Real Estate Investment assignment: Navigating the Risks and Opportunities in Kingfisher Bay
Question
Task: How can investors mitigate risks and maximize returns in an increasingly unpredictable Real Estate Investment assignment market, particularly in Kingfisher Bay?
Answer
Introduction
The Real Estate has been classified as being among the most secure forms investment due to Real Estate Investment assignments delivering several benefits linked to consistently rising returns, stability, and rental income while they await resale. But investment in Real Estate is influenced by several factors which must be taken in to close consideration before a final decision related to the Real Estate can be made. To achieve these investors need to utilize different tools which can be used to run the data simulations (Grbich, 2012). These help determine correlations between factors and determine trends as well as influencing factors which can further be used to develop an accurate analysis of the data set and make the most accurate prediction.
For this assignment the Kingfisher Bay 2017 data set shall be reviewed using excel analysis tools but the data set will also be run through Rapid miner which will help detect and display certain links which may be linked and have been missed while performing the excel analysis. In addition, to determine correlating trends, utilizing second data mining tools will also help generate independent results which can be counter checked to ensure the final results are accurate before documentation and reporting (Ramsay & Silverman, 2013). Data analysis’s consists of reviewing, analyzing and scrutinizing raw data to determine influencing factors which can be used by investors to make the decisions that will help them secure the best and most accurate results of raw data.
Available and utilized data
The Kingfisher Bay 2017 dataset comprises of 24 data columns, each delivering data related to the investors choice while buying properties. The raw data shall be mined to determine factors investors prioritized before making an investment; the results will then be used to determine the preferred Real Estate characteristics which can be used to improve making and sales for the organization (Göbel & Teixeira, 2012). To narrow down the number of data columns used on the project a decision tree shall first be generated using Rapid miner for all data columns. This will help list of the most influential factors on the data set which can then be used to prepare the reported.
Data Analysis Tools
Rapid Miner Data Analysis
The analysis begins by running the available raw data through rapid miner using only the Decision tree operator to generate a decision tree which interlinks the different components of data (Hofmann & Klinkenberg, 2016). Rapid miner will also be used to read the excel file and generate results which can be used to compare the Ms. Excel results to determine accuracy as well as correlating trends which may not be visible on MS Office Excel.
Decision Tree
They were directly run by Rapid miner using only the decision tree operator and the following results were generated (Z & Lior, 2014). These results show a clear interconnection and relationship between the different data set columns and clearly indicate primary factors influencing investor decision before purchasing the Real Estate.
Generating a decision tree of the raw data is very important as this allows the analysis visualize important trends influencing buying decisions. This is very important as it delivers the foundation on which the research and analysis can begin as well as delivers a direction in which the data may be pointing (Wikipedia, Source Wikipedia). Each columns correction to the other is interlinked using directional arrows which clearly display the primary, secondary and other factors which influence customer decision while considering Real Estate Investment assignment at Kingfisher Bay.
Decision Tree Directional influences
In addition to the decision tree, the directional decision tree without nodes and data helps further understand the direction and distribution of factors considered before investing in the properties (Brath & Jonker, 2015). This now delivers an overview of data distribution in levels that can now be used to further mine the data and locate interrelated trends.
Rapid Miner - Decision Tree observations
The decision tree is clearly broken down into different levels each connected to the other and clearly displaying the directional influence. The observation shows a clear preference related to the Suburb which is the topmost level. This is then split into two factors namely suburb and Real Estate Age which fall under the second level of influencing factors. These are then further broken down into Lot Size, Area, and Rental Status (Perner, 2013). The data continues breaking down further while incorporating other factors which may not be directly linked to influencing the main decision but associated with the final decision. Using the decision tree generated on a rapid minor, the analysis can move on to using the MS Office Excel analysis’s tools which can be focused towards certain aspects which have demonstrated a direct effect and relationship to consumer choice.
MS Excel Analysis
Excel has been classified as being the amount the most powerful and accurate tools used in data analysis thus it’s critical to utilize this tool towards mining data on the Kingfisher Bay 2017 data set (Albright, Winston, & Zappe, 2008). With there being several dozen operations that can be performed using the 24 data columns originally given on the data set, the requirement to narrow down the categories is vital.
Kingfisher Bay Real Estate Investment assignment indicator analysis
To understand the consumer preferences for Kingfisher Bay we shall be analyzing the using specific categories which would help pinpoint specific information and trends related to past customer preferences (Olson & Delen, 2008). The data shall be run by both Rapid miners as well as through Microsoft excel and respective image results posted on the report for easier visual translation. Each process shall be analyzed and findings reported after which a final analysis’s compiled with the overall finding compiled clarity.
House prices Analysis
The first data analysis process used for this research involves comparing the customer preferences towards purchasing properties based on the Real Estate price. On this process, only the Real Estate prices are utilized to generate a scatter charts as below
Rapid miner price scatter chart
Excel Price scatter chart
Analyzing the two above charts allows the analyst to quickly identify a clear distinction related to customer Real Estate preferences based on the Real Estate price. This is observed more clearly on the rapid miner results on which the data clearly shows considerable investor interest in properties falling under the price range of 500,000 and 1,200,000. This data can also be confirmed again analyzing the excel results for the same data sets which also shows clear distinction related to this preferences investment price range preferences. To further confirm this analysis the linear regression tool on Excel has been used to deliver the extract customer investment price ranges gathered from the data (Kudyba & Hoptroff, 2001).
The linear regression clearly shows plots the preferred range beginning around 620,000 and climbing to approximately 1,170,000. With this data, customer preferences can be determined which can then be used to develop effective Real Estate marketing plans which are likely to deliver higher returns over the short term period. Properties valuedbelow500, 000 and those above 1.2million should, therefore, be avoided as there is a lower consumer interest towards these properties thus could result in blocking investment capital and experiencing long turn over periods.
House prices vs. condition/suburb
Another influencing factor linked to Real Estate Investment assignment as detected early during the data analysis’s the suburb the Real Estate is located. This was observed on the initial decision tree model generated on Rapid miner where suburbs were ranked as the primary characteristic considered by investors before purchasing the Real Estate (Koulizos, 2016). To confirm these findings the data shall be price suburb data shall once again be run through Rapid Miner and excel to identify the relevance, as well as important investor preference, ranges linked to the Real Estate price vs. suburb characteristics.
Rapid miner results
Excel results
Once again with the help of both data analysis tools a clear distinction related to the relationship between suburbs selected and the price can be viewed. Studying the results quickly reveals that medium budget Real Estate investors preferred suburb 1 and 2 were with investment purchased properties for between 500000 to 1.2 million. On the other hand, high budget investors preferred Suburb 3 with their average investment range falling between Medium budget investors preferred suburb 3 with the maximum number of investors purchasing properties in the suburb 3 for between 800000 and 1.6 million. When further reviewed in more detail it can also be reported the Suburb a had the highest concentration investors prepared to invest on properties for between 400 and 800 thousand but several extended their budget to while purchasing properties in the suburb.
House prices vs. factors influencing house prices
Another influencing factor observed on the data is related to rental income. This has a direct link to the price and demand of properties in the Kingfisher Bay region thus making it another important factor to take into consideration while analyzing the data. The scatter charts provided below help visualizes the relationship between the Real Estate price and expected rental income.
Rapid miner weekly rental analysis
MS Excel weekly rental
The visual reports generated above again demonstrate a familiar and repeating trend on the data sets selected for this simulation. Again the data on depicts the clear relationship between the investment price and expected rental income. Again the projected results are very similar to the price analysis but differ in that investors are also considering the rental income that can be generated from the Real Estate. The average expectation for rent on the dataset is clearly plotted using the linear regression trend line which allows the annalist to determine the exact point and amount of rental income investors expect from the Real Estate based on its value (Kenney, 2012).
Once again the same characteristics are displayed in the price vs. rental income charts with a similar trend line which clearly indicates there is a direct relationship between the Real Estate prices, suburbs and expected rental income from the properties. Each of these factors is directly interlinked to the investor's decision making it important for the real estate industry to also perform refined research studies to determine other integrate factors which may be influencing the products.
Concerns raised by real estate agents and developers
The case study depicts Real Estate Company and agents concerns related to a restriction on Real Estate value appreciation rates whereby investors are limiting themselves to purchases of up to a million dollars. Traditionally Real Estate values register continually rising prices but this may have been linked to a limited number low number of properties available in the past. As the number of real estate developers increases competition has also increased and it has had a direct effort on the overall Real Estate prices (Schulte, 2012).
Unlike the past when there was a limited number of properties forcing Real Estate value to constantly rise, today numerous real estate project result in setting a paramount for Real Estate valuation. This makes it important for Real Estate developers and agencies to consider adopting alternative strategies linked to evaluating and assessing properties.
Future Surveys
With paramount set for Real Estate valuation Real Estate developers, renovators and agents will need to change their strategies focusing on other aspects investors are considering while purchasing properties. With no longer being a concern due to their being a wide range available to select from, investors are turning their attention to other aspects such as the Real Estate suburbs and rental returns that can be generated from a Real Estate. With these being the main points influencing investor preference while selecting a Real Estate, developers, real estate companies and agents must consider alternative factors to increase the number of sales and revenue generated from real estate at Kingfisher Bay.
Many investors have been observed to consider the rental income Real Estate can generate thus many are likely to invest in the Real Estate as a means of generating extra income (Hoesli & Macgregor, 2014). In this case, many will be looking for affordable properties located in prime locations and suburbs. They also need to be well-connected to the train and bus service as well as have convinced store located close by. This will automatically increase the tenancy demand and rental value for the properties in the region as opposed to considering high returns from long-term Real Estate Investment assignment.
Conclusion
Within increasing number ordeal estate developers developing housing projects across Kingfisher Bay and Australia as a whole, Real Estate prices are now likely to hit a paramount price and not go beyond. This makes it important to change real estate marketing forces from long investment to alternative income investment such rental income which is likely to generate a higher return over the long-term period as opposed to solely expecting high returns on Real Estate value.
References:
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