Hedonic Pricing

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.

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.

What is a Regression Analysis?

Regression analysis is a reliable approach to determining which variables influence a particular issue. Regression analysis allows you to determine which elements may be important in influencing a particular issue and quantify the relationships between variables.

To completely understand regression analysis, you must first understand the following terms:

• Dependent Variable: This is the most crucial variable you're attempting to predict
• Independent Variables: These are the factors that you believe impact your dependent variable

Simple linear regression is a model that examines the relationship between a dependent variable and an independent variable. The following equation represents the simple linear model:

Y = b1 + b2X + e

Where:

• Y – Dependent variable
• X – Independent variable
• b1 – Intercept
• b2 – Slope
• e – Residual (error)

Multiple Linear Regression Analysis

Multiple linear regression analysis is identical to simple linear regression analysis except for using multiple independent variables. Multiple linear regression is expressed mathematically as:

Y = b1 + b2X2 + b3X3 + b4X4 + e

Where:

• Y – Dependent variable
• X2, X3, X4 – Independent (explanatory) variables
• b1 – Intercept
• b2, b3, b4 – Slopes
• e – Residual (error)

The requirements for multiple linear regression are the same as for the basic linear model. However, because multiple linear analysis involves several independent variables, there is another requirement for the model: non-collinearity.

It implies that independent variables should have a low correlation with each other to mitigate multicollinearity issues.

Hedonic Regression and Analysis Models

There are two steps to hedonic regression analysis. 

Stage One

The first stage is to gather information on residential property sales in the area over a certain time frame (usually one year). It calculates the link between an asset's price (the dependent variable) and all of its attributes (independent variables).

The required data usually includes:

• Residential property selling prices and locations
• Lot size, number and size of rooms, and the number of bathrooms are all factors that influence selling prices
• Property taxes, crime rates, and the quality of schools are all factors that influence selling prices in a community
• Distances to work and shopping centers, as well as public transportation availability, are all factors that influence prices
• Environmental factors that influence prices

Stage Two

A hedonic price function, for example, can be used to summarize the price of a house in the following manner:

P= f(LOCATION, TYPE, SIZE, VIEW, NEIGHBORHOOD)

A house's price (P) is determined by factors such as

• Its location
• Type
• Plot size
• View quality
• Neighborhood qualities such as school quality and crime

The hedonic price is the change in a house price caused by a minor change in one of these features (sometimes called the implicit price or rent differential). As a result, the hedonic price can be considered the extra cost of buying a marginally 'better' home in terms of a specific quality.

Stage Three

When calculating hedonic pricing, most researchers assume that the hedonic price function has a multiplicative functional form. This indicates that as a trait improves (or gets better), housing prices rise, but at a slower pace.

This is stated in the following manner:

P=B0(B1LOCATION)(B2TYPE)(B3SIZE)(B4VIEW)(B5 NEIGHBORHOOD)

Elasticities are represented by the coefficients B1 through B5.

These metrics measure the proportionate change in pricing due to proportional changes in features. For instance, we would expect B3 >0 because housing costs rise as plot sizes grow. 

As a result, the slope of this equation with regard to a particular attribute is the hedonic price of that quality. The hedonic price of plot size, for example, can be written as:

P=B3*(P/SIZE) >0

The value of parameter B3, the house's price, and the house's size all influence the hedonic price of house sizes. The willingness of families to pay for a marginal increase in a characteristic is known as the hedonic price.

Stage Four

The second step of the hedonic regression technique measures households' willingness to pay while accounting for households with varying incomes and tastes. As a result, the willingness-to-pay function is:

P= W(SIZE, Y, X)

Where the willingness to pay for a size characteristic is determined by the house's size (SIZE), the household's income (Y), and a vector (X) that represents tastes (based on age, race, social background, family size, etc.).

The proximity to open space is the environmental characteristic of concern in this scenario. Therefore, the researcher could gather information on the amount and type of open space within a certain radius of each property and whether or not the property is directly adjacent to the open space.

Problems with Hedonic Models

There are several limitations to the use of the hedonic pricing method. These include:

1. Information: The hedonic pricing model assumes that individuals are fully aware of potential externalities, such as pollution levels, which may not always align with reality due to information asymmetry
2. Measurement validity:  Measurement validity is crucial in hedonic pricing models, especially when proxy measurements are used, as they may sometimes lead to inaccurate coefficients in regression analyses
3. Market limitations: The model ideal requires that various houses be available so that people can get the house of their choice with the features they want. A family looking for a large house with a garden in a bustling city center location may discover that the city center only has tiny houses or houses without gardens
4. Multicollinearity: Multicollinearity issues may arise when variables like house size and pollution levels are closely associated, making it challenging to separate their effects in the model accurately
5. Price changes: The model assumes that market prices promptly adapt to changes in attributes, although there may be a lag, especially in areas with infrequent home sales and purchases

Benefits of the Hedonic Pricing Method

Some of the benefits include:

• The method's key advantage is that it can estimate values based on actual choices
• Property markets can be strong value indicators since they are reasonably efficient in responding to information
• Property records are usually entirely trustworthy
• Data on property sales and attributes can be found in a variety of places and can be combined with secondary data to create descriptive variables for the analysis
• The method is adaptable and can be used to evaluate a variety of market goods and environmental quality interactions

Limitations of Hedonic Pricing Models

The limitations include:

• The range of environmental advantages that can be measured is restricted to those linked to housing costs
• Only people's willingness to pay for perceived variations in environmental qualities and their direct repercussions will be captured by this method. As a result, if consumers are unaware of the connections between environmental attributes and personal or property benefits, the attribute's value will not be reflected in home prices
• Given their wealth, according to the technique, consumers can choose the mix of attributes they desire. Outside forces, such as taxes, interest rates, and other considerations, may impact the housing market
• The method is difficult to implement and interpret, and it necessitates a high level of statistical knowledge
• The model specification has a significant impact on the outcome
• It is necessary to collect and manipulate large amounts of data
• The amount of time and money it takes to complete an application is determined by the availability and accessibility of data

Summary

The hedonic pricing approach determines the value of environmental amenities that impact the cost of marketed commodities. Most applications use residential housing costs to quantify the value of environmental amenities.

The methodology is based on the notion that people appreciate a product's attributes or services more than the product itself. As a result, pricing will represent the value of a collection of traits, including environmental characteristics, that individuals value when buying a product.

The hedonic pricing method is generally simple and uncontroversial to adopt because it is based on actual market prices and easily observed data. If the data is readily available, applying it can be relatively affordable.

However, if data must be collected and compiled, the cost of application of the same can skyrocket.

Free Resources

To continue learning and advancing your career, check out these additional helpful WSO resources:

• Appraisal-based Indices
• Asset-Based Valuation
• Comparable Company Analysis
• Discount for Lack of Marketability (DLOM)
• Market Approach Valuation