EL SOCIALISMO Y EL COMUNISMO CRITICO-UTOPICOS

DCEs are used to measure a decision unit’s valuation o f a good or service where revealed

preference data are unavailable, for example, for non-m arket services, new commercial

goods or for testing new policies in existing markets (see Hensher et al. (2005) for an

introduction). In the survey, hypothetical choice situations, based on a controlled and

bounded set o f attributes, are presented to a sample o f likely m arket participants. Attribute

levels and respondent choices are then used to estimate marginal willingness-to-pay (for

attributes) and marginal rates o f substitution (between attributes). The approach has been

used in numerous sectors, including health (see Ryan and Gerard (2003), for example),

transport (Greene and Hensher, 2003) and environmental evaluation (Hanley et al., 1998).

W ithin energy-related research, DCEs have been employed for valuing appliances (Revelt

and Train, 1998), renewable generation (Bergmann et al., 2006), household energy-saving

technologies (Banfi et al., 2008), micro-generation (Scarpa and Willis, 2010) and electric

cars (Hidrue et al., 2011).

In the current setting we present respondents with apartment choices which differ

according to a num ber o f property attributes, including energy efficiency. For the latter, we

use the current informational policy in Ireland - the BER. W e employ a stated preference

technique as the current rating system, although a legal requirement for landlords, is

during negotiations, is commonplace.'*^ For example, a simple manual search on daft.ie reveals that around two thirds o f advertisements do not include a BER rating."*^ This proportion is sim ilar to official national BER records (28%) and would therefore suggest that non-advertisement is largely due to non-certification (failure o f landlords to commission certification).''^ However, the proportion o f certified rental properties is currently unknown, and it is also possible that non-disclosure is due to deliberate attempts to withhold poor efficiency information. In this regard, recent advertisement data from daft.ie presents some evidence o f misinformation in landlord-tenant negotiations. For example, while 25% o f rated properties have low efficiency levels ( ‘E ’, ‘F ’ or ‘G ’) (official nation data), ju st 10% o f properties advertised on daft.ie are in this category (Table 4.1). Furthermore, the advertised data also shows an overrepresentation o f ‘A ’ rated properties (10%), with stark differences com pared to national statistics (less than 1%). Also, these proportions do not show the owner-renter efficiency divide which is present in Ireland (even allowing for a smaller divide in Table 4.1 due to the inclusion o f rented properties in official BER figures).

Table 4.1: BER shares nationally (owner plus rented) and in rental markets (advertised)__________________

N ational BER shares A dvertised BER shares A 0.73% 10.52% B 12.70% 23.72% C 36.22% 37.83% D 25.28% 17.57% E 12.31% 6.91% F o r G 12.76% 3.45%

Note: National BER shares are calculated using data from the SEAI National BER Research Tool (accessed July 2014 and available at www.seaLie) and advertised BER shares are based on d a ftie rental data, highlighted in Hyland et al. (2013).

Since January 2009, all properties sold o r rented are required to have com pleted B E R certification. Since January 2013, it is also illegal to advertise a property w ithout a B ER certificate.

Based on 100 property advertisem ents (first 100) in Dublin city on daft.ie with rents betw een €800 and €1400 and with one to three bedroom s (searched on 4* July 2014).

N ational B E R records dow nloadable from

w w w .seai.ie/Y our_B uilding/B ER /N ational_B E R _R esearch_T ool/ [accessed July 4*, 2014]

DCEs also allow us to re-create a realistic rental choice situation by including very specific and valued property attributes (highlighted in our focus group) which are not readily available in revealed preference data (condition or area safety, for example). Furthermore, through experimental design, we can isolate the independent effects o f each attribute; a task which can be problem atic in revealed preference analysis when independent variables are often correlated with each other, or with the error term - for example, if a property’s condition is unknown but correlated with energy efficiency, the relationship between efficiency and choice will be biased upwards (provided condition is valued). Finally, the method offers us full control o f the experimental setting, thus enabling us to estimate the likely future effects o f the BER policy (when com pliance and disclosure is universal).

The hypothetical nature o f choice experiments has, however, received criticism in the literature. While much o f this has been directed towards the contingent valuation method (CVM) (see Diamond and Hausman (1994) Kling et al. (2012) Hausman (2012)), the possibility o f ‘hypothetical bias’ in choice experiments is also an obvious concern. However, for CVM, some studies have found (see Carson et al. (1996), for example) that WTP estimates are in fact a useful predictor o f actual valuation, although, on average, are

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slightly below. For choice experiments (see Carlsson (2011)), there are also studies which find no bias in marginal WTP estimates (Carlsson and M artinsson (2001) and Lusk and Schroeder (2004))."^^ There are also, o f course, studies which find the opposite (Johansson-Stenman and Svedsater, 2008).

There are some more practical issues in choice experiments which could lead to biases in estimates. These are discussed for laboratory experiments by Carlsson (2011), but are equally relevant for this applied methodology. For example, experiments are normally

Carlsson (2011) provides a summary o f studies exploring these biases (pg. 204), in the CVM, choice experiment and experimental settings.

conducted out o f context, and respondents are asked to choose a good in a setting that is unfamiHar to where the transaction normally takes place. Second, it is possible that respondents m ay adjust their behaviour as they feel their choices are under scrutiny. Finally, the presented alternatives m ay not resemble real-life choice sets, as they are clearly defined and norm ally restricted to two or three (when actual choice sets can be vast and decisions are often made with imperfect information). Such simplicity and clarity o f attributes m ay lead to biases in hypothetical decision-making, relative to the real-world. However, biases and drawbacks withstanding, the main motivation for conducting stated preference surveys remains - the unavailability o f revealed preference data - and in this respect, choice experiments are one o f the few viable methods available. The challenge for the researcher is to ensure that the situation presented resembles a real-life choice situation, is noncomplex and clearly understood by the respondent.

4.2.2 Theoretical model and econom etric method

The total cost o f renting a property equals the sum o f rent and auxiliary costs. The rent is a function o f the property’s attributes, including, for example, location, size and structural finish. Although there are many potential auxiliary costs - telephone, waste and water charges, for example - we will consider energy costs only. For the prospective renter then, the expected total cost for a property then equals:

E[ Cm . i ] = 6m) + E[Pe * 1^6^)] (1)

where is the cost o f property m (from a set o f M properties) for individual i, Rm is the

rent, is a vector o f k property attributes (excluding the efficiency level), is the price

o f energy and li is the quantity o f energy units consumed. Both the rent and auxiliary costs

are a function o f the property’s energy efficiency level positively and negatively respectively. The energy efficiency level o f the property is a function o f the level and quality o f wall and attic insulation, window glazing, the space and w ater heating technologies installed and the efficiency o f installed appliances.^'’

The rental choice problem can be presented within a more general utility maximization problem (see Alpfzar et al. (2001), Hensher et al. (2005), for example);

maxz,meMUi(.Z,X,n) s . t . Z < Y i ~ R m (2)

where t/ i( .) is the utility function o f individual i, Z is a vector o f consumer goods (with price vector normalized to 1) and is income. The individual will choose property m if:

yim(^m> Yi -

^m)

where K (.) is the indirect utility fianction. Assuming that utility is a linear additive function o f property attributes, and allowing for a random noise term for measurement error, unobserved individual characteristics, missing attributes and/or heterogeneity o f preferences, the utility associated with property m for individual i is then:

l ] i m = P ' X m + y { y i - R m ) + ^im (4)

where /? is the coefficient vector (marginal utilities o f attributes), y is the marginal utility o f income and is an unknown individual/alternative specific noise term. The marginal willingness-to-pay (MWTP) for an attribute then equals the marginal rate o f substitution (MRS) between the attribute and money:

M W T P = ('5')

- d U i m / d Y i Y ^ ’

where is the k‘*’ element o f attribute vector , and it’s coefficient. Overall, the

probability o f choosing property m equals^':

P ( m : M . P .y) = P r [ p X ^ - y{Yt - R ^ ) + 8^^ > PX^ - y(K^ - (6)

= Pr{Sin - £im <

PXm

which demonstrates that the choice probabilities are determined by indirect utility

differences, rather than absolute utilities. The effects o f variables that do not vary across

attributes (such as income) disappear, and can be excluded. Assuming that the error terms

are i.i.d. Type 1 extreme value distributed gives the conditional logit model:

V n 6 M ; n : ^ m (7)

where param eter estimates are norm ally obtained through maximum likelihood.

Estimation issues associated with the standard conditional logit model are discussed by

Adamowicz and Swait (2011). The model, as described above, does not allow for

unobserved heterogeneity (individual characteristics or missing attributes which affect the

decision), and m any authors attempt to control for such unobservables by estimating

M ixed Logit (originally proposed by M cFadden and Train (2000)) and Latent-Class

models (originally proposed by Swait (1994)). In the Latent-Class models, the marginal

utilities are allowed to differ (scaled) according to some unknown discrete class or

indicator. In the M ixed Logit, some param eters (chosen by the researcher) are assumed to

be random draws from a known distribution (normal or log-normal, for example) and the

model is estimated by Simulated M aximum Likelihood (see Train (2009)). This effectively

allows the coefficients to vary by individual and, in doing so, permits tastes for the

In choice models it is the utility difference that determines the probability o f choice. As the marginal utility o f income is constant across choices it cancels out (Adam owicz and Swait, 2011)

particular attribute to vary. However, such methods bundle all possible sources o f heterogeneity into a single scaling factor (latent-class) or random effect (mixed logit), without providing an explanation o f the underlying differences. W ithin the m ixed logit model, it is also possible to allow the means o f the random coefficient to be heterogeneous with observed choice-invariant individual data z^. The model also accounts for the quasi­ panel setup o f the data (seven choices per respondent in our application). For attribute k,

the random coefficient for individual i is:

Pki ~ Pk

^k^ki

(^)

where 5/^ is a vector o f coefficients, Vj^i is the individual specific heterogeneity (with mean

zero and standard deviation one) and (7^ is the standard deviation o f the distribution o f around The model is easily estimated in N LOGIT version 5.