Thank you for making this video. It was very helpful and makes hedonic pricing a little clearer to me. I'll definitely be re-watching this again and again to help me learn the concept. Cheers!
Hi there, thanks for the video, using regression analysis to see the effects on market value by EPC certificates, would the EPC ratings (A,B,C etc) be dummy variables?
You are most welcome. You are right, it is just a linear regression. One definition of the word Hedonic is "characterized by"; we call this type of linear regression hedonic because we decompose the characteristics of a certain product/object, and see their independent contributions to the formation of prices.
@@AliNasserEddine Thank you for your answer. I have a second question. Isn't it a problem that the two dummy variables RL and CL are complementary? Or the multicolinearity is not a problem with hedonic regression?
You are most welcome. Yes, multicollinearity represents a problem within the hedonic regression framework and with all other linear regression models. However, we must keep in mind that the example used here is strictly for illustrative purpose: to make it simple and clear. More importantly, the correlation between RL and CL doesn’t affect the prediction we are after, and this is observable. Please have a look at this article bit.ly/2XwWCPc; particularly, look at the third point the author raises about fixing multicollinearity. On the other hand, multicollinearity becomes a real issue when we can’t isolate the effect of an independent variable on the regressand; and this is not the case in our example.
You are most welcome. Generally speaking, the more data you have the better it is as long as there is no overfitting, but it really depends on the case. Sometimes, 5 data points are representable for a million. Also, the nature of the matter plays a role. For example, it is easier to get data for rental properties in New York than it is for getting data about heart surgeries due to frequency. Also, it is easier to get data on rental properties in Sweden than in a third-world country due to the level of corruption, professionalism and technology. There is no given answer to your question, but many argue that 10 data points for each variable should be sufficient; personally, I judge the matter and form my opinion accordingly. Please note that you can still run the regression with fewer data points, and the reliability of the results depends on the nature of your data. You may also want to check the one-in-ten rule.
This is very well explained and easy to understand. But sir if the houses are different, say one is a detached house and the other is a terrace house, does it need to be included as a dummy variable?
Thanks for the clear description. I have a question that is the hedonic model only have dummy variable as X or it can take numerical variable as X too? Thanks.
You are most welcome. It can have both. It depends on the type of the variable. For example, for years, we use dummy variables, but for an area on the other hand, we use a numerical variable. Sorry for the late reply, please let me know if you have any further questions.
I've got a question. Why we have to delete the time variable for 2015? I know that it is out 0 point, but is it neccessary? What if we didn't remove it and made computations? How then should we interprete the model? And the second question: where could we find some math proofs or stuff like that for this technique?* Thanks in advance for the answer and for making this video - great job :) * I mean, I know that there are a lot of papers on the Internet, but I think about these which are not so hard to digest.
We remove it because we need to have a reference variable, so that we can tell the changes. If we don't remove it, the model won't work. You can refer to Court 1939 as it is believed to be the start of this model. You are most welcome.
Thank you very much sir for the tutorial,well explained. It is my first time to hear about Hedonic regression,and I really appreciate your explanation. somewhere in the tutorial you said that "the more different values we have,the lower the accuracy of estimations would be" is it not wrong to have a lower accuracy?,...please may you explain the statement. thank you
You are most welcome. That is correct. Please consider the following: we buy two similar houses in the same location for $100K each. Then, we go for another one and buy it again for $100K. If someone asks us how much does a similar house cost you there, we firmly say $100K. After one month, we buy a fourth with similar specifications in a closeby location for $110K. Afterward, if someone asks us about the cost in the greater area, we answer about $100K; 'about' and not forsure. Now, imagine too many houses with different specifications and locations, too many different values; whatever we say, we won't be as accurate.
Hello. Indeed we do. This tutorial is for explanatory purpose. Please refer to the tutorial on hedonic regression with large data, where we use the natural log prices.
You treat them the same way you treat the yearly dummy variables. For example, if you have houses with 1, 2 and 3 rooms only, you create three dummy variables, one for each. Then, you give it a value of 1 if it matches, and 0 otherwise. For example, if the house has two rooms, the dummy variable of "2 rooms" would be 1, and 0 for the other two.
@@AliNasserEddine okay but is it not also possible to add dummy variables and normal variables like rooms and then type the number of rooms or will that create wrong results?
I would add it as a dummy variables set as explained. This is the common practice. You may try using the number of rooms as a numerical variable, one for all; I assume this should lead to similar results. If you used the log price as the dependent variable, you should use the log of the number of rooms.
We drop one variable in a category of dummy variables for many reasons. In our case, the category is the yearly dummy variables. The first reason is to avoid multicollinearity, which otherwise would be present. The sum of dummy variables inside a category is 1, thus, if V2016 and V2017 are both 0s, then, the model considers these data points as for the absent variable V2015; note that in this case, we avoid redundancy. Another reason is the reference point; since 2015 is omitted, the coefficients of the other years become in reference to it, and not with each other's. Try the example while omitting 2017 instead of 2015 and notice the difference. Note particularly what happens to the coefficient of 2016, because it is now in reference to 2017. Last, please have a look at the dummy variable trap.
This is a wonderful and full informative video which I was searched for years. Clear explanations. Many thanks!
You are most welcome Devindi. Happy to hear so.
I have been studying this concept for a while but did not get it until i saw this video. I cannot thank you enough.
You are most welcome.
Thank you for making this video. It was very helpful and makes hedonic pricing a little clearer to me. I'll definitely be re-watching this again and again to help me learn the concept. Cheers!
You are most welcome. Glad to hear this.
Thanks a lot, this video helps me understand more about hedonic price method!
You are most welcome.
Thank you very much！ The explanations are very clear
You are most welcome.
Really easy to understand ! Thank you very much!
You are most welcome.
Thank you so much for this useful tutorial!
You are most welcome.
Thanks a lot for a clear explanation
You are most welcome.
Hi there, thanks for the video, using regression analysis to see the effects on market value by EPC certificates, would the EPC ratings (A,B,C etc) be dummy variables?
Hi Oliver, you are most welcome. Yes, they would.
Great videos. Thank you! However, I still do not understand the difference with a normal regression. What makes it "hedonic"?
You are most welcome. You are right, it is just a linear regression. One definition of the word Hedonic is "characterized by"; we call this type of linear regression hedonic because we decompose the characteristics of a certain product/object, and see their independent contributions to the formation of prices.
@@AliNasserEddine Thank you for your answer. I have a second question. Isn't it a problem that the two dummy variables RL and CL are complementary? Or the multicolinearity is not a problem with hedonic regression?
You are most welcome. Yes, multicollinearity represents a problem within the hedonic regression framework and with all other linear regression models. However, we must keep in mind that the example used here is strictly for illustrative purpose: to make it simple and clear. More importantly, the correlation between RL and CL doesn’t affect the prediction we are after, and this is observable. Please have a look at this article bit.ly/2XwWCPc; particularly, look at the third point the author raises about fixing multicollinearity. On the other hand, multicollinearity becomes a real issue when we can’t isolate the effect of an independent variable on the regressand; and this is not the case in our example.
excellent! and well explained. Thank you
You are most welcome.
Thank you for your explained video! Sir, how many data (minimum samples) can be used in hedonic regression?
You are most welcome. Generally speaking, the more data you have the better it is as long as there is no overfitting, but it really depends on the case. Sometimes, 5 data points are representable for a million. Also, the nature of the matter plays a role. For example, it is easier to get data for rental properties in New York than it is for getting data about heart surgeries due to frequency. Also, it is easier to get data on rental properties in Sweden than in a third-world country due to the level of corruption, professionalism and technology. There is no given answer to your question, but many argue that 10 data points for each variable should be sufficient; personally, I judge the matter and form my opinion accordingly. Please note that you can still run the regression with fewer data points, and the reliability of the results depends on the nature of your data. You may also want to check the one-in-ten rule.
This is very well explained and easy to understand. But sir if the houses are different, say one is a detached house and the other is a terrace house, does it need to be included as a dummy variable?
Yes, I believe it is to be added because the variation of house type affects the price.
Very neatly explained sir.Thank you very much.:-)
Many thanks for the feedback Siddhant. You are most welcome.
Thanks for the clear description. I have a question that is the hedonic model only have dummy variable as X or it can take numerical variable as X too? Thanks.
You are most welcome. It can have both. It depends on the type of the variable. For example, for years, we use dummy variables, but for an area on the other hand, we use a numerical variable.
Sorry for the late reply, please let me know if you have any further questions.
I've got a question. Why we have to delete the time variable for 2015? I know that it is out 0 point, but is it neccessary? What if we didn't remove it and made computations? How then should we interprete the model?
And the second question: where could we find some math proofs or stuff like that for this technique?*
Thanks in advance for the answer and for making this video - great job :)
* I mean, I know that there are a lot of papers on the Internet, but I think about these which are not so hard to digest.
We remove it because we need to have a reference variable, so that we can tell the changes. If we don't remove it, the model won't work.
You can refer to Court 1939 as it is believed to be the start of this model.
You are most welcome.
why do you say the average prices, despite there being duplicate values in all years?
Because average price is an average, whether there are duplicates or not.
Thank you very much sir for the tutorial,well explained. It is my first time to hear about Hedonic regression,and I really appreciate your explanation. somewhere in the tutorial you said that "the more different values we have,the lower the accuracy of estimations would be" is it not wrong to have a lower accuracy?,...please may you explain the statement. thank you
You are most welcome. That is correct. Please consider the following: we buy two similar houses in the same location for $100K each. Then, we go for another one and buy it again for $100K. If someone asks us how much does a similar house cost you there, we firmly say $100K. After one month, we buy a fourth with similar specifications in a closeby location for $110K. Afterward, if someone asks us about the cost in the greater area, we answer about $100K; 'about' and not forsure. Now, imagine too many houses with different specifications and locations, too many different values; whatever we say, we won't be as accurate.
@@AliNasserEddine thank you sir
very helpful! Thanks !!
You are most welcome.
Hello Shouln't we use the log(price) instead of price in hedonic regression?
Hello. Indeed we do. This tutorial is for explanatory purpose. Please refer to the tutorial on hedonic regression with large data, where we use the natural log prices.
@@AliNasserEddine we use normal reggresion command but we use log price and thats only change, am I right?
Yes, you are right.
what would you do if there was a variable like rooms where you can't enter only 1 and 0?
You treat them the same way you treat the yearly dummy variables. For example, if you have houses with 1, 2 and 3 rooms only, you create three dummy variables, one for each. Then, you give it a value of 1 if it matches, and 0 otherwise. For example, if the house has two rooms, the dummy variable of "2 rooms" would be 1, and 0 for the other two.
@@AliNasserEddine okay but is it not also possible to add dummy variables and normal variables like rooms and then type the number of rooms or will that create wrong results?
I would add it as a dummy variables set as explained. This is the common practice. You may try using the number of rooms as a numerical variable, one for all; I assume this should lead to similar results. If you used the log price as the dependent variable, you should use the log of the number of rooms.
great video how ever i think he didn't explain IN DETAIL why he deleted the 2015 column.
We drop one variable in a category of dummy variables for many reasons. In our case, the category is the yearly dummy variables. The first reason is to avoid multicollinearity, which otherwise would be present. The sum of dummy variables inside a category is 1, thus, if V2016 and V2017 are both 0s, then, the model considers these data points as for the absent variable V2015; note that in this case, we avoid redundancy. Another reason is the reference point; since 2015 is omitted, the coefficients of the other years become in reference to it, and not with each other's. Try the example while omitting 2017 instead of 2015 and notice the difference. Note particularly what happens to the coefficient of 2016, because it is now in reference to 2017. Last, please have a look at the dummy variable trap.
great content, thanks
You are most welcome.
Great ❤
Happy to hear so!
I need help regarding the hedonic model let me know if you can help me.
Of course I can. Please let me know what you need.
Sorry for the late reply.
I'm still struggling to understand this concept as I'm trying to complete a online course with the LSE.
Please ask me if you have any questions.
Hi, really helpful content. i am looking for the art database and need your help pls
Hi, glad to hear that. Please let me know how I can help.
gracias!
You are most welcome Janeth.
What if a coefficient is negative?
It means that this variable negatively affect the price.