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Thread: Hedonic Regression

  1. #1 Hedonic Regression 
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    Dear,

    Can someone explain or have any step-by-step tutorial to recommend which explains Hedonic Regression?

    I've tried Google, but it's not what I have been looking for, I need something like Hedonic Regression for Dummies. To master the basics, for start.

    Thanks in advance.

    Best regards,
    I.


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  3. #2  
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    OK. I'm not sure that a "for dummies" version is available.

    I took a look at the wiki, and this list of limitations of the concept leapt off the page at me. (Note the portions I've bolded.) Hedonic regression - Wikipedia, the free encyclopedia

    LIMITATIONS
    • The scope of applying this model is restricted and limited to measuring the environmental benefits related to housing prices only.
    • The amount of data that needs to be collected and worked with is very large.
    • A prerequisite of the model is that everyone should have prior knowledge of the potential positive and negative externalities that are associated with purchasing the real estate property. For example, it is important that they know before-hand about the level of pollution in a locality situated near an industrial site. This however is not the case in reality.
    • The availability and accessibility of data directly affects the amount of time and the expense that will be undertaken to carry out an application of the model.
    • This method estimates people’s willingness to pay for the supposed variation in environmental qualities and their consequences. However, if the people are unaware of the relation between the environmental qualities and their benefits to them or the property, then the value will not be reflected in the price of the property.
    • Market Limitations: This model makes an assumption that, given their income, people have the opportunity to choose the combination of attributes they prefer. What it fails to see is that the real estate market can also be affected by external factors such as interest rates, taxation, etc. For example : Suppose a family wishes to purchase a property near a popular city center, having a garden and of a large area. In reality - it may be possible that a house near the city center is comparatively smaller in size or does not have a garden.
    • Multicollinearity: Sometimes, there could be a case when Larger properties are only available in cleaner non-polluted areas and smaller properties are found in more urban and polluted environments. In such cases, it would be difficult to separate pollution and the size of property exactly.
    • Price Changes: Another assumption is that prices in the market will automatically adjust to any changes in the attributes. In reality, there is a lag especially in localities where purchase and sale of real estate is limited.
    • The model is relatively complex to interpret and requires a high level of statistical knowledge and expertise.
    If you're interested in this area, getting on top of data gathering and complex statistical analysis seems to be a prerequisite. If you're only interested in the general concept, then following the wiki links might get you somewhere useful.


    "Courage is what it takes to stand up and speak; courage is also what it takes to sit down and listen." Winston Churchill
    "nature is like a game of Jenga; you never know which brick you pull out will cause the whole stack to collapse" Lucy Cooke
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  4. #3  
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    Yes, I'm aware of that. I need a solved example (step-by-step) because everything I've found on the Internet, doesn't have that calculation part with possible explanations - just it gives info about hedonic regression and then it skips to results.For start, I can make up some data for learning purposes only - I just need to learn the calculation part.


    I.
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  5. #4  
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    I just need to learn the calculation part.
    What's your statistics background?
    "Courage is what it takes to stand up and speak; courage is also what it takes to sit down and listen." Winston Churchill
    "nature is like a game of Jenga; you never know which brick you pull out will cause the whole stack to collapse" Lucy Cooke
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  6. #5  
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    I worked with statistical inference (probability distributions, sampling distributions, goodness of fit - Chi squared, confidence intervals, test of significance) and simple linear regression.
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