For instance, policymakers can look at how much more individuals need to be paid to take on riskier jobs that increase the probability of fatality. However, the drawbacks with revealed preferences also arise – in this case, if the riskier jobs increase the probability of not only death but also injury, or are also unpleasant in other respects, the higher wages may incorporate the other factors, misrepresenting the result. Hedonic pricing Hedonic pricing is a model that uses regression analysis to isolate the value of a specific intangible cost or benefit. It is based on the premise that that price is determined by both internal characteristics and external factors. It also assumes that individuals value the characteristics of a good rather than the good itself, which implies that price will reflect a set of internal and external characteristics. It is most often used to calculate variances in housing prices that reflect the value of local environmental factors. The model is based on widely-available and relatively accurate market data, making this method uncontroversial and inexpensive to use. As such, one of hedonic pricing's main advantages is that it can be used to estimate values on actual choices. This method is also very versatile and can be adapted to incorporate multiple other interactions with other factors. However, one of its major downfalls is that it is rather limited – it can mostly only measure things that are related to housing prices. It also assumes that individuals have the freedom and power to select the preferred combination given their income but in actuality, this may not be the case as the market may be influenced by changes in taxes and interest rates. In the freeway project example, hedonic pricing may be useful to value the benefits of reduced air pollution. It can run a regression of home values on clean air with a variety of control variables that can include home size, age of home, number of bedrooms and bathrooms, crime statistics, school qualities, etc. Hedonic pricing may also be considered in quantifying the monetary value of time saved. It can run a regression of home values on proximity to work with a similar set of control variables. Illustration #1 Suppose a consumer with utility function faces prices and is endowed with income Then the consumer's problem is: Forming the Lagrangian auxiliary function taking first-order conditions and solving for its saddle point we