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There are simple and complecated s

I think the field you are interesting in is related to conjoint analysis. Sawtooth, to my knowledge, are the leaders in this type of analysis, though SPSS and R also provide the tools and libraries to perform the conjoint analysis.

This part of the applied statistics consider the questions related to how a particular group of costumers actually choose the product valuing different features of the latter, but jointly. The design of a questionnaire can be concentrated upon individual preferences of a particular person, or you would probably like to generalize the preferences of a certain group of people.

To apply this analysis in practice you have to know what cars, apartments, computers, cell phone plans where actually bought or chosen, or at least the sample of respondents you study would buy or choose, if not facing the budget constraint directly. However, if you just collect the advertisements from newspapers or Internet the actual choices are hidden, because you do not observe them. To put it another way, you do not know in advance the qualitative answer to the question if there was a person with high enough reservation price and/or money in the pocket to like this suggested set of attributes (notice that price is the attribute as well) at all. Knowing the latter, you could build some kind of multinomial logit model, where the price would be the impact variable.

So to add more details, I think, we all need more details on:

1. What do you actually observe: the set of attributes only (price IS the attribute, for example, I would like to rent the cheapest car with the best other attributes available, why not?) or some additional data not presented in your context part
2. What you believe could be your dependent variable (the probability of purchase to my mind is the most suitable here)
3. Do you need the model for forecasting needs or just to understand what attributes are the most influential

And a bit more on the topic...

There are, to my mind, several still open related questions:

1. Decision making involves steps: do I need a car, what features I value most, how much I willing to pay for the features (answering the last question you indeed perform something like simple regression model)
2. What prices do you observe? Those that are set by sellers, by buyers or the actual transaction. Without knowing the latter, suppose you observe 100 units of

V6, red, automatic, 140hp, 2010, $300

that where not purchased and 1

V6, blue, manual, 140hp, 2005, $100

that actually was? So what your conclusions based on regression analysis would be than?

What I'm looking for is something practical and simple, but I would also like to hear about more complex approaches how to model something like this.

After some sort of a discussion, here is my complete view of the things

The problem

Aim: to understand how to price the cars in a better way

Context: in their decision process people solve several questions: do I need a car, if I do, what attributes I prefer most (including the price, because, being rational, I would like to have a car with best quality/price ratio), compare the number of attributes between different cars and choosing valuing them jointly.

From the seller position, I would like to set the price as high as possible, and sell the car as quickly as possible. So if I set the price too high and am waiting for months it could be considered as not demanded on the market and marked with 0 comparing to very demanded attribute sets.

Observations: real deals that relates the attributes of a particular car with the price set within the bargaining process (regarding the previous remark it is important to know how long it take to set the deal).

Pros: you do observe the things that were actually bought on the market, so you are not guessing if there exist a person with high enough reservation price that wants to buy a particular car

Cons:

1. your assumption is that market is efficient, meaning the prices you observe are close to equilibrium
2. you ignore the variants of car attributes that were not purchased or took too long to set the deal, meaning your insights are biased, so you actually do work with latent variable models
3. Observing the data for a long time you need to deflate them, though the inclusion of the car age partly compensates this.

Solution methods

The first one, as suggested by whuber, is the classical least squares regression model

Pros:

1. indeed the simplest solution as it is the work-horse of econometrics

Cons:

1. ignores that you do observe the things incompletely (latent variables)
2. acts as the regressors are independent one of the other, so the basic model ignores the fact that you may like blue Ford differently from blue Mercedes, but it is not the sum of marginal influence that comes from blue and Ford

In case of classical regression, since you are not limited in the degrees of freedom, to try also different interaction terms.

Therefore more complicated solution would be either tobit or Heckman model, you may want to consult A.C. Cameron and P.K. Trivedi Microeconometrics: methods and applications for more details on core methods.

Pros:

1. you do separate the fact that people may not like some sets of attributes at all, or some set of attributes has a small probability to be bought from the actual price setting
2. your results are not biased (or at least less than in the first case)
3. in case of Heckman you separate the reasons that motivates to buy the particular car from the pricing decision of how much I would like to pay for this car: the first one is influenced by individual preferences, the second one by budget constraint

Cons:

1. Both models are more data greedy, i.e. we need to observe either the time length between the ask and bid to equalize (if it is fairly short put 1, else 0), or to observe the sets that were ignored by the market

And, finally, if you simply interested in how price influences the probability to be bought you may work with some kind of logit models.

We agreed, that conjoint analysis is not suitable here, because you do have different context and observations.

Good luck.

I think the field you are interesting in is related to conjoint analysis. Sawtooth, to my knowledge, are the leaders in this type of analysis, though SPSS and R also provide the tools and libraries to perform the conjoint analysis.

This part of the applied statistics consider the questions related to how a particular group of costumers actually choose the product valuing different features of the latter, but jointly. The design of a questionnaire can be concentrated upon individual preferences of a particular person, or you would probably like to generalize the preferences of a certain group of people.

To apply this analysis in practice you have to know what cars, apartments, computers, cell phone plans where actually bought or chosen, or at least the sample of respondents you study would buy or choose, if not facing the budget constraint directly. However, if you just collect the advertisements from newspapers or Internet the actual choices are hidden, because you do not observe them. To put it another way, you do not know in advance the qualitative answer to the question if there was a person with high enough reservation price and/or money in the pocket to like this suggested set of attributes (notice that price is the attribute as well) at all. Knowing the latter, you could build some kind of multinomial logit model, where the price would be the impact variable.

So to add more details, I think, we all need more details on:

1. What do you actually observe: the set of attributes only (price IS the attribute, for example, I would like to rent the cheapest car with the best other attributes available, why not?) or some additional data not presented in your context part
2. What you believe could be your dependent variable (the probability of purchase to my mind is the most suitable here)
3. Do you need the model for forecasting needs or just to understand what attributes are the most influential

And a bit more on the topic...

There are, to my mind, several still open related questions:

1. Decision making involves steps: do I need a car, what features I value most, how much I willing to pay for the features (answering the last question you indeed perform something like simple regression model)
2. What prices do you observe? Those that are set by sellers, by buyers or the actual transaction. Without knowing the latter, suppose you observe 100 units of

V6, red, automatic, 140hp, 2010, $300

that where not purchased and 1

V6, blue, manual, 140hp, 2005, $100

that actually was? So what your conclusions based on regression analysis would be than?

There are simple and complecated s

I think the field you are interesting in is related to conjoint analysis. Sawtooth, to my knowledge, are the leaders in this type of analysis, though SPSS and R also provide the tools and libraries to perform the conjoint analysis.

This part of the applied statistics consider the questions related to how a particular group of costumers actually choose the product valuing different features of the latter, but jointly. The design of a questionnaire can be concentrated upon individual preferences of a particular person, or you would probably like to generalize the preferences of a certain group of people.

To apply this analysis in practice you have to know what cars, apartments, computers, cell phone plans where actually bought or chosen, or at least the sample of respondents you study would buy or choose, if not facing the budget constraint directly. However, if you just collect the advertisements from newspapers or Internet the actual choices are hidden, because you do not observe them. To put it another way, you do not know in advance the qualitative answer to the question if there was a person with high enough reservation price and/or money in the pocket to like this suggested set of attributes (notice that price is the attribute as well) at all. Knowing the latter, you could build some kind of multinomial logit model, where the price would be the impact variable.

So to add more details, I think, we all need more details on:

1. What do you actually observe: the set of attributes only (price IS the attribute, for example, I would like to rent the cheapest car with the best other attributes available, why not?) or some additional data not presented in your context part
2. What you believe could be your dependent variable (the probability of purchase to my mind is the most suitable here)
3. Do you need the model for forecasting needs or just to understand what attributes are the most influential

And a bit more on the topic...

There are, to my mind, several still open related questions:

1. Decision making involves steps: do I need a car, what features I value most, how much I willing to pay for the features (answering the last question you indeed perform something like simple regression model)
2. What prices do you observe? Those that are set by sellers, by buyers or the actual transaction. Without knowing the latter, suppose you observe 100 units of

V6, red, automatic, 140hp, 2010, $300

that where not purchased and 1

V6, blue, manual, 140hp, 2005, $100

that actually was? So what your conclusions based on regression analysis would be than?

I think the field you are interesting in is related to conjoint analysis. Sawtooth, to my knowledge, are the leaders in this type of analysis, though SPSS and R also provide the tools and libraries to perform the conjoint analysis.

This part of the applied statistics consider the questions related to how a particular group of costumers actually choose the product valuing different features of the latter, but jointly. The design of a questionnaire can be concentrated upon individual preferences of a particular person, or you would probably like to generalize the preferences of a certain group of people.

To apply this analysis in practice you have to know what cars, apartments, computers, cell phone plans where actually bought or chosen, or at least the sample of respondents you study would buy or choose, if not facing the budget constraint directly. However, if you just collect the advertisements from newspapers or Internet the actual choices are hidden, because you do not observe them. To put it another way, you do not know in advance the qualitative answer to the question if there was a person with high enough reservation price and/or money in the pocket to like this suggested set of attributes (notice that price is the attribute as well) at all. Knowing the latter, you could build some kind of multinomial logit model, where the price would be the impact variable.

So to add more details, I think, we all need more details on:

1. What do you actually observe: the set of attributes only (price IS the attribute, for example, I would like to rent the cheapest car with the best other attributes available, why not?) or some additional data not presented in your context part
2. What you believe could be your dependent variable (the probability of purchase to my mind is the most suitable here)
3. Do you need the model for forecasting needs or just to understand what attributes are the most influential

And a bit more on the topic...

There are, to my mind, several still open related questions:

1. Decision making involves steps: do I need a car, what features I value most, how much I willing to pay for the features (answering the last question you indeed perform something like simple regression model)
2. What prices do you observe? Those that are set by sellers, by buyers or the actual transaction. Without knowing the latter, suppose you observe 100 units of

V6, red, automatic, 140hp, 2010, $300

that where not purchased and 1

V6, blue, manual, 140hp, 2005, $100

that actually was? So what your conclusions based on regression analysis would be than?

Sincerely, yours minus one.

I think the field you are interesting in is related to conjoint analysis. Sawtooth, to my knowledge, are the leaders in this type of analysis, though SPSS and R also provide the tools and libraries to perform the conjoint analysis.

This part of the applied statistics consider the questions related to how a particular group of costumers actually choose the product valuing different features of the latter, but jointly. The design of a questionnaire can be concentrated upon individual preferences of a particular person, or you would probably like to generalize the preferences of a certain group of people.

To apply this analysis in practice you have to know what cars, apartments, computers, cell phone plans where actually bought or chosen, or at least the sample of respondents you study would buy or choose, if not facing the budget constraint directly. However, if you just collect the advertisements from newspapers or Internet the actual choices are hidden, because you do not observe them. To put it another way, you do not know in advance the qualitative answer to the question if there was a person with high enough reservation price and/or money in the pocket to like this suggested set of attributes (notice that price is the attribute as well) at all. Knowing the latter, you could build some kind of multinomial logit model, where the price would be the impact variable.

So to add more details, I think, we all need more details on:

1. What do you actually observe: the set of attributes only (price IS the attribute, for example, I would like to rent the cheapest car with the best other attributes available, why not?) or some additional data not presented in your context part
2. What you believe could be your dependent variable (the probability of purchase to my mind is the most suitable here)
3. Do you need the model for forecasting needs or just to understand what attributes are the most influential

And a bit more on the topic...

There are, to my mind, several still open related questions:

1. Decision making involves steps: do I need a car, what features I value most, how much I willing to pay for the features (answering the last question you indeed perform something like simple regression model)
2. What prices do you observe? Those that are set by sellers, by buyers or the actual transaction. Without knowing the latter, suppose you observe 100 units of

V6, red, automatic, 140hp, 2010, $300

that where not purchased and 1

V6, blue, manual, 140hp, 2005, $100

that actually was? So what your conclusions based on regression analysis would be than?