Definition of hedonic property value

A method for determining ecosystem or environmental service economic values that directly impact market prices

• What Is Hedonic Pricing?
• Using Housing Prices To Apply The Hedonic Pricing Method
• What Is A Regression Analysis?
• Hedonic Regression And Analysis Models
• Problems With Hedonic Models
• Benefits Of The Hedonic Pricing Method
• Limitations Of Hedonic Pricing Models
• Summary

What Is Hedonic Pricing?

Hedonic pricing is a method for determining ecosystem or environmental service economic values that directly impact market prices. It is most commonly used to indicate fluctuations in housing prices due to local environmental conditions.

It can be used to calculate the economic advantages or costs of:

• Air pollution, water pollution, or noise; that is, environmental quality
• Aesthetic views or proximity to recreational sites that is, environmental amenities

The hedonic pricing method's main idea is that a good's price is related to its attributes, including both tangible and intangible characteristics.

For example, a car's price reflects its features, such as transportation, comfort, style, luxury, and fuel economy . As a result, we can value a car's or other good's particular features by observing how the price people are ready to pay for it varies as the attributes change.

Environmental amenities that affect the price of residential properties are frequently valued using the hedonic pricing method.

Key Takeaways

• Hedonic pricing evaluates the economic values of ecosystems or environmental services affecting market prices, often used to gauge housing price changes due to local environmental conditions.
• It assesses how property attributes, neighborhood factors, and environmental factors influence home prices, isolating environmental quality impacts from other factors.
• Data on property values and environmental measures are analyzed using regression analysis to understand the relationship between property prices, attributes, and environmental features.
• Regression analysis identifies variables influencing a phenomenon, like housing prices, by examining the relationship between dependent (e.g., property price) and independent variables (e.g., environmental quality).
• Hedonic regression involves stages of data collection, modeling, and assessing willingness to pay. It uses mathematical models to estimate the economic value of environmental amenities that affect housing prices.

Hedonic Pricing and Valuing Environmental Factors

The attributes of the house and property, as well as the neighborhood, community's traits, and environmental elements, all influence the price of a home. Any remaining pricing differences can be attributed to changes in environmental quality after correcting for non-environmental factors.

For example, houses with better air quality would be more expensive if all other characteristics of houses and communities in a specific region were the same but for air pollution levels.

People who purchase properties in the area place a premium on cleaner air, reflected in the higher price.

Process

The following data must be gathered before using the hedonic pricing method:

• A measure or gauge of a desired environmental amenity
• Cross-section and time-series data on property values and property and household characteristics for a well-defined market area that includes properties with varying levels of environmental quality or homes located at varying distances from an environmental amenity, such as open space or the seaside

The data is analyzed using regression analysis, examining the relationship between property prices, property attributes, and environmental features of interest. As a result, various quality pricing implications can be assessed.

The regression results illustrate how much property values will change if each characteristic is slightly altered while all other quantities remain the same.

Several factors could complicate the analysis. For example, the relationship between price and property traits may not be linear; prices may grow or fall as characteristics alter. Furthermore, many variables are likely to be associated, meaning their values will fluctuate similarly.

As a result, the significance of some analytical factors may be exaggerated.

Note

The analysis must consider multiple functional forms and model needs.