The hedonic price of a characteristic can be interpreted as the willingness to pay of households for a marginal increase in that particular characteristic. The hedonic price of house sizes is dependent on the value of the parameter β3, the price of the house, and the size of the house. For example, the hedonic price of plot size is expressed as: The hedonic price of a particular characteristic is therefore the slope of this equation with respect to that particular characteristic. For example, we would expect β3 > 0 as house prices will increase as plot size increases. These parameters measure the proportional change in prices caused by proportional changes in characteristics. Here the parameters β1 to β5 are elasticities. This means that as a characteristic increases (or improves) the house prices increase but at a decreasing rate. Usually researchers estimating hedonic prices assume the hedonic price function has a multiplicative functional form. The hedonic price can therefore be interpreted as the additional cost of purchasing a house that is marginally ‘better’ in terms of a particular characteristic. The change in a house price resulting from the marginal change in one of these characteristics is called the hedonic price (sometimes referred to as the implicit price or rent differential). Where the price of a house (P), is a function of its location relative to a local urban centre (LOC), the type of house (TYPE), the size of the plot (SIZE), the quality of its view (VIEW), and neighbourhood characteristics (NEIGH) such as school quality and crime. For example, the price of a house can be summarised using a hedonic price function as below: The first step estimates the relationship between the price of an asset (the dependent variable) and all of its various characteristics (independent variables). The hedonic regression analysis is conducted in two steps. Hedonic Regression Analysis (adapted from Boardman et al, 2001, 349-352) It may be found that a 1km movement away from the open cast site equates to an increase of £5,000 on a house price. The regression analysis can also be used to provide a value for the size of the relative impact. If, for example, through regression analyses increased distance from an open cast mining site is found to be correlated with increased house prices, it can be ascertained that the open cast site is having a negative impact on house prices. The log linear approach suggests a huge effect of different quality categories on the wine prices for Riesling with the highest price premiums for Auslese and “Beerenauslese/Trockenbeerenauslese/Eiswein (Batbaice),” while the machine learning model shows, that additionally the alcohol level has a positive effect on wines in the quality categories “QbA,” “Kabinett,” and “Spätlese,” and a mostly negative one in the categories “Auslese” and “Batbaice.” Weather variables exert different affects per grape variety, but all grape varieties have problems coping with rising maximum temperatures in the winter and with rising minimum and maximum temperatures in the harvest season.The hedonic price method is used to measure the relative importance – through use of regression analyses – of these independent ‘explanatory’ variables on house and property prices. Gault&Millau points are shown to have a significant positive impact on German wine prices. Machine learning exhibits slightly greater explanatory power, suggests adding additional variables, and allows for a more detailed interpretation of results. A log linear regression model is first applied only for Riesling, and then machine learning is used to find hedonic price models for Riesling, Silvaner, Pinot Blanc, and Pinot Noir. This article examines whether there are different hedonic price models for different German wines by grape variety, and identifies influential factors that focus on weather variables and direct and indirect quality measures for wine prices.
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