Cálculos de la densidad de espuma a 50°C

This volume includes applications of both the hedonic price and the LS approaches. The former approach (Rosen 1979; Roback 1982; Blomquist, Berger, and Hoehn 1988; and Gyourko 1991) was developed to obtain

monetary valuations of public goods, such as green areas, security, conges- tion, and other urban features. The rationale for this approach is quite simple: if two otherwise identical houses are located in safe and unsafe parts of a city, and if the monthly rent for the safe-area house is $1,000 while the monthly rent for the unsafe-area house is $500, it then seems plausible to assign the difference in value to the difference in safety. The value of safety per month may thus be evaluated by the rent difference of $500.

This idea may be generalized to a great extent. Assume that the rent per month of a specific house n located at a specific location within neighborhood j(n) is pn.The rent will vary with the characteristics of the

house, such as the number of rooms, whether it is an apartment or an independent house, how far it is from key amenities or services, and the physical infrastructure and other attributes of the neighborhood. In short, the rent of a house depends on a vector of characteristics of the house and its specific location, Hn, and a vector of neighborhood characteristics, Zj(n)

(characteristics that are common to all the houses in the neighborhood). It therefore follows that relationships of the type pn = p(Hn, Zj(n)) may be

posited. For instance, let Z1j be for the neighborhood j(n) the number of minutes it takes the police to arrive after an emergency, and let Z2j be the walking distance (in minutes) to the nearest subway station. After suitable transformations of the variables, a linear relationship for the logarithm of the rent could be estimated as

ln (pn) = a0 + a1Z1j(n) + a2Z2j(n) + a3Hn+a4Z¨ j(n), (3.1)

where Z¨ j(n) stands for the vector Z without the two first components; α₀, a_1, and a₂ are scalars; and a₃ and a₄ are vectors. Let us assume that we estimate aˆ ₁ = −0.02 and aˆ ₂ = −0.01. These estimates imply that for every extra minute it takes the police to arrive, rents would drop by 2 percent. Similarly, every minute less of walking time to the nearest subway station would increase rent by 1 percent.2 It therefore seems attractive to interpret these coefficients as shadow prices.

Notice that Z1j(n) and Z2j(n) are hedonic dimensions that cannot be bought separately in a different market. Also, problems in one dimension could be compensated for by advantages in another. For example, two extra minutes of walking time to the subway could be compensated by one minute less in police arrival time. In this way, it is possible to use a common denominator (that is, the amount of rent to be paid) to compare different factors. This ratio, the market substitution between two charac- teristics, is equal to a1/a2.

Equation (3.1) now may be seen as a budget curve in the sense that it describes all houses as bundles of characteristics that can be leased at a specific rent level p. Assume that individuals have direct utility functions of the type U(Hn, Zj(n)); and that, in equilibrium, indifference curves (in

an urban quality of life index: theory and methods 69

the characteristics space) will be tangent to the budget curve. Formally, this means that

∂ ∂ ∂ ∂ ∂ ∂ ∂ ∂ U Z U Z p Z p Z j n j n j n j n 1( ) 2( ) 1( ) 2( ) 1 2 . = = α α (3.2)

It follows that the ratios a1/a2 also describe the slope of the indifference curve, if the individual consumer is assumed to be in his or her optimum. Under that assumption, there is equality between the market substitution ratio and the subjective trade-off between characteristics. It follows also that the rent equation (3.1) may be interpreted as a local approximation of the indifference curve (up to a positive factor of multiplication). Relative utility changes thus can be assessed by

ΔU ≈ a1ΔZ1j(n)+ a2ΔZ2j(n)+ a3ΔHn+ a4ΔZ¨ j(n) (3.3)

for a specific individual if the bundle (Hn, Zj(n)) is changed into (Hn,

Zj(n)) + (ΔHn, ΔZj(n)). However, this is only a local approximation. In this

way, the relative impact of changes in different variables on subjective well-being may be compared.

The hedonic price approach, used by all research groups in this vol- ume, offers interesting and even surprising results. All the groups were able to find a wealth of information in public records, including data on factors such as domiciliary services, distance to schools, and vulnerabil- ity to natural disasters. These data were complemented by information drawn from individual questionnaires filled out by the inhabitants of the houses considered.

One problem, however, renders this method less attractive than it might appear at first: the underlying assumption of neoclassical econom- ics that each person not only looks for the best position he or she can reach, but also is able to reach it. In other words, the housing market is a free-access competitive market and is always in equilibrium. That assump- tion, which implies that every observed respondent is in the best position he or she can attain, is unlikely to hold in practice. This is especially true for the housing market, where regulations and lack of information on many relevant variables may prevent full competition, where individuals’ choices—in most cases, made many years ago—are very costly to recon- sider, and where the monetary and psychic transaction costs of moving to a new home are considerable. Moreover, housing markets frequently are rationed—for example, by public housing programs or zoning restrictions. If the equilibrium assumption does not hold, then market substitution rates will not equal subjectively perceived substitution rates.

Indeed, it is not reasonable to assume that individuals observed in a dynamic reality live in an optimal situation. Most serious decisions— such as those involving education, job choice, housing, the number of

children, and the choice of a partner in life—are long-term decisions with long-lasting consequences. It is by no means certain that individuals at any arbitrary later moment still feel that they have reached the greatest possible well-being or level of satisfaction, even if all those decisions were made in a fully rational manner. It is much more probable that, at pres- ent, individuals would choose other situations, if they were not hampered by rather enormous transition costs (see Bruni and Sugden 2007). Hence, individuals in reality are rarely in an optimum situation.

A second problem with this method in relation to housing is its implicit assumption that the choice of a house is the only consumer choice affected by external factors such as safety, commuting distance, and sanitation. In fact, the choice of a car and the decision whether to buy a car are deter- mined partly by such external factors as well. For instance, the decision to buy a car depends on such considerations as road congestion, road condi- tions, and auto accessibility to schools and hospitals. Likewise, external variables may affect many other choices. It is not obvious that the trade- off ratios between urban variables when buying a car would be the same as when renting a house; and if the trade-off ratios between urban variables are unequal, depending on the specific commodity considered, it is unclear which trade-off ratio should be analyzed. Although having well-defined estimates of the rent equation pn = p(Hn, Zj(n)) remains interesting, because

it describes the possibilities on the housing market, it does not necessar- ily give a complete evaluation of the individually perceived trade ratios between, for example, safety and distance to the subway.

A feature of the hedonic approach is that it only assumes an ordinal utility function. Using the ordinal assumption only does not allow for translating an income gain into a utility gain; nor does it allow for inter- personal comparison of levels of well-being (QoL).

Here is where the LS approach comes into play. That approach does not assume that the individual is at any time observed in his optimum in a perfect market. Moreover, the LS approach allows for both ordinal and cardinal satisfaction measurements.