Tacit Collusion in Housing Markets The Case of Santiago, Chile Fernando Lefort * and Miguel Vargas † Facultad de Econom´ ıa y Empresa Universidad Diego Portales Abstract In this paper we investigate the potential existence of tacit collusion in housing markets using a detailed micro data base from Santiago, Chile. In order to perform the test, we first split Santiago’s housing market into four different sub-markets using hedonic price analysis and households socioeconomics characteristics. This procedure is important because facilitates a more precise characterization of markets and calculation of markups. Secondly, using a GMM panel data regression model we run regressions, for each sub-market, correlating industry’s markups with the aggregate level of activity. The main finding is that low and middle income sub- markets present higher average markups and a pro-cyclical behavior. This finding is consistent with a market where participants do not face capacity constraints and behave strategically to sustain tacit collusion during increasing demand periods. 1 Introduction It has been shown, (see for instance, Straszheim (1975)) that there is a strong relationship between home ownership, access to credit, productivity in family- owned businesses, labor * [email protected]† [email protected]1
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Tacit Collusion in Housing Markets
The Case of Santiago, Chile
Fernando Lefort∗and Miguel Vargas†
Facultad de Economıa y Empresa
Universidad Diego Portales
Abstract
In this paper we investigate the potential existence of tacit collusion in housing
markets using a detailed micro data base from Santiago, Chile. In order to perform
the test, we first split Santiago’s housing market into four different sub-markets
using hedonic price analysis and households socioeconomics characteristics. This
procedure is important because facilitates a more precise characterization of markets
and calculation of markups. Secondly, using a GMM panel data regression model
we run regressions, for each sub-market, correlating industry’s markups with the
aggregate level of activity. The main finding is that low and middle income sub-
markets present higher average markups and a pro-cyclical behavior. This finding
is consistent with a market where participants do not face capacity constraints and
behave strategically to sustain tacit collusion during increasing demand periods.
1 Introduction
It has been shown, (see for instance, Straszheim (1975)) that there is a strong relationship
between home ownership, access to credit, productivity in family- owned businesses, labor
market insertion and household income. Hence, a well-functioning, competitive housing
market can promote development, poverty reduction and improvements in quality of life
for citizens.
However, some particularities of housing markets may preclude the perfectly compet-
itive behavior of their participants. On the one hand, the special features of dwelling
units, particularly regarding location and quality heterogeneity, facilitate the existence of
monopolistic competition in housing markets, even in the presence of many competing
players.1
On the other hand, according to industrial organization theory, even a large number of
suppliers cannot guarantee competitive behavior of players due to the potential emergence
of tacit collusion, especially when, as it is the case in housing markets, multi-market
contact increases the frequency of the interaction between firms. 2
In general, the participants of a market operating either under monopolistic competi-
tion or tacit collusion will earn abnormal returns during some periods of time. Ching and
Fu (2003) empirically test this hypothesis for the Hong Kong urban land market and find
evidence of positive expected abnormal returns earned by developers. Although, monop-
olistic competition and tacit collusion are both departures from perfect competition, they
produce different private and social outcomes in the market. Furthermore, in theory, in
the absence of entry barriers only a collusive behavior will be able to maintain abnormal
returns in the long run.
Given the doubts cast by the economic literature regarding the level of competition
in housing markets, in this paper we investigate the potential existence of tacit collusion
1See for instance Taltavull de la Paz (2001).2As clearly stated by Ivaldi et al. (2003) collusion arises from dynamic interaction, a pervasive situation
in housing markets. For example, Straszheim (1975) indicates that variation in housing characteristics
and prices by location is a fundamental characteristic of the urban housing market, while Goodman
and Thibodeau (2003) point out that metropolitan housing markets are, in fact, segmented into smaller
submarkets, due to sector specific supply and demand factors. Furthermore, Bernheim and Whinston
(1990) show, using supergame analysis, that markets arranged in multi-markets, such as the housing
submarkets, facilitate collusive behavior in a wide range of circumstances.
2
in the Santiago de Chile housing markets. However, the economic empirical literature
has shown that, under tacit collusive behavior, i.e. in the absence of a smoking gun, it is
difficult to prove the existence of collusive behavior in a market.3
An important amount of empirical studies about tacit collusion have analyzed the
time pattern of mark-ups. The reason for this is that tacit collusion equilibrium may be
unstable and, hence, it should be expected to observe periods of high mark-ups followed
by periods with no abnormal returns. As an example, consider the situation where a
reduction in profits caused by an exogenous factor or the defection of some of the colluding
firms, triggers retaliation behavior by firms causing a period of low profits in the market.
Examples like the above have motivated researchers to empirically analyze the rela-
tionship between mark-up and the business cycle. This is because the pattern of mark-ups
during the business cycle may provide evidence of strategic behavior by firms. An addi-
tional difficulty faced by this line of research is that theory provides different conclusions
regarding how mark-ups should behave over the business cycle depending upon the as-
sumptions of the model.
Theoretical models of collusive behavior are framed under repeated games where firms
try to sustain collaborative high levels of profits through the threat of punishing defectors
increasing supply. In such a context, there are two key assumptions that shape the
theoretical relationship between mark-ups and the level of economic activity.
On the one hand, the predictability of demand conditions affects the ability to sustain
collusion. Intuitively, tacit collusion will be easier to sustain in booming markets, when
future profits are expected to be high and, hence, the expected cost of retaliation is also
high. Conversely, collusion is more difficult to sustain in declining markets because the
short run gain of defecting will tend to compensate the limited expected cost of future
retaliation.
The strategic behavior simply outlined above has two empirical implications. First,
in general, tacit collusion will be less sustainable in markets that are subject to demand
3See for instance Rapson (2009).
3
fluctuations especially when they are predictable.4 The second testable implication is that
tacit collusion, under predictable demand shocks and relatively homogeneous players will
tend to generate a pro-cyclical pattern of mark-ups.
The second key issue that may have an effect on the relationship between mark-ups
and the level of activity is the industry cost structure. In general, an asymmetric cost
structure will hinder collusion and condition market shares of participants.5 In theory,
however, capacity constraints have an ambiguous effect on the sustainability of collusion.
The reason is that although a capacity-constrained firm has less to gain when defecting a
tacit collaboration, it also has less retaliatory power against other defecting companies.
Because capacity constraints affect the strategic behavior of colluded firms, they may
also affect the relationship between mark-ups and the level of activity. Specifically, Fabra
(2006) shows that, if capacity constraints are sufficiently high, firms will find more difficult
to collude when facing increasing demand. Intuitively, when capacity constraints are
severe enough, the lack of excess capacity during a boom implies that the future costs
of being punished are low. Thus, the losses from cheating decrease even if collusive
profits are rising. In contrast, the emergence of excess capacity during a recession makes
the punishment threat more severe, and thereby induces an increase in the losses from
cheating even if collusive profits decline. Hence, a housing market where participant
companies have severe capacity constraints will tend to show counter cyclical mark-ups,
because firms will be able to coordinate better in times of decreasing demand.
In accordance with the above discussion, in this paper, we implement a test of tacit
collusion using a detailed housing sales’ data base from Santiago, Chile. The data includes
information about dwellings’ price, surface, number of bedrooms and bathrooms, and
location. We also have information on each location specific socioeconomic characteristics
and the quality of facilities available to households.
4This general idea was stated by Rotemberg and Saloner (1986) and Haltiwanger and Harrington
(1991). See also Ivaldi et al. (2003) for a clear discussion of this issue.5See Ivaldi et al. (2003) for a discussion in this issue.
4
In order to perform the test, we first split Santiago’s housing market into four different
sub-markets using hedonic price analysis and households socioeconomics characteristics.
This procedure is important because facilitates a more precise characterization of markets
and calculation of mark-ups. Furthermore, because there is more homogeneity among
dwelling units belonging to the same specific sub-market, sub-market markups are less
likely to reflect non-cooperative monopolistic competition. We find clear evidence of
positive average mark-ups in most sub-markets.
We, then, implement a test of the correlation between mark-ups and the level of activ-
ity for each sub-market, using a GMM panel data regression model regressing industry’s
sub-market markups against business cycle. The main finding is that low and middle
income sub-markets present both higher average markups and positive correlation with
the level of economic activity. This finding is consistent with a market where participants
do not face capacity constraints and behave strategically to sustain tacit collusion during
increasing demand periods.
Section 2 of the paper presents the methodology used to segment markets and calculate
mark-ups. Section 3 describes the Santiago housing market and the data used for the
econometric analysis. In section 4, we perform hedonic price regression in order to properly
identify the specific sub-market characteristics. In section 5, we use a GMM estimator
to obtain estimates of the correlation between mark-ups and economic activity for each
sub-market and analyze the overall results. Section 6 concludes.
2 Methodology and Data
The methodology proposed has been developed in order to test the presence of tacit collu-
sion in Santiago of Chile housing market. This test is based upon the works of Rotemberg
and Saloner (1986) and Green and Porter (1984), which establish that the relationship
between firms profits and business cycle will provide information about markets level of
competition. However, in order to do a more accurate analysis of the firms behavior we
need to identify sub-markets, given the particular features that these markets present.
5
After the sub-markets have been identified, the tacit collusion test will be performed for
each sub-market.
A simple algorithm of the methodology proposed here establishes the following steps:
• The estimation of a hedonic model for the city as a whole as a way to identify the
variables that are behind housing prices
• To cluster basic geographical units of analysis, like census tracks, according to a
criterion based upon households socio-economic characteristics. For instance to
cluster census tracks that have a similar average household income.
• Once the potential sub-markets have been defined the next step will be run a hedonic
regression for each one of them and then to test if the parameters estimated are
different between sub-markets.
• Once the sub-markets have been defined the firms markups will be estimated for
each sub-markets
• Finally, every sub-markets firms markups will be compare with the business cycle
in order to undertake the tacit collusion test
All these issues are discussed with further details in the following subsections.
2.1 Housing demand and hedonic prices estimation
Because dwellings and housing services are highly heterogeneous it is a difficult task to
estimate a generically demand function for them. Instead, dwellings can be decomposed
into its constituent characteristics and then estimates prices and elasticities for each one of
them. The way of doing that is using the hedonic regression due to Rosen (1974), which
faces the fact that observed choices over housing reveals to the researcher information
about the underlying preferences for these amenities or other characteristics of interest
(Taylor, 2008).
6
Let us consider that Pi is the dwelling price, which is a heterogeneous good, and xi is
a vector that includes its structural attributes of size and quality, characteristics of the
immediate neighborhood and indicators of its environment and accessibility. b is a vector
of parameters that must be estimated for the characteristics.
Pi = f(xi;b) + ui (1)
Having estimated the equation 1, it can be possible to predict the price of any dwelling
i whit attributes xi.
Pi = f(xi; b) (2)
For discrete characteristics the implicit price of the attribute kth can be calculated as
follows:
pk = f(xk + 1, x−k; b)− f(xk, x−k; b) (3)
and for the continuous case:
pk =∂f(xi; b)
∂xk
(4)
The implicit prices reveal the implicit marginal willingness to pay for an increment in
any of the dwellings attributes.
As Taylor (2008) points out, the hedonic price function has no theoretical guidance
for its specification, due to the fact that it is an envelope function. The most used
specification is a semi-log:
ln(Pi) = a+K∑
k=1
bkxki + ui (5)
The most common way of estimating 5 is by either OLS or maximum likelihood.
The set of the relevant attributes for price determination can be categorized into three
groups:
7
• characteristics of the dwelling and the lot
• features of the neighborhood, like crime rate
• the property locational characteristics, like the proximity to employment centers
2.2 Sub-markets definition
Despite the housing sub-markets, since their first appearance in the seminal work of
Maclennan (1977), have been widely studied in a theoretical framework, there is little
consensus about how sub-markets should be identified for applied studies (Alkay, 2008;
Royuela and Vargas, 2009).
In empirical works, sub-markets have been defined in different ways such as by de-
mand and supply factors, geographical characteristics, spatial characteristics, structural
characteristics and neighbourhood characteristics.
Researchers have offered different stratification schemes for their sub-markets defini-
tions: dwellings age, floor area, lot size, number of rooms, number of bathrooms, parking
lot, lift, wall material, roof material are given as examples of structural stratifiers. Also
socioeconomic characteristics and race have been used, and spatial dimensions as census
boundaries, neighborhood boundaries, municipal boundaries, school districts, inner and
outer urban areas. Income levels or household size in addition to neighborhood bound-
aries or inner and outer urban areas or construction type are examples of stratifiers of
joint influence.
Jones et al. (2004) defined sub-markets based on households intra-urban mobility. This
approach is an attempt to avoid researchers’ bias. In turn, within this structure different
approaches can be found too, such as travel-to-work areas and migration data.
Here the methodology introduced by Schnare and Struyk (1976), following the ex-
planation by Alkay (2008), is proposed. As sub-markets are not known in advance, the
first step must be to determine if segmentation exists. In order to do that, potential
sub-markets should be proposed, for instance, clustering census tracks with a similar av-
erage households income, and then to test if the parameters estimated for these potential
8
sub-markets are different between them. Second, if a segmentation structure is observed,
it must be tested if the resulting variation in prices is significant.
This test procedure can be split into three stages:
• First, to estimate a hedonic housing price function for each potential sub-market
in order to compare these potential sub-markets prices. If there are large and
significant differences in the estimated parameters of those potential sub-markets,
the differences might be accepted as evidence of market segmentation.
• Second, to compute the tests to establish whether significant differences exist be-
tween the sub-markets specific prices.
• Third, since the primary interest is in the price of housing instead in the price of
the individual housing characteristic, the difference between the whole market model
and sub-market models must be emphasized. Testing for the relative importance of
this variation the standard errors of the whole market model and the sub-markets
models can be compared.
2.3 Firms markups and the sub-markets level of competition
Machin and Van Reenen (1993) propose a procedure based upon super-games models
developed by Rotemberg and Saloner (1986) and Green and Porter (1984) to test the
extent of competition of an industry. These models have clearcuts predictions regarding
the behavior of markups over the business cycle: Rotemberg and Saloner (1986) predict
that markups should exhibit countercyclical behavior meanwhile Green and Porter (1984)
suggest pro-cyclical markups. The former prediction rely on the assumption that firms can
discriminate amongst aggregate and idiosyncratic shocks, whilst the latter prediction is
based upon the assumption that firms cannot do it. Therefore, if a systematic relationship
between profits and business cycle is found, it will be evidence of oligopolistic behavior.