NIST Technical Note 1811

Applying the Hedonic Method

Stanley W. Gilbert
NIST Technical Note 1811 

Applying the Hedonic Method

Stanley W. Gilbert

Office of Applied Economics

Engineering Laboratory

September 2013

U.S. Department of Commerce 

Penny Pritzker, Secretary

National Institute of Standards and Technology 

Patrick D. Gallagher, Under Secretary of Commerce for Standards and Technology and Director 
Certain commercial entities, equipment, or materials may be identified in this

document in order to describe an experimental procedure or concept adequately.

Such identification is not intended to imply recommendation or endorsement by the

National Institute of Standards and Technology, nor is it intended to imply that the

entities, materials, or equipment are necessarily the best available for the purpose. 

National Institute of Standards and Technology Technical Note 1811

Natl. Inst. Stand. Technol. Tech. Note 1811, 32 pages (September 2013) 

CODEN: NTNOEF
iii

Abstract

Often, advances in measurement science will enable the development of new product innovations. 

When trying to estimate the value of the benefits of an advance in measurement science, it can be 

difficult to estimate the value of those new product innovations, especially if they are contained within 

composite goods.

The hedonic method provides a mechanism for the impact on the price of a composite good of a new 

product innovation. Under the right circumstances, and with the right data, it can even help estimate the 

value to the consumer of those new product innovations. This helps in estimating the benefits of 

advances in measurement science, and in some cases can help show where additional resources could be 

profitably spent in seeking further advances.

This Technical Note describes what the hedonic method does, how one is done, what its limitations are. 

It starts with a discussion of the theory behind hedonic studies, including a discussion of the basic 

assumptions behind it. It then goes on to describe how such studies are done. It specifically discusses 

basic studies that simply estimate the price function for a composite good, including some of the 

potential problems that could arise in such a study. It then goes on to discuss more complete studies that 

estimate supply and or demand functions for the market for a composite good. It concludes with an 

annotated bibliography.

Keywords: Hedonic Method; Hedonic Bibliography; Composite Goods; Theory and Practice
iv
v

Acknowledgements 

The author wishes to thank all those who contributed so many excellent ideas and suggestions to 

this report. They include Dr. Robert Chapman, Chief of the Applied Economic Office (AEO) at the 

National Institute of Standards and Technology who provided support and many useful 

comments. Dr. David Butry, an AEO economist, provided many useful comments. Ms. Shannon 

Takach of the AEO assisted greatly in preparing the manuscript for review and publication. As 

always, any remaining flaws and errors are the responsibility of the author.

Disclaimer regarding Non-Metric Units

The policy of the National Institute of Standards and Technology is to use metric units in all of its 

published materials. Because this report is intended for the U.S. construction industry that uses 

U.S. customary units, it is more practical and less confusing to use U.S. customary units rather than 

metric units. Measurement values in this report are therefore stated in U.S. customary units first, 

followed by the corresponding values in metric units within parentheses.
vi
vii

Contents

Abstract......................................................................................................................................................iii

Acknowledgements..................................................................................................................................... v

Introduction................................................................................................................................................. 1

Theory......................................................................................................................................................... 3

Basic Assumptions.................................................................................................................................. 4

Completeness...................................................................................................................................... 4

Availability ......................................................................................................................................... 4

Market Power...................................................................................................................................... 4

Preference / Cost Estimation................................................................................................................... 5

Estimating the Price Function..................................................................................................................... 6

Assumptions............................................................................................................................................ 6

Functional Form.................................................................................................................................. 6

Characteristics included in the Analysis............................................................................................. 7

Spatial Correlation .............................................................................................................................. 8

Techniques to Address These Issues................................................................................................... 9

Estimating Preferences................................................................................................................................ 9

Conclusion ................................................................................................................................................ 10

Annotated Bibliography............................................................................................................................ 13

Theory of Hedonic Analysis................................................................................................................. 13

Preferences and Costs........................................................................................................................... 15

Price Function ....................................................................................................................................... 18
viii
1

Introduction

A hedonic study is an effort to understand what characteristics contribute to the value of a good. Most 

goods have a variety of characteristics that contribute to or detract from its value. For example, the 

characteristics of a ballpoint pen that contribute to (or detract from) its value would include the color of 

ink, durability of ink, how fast the ink dries, how fine a point it has, how big it is, how readily it leaks 

(for example, does it tend to leak on airplanes), how easy the ink is to refill, and how stylish it is. Goods 

like this are called Composite Goods.

A Composite Good is a good that consists of a bundle of characteristics that are relevant to people’s 

decision to buy them. Examples of composite goods include houses, cars, phones, and food. A car, for 

example, has a number of characteristics that factor into how much people are willing to pay for it. 

These include color, body style, seating capacity, fuel efficiency, and horsepower. The purpose of a 

hedonic study is to determine how much each characteristic contributes to the value of the car at the

margin. For example, Goodman (1983) found that for cars manufactured in 1975 and sold on the usedcar market in 1977, a one MPG increase in fuel efficiency increased the resale price by $93.

Hedonic studies are primarily useful for estimating the value of non-market goods where those nonmarket goods form part of some larger composite good. Examples of studies that have done this include 

studies that estimated the value of fire sprinklers in single-family residential houses, energy efficiency in 

air conditioners, the value of trees in a residential neighborhood, and the value of brand names for laser 

printers. This sort of a study could be useful in developing cost-benefit analyses of proposed seismic or 

fire design codes (where, for example, code changes could eliminate (or enable) building features or 

building types that people value for their own sake); it could aid in developing cost-benefit analyses of

low- (or zero-) energy-use structural designs; or it could enable a value to be placed on improved 

building comfort when (for example) commissioning buildings.

A Hedonic study can have at least one of two basic objectives. First, and most commonly, hedonic 

studies seek to define the relationship between the characteristics that make up the Composite Good and 

its price. Second, hedonic studies sometimes also attempt to describe the individual preferences that 

drive people’s decisions about what they buy. The latter is much more difficult to do correctly, and so 

tends to be done only rarely. The classic example of a preference study is Witte, et al. (1979).

The basic approach to a pricing study is illustrated by Rao and Lynch (1993) in their hedonic study of 

computer workstations1

. We assume that the products in the market being studied are completely 

described by some vector Z of characteristics. Thus any particular product, i, will be defined by its 

bundle of characteristics, zi, and will sell for price pi. Then, the basic assumption of any hedonic pricing 

study is that there is a function, p, that determines the price of all goods in the market, where pi = p( zi ).

There are two basic decisions that need to be made before the study really commences. First, is the 

decision of what characteristics to include in the description of goods in the market. Typically no claim 

is made that the characteristics selected provide a complete description of the Composite Good. The 

second decision that must be made is what functional form to use in modeling the pricing relationship.

 1 Workstations were an intermediate class of computer between the standard desktop microcomputer and the mainframe.
2

In the Rao and Lynch (RL) study, they finally settled on six variables describing the system, and three 

dummy variables representing the manufacturer (see table). The Manufacturer dummy was included 

because it was believed that the reputation for quality and the level and quality of service provided 

contributed to the value of the system for buyers.

RL made an attempt to determine the functional form that best fit the data. Based on those efforts, they 

settled on a linear specification. That is, they assumed that price was related to the characteristics by the 

following function:

pi = β0 + β1DASDi + β2RAMLOWi + β3MONOi + β4SCSIi + β5MIPSi

+β6GRAPHi + β7SUNi + β8DECi + β9HPi + εi

Here the variable ε is a random variable accounting for the fact that they didn’t actually have a complete 

specification of the Composite Good.

At that point, RL simply regressed price 

against the workstation characteristics using 

publically available pricing data. Based on 

their regression, they found that the 

variables with the greatest effect on 

workstation price were DASD, SCSI (which 

negatively affected price), and RAMLOW.

Many studies simply assume that the results 

of the price study are a direct reflection of 

underlying consumer preferences. It must

be emphasized that this is not necessarily the 

case. First, price is a product of both supply 

and demand. Factors that affect the supply 

side of the market will be reflected in the 

price even if they have no impact 

whatsoever on consumer preferences. For 

example, the DEC manufacturer variable 

was strongly negative. One possible 

explanation is that customers considered 

DEC machines to be of lower quality than 

those of other manufacturers, or that 

consumers found the service package provided by DEC to be less valuable than that of other 

manufacturers. However RL attributed the price difference to supply differences: specifically they 

observed that DEC had been the first manufacturer to significantly lower its price in search of market 

share, thus setting off a price war in the workstation market.

Second, it is likely that consumer preferences are heterogeneous. In such a case, a simple description of 

preferences becomes difficult at best. For example, in the RL study, the SCSI variable is strongly 

negative. RL suggest that the SCSI variable marks the difference between a standard workstation which 

came equipped with a SCSI Bus and special purpose workstations which often did not, and tended to be 

much more highly priced. In this case the SCSI variable reflected a divide not only between different 

Variable Description

DASD Maximum size hard drive (or other 

direct-access secondary storage) 

supported.

RAMLOW Minimum amount of RAM required.

MONO Dummy Variable: Does the machine 

support a monochrome monitor?

SCSI Dummy Variable: Does the machine 

use a SCSI mass storage disk 

controller?

MIPS A measure of system performance.

GRAPH Number of graphics standard 

supported.

SUN, DEC, HP Manufacturer dummies.
3

types of workstations, but also between different types of customers who valued different characteristics 

in the products they were buying.

This report describes what the hedonic method does, how a hedonistic study is done, what its limitations 

are. It starts with a discussion of the theory behind hedonic studies, including a discussion of the basic 

assumptions behind it. It then goes on to describe how such studies are done. First it discusses how a 

basic study that simply estimates the price function for a composite good is conducted, including some 

of the potential problems that could arise in such a study. It then goes on to describe how a more 

complete study that estimates supply and or demand functions for the market for a composite good 

would be conducted. It concludes with an annotated bibliography.

Theory

One of the earliest treatments of the idea of a Composite Good was Lancaster (1966). He developed a 

consumer theory where all goods are composite, and people buy those goods for the characteristics that 

make them up rather than for the goods themselves. However, the seminal treatment of the idea, and the 

one that placed the Hedonic study on firm footing was Rosen (1974). His model assumes that both 

consumers and producers are price takers (i.e., they must both take the market price). He then derives 

supply and demand from the consumers’ preferences and from the producers’ profit-maximization 

decision.

Assume that there exists a price function p( z ), where z is a vector of characteristics completely 

describing the Composite Good being studied. That is, the characteristics in z completely determine the 

price paid every time a consumer buys one of the Composite Goods being studied. Also assume that

consumers have utility functions U( c, z; x ), where c represents consumption of all other goods, and x

parameterizes different consumers. Note that this model explicitly allows different consumers to have 

different preferences. Each consumer chooses the version of the Composite Good to purchase in order 

to maximize his or her utility, taking into account price. That is, the consumer solves the problem:

max z U(y − p(z), z; x)

Here y represents the consumer’s income.

Similarly for producers, we have a cost function, C( M, z; β ), where M is the amount produced, z is the 

vector of characteristics of the good produced, and β is a vector of characteristics that define the 

different producers. Again, the model explicitly allows different producers to have different cost 

structures. Each producer selects the specific Composite Good (z) to produce and how much to produce. 

That is, the producer solves the problem:

max

M,z Mp(z) − C(M, z; β)

From these we can derive supply and demand for each z. Specifically QD( p, z ) would be the number of 

consumers who choose to consume bundle z given the price function p, and QS( p, z ) would be the total 

amount of good z produced given the price function p. Supply and demand must be equal for all values 

of z. The price, then, is the function that results in QD( p, z ) = QS( p, z ) for all z. Note that this will 

typically require the solution of a highly non-linear second-order differential equation.
4

In practice, we aren’t interested in solving that differential equation. In fact, the data we typically have 

generally allows us to estimate p( z ) directly. What we are interested in doing, at least some of the time, 

is solving the inverse problem: that is, trying to estimate the utility function(s) and / or the cost 

function(s) from the pricing data.

The fundamental assumption that any hedonic study makes is that a stable price function p( z ) exists. 

This in turn implies an additional number of assumptions that typically include:

• Completeness: All possible products within the product space are available for sale.

• Availability: At any given time within a single market, all products are available to all 

consumers—that is, we are looking at a single unified market.

• Market Power: No consumer or producer has market power—that is, all participants in the 

market are price-takers.

As a practical matter, of course, these assumptions are often violated in practice. How serious an impact 

these violations have on the study depends on the situation. With that in mind, each of the assumptions 

is discussed in turn.

Basic Assumptions

Completeness

Completeness mainly ensures that each consumer gets his or her optimal bundle. If it does not hold, 

then consumers will tend to “bunch” up on the boundary of their choice set. That will be reflected in the 

estimated price function. If not properly accounted for it could easily lead to misinterpretations of the 

results.

In practice, this condition is rarely fully met. How serious the problem is depends on how incomplete

the market is, and what form the market incompleteness takes. In some cases, the incompleteness can be 

safely ignored (and it often is ignored in housing studies). However, if significant portions of the product 

space are simply not available for sale, then boundary effects may need to be taken into account. 

Certainly extrapolation beyond the bounds of the market is likely to be seriously problematic.

Availability

Availability matters because if some non-negligible segment of the population has no access to some of 

the products on the market, then prices will be too low for the inaccessible market segment, and likely 

too high for the remainder of the market. Remember that prices are a product of supply and demand. If 

demand is artificially restricted for some segment of the market, then prices will partially reflect that 

restriction.

Market Power

Market power matters because interpretation of the price function can become seriously problematic 

when some party (or parties) have pricing power. For example, in a monopoly market, price is 

determined by the monopolist. That enables complications like price discrimination (where different 

customers are charged different prices for the same good) and strategic pricing decisions on the part of 
5

the monopolist (for example, the monopolist might charge higher prices for one good in an effort to 

channel customers to a related but more profitable good).

This assumption is somewhat less likely to be violated in practice, but is still violated from time to time. 

It is reasonable to assume that housing markets fit this assumption. An example of a study where the 

assumption was violated is the RL study above. In it three companies controlled 70 % of the market, 

suggesting that they had some pricing power. The study also makes it clear that price discrimination 

was routinely practiced in the market. The prices used in the study were the list prices, not the actual 

prices paid by customers. Since price discrimination was routinely practiced, the list prices likely had a

significant strategic component to them. 

If some market participant has market power, then care needs to be taken in interpreting the data. 

Furthermore, since pricing is a strategic decision, then predicting what will happen as a result of market 

changes becomes much more difficult.

Preference / Cost Estimation

As previously discussed, the results of a pricing study need to be interpreted carefully. An example 

provides an additional illustration of this. Goodman (1983) attempted to estimate the value of fuel 

efficiency for cars on the U.S. used car market. He specifically looked at a select subset of models, sold 

on the used car market in 1977 and 1979, and that were two years old at the time. In 1977 he found that 

a one MPG increase in fuel efficiency increased the resale price of 1975-model cars by approximately 

$93 (or $219 for a one km / l increase). However, in 1979, he found that an increase in fuel efficiency 

reduced the resale price of a 1977-model car. In spite of valiant effort, he was unable to overturn that 

conclusion. That result is counter-intuitive, since we would expect fuel efficiency to have a positive 

value for consumers (as further indicated by the 1977 results).

However, supply-related factors may serve to explain his results. The Corporate Average Fuel Economy 

(CAFE) standards came into force in 1978 and were significantly tightened for the 1979 model year. 

Those standards require auto makers to maintain a minimum average fuel economy for the automobiles 

sold in a specific year. Assuming (as seems likely) that the standards mandated a mix of cars different 

from the one that consumers would have purchased on their own in 1979, then low-fuel-efficiency new 

cars would have carried an additional cost for complying with the CAFE mandate. Assuming that new 

and used cars are to some extent substitutes, and since the increase in price did not affect demand, the 

“overpricing” of low-fuel-efficiency new cars would to some extent have been compensated for by a 

similar “overpricing” of low-fuel-efficiency used cars.

A number of techniques have been discussed and used to estimate utility parameters or cost parameters 

from the pricing data. Rosen (1974) suggested one approach. To implement that technique required 

data from multiple separate markets. Essentially, he required multiple instances where supply and 

demand were different. These could be geographically distinct markets, or it could be the same 

geographic market observed at different times when supply and / or demand would have shifted. Then, 

given the pricing function (which is estimated first), and the marginal price (determined from the 

estimated pricing function), he suggested simultaneously estimating the system of equations 

representing supply and demand.
6

As Brown and Rosen (1982) discuss, it is essential that multiple markets be used in this technique, or 

that prices be non-linear. They observe that if marginal prices are linear, then the method described 

above produces no information about the underlying supply and demand functions. On the basis of their 

results, they argued that multiple markets were imperative if an attempt was to be made to recover 

structural information about supply and demand.

Ekeland et al. (2004) soften this conclusion by noting that a non-linear pricing structure will allow 

structural information to be recovered. They also note that marginal price is “almost always” non-linear 

(where “almost always” had a technical definition that is beyond the scope of this paper). Nevertheless, 

the key observation of Brown and Rosen holds: care must be taken when trying to estimate structural 

parameters, or the results will be meaningless.

Estimating the Price Function

Assumptions

As a practical matter, when trying to estimate the price function p( z ), all the factors that affect the price

are not known. So, in practice, the related function, p( z, ε ), is what is estimated, where z are those 

characteristics of the good that are known to the researcher, and ε are those characteristics that are not 

known. In particular, it is assumed that the unknown characteristics can be described by a single 

random variable that is distributed according to some probability distribution F( ε )

2

. Estimation of the 

price function then is the process of estimating p( z, ε ) accounting, as necessary, for the probability 

distribution F( ε ). In some cases the parameters of the probability distribution, F, are also estimated.

Functional Form

As with any statistical analysis, there are a number of decisions that must be made. In estimating the 

price function, the same considerations apply that apply to any regression model. First and foremost is 

the functional form to be estimated. As a general rule most studies assume a linear relationship between 

the price and some function of the characteristics, or some form that can be transformed to linear. Loglog models and log-linear models are common variants.

The Box-Cox transformation (which includes linear and log models as special cases) is used somewhat 

less frequently. The Box-Cox transformation is shown below.

x(λ) = �

xλ − 1

λ λ > 0

ln x λ = 0

Some researchers have used the Box-Cox transformation and estimated the optimal exponent as part of 

the regression (including Goodman, 1983 and especially Halstead et al. 1997).

Non-Linear models are also occasionally estimated. For a particularly complex example see Epple et al. 

(2006). Many non-linear models can be readily estimated using modern software and methods; 

 2 More complex assumptions can be made, but that is rarely done.
7

however, non-linear models are rare because they are more complex to set up, convergence issues are 

more likely to crop up, and generally they require an a priori justification for their form.

Functional form is important because an incorrect form can produce incorrect results. How large the 

error is in the results will depend on the problem but could be quite large.

Characteristics included in the Analysis

Which characteristics to include in the description of the composite good is also important. Two 

problems are considered: inclusion of irrelevant characteristics and failure to include relevant 

characteristics.

Inclusion of irrelevant characteristics will not generally bias the results of the estimated price function; 

however, it can reduce the significance of the results obtained or produce spurious results through 

simple random variation. An example will illustrate the latter problem. Kochi et al. (2012)3

, estimate 

the mortality impact of urban air pollution from the 2003 Southern California wildfires. They conducted 

a total of 36 regressions for twelve different portions of the study area and three different time periods 

during and following the fire. They found significant increases in mortality in three of the regions 

studied. One region was found to have increased mortality during the fires (at the 5 % level). Two other 

regions were found to have increased mortality during the two weeks following the fire (one at the 10 % 

level and one at the 1 % level). The problem is that these results are difficult to distinguish from random 

variation. Looking exclusively at the results for mortality during the fire, the likelihood of producing at 

least one result with a 5 % level of significance out of twelve regressions is about 45 % (their 

conclusions for the follow-up period were somewhat stronger).

If some of the irrelevant variables correlate with the relevant characteristics, then significance levels for 

the results will dramatically decline. In short, if an irrelevant characteristic is included, results will still 

be unbiased, but they may become confused as significance levels decline or spurious correlations start 

to appear.

Exclusion of relevant characteristics can be more serious. When an omitted characteristic significantly 

affects price and is correlated with an included characteristic then the results for the included 

characteristic will misrepresent its impact on price. For example, Bayer et al. (2008) estimated the value 

that people place on living in a good quality school district. Since good quality school districts tend to 

be associated with good “quality” neighborhoods, there is a correlation between district quality and 

neighborhood quality. Not accounting for neighborhood quality will result in an overestimate of the 

value that people place on being in a good quality school district. Bayer at al. use boundary 

discontinuity techniques by assuming that neighborhoods that are very close together but on opposite 

sides of a school district boundary will be similar in quality. That allows them to separate the effects of 

neighborhood quality from school district quality.

Related are situations where a characteristic is simultaneously determined with price. The classic 

example of this problem is the simultaneous determination of quantity and price of some market good. 

In the classic model, both the quantity and price sold of a market good are determined by the point 

where supply and demand are equal. That model can be described using the following set of 

simultaneous equations:

 3 Kochi, I., P. A. Champ, J. B. Loomis, and G. H. Donovan. 2012. “Valuing Mortality Impacts of Smoke Exposure from 

Major Southern California Wildfires.” Journal of Forest Economics 18 (1): 61–75.
8

Q = QD(P, X)

Q = QS(P, Y)

By the Implicit Function Theorem, those equations can be recast as:

P = P(X, Y)

Q = Q(X, Y)

That allows us to estimate either price or quantity separately using the techniques described above. This 

is just restatement of the results of Rosen (1974) in a different form. However, if, in trying to estimate 

price, we include quantity as one of the regressors (and, as usual, use only a subset of the 

characteristics); that is, if we try to estimate:

P = P�(Z,Q, ξ)

Where Z ⊂ X ∪ Y (and as usual we assume that the excluded variables can be summarized by ξ with a 

probability distribution F( ξ )) then we will run into problems. Specifically, the coefficients on Z will be 

biased, and will not represent the true effect of those characteristics on price. In addition, interpreting 

the coefficient on the endogenous regressor is difficult at best.

Fuerst and McAllister (2011) investigated the effect of eco-labeling on rental rates, sales prices, and 

occupancy for office buildings. Specifically, they estimated the rental and sales price premiums from an 

Energy Star rating and a LEED rating on office buildings. They found that either rating produces a 

significant increase in rental rate and sales price. Dual certification provides an additional premium. 

They found that an Energy Star rating significantly increased occupancy, while an increase in occupancy 

for the LEED rating could not be confirmed. However, in estimating rental rates, they included 

occupancy rate as a regressor, when the two are simultaneously determined through equilibration of 

supply and demand. That casts doubt on their remaining results.

Spatial Correlation

One common example of an important omitted regressor involves spatial correlation in housing markets. 

In spatial correlation, the price of two nearby houses are correlated even after taking into account the 

known characteristics of the houses. Remember that the objective is typically to estimate price using 

some functional form similar to:

yi = xi

′

β + εi

Spatial correlation can occur in three different (non-exclusive) forms. First, the omitted characteristics 

could be correlated. That is, for two different (but “nearby”) houses, εi could be correlated with εj. In 

the housing example, that would be the equivalent of having housing characteristics not accounted for in 

the regression more likely to be similar if the houses are nearby than if they are far apart. A second form 

would be if the price of a house depends on the characteristics of houses nearby. An example would be 

if a new house increases the price for all houses in the neighborhood regardless of their age. A third 

form would be if the price of a house depends on the price of the nearby houses.

The first form of spatial correlation does not introduce any bias into estimates of the regression 

coefficients, although it means that it takes larger samples to get reasonable results. Either of the 
9

remaining forms of spatial correlation, if present, will bias the results, and estimated coefficients on the 

regressors will generally misstate the relationship between them and price.

Techniques to Address These Issues

In all of these cases, there are techniques available for dealing with these problems. In the case of an 

omitted correlated variable, instrumental variables techniques are available for addressing the problem. 

As an example, Palmquist (1984) used instrumental variables in a multi-market model to estimate the 

underlying demand for housing. When dealing with endogenous regressors, simultaneous equation 

methods allow for the estimation of the endogenous regressors at the same time as the quantity of 

interest. Alternatively, instrumental variables methods will also work with endogenous regressors.

A set of techniques have also been developed to handle spatial correlation. As an example, Hui et al. 

2007 and Hui et al. 2012 investigated the impact of a number of environmental and neighborhood 

amenities on the price of housing in Hong Kong. The amenities evaluated included view, the presence 

of nearby greenbelt areas, air quality and noise level. Their studies explicitly took spatial correlation 

into account. Hui et al. (2007) separately estimated models where the omitted characteristics were 

correlated (i.e., a Spatial Error Model), and where price depends on the price of nearby houses (i.e., a 

Spatial Autocorrelation Model). Hui et al. (2012) estimated a model where two forms of spatial 

correlation were present simultaneously. The parameters for the spatial regression were significant for 

both studies.

Estimating Preferences

When preferences or cost functions are the object of the study, a number of techniques have been 

developed. The techniques depend on one of two approaches. The first requires estimation of price 

across multiple markets. The second requires the price function to be non-linear. Both approaches are 

discussed below. Brown and Rosen (1982) made the point that without one or the other, no information 

can be obtained about underlying preferences or production cost functions.

One of the earliest studies that estimated underlying preferences was Witte et al. (1982) who estimated 

preferences for rental housing and the cost function for provision of rental housing in non-metropolitan 

cities in North Carolina. They used price information from four geographically distinct markets in the 

state to estimate supply and demand parameters. They assumed that there was a single parameterized 

utility function and cost function (with the parameters describing the difference in costs between 

different producers, as well as the difference in preferences between different consumers). First, they 

estimated the price function for each of the markets they analyzed. From that, they obtained marginal 

price information. Then they used the price information from multiple markets to estimate underlying 

preferences using standard multiple-equation techniques. Their supply and demand functions had the 

expected slope: that is, demand declined as its price increased and supply increased as its price 

increased. In demand, most parts of the composite good that formed the house were complements: that 

is, an increase in one good increased demand for the others that make up the house.

Palmquist (1984) estimated demand for housing without using the simultaneous equation techniques 

used in Witte et al. (1982). He used multiple markets as well. Specifically he used six mid-level 

metropolitan areas from across the country. He also estimated a price function separately for each city, 

and used the marginal prices computed from that to estimate demand. In order to consistently estimate 

demand, he used a set of instrumental variables to correct for the inherent endogeneity of price. He 
10

found results that made sense economically (higher prices resulted in lower quantity demanded for 

housing characteristics).

A slightly different approach was used by Kristofersson and Rickertsen (2004 and 2007). They 

estimated the demand for fish on Iceland’s fish auction markets. In their case, the customers whose 

preferences make up demand are fish processing firms. Their study used price information from 

multiple markets as well, except in their case the multiple markets are auctions that occur on different 

days. They assumed that supply in each daily market was inelastic, since the fish on each days market 

had already been caught. That allowed them to avoid a simultaneous equations model. They also 

developed a random-coefficients model for demand. In a random-coefficients approach, instead of 

estimating regression parameters, the researcher estimates a probability distribution over the parameters. 

They found that price per unit weight for Cod increased as the fish got larger. They were also able to 

estimate how much the price per unit weight declined per day of storage or if the fish was not gutted.

As an alternative to the multiple markets approach, preferences can be estimated if the price function is 

nonlinear. Ekeland et al. (2004) argue that the price function will almost always be non-linear, where 

“almost always” had a technical definition that is beyond the scope of this paper. 

Epple et al. (2006) implicitly used the non-linearity of price to estimate preferences for higher education, 

as well as the cost-function for providing higher education. Their complex technique involved a twoloop iterative procedure. The outer loop was a maximum likelihood estimation of parameters for 

preferences and costs. The inner loop used the candidate parameters for preferences and costs to solve 

for the equilibrium price for all the different levels of educational quality in the model. Their model 

gave rise “to a strict hierarchy of colleges that differ by the educational quality provided to the students.” 

Based on their model they were able to “simultaneously [predict] student selection into institutions of 

higher education, financial aid, educational expenditures, and educational outcomes.” However, the 

model was very computationally intensive. They had to use bootstrapping to estimate errors and each 

bootstrap iteration took 24 hours on a PC to compute.

While such a complex model is a possibility in some cases, there are other techniques available that 

exploit non-linearity without resorting to such computationally intensive methods. Ekeland et al. (2004)

suggested the use of instrumental variables to allow for consistent estimation of preferences. They 

showed that the variables that parameterize preferences or the cost function will serve adequately as 

instrumental variables for the estimation.

Conclusion

Often, advances in measurement science will enable the development of new product innovations. 

When trying to estimate the value of the benefits of an advance in measurement science, it can be 

difficult to estimate the value of those new product innovations, especially if they are contained within 

composite goods.

The hedonic method provides a mechanism to estimate the impact on the price of a composite good of a 

new product innovation. Under the right circumstances, and with the right data, it can even help 

estimate the value to the consumer of those new product innovations. This helps in estimating the 

benefits of advances in measurement science.

In some cases a hedonic study can help focus efforts where additional resources would most profitably 

be spent. Consider the hypothetical example where a composite good has one characteristic with a very 
11

high marginal price. Any innovation that makes that characteristic cheaper to produce or integrate into 

the good will make people better off, either by enabling more of the characteristic to be integrated into 

the good or by allowing the same amount to be supplied more cheaply.

For example, Deligiorgi et al. (2007) estimated the price effects of different characteristics of broadband 

service in Europe. They found that the characteristic that most strongly affected price was download 

speed. That implies that research and development resources would most productively be funneled 

primarily toward increasing download speeds, where the benefit is greatest.

13

Annotated Bibliography

This annotated bibliography is divided into three sections. The first is a list of papers that address the 

theory of hedonic analysis. The second section lists papers that attempt to identify supply or demand

(preferences or cost functions) for the market analyzed. The third section lists papers that focus on

identifying the price function for the market analyzed.

Theory of Hedonic Analysis

Brown, James N., and Harvey S. Rosen. 1982. “On the Estimation of Structural Hedonic Price Models.” 

Econometrica 50 (3) (May): 765–768.

This paper is interested in the limits of studies that attempt to identify demand and / or supply or 

other structural characteristics. They observe with an example that if the marginal price function 

is a linear combination of the terms of the supply and demand functions, then the attempt to 

estimate structural characteristics will provide no information about those characteristics. 

Therefore, if information about structural characteristics is sought, multiple markets are essential, 

and / or, the marginal prices for characteristics must be non-linear.

Ekeland, I., J. J. Heckman, and L. P. Nesheim. 2004. “Identification and Estimation of Hedonic 

Models.” Journal of Political Economy 112 (1): 60–109.

This study supports the conclusions of Brown and Rosen (1982) that in order to obtain 

information about structural characteristics, multiple markets are essential, and / or, the marginal 

prices for characteristics must be non-linear. They differ from them in that they argue that 

marginal prices are non-linear in “almost all” markets (where “almost all” has a technical 

definition beyond the scope of this paper). Assuming that marginal prices are non-linear, they 

argue that it is possible to get to preferences even in single-market by exploiting the nonlinearity. They propose a couple of techniques to do so, including proposing a set of 

instrumental variables that will work.

Lancaster, K. J. 1966. “A New Approach to Consumer Theory.” The Journal of Political Economy 74 

(2): 132–157.

This is one of the earliest treatments of a Composite Good. He developed a consumer theory 

where all goods are composite, and people buy those goods for the characteristics that make 

them up rather than for the goods themselves.
14

Rosen, S. 1974. “Hedonic Prices and Implicit Markets: Product Differentiation in Pure Competition.” 

The Journal of Political Economy: 34–55.

This is the seminal treatment of Hedonic theory. He attempts to get information about 

preferences from market data. His main observation is that the normal form of Hedonic analysis 

does not do that. He goes on to suggest an approach that will get preferences provided sufficient 

data of the right type is available.

What is typically estimated in Hedonic analysis is the price functional. He notes that the price 

functional cannot be (correctly) interpreted as a reflection of underlying preferences. For 

example, suppose there is only one type of seller, but many types of consumers. In that case, the 

differences in price will reflect different types of consumers rather than giving the price v. 

attribute function for any consumer.
15

Preferences and Costs

Bayer, Patrick, Fernando Ferreira, and Robert McMillan. 2007. “A Unified Framework for Measuring 

Preferences for Schools and Neighborhoods.” Journal of Political Economy 115 (4) (August): 588–638.

This paper estimates household preferences for school district quality and neighborhood quality, 

where the two are highly correlated. In order to disentangle the effects of school district from 

those for neighborhood quality, they use boundary discontinuity techniques. Specifically, they 

assume that neighborhoods on the opposite sides of a school district boundary will be very 

similar. Otherwise, they use techniques similar to those described in Rosen (1974) to estimate 

structural parameters.

They also attempt to determine preferences for some of the characteristics that make up 

neighborhood and school-district “quality.” For example, they find that “households are willing 

to pay less than 1 percent more in house prices when the average performance of the local school 

increases by 5 percent.” They also find that “there is considerable heterogeneity in preferences 

for schools and neighbors, with households preferring to self‐segregate on the basis of both race 

and education.”

Chintagunta, P., J. P. Dube, and K. Y. Goh. 2005. “Beyond the Endogeneity Bias: The Effect of 

Unmeasured Brand Characteristics on Household-level Brand Choice Models.” Management Science 51 

(5): 832–849.

This study develops a theoretical model of brand choice, where they estimate the distribution 

over preferences. In particular they account for endogeneity in prices resulting from unaccounted-for covariates by using an instrumental variables approach.

They use the model to estimate preferences for margarine in Denver.

Elrod, Terry, and Michael P. Keane. 1995. “A Factor-Analytic Probit Model for Representing the 

Market Structure in Panel Data.” Journal of Marketing Research 32 (1) (February): 1–16.

This paper develops procedures for estimating preference parameters from market data. 

Specifically they assume that preferences are randomly distributed, and estimate the parameters 

of the distribution. 

They compare a series of product-preference models to a pair of models that assume no 

correlation between "related" brands. That is, preference for brand j gives no information about 

the relative preference for brands m and n even though they may share similar characteristics. 

These simpler “Independence of Irrelevant Alternatives” models tended to perform as well as or 

better than any of the other models.
16

Epple, D., R. Romano, and H. Sieg. 2006. “Admission, Tuition, and Financial Aid Policies in the Market 

for Higher Education.” Econometrica 74 (4) (July): 885–928.

This study does not look much like any other study in the Hedonic literature, but it solves 

essentially the same problem. They implicitly used the non-linearity of price to estimate 

preferences for higher education, as well as the cost-function for providing higher education. 

They explicitly develop a supply-demand model for higher education. Then they estimate the 

parameters in the model using a two-loop iterative procedure. The outer loop was a maximum 

likelihood estimation of parameters for preferences and costs. The inner loop used the candidate 

parameters for preferences and costs to solve for the equilibrium price for all the different levels 

of educational quality in the model. Their model gave rise “to a strict hierarchy of colleges that 

differ by the educational quality provided to the students.” Based on their model they were able 

to “simultaneously [predict] student selection into institutions of higher education, financial aid, 

educational expenditures, and educational outcomes.” However, the model was very 

computationally intensive. They had to use bootstrapping to estimate errors and each bootstrap 

iteration took 24 hours on a PC to compute.

Kristofersson, Dadi, and Kyrre Rickertsen. 2004. “Efficient Estimation of Hedonic Inverse Input 

Damand Systems.” American Journal of Agricultural Economics 86 (4) (November): 1127–1137.

Kristofersson, D., and K. Rickertsen. 2007. “Hedonic Price Models for Dynamic Markets.” Oxford 

Bulletin of Economics and Statistics 69 (3): 387–412.

These studies estimate the demand for fish on Iceland’s fish auction markets. They are studying 

an intermediate market, and the customers whose preferences make up demand are fish 

processing firms. They used price information from multiple markets, except in their case the 

multiple markets are auctions that occur on different days. They assumed that supply in each 

daily market was inelastic, since the fish on each days market had already been caught. That 

allowed them to avoid a simultaneous equations model. They also developed a randomcoefficients model for demand. In a random-coefficients approach, instead of estimating 

regression parameters, the researcher estimates a probability distribution over the parameters. 

They found that price per unit weight for Cod increased as the fish got larger. They were also 

able to estimate how much the price per unit weight declined per day of storage or if the fish was 

not gutted.

Palmquist, Raymond. 1994. “Estimating the Demand for the Characteristics of Housing.” The Review of 

Economics and Statistics 66 (3) (August): 394–404.

This paper estimates demand and preferences for housing. He starts by estimating the price 

function for several different metropolitan areas. Instead of using the information to 

simultaneously estimate supply and demand, (like Witte et al. (1979), discussed below), he uses 

instrumental variables to identify the characteristics of demand.
17

Witte, A. D., H. J. Sumka, and H. Erekson. 1979. “An Estimate of a Structural Hedonic Price Model of 

the Housing Market: An Application of Rosen’s Theory of Implicit Markets.” Econometrica: Journal of 

the Econometric Society: 1151–1173.

This paper estimates preferences for rental housing and the cost function for provision of rental 

housing in non-metropolitan cities in North Carolina. They use price information from four 

geographically distinct markets in the state to estimate supply and demand parameters. They 

assume that there was a single parameterized utility function and cost function (with the 

parameters describing the difference in costs between different producers, as well as the 

difference in preferences between different consumers). First, they estimate the price function 

for each of the markets they analyzed. From that, they obtain marginal price information. Then 

they use the price information from multiple markets to estimate underlying preferences using 

standard multiple-equation techniques. Their supply and demand functions had the expected 

slope: that is, demand declined as its price increased and supply increased as its price increased. 

In demand, most parts of the composite good that formed the house were complements: that is, 

an increase in one good increased demand for the other that make up the house.
18

Price Function

Ahmad, Waseem, and Sven Anders. 2012. “The Value of Brand and Convenience Attributes in Highly 

Processed Food Products.” Canadian Journal of Agricultural Economics-Revue Canadienne D 

Agroeconomie 60 (1) (March): 113–133.

This is a standard hedonic regression. They look at prepackaged dinners for chicken and fish to 

estimate the monetary value of brand, convenience, and other quality attributes. The data they 

use is the 2006 Nielsen aggregate weekly scanner data. They “find evidence of consumer 

preferences for perceived natural and health attributes over products with higher degrees of 

processing.”

Ambrose, Brent. 1990. “An Analysis of the Factors Affecting Light Industrial Property Valuation.” 

Journal of Real Estate Research 5 (3) (January 1): 355–370.

This is a straightforward estimation of the price functional for light industrial space. It is 

different from most studies in that it is applied to an intermediate good rather than a final good.

Boyle, Kevin J., Nicolai V. Kuminoff, Congwen Zhang, Michael Devanney, and Kathleen P. Bell. 2010. 

“Does a Property-specific Environmental Health Risk Create a ‘Neighborhood’ Housing Price Stigma? 

Arsenic in Private Well Water.” Water Resources Research 46 (March 9).

This study evaluates the impact on the price of housing of having arsenic in wells in the 

neighborhood. Their data set are housing prices from two towns in Maine where the existence of 

arsenic in well water was well-publicized. They found that housing prices suffered a temporary 

two-year decline following media coverage of the arsenic contamination, after which prices 

recovered.

Carew, Richard, Wojciech J. Florkowski, and Elwin G. Smith. 2012. “Hedonic Analysis of Apple 

Attributes in Metropolitan Markets of Western Canada.” Agribusiness 28 (3): 293–309.

This is a standard hedonic analysis of wholesale apple prices in western Canada. They estimate 

the price premiums paid for newer varieties, higher grades, and larger fruit size, as well as the 

price effects of cold-storage and seasonality. 

Carter, David W., and Christopher Liese. 2010. “Hedonic Valuation of Sportfishing Harvest.” Marine 

Resource Economics 25 (4): 391–407.

This is an application of Hedonic methods to sportfishing charters in the Gulf of Mexico.
19

Deligiorgi, C., C. Michalakelis, A. Vavoulas, and D. Varoutas. 2007. “Nonparametric Estimation of a 

Hedonic Price Index for ADSL Connections in the European Market Using the Akaike Information 

Criterion.” Telecommunication Systems 36 (4) (December): 173–179.

This study estimates the characteristics that determine price in Asynchronous DSL lines in 

Europe. They allowed the price to be a non-linear function of characteristics, and found that 

price was strongly related only to downlink data rate. They allow the relationship between price 

and download speed to be non-linear by using Sliced Inverse Regression.

Donovan, Geoffrey H., and David T. Butry. 2010. “Trees in the City: Valuing Street Trees in Portland, 

Oregon.” Landscape and Urban Planning 94 (2) (February 28): 77–83.

This paper uses housing prices to estimate the value of having trees on the street. They take 

spatial correlation into account, and simultaneously estimate time on market. On average, street 

trees add $8870 to sales price and reduce time on the market by 1.7 days. In addition, they found 

that the benefits of street trees spill over to neighboring houses.

Fuerst, Franz, and Pat McAllister. 2011. “Eco-labeling in Commercial Office Markets: Do LEED and 

Energy Star Offices Obtain Multiple Premiums?” Ecological Economics 70 (6) (April 15): 1220–1230.

This is a fairly straightforward hedonic regression on the market for rented commercial 

space. They look both at the rental market (rent / unit of floor space) and at the sales market for 

the same set of buildings. They find that an energy efficient rating increases rental premia by 

about 3 – 5 %. Dual certification increases rents additively. Sales price increases 18 – 25 % for 

a single certification wile dual certification increases sales price 28 – 29%.

For their rental model, they use occupancy rate as one of the explanatory characteristics. 

However, occupancy is codetermined with price as the balancing of supply and demand. By 

including occupancy as one of the covariates they introduce a potential endogeneity bias that 

they fail to correct for.

Gallaugher, JM, and YM Wang. 2002. “Understanding Network Effects in Software Markets: Evidence 

from Web Server Pricing.” MIS Quarterly 26 (4) (December): 303–327.

This paper estimates the characteristics that affect price in server software. They are particularly 

interested in determining whether network effects impacted price. A “network effect” would 

exist if server software with a large customer base is more valuable to customers because it has a 

large customer base. For example, a large customer base may inherently be associated with a 

large network of third-party support firms. They try to determine whether having a large market 

share (which presumably includes an associated network affect) contributed to the price of server 

software. They found that a network effect existed. Unfortunately, market share is endogenous: 

how many customers buy a particular suite of server software is itself a function of its price (as 

well as the characteristics of software). They did not account for the endogeneity, and that could 

bias their results.
20

Goodman, A. C. 1983. “Willingness to Pay for Car Efficiency: A Hedonic Price Approach.” Journal of 

Transport Economics and Policy: 247–266.

This study estimates the value of fuel efficiency for cars on the used car market. He specifically 

looked at a select subset of models, sold on the used car market in 1977 and 1979, and that were 

two years old at the time. In 1977 he found that a one unit increase in fuel efficiency increased 

the resale price of 1975-model cars by approximately $93. However, in 1979, he found that an 

increase in fuel efficiency reduced the resale price of a 1977-model car. In spite of valiant effort, 

he was unable to overturn that conclusion. That result is counter-intuitive, since fuel efficiency 

would seem to be a good (as further indicated by the 1977 results).

These results are probably explained by supply-related factors. The Corporate Average Fuel 

Economy (CAFE) standards came into force in 1978 and were significantly tightened for the 

1979 model year. Those standards require auto makers to maintain a minimum average fuel 

economy for the automobiles sold in a specific year. Assuming (as seems likely) that the 

standards mandated a mix of cars different from the one that consumers would have purchased 

on their own in 1979, then less fuel efficient new cars would have carried a supply-related 

surcharge. Since that increase in price did not affect demand (and assuming that new and used 

cars are to some extent substitutes), the “overpricing” of less fuel efficient new cars would to 

some extent have been compensated for by a similar “overpricing” of less fuel efficient used 

cars.

Halstead, J. M., R. A. Bouvier, and B. E. Hansen. 1997. “On the Issue of Functional Form Choice in 

Hedonic Price Functions: Further Evidence.” Environmental Management 21 (5): 759–765.

This study estimates the impacts on housing value of being near a closed landfill. They found 

that proximity to the landfill had no impact on property values. What distinguishes this study 

from others is their effort to identify the functional form of the price function. They use the BoxCox transformation, using different transformation coefficients on different variables. They 

estimate the optimal Box-Cox coefficient using maximum likelihood techniques.

Hough, Douglas E., and Charles G. Kratz. 1983. “Can ‘good’ Architecture Meet the Market Test?” 

Journal of Urban Economics 14 (1) (July): 40–54.

This paper estimates the price-characteristics relationship for office space in Chicago. In 

particular, they are interested in determining whether there was a price premium for “good” 

architecture. They defined “good” architecture as buildings that had been declared national 

landmarks or Chicago landmarks for architectural reasons, or buildings that had won an award 

for architectural excellence from the Chicago-American Institute of Architects. They found that 

“good” architecture in newer buildings carried a price premium, while “good” architecture in 

older buildings did not.
21

Hui, E., C. K. Chau, L. Pun, and M. Y. Law. 2007. “Measuring the Neighboring and Environmental 

Effects on Residential Property Value: Using Spatial Weighting Matrix.” Building and Environment 42 

(6): 2333–2343.

Hui, E., J. W. Zhong, and K. H. Yu. 2012. “The Impact of Landscape Views and Storey Levels on 

Property Prices.” Landscape and Urban Planning 105: 86–93.

These are standard studies of housing prices for Hong Kong, in densely populated high-rise 

neighborhoods. They are particularly interested in the impact of having a sea view and better air 

quality on housing price. They also look at the impact of story, noise level, and proximity to 

green-belt spaces on price. They specifically take spatial correlation into account. For anyone 

interested in spatial correlation, these studies are a good place to start.

Jani, A. B., and S. Hellman. 2008. “Early Prostate Cancer: Hedonic Prices Model of Provider-patient 

Interactions and Decisions.” International Journal of Radiation Oncology Biology Physics 70 (4) 

(March 15): 1158–1168.

This paper applies a version of Hedonic prices to determine what factors are most influential in 

the choices of therapy for Prostate Cancer.

Lavee, Doron, Tomer Ash, and Gilat Baniad. 2012. “Cost-benefit Analysis of Soil Remediation in 

Israeli Industrial Zones.” Natural Resources Forum 36 (4) (November): 285–299.

This is a cost-benefit analysis of remediating all soil-contamination sites in Israel. They use a 

hedonic approach to estimate the benefits.

Liu, Jin-Long, Pe-I. Chang, and Su-Juan Den. 2012. “Consumer Willingness to Pay for Energy 

Conservation: a Comparison Between Revealed and Stated Preference Method.” Procedia 

Environmental Sciences 17: 620-629.

This is a standard Hedonic model estimating the impact of an Energy Efficiency rating on the 

price of air conditioners in Taiwan.

Nguyen, Thong Tien. 2012. “Implicit Price of Mussel Characteristics in the Auction Market.” 

Aquaculture International 20 (4) (August): 605–618.

This is a standard hedonic model of mussel values in a wholesale market in the Netherlands. He 

finds that meat content and size count, are the most important characteristics determining the 

price. He finds that impurity of mussel lots is a significant discounting factor on the price.
22

Rao, HR, and BD Lynch. 1993. “Hedonic Price Analysis of Workstation Attributes.” Communications 

of the ACM 36 (12) (December): 95–102.

This is a standard hedonic model of workstation prices. By workstation, they mean the highpowered computer that was in between the Personal Computer and the Minicomputer in power. 

They found that the variables with the greatest effect on workstation price were the maximum 

size hard drive, whether or not the workstation came with a SCSI bus, and minimum RAM.

Robst, John. 2006. “Estimation of a Hedonic Pricing Model for Medigap Insurance.” Health Services 

Research 41 (6) (December): 2097–2113.

This paper uses hedonic methods to estimate the characteristics that affect price of Medi-Gap 

insurance plans. His study differs from the others in that he is primarily interested in 

understanding the supply side of the market rather than the demand side. However, his 

methodology is essentially the same, and his data only allows the relationship of characteristics 

to price to be inferred.

Sirmans, Stacy, David Macpherson, and Emily Zietz. 2005. “The Composition of Hedonic Pricing 

Models.” Journal of Real Estate Literature 13 (1) (January 1): 1–44.

This is a review of hedonic studies of housing. They are mainly interested in identifying 

consistently significant variables from those studies. It is a good round-up of the possible 

variables that could be used, and their expected sign and significance. It is a summary of their 

earlier study published by The National Association of Realtors.

Stetler, Kyle M., Tyron J. Venn, and David E. Calkin. 2010. “The Effects of Wildfire and 

Environmental Amenities on Property Values in Northwest Montana, USA.” Ecological Economics 69

(11) (September 15): 2233–2243.

This study employed the hedonic price framework to examine the effects of wildfires and 

environmental amenities on home values in northwest Montana. They found that some 

environmental amenities, including proximity to lakes, golf courses, national forests, and Glacier 

National Park increased the value of properties. They also found that proximity to wildfire burn 

areas had a negative effect on price.

Von Auer, L., and M. Trede. 2012. “The Dynamics of Brand Equity: a Hedonic Regression Approach to 

the Laser Printer Market.” Journal of the Operational Research Society 63 (10) (October): 1351–1362.

This is a hedonic analysis of the laser printer market in Germany. They are primarily interested 

in the price premiums associated with different brand names, and how those premiums vary over 

time. To account for the variation over time, they assume that the brand premium follows a 

random walk, and estimate the path it follows using Bayesian techniques.
23

Yu, Danlin. 2007. “Modeling Owner-occupied Single Family House Values in the City of Milwaukee: A 

Geographically Weighted Regression Approach.” GIScience & Remote Sensing 44 (3): 267–282.

This study estimated housing prices for the city of Milwaukee, Wisconsin. It used a subset of the 

standard parameters used in housing. What distinguished this study from others like it is that it 

allowed prices to differ depending on location. It used a technique called “Geographically 

Weighted Regression” to estimate a non-parametric function of price as a function of location for 

each hedonic characteristic. It found that price for characteristics varied from location to 

location. For example, price for floor space varied from a low of $18 per square foot ($194 / m2

)

to a high of $120 per square foot ($1,290 / m2) in different parts of the city.

Zietz, Joachim, Emily Zietz, and G. Sirmans. 2008. “Determinants of House Prices: A Quantile 

Regression Approach.” The Journal of Real Estate Finance and Economics 37 (4): 317–333.

This is a hedonic study of housing values in Orem/Provo Utah. They explore the possibility that 

the value of some characteristics may vary non-linearly. To examine this issue, they use quantile 

regression. They estimate the coefficients on housing characteristics for several different 

quantiles. “The results show that purchasers of higher-priced homes value certain housing 

characteristics such as floor area and the number of bathrooms differently from buyers of lowerpriced homes. Other variables such as age are also shown to vary across the distribution of house 

prices.”