Journal of Housing Economics 63 (2024) 101970
Contents lists available at ScienceDirect
Journal of Housing Economics
journal homepage: www.elsevier.com/locate/jhec
The impact of subsidies on house prices in Mexico’s mortgage market for
low-income households 2008–2019
Gabriel Darío Ramírez Sierra *, Alayn Alejandro González Martínez , Miguel Ángel Monroy Cruz ,
Luis Gerardo Zapata Barrientos
Economic Studies Unit, Institute of the National Housing Fund for Workers (Infonavit), Mexico
A R T I C L E I N F O
A B S T R A C T
JEL classification:
R210
R310
R380
D820
H230
We estimate the effect of Mexico’s primary house-purchase subsidy program for low-income individuals on house
prices between 2008 and 2019, using administrative records from Infonavit, the nation’s largest mortgage
originator. We employ a fuzzy regression discontinuity design that leverages the existence of a threshold on the
borrower’s income that determined access to the subsidy program to identify the effect on house prices. Our
estimations yield statistically significant evidence that the subsidy led to an average increase in house prices of
863 US dollars for the program participants at the threshold during those years. This effect represents 28.9 % of
the average subsidy amount and 5.4 % of the average house price. The estimations control for individual, house,
and location characteristics. Furthermore, we find evidence that when an intermediary is involved in the
mortgage application process, there is a statistically significant price difference of 867 dollars for subsidy re­
cipients. On the contrary, this impact disappears when no external broker is involved. These intermediaries are
primarily real estate developers that build and sell the houses associated with the mortgages. These findings shed
light on how market structure could have nonnegligible impacts on equilibrium outcomes and on the welfare
effects of economic policy.
Keywords:
Housing market
Mortgage market
Housing subsidies
Price discrimination
1. Introduction
This study assesses the impact of Mexico’s primary house-purchase
subsidy program for low-income individuals during the period from
2008 to 2019. Specifically, it examines the effect of the subsidy program,
administered by the National Housing Commission (Conavi), the agency
in charge of implementing the Government’s housing policy, on house
price formation in that market. We employ administrative mortgage
records from the Institute of the National Housing Fund for Workers
(Infonavit) to study differences in house purchase prices between re­
cipients and non-recipients of the subsidy. Infonavit, a public financial
institution and the largest mortgage originator in the country, partici­
pated closely in the allocation of the subsidy among its clients.
Between 2008 and 2019, Infonavit originated 56.5 % of all mort­
gages nationwide and distributed 93.0 % of the housing subsidies. Over
this timeframe, the average subsidy amount an individual received for a
house purchase stood at 3338.6 dollars, this represents 17.8 % of the
total house price. Thus, for low-income individuals, the subsidy repre­
sents a significant proportion of the house price.
Utilizing a pseudo-experimental methodology grounded in a
discontinuity design, contingent upon eligibility criteria tied to indi­
vidual income, we assessed the difference between the price of subsi­
dized and non-subsidized houses over the period from 2008 to 2019.
Employing a fuzzy regression discontinuity design, we find statistically
significant evidence that the subsidy resulted in an average house price
increase of 863 US Dollars for beneficiaries of the program from 2008 to
2014. This effect represents 28.9 % of the average subsidy amount.1 The
estimation controls for state level and year fixed effects, as well as for
house and location characteristics.
Interestingly, we find evidence that these price differences are
accounted for mostly by supply-side considerations and the structure of
the market. When analyzing the effect of subsidies in house prices for
* Corresponding author at: Gustavo E. Campa 60, Guadalupe Inn 01020, Álvaro Obregón, Mexico City, Mexico.
E-mail addresses: gramirez@infonavit.org.mx (G.D. Ramírez Sierra), aagonzalez@infonavit.org.mx (A.A. González Martínez), mmonroyc@infonavit.org.mx
(M.Á. Monroy Cruz), lzapatab@infonavit.org.mx (L.G. Zapata Barrientos).
1
We consider the ratio of our estimated price difference between beneficiaries and non-beneficiaries (863 USD) to the average subsidy between 2008 and 2014
(2,991 USD).
https://doi.org/10.1016/j.jhe.2023.101970
Received 2 August 2022; Received in revised form 19 October 2023; Accepted 3 November 2023
Available online 12 November 2023
1051-1377/© 2023 Elsevier Inc. All rights reserved.
G.D. Ramírez Sierra et al.
Journal of Housing Economics 63 (2024) 101970
borrowers who received assistance in the mortgage application process
from an intermediary, our analysis reveals a statistically significant price
difference of 867 dollars between subsidized houses and not subsidized
houses in the same period. Furthermore, this impact disappears for the
subset of borrowers that did not receive the assistance of an interme­
diary.2 These intermediaries are primarily real estate developers who
construct and sell the houses linked with the mortgages. They partici­
pate actively in the mortgage origination process and often serve as
informal intermediaries for mortgage applications. As a result, real es­
tate developers wield substantial influence over individuals seeking to
purchase houses financed by Infonavit.
Standard economic theory predicts that when demand is subsidized
and supply exhibits inelasticity, the equilibrium price increases for all
market participants. The subsidy’s incidence depends on the relative
importance of demand and supply elasticities. However, theory also
suggests that when suppliers of a homogenous good have market power
and the ability to segment markets, they find optimal to set different
prices in each of these markets exploiting the differences in demand
elasticities.3 We argue that this phenomenon, in part, explains the price
effect that we estimated in the data. Developers, who possess knowledge
of the eligibility criteria for the subsidy, may employ market segmen­
tation strategies to set different prices for houses with similar attributes.
This paper contributes to a growing literature that analyzes the
relevance of market structure for public policy outcomes. Our results
provide valuable insights on how market structure could have non­
negligible impacts on equilibrium outcomes and on the welfare effects of
economic policy.
Regarding empirical research on the impact of housing subsidies, a
notable literature review conducted by Brackertz et al. (2015) examines
published studies on rent subsidies and their effects on prices. Their
primary finding relates to the regularity of the phenomenon across
different countries: it is common for property owners to partially absorb
monetary transfers intended for rent payments.4 The most recurrent
explanation provided by the authors is attributed to the short-term
inelasticity of housing supply. Due to the inability to adjust rapidly to
shifts in demand, suppliers are unable to increase the quantity or quality
of available housing. As a result, market mechanisms exert upward
pressure on rents.
From a theoretical perspective, the findings of this study demon­
strate the importance of examining market failures in the housing
market. Numerous research studies explain how this market is prone to
inefficient resource allocation. In general, the house placement business
carries fixed costs and sector-specific knowledge that serve as entry
barriers, resulting in a limited number of participants with high market
power. Notable works by Gyourko and Saiz (2006), Saiz (2010) and
Murphy (2018) focus on studying how building permit restrictions
constitute a significant barrier to entry in the housing market, as they
function as fixed costs that require substantial investments from
builders. Garmaise and Moskowitz (2004) examine the effect of infor­
mation asymmetries among builders on the real estate market. The au­
thors argue that the local knowledge possessed by certain producers
about the specific regional market in which they operate (particularly
regarding local permits) gives them greater power compared to other
potential market entrants.
In the literature on the introduction of direct transfers to demand,
Polyakova and Ryan (2021) study the impact of targeted subsidies for
low-income groups on market dynamics. By examining the insurance
market in the United States, the authors find that as fewer suppliers
participate, the mechanism that drives prices upward intensifies.
Furthermore, they identify externalities in prices affecting other groups
that are not beneficiaries of the subsidy. While their research focuses on
the insurance market, their conclusions are also relevant for other sec­
tors, such as housing. Similarly, Fillmore (2021) investigates the effects
of federal government financial support given to undergraduate and
graduate students in the United States on tuition fees. The author finds
that the universities’ access to information about the student applica­
tions for the program leads to a situation of price discrimination, which
intensifies when there is less competition.5
The structure of this paper unfolds as follows. First, we provide an
overview of the context in which the program was implemented, high­
lighting the main characteristics of the subsidy and the housing market
in Mexico for low-income individuals. Second, we discuss the data
sources, describing the variables contained in the database and speci­
fying their scope and the empirical strategy used to evaluate the subsidy
impact. Thirdly, we present the results of the analysis, and finally, we
provide our key insights and concluding remarks.
2. Institutional and housing policy background in Mexico
2.1. Conavi housing subsidy
The public policy we analyze is a nationwide housing subsidy aimed
at providing low-income individuals with access to suitable housing
solutions. In 2007, the National Housing Commission (Conavi), a
Mexican federal public agency, operated the program and established its
guidelines, which included different modalities such as the purchase of
new or used houses, house improvements, land purchase, and selfproduction. The resources could be allocated to cover a portion of the
house’s price, as well as mortgage origination fees, permits, taxes, and
other administrative expenses. This study specifically focuses on the
subsidy granted to the house purchase.
The program was implemented in 2007 and experienced its period of
greatest growth between 2014 and 2015. Over the period from 2007 to
2019, the amount of the subsidy that an individual received for a house
purchase was on average 3233.6 dollars and represented 17.8 % of the
total price of the house.6 Table 1 illustrates the program’s budget evo­
lution. The program began with a budget of 147.6 million USD in 2007
and grew up to 621.8 million USD in 2015. From 2017 onwards, the
program decreased in scope, with a budget of 25.7 million USD in 2019.
Additionally, throughout the analyzed period, Infonavit loans that were
associated with subsidized purchases accounted for between 87.5 % and
100 % of the total program, reaching its highest level in 2019, with
100.0 % of subsidies channeled through Infonavit.
The operation of the subsidy program involved three key partici­
pants: Conavi, the beneficiaries, and a fund-distribution entity, collec­
tively referred to as "Onavi". Onavis are mostly organizations with
nationwide coverage whose purpose is to provide financing and credit
support for homebuyers. The most important of these entities is Info­
navit. This paper exclusively focuses on beneficiaries who were also
Infonavit borrowers, which distributed 93.0 % of the total subsidies
between 2007 and 2019. Conavi, as part of the Federal Government,
received a budget to finance the program and retained authority over its
direction, the design of its operational rules, its parameters’ update, and
the resolution of unforeseen issues. Subsidies were assigned until the
budget resources were spent totally. The potential beneficiary of the
program submitted a subsidy application at the same time when
5
The authors draw this conclusion by studying the effects of including a
greater number of applications to universities (more competitors for a given
student) in the program application process.
6
The average transfer of 3,233.6 dollars refers to the average across all the
program beneficiaries (with a mortgage from Infonavit and from other in­
stitutions). In the case of the subsidies channeled through Infonavit, their
average value was 3,338.6 USD between 2008 and 2019.
2
All monetary values are expressed in US Dollars, which we calculated using
an average exchange rate between 2008 and 2019 of 15.1 pesos per US Dollar.
3
This is an example of third-degree price discrimination.
4
The authors review articles that study the topic in New Zealand, the United
Kingdom, France, and Finland.
2
G.D. Ramírez Sierra et al.
Journal of Housing Economics 63 (2024) 101970
established a maximum price that the property could have, and starting
from 2012, additional measurements were incorporated that modified
the maximum amount for the subsidy. Specifically, Conavi included
variables related to the availability of infrastructure and services in the
neighborhood, its living conditions, ecological sustainability, and
accessibility.9
The amount of the subsidy was calculated as the difference between
the value of the house to be financed and the sum of the maximum loan
amount with the down payment of the borrower. However, there were
certain limits imposed on the subsidy amount based on the quality
conditions of the property and whether it was a newly built house or an
existing one. The average subsidy amount for Infonavit borrowers
changed during the study period: in 2008, it was of 2.2 thousand USD,
and in 2019, it was of 3.6 thousand USD. It is worth mentioning that in
2016, the average subsidy reached its maximum level of 4.2 thousand
USD.
Table 1
Conavi Housing Subsidy Growth.
Nationwide
2007
2008
2009
2010
2011
2012
2013
2014
2015
2016
2017
2018
2019
Total 2007
- 2019
Infonavit
Number of
loans
Transfer
amount
(mUSD)1
Average
transfer
(USD)1
Share in the
number of loans
76,268
109,793
95,975
103,070
102,421
117,090
107,618
156,170
159,493
125,727
80,168
65,492
7060
1306,345
147.6
239.8
243.8
277.1
270.2
379.2
404.4
602.2
621.8
524.0
236.3
252.1
25.7
4224.2
1934.9
2184.0
2540.2
2688.1
2638.3
3238.4
3757.6
3856.0
3898.9
4167.9
2947.6
3849.7
3640.5
3233.6
95.5 %
90.1 %
87.5 %
90.8 %
90.2 %
92.2 %
94.4 %
97.3 %
92.4 %
92.3 %
96.9 %
98.2 %
100.0 %
93.0 %
2.2. Housing in Mexico
Note: Considers new and existing houses. 1 US Dollars, calculated using an
average exchange rate between 2008 and 2019 of 15.1 pesos per US Dollar.
mUSD referes to millions of dollars. Source: (Comisión Nacional de Vivienda,
2023)
To understand the context in which the program was implemented, it
is pertinent to identify the main participants involved in the housing
market in Mexico for low-income households:10 individual homebuyers,
real estate developers, real estate appraisers, public and private
financing companies, and the state as a regulator. Real estate developers
plan and build housing projects based on their own assessment of the
market and subject to housing regulations at the federal, state, and
municipality levels of government. They often play an active role as well
in the mortgage origination process as sales intermediaries.
The homebuyer selects the house he wishes to purchase and agrees
on a price with the seller based on the information available in the
market and can choose to finance it through private banks or through an
Onavi.11 Once with the price set, if the buyer applies for a mortgage loan
through an Onavi, he can use his accumulated savings in the Housing
Savings Account, which is part of the Pension Saving System (SAR), as a
down payment.12 At this step is when the borrower had the opportunity
to apply for a subsidy. The purchase transaction is then formalized with
the registration of the property deed carried out by public notaries.
In the Mexican housing market, there are specific conditions under
which the Conavi subsidy could have created distortions. On the one
hand, there is the way individuals inform themselves to choose a house
and its financing method. For years Infonavit has relied on sales in­
termediaries to originate mortgages, as it does not have many branches,
it only has 92 service centers across the country. Developers’ main
business activity is residential construction, but they play an essential
role in originating mortgages for Infonavit. They play a dual role: con­
structing developments of thousands of houses (known in Infonavit as
“bulk projects”) with the expectation of selling them to Infonavit cus­
tomers, and closely assisting these customers to apply for a mortgage at
Infonavit. As for the loan granting process, they help the customer
collect all the relevant information and requirements needed for a
applying for a mortgage with Infonavit, and Conavi directly provided
the subsidy to finance the house purchase. Thus, the subsidy functioned
as a non-repayable amount, delivered in a single payment, and did not
have allocation rules such as lotteries or waiting lists. It is important to
note that Infonavit distributed the subsidy without having any further
benefit or assuming any administrative or managerial responsibility.
Regarding the mortgage origination process, Infonavit, a public
institution offering universal housing financing schemes, uses a
distinctive credit procedure that distinguishes it from private mortgage
banks. Instead of evaluating credit applications based on the borrower’s
risk profile, Infonavit enables eligible beneficiaries to get housing
mortgages irrespective of their risk level, provided they meet specific
requirements primarily associated with job tenure. Consequently, the
beneficiaries with any level of income can obtain a mortgage loan.
Conavi neither established a registry of potential subsidy benefi­
ciaries nor executed extensive promotional campaigns. Individuals
became aware of the program while applying for their mortgage to
purchase a house when they received guidance on applying for the
subsidy to finance their pre-selected house. Furthermore, the subsidy
operated on a first-come, first-served basis, with subsidies being granted
until the annual budget was exhausted. As a result, it is possible that two
individuals with similar characteristics and housing needs could have
different outcomes, with one receiving the subsidy and the other not,
depending on whether budgetary resources were available at the time of
application.
The program’s target population consisted of low-income house­
holds with housing needs.7 As an initial criterion to identify potential
beneficiaries, two metrics were established: (1) the applicant’s income
and (2) the characteristics of the financed house. Regarding income,
maximum thresholds were established to determine eligibility. The
threshold was of approximately USD 11.5 per day in 2008 and under­
went three modifications in the following years.8 In terms of the pro­
gram rules related to the houses eligible for subsidy financing, Conavi
9
The use of the subsidy was limited to the purchase of houses with purchase
prices below certain thresholds that changed according to the property kind
(single family house or department) and its condition (new or existing).
10
There is a large mortgage and housing market in Mexico for higher income
individuals with a widespread participation of commercial banks. This paper
focuses in low-income population which is one of the main targets of Infonavit.
Nonetheless, Infonavit does originate mortgages for individuals with high
incomes.
11
As mentioned before, Onavis are mostly organizations with nationwide
coverage whose purpose is to provide financing and credit support for
homebuyers.
12
The Pension Saving System was initiated in Mexico in July 1997 and
designed the creation of an individual account for workers in the formal mar­
ket. The worker, the employer, and the state make regular contributions to
provide the worker with housing finance solutions and a pension.
7
The information was self-reported by the beneficiaries and Infonavit vali­
dated it according to the program’s operational rules.
8
As a reference, 44.1% of Infonavit borrowers between 2008 and 2014
earned 11.5 USD per day or lower at the time of applying for a mortgage.
During the peak period of subsidy growth in 2015, when the maximum
threshold for eligibility was set at 22.5 USD per day, 80.5% of Infonavit bor­
rowers had an income at or below that level.
3
G.D. Ramírez Sierra et al.
Journal of Housing Economics 63 (2024) 101970
mortgage (and presumably for the subsidy) and assist them in applying
for it.
On the other hand, developers have a vast knowledge about Info­
navit’s mortgage origination process as a result of their active involve­
ment in the sales process. As a result, when selling the houses, the
customers are more likely to be price-takers in the price bargain process
with the developer. It is worth emphasizing that internal estimates
reveal that approximately 65 % of sales intermediaries registered with
Infonavit are housing developers themselves. In the analytical frame­
work presented in this study, we refer to loans assisted by sales in­
termediaries as those in which the transaction was assisted by an
intermediary, as well as those coming from bulk projects. This inclusion
is justified by the observed active involvement of developers in the
marketing and promotion of both the houses in bulk projects and the
associated credit facilities.
number of housing subsidies between 2008 and 2019. On average, it
accounted for 93.0 % of the subsidized housing loans granted nation­
wide, as shown in Table 1. Based on this, we can state that, on the one
hand, our data covers the biggest portion of the mortgage market in
Mexico and, on the other hand, it lets us study nearly all the Conavi
subsidy recipients.
To study the conditions in which the houses in our data were pur­
chased, we refer to two operational aspects of Infonavit’s financing
process. First, the institute records the transaction price and all relevant
data regarding the appraisal of purchased houses. Infonavit collects
relevant information about the physical characteristics of the property,
the amenities of its neighborhood, and its location. Therefore, the data
source allows us to incorporate into the analysis the purchase price and
all variables that are used to appraise a house and that affect its value.
Second, we can identify in the data which houses were purchased with
the assistance of a sales intermediary. As discussed in the introduction,
Infonavit has historically relied on sales intermediaries to originate
loans. The developers possess a vast knowledge about Infonavit’s
mortgage origination process and about the economic housing market
conditions. As a result, when selling the houses, the customers are more
likely to be price-takers in the price bargain process with the developer.
Table 2 shows the main characteristics of the loans granted between
2008 and 2019 for the overall borrower universe of Infonavit, as well as
for those loans that were placed with a subsidy. For illustrative purposes,
we only show the mean and standard deviation of each variable in the
years 2008, 2010, 2012, 2014, 2016, 2018, and 2019. On the one hand,
we observe that Infonavit’s annual mortgage origination ranged from
241.4 thousand to 351.4 thousand credits during the study period,
showing a decreasing trend. The number of loans granted with a sub­
sidy, as well as their share in the total placement, grew between 2008
and 2014 and then progressively decreased until 2019. This trend is
consistent with the evolution of the subsidy program’s budget discussed
in the policy background.
On the other hand, Infonavit borrowers in the period from 2008 to
2019 had an average income of 17.6 USD per day, exceeding the median
income of workers in Mexico (13.6 USD per day) by 28.4 % when
averaging the annual surplus in the period.13 The decision to purchase a
house occurred around ages 32–34 years old with 4–6 years of work
experience, showing a declining trend over time. Men were over­
represented among borrowers, possibly reflecting gender disparities in
the labor market.14 In terms of the purchased house price, its value more
than doubled between 2008 and 2019, from 16,043 USD to 33,203 USD,
exhibiting a compound annual growth rate of 6.8 %. To cover this price,
borrowers contributed an average down payment ranging from 1813
USD to 3707 USD, which remained stable as a proportion of the house
price (averaging 12.1 % over the period).
In contrast, the monthly income of the subsidized borrowers, and the
price of their houses remained lower than the average Infonavit loan.
While the average income of borrowers remained, on average, 28.4 %
above the national median for the employed population, borrowers with
the subsidy had an income that was, on average, 36.5 % lower than the
national median. The price of houses purchased with a subsidy was, on
average, 20.8 % lower than that of the complete universe of Infonavit
borrowers. Additionally, there was a higher participation of women in
the program and of workers with less job tenure at the time of applying
for the loan. Finally, a significant aspect highlighted by Table 2 is that
sales intermediaries had consistently higher participation in the place­
ment of subsidized houses compared to non-subsidized ones, ranging
from 89 % to 100 %.The individual income of the subsidy recipient
3. Conceptual framework
In this section we provide the theoretical framework needed for our
analysis. The subsidy works as an incentive granted by the government
to house demanders and producers, aiming to increase the quantity
demanded by the former and produced by the latter. A recurring theo­
retical finding in the literature is that a subsidy generates a deadweight
loss that can be offset by positive externalities. If we use a partial
equilibrium model assuming perfect competition, after delivering a
lump-sum subsidy to buyers for the purchase of a house, the new equi­
librium will feature higher housing consumption, and higher prices with
respect to the scenario without a subsidy. Therefore, the subsidy’s
incidence will depend on the relative elasticities between supply and
demand.
In contrast, in a monopolistic market setting with third-degree price
discrimination, suppliers of a homogenous good have market power and
the ability to segment markets, and they find optimal to set different
prices in each of these markets exploiting the differences in demands
elasticities. Individuals with similar characteristics that are eligible for
the subsidy that increases their income have different demands than
those that are not eligible for the subsidy. These income differences,
depending on their preferences, might induce different demand elas­
ticities that the supplier could exploit to maximize its profits charging
different prices. Fig. 1 shows that in such a setting, individuals receiving
subsidies will face a higher price (P*) than those who do not receive one
(P).
Finally, it is important to note that if the producer does not have the
ability to engage in price discrimination, the price charged by the sup­
plier to demanders would lay at some point between (P*) and (P), uni­
formly across both groups. However, the price paid by the subsidized
demand would still be lower than the price paid by the non-subsidized
demand. This indicates that in the absence of a monopolistic structure
with the ability to engage in price discrimination, consumer surplus
would be greater compared to the existence of such a structure.
4. Data and empirical strategy
4.1. Data
We used Infonavit’s administrative records of the mortgage granting
process to conduct the analysis. We studied a total of 3.6 million
approved loans granted between January 1, 2008, and December 31,
2019, out of which 1.1 million (30.5 %) were granted with a Conavi
subsidy. Our study universe is limited to those loans used to purchase a
house regardless of its condition (newly built or existing) and that were
fully financed by Infonavit. Regarding the value of our data source, it is
important to highlight that Infonavit is the largest mortgage lender in
Mexico. Its total mortgage origination accounted for 56.5 % of the total
market during the study period. Furthermore, as discussed in the
introduction, Infonavit was the institution that channeled the highest
13
Refers to the median national income of the employed population, as re­
ported by the National Institute of Statistics and Geography through its Na­
tional Survey of Occupation and Employment.
14
For further details, refer to the research titled "Infonavit from a Gender
Perspective" (Infonavit bajo la perspectiva de género) in Infonavit (2019).
4
G.D. Ramírez Sierra et al.
Journal of Housing Economics 63 (2024) 101970
Fig. 1. The effect of a lump-sum subsidy in a third-degree price discrimination setting.
Table 2
Mortgage application variables.
Total mortgages originated
With subsidy
With subsidy (% of total)
All mortgages
Individual Daily Income (USD)1
Age (years)
Sex (Man=1)
Down payment (USD)1
Current job tenure (years)
House price (USD)1
Intermediary (=1)2
Mortgages with a subsidy
Individual Daily Income (USD)1
Age (years)
Sex (Man=1)
Down payment (USD)1
Current job tenure (years)
House price (USD)1
Subsidy amount (USD)1
Intermediary (=1)2
2008
2010
2012
2014
2016
2018
2019
347,579
101,183
29 %
351,399
90,675
26 %
305,952
100,434
33 %
300,848
152,114
51 %
281,468
111,434
40 %
276,821
59,109
21 %
241,420
7060
3%
13.48
(11.6)
32.19
(8.4)
0.65
(0.5)
1813
(2568.9)
4.69
(3.0)
16,043
(4301.4)
0.82
(0.4)
15.27
(14.5)
33.49
(8.7)
0.65
(0.5)
2576
(3902.7)
5.20
(3.3)
18,016
(4837.9)
0.83
(0.4)
14.12
(13.3)
33.05
(8.3)
0.65
(0.5)
2378
(3680.6)
3.81
(1.9)
19,274
(4959.0)
0.85
(0.4)
15.97
(14.3)
32.43
(8.2)
0.66
(0.5)
2509
(3975.9)
3.53
(1.3)
22,395
(7726.3)
0.82
(0.4)
19.83
(17.1)
32.46
(8.2)
0.67
(0.5)
3054
(4785.6)
3.63
(1.3)
25,863
(10,086.7)
0.75
(0.4)
24.27
(20.4)
32.49
(8.0)
0.66
(0.5)
3576
(5501.8)
3.69
(1.3)
30,960
(14,143.6)
0.73
(0.4)
26.31
(20.5)
32.23
(7.8)
0.67
(0.5)
3707
(5529.6)
3.71
(1.3)
33,203
(14,604.4)
0.71
(0.5)
6.35
(1.7)
32.42
(8.7)
0.57
(0.5)
847
(640.0)
4.11
(2.7)
12,941
(1097.9)
2125
(807.5)
0.89
(0.3)
6.79
(2.0)
33.01
(8.2)
0.57
(0.5)
1094
(821.2)
4.54
(2.8)
14,941
(1236.6)
2699
(1011.5)
0.92
(0.3)
7.43
(3.0)
33.17
(8.1)
0.59
(0.5)
1248
(1008.8)
3.68
(1.8)
16,744
(1900.6)
3246
(1203.7)
0.95
(0.2)
8.83
(3.5)
32.12
(8.2)
0.62
(0.5)
1341
(1107.2)
3.32
(1.2)
19,241
(2370.8)
3854
(873.9)
0.95
(0.2)
11.38
(4.3)
31.98
(8.1)
0.63
(0.5)
1536
(1323.2)
3.40
(1.3)
22,499
(3294.8)
4184
(974.4)
0.98
(0.2)
10.62
(2.8)
32.26
(7.8)
0.60
(0.5)
1520
(1302.6)
3.35
(1.2)
23,842
(2603.7)
3882
(1308.4)
0.98
(0.1)
11.35
(2.7)
32.32
(7.5)
0.56
(0.5)
1600
(1317.8)
3.54
(1.2)
25,065
(2560.1)
3641
(978.1)
1.00
(0.0)
Note: All variables with (=1) are indicator variables. Standard deviations (in parenthesis). 1 All monetary values are expressed in US Dollars, which we calculated using
an average exchange rate between 2008 and 2019 of 15.1 pesos per US Dollar. 2 Refers to the proportion of houses purchased with the assistance of a sales
intermediary.
population increased during the study period. The operating rules of the
program stated that it would be granted to people who could apply for
an Infonavit loan and who had an individual income (as registered in the
social security system) below a certain threshold that changed at
different points. We use this threshold to implement a regression
discontinuity design, as detailed in the methodology section. Between
2008 and 2014, the threshold was equivalent to USD 11.5 per day;
between 2015 and February 2017, it was equivalent to USD 22.2 per
day; between February 2017 and March 2018, it was equivalent to USD
17.7 per day, and finally between March 2018 and December 2019, it
was USD 12.4 per day.
A significant percentage (44.1 %) of the overall borrower population
between 2008 and 2014 exhibited a daily income below the threshold of
11.5 USD, thereby meeting the eligibility criteria for the subsidy
5
G.D. Ramírez Sierra et al.
Journal of Housing Economics 63 (2024) 101970
program. In 2015, the income requirement was substantially relaxed,
resulting in a higher percentage of the borrower population (80.5 %)
potentially being eligible for the subsidy in that year. However, the
percentage gradually declined thereafter, eventually leading to the
program’s complete termination in 2019.
Table 3 summarizes on the one hand the key characteristics of the
universe of houses acquired with an Infonavit loan, and on the other
hand, it summarizes the same characteristics of the universe that
received a subsidy. The table provides information for the years 2008,
2010, 2012, 2014, 2016, 2018, and 2019. Although a limited set of
variables is presented for illustrative purposes, a comprehensive table
with the complete set of characteristics, which were used as control
variables in the econometric analysis, is provided in the appendix
(Tables A.1 and A.2).
Regarding the provision of basic services, it is important to highlight
that most houses (over 90 %) had access to the drinking water and the
sewage collection networks (Table A.1 in the appendix). However,
approximately one-fourth of the entire universe of houses had a natural
gas connection (26.2 % on average between 2008 and 2019). As for the
property size, it changed importantly in this period. In terms of built-up
area, there was a growing trend in which the average size increased from
48.59 m2 in 2008 to 57.23 m2 in 2019. The lot size experienced more
substantial fluctuations: it changed from 116.04 m2 in 2008 to 96.07 m2
in 2019, but it did not consistently decrease in those years. It initially
increased to an average level of 141.76 m2 in 2012 and then declined
until 2019. Finally, regarding the location and neighborhood amenities,
it was found that, on average, 30.7 % of the houses had an extended set
of amenities (at least a market and a church within a 2.0-kilometer
radius of the property or other amenities such as public squares, hos­
pitals, and banks). Additionally, on average, 90.7 % of houses were in an
economic housing area, meeting minimum habitability standards or part
of a standardized prototype development (bulk projects, explained in
the background section).
In contrast, subsidized houses had consistently smaller built-up areas
(14.2 % smaller on average) and, in some years, it is observed that the lot
size of these subsidized houses exceeded that of the average Infonavitpurchased house. Additionally, they had a lower provision of services
(such as natural gas installation), a lower provision of infrastructure in
the neighborhood (such as public squares, hospitals, and banks) and
throughout all years, subsidized houses were more frequently located in
sprawl areas (on average 4 percentage points more likely). These char­
acteristics are consistent with the housing market trends observed be­
tween 2008 and 2019: the house developers focused on promoting the
purchase of smaller houses situated on the suburbs of urban centers.
eligibility cutoff that we consider to be comparable to each other. They
are part of same income groups, but those with an income marginally
bigger than the cutoff have an abrupt drop in their probability of
receiving the subsidy. This setting allows us to increase the compara­
bility between two groups: those slightly below the cutoff (i.e., those
with an income slightly smaller than the eligibility cutoff) and those
slightly above it. These two groups are supposed to be similar enough to
let us estimate a local average treatment effect when comparing their
housing choices.
More formally, let Wi ∈ {0, 1} denote the treatment variable, with
Wi = 1 if individual i received the subsidy and Wi = 0 otherwise.15 We
are interested on calculating an average treatment effect of Wi on the
house price Pi . In the standard Sharp Regression Discontinuity Design
(SRD), one observes that all the individuals that score below a certain
value c of a running variable Xi , income, receive the treatment and all
those above c do not receive it. In such a perfect compliance setting, the
probability of receiving the treatment drops from 1 to 0 when crossing
the cutoff. In our study, however, there is not perfect compliance. It is
true that limPr(Wi = 1|Xi = x) ∕
= limPr(Wi = 1|Xi = x). Therefore, we
x↓c
x↑c
observe a discontinuity at the cutoff on the probability of receiving the
subsidy, but the drop is not as large as in the SRD: there are individuals
above c receiving the subsidy and others below c not receiving it. The
Fuzzy Regression Discontinuity Design (FRD) estimator of the average
treatment effect of Wi on Pi is
limE(Pi |Xi = x) − limE(Pi |Xi = x)
τFRD =
x↓c
x↑c
limE(Wi |Xi = x) − limE(Wi |Xi = x)
x↓c
(1)
x↑c
As in a local average treatment effect setting, τFRD can be interpreted
as the treatment effect on Yi for those individuals that received the
treatment because their income was smaller than the eligibility cutoff c
and that would have not received it if their income had been bigger than
c. In other words, it adjusts a sharp design estimator so it incorporates
the fact that some people below the cutoff would have not participated
in the program despite being eligible and some people above the cutoff
would have participated regardless of having an income higher than the
cutoff. To identify τFRD we use a two-stage strategy that calculates a
reduced form estimate (which is equivalent to SRD) on the one hand and
a first stage estimate (that measures the treatment probability jump at
the cutoff) on the other hand. More specifically, the two stage models are
calculated as follows.
4.3. Reduced form
We restrict the data to a window of borrowers within a distance h of
the cutoff on the income. We estimated a weighted linear regression
function with triangular kernel weights that decrease with the distance
to c and, in our preferred specification, a set of covariates Zi (credit
application, house, and neighborhood characteristics, state-level, and
year fixed effects). Our estimation method solves the following optimi­
zation problem.
4.2. Empirical strategy
To evaluate the impact of Conavi’s subsidy, we compare house prices
across properties purchased by individuals who were part of the pro­
gram (treated population) and properties purchased by individuals who
were not. Specifically, we use a regression discontinuity design (RDD) to
measure the average treatment effect on prices at the individual income
cutoff that determined the possibility to participate in the program. The
estimate incorporates control variables for the house characteristics and
neighborhood characteristics. Since our database was built with Info­
navit’s administrative records, we refer to individuals in the database as
borrowers.
Given that Conavi subsidy’s operating rules established a cutoff on
the personal income of an applicant to determine his or her eligibility to
participate in the program, we exploit the discontinuity that such rule
generated on the proportion of borrowers receiving the subsidy. Since
the cutoff changed between 2008 and 2019, we study separately the
periods in which the cutoff had different values. Periods of analysis have
different lengths because the operating rules changed at different points
in time, as explained in the previous section. For each of these period
specifications, we find sets of borrowers in a neighborhood of the
N
∑
min
αP− ,αP+ ,βP− ,βP+ ,γ
i=1
[Pi − 1(Xi < c)(αP− + βP− Xi ) − 1(Xi ≥ c)(αP+ + βP+ Xi ) − Zi ]2
(2)
The values of αP+ and αP− lead then to the estimator of the reduced
form, τRF , expressed as follows.
̂
τRF = α̂
P+ − α
P−
(3)
15
Our notation is based on Imbens and Lemieux (2008) and on Calonico et al.
(2017).
6
G.D. Ramírez Sierra et al.
Journal of Housing Economics 63 (2024) 101970
Table 3
House characteristics of all houses purchased with an Infonavit loan.
All mortgages
With a natural gas connection (=1)
2
Built-up area (m )
Lot size (m2)
Single family home (=1)1
Extended urban infraestructure (=1)2
Sprawl location (=1)3
Economic housing area (=1)4
Mortgages with a subsidy
With a natural gas connection (=1)
Built-up area (m2)
Lot size (m2)
Single family home (=1)1
Extended urban infraestructure (=1)2
Sprawl location (=1)3
Economic housing area (=1)4
2008
2010
2012
2014
2016
2018
2019
0.37
(0.5)
48.59
(22.4)
116.04
(298.8)
0.94
(0.2)
0.46
(0.5)
0.21
(0.4)
0.91
(0.3)
0.26
(0.4)
49.63
(47.5)
110.89
(414.2)
0.87
(0.3)
0.24
(0.4)
0.24
(0.4)
0.90
(0.3)
0.21
(0.4)
50.45
(18.2)
141.76
(1001.6)
0.75
(0.4)
0.26
(0.4)
0.28
(0.4)
0.89
(0.3)
0.24
(0.4)
54.30
(20.3)
104.34
(280.3)
0.76
(0.4)
0.28
(0.5)
0.22
(0.4)
0.92
(0.3)
0.26
(0.4)
55.78
(20.8)
96.83
(255.2)
0.75
(0.4)
0.30
(0.5)
0.18
(0.4)
0.92
(0.3)
0.26
(0.4)
57.25
(22.6)
96.03
(235.6)
0.73
(0.4)
0.29
(0.5)
0.13
(0.3)
0.90
(0.3)
0.27
(0.4)
57.23
(22.4)
96.07
(201.7)
0.75
(0.4)
0.28
(0.4)
0.12
(0.3)
0.89
(0.3)
0.33
(0.5)
41.90
(11.9)
98.31
(94.6)
0.94
(0.2)
0.46
(0.5)
0.23
(0.4)
0.94
(0.2)
0.23
(0.4)
42.37
(11.7)
115.75
(438.8)
0.82
(0.4)
0.29
(0.5)
0.28
(0.5)
0.93
(0.3)
0.15
(0.4)
44.90
(10.6)
161.18
(1343.0)
0.56
(0.5)
0.27
(0.4)
0.32
(0.5)
0.91
(0.3)
0.22
(0.4)
47.21
(10.3)
89.37
(264.9)
0.66
(0.5)
0.32
(0.5)
0.27
(0.4)
0.96
(0.2)
0.21
(0.4)
48.00
(8.3)
74.80
(119.8)
0.60
(0.5)
0.34
(0.5)
0.22
(0.4)
0.98
(0.1)
0.19
(0.4)
47.33
(6.7)
67.70
(36.4)
0.49
(0.5)
0.32
(0.5)
0.13
(0.3)
0.98
(0.1)
0.12
(0.3)
46.66
(4.8)
68.35
(37.1)
0.44
(0.5)
0.38
(0.5)
0.19
(0.4)
0.99
(0.1)
Note: All variables with (=1) are indicator variables. Standard deviations (in parenthesis). 1 Refers to the type of building, which can be a single-family home, a
condominium, an apartment, or a multiple dwelling. Multiple dwelling refers to a house in which more than one family lives. 2 Refers to houses with additional
amenities, including at least a church, and a market within a 2.0-kilometer radius of the house. 3 Refers to the location of the property based on its accessibility and its
recognition by the government as part of the urban area. The proximity classification can be central, intermediate, peripheral, sprawl and rural. Sprawl locations are
recognized by the government as potential growth areas, but that are not currently bounded by primary roads or expressways. 4 Refers to the predominant classification
of houses in the area. We group houses as "social interest" when they either meet minimum habitability standards or that are constructed in clusters based on stan­
dardized prototypes.
4.4. First stage
difference of 863 USD between beneficiaries and non-beneficiaries close
to the income eligibility cutoff of 11.5 daily USD for the period that
spans between 2008 and 2014. This Local Average Treatment Effect for
those borrowers close to the cutoff represents 5.4 % of the average house
purchased with a subsidy and 28.9 % of the average subsidy amount.
The estimates come from a Fuzzy Regression Discontinuity Design (FRD)
that measures the local average treatment effect on prices at the income
cutoff. Our estimates remain robust when we add control variables for
housing and neighborhood characteristics, yearly, and state-level fixed
effects. We also find that this effect was only statistically significant in
the group of borrowers that bought the property with the assistance of a
sales intermediary.
Fig. 2 shows the distribution of the borrowers’ income, which is the
running variable of our FRD models. Each of the four graphs in the figure
display a specific period of analysis in which the cutoff changed between
2008 and 2019, as explained in the data section. The first period runs
from January 1st, 2008, to December 31st, 2014; the second one, from
January 1st, 2015, to February 3rd, 2017; the third one, from February
4th, 2017, to March 7th, 2018, and the final one, from March 8th, 2018,
to December 31st, 2019. The cutoff of each period is depicted with a red
vertical line. Visually, we cannot affirm that the density function is
continuous at the cutoff in all periods. Further, we visually identify that
the density function shows discontinuities at 11.5 USD between 2008
and 2014 and at 17.7 USD between 2017 and 2018.
With the McCrary test for continuity, we reject at the 0.01 level the
null hypothesis of continuity at the cutoff in three out of the four periods
studied (the period from 2018 to 2019 is the only one in which we did
not reject the null hypothesis). However, we know that borrowers above
As in the reduced form, we restrict the data to a window of borrowers
within a distance h to the cutoff on the income, but with the treatment
indicator Wi as the dependent variable instead of the price Pi .
N
∑
min
αW− ,αW+ ,βW− ,βW+
[Wi − 1(Xi < c)(αW− + βW− Xi ) − 1(Xi ≥ c)(αW+ + βW+ Xi )]2
i=1
(4)
The values αW+ and αW− are then used to derive the estimator for the
reduced form, denoted as τFS , which can be expressed as follows.
̂
τFS = α̂
W+ − α
W−
(5)
4.5. Fuzzy regression discontinuity design estimator
The interest effect from Eq. (1) can be calculated then as follows.
τFRD =
̂
α̂
τRF
P+ − α
P−
=
̂
τFS
α̂
W+ − α
W−
(7)
For the estimation of τFRD we use the bias-corrected inference pro­
cedure proposed by Calonico et al., 2017.
5. The effect of subsidies on house prices
5.1. The effect of CONAVI’s subsidy on house purchase prices
Our main result is that Conavi’s housing subsidy generated a price
7
G.D. Ramírez Sierra et al.
Journal of Housing Economics 63 (2024) 101970
Fig. 2. Density of the borrowers’ income.
the cutoff would have not been able to manipulate their income value.16
To address the discontinuity in our income density function, we
examine eight conditional mean functions for mortgage application
variables covering the period from 2008 to 2014 (Fig. A.1 in the ap­
pendix). We can visually detect that down payment, age, and marital
status present different mean values below and above the income cutoff.
To a smaller extent, sex presents a mean difference at the cutoff as well.
All of these conditional mean functions for the covariates are expected to
be continuous at the cutoff point, as the relationship between income
and these variables should remain unaffected by receiving a housing
subsidy. However, we do observe that borrowers on the left-hand side
were at the cutoff less likely to be married with respect to the ones in the
right-hand side, paid a lower down payment for their mortgage (which is
in this case the Housing Savings Account amount they had at Infonavit)
house, were less than a year older, and marginally more likely to be a
woman.
Figs. 3 and 4 show, respectively, conditional mean functions for
program receipt and house prices below and above the income cutoff.
On the one hand, the conditional program receipt (Fig. 3) presents a
jump at the income cutoff in three out of the four study periods (only the
period from 2017 to 2018 does not show a discontinuity). Further, the
largest difference of 29 percentage points was calculated in the period
from 2008 to 2014.17 On the other hand, mean house prices show a
significant jump in the period from 2008 to 2014: house prices are
estimated 281 USD higher in the left-hand side of the cutoff than in the
right-hand side.18 As mentioned before, the fact that we observe such a
sharp discontinuity on the price exactly above 11.5 USD contributes to
the hypothesis of a market segmentation implemented by the house
sellers, since it is based on a borrower characteristic rather than on a
difference of the house he or she purchased. However, we further study
the conditional mean functions of the house variables as well.
Regarding the conditional mean functions of house characteristics,
we examine those described in the data section to evaluate their possible
impact on the price difference we found at the income cutoff (Fig. A.2 in
the appendix, for the period from 2008 to 2014). These house and
neighborhood variables are widely used in the appraisal process that is
necessary to purchase a property with a mortgage from Infonavit.
Although three out of eleven of them present statistically significant
differences at the cutoff at a 0.05 level, we identify that only two could
lead up to an identification problem of the effect of the subsidy itself on
prices.
An interesting result is that property size and location seem to have
been affected by the subsidy. Built-up area is calculated to be less than a
square meter bigger on the right-hand side of the cutoff with respect to
the left-hand side of it. Similarly, houses with a parking space or with a
natural gas connection are less than a percentage point more frequent in
one side of the cutoff with respect to the other. Only lot size and location
appear to be more significant: houses on the left side were calculated to
be at the cutoff 29.79 m2 bigger, 0.87 percentage points more frequent
in a sprawl area, and 1.1 percentage points less frequent in an inter­
mediate area.19 To assess the effect of these differences on our calcula­
tion, we add these characteristics as control variables across our model
specifications to see if they change the price difference estimate at the
cutoff.
To assess the economic significance of the covariate differences
observed at the discontinuity, we conducted a standardized balance test,
as presented in the appendix (Fig. A.3). Notably, price exhibits the most
substantial standardized deviation when compared to all other study
16
The income used to evaluate program eligibility was calculated automati­
cally based on the monetary contributions made by the employers to Infonavit,
as a social security institution that collects such contributions to save into
employee Housing Savings Accounts.
17
This estimate comes from the first stage estimation used in our FRD model
without control variables. As in the main result, the estimation was made using
a bandwidth of 0.6 USD.
18
This estimate comes from the reduced form estimation used in our FRD
model without control variables. As in the main result, the estimation was made
using a bandwidth of 0.6 USD.
19
The initial balance analysis comes from estimating Sharp Regression
Discontinuity Designs for each of the covariates in the period between 2008 and
2014. As in the main result, the estimation was made using a bandwidth of 0.6
USD. The proximity classification can be central, intermediate, peripheral,
sprawl and rural. Sprawl locations are recognized by the government as po­
tential growth areas, but that are not currently bounded by primary roads or
expressways. Intermediate locations refer to those with access to primary roads
but typically bounded by intermediate speed ones.
8
G.D. Ramírez Sierra et al.
Journal of Housing Economics 63 (2024) 101970
Fig. 3. Program receipt around the cutoff.
variables, with a deviation of 0.13. The next larger deviations are
associated with credit application variables, particularly age (0.09) and
marital status (− 0.06). As for the house characteristics, the indicators
for natural gas on the property registered a deviation of − 0.05, while
sprawl location showed a deviation of 0.04. These findings suggest that
the covariate differences identified at the cutoff are not the primary
drivers of our main results, which are presented in Table 4.
We show our main results in Table 4. Our identification strategy
leads to an estimate of 963 USD in the specification without controls for
the period between 2008 and 2014. When adjusted for the exposure to
the subsidy, an initial difference of 227 USD (Fig. 4) increases more than
threefold.20 In order to assess the robustness of our results to the in­
clusion of control variables, we begin by incorporating those used in the
credit application process. These variables, while not expected to be
influenced by the receipt of the subsidy, were identified as not entirely
balanced at the cutoff. Although the estimate increases to 1115 USD, it
remains positive and statistically significant at the 0.01 level. Then, our
third model adds house and neighborhood characteristics as control
variables to find that the effect is only 31 USD smaller than the speci­
fication with no controls.
20
The initial difference of 227 USD comes from the reduced form estimation
used in our FRD model without control variables.
9
G.D. Ramírez Sierra et al.
Journal of Housing Economics 63 (2024) 101970
Fig. 4. House prices around the cutoff.
Finally, our fourth specification led to our preferred result of 863
USD when controlling for yearly and state-level fixed effects. Our esti­
mate shows then to be robust for the 2008 to 2014 period. Regarding the
relative size of the house price difference we found, our preferred esti­
mate represents 5.4 % of the average house price purchased between
2008 and 2014 and 28.9 % of the average transfer in the same period. As
for the rest of the periods, we do not find statistically significant effects
in any of the other specifications. Therefore, we do not consider any
other estimate as one in our results. Our model exhibits significant ef­
fects only in the period between 2008 and 2014, when the subsidy was
specifically aimed at the lowest income population.
We believe that there may be several reasons for this result, but our
hypotheses follow three lines. First, it is likely that the increase in the
income threshold (from $11.5 to $22.2 per day) may have altered the
profile of the borrowers at the cutoff. It could be the case that borrowers
at higher income levels display greater financial knowledge, giving them
a better position to negotiate with the seller or exhibit more price
elasticity. Second, the discontinuity in participation is less pronounced
after 2014. Probably, a smaller jump in participation at the cutoff leads
to less precise estimates from our model for the years from 2015 on­
wards. Finally, third, there is a possibility of greater homogeneity among
houses targeted at individuals with a daily income of $11.5 or less
compared to those with $22.2. The substantial changes in our estimation
results for periods after 2014, as well as the increased size of the
10
G.D. Ramírez Sierra et al.
Journal of Housing Economics 63 (2024) 101970
Table 4
Regression Discontinuity Models of Changes in House Prices in the Presence of a Subsidy.
(Dollars,1 Fuzzy Regression Discontinuity Designs)
R1
Cutoff1
January 2008 to December 2014
11.5
January 2015 to February 2017
22.2
February 2017 to March 2018
17.7
March 2018 to December 2019
12.4
963
(111.5)
− 18
(1043.1)
21,879
(33,258.8)
− 729
(684.2)
Controls
Credit Application
X
House characteristics
Neighborhood characteristics
State-level fixed effects
Year fixed effects
***
R2
R3
1115
(109.8)
− 147
(1008.5)
16,848
(22,735.5)
− 618
(643.2)
***
R4
932
(113.8)
− 213
(1031.8)
22,023
(37,194.2)
− 769
(670.7)
***
X
X
X
863
(115.2)
− 164
(998.2)
39,488
(140,202.9)
− 87
(673.2)
***
X
X
X
X
Note: Standard errors in parenthesis and using the bias-corrected inference procedure proposed by Calonico et al. (2017). All estimations were calculated withtin a
distance of 0.6 Dollars around the cutoff. House variables include the built-up area, floor area, and indicator variables for more than one bedroom, more than one
bathroom, parking space, natural gas connection, and metropolitan area. Neighrborhood variables include neighborhood kind (economic housing or other) and urban
proximity classification (sprawl or other). 1 US Dollars, calculated using an average exchange rate between 2008 and 2019 of 15.1 pesos per US Dollar.
Table 5
Regression Discontinuity Models of Changes in House Prices in the Presence of a Subsidy for Groups of Borrowers with and without the Assistance of a Sales
Intermediary.
(Dollars,1 Fuzzy Regression Discontinuity Designs)
R5
Purchased without an intermediary
Purchased with an intermediary
Controls
Credit application
House variables
Neighborhood variables
State-level fixed effects
Year fixed effects
− 6
(751.0)
1038
(111.2)
***
R6
R7
R8
99
(703.2)
1196
(109.1)
81
(741.0)
1007
(113.7)
627
(603.4)
867
(112.1)
X
***
X
X
***
***
X
X
X
X
Note: Standard errors in parenthesis and using the bias-corrected inference procedure proposed by Calonico et al. (2014). All estimations were calculated withtin a
distance of 0.6 Dollars around the cutoff. House variables include the built-up area, floor area, and indicator variables for more than one bedroom, more than one
bathroom, parking space, natural gas connection, and metropolitan area. Neighrborhood variables include neighborhood kind (economic housing or other) and urban
proximity classification (sprawl or other). 1 US Dollars, calculated using an average exchange rate between 2008 and 2019 of 15.1 pesos per US Dollar.
standard errors for those periods, may reflect increased variability in the
types of houses purchased.
We observe these results to be differentiated in the years that our
main period (2008- 2014) covers (Table A.3 in the appendix). Between
2008 and 2010, the estimate was calculated to be of between 835 and
1035 USD, reaching its biggest magnitude in 2010. From 2011 onwards,
we observe a change in which the effect is reduced first (it decreases its
size and statistical significance) and increases very significantly to 1461
USD in 2014. We consider this could be related to the fact a new gov­
ernment took office in Mexico in that year and published later in 2014 its
housing policy guidelines. This led to an overall higher house demand,
as Infonavit’s mortgage origination increased by 10.2 % in that year. As
shown in Table 1 in the introduction of this document, the number of
borrowers with a subsidy increased by 45.1 % between 2013 and 2014.
As noted previously, the introduction of the subsidy very probably
had an impact on the quality of houses purchased by the subsidized
population. However, the robustness of our findings when controlling
for crucial variables in the appraisal process suggests that another un­
derlying mechanism could explain the observed price difference. Spe­
cifically, we explore the role of sales intermediaries in inducing an
effect, which will be discussed in the following section.
When it comes to the spillover effects that the program may have
generated, it is true that it had a significant penetration in the housing
market. As discussed in Table 2, subsidized mortgages accounted for
more than 20 % of the total origination of Infonavit until 2018. There­
fore, although it exceeds the scope of this study, we acknowledge that
the program could have raised the average level of house prices beyond
the difference we estimated at the eligibility cutoff. As a result, our
estimation of the total effects of the subsidy on price levels in the market
may underestimate its overall impact, as it is limited to the local effect
observed close to the eligibility threshold on income and does not pro­
vide a counterfactual of how prices would have evolved in the market in
the absence of the subsidy.
11
G.D. Ramírez Sierra et al.
Journal of Housing Economics 63 (2024) 101970
5.2. Intermediation, subsidies, and effects on house prices
We believe that the combination of inefficiencies in the subsidy
allocation rules, along with the characteristics of the Mexican housing
market, resulted in an information asymmetry between house suppliers
and subsidy beneficiaries. Sales intermediaries, who had a better un­
derstanding of the program, could have imposed higher prices on sub­
sidized houses through a third-degree price discrimination setting.
These results highlight the significance of addressing inefficiencies and
information asymmetries in the housing markets. Such a situation holds
significant relevance for the development of public policies aimed at
ensuring the provision of affordable and quality housing for low-income
populations. It is crucial to adjust the operational rules of such programs
to ensure they achieve their objectives while minimizing the market
distortions they could generate.
This research also underscores the need for regular and rigorous
evaluation models to assess the impact of such programs. It is essential to
explore alternative approaches that mitigate the inherent information
asymmetries in Mexico’s housing market, which stem from factors such
as the decentralized nature of housing supply. Coordinated efforts be­
tween individuals, suppliers, and the government are crucial for estab­
lishing a national housing supply database that allows all market
participants to geolocate and access relevant information regarding
housing availability, characteristics, and prices. Moreover, within the
academic sphere, it is vital to extend research on information asym­
metries in the Mexican real estate market and its core inefficiencies. By
addressing these issues and fostering greater transparency and coordi­
nation, policymakers and researchers can work to create a more efficient
and equitable housing market that meets the needs of the population.
In concern to the channels that generated the estimated house price
difference, we argue that the presence of third-degree price discrimi­
nation practiced by housing developers targeting potential buyers could
play a relevant role in the price effect. We base our hypothesis on the
notion that developers may have selectively offered different prices
depending on their customer characteristics, which they could have
identified in the process to originate mortgages. To explore this hy­
pothesis, we changed our strategy to focus on the subset of buyers that
actively engaged with sales intermediaries during their house purchas­
ing process, as explained in the introduction.
We show in Table 5 that our price difference is driven by the
intermediary-assisted group, attaining a significance level of 0.01. The
magnitude of this effect ranged from 867 to 1196 USD. Our preferred
specification, which incorporates fixed effects at both state and year
levels, yielded an estimated effect of 867 USD. In contrast, in the group
of buyers without developer assistance, statistical significance was not
observed in any of the four specifications, and its size was not robust
across our sets of control variables.
Our results show that there was a statistically significant difference
between the prices of houses purchased with and without a housing
subsidy. Our estimate was driven by the group of borrowers that had the
assistance of a sales intermediary to purchase a property. This result was
calculated at the individual income level that was required to qualify for
the subsidy program, ensuring better comparability within houses than
other methodologies. Furthermore, it is shown that the differences
persist when adding control variables for characteristics that were not
fully balanced at the cutoff. Based on our results, we believe that the
estimated price difference could be consistent with a phenomenon of
price discrimination by sales intermediaries, who may have actively
participated in promoting the subsidy with their customers.
The active role of sales intermediaries on mortgage origination could
have played an important role to let them segment the market, as they
help the customer collect all the relevant information and requirements
needed for a mortgage and assist them in applying for it. In this setting,
demanders were likely to reveal relevant information about their in­
come profiles and identify themselves them as potential beneficiaries of
the subsidy. Furthermore, in the Mexican market, sales intermediaries
play a role wider than merely supplying houses to the market. They also
actively promote financing solutions for their purchase. In this context,
they have firsthand information about Infonavit’s credit process and the
interaction it may have with housing policy instruments, such as the
Conavi subsidy.
CRediT authorship contribution statement
Gabriel Darío Ramírez Sierra: Conceptualization, Methodology,
Writing – review & editing. Alayn Alejandro González Martínez:
Conceptualization, Formal analysis. Miguel Ángel Monroy Cruz:
Software, Data curation. Luis Gerardo Zapata Barrientos: Formal
analysis, Writing – original draft.
Declarations of Competing Interest
The authors declare that they have no known competing financial
interests or personal relationships that could have appeared to influence
the work reported in this paper.
Data availability
The data that has been used is confidential.
6. Concluding remarks
This study presents an assessment of the impact of Conavi’s housing
purchase subsidy on price dynamics. Our research hypothesis argues the
existence of a price premium paid by the program beneficiaries for very
similar houses compared to the ones purchased by non-beneficiaries.
Our empirical findings reveal that the implementation of Conavi’s
subsidy is associated with an increase in the average purchase price for
beneficiaries from 2008 to 2014, amounting to 863 USD. This increase
represents 28.9 % of the average subsidy value in that period. Moreover,
our analysis suggests that the participation of sales intermediaries as key
stakeholders in the house purchase process could explain the price
premium. We found robust and statistically significant price differences
in the subset of buyers that actively engaged with sales intermediaries
during their house purchase process, while no evidence of price in­
creases was found in subsidized housing transactions without
intermediaries.
Acknowledgments
Hugo Alejandro Garduño Arredondo, Óscar Vela Treviño, Mitzi Yael
Camba Almonaci, Sebastián Ocampo Palacios, Isaac Medina Martínez
and Francisco Felipe Villegas Rojas, from Infonavit’s General SubDirectorate of Financial Planning and Fiscalization, for their technical
comments and support in the construction of inputs, as well as to Rosa
María Escobar Briones, Moisés Nahmad Fierro and Rosa Isela Rodríguez
Ayala, from Infonavit’s Credit Sub-Directorate, for their support in un­
derstanding in depth the housing appraisal process in Mexico. This
research did not receive any specific grant from funding agencies in the
public, commercial, or not-for-profit sectors. During the preparation of
this work the authors used ChatGPT in order to improve writing. After
using this tool/service, the authors reviewed and edited the content as
needed and takes full responsibility for the content of the publication.
12
G.D. Ramírez Sierra et al.
Journal of Housing Economics 63 (2024) 101970
Appendix
Table A.1
House characteristics of all houses purchased with an Infonavit loan.
Basic services
With drinking water supply (=1)
Sewage collection access (=1)
With a natural gas connection (=1)
House characteristics
Built-up area (m2)
2
Lot size (m )
Single family home (=1)1
More than one bathroom (=1)
More than one bedroom (=1)
With a parking space (=1)
Neighborhood characteristics
Extended urban infraestructure (=1)2
Central location (=1)
3
Sprawl location (=1)3
Intermediate location (=1)3
Peripheral location (=1)3
Rural location (=1)3
Economic housing area (=1)4
Mean housing area (=1)4
Residential housing area (=1)4
In a metropolitan area (=1)
2008
2010
2012
2014
2016
2018
2019
0.96
(0.2)
0.96
(0.2)
0.37
(0.5)
0.98
(0.2)
0.93
(0.3)
0.26
(0.4)
0.97
(0.2)
0.92
(0.3)
0.21
(0.4)
0.97
(0.2)
0.94
(0.2)
0.24
(0.4)
1.00
(0.1)
0.98
(0.2)
0.26
(0.4)
1.00
(0.0)
0.98
(0.1)
0.26
(0.4)
1.00
(0.0)
0.97
(0.2)
0.27
(0.4)
48.59
(22.4)
116.04
(298.8)
0.94
(0.2)
0.02
(0.1)
0.63
(0.5)
0.93
(0.3)
49.63
(47.5)
110.89
(414.2)
0.87
(0.3)
0.05
(0.2)
0.66
(0.5)
0.92
(0.3)
50.45
(18.2)
141.76
(1001.6)
0.75
(0.4)
0.03
(0.2)
0.76
(0.4)
0.91
(0.3)
54.30
(20.3)
104.34
(280.3)
0.76
(0.4)
0.05
(0.2)
0.85
(0.4)
0.91
(0.3)
55.78
(20.8)
96.83
(255.2)
0.75
(0.4)
0.06
(0.2)
0.88
(0.3)
0.91
(0.3)
57.25
(22.6)
96.03
(235.6)
0.73
(0.4)
0.08
(0.3)
0.90
(0.3)
0.91
(0.3)
57.23
(22.4)
96.07
(201.7)
0.75
(0.4)
0.08
(0.3)
0.90
(0.3)
0.91
(0.3)
0.46
(0.5)
0.02
(0.1)
0.21
(0.4)
0.20
(0.4)
0.57
(0.5)
0.00
(0.0)
0.91
(0.3)
0.09
(0.3)
0.01
(0.1)
0.88
(0.3)
0.24
(0.4)
0.02
(0.1)
0.24
(0.4)
0.19
(0.4)
0.55
(0.5)
0.00
(0.0)
0.90
(0.3)
0.08
(0.3)
0.02
(0.1)
0.88
(0.3)
0.26
(0.4)
0.02
(0.1)
0.28
(0.4)
0.18
(0.4)
0.51
(0.5)
0.00
(0.1)
0.89
(0.3)
0.08
(0.3)
0.02
(0.2)
0.87
(0.3)
0.28
(0.5)
0.03
(0.2)
0.22
(0.4)
0.22
(0.4)
0.53
(0.5)
0.00
(0.1)
0.92
(0.3)
0.08
(0.3)
0.00
(0.0)
0.86
(0.3)
0.30
(0.5)
0.02
(0.1)
0.18
(0.4)
0.27
(0.4)
0.53
(0.5)
0.00
(0.0)
0.92
(0.3)
0.08
(0.3)
0.00
(0.0)
0.87
(0.3)
0.29
(0.5)
0.02
(0.1)
0.13
(0.3)
0.34
(0.5)
0.51
(0.5)
0.00
(0.0)
0.90
(0.3)
0.10
(0.3)
0.00
(0.0)
0.87
(0.3)
0.28
(0.4)
0.02
(0.1)
0.12
(0.3)
0.38
(0.5)
0.47
(0.5)
0.00
(0.0)
0.89
(0.3)
0.10
(0.3)
0.00
(0.0)
0.85
(0.4)
Note: All variables with (=1) are indicator variables. Standard deviations (in parenthesis). 1 Refers to the type of building, which can be a single-family home, a
condominium, an apartment, or a multiple dwelling. Multiple dwelling refers to a house in which more than one family lives. 2 Refers to houses with additional
amenities, including at least a church, and a market within a 2.0-kilometer radius of the house. 3 Refers to the location of the property based on its accessibility and its
recognition by the government as part of the urban area. The proximity classification can be central, intermediate, peripheral, sprawl and rural. Sprawl locations are
recognized by the government as potential growth areas, but that are not currently bounded by primary roads or expressways. 4 Refers to the predominant classification
of houses in the area. We group houses as "social interest" when they either meet minimum habitability standards or that are constructed in clusters based on stan­
dardized prototypes.
13
G.D. Ramírez Sierra et al.
Journal of Housing Economics 63 (2024) 101970
Table A.2
House characteristics of all houses purchased with an Infonavit loan and with a subsidy from Conavi.
Basic services
With drinking water supply (=1)
Sewage collection access (=1)
With a natural gas connection (=1)
House characteristics
Built-up area (m2)
2
Lot size (m )
Single family home (=1)1
More than one bathroom (=1)
More than one bedroom (=1)
With a parking space (=1)
Neighborhood characteristics
Extended urban infraestructure (=1)2
Central location (=1)
3
Sprawl location (=1)3
Intermediate location (=1)3
Peripheral location (=1)3
Rural location (=1)3
Economic housing area (=1)4
Mean housing area (=1)4
Residential housing area (=1)4
In a metropolitan area (=1)
2008
2010
2012
2014
2016
2018
2019
0.95
(0.2)
0.95
(0.2)
0.33
(0.5)
0.98
(0.1)
0.93
(0.3)
0.23
(0.4)
0.96
(0.2)
0.92
(0.3)
0.15
(0.4)
0.97
(0.2)
0.95
(0.2)
0.22
(0.4)
1.00
(0.1)
0.99
(0.1)
0.21
(0.4)
1.00
(0.0)
0.98
(0.1)
0.19
(0.4)
1.00
(0.0)
0.97
(0.2)
0.12
(0.3)
41.90
(11.9)
98.31
(94.6)
0.94
(0.2)
0.00
(0.1)
0.50
(0.5)
0.94
(0.2)
42.37
(11.7)
115.75
(438.8)
0.82
(0.4)
0.03
(0.2)
0.53
(0.5)
0.94
(0.2)
44.90
(10.6)
161.18
(1343.0)
0.56
(0.5)
0.01
(0.1)
0.75
(0.4)
0.93
(0.3)
47.21
(10.3)
89.37
(264.9)
0.66
(0.5)
0.01
(0.1)
0.84
(0.4)
0.93
(0.3)
48.00
(8.3)
74.80
(119.8)
0.60
(0.5)
0.01
(0.1)
0.92
(0.3)
0.95
(0.2)
47.33
(6.7)
67.70
(36.4)
0.49
(0.5)
0.00
(0.1)
0.96
(0.2)
0.93
(0.3)
46.66
(4.8)
68.35
(37.1)
0.44
(0.5)
0.00
(0.0)
0.98
(0.2)
0.91
(0.3)
0.46
(0.5)
0.01
(0.1)
0.23
(0.4)
0.17
(0.4)
0.59
(0.5)
0.00
(0.0)
0.94
(0.2)
0.05
(0.2)
0.01
(0.1)
0.85
(0.4)
0.29
(0.5)
0.01
(0.1)
0.28
(0.5)
0.14
(0.3)
0.56
(0.5)
0.00
(0.1)
0.93
(0.3)
0.06
(0.2)
0.01
(0.1)
0.87
(0.3)
0.27
(0.4)
0.02
(0.1)
0.32
(0.5)
0.13
(0.3)
0.53
(0.5)
0.01
(0.1)
0.91
(0.3)
0.07
(0.2)
0.02
(0.1)
0.88
(0.3)
0.32
(0.5)
0.02
(0.1)
0.27
(0.4)
0.15
(0.4)
0.56
(0.5)
0.00
(0.0)
0.96
(0.2)
0.04
(0.2)
0.00
(0.0)
0.88
(0.3)
0.34
(0.5)
0.01
(0.1)
0.22
(0.4)
0.16
(0.4)
0.61
(0.5)
0.00
(0.0)
0.98
(0.1)
0.02
(0.1)
0.00
(0.0)
0.90
(0.3)
0.32
(0.5)
0.01
(0.1)
0.13
(0.3)
0.25
(0.4)
0.62
(0.5)
0.00
(0.0)
0.98
(0.1)
0.02
(0.1)
0.00
(0.0)
0.90
(0.3)
0.38
(0.5)
0.01
(0.1)
0.19
(0.4)
0.24
(0.4)
0.56
(0.5)
0.00
(0.0)
0.99
(0.1)
0.01
(0.1)
0.00
(0.0)
0.82
(0.4)
Note: All variables with (=1) are indicator variables. Standard deviations (in parenthesis). 1 Refers to the type of building, which can be a single-family home, a
condominium, an apartment, or a multiple dwelling. Multiple dwelling refers to a house in which more than one family lives. 2 Refers to houses with additional
amenities, including at least a church, and a market within a 2.0-kilometer radius of the house. 3 Refers to the location of the property based on its accessibility and its
recognition by the government as part of the urban area. The proximity classification can be central, intermediate, peripheral, sprawl and rural. Sprawl locations are
recognized by the government as potential growth areas, but that are not currently bounded by primary roads or expressways. 4 Refers to the predominant classification
of houses in the area. We group houses as "social interest" when they either meet minimum habitability standards or that are constructed in clusters based on stan­
dardized prototypes.
14
G.D. Ramírez Sierra et al.
Journal of Housing Economics 63 (2024) 101970
Fig. A.1. Conditional mean functions for credit application and individuals’ characteristics variables around the cutoff.
15
G.D. Ramírez Sierra et al.
Journal of Housing Economics 63 (2024) 101970
Fig. A.2. Conditional mean functions for House characteristics and neighborhood characteristics variables around the cutoff.
16
G.D. Ramírez Sierra et al.
Journal of Housing Economics 63 (2024) 101970
Table A.3
Main results estimated for each year between 2008 and 2014.
(Dollars,1 Fuzzy Regression Discontinuity Designs)
First stage
Second stage
2008
2009
2010
2011
2012
2013
2014
− 0.47
(0.015)
− 0.25
(0.016)
− 0.20
(0.014)
− 0.22
(0.012)
− 0.24
(0.014)
− 0.17
(0.013)
− 0.27
(0.018)
834.82
(156.496)
949.37
(269.570)
1034.71
(302.725)
403.57
(349.701)
756.44
(415.289)
272.28
(581.190)
1460.95
(295.293)
***
***
***
***
Note: Standard errors in parenthesis and using the bias-corrected inference procedure proposed by
Calonico et al. (2017). All estimations were calculated withtin a distance of 0.6 Dollars around the cutoff.
House variables include the built-up area, floor area, and indicator variables for more than one bedroom,
more than one bathroom, parking space, natural gas connection, and metropolitan area. Neighrborhood
variables include neighborhood kind (economic housing or other) and urban proximity classification
(sprawl or other). 1 US Dollars, calculated using an average exchange rate between 2008 and 2019 of
15.1 pesos per US Dollar.
Fig. A.3. Covariate balance. (Standardized mean differences, 2008–2014).
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17