Business Intelligence and Data Visualisation - Business Analysis Report Assessment Answer

Business Intelligence and Data VisualisationAssignment Help

Assignment Task

1. Is AirBNB a worthwhile market place to invest in?
2. What are the best suburbs in Melbourne to purchase properties for AirBNB to guarantee the greatest return on investment?
3. What types of properties will generate the greatest return on investment?Apartments or Houses?
4. 4. What types of rooms will generate the highest return on investment? Shared / Private / Whole House

PROS AND CONS OF INVESTMENT IN AIRBNB

The prospects of any investment is determined by two main factors-i) the amount required to invest and ii) the return on invetment. If the amount required to invest is very high and beyond the capicity of the investor, then the investor would be in a huge debt, which is very highly risky and may lead to serious losses on his part. On the other hand, if the investment is within the reach of the investor, even then,if the return on investments is very less, then the investor would gain very less which would be required to just about break even and/or recover and pay loans and mortgages. Considering these two factors, we see that given Rachel’s invetment capital of $2 million, the investment in property in Melbourne market in AirBNB is not very highly risky from purchasing power point of view. But, considering the return on investments, the investment seems not to be worthwhile. The prospects of investing in AirBNB, according to recent reports, is not that benefecial for hosts.(SCHETZER A. and BATTERSBY L., 2016) However, in case the investment in the Melbourne market in real estate is a must, then AirBNB is a better option than traditional tenants.(MEGGINSON,S., 2017)Even then, investing outside hot spot suburbs can be risky according to analyses (CORDEROY, J., 2017). Especially, the main concern is getting consistent clients throughout the year. Even the frequently booked places reported an occupancy rate of about 50 per cent. The remaining suburbs reported occupancy rates on an average upto 21 per cent. According to reports, the return on investments outside of hot suburbs is close to 10 times less than those than the return on investment in the main suburbs in the Melbourne market.(SCHETZER A. and BATTERSBY L.,2016) The analysis done by the report here also closely confirms this. Areas in Melbourne report a loss of investment, sometimes even prominently. In addition to the two factors mentioned above, some other unseen factors such as legal and taxation issues are also a problem to investors and cost the investorsextra capital and time burden. There are legal troubles in investment in AirBNB arising from the fact that some short term occupancies are being used for parties and creating excessive noises.(SCHETZER A. and BATTERSBY L.,2016) Reports are also pointing to the fact that the hosts have to lose a part of their returns on tax implications. Thus, investment on AirBNB, though better for investment compared to traditional clients in the case of Real Estate, is not much recommended as compared to other modes of investment like shares and/or mutual funds. Thus, the analysis here and already existing reports point out much general investment plans than real estate for maximizing profit. However, if Rachel is bent on investing in Real Estate in Melbourne market, then, yes, AirBNB would be a better option compared to traditional method of client tenancy.

CHIEF SUBURBS IN MELBOURNE MARKET

To analyse the chief/best suburbs in the melbourne market, the given dataset(listings.csv) is analysed using the tool R. The technique used is quite similar to pivot table summarization in tools like SQL or Microsoft Excel. The procedure involves casting first the city with respect to its price using the ‘cast’ command from the ‘reshape’ package in R. The ‘reshape’ package is seen to be not that effective in performing analysis, because the data set has several blank cells and several character cells in the columns meant for numerical variables. Thus, the ‘dplyr’ package is more suitable for the analysis which overcomes the above problem. Note that the above problems would persist if we used power pivot feature in Microsoft Excel and one needs to manually filter the columns to make it more excel friendly. The sum of all prices corresponding to different categories under city is performed using the ‘ddply’ command from the ‘dplyr’ package in R. Then, to analyze the return on investment, the price is subtracted from the sum of weekly price, monthly price, security deposit, cleaning fee and fees for extra people respectively given in the columns with heads weekly_price, monthly_price, security_deposit, cleaning_fee, extra_people. The resulting output is made into a csv form for easy reading. The code used in the analysis is as follows:

library(reshape)

library(plyr)

library(dplyr)

a=read.csv("/home/prajnan/Downloads/listings.csv",header=T)

b=cast(a,city~price)

write.csv(b,file="b2.csv")

a$price=as.numeric(a$price)

d=ddply(a,.(city),summarize,sum=sum(price))

d

plot(d)

a$weekly_price=as.numeric(a$weekly_price)

a$monthly_price=as.numeric(a$monthly_price)

a$security_deposit=as.numeric(a$security_deposit)

a$cleaning_fee=as.numeric(a$cleaning_fee)

a$extra_people=as.numeric(a$extra_people)

d1=ddply(a,.(city),summarize,sum1=sum(weekly_price))

d2=ddply(a,.(city),summarize,sum2=sum(monthly_price))

d3=ddply(a,.(city),summarize,sum3=sum(security_deposit))

d4=ddply(a,.(city),summarize,sum4=sum(cleaning_fee))

d5=ddply(a,.(city),summarize,sum5=sum(extra_people))

d6=d5$sum5+d4$sum4+d3$sum3+d2$sum2+d1$sum1-d$sum

d6

d7=order(d6,decreasing=TRUE)

write.csv(d7,file="d2.csv")

The visualization for the purchase price with respect to city is as shown below:

The final output in the form of csv file clearly shows that the returns on invetment is the highest in the following top ten suburbs:

1) Melbourne-$343075

2) St Kilda-$120011

3) South Morang-$107710

4) Southbank-$81091

5) Fitzroy-$48146

6) Richmond-$44252

7) South Melbourne-$38919

8) Saint Kilda-$34826

9) Elwood-$29953

10) East Melbourne, Victoria, AU-$28461.

Comparing the above data with already available data from other sources on the best suburbs in Melbourne market leads to several similarities.According to (SCHETZER A. and BATTERSBY L., 2016), it is seen that the top ten properties in Melbourne market zone are:

Melbourne, 965 properties with Average price per night: $160.55; St Kilda, 544 properties with average price per night: $142.28; South Yarra, 436 properties with average price per night:$141.64; Southbank, 367 properties with average price per night: $183.68; Richmond, 283 properties with average price per night: $162.79; Brunswick, 265 properties with average price per night:$89.46; Fitzroy, 238 properties with average price per night: $146.67; Elwood, 231 properties with average price per night:$155.86; Carlton, 225 properties with average price per night: $123.45; South Melbourne, 184 properties with average price per night: $189.52. Thus, the analysis done here is in close conformity with this data thus clearly showing good accuracy of the results here.

It is clearly seen that the return on investment rapidly decreases as one moves along the top ten popular suburbs. The difference is as much as ten times from the most popular to the least popular suburb. Thus, for best return on investment, it is ideal to invest in a property at Melbourne itself, followed by St Kilda, then South Morang, which have their returns in the million range. The other options do not yield high returns on investment.

BEST PROPERTY TYPES IN THE MELBOUNE MARKET

The analysis in the case of predicting the best property type in the melboune market is similar to the analysis made above for determing the best suburbs. First, using the ‘cast’ command from the library ‘reshape’, the price is cast against property types. There are 26 property types in all. The ‘ddply’ command from the package ‘dplyr’ is then used to sum all prices corresponding to a particular category of property type. The purchase prices are then subtracted from the sum of weekly price, monthly price, security deposit, cleaning fee and fee for extra people. The R Code to perform the analysis is as follows:

library(reshape)

library(plyr)

library(dplyr)

a=read.csv("/home/prajnan/Downloads/listings.csv",header=T)

b=cast(a,property_type~price)

write.csv(b,file="b3.csv")

a$price=as.numeric(a$price)

d=ddply(a,.(property_type),summarize,sum=sum(price))

d

plot(d)

a$weekly_price=as.numeric(a$weekly_price)

a$monthly_price=as.numeric(a$monthly_price)

a$security_deposit=as.numeric(a$security_deposit)

a$cleaning_fee=as.numeric(a$cleaning_fee)

a$extra_people=as.numeric(a$extra_people)

d1=ddply(a,.(property_type),summarize,sum1=sum(weekly_price))

d2=ddply(a,.(property_type),summarize,sum2=sum(monthly_price))

d3=ddply(a,.(property_type),summarize,sum3=sum(security_deposit))

d4=ddply(a,.(property_type),summarize,sum4=sum(cleaning_fee))

d5=ddply(a,.(property_type),summarize,sum5=sum(extra_people))

d6=d5$sum5+d4$sum4+d3$sum3+d2$sum2+d1$sum1-d$sum

d6

d7=order(d6,decreasing=TRUE)

write.csv(d7,file="d3.csv")

The visualization for the purchase price with respect to property types is as shown below:

From the final output in the form of csv file, it is clear that among the property types required to be compared, apartments and houses; apartments have a higher return on investment at $1154570 as compared to houses at $111740. Thus, the difference is about ten times between the two property types. This clearly indicates that it is better to invest in apartments rather than houses. In addition, apartments have the highest return on investment on all the property types. But, investment in apartments is liable to increase the risk of falling into legal troubles as the tenants might cause the neighbours quite a lot of trouble. Wheras in individual houses, the noises would not be that prominent and may be overcome, the noises created by tenants in the process of partying and the like would cause frequent trouble to neighbours due to their proximity. Again, maintenance and cleanliness would also be a cause of concern.

BEST ROOM TYPES FOR INVESTMENT

A similar method of analysis can be used for analysing the best room types to guarantee a high return on investment. The ‘cast’ function from the package ‘reshape’ in R and cast or tabulate purchase price vs the room types is used. To calculate the return on investment one uses the ‘ddply’ function from the ‘dplyr’ package to first generate the sum of all prices corresponding to a particular room type, then subtract it from the sum of monthly price, weekly price, security deposit, cleaning fee and price for extra people. The R Code is as follows:

library(reshape)

library(plyr)

library(dplyr)

a=read.csv("/home/prajnan/Downloads/listings.csv",header=T)

b=cast(a,room_type~price)

write.csv(b,file="b4.csv")

a$price=as.numeric(a$price)

d=ddply(a,.(room_type),summarize,sum=sum(price))

d

plot(d)

a$weekly_price=as.numeric(a$weekly_price)

a$monthly_price=as.numeric(a$monthly_price)

a$security_deposit=as.numeric(a$security_deposit)

a$cleaning_fee=as.numeric(a$cleaning_fee)

a$extra_people=as.numeric(a$extra_people)

d1=ddply(a,.(room_type),summarize,sum1=sum(weekly_price))

d2=ddply(a,.(room_type),summarize,sum2=sum(monthly_price))

d3=ddply(a,.(room_type),summarize,sum3=sum(security_deposit))

d4=ddply(a,.(room_type),summarize,sum4=sum(cleaning_fee))

d5=ddply(a,.(room_type),summarize,sum5=sum(extra_people))

d6=d5$sum5+d4$sum4+d3$sum3+d2$sum2+d1$sum1-d$sum

d6

d7=order(d6,decreasing=TRUE)

write.csv(d7,file="d4.csv")

The visualization for the purchase price with respect to room types is as shown below:

The end output in the form of csv files clearly show that the Whole house have the highest return on investment at $1770949 wheras the return on investment in the case of shared room and private room are negative at $(-31197) and $(-446428) respectively, thus clearly indicating that investment must be done compulsorily only on whole houses/apartments than on shared or private rooms. But, again, there is the consequence of cleanliness which would come in the way of investment.

CONCLUSION

The above analyses clearly point that investment in AirBNB is a highly risky venture yielding less than the amount invested as return on investment in all cases Rachel is interested in. The profit earned by way of return on investment would be used to repay mortgages and also to solve legal issues involved in the occupancy by wrong clients. The interest rates are constantly rising through the years, and; if Rachel’s capital depends on some form of loan, then the heat of the interest and mortgages would be entirely on Rachel.(MEGGINSON,S.2017) This is clearly seen, as Rachel has $2 million as the starting investment amount. But, we see that the return on investment in the best suburb, Melbourne area, is only $343075. In addition, the taxation laws, which are always fluctuating, will also be a source of trouble for Rachel. Considering the fact that she invests in more than one suburb, even then, the return on investment on apartments, which is the highest, is $1154570, which is less than $2 million. Even considering shared rooms and/or private rooms, the higheat return on investment is on whole houses, which is at $1770949. We also see some investment options having negative return on investments, which means losses. Thus, the report we analyse is in consonance with reports found in latest reports on websites. The returns obtained on investments would be mostly used to repay their mortgages. Thus, caution is needed to be invested in AirBNB and it is best if it is given up in favour of other investment options. In case Rachel wishes to improve her chances of getting high return on investment, she should try to advertise more apartments with complete house renting in the top ten ten suburbs listed above. Even then, caution needs to be exercised as the tenants need not be as trustworthy as traditional tenants. In addition, homesharing companies like AirBNB are illegal in several countries, and have a bad reputation for rising up the rentals of homes.