Predicting Realty Price Fluctuation in Russia using Data Analysis

This project delves into the analysis of realty price fluctuations in Russia, utilizing a dataset provided by Sberbank. The data, divided into train and test sets, spans from 2011 to 2016. The analysis investigates the prevalence of investment versus owner-occupied properties, price variations across different locations, and the impact of various factors such as square area, living area, kitchen area, and population demographics on realty prices. Statistical modeling reveals relationships between these factors and property prices, highlighting the influence of nearby amenities like industries, offices, green zones, and markets. The study concludes that while certain factors show statistically significant correlations with price, their predictive power varies, offering insights into the dynamics of Russia's realty market. Desklib offers similar solved assignments for students.

Running head: Data Analysis
Data Analysis
Name of the student
Name of the university
Author’s note

1Data Analysis
Table of Contents
Introduction......................................................................................................................................2
Train Set...........................................................................................................................................2
Test Set..........................................................................................................................................16
Conclusion.....................................................................................................................................19
Reference.......................................................................................................................................20

2Data Analysis
Introduction
The present analysis delves into the price fluctuations of reality prices in Russia. The
prices of property have been analysed through the data set provided by Sberbank. The data has
been divided into two sets – Train and Test. The reality prices have been analysed for the period
of analysis is 2011 – 2016. The data was visualised using Tableau Software (Murray, 2016) and
predictive analysis was done through SPSS (Field, 2013)
Train Set
The investigation into the product type reveals that there are two types of reality –
Investment and Owner Occupied. Owner Occupied buildings have a higher frequency than
Investment type buildings. In addition, “Nekrasovka” has the highest frequency of both – Owner
occupied and investment buildings.
Figure 1: Product Type with Location
Table 1: Cross table of sub_area & product_type
product_type
TotalInvestment OwnerOccupier
sub_area Ajeroport 1 1 2

3Data Analysis
Akademicheskoe 19 0 19
Alekseevskoe 1 2 3
Babushkinskoe 1 1 2
Begovoe 3 0 3
Beskudnikovskoe 8 0 8
Bibirevo 0 1 1
Birjulevo Vostochnoe 28 1 29
Birjulevo Zapadnoe 1 0 1
Bogorodskoe 20 0 20
Butyrskoe 3 0 3
Caricyno 1 0 1
Cheremushki 1 0 1
Chertanovo Central'noe 4 9 13
Chertanovo Juzhnoe 1 10 11
Chertanovo Severnoe 0 16 16
Danilovskoe 1 11 12
Dmitrovskoe 2 0 2
Donskoe 3 1 4
Filevskij Park 0 32 32
Fili Davydkovo 1 0 1
Gagarinskoe 1 0 1
Gol'janovo 3 0 3
Golovinskoe 6 3 9
Hamovniki 1 1 2
Horoshevo-Mnevniki 13 5 18
Horoshevskoe 1 8 9
Hovrino 1 19 20
Ivanovskoe 1 0 1
Izmajlovo 15 35 50
Jakimanka 6 2 8
Jaroslavskoe 2 0 2
Jasenevo 0 2 2
Juzhnoe Butovo 7 2 9
Juzhnoe Medvedkovo 4 0 4
Juzhnoe Tushino 9 0 9
Juzhnoportovoe 3 0 3
Kapotnja 0 1 1
Kon'kovo 1 0 1

4Data Analysis
Koptevo 1 2 3
Kotlovka 3 0 3
Krasnosel'skoe 0 1 1
Krjukovo 11 13 24
Krylatskoe 0 5 5
Kuncevo 6 2 8
Kuz'minki 1 0 1
Levoberezhnoe 1 33 34
Ljublino 1 0 1
Lomonosovskoe 1 1 2
Losinoostrovskoe 1 12 13
Mar'ina Roshha 1 5 6
Mar'ino 0 1 1
Marfino 1 0 1
Mitino 4 144 148
Molzhaninovskoe 3 0 3
Mozhajskoe 9 0 9
Nagatino-Sadovniki 6 2 8
Nagatinskij Zaton 0 56 56
Nagornoe 1 81 82
Nekrasovka 69 295 364
Novo-Peredelkino 0 3 3
Novogireevo 6 0 6
Obruchevskoe 2 8 10
Ochakovo-Matveevskoe 14 6 20
Orehovo-Borisovo Severnoe 1 1 2
Ostankinskoe 1 0 1
Otradnoe 1 0 1
Perovo 2 0 2
Pokrovskoe Streshnevo 1 10 11
Poselenie Desjonovskoe 13 111 124
Poselenie Filimonkovskoe 0 167 167
Poselenie Kievskij 0 1 1
Poselenie Kokoshkino 1 1 2
Poselenie Krasnopahorskoe 0 10 10
Poselenie Moskovskij 50 55 105
Poselenie Mosrentgen 1 0 1
Poselenie Novofedorovskoe 0 34 34

5Data Analysis
Poselenie Pervomajskoe 5 21 26
Poselenie Rjazanovskoe 6 1 7
Poselenie Rogovskoe 0 4 4
Poselenie Shhapovskoe 1 0 1
Poselenie Shherbinka 16 101 117
Poselenie Sosenskoe 79 250 329
Poselenie Vnukovskoe 52 169 221
Poselenie Voronovskoe 1 1 2
Poselenie Voskresenskoe 6 138 144
Preobrazhenskoe 1 0 1
Presnenskoe 0 24 24
Prospekt Vernadskogo 1 5 6
Ramenki 9 23 32
Rostokino 1 7 8
Savelki 0 1 1
Savelovskoe 0 1 1
Severnoe 0 1 1
Severnoe Butovo 1 1 2
Severnoe Medvedkovo 6 0 6
Severnoe Tushino 0 9 9
Shhukino 1 0 1
Sokol 6 0 6
Sokolinaja Gora 6 0 6
Solncevo 5 32 37
Staroe Krjukovo 3 0 3
Strogino 6 60 66
Sviblovo 2 14 16
Taganskoe 1 0 1
Tekstil'shhiki 1 9 10
Timirjazevskoe 1 0 1
Troickij okrug 19 11 30
Troparevo-Nikulino 3 0 3
Tverskoe 2 170 172
Veshnjaki 0 1 1
Vojkovskoe 2 0 2
Vostochnoe Izmajlovo 1 0 1
Zamoskvorech'e 3 3 6
Zapadnoe Degunino 1 45 46

6Data Analysis
Zjuzino 18 0 18
Total 652 2325 2977
Figure 2: Comparison of Average Price of Reality
The comparison shows that the average price of Owner occupied buildings is higher that
Investment.

7Data Analysis
Figure 3: Average prices of Owner Occupied buildings
The price of owner occupied buildings has a great deal of variation with different places.
The average “Ajeroport” is the highest while for “Poselenie Novofedorovskoe” is the lowest
(Zabortseva, 2014).
Table 2: Average Price of Owner Occupied Buildings
price_doc
Mean
sub_area Ajeroport 71249624
Alekseevskoe 22777630
Babushkinskoe 13830094
Bibirevo 4535370
Birjulevo Vostochnoe 9003162
Chertanovo Central'noe 12222477
Chertanovo Juzhnoe 7774983
Chertanovo Severnoe 8736537
Danilovskoe 15349536
Donskoe 23045878
Filevskij Park 9219095

8Data Analysis
Golovinskoe 7692365
Hamovniki 51650004
Horoshevo-Mnevniki 14080880
Horoshevskoe 6282009
Hovrino 12504879
Izmajlovo 16815710
Jakimanka 23229570
Jasenevo 26014650
Juzhnoe Butovo 5133370
Kapotnja 4744953
Koptevo 9598150
Krasnosel'skoe 22080290
Krjukovo 6390628
Krylatskoe 9033430
Kuncevo 12648500
Levoberezhnoe 12112517
Lomonosovskoe 19697464
Losinoostrovskoe 11446086
Mar'ina Roshha 7111597
Mar'ino 9601000
Mitino 9838439
Nagatino-Sadovniki 13050802
Nagatinskij Zaton 10010402
Nagornoe 12039389
Nekrasovka 5905372
Novo-Peredelkino 5238013
Obruchevskoe 16095892
Ochakovo-Matveevskoe 8477617
Orehovo-Borisovo Severnoe 4156271
Pokrovskoe Streshnevo 11125287
Poselenie Desjonovskoe 4576974
Poselenie Filimonkovskoe 3500230
Poselenie Kievskij 15190446
Poselenie Kokoshkino 3046392
Poselenie Krasnopahorskoe 3503867
Poselenie Moskovskij 7124272
Poselenie Novofedorovskoe 2649019
Poselenie Pervomajskoe 4154571

9Data Analysis
Poselenie Rjazanovskoe 5821080
Poselenie Rogovskoe 5610886
Poselenie Shherbinka 4161179
Poselenie Sosenskoe 6232075
Poselenie Vnukovskoe 6708997
Poselenie Voronovskoe 6093691
Poselenie Voskresenskoe 6105789
Presnenskoe 22994072
Prospekt Vernadskogo 13501442
Ramenki 12742409
Rostokino 16901923
Savelki 6828600
Savelovskoe 18713200
Severnoe 6890863
Severnoe Butovo 4287500
Severnoe Tushino 11031631
Solncevo 5437991
Strogino 12687070
Sviblovo 11072582
Tekstil'shhiki 7627389
Troickij okrug 5094464
Tverskoe 5313056
Veshnjaki 3299400
Zamoskvorech'e 36156461
Zapadnoe Degunino 4389321

10Data Analysis
Figure 5: Price variations of Investment Buildings
Like in Owner Occupied buildings there is also a variation in the average prices of
investment buildings (Beliakov, Boriskina and Gnevanov, 2017).
Figure 6: Relation of Square Area to reality type

11Data Analysis
The mean full square area of investment buildings is more than owner occupied
buildings. On the other hand, the mean living area (sq m) is more for owner occupied buildings
than investment type buildings.
Figure 7: Average price of Reality
There has been a great deal of variation in the prices of reality during the period 2011 –
16. The price has steadily grown during the period except for 2012 when it suddenly rose.