162

maitha254

Oct 22

Objective:
The major objective of this study was to analyze factors that affect the price of a house. In this case, 60 apartments were chosen for analytical purposes using quantitative methods in statistics. The variables extracted from online databases included; Lägenhet, Pris tkr, Antal rum and kvadratmeter(m^2). These were simply; name of apartment, price in tkr, number of rooms and size in square meters. The following table shows the dataset alongside the individual links for selected apartments.
Lägenhet Pris tkr Antal rum kvadratmeter(m^2) Link
Förlandsgränd 4A 1410 2 48 https://www.booli.se/bostad/364552
Storsjöstråket 14 1310 1 36 https://www.booli.se/bostad/4058329
Rosenlundsvägen 33 350 1 41 https://www.booli.se/annons/4765239
Skogsvägen 8C 4250 4 98.5 https://www.booli.se/bostad/3997999
Midgårdsgatan 3E 1825 4 90 https://www.booli.se/annons/4786911
Köpmangatan 44B 1700 3 67.5 https://www.booli.se/annons/4792534
Snorres väg 215 875 2 63.5 https://www.booli.se/bostad/4247205
Bergsgatan 20A 1000 4 94 https://www.booli.se/bostad/360704
Köpmangatan 44B 900 2 64 https://www.booli.se/annons/4729167
Stenstigen 2B 1770 3 78 https://www.booli.se/annons/4770932
Krondikesvägen 34 1200 2 56 https://www.booli.se/annons/4742304
Fritzhemsgatan 63A 1310 2 56.5 https://www.booli.se/annons/4769240
Vickervägen 36 1725 3 78 https://www.booli.se/bostad/4247581
Köpmangatan 63A 2900 4 90.5 https://www.booli.se/annons/4725836
Brunflovägen 6A 1375 2 48 https://www.booli.se/bostad/369074
Mariebyvägen 10S 1925 4 110 https://www.booli.se/annons/4737260
Övre Hantverksgata2 520 1 27 https://www.booli.se/bostad/4172300
Frösövägen 53 1250 2 61 https://www.booli.se/bostad/362029
Reveljgränd 15 1225 3 84 https://www.booli.se/bostad/365183
Odenskogsvägen 66 850 2 49.5 https://www.booli.se/bostad/362308
Karlsviksvägen 4A 355 1 40 https://www.booli.se/bostad/367356
Strandgatan 22B 750 1 24 https://www.booli.se/annons/4763315
Färgargränd 37 1390 3 74.5 https://www.booli.se/bostad/360970
Hornsgatan 12B 1280 2 64.5 https://www.booli.se/annons/4722207
Mjölnargränd 15 895 2 64 https://www.booli.se/bostad/360848
Ärtvägen 20 1000 1 43 https://www.booli.se/bostad/4247097
Fritzhemsgatan 61A 1550 3 67 https://www.booli.se/bostad/363261
Snorresväg 209 1475 3 74 https://www.booli.se/annons/4775500
Fagervallsgränd 15A 930 1 41 https://www.booli.se/bostad/3882570
Allégatan 20B 1350 2 47 https://www.booli.se/bostad/4172588
Risslersgatan 25 2150 4 89 https://www.booli.se/bostad/366971
Köpmangatan 42A 2000 3 73 https://www.booli.se/bostad/4058394
Kopparslagargränd 4 950 3 74.5 https://www.booli.se/annons/4760183
Rosenlundsvägen 33 600 2 63.5 https://www.booli.se/annons/4762437
Krondikesvägen 22 1150 3 75 https://www.booli.se/annons/4699056
Södra Torlandsgatan 750 1 28.5 https://www.booli.se/bostad/366893
Jägarstigen 2 600 1 26 https://www.booli.se/bostad/4172250
Hornsgatan 17A 1550 3 76 https://www.booli.se/annons/4085594
Fröjavägen 11B 1405 2 5 https://www.booli.se/annons/4777050
Konstapelgränd 22 1000 3 84 https://www.booli.se/annons/4774955
Gränsvägen 8B 870 3 78.5 https://www.booli.se/bostad/367259
Gulsparvvägen 26C 2350 4 108 https://www.booli.se/bostad/4115898
Mjölnargränd 1 1650 4 95 https://www.booli.se/bostad/360817
Rådhusgatan 61F 1300 2 52.5 https://www.booli.se/bostad/363942
Taptogränd 9 1600 4 103 https://www.booli.se/annons/4691685
Färgargränd 9 1050 3 74.5 https://www.booli.se/annons/4750037
Freskvägen 9 1650 2 62 https://www.booli.se/annons/4732851
Litsvägen 16B 1100 1 39 https://www.booli.se/bostad/362666
Norra Torlandsgatan 1710 3 74 https://www.booli.se/annons/4755889
Rosenlundsvägen 11 995 3 80 https://www.booli.se/annons/4327249
Tallåsvägen 4B 1600 3 82 https://www.booli.se/annons/4739780
Krondikesvägen 56A 1440 2 57.5 https://www.booli.se/annons/4647381
Södra Torlandsgatan 1250 2 65.5 https://www.booli.se/annons/4750076
Karlsviksvägen 4G 450 2 63 https://www.booli.se/annons/4761748
Sjövägen 20 1595 3 85.5 https://www.booli.se/annons/4717071
Sjövägen 158 820 3 80 https://www.booli.se/annons/4734584
Taptogränd 20 820 2 67 https://www.booli.se/annons/4750251
Krutvaktargränd 9 720 2 62 https://www.booli.se/bostad/4247094
Rådhusgatan 59D
2000 3 92 https://www.booli.se/bostad/363977
Rådhusgatan 61D
1500 2 64 https://www.booli.se/bostad/363926

TASK A: Part 1
The variables extracted are all discrete in nature because they are numerical. A discrete random variable is the one whose value can be obtained through counting or measuring. This is the case study in our analysis since we can quantify price, and number of rooms, and size of a house.
TASK A: Part 2
In statistics, metrics refer to quantitatively measuring variables through analytical approaches. Price and number of rooms were selected for analysis in our case study. The following are the descriptive statistics

Table 1.1: Descriptive Statistics for house prices and number of rooms
From the analysis, the minimum and maximum house prices are 350tkr and 4250tkr respectively with a mean of 1321.17. On the other hand, the minimum and maximum number of rooms are 1 and 4 and mean is equal to approximately 2.47 or 2 as per the analysis.
ANALYSIS B: GRAPHS
Graphs were used to explore the distribution of price, size and number of rooms in selected regions.

Number of rooms

Figure 1.1: Distribution of rooms
From the analysis, there was a fair distribution of the number of rooms. However, most of the houses had 2 to 3 rooms with a frequency of 20 and 21 respectively. The least frequency was approximately 9 for houses with 4 rooms.
Price of houses

Figure 1.2: Distribution of Price
From the boxplot above, the price of houses followed a standard normal distribution where only one outlier was detected. Price was skewed to the right and had a belt like shape. Also, the data was not spread out completely as indicated by central tendencies. This means that we can carry out quantitative inferential analysis in SPSS.

ANALYSIS C: CORRELATIONS ANALYSIS
Correlation is a statistical measure that indicates the relationship between two or more variables. It shows the strength and direction of association between selected variables (Wang & Tsai, 2013, pp 3-5). A value of -1 shows a strong negative association while +1 shows a positive linear relationship between two or more variables. The following correlations were analyzed from SPSS.

Table 1.2: Correlations
From the analysis, all correlations were statistically significant at a 5% level since they had p values all less than 0.05. The correlation between price and number of rooms was r=0.662,p<0.000 which is strong positive and significant. Besides, the correlation between size and price was r=0.568, p<0.000 which was also significant and moderate. This indicates that size and number of rooms are associated with price of a house.

ANALYSIS D: REGRESSION
The R squared, also referred to as the coefficient of determination is a measure that explains the variation of dependent variable explained by explanatory variables. In the present analysis, the number of rooms best suited for the price of a home in tkr. Price was regressed against size and number of rooms. The following results were obtained from SPSS.

Table 1.3: Model summary
From the analysis, 65.2% of the variation in house price (tkr) was explained by number of rooms and houses as shown above.

Table 1.4: F statistics
The f value was significant with F(2,57)=21.121, p<0.00 indicating adequacy of the model.

Table 1.5: Coefficients table
From the table of coefficients, only the number of rooms was statistically significant with p<0.001. The intercept and size had p values greater than 0.05 (p1>p2>0.4319>0.097). The slope means that an increase in size and number of rooms causes an increment of price by 875.816 tkr or 875816 k$ The most appropriate equation to predict price of a home is;
Y=336.609+544.646x1-5.439x2;
Where y is the price(tkr), x1=number of rooms(antal rum) and x2=size (kvadratmeter(m^2).
Graph of room size versus price

The conclusion is that the number of rooms and size have a positive linear relationship with the price. The larger the lot size the more costly a house will be. Conversely, the more the number of rooms, the higher the price of any chosen house.

References
Wang, H.-Q. and Tsai, C.-J. (2013) "Corsig: A general framework for estimating statistical significance of correlation and its application to gene co-expression analysis," PLoS ONE, 8(10) pp 3-5. Available at: https://doi.org/10.1371/journal.pone.0077429.
Hitta Bostad I östersund - booli.se. (n.d.). Retrieved October 22, 2022, from https://www.booli.se/slutpriser/ostersund/2717?objectType=L%C3%A4genhet

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