The paper is based on the data on Real estate prices in Moscow from January 2000 till January 2016. The purpose of this work and the future research is analyze the dependence of the price of square meter from different macroeconomic factors such as oil price, exchange rate ruble/dollar, RTS index. As a first step, we consider that we have only one category of real estate. It means that we can not differ the prices for the different square’s apartments or we can not differ the prices of apartments with the different types of buildings in which they are. As the set of parameters, which potentially influence on the real estate price, I consider such factors as square of the flat or quality of building, the number of floor, the type of building and the region of Moscow city where the building is situated.
The model is constructed using the regular regression for monthly returns of these factors. In the paper, I describe in detail this regression. As the second step, I try to analyze the shift in months of the reaction in the changes of return of real estate prices under the changes of returns in the macroeconomic factors. This research is the first step towards answering various theoretical and practical questions regarding the real estate market in Moscow and in general. The topic is compelling in both scientific and business ways of research.
Motivation: Real estate market in Moscow
Real estate market in Moscow is significantly large: it is weekly up for sale nearly 40.000 apartments, it is nearly 3,3 million square meters and it costs nearly 7,4*〖10〗^11 rubles. This market grew up since 1991 as Russian government allowed citizens to privatize their apartments and then sell them. However, current macroeconomic situation creates considerable threats for the market in Moscow. Taking in account the depreciation of the national currency, analysts expect the decrease by 10-20% in 2016. The optimistic forecast is to expect growth of the market by 2020.
This market is the most important because it is both an investment instrument for savings deposit and is a product necessary for life. This product is more understandable to ordinary citizens compared to stocks, bonds or other derivatives. As the shares or bonds this product gives dividends which are calculated as monthly fee for rent. Therefore, we can compare the profitability of the product with a yield of well-known indexes such as RTS. As we know, Russia is one of the leaders of the world's oil exports. Therefore, I am interested in tracking changes in prices per square meter, depending on the cost of a barrel of oil.
I understand that in addition to stocks and bonds ordinary citizens keep their savings in foreign currencies such as the dollar or euro. So, I decided to find the dependence of the cost per square meter, depending on the change in the ruble / dollar.
On the other hand, if we compare the value of the property to the value of the shares then we understand that the value of real estate can not quickly respond to changes in macroeconomic factors. It means that we will see these changes only after some period. That’s why if we get a reliable estimate for the cost of real estate in dependence on macroeconomic factors, and if we can determine this time shift, then we can well predict the future value of the property.
In this paper, I use the next regression:
〖∆Real estate price〗_i=a+β_1*〖∆oil price〗_i+β_2*∆exchange rate ruble/dollar i+β_3*〖∆RTS index〗_i+u_i , where
〖∆Real estate price〗_i - is monthly return of real estate price,
〖∆oil price〗_i - is monthly return of oil price per 1 barrel of Brent mark,
〖∆RTS index〗_i - is monthly return of RTS index,
∆exchange rate ruble/dollar i - is monthly return of exchange rate of ruble per 1 dollar US.
The data describes average price per 1 square meter in Moscow during the period from January 2000 till January 2016. It contains 5 variables and 191 observations.
The table contains the following information:
returnrealestate – as it was already mentioned in the paper.
returnoil – return of oil price.
returnrubdol – return of exchange rate ruble/dollar.
returnrts – return of RTS index
Estimation and Results
This page contains the results of some regressions for different values of the shift in the months from 0 to 11. I consider that the return of real estate price must react on the changes in the returns of macroeconomic factors. It means that the shift is the best if some coefficients are significant.
All results and pictures of all variables are attached in Appendix A and B.
My result for the dependence of the return of real estate price is:
Return of exch. rate r/$ - 3 months(shift) coefficient= -0,21 on 1% level
Return of oil price - 8 months(shift) coefficient=0,022 on 1% level
Return of RTS index - 7 months(shift) coefficient= 0,057 on 1% level
It means that return of real estate price is positive correlated with return of oil price and return of RTS index and is negative correlated with return of exchange rate of ruble/dollar.
I can explain this result by the next economic reason: if the price on oil and RTS index grow up then Russian economy also grow up. People start to get more profit and that’s why they start to consume more. And, as a result the price of real estate start to grow up. On the other hand, if the exchange rate of ruble/dollar US grow up then Russian currency depreciates and Russian economy fell down. So, people get less profit and start to buy less real estate and that’s why the price for the rea1 estate start to decrease.
12 ноября 2016
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