Hedonic Pricing
Approach for Urban
Infrastructure
Guidance Note
June 2022
Alvina Erman and Ingrid Dallmann
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Acknowledgments
This guidance note has been prepared under the World Bank
Advisory Services and Analytics project “Mobilizing Finance
through Anticipating Impact of Urban Infrastructure” (P173320).
Financial support was provided by the Sustainable Urban and
Regional Development program (SURGE), a World Bank Umbrella
Multi-Donor Trust Fund funded by the Swiss State Secretariat
for Economic Affairs (SECO). The note complements the broader
guidance paper “Assessing Wider Socioeconomic Impacts of Urban
Infrastructure Ex Ante” that has also been prepared under this
project. The authors are grateful to Valerie Joy-Santos (Senior
Urban Specialist) and Mark Roberts (Lead Urban Specialist) for
their feedback on earlier drafts of this note, as well as to Paula
Restrepo Cadavid (Senior Urban Specialist) and Judy Baker (Lead
Economist) of the World Bank’s Global Practice for Urban, Disaster
Risk Management, Resilience, and Land for their comments during
the peer-review process.
Cover photo: Mayur_Mehta/Shutterstock.com
Cover design and production editing: Lincoln Lewis
Recommended Citation
Erman, Alvina, and Ingrid Dallmann. 2022. Hedonic Pricing
Approach for Urban Infrastructure: A Project Economic Analysis
Guidance Note. Washington, DC: The World Bank.
Hedonic Pricing Approach
for Urban Infrastructure
Guidance Note
June 2022
Alvina Erman and Ingrid Dallmann
Background
Urban infrastructure investments often have benefits that are particularly challenging to quantify.
Especially investments – such as investments in parks, recreational areas, noise reduction or
streetscaping –which carry large potential positive externalities, or co-benefits. The challenge is even
more prominent in data-scarce developing country environments. When the economic analysis that
informs investment decisions does not incorporate the indirect and intangible benefits of investments,
socially beneficial projects may not be implemented, and policy makers may end up making the wrong
decisions from an economic standpoint. Financing of investment projects by the World Bank and other
International Financial Institutions may also be sub-optimally allocated.
The hedonic pricing method is a data-driven decision-making tool that can be used by policy makers
and researchers to quantify the benefits of urban infrastructure investments. This note is an
introduction to the hedonic pricing method and provides guidance on its application to assess the
benefits of urban infrastructure investments, with a focus on developing countries. The objective is to
support task teams in building a value proposition of urban investments that can help inform decisions
and to enhance the quality of project economic analysis of urban infrastructure investments. By
enabling the monetization of the benefits of a wide range of urban investments, the hedonic pricing
method is a tool particularly well-placed to support this effort. At the same time, it is also important
to be aware of the method’s limitations to prevent its inappropriate application. These limitations will
also be discussed in this note.
The assessment can be done ex ante, to assess the feasibility of a proposed investment, or ex post,
to evaluate the benefit of existing infrastructure. Most commonly, the hedonic pricing approach is
used to assess the potential benefit of a planned investment, and this will be the focus of this note.
However, the approach can also be applied ex post to evaluate the value that households attribute to
a given urban infrastructure.
Property pricing is a result of myriad push and pull factors. When people make decisions on where to
live, they consider not only the quality of housing and services, but also access to jobs, stores, schools,
personal safety, recreational opportunities, public transport, and environmental factors, such as risk
of exposure to hazards (e.g., flooding and air pollution) or noise. The hedonic pricing method is a
revealed preference approach, that captures the willingness to pay (WTP) for these and other
“amenities” (or “disamenities”) by analyzing the drivers of property pricing, based on bundles of
housing, amenities, and location characteristics (Bishop et al., 2020; Brueckner, 2011; Rosen, 1974).
By estimating the WTP, the method captures the effect that each attribute has on the property value,
holding other factors constant. According to consumer demand theory, the WTP equals the value that
1
the consumer assigns to an amenity (or the avoidance of a disamenity) and can therefore be proxied
as the benefit of (avoiding) the said (dis)amenity.
The data needed to apply a hedonic model are often present in household surveys, and are thus
reliable and readily available, even in data scarce environments. Data on property pricing is used to
apply the hedonic pricing model. Real estate transaction data is preferred. However, other types of
data can be used. For example, rent values or data from property listings. Since self-reported rent
values are sometimes captured in household survey data, the hedonic model is a promising approach
for developing countries, where household survey data are often readily available. The pros and cons
and availability of different data options are discussed further below. Since the approach uses
observed property prices (mostly), the values obtained are based on actual, rather than assumed or
predicted preferences, which is common for other methods that quantify infrastructure benefits. This
makes the hedonic pricing method comparatively robust.
This note provides step-by-step guidance on how to apply the hedonic pricing method, focusing
particularly on considerations for developing countries. Since an almost unlimited list of urban
characteristics can drive property pricing, the focus is on types of data needed to capture the most
relevant variables for the purpose of project evaluation − namely, data on property values, data that
capture access to the urban infrastructure being evaluated, and data for the most important control
variables. The note also discusses the challenges and limitations of the hedonic pricing method.
How to apply a hedonic pricing model
The hedonic pricing method can assess the effects of a large range of urban infrastructure types on
property pricing. The data used by the method typically comes from a cross-section of households,
with information on property values, housing characteristics, access to public services, and other
relevant location-specific characteristics that could influence property pricing and which may be
correlated with the urban infrastructure being investigated.
A hedonic pricing function relates the price of a dwelling to the levels of its various attributes. A
classic and straightforward specification of a hedonic model for a cross-section of households is:
ln ������������������������ = ������������ + ������������������������������������������������ + ������������������������������������ + ������������������������
2
The outcome variable y measures the property value. The property value y is measured in USD or
local currency and should be in natural logs and i indexes the households. 1. The type of data that can
be used to estimate property values is described below.
UI is a vector of the variables or a single variable of interest, which proxies or directly measures
access to the urban infrastructure that is being assessed. For example, to look at the benefit of
sanitation services, variables capturing whether a household has access to piped water, sewage
infrastructure, and solid waste collection could be included in UI. As a result, β is a vector of coefficients
or a single coefficient estimating the marginal WTP for the urban infrastructure(s) of interest. If, for
instance, UI is a dummy variable that equals 1 if a household has access to piped water and zero
̂ = 0.15, and the average rent value is 1000 LCU/month, 2
otherwise, its coefficient ������������ is estimated as ������������
the interpretation is that gaining access to piped water will increase the property price by
approximately 16.2 percent, 3 or 162 LCU. This means that the benefit of having access to piped water
in the project area is the number of households in the area that will benefit from the investment (i.e.,
the number of households which will be provided with access to piped water) multiplied by 162 LCU. 4
The type of data that can be used to estimate access to infrastructure is described below.
The vector X includes control variables which are additional attributes that influence property
pricing and γ is a vector of the implicit prices of these attributes. Adding control variables will not
only provide insightful information on the drivers of property pricing in the area being studied, but it
is also crucial for the accuracy of the estimates. Particular attention should be given to variables that
are correlated with UI and impact y. For instance, if the investment of interest is slum upgrading, it is
important to control for tenure arrangements since the type of tenure affects property values and, at
the same time, is correlated with being located in an area eligible for slum upgrading. If the benefit of
the infrastructure that is being provided is correlated with other factors that themselves influence
housing prices, then failure to include control variables that capture these factors, will lead to omitted
variable bias (over- or under-estimation of the benefits of the infrastructure). Variables that are
important drivers of property pricing and that are also generally straightforward to capture in available
data include dwelling type, i.e., whether it is a house or apartment, size and quality of property, and
tenure arrangement. How to obtain these data is discussed below.
1
Implicit prices estimated in the hedonic model are assumed to be non-constant, and thus the hedonic
estimation should be non-linear.
2
LCU is an acronym for Local Currency Unit.
�
3
The estimated marginal effect of a dummy variable in a log-linear specification is given by (������������ ������������ -1) × 100. In this
̂ × 100.
case, exp(0.15)-1 × 100=16.2. The coefficients of continuous variables are interpreted as ������������
4
Data on property value should be corrected by the inflation.
3
Finally, the equation can be estimated using Ordinary Least Squares (OLS). The error term ������������������������ is
typically modeled using robust standard to account for heteroskedasticity.
Measuring property value
Property values can be measured by using either the value of the dwelling, or the rent paid by the
dweller. For property value, transaction prices, asking prices, modeled property values using several
inputs, 5 or property values as recalled or “predicted” by the owner could be considered. 6 Rent values
can be measured with landlords’ asking prices, or the actual rent paid, as reported by the renter or
rent values, or as “predicted” by the owner. 7 In some cases and when relevant, commercial property
prices could be used, both, property or rent values. Appendix Table A1 shows different types of data
used to capture property value in hedonic model applications in different countries, including types of
data sources.
Property transaction prices, asking prices, or estimated property prices through valuation models
are the most reliable measurements of property value for a hedonic application. Among these
sources, transaction prices are preferred compared to asking prices that can be potentially influenced
by the sellers’ perceived valuation (Bishop et al., 2020). Property transaction and asking prices, as well
as input data for property valuation models, are obtained from public records, real estate databases
and websites. Ready-to-use datasets are generally not available, even in developed countries, and
significant effort is usually needed to access and construct the datasets needed for analysis (e.g.,
bureaucracy to access public data, scraping websites for data, digitalizing paper records, etc.). Despite
this, examples of hedonic applications to developing countries using these data types exist for
Argentina, China, and Indonesia (see, respectively, Rabassa and Zoloa, 2016;Wen, et al., 2018; and
Cobian Alvarez and Resosudarmo, 2019). Property transaction data also exist in Chile, Hungary and
Poland according to a review carried out by Bishop at al. (2020) but access is more or less restricted.
Household survey data is a good source of data on property pricing, especially when measured with
rent value and it is the most used data source for developing country applications. Household survey
questionnaires typically include questions on rent and since respondents are likely to know how much
rent they pay; this generates a reliable estimate. Another advantage of using rent as a property value
estimate is that the rental market is more transactional, and prices may therefore better reflect the
currently available (dis)amenities, including urban infrastructure. A disadvantage of using reported
5
In this case, it is important to make sure that the variables the modeled price is based on are not directly
associated with the variables of interest in the hedonic model.
6
Recalled is how much the property was originally purchased for and “predicted” is how much the respondents
believe the property would sell for today.
7
Some surveys ask respondents what they believe their property would rent for if rented today.
4
rent is that renters may not be representative of the more general population, including homeowners.
Also, in many cities, renters make up a minority of the population which limits the sample size. The
recalled purchase price of the property is also based on actual prices, compared to the “predicted”
value, discussed below. However, it relies on the respondents’ recall capacity and, more importantly,
the data points will reflect historic prices from when the property was purchased and the timing of the
recalled transaction will differ across households, making the data potentially unsuited for a hedonic
application.
“Predicted” property value has more uncertainty but often better coverage of the population than
actual price information. Surveys often capture how much the respondent thinks his/her property will
sell or rent for today. The advantage of this measure is that the question can be asked to the total
survey population, regardless of their tenure status, increasing the sample size and ensuring the data
is more representative. The disadvantage is that the measurement error may be larger than real prices
since it is based on hypothetical questions. Furthermore, measurement error may not be random. Self-
assessment of housing prices may depend on the respondent’s education level, his/her knowledge of
the current housing market, and even his/her subjective attachment to his property. The pricing
assessment can also be influenced by expectations of price increase if information of planned
infrastructure investments is public. To reduce the bias that results from predicted property values,
the hedonic model could control for characteristics of the respondent (such as education, profession,
age, and gender) and location fixed effects (like neighborhood fixed effects).
In some cases, commercial rather than residential property pricing is used to assess the impact of
urban infrastructure. There may be interest to evaluate the impact of urban infrastructure on
commercial property pricing. For example, Berawi et al., (2020) assess how proximity to rail stations
affects commercial property prices in Jakarta, Indonesia. In some cases, the use of commercial, as
opposed to residential, property prices is more relevant to an evaluation. For instance, the benefits of
investing in infrastructure that promotes tourism and creates jobs may be better reflected in
commercial rather than residential property prices. In these cases, the model would estimate the
private sector WTP to access the infrastructure, which is most likely associated with its impact on
profits. This contrasts to households’ WTP to access an amenity, which is interpreted as the value
households attribute to the service (welfare) and not profitability. In terms of data, commercial
property price data are even more rare than data on residential prices.
5
Capturing the impact of urban infrastructure (co-benefits)
To select an urban infrastructure variable for the hedonic application, it is first important to
understand the impact channel of the infrastructure of interest on the property value. There needs
to be a direct link between the co-benefit and property pricing. Urban infrastructure can be classified
into water, sanitation, and waste management; transport (overall, road and highway, and public
transport); infrastructure for disaster risk reduction; housing and slum upgrading; tourism;
streetscaping; and energy. While some of these investments, such as electricity access, are more
directly linked to property values, for others, the link is less clear, such as for streetscaping. The data
and approach used to capture the effects of urban infrastructure depends on the type of infrastructure
that is being assessed.
The impact of urban infrastructure is measured with level of access to the service provided by the
infrastructure. If the urban investment of interest is a proposed investment (ex ante) then the variable
used in the model will reflect access to similar existing infrastructure in the city of interest. If the
objective of the application is to assess the impact of an already existing infrastructure (ex post), then
the variable used will capture access to that particular infrastructure. Data sources and approaches are
similar for the two scenarios. However, in special cases, when the investment planned is for an
infrastructure that doesn’t already exist in the city or when the data needed for the analysis is not
available, results from hedonic pricing model applications in comparable cities could be exploited to
shed some light on the potential impact of the planned investment. Naturally, such extrapolation of
results incurs lot of uncertainties. Not only do the cities need to be comparable, but the investments
(the infrastructure assessed and the one proposed) also need to be similar (type, location, type of
beneficiaries, etc.). To take into account some of the uncertainty in the cost-benefit analysis, the team
could use a range, such as 10 percent higher and lower than the point estimate, to assess sensitivity.
6
Box 1. Kinshasa multisector development and urban resilience project – Kin-Elenda, World Bank
Box 2. A
Project hedonic valuation
Appraisal Document, sanitation
of March services
2021, in )Guatemala (Vasquez and Beaudin, 2020)
(P171141
The objective
The of the
objective of the project wasto
paper was to estimate institutional
improvethe havingfor
capacity
benefits of urban
access tomanagement and access
solid waste collection to select
and sewer
infrastructure and
infrastructure services in Kinshasa.
in Guatemala.
The valuation
The analysis of economic
used a subsamplebenefits of the project
of households relied
from the on hedonic
Living regression
Standards using data
Measurement from
Surveys a household
(LSMS) 2006,
survey conducted
2011 and in 2018-2019
2014, composed renters. The
in Kinshasa.
of urban analysisanalysis
A hedonic was limited to renters
was applied to only. The the
estimate specification
value thatwas as
urban
follows:assign to sanitation services. The following specification was used:
renters
ln ������������
ln ������������������������������������������������= ������������ + ������������������������������������������������ + ������������������������������������ + ������������������������
������������ + ������������������������������������������������
������������ = ������������������������������������������������������������������������ + ������������������������������������������������������������ + ������������������������������������ + ������������������������
where y measures the self-reported rent value for each household i. UI is a set of categorical and dummy variables
where y is a self-reported rent value for each household i, located in department j, in year t. UI is a set of dummy
that measure access to different types of water sources, sanitation systems, solid waste management, electricity,
variables that capture access to municipal solid waste management and private solid waste management,
as well as risk of flooding (captured by whether a household had previously being exposed to a flood) as a proxy
respectively, as well as whether the property is hooked up to a sewer system. X is a set of control variables,
for access to risk reduction infrastructure. X is a set of control variables, which contain housing and location
including access to other services (piped water, electricity, and landline communication), number of rooms, and
characteristics.
other dwelling features. D and Y are department and time fixed effects, respectively. The estimation was done
OLS with
using results
The data.
pooledthat
showed having access to improved piped water, sanitation, excreta disposals, solid waste
management, flood risk reduction and electricity are associated with higher rents. For example, it was found that
The results showed that having access to municipal solid waste services was associated with about 23 percent
households that had not been exposed to floods pay between 17 to 22 percent higher rents, indicating that
higher rent. Having access to a private provider was valued even higher, 32 percent and 26 percent higher rent
households are willing to pay a premium to live in safer areas. By applying the estimated marginal WTP for each
for solid waste and sewer infrastructure, respectively.
type of urban infrastructure to the average rent value, considering the number of beneficiaries by infrastructure
type, the assessment showed that the project has a net present value of US$ 247.5 million.
Information on access to infrastructure can be obtained in most household survey datasets,
especially for amenities that are supplied directly to households, such as water and sanitation, and
electricity. Household survey data sometimes also include information on access to services that are
more public by nature, such as access to waste management, quality of road and drainage outside
dwelling and information on exposure to disasters (whether the household has been affected by or
has a perceived risk of future exposure). The latter could be used to capture the benefit of risk
reduction investments. Boxes 1 and 2 detail hedonic price model applications in Kinshasa, Democratic
Republic of the Congo (DRC) and Guatemala, respectively, that use household survey data to evaluate
the benefit of different urban infrastructure. The application in Kinshasa evaluates the benefits of a
World Bank-financed urban resilience project, including improved access to services, such water, and
electricity and flood protection. The application in Guatemala assessed the benefit of having access to
solid waste collection and sewer infrastructure. Some household survey instruments, such as the
World Bank’s Living Standards Measurement Survey (LSMS), include community level surveys that
capture information on the quality of, and access to, services at the community level and therefore
tend to contain more information on services that are more public by nature than household surveys.
7
Most household survey data can be accessed on the Microdata Library 8 or via the National Statistical
Office in the country. In case the team is using non-household survey property value data, the data
most likely also contain information on access to services that are more private by nature, such as
water and electricity. For information on other services, spatial data may be leveraged.
Box 2. A hedonic valuation of sanitation services in Guatemala (Vasquez and Beaudin, 2020)
The objective of the paper was to estimate the benefits of having access to solid waste collection and sewer
infrastructure in Guatemala.
The analysis used a subsample of households from the Living Standards Measurement Surveys (LSMS) 2006,
2011 and 2014, composed of urban renters. A hedonic analysis was applied to estimate the value that urban
renters assign to sanitation services. The following specification was used:
ln ������������������������������������������������ = ������������������������������������������������������������������������ + ������������������������������������������������������������ + ������������������������������������ + ������������������������������������ + ������������������������������������������������
where y is a self-reported rent value for each household i, located in department j, in year t. UI is a set of dummy
variables that capture access to municipal solid waste management and private solid waste management,
respectively, as well as whether the property is hooked up to a sewer system. X is a set of control variables,
including access to other services (piped water, electricity, and landline communication), number of rooms, and
other dwelling features. D and Y are department and time fixed effects, respectively. The estimation was done
using OLS with pooled data.
The results showed that having access to municipal solid waste services was associated with about 23 percent
higher rent. Having access to a private provider was valued even higher, 32 percent and 26 percent higher rent
for solid waste and sewer infrastructure, respectively.
Spatial data is a powerful source of information on city amenities that can be used to capture the
co-benefits of different urban infrastructures, especially the ones that are more public by nature. To
capture the effect of more types of infrastructure, such as transport, slum upgrading, disaster risk
reduction or streetscaping, complementary data, and in particular spatial data, can be leveraged. For
instance, Open Street Map (OSM) 9 has information on the locations of health and education facilities,
parks, bus stations, road networks, etc. The benefit of having access to these different amenities, as
well as the benefit of the transport network itself, can be captured by, for example, measuring a
household’s distance or travel time to the amenity. Hazard risk maps or spatial disaster data based on
historic events can be matched with household or property location data to assess the role of disaster
risk exposure on property values. The risk level can be captured either using a binary variable (e.g.,
8
https://microdata.worldbank.org/
9
https://www.openstreetmap.org
8
located in high risk or low risk area) or categorical (not affected, affected once, affected multiple times
in the past, if using historical disaster data) or continuous variables (e.g., predicted flood depth)
depending on the type of hazard map used.
Table A2 in Appendix includes examples of measurements used to capture the effect of more public
urban infrastructure in hedonic applications in developing countries.
Resources needed to apply a hedonic pricing model
While the hedonic pricing model itself is straightforward and quick to implement, the effort required
to collect and manage the data can be considerable and depends largely on the data that is available
(see table 1 for a matrix that indicates the time and effort required for each data type scenario).
• Effort: Easy. If household survey data from the study area is available and it contains both a
property value variable and information on access to relevant urban infrastructures, as well as
control variables, then the analysis can be done in less than a week by a junior analyst with
familiarity with household survey and regression analysis.
• Effort: Medium. If access to the urban infrastructure of interest is not captured in the
household survey data, but publicly available spatial data, such as OSM data or hazard maps
are readily available online, they could be leveraged to compute the variables of interest. This
requires that the household data disclose information on the location of households. To create
the variables, additional support from a geospatial specialist, with strong knowledge of
geographic information system (GIS) techniques, is needed. This computational work, plus the
analysis, should not take more than 2-3 weeks.
• Effort: Significant. When using asking or transaction price data, things are not as
straightforward. In a best-case scenario, processed data are available and minimum work is
needed to make it fit for use. If the data are geo-located, it will be easy to merge with urban
infrastructure variables of interest. For this special case, processing and analysis may only take
a few weeks. However, as discussed above, observed property price data is rarely readily
available. When data are obtained from websites, which is the case primarily with asking
prices, expertise in extracting information off websites (data/web scraping) and deep
knowledge of the housing market in the area studied are needed. If data is owned by the
government administration, as often is the case with transaction data, obtaining records may
require making an official request which can take several weeks or months. Once data is
obtained, whether from the web of from public records, significant data cleaning will most
likely be needed. It is difficult to predict how much effort is required to be able to map out the
property data spatially to be able to link them with the spatial infrastructure data. Obtaining
9
and processing asking or transaction price data is estimated to take months and the length will
depend on country specific factors. Beyond a junior economist and geospatial specialist,
obtaining and processing the data may require more advanced programming and data science
skills. While this type of data is more challenging to obtain and use, they are the preferred
options in terms of robustness of the analysis.
• Effort: Significant. When the urban infrastructure variables of interest cannot be computed
using readily available data, further work will be required. This will be the case, for example, if
hazard maps have to be obtained from a governmental agency or satellite imagery has to be
leveraged to compute variables, such as access to green spaces or a slum index. The time and
effort needed will depend on the type of data and analysis needed and may require advanced
geospatial skills.
• Effort: Significant. In countries where household surveys are too old, and asking and
transaction price data are not available, a household survey could be conducted. The
advantage of collecting data is that questionnaires can be customized according to the
information needed. Data collection does not require advanced technical skills and could be
managed by the program manager and an economist experienced with household survey data
design and collection (with the actual data collection outsourced to a survey firm). However,
the process takes 6 months to a year and is expensive. Still, if a big urban development
investment is planned for a city without available data, this effort may be worth the
investment since the data can be used for many purposes throughout the project and can also
serve as baseline data for the investment.
Table 1. Effort and time matrix for property value and urban infrastructure data
Effort Property value Urban infrastructure
Time
level Self-reported Observed Self-reported Observed
Easy 1 week Survey available - Survey available, - -
incl. property incl. relevant
value info infrastructure
info
Medium 2 weeks Survey available - - Data available -
incl. property (OSM, hazard
value info maps, etc.)
Significant 1-6 - Asking/transacti - Data not readily
months on price data available and
available (online need
data or public computation
data) (satellite data,
public data, etc.)
Significant 6-12 Survey data - Survey data - -
months collection collection
10
Challenges and limitations of the hedonic pricing method
The hedonic pricing approach assumes that the housing market is efficient, i.e., buyers/renters are
fully informed and free to move within the market, market prices are set, and transactions costs are
equal to zero. This means that identical dwellings should be sold/rented at the same price throughout
the defined market. It assumes that people have the ability to select a combination of amenities they
prefer, given their income, in a housing market that is free from distortions associated with, among
other things, subsidies, taxes, informal and insecure tenure arrangements, and, in some cases,
misinformation.
Hedonic price models are applied using data representing a limited period. The results therefore
reflect the revealed preferences of dwellers in that moment. If the results are used as an input to a
cost-benefit assessment of an investment with a lifespan of several decades, it is important to keep in
mind that the analysis is based on “current” observed preferences, and that these may change over
time. For example, the implicit price of better access to public transport may decrease over time if
more people start working from home.
Informality and weak institutions characterizing many housing markets in developing countries
present an additional challenge, since this can result in discrepancies in prices of similar dwellings
within the same market. While few housing markets are completely perfect, there are some
characteristics in highly informal and underdeveloped housing markets that are important to point
out. Both renters and landlords, and buyers and sellers face higher risks in countries with unreliable
legal systems and this has consequences for the market functionality. For example, in some contexts,
prices tend to be set inside of informal networks, such as family, religious groups or other social groups,
instead of in an open market due to the trust that the networks provide. Landlords also tend to request
large down payments for renters in less developed contexts, which affects dwellers’ mobility and, by
extension, the functioning of the market. In this context, it is important to control for tenure
arrangements and tenure security if possible. Also, government housing subsidies cause price
distortions, and if possible, subsidized dwellings should not be included in the analysis, unless data
availability allows for a correction of this.
This brief guide does not cover all the theoretical and econometric considerations for an application
of a hedonic price function. For more detail on the hedonic pricing approach, see the seminal paper
of Rosen (1974); best practices covered in Bishop et al., (2020); and a guide to the theory and
econometrics of the hedonic method in Taylor (2003).
11
References
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Timmins, C. D. (2020). Best practices for using hedonic property value models to measure willingness
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Brueckner, J. K. (2011). Lectures on urban economics. Cambridge, Massachusetts; London, England:
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megacities: a hedonic price analysis of the Jakarta housing market, Indonesia. Environmental
Economics and Policy Studies, 21(4), 555-577.
Donovan, G. H., Landry, S., Winter, C. (2019) Urban trees, house price, and redevelopment pressure in
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12
da Piedade Morais, M., & de Oliveira Cruz, B. (2015). Demand for housing and urban services in Brazil:
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13
APPENDIX
Table A1: Property pricing measurement
Property value
measure/variable Countries/Applications Source
Transaction prices Chile, Hungary, Poland, China Central Bank (Chile), Statistics Office
(Hungary, Poland), Real Estate Administration
(China)
Property asking prices La Plata, Argentina; Shanghai, Publication of prices from local real estate
China agencies
Modeled property Jakarta, Indonesia Online data, from the National Land Agency
value for property tax purposes. Modeled property
value with data from brokers, online
websites, administrative and notary offices,
including market transaction prices.
Rent reported Brazil; Kinshasa, DRC; Accra, Household surveys
Ghana; Urban areas, Guatemala;
Kisumu, Kenya; Islamabad,
Pakistan; Kigali, Rwanda; Peri-
urban Lusaka, Zambia
Predicted property Dapaong city, Togo Survey
value
Predicted rent value Mumbai, India Survey
Commercial property Jakarta, Indonesia Online real estate marketplaces
asking prices
Table A2: Urban infrastructure measurement
Example of
Sector Urban infrastructure Urban infrastructure measure/variable application
Water; Access to public and Dummy for access to the infrastructure Brazil; Kinshasa, DRC;
Sanitation; private solid waste Urban areas,
Waste collection, access to Guatemala; Kigali,
management; sewerage system; Rwanda; Dapaong,
Electricity access to latrine and Togo; Lusaka,
toilet; access to piped Zambia; all the
water countries with LSMS
data
Disaster risk Hydraulic Flood risks map; historic flood map; La Plata, Buenos
reduction: infrastructure; Self-reported exposure to floods. Aires, Argentina;
flooding Drainage system; Kinshasa, Congo;
Flood risk mitigation Accra, Ghana;
and erosion control Antananarivo,
infrastructure Madagascar; Dar es
Salaam, Tanzania
Housing; Slum Slum upgrading and Access to jobs (distance to job, and Mumbai, India;
upgrading relocation programs; average distance from dwelling to the Jakarta, Indonesia;
improve roads and 100 nearest jobs), housing quality (roof, Islamabad, Pakistan
basic public goods. floor, wall materials, etc.); Index of
informality of settlements taking values
0 to 4 (0 = formal, 4 = very informal)
14
based on slum mapping methods;
Distance to nearest slum, household
perception of living (or not) in slums.
Transport: Dar es Salaam Bus Travel time, km per bus and per day, x Dar es Salaam,
Public transport Rapid Transit (BRT) fuel liters per km. Tanzania
Co-benefits:
reduction of travel
times, reduction of air
pollution
Streetscaping Street greenery Share of certain area covered in green Shanghai
space
Tourism Tourism development Data on number of overnight stays Kakheti, Georgia;
and improved (days), number of hotel rooms, number South of Albania
infrastructure of beds in hotels, number of
Co-benefits: Increase guesthouses; visitors to the main
in property and rent touristic attractions; tourist spending
values, and income
revenues. Job
creation.
15
The hedonic pricing method is a data-driven decision-
making tool that can be used by policymakers
and researchers to quantify the benefits of urban
infrastructure investments. This note is an introduction
to the hedonic pricing method and provides guidance
on its application to assess the benefits of urban
infrastructure investments, with a focus on developing
countries. The objective is to support World Bank
task teams in building a value proposition of urban
investments that can help inform decisions and to
enhance the quality of project economic analysis
of urban infrastructure investments. By enabling
the monetization of the benefits of a wide range of
urban investments, the hedonic pricing method is a
tool particularly well-placed to support this effort.
At the same time, it is also important to be aware of
the method’s limitations to prevent its inappropriate
application, which are also discussed in this note.
Wichtiger HINWEIS !
Innerhalb der Schutzzone (hellblauer Rahmen) darf
kein anderes Element platziert werden!
Ebenso darf der Abstand zu Format- resp. Papierrand
die Schutzzone nicht verletzen!
Hellblauen Rahmen der Schutzzone nie drucken!
Siehe auch Handbuch
„Corporate Design der Schweizerischen Bundesverwaltung“
Kapitel „Grundlagen“, 1.5 / Schutzzone
www. cdbund.admin.ch