Increasing Real Estate Management Profits: Harnessing Data Analytics , week(1-7) All Quiz Answers.

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 Increasing Real Estate Management Profits: Harnessing Data Analytics






Week 1 Assignment :

PROMPT

List one stakeholder/role, explain whether this stakeholder is inside or outside of Watershed, and list at least one question you would ask that stakeholder.

Stake Holder = Owner of property for rent Question = When can you expect to get a return from the short-term rental business?


PROMPT

List one stakeholder explain whether this stakeholder is inside or outside of Watershed, and list at least one question you would ask that individual.

Stake Holder = Short term lease clients Question = What kind of benefits do you need to rent a property? 


PROMPT

List one stakeholder/role, explain whether this stakeholder is inside or outside of Watershed, and list at least one question you would ask that individual.


Stake holder = Operations Manager(COO) Question = Is there any technological platform to support the operation of the short term rental business?


Week 7 Assignment :


PROMPT

Dashboard:

Submit your Tableau Public Dashboard URL in the text box below.

Double-check to be sure it is publicly accessible, so your classmate can access it!

https://public.tableau.com/profile/victor5187#!/vizhome/WatershedDashboardSensibilityAnalysis/SensibilityDashboard



PROMPT

White Paper:

Copy the entire contents of your white paper document and paste into the text box below. Verify that the original formatting generally remains, when you preview the assignment.

I recommend that Watershed enter the short term rental market, but not with all their proprieties. They should convert the 41 profitable proprieties, the majority of those proprieties are located in Miami, Austin, New York and San Diego. The conversion should be done on 2 steps, on the first one the 16 most profitable proprieties should be converted on Short Term Rental and after that the other 25 should be converted when the revenue generated by those first 16 cover the cost of conversion of the other 25. The analysis that serves as the basis of my recommendation indicates that Watershed and its client would benefit from $1,37 Millions of increased profits during the first year, and yearly profits of $1,12 Millions every year thereafter if my recommendation is enacted. The initial capital investment needed to implement my recommendation would be $500.000. This analysis is based on financial assumptions that were confirmed by company and industry experts, but sensitivity analyses indicate that Watershed should enter the short-term rental market with their client, even if these initial assumptions need to be revised. Below, I describe the analyses I used to arrive at my conclusion, and report the results of my sensitivity analysis that assesses how expected profits and needed capital expenditure would change if my assumptions are modified. Analysis Summary I modeled the relationship between nightly rental price and occupancy rate for short-term rental properties using data from current short-term rentals managed by other companies and owners. I used this model to predict the short-term rental price that would maximize profits from each of Watershed’s client’s I was provided with four types of information about short-term rentals of the same type (number of bedrooms, apartment or house, kitchen availability, unshared property) and in the same location as Watershed’s client’s 244 properties: a typical short-term nightly rental rate, the corresponding occupancy rate for the property with that rental rate, the 10th percentile nightly rental rate, and the 90th percentile nightly rental rate. When the typical rental prices were expressed in terms of percentiles relative to properties of the same type and in the same location—but not when they were analyzed as raw dollar values—they correlated linearly with occupancy rates: I used the parameters of the regression line and Excel’s Solver optimization function to find the rental price and occupancy rate that would maximize the profits expected from each of Watershed’s client’s 244 properties. Any optimized price below the 10th percentile rate was replaced with the 10th percentile rate, and any optimized price above the 90th percentile rate was replaced with the 90th percentile rate, in order to account for lack of data outside of these ranges in the linear model. These optimized rental rates were entered into a financial cash flow and profit model that computed the expected revenue from each property based on its projected occupancy rate.



PROMPT

Presentation:

Submit your presentation URL below.

Double-check that it is publicly accessible so your classmates can access it to provide their evaluation feedback.

https://www.screencast.com/t/FFcNAKhrt















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