 percent LTV ceiling);
  - mortgages usually contracted at a fixed interest rate;
  - full recourse on mortgages.

### II. Recent developments in the housing and mortgage markets — key statistics
- Households and tenure (2015):
  - Total households: 14 million.
  - Own home: 46 percent.
  - Rent: 38 percent.
  - Free usufruct: 16 percent.
- Housing deficit:
  - Total housing deficit: 25 percent of households in 2014 (down from 54 percent in 1993).
  - Quantitative deficit (2014): 9 percent of households.
  - Qualitative deficit (2014): 15 percent of households.
  - Rural total deficit (2014): 48 percent.
  - Urban total deficit (2014): 18 percent.
- Construction sector:
  - Construction share of GDP bottomed at around 4 percent in 2000.
  - Construction share of GDP: 9.7 percent in 2015.
  - Construction licenses declined in 2016.
- Cross-city house-price growth (new houses, 2005–2016 annual real rates):
  - Average home-price index: 5.18 percent.
  - Bucaramanga: 8 percent.
  - Armenia: 3.4 percent.
  - Prices grew stronger for middle and upper income levels across cities.
- Mortgage credit:
  - Real mortgage credit annual average growth since 2011: 13 percent.
  - Mortgage credit stock: about 5 percent of GDP.
  - Around 30 percent of mortgages originated in 2016 were subsidized.

### III. Housing finance characteristics — comparative and Colombian specifics
- Maximum observed LTV (cross-country):
  - Median maximum observed LTV: 83 percent.
  - Range in sample: 70 percent (Colombia, Hong Kong SAR, Hungary) to 125 percent (the Netherlands).
  - Colombia regulatory LTV limits:
    - VIS loans: 80 percent.
    - All other mortgage loans: 70 percent.
  - Actual LTV ratio in Colombia: 53 percent (below regulatory ceiling).
- Term to maturity:
  - Cross-country range: 7 years (Turkey) to 45 years (Sweden); median 25 years.
  - Colombia average mortgage maturity: 15 years.
  - Colombia effective duration: around 6‒7 years due to early prepayments.
- Funding model:
  - Most countries: retail deposits as primary source of mortgage financing; same for Colombia.
- Degree of lender recourse:
  - Colombia: full recourse on mortgages.
- Interest type:
  - Cross-sample: variable-rate mortgages dominant in 30 countries, fixed-rate in 12 countries, both types in 14 countries.
  - Variable rates more common in emerging economies.
  - Colombia: both variable- and fixed-rate mortgages offered; most households tend to choose fixed-rate mortgages.
- Prepayment penalties:
  - Colombia: no prepayment penalties; borrowers frequently make partial prepayments.
- VIS definition and benchmark:
  - VIS: a house or apartment whose value is below 135 monthly minimum wages: around USD$31,000 in 2016.

### IV. House price determinants — model, variables, and estimation
- Model framework:
  - Uses an error-correction model relating short-term changes in real house prices to a long-term equilibrium (affordability), interest rates, changes in income per capita, mortgage credit growth, past house-price growth, and population growth.
  - Quarterly data from Q1 1994 to Q1 2016.
  - Price measure: existing home price index from BanRep deflated by CPI; index covers existing houses in Bogota, Medellin, Cali, Barranquilla, Bucaramanga, Cucuta, Manizales, Neiva, Villavicencio and Soacha.
  - Housing affordability measured as the ratio of house prices to real GDP per capita.
- Key regression insights:
  - Long-run reversion to fundamentals: affordability is negatively related to changes in house prices and statistically significant.
  - Speed of correction: about 13 percent every quarter (misalignment is corrected gradually at this rate).
  - Economic fundamentals:
    - Mortgage credit growth and higher income per capita promote higher house prices.
    - Demographic factors (population growth) have a positive effect on house prices.
    - Interest rates have the expected negative sign.
  - Past growth rate coefficient unexpected: current growth rate negatively correlated with past growth rate.
  - Model fit and sample:
    - Number of observations: 85.
    - R-squared = 0.4565.
    - Adj R-squared = 0.4147.
    - Root MSE = .02293.
    - F(6, 78) = 10.92; Prob > F = 0.0000.
- Selected coefficient estimates (Table 1):
  - Constant (_cons): .1242847 (Std. Err. .0373041; t = 3.33; P = 0.001).
  - dlnpop: 5.079725 (Std. Err. 2.821283; t = 1.80; P = 0.076).
  - dlnc: .1200901 (Std. Err. .0214842; t = 5.59; P = 0.000).
  - rate: -.0017835 (Std. Err. .0008625; t = -2.07; P = 0.042).
  - dlnypc: .2093321 (Std. Err. .1252894; t = 1.67; P = 0.099).
  - lnafford1: -.132468 (Std. Err. .0315063; t = -4.20; P = 0.000).
  - hpi1: -.4009662 (Std. Err. .0926514; t = -4.33; P = 0.000).

### V. Misalignment estimates and valuation measures
- Misalignment estimation approach:
  - Assumes house prices are at equilibrium in selected base years (first quarter in years from 2005 to 2008 set to index 100), then accumulates predicted house price changes to compute fitted index values; estimated price gap = actual index − predicted index.
  - Misalignment averaged over base years for robustness.
  - Overvaluation threshold: average estimated gap above 10 percent of equilibrium price.
- Result:
  - Average estimated price gap: 13.5 percent.
  - Conclusion: house prices are moderately misaligned with respect to economic fundamentals (average gap > 10 percent).
- Traditional valuation ratios:
  - Price-to-income and price-to-rent ratios have been increasing steadily since 2011.
  - Only the price-to-rent ratio is above its overvaluation threshold (defined as at least one standard deviation above its historical average).

### VI. Macroeconomic effects of housing shocks — methodology and VAR results
- Methodology:
  - VAR model following Aspachs-Bracons and Rabanal (2011) and Igan and Loungani (2012).
  - Variables: real house prices, GDP, private consumption, investment, and a nominal short-term interest rate.
  - House price shocks identified using generalized impulse response approach (invariant to ordering).
  - Quarterly data from Q1 1994 until Q2 (text cutoff).
- VAR estimation and impulse-response to a house price shock:
  - Variables included: real GDP, real private consumption, real investment, the 90-day certificates of deposit interest rate, and the real existing house price index.
  - Data treatment: all variables introduced in the VAR in levels after taking natural logarithms, except for the nominal interest rate, which is introduced directly in levels.
  - Lag specification: the VAR is estimated using 3 lags.
  - Shock description: negative house price shock; responses shown with ±2 S.E. confidence bands.
- Key numerical responses reported:
  - House prices:
    - decline of 2.5 percent after one period.
    - cumulative decline of about 15 percent after two years.
  - Real investment:
    - initial decline of 2 percent.
    - cumulative impact of -21 percent over two years.
  - Real GDP and private consumption:
    - both decline by more than 3 percent (cumulative) after 8 quarters.
  - Statistical significance windows (based on reported confidence intervals):
    - investment: effect not statistically different from zero after the 5th quarter.
    - GDP: effect not statistically different from zero after the 3rd quarter.
    - consumption: effect not statistically different from zero after the 6th quarter.

### Conclusions on housing market risks and policy implications
- Overall assessment: risks from the housing market seem to be contained.
- Valuation gap: the average estimated gap is at 13 percent.
- Macroprudential measures: after the 1999 financial crisis the authorities have adopted measures such as the use of LTV limits; these, together with other housing financing characteristics, limit vulnerabilities stemming from the housing market.
- Current dynamics: the ongoing slowdown in economic activity should decelerate mortgage growth and impact the growth of house prices, which has started to show some signs of weakening.
- Recommendation: the authorities should continue to monitor closely the developments in credit and house price growth.

### Government housing programs (Annex I)
- Objectives and recent expansion:
  - traditionally aimed to reduce gaps in home ownership and improve social conditions.
  - recently used as countercyclical response to country-wide economic slowdown, exploiting construction spillovers (official estimates: construction sector is interconnected with 25 percent of the industrial sector).
  - PIPE (Plan to Boost Productivity and Employment) launched in 2013 and PIPE 2.0 in 2015 as examples.
- Output of programs:
  - number of houses sold with mortgage subsidies increased from about 21,000 in 2013 to about 33,000 in 2016.
- Program features and eligibility:
  - Mi Casa Ya targets households with income between 2 and 4 minimum wages and for houses worth up to US$30,000; offers support for down payments and a subsidy for interest payments.
  - FRECH offers interest rate subsidies of 4−5 percentage points and targets households earning up to 8 minimum wages and houses worth up to US$50,000.
  - FRECH is included in Mi Casa Ya through the program Mi Casa Ya subsidio a la tasa.
  - FRECH No-VIS: provides interest rate subsidies of 2.5 percentage points for houses worth up to US$82,378 and is open to any household regardless of income level.
  - Mi Casa Ya para Ahorradores: extension for households with income lower than 2 minimum wages.
- Administrative features:
  - government sets annual ceilings (cupos) for the number of houses to be subsidized under each program.
  - the programs are only for new houses.
- Impact on construction and fiscal multipliers:
  - construction sector outpaced total GDP growth during most recent years, supported in part by increase in residential construction.
  - clear boost to construction at the time of PIPE in 2013; response to PIPE 2.0 more muted.
  - official estimates of fiscal multipliers of housing programs vary from 10 for FRECH No-VIS to 1 for the free housing program.
- Notes on program metrics conversion:
  - House value limits are defined in number of minimum wages. For 2016, the values are converted using the official monthly minimum wage of US$230.
  - Data point: Excludes houses worth more than US$30,000.

### The 1998−99 financial crisis (Annex II) — origins and macrofinancial dynamics
- Background developments in the early 1990s:
  - process of financial and trade liberalization.
  - considerable reduction in public and private savings.
  - public spending increased substantially after the constitutional reform of 1991, introducing large social programs and higher expenditure by regional and municipal governments.
- Fiscal and saving outcomes:
  - public deficit deteriorated from near balance in 1992 to around 4 percent in 1998.
  - private savings fell from 14.1 percent of GDP in 1990 to 8.7 percent of GDP in 1998.
  - current account: from a 2 percent surplus in 1992 to a 6 percent deficit in 1998.
- Financial intermediation and asset boom:
  - large capital inflows intermediated by domestic financial system contributed to decline in private savings and a large credit expansion that fueled a boom in asset prices, particularly real estate.
  - bank credit as a share of GDP doubled between 1991 and 1997.
  - financial system vulnerabilities due to weak regulatory and supervisory systems.
- Crisis triggers and transmission:
  - between 1997 and 1999, reversal in capital flows and speculative attacks on the domestic currency following the Asian crisis.
  - authorities depreciated the exchange rate band and tightened monetary policy.
  - tightening and reversal in capital flows reduced liquidity and increased cost of funds.
  - mortgage loan indexing change in 1993: from the UPAC (an inflation index) to a market interest rate (the 90-day deposit rate).
  - rise in real interest rates and fall in house prices increased household financial burden, raising NPLs and worsening solvency ratios of intermediaries.
- Real economy and housing outcomes:
  - output fell by more than 4 percent in 1999.
  - real estate prices contracted by nearly 27 percent in real terms.

*Source: wp17190 (IMF Working Paper content provided).*

### References   _____________________________________________________________________________________  15

### References

### I. Introduction — high-level findings
- House prices in the main three cities of Colombia (Bogota, Cali, and Medellin) rose by around 200 percent in nominal terms (110 percent in real terms) from 2005 to mid-2016.
- Some housing indicators reached historically high levels: housing affordability ratio, price-to-rent ratio, and mortgage debt.
- Model-based estimate: house prices are around 13 percent above equilibrium.
- Housing finance characteristics that mitigate risk:
  - ceiling on loan-to-value ratios of 70 percent for most loans (VIS loans have an 80 percent LTV ceiling);
  - mortgages usually contracted at a fixed interest rate;
  - full recourse on mortgages.

### II. Recent developments in the housing and mortgage markets — key statistics
- Households and tenure (2015):
  - Total households: 14 million.
  - Own home: 46 percent.
  - Rent: 38 percent.
  - Free usufruct: 16 percent.
- Housing deficit:
  - Total housing deficit: 25 percent of households in 2014 (down from 54 percent in 1993).
  - Quantitative deficit (2014): 9 percent of households.
  - Qualitative deficit (2014): 15 percent of households.
  - Rural total deficit (2014): 48 percent.
  - Urban total deficit (2014): 18 percent.
- Construction sector:
  - Construction share of GDP bottomed at around 4 percent in 2000.
  - Construction share of GDP: 9.7 percent in 2015.
  - Construction licenses declined in 2016.
- Cross-city house-price growth (new houses, 2005–2016 annual real rates):
  - Average home-price index: 5.18 percent.
  - Bucaramanga: 8 percent.
  - Armenia: 3.4 percent.
  - Prices grew stronger for middle and upper income levels across cities.
- Mortgage credit:
  - Real mortgage credit annual average growth since 2011: 13 percent.
  - Mortgage credit stock: about 5 percent of GDP.
  - Around 30 percent of mortgages originated in 2016 were subsidized.

### III. Housing finance characteristics — comparative and Colombian specifics
- Maximum observed LTV (cross-country):
  - Median maximum observed LTV: 83 percent.
  - Range in sample: 70 percent (Colombia, Hong Kong SAR, Hungary) to 125 percent (the Netherlands).
  - Colombia regulatory LTV limits:
    - VIS loans: 80 percent.
    - All other mortgage loans: 70 percent.
  - Actual LTV ratio in Colombia: 53 percent (below regulatory ceiling).
- Term to maturity:
  - Cross-country range: 7 years (Turkey) to 45 years (Sweden); median 25 years.
  - Colombia average mortgage maturity: 15 years.
  - Colombia effective duration: around 6‒7 years due to early prepayments.
- Funding model:
  - Most countries: retail deposits as primary source of mortgage financing; same for Colombia.
- Degree of lender recourse:
  - Colombia: full recourse on mortgages.
- Interest type:
  - Cross-sample: variable-rate mortgages dominant in 30 countries, fixed-rate in 12 countries, both types in 14 countries.
  - Variable rates more common in emerging economies.
  - Colombia: both variable- and fixed-rate mortgages offered; most households tend to choose fixed-rate mortgages.
- Prepayment penalties:
  - Colombia: no prepayment penalties; borrowers frequently make partial prepayments.
- VIS definition and benchmark:
  - VIS: a house or apartment whose value is below 135 monthly minimum wages: around USD$31,000 in 2016.

### IV. House price determinants — model, variables, and estimation
- Model framework:
  - Uses an error-correction model relating short-term changes in real house prices to a long-term equilibrium (affordability), interest rates, changes in income per capita, mortgage credit growth, past house-price growth, and population growth.
  - Quarterly data from Q1 1994 to Q1 2016.
  - Price measure: existing home price index from BanRep deflated by CPI; index covers existing houses in Bogota, Medellin, Cali, Barranquilla, Bucaramanga, Cucuta, Manizales, Neiva, Villavicencio and Soacha.
  - Housing affordability measured as the ratio of house prices to real GDP per capita.
- Key regression insights:
  - Long-run reversion to fundamentals: affordability is negatively related to changes in house prices and statistically significant.
  - Speed of correction: about 13 percent every quarter (misalignment is corrected gradually at this rate).
  - Economic fundamentals:
    - Mortgage credit growth and higher income per capita promote higher house prices.
    - Demographic factors (population growth) have a positive effect on house prices.
    - Interest rates have the expected negative sign.
  - Past growth rate coefficient unexpected: current growth rate negatively correlated with past growth rate.
  - Model fit and sample:
    - Number of observations: 85.
    - R-squared = 0.4565.
    - Adj R-squared = 0.4147.
    - Root MSE = .02293.
    - F(6, 78) = 10.92; Prob > F = 0.0000.

- Selected coefficient estimates (Table 1):
  - Constant (_cons): .1242847 (Std. Err. .0373041; t = 3.33; P = 0.001).
  - dlnpop: 5.079725 (Std. Err. 2.821283; t = 1.80; P = 0.076).
  - dlnc: .1200901 (Std. Err. .0214842; t = 5.59; P = 0.000).
  - rate: -.0017835 (Std. Err. .0008625; t = -2.07; P = 0.042).
  - dlnypc: .2093321 (Std. Err. .1252894; t = 1.67; P = 0.099).
  - lnafford1: -.132468 (Std. Err. .0315063; t = -4.20; P = 0.000).
  - hpi1: -.4009662 (Std. Err. .0926514; t = -4.33; P = 0.000).

### V. Misalignment estimates and valuation measures
- Misalignment estimation approach:
  - Assumes house prices are at equilibrium in selected base years (first quarter in years from 2005 to 2008 set to index 100), then accumulates predicted house price changes to compute fitted index values; estimated price gap = actual index − predicted index.
  - Misalignment averaged over base years for robustness.
  - Overvaluation threshold: average estimated gap above 10 percent of equilibrium price.
- Result:
  - Average estimated price gap: 13.5 percent.
  - Conclusion: house prices are moderately misaligned with respect to economic fundamentals (average gap > 10 percent).
- Traditional valuation ratios:
  - Price-to-income and price-to-rent ratios have been increasing steadily since 2011.
  - Only the price-to-rent ratio is above its overvaluation threshold (defined as at least one standard deviation above its historical average).

### VI. Macroeconomic effects of housing shocks — methodology
- Approach:
  - VAR model following Aspachs-Bracons and Rabanal (2011) and Igan and Loungani (2012).
  - Variables: real house prices, GDP, private consumption, investment, and a nominal short-term interest rate.
  - House price shocks identified using generalized impulse response approach (invariant to ordering).
- Data used:
  - Quarterly data from Q1 1994 until Q2 (text cutoff), with generalized impulse response analysis to assess effects on macro variables.

*Source: wp17190 - References (IMF Working Paper content provided).*

### 2016. The domestic variables of interest are real GDP, real private consumption, real

### wp17190 - 2016. The domestic variables of interest are real GDP, real private consumption, real

### VAR estimation and impulse-response to a house price shock
- Variables included: real GDP, real private consumption, real investment, the 90-day certificates of deposit interest rate, and the real existing house price index.
- Data treatment: all variables introduced in the VAR in levels after taking natural logarithms, except for the nominal interest rate, which is introduced directly in levels.
- Lag specification: the VAR is estimated using 3 lags.
- Shock description: negative house price shock; responses shown with ±2 S.E. confidence bands.

Key numerical responses reported
- House prices:
  - decline of 2.5 percent after one period.
  - cumulative decline of about 15 percent after two years.
- Real investment:
  - initial decline of 2 percent.
  - cumulative impact of -21 percent over two years.
- Real GDP and private consumption:
  - both decline by more than 3 percent (cumulative) after 8 quarters.
- Statistical significance windows (based on reported confidence intervals):
  - investment: effect not statistically different from zero after the 5th quarter.
  - GDP: effect not statistically different from zero after the 3rd quarter.
  - consumption: effect not statistically different from zero after the 6th quarter.

### Conclusions on housing market risks and policy implications
- Overall assessment: risks from the housing market seem to be contained.
- Valuation gap: the average estimated gap is at 13 percent.
- Macroprudential measures: after the 1999 financial crisis the authorities have adopted measures such as the use of LTV limits; these, together with other housing financing characteristics, limit vulnerabilities stemming from the housing market.
- Current dynamics: the ongoing slowdown in economic activity should decelerate mortgage growth and impact the growth of house prices, which has started to show some signs of weakening.
- Recommendation: the authorities should continue to monitor closely the developments in credit and house price growth.

### Government housing programs (Annex I)
- Objectives and recent expansion:
  - traditionally aimed to reduce gaps in home ownership and improve social conditions.
  - recently used as countercyclical response to country-wide economic slowdown, exploiting construction spillovers (official estimates: construction sector is interconnected with 25 percent of the industrial sector).
  - PIPE (Plan to Boost Productivity and Employment) launched in 2013 and PIPE 2.0 in 2015 as examples.
- Output of programs:
  - number of houses sold with mortgage subsidies increased from about 21,000 in 2013 to about 33,000 in 2016.
- Program features and eligibility:
  - Mi Casa Ya targets households with income between 2 and 4 minimum wages and for houses worth up to US$30,000; offers support for down payments and a subsidy for interest payments.
  - FRECH offers interest rate subsidies of 4−5 percentage points and targets households earning up to 8 minimum wages and houses worth up to US$50,000.
  - FRECH is included in Mi Casa Ya through the program Mi Casa Ya subsidio a la tasa.
  - FRECH No-VIS: provides interest rate subsidies of 2.5 percentage points for houses worth up to US$82,378 and is open to any household regardless of income level.
  - Mi Casa Ya para Ahorradores: extension for households with income lower than 2 minimum wages.
- Administrative features:
  - government sets annual ceilings (cupos) for the number of houses to be subsidized under each program.
  - the programs are only for new houses.
- Impact on construction and fiscal multipliers:
  - construction sector outpaced total GDP growth during most recent years, supported in part by increase in residential construction.
  - clear boost to construction at the time of PIPE in 2013; response to PIPE 2.0 more muted.
  - official estimates of fiscal multipliers of housing programs vary from 10 for FRECH No-VIS to 1 for the free housing program.

Notes on program metrics conversion
- House value limits are defined in number of minimum wages. For 2016, the values are converted using the official monthly minimum wage of US$230.
- Data point: Excludes houses worth more than US$30,000.

### The 1998−99 financial crisis (Annex II) — origins and macrofinancial dynamics
- Background developments in the early 1990s:
  - process of financial and trade liberalization.
  - considerable reduction in public and private savings.
  - public spending increased substantially after the constitutional reform of 1991, introducing large social programs and higher expenditure by regional and municipal governments.
- Fiscal and saving outcomes:
  - public deficit deteriorated from near balance in 1992 to around 4 percent in 1998.
  - private savings fell from 14.1 percent of GDP in 1990 to 8.7 percent of GDP in 1998.
  - current account: from a 2 percent surplus in 1992 to a 6 percent deficit in 1998.
- Financial intermediation and asset boom:
  - large capital inflows intermediated by domestic financial system contributed to decline in private savings and a large credit expansion that fueled a boom in asset prices, particularly real estate.
  - bank credit as a share of GDP doubled between 1991 and 1997.
  - financial system vulnerabilities due to weak regulatory and supervisory systems.
- Crisis triggers and transmission:
  - between 1997 and 1999, reversal in capital flows and speculative attacks on the domestic currency following the Asian crisis.
  - authorities depreciated the exchange rate band and tightened monetary policy.
  - tightening and reversal in capital flows reduced liquidity and increased cost of funds.
  - mortgage loan indexing change in 1993: from the UPAC (an inflation index) to a market interest rate (the 90-day deposit rate).
  - rise in real interest rates and fall in house prices increased household financial burden, raising NPLs and worsening solvency ratios of intermediaries.
- Real economy and housing outcomes:
  - output fell by more than 4 percent in 1999.
  - real estate prices contracted by nearly 27 percent in real terms.

*Source: wp17190 - 2016 (IMF).*

---


_Source: https://www.imf.org/-/media/files/publications/wp/2017/wp17190.pdf_