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Social Housing Finance in Colombia
Research Proposal
Fedesarrollo
I.
MOTIVATION
Housing deficit in Colombia remains high, despite the recovery of the sector after the
crisis of the late nineties. According to the last census, the quantitative housing shortage
is 12.4% and there are 23.8% of households dwell in inadequate units, which adds up to
an effective housing deficit of 36.2%1. The literature suggests that formal housing in
Colombia is constrained, among other factors, by land markets failures, lengthy and
costly procedures to obtain building permits and low access to credit.
As a matter of fact, housing finance in Colombia is small as compared with Latin
American standards. Mortgage loans ranged from 3.0% to 3.5% of GDP during the last
five years, while the regional average is around 5%2. After the financial crisis of the late
nineties, which lasted from 1998 to 2001, the Colombian mortgage market recovery has
been weak. While the ratio of total loans increased from 23.8% of GDP in 2001 to
46.2% in 2008, mortgage credit dropped from 6.2% of GDP in 2001 to 3.5% in 20083.
Mortgage lending in Colombia only funds a third of total housing, while the rest is
funded by informal lenders or self-funded. Formal housing loans are concentrated on
the formally employed segment, leaving out 70% of social housing demand4 which is
comprised of households that earn their living from informal activities5. In fact, the
problem of labor informality, which reaches almost 65% in Colombia, is behind the
housing deficit and the lack of credit, since standard financial instruments don’t suit the
particular needs of this population.
The Colombian literature has identified different factors that constraint the access to
credit, especially by low-income people, among which two are particularly relevant.
First, even though poor families usually manage to accumulate a significant amount of
capital over the years by self-building, they may not have access to credit because loan
providers perceive a high risk of default from borrowers with low and volatile income
(Galindo, 2005). In the same direction, Galindo and Hofstetter (2006) estimated the
determinants of mortgage-interest rates in Colombia and found that at the
microeconomic level the main cause of high level of the interest rates is the credit risk
assumed by lenders. Second, the supply of social housing credit is also constrained by
the lack of collateral due to deficiencies in deed registration and the high costs and
length of recovering the collateral (Cardenas y Badel, 2003).
1
According to the National Department of Statistics (DANE) in 1993 the effective housing deficit was
53.7%, dropping to 36.2% in 2005.
2
Warnock and Warnock (2008)
3
Colombian Financial Superintendence
4
National Planning Department, Visión 2019, Ciudades Amables.
5
Rocha et al. (2007) estimate that a formal employee has a higher probability to access housing credit
than an informal employee.
To promote the provision of social housing in Colombia, the government has
implemented a number of instruments including, mainly tax exemptions, provision of
guarantees, direct subsidies and rediscount credits. However, the scope and
effectiveness of these instruments have been limited. As mentioned above housing
access to the poorest segments of the population is still significantly low. The program
of subsidies for social housing is the most important, providing direct, one-time
subsidies to home buyers who demonstrate having savings for 10% of the total cost of
the house. It is also expected that the subsidy facilitates access to credit. The provision
of guarantees to social housing credit by the National Guarantees Fund (NFG), which
aims to address the obstacles of lack of collateral and its recovery, is another instrument
of significant incidence on Colombian housing finance - approximately 65% of the
social housing loans are backed by these guarantees.
This study will focus on the access to housing credit by the low-income population. The
main objective is to determine the impact of public policies addressed to stimulate
social housing (input) on the access to housing credit (output) by the poor. Emphasis
will be placed on the effect of housing subsidies on the access of low-income
households to mortgage, as well as on the role played by the NGF in solving the lack of
guarantees for social housing credits. Additionally, we will evaluate the risk involved in
social housing credits, through the estimation of the determinants of housing credit
default probability.
II.
LITERATURE REVIEW
The housing sector in Colombia has been widely studied, with papers focusing on the
housing market, the mortgage market and the social housing.
Recently, Clavijo et al (2005) studied the Colombian housing market development and
find evidence suggesting that household’s disposable income, new housing prices and
real interest rates on mortgage credit are important determinants of housing demand.
Arbelaez (2004) also estimated the demand for housing and finds that credit, real
interest rates, employment and labor income play an important role in determining
housing demand.
There are also relevant recent studies regarding the housing financial market in
Colombia. For instance, Cardenas and Badel (2003) suggest that the financial crisis of
the late nineties in Colombia was a consequence of the drop in housing prices and a
significant increase in the value indebted by households, which deteriorated the loan to
value ratio in the market. Murcia (2007) used the Quality of Life Survey of 2003 to
analyze the socio-economic determinants of access to mortgage loans and credit cards
and they find that the probability of having a mortgage loan increases by 11.7% for
households in the higher quintile of the income distribution. This probability also
increases if the household has a housing subsidy or if it is located in urban areas. The
main limitation of this study is that the estimations only include households applying for
one of the credit instruments, which induces auto-selection biases.
Among the studies focused on social housing finance, Cuellar (2006) presents a
thorough analysis of the regulatory framework evolution and its incidence on housing
finance development. Rocha et al. (2006) study the main barriers of access to credit by
low-income people, estimating the determinants of supply and demand of social
housing credit. They find that the probability of getting a mortgage loan increases when
households have been granted a subsidy, have high and stable income, and hold
programmed saving accounts. Programmed saving accounts are even more important in
the econometric estimations than working in the formal sector. However, the authors
suggest that credit supply to informal workers is constrained by the lack of appropriate
mechanisms of information sharing, and recommend promoting programmed savings
among this population as a factor that signals income stability to credit suppliers. The
results of this study are estimated from cross-section data which makes it difficult to
analyze the dynamics of housing credit and its determinants.
The National Planning Department of Colombia (2007) evaluates the Urban Social
Housing Subsidies Program. The evidence shows that assets ownership, education level,
and access to information are determinants of the program participation. According to
this study, the program has positive and significant impact on the house and
neighborhood physical conditions, as well as on the beneficiary households’
expenditure and savings. However, it does not estimate the effect of the subsidy on the
access to housing finance.
Some studies evaluate policy instruments that seek to facilitate the supply of social
housing credit. Marulanda et al (2006) measure the fiscal cost of the guarantees
provided by the National Fund of Guarantees to back social mortgage securities and
housing credits, as well as the fiscal cost of tax exemptions designed to promote social
housing supply. The authors provide an analysis of the performance of these
instruments over time and with respect to set targets. Silva (2007) provides a financial
and operative analysis of the social housing rediscount credit provided by FINDETER
and formulates recommendations to strengthen the instrument and ensure its financial
sustainability.
In contrast with previous evaluations of the subsidy, which rely on time series or crosssection, this study offers an impact evaluation using micro-data information for a panel
of individuals from 2008 to 2009. The advantage of this methodology is that it allows
estimating the impact of subsidies on changes in credit access by the estimation of a
difference-in-difference specification. In addition, one of the most important
instruments for promoting social housing credit, the National Fund of Guarantees, has
been studied form a fiscal perspective but not from the impact on social housing credit
perspective. In this study we will evaluate the role of the guarantees in easing the credit
and the characteristics of the beneficiaries using information at individual-level.
III.
OUTCOMES
The outcome analysis covers the characteristics and evolution of the Colombian housing
market and those of the housing finance system. We will include a general description
of both markets, focusing on social housing.
1. The Housing Market
The characterization of the housing market will include, among others:
- Cycle and trend of the housing sector GDP, built area, building costs and
housing prices
- Regional distribution of housing GDP
-
Housing prices by income segment
Cycle and trends of land prices
Rental market functioning
Impact of the construction activity on employment
Analysis of the role of institutions and regulations of the housing market
Focusing on low-income housing we will use Fedesarrollo´s Longitudinal Social Survey
(see annex for a complete detail of the Survey) to provide an overview of the housing
system in Colombia, emphasizing the mechanisms used by the poorest households to
satisfy their housing needs and the conditions of the housing units where they live.
We will characterize the occupancy conditions (rental, occupancy with no title deed,
formal ownership), the expenses of the family on housing as a percentage of their
monthly income, the physical conditions of the house and its access to public utilities,
the number of families living in the same housing unit, household relocations over the
last four years and the reasons for moving to a different unit, as well as changes in the
general conditions of the dwelling unit.
This section will include a review of the existing diagnosis of the failures on land
market regulation and their incidence on social housing supply and the main public
policy instruments adopted to overcome this constraint. For example, we will study the
evolution of the regulation on housing macro-projects and land use, with emphasis on
its modifications over the last year (2009).
2. The Housing Finance Market
The characterization of housing finance market will include, among others:
- Cycle and trend of the housing loans relative to total loans, and as a percentage
of GDP
- Interest rate volatility and level
- Housing loans by income-segment
- Participation in the financial market of rediscount funds, commercial banks,
public banks, cooperatives (other organizations such as cajas de compensación
familiar), microfinance entities, and other intermediaries
- Credit supply by income segment and geographical location
- Evolution of mortgage backed securities markets
- Analysis of the role of institutions and regulations of the housing finance market
Based on information from Fedesarrollo’s Longitudinal Social Survey, we will evaluate
the credit housing access, using the following indicators:
- Distribution of beneficiaries per income quintile
- Percentage of benefited households earning most of their income from informal
vs. formal activities
- Socio-economic characteristics of the household’s head receiving the subsidy
(education attainment, income level, gender, geographical location, number of
members in the household)
- Quality of housing of beneficiaries receiving credit and its evolution over the
last two years (building materials, access to public utilities).
IV.
INCOMES
This section will describe the main public policy instruments adopted to promote
housing finance development and their evolution over the last ten years. Especial
attention will be placed on the description of regulations that impact social housing
finance including:
-
-
Social Housing Subsidies
Programmed Saving Accounts
Rediscount Banks, with emphasis on FINDETER and the different measures
adopted to strengthen the financial resources of the bank
National Guarantees Fund
o Guarantees to social housing credits
o Guarantees to securities on social housing
Voluntary agreements between the Government and the commercial banking
sector to provide a percentage of (0,5%) gross loans to social housing financing
National Saving Fund (Fondo Nacional del Ahorro)
Tax exemptions on revenues from social housing loans, mortgage securities and
housing leasing
Inflation coverage from UVR fluctuations above the inflation target
Interest rate subsidy implemented on April 2009
A compilation of qualitative information and time series will be analyzed in order to
determine the scope of the instruments over the last five years.
Based on information from Fedesarrollo’s Longitudinal Social Survey, we will evaluate
the social housing subsidies targeting, using the following indicators:
i.
Distribution of beneficiaries per income quintile
ii.
Percentage of benefited households earning most of their income from
informal vs. formal activities
iii.
Socio-economic characteristics of the household’s head receiving the
subsidy (education attainment, income level, gender, geographical location,
number of members in the household)
iv.
Quality of housing of beneficiaries receiving the subsidy and its evolution
over the last two years (building materials, access to public utilities)
IV.
LINKS BETWEEN INCOMES AND OUTCOMES
This section focuses on establishing the incidence of the social housing program of
subsidies and the provision of financial guarantees on the development of social
housing finance.
1. Evaluation of the impact of subsidies
a. Impact of subsidies on the access to credit
The purpose of this section is to identify the impact of social housing subsidies on
access to housing credit. We are particularly interested in identifying the population
segments obtaining a higher benefit from these subsidies, by including information on
their income level and their working conditions (formal/informal). Another feature we
will focus on is the incidence of bancarization on access to credit. We will control for
socio-economic conditions such as household head educational attainment, gender and
household geographical location.
We will estimate a difference-in-differences model using the panel data available for the
years 2008 and 2009 of Fedesarrollo’s Longitudinal Social Survey, which provides
information of households with subsidies and without subsidies, as well as the evolution
of the access to credit for both groups over time. The specification will be:
Yit   0  1 X it   2Tt   3 Subsidy it   4Tt * Subsidy it   5Wit  ci   it
Where:
Yit denotes the outcome for household i in period t, i.e. it is a dichotomous variable with
value Yit = 1 if a household has a housing credit in period t, and Yit = 0 otherwise.
Subsidyit is a dummy variable taking the value 1 if the household has a housing subsidy
in period t and 0 otherwise; Tt is a dummy variable taking the value 1 in the posttreatment period (after the first year of been benefitted by the subsidy) and 0 in the pretreatment period (in the first period of been benefitted by the subsidy).
Xit is a vector of exogenous household´s characteristics including household head’s
labor conditions (formal/informal) gender, age, and educational attainment. Xit also
includes the number of members of the household, the income level, and indicators for
household bancarization and payment habits.
The indicator of bancarization will include the household’s access to financial products
and services including: current and saving accounts, investments (certificates of term
deposits), credit cards, microcredit and insurances.
Following Murcia (2007) we will also construct an indicator of payment habits. This
indicator will include information regarding the following variables:
 Lags in housing debt payments during the last 4 months
 Lags in utilities payments and others associated with housing
maintenance during the last 4 months
 Lags in tax payments
This indicator is calculated according to the following expression:
PH i  f1 
mi  m
ut  ut
tax  tax
 f2  i
 f3  i
sm
sut
stax
Where:
mi is a dummy variable taking the value 1 if the household reports a lag in the payment
of housing debts in the last 12 months and 0 otherwise; m is the mean of this variable
reported by the households who did not pay this amount, and sm is the standard
deviation. The coefficient f1 is the first estimated principal component. The variables ut
and tax correspond to the utilities and tax payments, respectively. The weighted sum of
these components corresponds to the index of payment habits which will allow us to
assign a credit score to each household.
Wit corresponds to region-level control variables such as Gross Domestic Product
(GDP), Consumer Price Index (CPI), Securitized housing loans-to-mortgage debt ratio,
and Social-Interest Housing Loan Guarantees-to-Mortgage Debt ratio. Securitization is
one of the methods of financing the purchase of new or used housing and it is an
alternative to mitigate maturity mismatch and interest rate risks. Therefore, controlling
for this variable will allow us to consider the effect of this instrument to promote the
access to housing credit in Colombia. The Social-Interest Housing Loan Guarantees
program is administered by the Fondo Nacional de Garantías (National fund of
guarantees, FNG) who backs loans whose destination is to finance the acquisition of
Social-Interest Housing. The availability of this program facilitates access to credit to
low-income households who do not have collateral to apply for a loan.
The error term it is composed of individual, family and community unobserved
characteristics and a stochastic disturbance term, and ci is an unobserved fixed effect.
The interaction Tt * Subsidyit is a dummy variable which takes the value of 1 only for
the treatment group in the post-treatment period.
In the case of binary outcomes (such as whether or not a household has access to a
mortgage-backed loan), we use a probit model specification. In non-linear models, the
Difference-in-Differences estimator of the impact of subsidy on access to housing
finance is the estimate of  4 , the coefficient of the interaction term of the treatment and
time dummy (Puhani, 2008), i.e. Tt. and Subsidyit
2. Scope of the Guarantees to Social Housing Credit Program
Based on information from the National Guarantees Fund and the National Department
of Statistics, we will evaluate the guarantees program for social housing credits, using
data at individual level. We will analyze the following aspects, among others:





Distributions of guarantees per type of social housing (which proxies the
beneficiary’s income segment)
Guarantees coverage in relation to the total social housing outstanding loans
Value of guaranteed credits and value covered by the guarantee
Intermediaries using guarantees
Default incidence on credits guaranteed by the fund
In order to determine the effectiveness of the guarantees provided by the NGF, we will
conduct interviews to the banks specialized on mortgage credit. The basic questions to
be included in the questionnaire will be related to the following topics:


Resources spent on collateral recovery of social housing credits
Costs faced by the commercial bank when claiming guarantees from the
NGF


Changes in the screening methods used by the bank to select the
beneficiaries
Changes in the borrowers payment habits due to the availability of the
guarantees
3. Payment habits in the low-income segments of the population
We are interested in determining the difference in the payment habits of population
according to their income level. To estimate this, we will follow Morandé and García
(2006). The authors estimate the determinants of the probability of mortgage payment
using the following Logit model:
P( Payment) 

 'X
1 
 'X
Where:
Payment is a dichotomous variable with value Payment = 1 if a household has paid the
mortgage during the last 4 months, and Payment = 0 otherwise.
X is a vector of exogenous characteristics of the household, including:
i.
An indicator for the household head formality/informality activity
ii.
Gender, age and education attainment of the household head
iii.
Number of household members, household income level and the monthly
payment of the mortgage debt or rent as a percentage of total household
expenditure
This estimation will include information from 2007 to 2009. The advantage of using
information for this period is that it comprises an important shift in Colombia´s
economic growth, capturing possible changes on household behavior in response to
changes in the economic cycle.
V.
DATA AVAILABILITY
This research will contemplate the use of the annual Encuesta Social (Social Survey)
conducted by Fedesarrollo, which is Colombia’s only longitudinal survey on social
conditions. This survey allows the estimation of socio-economic indicators related to
poverty, access to housing, employment, social security services, education and regular
household bills. Also, this survey provides data on migration, crime and victimization,
participation in welfare social programs, and access to health services and financial
products. The sample is representative for the six socio-economic strata in thirteen cities
in Colombia: Bogotá, Cali, Bucaramanga, Medellin, Cali, Barranquilla, Manizales,
Cartagena, Pasto, Pereira, Monteria, Villavicencio, Cucuta and Ibagué.
Another survey, Encuesta de Calidad de Vida conducted by DANE is another important
source of information. However, this survey is not designed as one of longitudinal type.
In contrast with a longitudinal study, this type of survey does not allow researchers to
exclude time-invariant unobserved individual differences (See template).
Table 1. DATA AND SOURCES OF INFORMATION
Variables
Sources
Period
Frequency
i. Housing sector variables
GDP of construction sector
Employment of construction sector
Total supply of Housing
Social and Non-social Housing Supply
Stock of social-interest housing (VIS): Finished and currently under construction
Stock of housing: Finished and currently under construction
Stock of housing: Rental market and ownership indicators
Average constructed area (Sq. Mts)by type (Social and Non-Social housing)
Costs of the industry (ICCV Spanish acronym)
Real index of Housing prices (Total and by income segment)
Land prices
Price Index of new housing (IPVN Spanish acronym)
Price Index of used housing (IPVU Spanish acronym)
DANE; National Planning Department
DANE
DANE
DANE
DANE
DANE
DANE
DANE
DANE
DANE; Jaramillo (2004)
Fedesarrollo; Jaramillo (2004)
DANE
DANE
1971 - 2009
1991 - 2009
1990 - 2009
1990 - 2009
2002 - 2009
1990 - 2009
1990 - 2009
2002 - 2009
2003 - 2009
1973 - 2009
1973 - 2004
2004 - 2009
2005 - 2009
Quarterly
Monthly
Monthly
Monthly
Monthly
Monthly
Quarterly
Monthly
Monthly
Monthly
Annual
Monthly
Monthly
ii. Housing finance market variables
Number of disbursed loans
Value of disbursed loans for adquisition and construction
Financial Superintendence of Colombia
Financial Superintendence of Colombia; CAMACOL
(Colombian construction chamber)
Nominal nterest rate
Financial Superintendence of Colombia; CAMACOL;
Banco de la
República (Central Bank)
Balance of mortgage loans
DANE; National Planning Department
Number and value of mortgage-loans (total, by type of housing and by geographical location)
DANE; National Planning Department
Number and value of housing subsidies
Ministerio de Medio ambiente, Vivienda y Desarrollo
Stock of mortgage-backed securities
Titularizadora Colombiana; CAMACOL
Index of Mortgage-Debt Quality
Financial Superintendence of Colombia
Participant institutions in housing finance market
Financial Superintendence of Colombia
Programmed Savings Acounts
CAMACOL
2005 - 2009 Quarterly
2003 - 2009 Monthly
1996 - 2009 Monthly
2000 - 2009
1996 - 2009
1997 - 2009
2002 - 2009
2000 - 2009
2001 - 2009
2000 - 2009
Monthly
Monthly
Monthly
Monthly
Monthly
Monthly
Monthly
iii. National Guarantees Fund (Fondo Nacional de Garantias)
Type of guarantee for social-interest housing finance
Municipality of the beneficiary of guaranteed loans
Value of disbursed guaranteed loans
Value of guarantee
Maturity of guaranteed loans
Loan-to-value ratio of guaranteed loans
Guarantee-to-Loan ratio of guaranteed loans
Number and value of unpaid guaranteed loans
National Guarantees Fund (Fondo Nacional de Garantias) 2005-2009
National Guarantees Fund (Fondo Nacional de Garantias) 2005-2009
National Guarantees Fund (Fondo Nacional de Garantias) 2005-2009
National Guarantees Fund (Fondo Nacional de Garantias) 2005-2009
National Guarantees Fund (Fondo Nacional de Garantias) 2005-2009
National Guarantees Fund (Fondo Nacional de Garantias) 2005-2009
National Guarantees Fund (Fondo Nacional de Garantias) 2005-2009
National Guarantees Fund (Fondo Nacional de Garantias) 2005-2009
Monthly
Monthly
Monthly
Monthly
Monthly
Monthly
Monthly
Monthly
Fedesarrollo
Fedesarrollo
Fedesarrollo
Fedesarrollo
Fedesarrollo
Fedesarrollo
Fedesarrollo
Fedesarrollo
Annual
Annual
Annual
Annual
Annual
Annual
Annual
Annual
iv. Fedesarrollo's Social Survey
Household socio-economic characteristics
Housing characteristics
Participation in social housing subsidies program
Access to housing credit
Bancarization
Education
Labor market (informality/formality)
Behavioural changes as a response to economic shocks
2007 - 2009
2007 - 2009
2007 - 2009
2007 - 2009
2007 - 2009
2007 - 2009
2007 - 2009
2007 - 2009
The questionaire of the Social Survey conducted by Fedesarrollo could be provided by
request.
v. Regulatory Data
Laws, Jurisdiction and Regulation
VI.
Cuellar (2006); Ministerio de Medio ambiente, Vivienda
y Desarrollo Territorial; Financial Superintendence of
Colombia, National Planning Department
WORK TEAM
The work team is composed of four researchers. Roberto Steiner will act as advisor of
the project, María Angélica Arbeláez will be the director of the project, Carolina
Camacho will be a senior researcher and Johanna Fajardo will be the research assistant.
VII.
REFERENCES
Arbeláez, M. (2006). “El Sector Hipotecario Colombiano”, FEDESARROLLO
(Mimeo)
Cárdenas, M. y Badel, A. (2003). “La crisis de financiamiento hipotecario en
Colombia: causas y consecuencias”. IDB. Documento de Trabajo No. 500.
Clavijo, S, M. Janna and S. Muñoz (2005). “The Housing Market in Colombia:
Socioeconomic and Financial Determinants”, IDB, Working Paper No. 522
Cuellar, M. (2006), “¿A la vivienda quien la ronda?”, Instituto Colombiano de Ahorro
y Vivienda, Universidad Externado de Colombia, p. 363-399
Galindo, A. and M. Hofstetter (2006) “Determinantes de la tasa de interés de los
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Galindo, A. and E. Lora (2005) “Foundations of Housing Finance” in IDB (ed.),
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Social Urbana: Impactos en la calidad de vida y evaluación del proceso de
focalización”
Puhani, P. (2008), “The Treatment Effect, the Cross Difference, and the Interaction
Term in Nonlinear “Difference-in-Differences” Models“, IZA, Discussion Paper No.
3478
Rocha, R., F. Sánchez and J. Tovar (2007), “Informalidad del mercado de crédito para
la vivienda de interés social”, Documento CEDE 2007-10.
Silva, J. (2007), “Línea de redescuento de Vivienda de Interés Social”, FINDETER
Warnock, V.C. and F. Warnock (2008), “Markets and Housing Finance”, Journal of
Housing Economics, 17:239-251.