# RECORD 6c0fa6707be0f8c5edd0d4088f7dfb356412099f
# TITLE: The demand for air quality: evidence from the housing market in Bogotá, Colombia
# YEAR: 2018.0  VENUE: Environment and Development Economics  DOI: 10.1017/s1355770x18000050
# AUTHORS: Fernando Carriazo; John Gomez-Mahecha
# PAGES: 18  CANDIDATE TABLE PAGES (1-based): [12, 14, 15]
# ABSTRACT: Abstract Identifying a demand function for air quality is a key input to calculate welfare measurements of pollution abatement policies. We defined intra-urban housing submarkets to apply a Second Stage hedonic pricing model that allowed us to identify an inverse demand function for PM10 reductions in Bogotá. The monthly benefits of compliance with the U.S. Environmental Protection Agency standard (50 μg/m3 – annual average), and the far more stringent World Health Organization standard (20 μg/m3 – annual average) are valued at US$12.16 and US$189.64 per household, respectively. These values, in turn, represent about 1.35 per cent and 21.04 per cent of the average household income. The hedonic model applied hereby shows that intra-urban housing submarkets are suitable for the identification of a demand function to be used by policy makers interested in evaluating non-marginal benefits (costs) from air quality improvements (deterioration).


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Environment and Development Economics (2018), 23, 121–138
doi:10.1017/S1355770X18000050
EDE
RESEARCH ARTICLE
The demand for air quality: evidence from the
housing market in Bogotá, Colombia
Fernando Carriazo1∗and John Alexander Gomez-Mahecha2
1 Universidad del Rosario, Calle 12C No. 6-25 – Bogotá DC, Colombia and
2 Universidad de Los Andes, Bogotá, Colombia
*Corresponding author. Email: fernando.carriazo@urosario.edu.co
(Submitted 30 June 2016; revised 1 January 2018; accepted 7 January 2018)
Abstract
Identifying a demand function for air quality is a key input to calculate welfare measurements
of pollution abatement policies. We defined intra-urban housing submarkets to apply a Sec-
ond Stage hedonic pricing model that allowed us to identify an inverse demand function
for PM10 reductions in Bogotá. The monthly benefits of compliance with the U.S. Environ-
mental Protection Agency standard (50 µg/m3 – annual average), and the far more stringent
World Health Organization standard (20 µg/m3 – annual average) are valued at US$12.16
and US$189.64 per household, respectively. These values, in turn, represent about 1.35 per
cent and 21.04 per cent of the average household income. The hedonic model applied hereby
shows that intra-urban housing submarkets are suitable for the identification of a demand
function to be used by policy makers interested in evaluating non-marginal benefits (costs)
from air quality improvements (deterioration).
Keywords: Air pollution; Bogotá; EPA; hedonic models; housing markets
JEL Classification: Q51; Q53; R31
1. Introduction
This study estimates welfare benefits related to air quality improvements that are indi-
rectly revealed through property values in Bogotá. Most stations from Bogotá’s mon-
itoring network recorded admissible levels for carbon monoxide (CO), sulfur (SO2)
and nitrogen dioxides (NO2), and ozone (O3). However, as judged by the local pol-
lutants standards, readings for PM10 exceeded the annual local air quality standard
of 50 µg per cubic meter, especially in Puente Aranda, the city’s main industrial area
(Gaitán et al., 2007).
Most studies examining individual preferences for clean air are based on the hedo-
nic model framework (Rosen, 1974) using the so-called First Stage (FS) estimation,
where residential prices are a function of structural characteristics, and neighborhood
and environmental amenities surrounding the housing unit location. Previous hedonic
© Cambridge University Press 2018. This is an Open Access article, distributed under the terms of the Creative Com-
mons Attribution licence (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted re-use, distribution,
and reproduction in any medium, provided the original work is properly cited.
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Fernando Carriazo and John Alexander Gomez-Mahecha
Table 1. Hedonic Price Function estimation
1
2
3
4
All
Apart.
Condos
House
PM10
−0.015372∗∗∗
−0.0061217∗∗
−0.024382∗
−0.021776∗∗∗
0
0
−0.01
−0.01
Structural
Yes
Yes
Yes
Yes
Security
Yes
Yes
Yes
Yes
Cadastral
Yes
Yes
Yes
Yes
Owner bias
Yes
Yes
Yes
Yes
Other pollutants
Yes
Yes
Yes
Yes
Spatial fix effects
Yes
Yes
Yes
Yes
Nσ 2
μ form
6,061 PM10, Strata,
4,153 PM10, Strata,
319 PM10, Strata,
1,562 PM10, Strata,
Strata∗PM10,
Strata∗PM10,
Strata∗PM10,
Strata∗PM10,
Spatial fix eff.
Spatial fix eff.
Spatial fix eff.
Spatial fix eff.
Standard errors in parentheses.
∗p < 0.1, ∗∗p < 0.05, ∗∗∗p < 0.01.
polling all types of properties. Columns (2), (3), and (4) show the submarket estimation
results for apartments, condominiums, and houses, respectively. All four models include
variables (shown in the online appendix table A5) related to structural dwelling charac-
teristics (Structural), neighborhood characteristic variables related to security (Security),
distances to principal amenities (Distance), and structural characteristics of the nearby
houses (Cadastral), locality fixed effects, CO, SO2 and O3 among others. The dependent
variable includes both the reported value of rent for renters and hypothetical (imputed)
rent value for owners. We intended to capture differences between these two prices by
adding a dummy equal to one in case the dwelling is rented as well as its interactions
with all the variables. We called this specification an ‘owner bias specification’. Finally,
the structure for the asymmetrical error variance is specified in the last row, following
the specification in equation (4).
Regarding PM10, our variable of interest, results suggest a negative relationship
between PM10 concentration and rental prices. The model predicts that an increase of
1 µg/m3 is accompanied by a monthly average rent reduction of 0.61 per cent for apart-
ments, of 2.43 per cent for condominiums, and of 2.17 per cent for houses. An increase
of 7.71 µg/m3 (the standard deviation of the PM10 concentration by household) reduces
rent prices by 4.7 per cent in the first submarket, 18.73 per cent in the second submarket,
and 16.73 per cent in the third one (1.62, 6.93, and 8.63 per cent of HH monthly income,
respectively).
Coefficient results for other variables (shown in the online appendix) had the
expected signs. The number of rooms (numCuartos) has a positive effect on the rents,
increasing them from 2.97 to 6.98 per cent for each additional room. An increase in one
bathroom (banios) increased rent from 12.29 to 16.23 per cent. The model also shows
that an increase in one case of homicide in the block is associated with a decrease in the
rent from 0.67 to 9.95p per cent. Neighborhood quality, measured by the cadastral score
for the census track where the residence is located, also has a positive effect on price, as
was expected.
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Environment and Development Economics
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5.2 Second stage estimation – demand for reductions of air pollution
Based on the predicted effect of the PM10 concentration on property rents, the implicit
price for the reduction of air pollution (W(PM10ij)) is calculated. Because defining
apartment, condominium, and house submarkets already solves the SS identification
issue by estimating information for multiple markets, the only problem that is still
unresolved is the endogeneity issue.
A 2SLS method is used to obtain consistent estimations of the demand parameters
under a linear specification. Optimum instrumental variables could be defined based
on those variables that affect PM10 concentrations, but not house prices. The selected
instruments are rainfall, wind speed and a PM10 spatial lag. At a large geographical scale,
rainfall would likely have a direct effect on property values, as Albouy et al. (2016) found
in the U.S for climate valuation. However, at an intra-urban scale, we argue that rainfall is
a suitable instrument since variation in rain patterns among neighborhoods is not likely
to influence property values within a city. Nonetheless, rain directly influences pollution
levels by washing the atmosphere, even at a small spatial scale. Also, there is no reason
to expect any direct effect on wind speed on dwellings’ prices in Bogotá. Still, previous
works have identified a strong relationship between wind speed and the concentration
of several pollutants (Verma and Desai, 2008), including PM10 (Tian et al., 2008). Tian
et al. (2008) identify this relationship as not linear so our instruments also include the
quadratic and cubic form of this variable. Finally, following Anselin and Lozano-Gracia
(2008) design, we included a PM10 spatial lag as an instrument. Table 2 shows a summary
of the first stage results of the 2SLS in column (1) and the second stage in column (2),
correcting again by clusters in the estimation.8 As was expected, more rain is associated
with less concentration of PM10, wind speed presents a non-linear relation with PM10
and the spatial lag (WPM10) is highly significant. The Hausman test, an instruments
relevance test, and Hansen’s J statistic for over-identification are also reported in table 2.
All three tests suggest that the model is well specified.
The second column also shows expected results in the estimation of the linear demand
for reduction in air pollution. The results show that air pollution reduction is a normal
good, meaning that it is affected positively by income. Other socio-demographic vari-
ables are reported in table A6 in the online appendix. Head of Household’s (HH) age
and education have a positive effect on the willingness to pay for pollution reductions.
We also found that some characteristics of the dwellings are substitutes of air quality,
including number of bedrooms (numCuartos) and neighborhood quality (Puntaje).
Finally, the SS hedonic estimation shows that the demand for air quality satisfies the
law of demand. In a low-middle income city like Bogotá, we expect a high response of air
quality to implicit price changes. At the mean, the demand for air quality will decrease
by 1.39 per cent with an increase of 1 per cent in price. Although this elasticity seems
relatively high (−1.39), our estimated elasticity lies in the range of previously estimated
elasticities using SS of the hedonic models for air quality. Brasington and Hite (2005)
and Zabel and Kiel (2000) find more inelastic demand functions, −0.12 and −0.219
respectively, while Chattopadhyay (1999) found a more elastic demand (−2.04)
5.3 Welfare measures
Welfare measures for non-localized amenities like air quality or any housing charac-
teristic whose changes affect all agents in the market should be calculated based on the
8Complete results are available in the online appendix. Please see table A6.
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Fernando Carriazo and John Alexander Gomez-Mahecha
Table 2. Demand for air quality estimation
(1)
(2)
First stage
Second stage
PM10
−0.057522∗∗∗
−0.01
Rainfall
−0.0057113∗∗
0
Wind speed
19.996∗∗∗
−5.92
Wind speed2
−9.3602∗∗∗
−3.17
Wind speed3
1.4206∗∗∗
−0.54
WPM10
1.0011∗∗∗
−0.02
Ingresos
−5.03 × 10−06
0.0001853∗∗∗
0
0
R2
0.28
N
5,694
5,694
Other socio-demographical variables
Educations, HH sex, HH age, HH number of kids,
partipating in env. organizations, other dwelling
characteristics
Hausman pValor
0.01
Relevance pValor
0
Hansen pValor
0.73
Var. Est.
Robust cluster
Robust cluster
Standard errors in parentheses.
∗∗p < 0.05, ∗∗∗p < 0.01.
demand from the SS and not from the FS hedonic estimation (Taylor, 2003). The area
under the derived demands will show the total willingness to pay for a certain reduction
of PM10.
Figure 2 shows the demand curves estimated in table 2. The shaded areas of the graph
show the total monthly willingness to pay per household for air quality that satisfies
both the WHO standard (left) and the national standard (right) given an initial level
of pollution of 51.78 µg/m3. A reduction in air pollution of PM10 up to 50 µg/m3 will
equate with a total willingness to pay of US$12.16 per month per household, or 1.35
per cent of monthly income. The total willingness to pay for a PM10 reduction up to
20 µg/m3 amounts to US$189.64 per month per household, which represents 21.04 per
cent of monthly income. Table 3 presents these results and disaggregates the effects by
low, medium, and high socioeconomic strata.
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Environment and Development Economics
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Figure 2. Demand for Air Quality
Table 3. Welfare measures
Elasticity (%)
TWP for 20 µm/m3
TWP for 50 µm/m3
General
−1.31
189.64
(21.04)
12.16
(1.35)
Low Strata
−0.82
152.42
(37.47)
10.07
(2.48)
Med Strata
−1.52
211.41
(19.88)
13.38
(1.26)
High Strata
−2.88
263.69
(8.12)
16.31
(0.5)
Percentage of monthly income in square brackets.
We carried out several robustness checks: first, we tested the suitability of the pro-
posed market segmentation by applying a Chow Test and a Two Step Clustering Method.
Second, we tested different functional forms of the hedonic function as well as different
air quality measures. Third, we compared OLS estimations of the FS hedonic model with
the Frontier Model Specification. Finally, we checked the robustness of the demand func-
tion for air quality to different instruments. Complete results of robustness checks are
discussed in the online appendix (please see tables RC1 and RC2 in the online appendix).
6. Discussion
Results from the FS hedonic model confirm that air quality is capitalized into property
values in Bogotá. However, such capitalization varies from one housing submarket to
another. A Chow test concludes in favor of separate housing submarkets and, therefore,
this test suggests a non-unique HPF for Bogotá.
The proposed market segmentation within the city helped us estimate a SS hedonic
model to identify a demand for air quality while relying on information obtained from
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136
Fernando Carriazo and John Alexander Gomez-Mahecha
the market process. This market segmentation could be defined by non-market valu-
ation practitioners interested in applying the SS hedonic model within any urban area
where apartments, houses and condominiums are the most prevalent types of residential
housing.
We address identification and endogeneity issues by using housing submarkets and
an IV econometric strategy. Results of the SS confirm the demand law and the hypothesis
that air quality is a normal good. An identification of a demand function for air quality is
a suitable and very flexible tool for policy makers interested in identifying non-marginal
benefits (costs) from air quality improvements (deterioration). We illustrated the flex-
ibility with which the demand function could be used to calculate benefits for various
ambient quality scenarios: benefits from compliance to the EPA standard amount to
US$12.16/month/household, whereas compliance with the more stringent WHO stan-
dard brings benefits close to US$189.64/month/household. The latter may seem high
in magnitude for a city like Bogotá. Nonetheless, these estimates correspond to non-
marginal changes of pollution levels calculated from the area below a demand curve
(Total Willingness to Pay.) These estimates do not entail the usually reported marginal
implicit price from FS hedonic applications. Furthermore, the non-marginal changes
correspond to a reduction of more than 50 per cent of the average PM10 pollution levels.
Nonetheless, our estimated values from revealed preferences could be incorporated in a
cost/benefit analysis for regulatory policies to control urban air pollution.
Supplementary material
The supplementary material for this article can be found at https://doi.org/10.1017/
S1355770X18000050.
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