Mortgage and Depreciation Expense and Tax Analysis Essay

Mortgage and Depreciation Expense and Tax Analysis - Essay Example However, although the mortgage rate rises from 5% to 10% in the current scenario, the appreciation of houses that consumers can buy rises from 2% to 9%. This implies that demand for consumers wanting to purchase a house goes down. With such deliberations, it is apparent that an investor will find it difficult getting customers willing or able to buy a house. This becomes tricky unless the investors have some other sources of obtaining funds to service the mortgage. To many investors, availability of consumers to purchase their house is a vital factor to consider when mortgaging a house because rents collected from them highly help pay for the mortgage (Lank, 2003). In another dimension, it is argued that in any investment, it is important to venture into business when prices are low, and exit or dispose when prices are high. However, in this scenario, both interest rates and prices of housing are high. In this regard, investors need to consider other factors such as growth in the eco nomy, local employment rates, and the growth of population in the area they wish to invest in. this means that if predictions about these factors turns out to be in his favor, the investor can go ahead with the mortgage. Second scenario If interest rates were able to be deducted from investor’s income, it is an option that many people would like to go for. However, since every investor aims at making a profit and avoid making losses, having interest rates for the mortgage being deducted from their income poses a great threat in servicing the mortgage and meeting other needs that are planned to be addressed by the income (Lank, 2003). Obviously, the investor has other obligations to meet with the income. So when his income starts servicing the mortgage, this means that some of his other projects would be at haul. However, this option is only applicable to first real estate investors who have not experience on serving their mortgage. When this happens, there is a possibility of the investor to service the mortgage on his own as he awaits such a time when prices goes up for him to dispose the house. However, this being the only option the investor has to service the mortgage; it is risky because unlike when the investor would have other means of servicing the mortgage, if consumers are unable to pay or even decides to move to other houses, the investor risks loosing the house unless they turn into their personal income to pay for it (Lank, 2003). All in all, if the income generated from the house can be able to pay for the mortgage, the better. This ensures that an investor’s other businesses or incomes are not disrupted to service the mortgage. Third scenario Deducting taxes from the income earned from the property can be argued to be the best option. The deduction is partial recovery of the cost of the property. Generally, when the property is able to take care of taxes, investors are assured of effective payment of the mortgage. This is unlike wh en they have to pay for the taxes from other sources. In fact, when taxes are deducted from the income generated from the property, the investor is in a better position to claim a tax reduction whenever there is depreciation of the property that generates the income (Lank, 2003). More importantly, when taxes are deducted from the income, the investor can take advantage of the internal revenue services provisions in the area where the property is located. Fourth scenario Every investor would be happy to have

Leadership and Management Theories Essay Example | Topics and Well Written Essays - 1500 words - 1

Leadership and Management Theories - Essay Example A person through learning acquires exemplary leadership; leadership skills and knowledge, however, processed by a leader. These leadership skills and knowledge mainly influenced by beliefs, character, values, and ethics; these factors contribute to leadership process. Leadership process has four principal factors that are the leader, situation, communication, and followers. These factors form the basis of outstanding leadership in an organization, hence the organization’s success while undertaking organizational operations. Several theories of leadership have been produced by students of leadership these theories include Trait, Skills, Styles, Situational, Contingency, Path-Goal, Leader-Member Exchange, Transformational, Servant, Authentic, Team and Psychodynamic theories (Chemers, 1997). These theories are vital in maintaining a sound leadership in an organization, the most common theories in an organization, which will be dealt with in this paper are the transformational the ory, situational theory, and contingency theory. According to transformational theory, leadership is a process through which a person’s engagement with others establishes a connection, which normally results in encouragement of morality and increased motivation for leaders and its followers. The theory attributes leaders with qualities such as confidence, extroversion, and the values stated; with these qualities, the leaders are able to motivate followers (Chemers, 1997). Under transformational leadership, the leader needs to pay attention to the follower’s needs and motives if their potential is to be attained. Transformational leadership does attempt to explain leader’s efforts to implement initiatives and develop crucial and significant changes in any given organization.

Citrus industry in Florida Research Paper Example | Topics and Well Written Essays - 1250 words

Citrus industry in Florida - Research Paper Example In 1834, citrus groves were being cultivated by farmers, which were interrupted by the occurrence of a freeze in February of 1835 (Florida’s Citrus Production 2013). The freeze, which happened on February of 1835, killed all the fruit trees in St. Augustine as temperature dropped to seven degrees above zero, thus, robbing people of their income (Dobson 2009). The farmers’ recovered production for the succeeding fifty-one years as the state only experienced warm winters; during this time, northeast Florida, as well as St. John’s County, became the hub of citrus supply (Dobson 2009). In the 1890’s, citrus production increased to five million boxes per year due to the demand for the said fruit in the northeast and the existence of rail lines, which promoted long distance shipping of the citrus fruits (About Citrus 2012). In fact, in the year 1894, the shipment of crates of citrus to the north amounted up to 5,000,000 (Dobson 2009). On December of 1894, anothe r freeze happened, killing all of Florida’s orange crops in its wake. On the eighth of February the following year, another freeze came about, bringing about the same disastrous effects; such was its impact that on 1896, Florida was only able to ship a little above 100,000 crates of oranges (Dobson 2009). The freeze caused the abandonment of citrus groves in the North of Florida and the production of melons and potatoes in its place (Dobson 2009). This was the most severe in the history of freezes that Florida had undergone (Timeline of Major Florida Freezes 2013). In 1901, there were little above 1,000,000 crates produced (Dobson 2009). In 1917 and 1934, still the state was plagued with the same natural calamity; the freeze of 1934 resulted in the formation of the Federal Frost Warning Service -- a replacement of the train whistles, which warned people of imminent frosts in the previous years (Dobson 2009). The occurrence of continuous freezes in December of 1934, as well as on February of 1935, yielded a negative impact as it reduce production from a million boxes to just below 150,000 boxes of citrus (About Citrus 2013). Again, the farmers planted their citrus crops, yet another freeze took its toll in

Case Study 2 -part 2 Essay Example | Topics and Well Written Essays - 750 words - 1

Case Study 2 -part 2 - Essay Example The average for the machine type one is the highest at 639.093 parts per minute with the highest standard deviation at 60.481, meaning that it also has the highest variability in machine production. Machine type two on the other hand has the lowest average rate at 120.765 ppm and the lowest standard deviation at 13.73011 attributing to its lower variability in machine production per minute. Machine type three has an average of 156.48 in the sample, a close rate in the production rate of 155 ppm as noted earlier. On the production per day sampling, machine type 2 had an average of 120.765 ppm, while its official rating is 200 ppm. Having the number of observation, the production levels, and the time period worked by each machine type as one block of independent variables, and the machine type as the dependent variable, the p-value of the summary table shown above, that t-statistic is negative for observation and time period worked by the machine, but 0.558 for observation. This indicates that the level of significance is favorable for obtaining the observed results when the null hypothesis is true. As shown by table 4 above, we shall reject the null hypothesis which states that machine type 1= 700, as accept the alternative hypothesis which states that machine type one is not equal to 700. This is because the calculated value of p= 0.0000002 is less than p value=0.05. For the machine type 2, the value of calculated p is close to zero which means that we reject the null hypothesis and accept the alternative hypothesis which states that Machine type 2 (rated 200 ppm) not equal to 200. While for the case of machine type 3 (rated 155 ppm) the value of calculated P=0.8258 which is greater than p=0.05 this prompt us not to reject null hypothesis and conclude that Machine type

Mercy killing Essay Example | Topics and Well Written Essays - 2000 words

Mercy killing - Essay Example The terminology mercy killing on the other hand refers to someone taking a direct action to terminate the life of a patient without permission from the patient. The decision to take such an action is usually made on the assumption that the patient’s life is no longer meaningful or that if the patient was in a position to say so, he would express his desire to die (Padilla 219). The distinction between mercy death and mercy killing is that mercy death is voluntary and is conducted with the permission of the patient and often at his request while mercy killing is involuntary and does not involve the patient’s permission or request. None of the actions is more morally acceptable than the other and arguments exist against these actions. Many arguments used against suicide are applicable to mercy death to some extent but the issues surrounding mercy death are complicated by the fact that another person has to do the killing (Padilla 227). If patients who request for mercy de ath would wait to see the results of medical therapy and science, they would probably adjust to their situations and change their minds about dying. Mercy killing is also complicated by the fact that it is done without the consent of the patient and this is a violation of the Value of Life Principle, no one has the right to decide whether a person’s life is worthy. Human beings also have rights and they are not the same as those of animals and no matter what science may say no human being is merely an animal. Question 2: What are the arguments for and against mercy death? Is it morally justifiable in some situations? The first argument about mercy death is that people who are suffering and in pain are usually in a state of fear and depression and therefore cannot simply make rational decisions, if such patients were to wait and see what medical science and therapy can do for them they would probably adjust to their situation and change their minds about dying. The second argu ment states that just as we are generally willing to put animals out of their misery when they suffer, we should do the same for human beings but the rights of human beings to live and die are not the same as those of animals. Western religions maintain that human beings have immortal souls and even non religious humanists talk about the human spirit or personality stating that it should be accorded greater respect than the mere physical self (Padilla 230). Mercy killing is a direct violation of the Principle Value of Life mainly because it involves taking the life of an innocent person, murder is murder regardless of the motive and this is cemented by the fact that patients have not or cannot give their consent for the termination of their lives. The domino argument states that because the consent of patients cannot be obtained, an outside decision about the worth, value and meaning of a patient’s life has to be made but this is a dangerous move because no one has the right to decide if a person’s life is worthy, has value or is meaningful. There is also a possibility of finding cures in future and patients could therefore continue living. In cases of financial and emotional burdens to the family but finances and emotions should not be determining factors where human life is concerned. Both mercy death and mercy killing are not morally justifiable because humane alternatives for both mercy death an

LSP 5 Ethics - Discrimination and Affirmative Action Essay

LSP 5 Ethics - Discrimination and Affirmative Action - Essay Example The most dangerous job at the company is working at the factory. â€Å"The wood products industry may be divided into the following sub categories:  logging, pulp, paper board mills and saw mills and woodworking†. (Occupational Safety and Health Administration-OSHA). These five processes are equally risky because they involve operating machines and equipment. There are several hazards associated with working in this factory ranging from chemicals used in the processes; machines and equipment when faulty, poor design of work equipment and improper lifting are all common dangers associated with working in factories like this. Sometimes clothes worn by employees can also be trapped in the machines if not the recommended ones. Occupational noise, dust and heat could also be possible risks. I feel that employees are adequately informed of the various risks involved in working in such environments. Due to government regulations and reforms companies are under pressure to formulate safe workplace policies and make sure all employees abide by the set rules and regulations. The government enforces these laws to companies through the Federal Department of Labor, OSHA. Employers abide by the set standards and communicate hazards to their employees. They are also required to carry regular training on workplace safety. Working in the wood mills is the most dangerous occupations in the United States according to OSHA with hazards ranging from wiring, static and moving equipment, and components of products being manufactured to occupational noise and dangers of respiratory infections. These risks are acceptable and reasonable as long as proper communication about them is in place and that employees in the company take precaution as required to avoid incidences of work place infections, injury and accidents. This is because the products made by the company are necessary we

A glimpse of Big Data Essay Example for Free

A glimpse of Big Data Essay â€Å"Big data is not a precise term; rather its a characterization of the never ending accumulation of all kinds of data, most of it unstructured. It describes data sets that are growing exponentially and that are too large, too raw or too unstructured for analysis using relational database techniques. Whether terabytes or petabytes, the precise amount is less the issue than where the data ends up and how it is used.†Cite from EMC’s report â€Å"Big data: Big opportunity to create business value†. When explosion happened in mobile network, cloud computing and internet technology, more and more different information appeared. In the past, the numerous terabyte data could be a disaster for any company, because it means high cost of storage and high performance CPU. However, in nowadays, companies discovered many facts they haven’t thought about these data before. Companies started to use data analytics technology to find business values from these terabyte or petabyte data. It seems to be a big opportunity instead of disaster for companies now. Data is not only defined as structured data. When we talking about big data, it could be categorized into three types of data: structured data, unstructured data, and semistructured data (Please see Chart I). Especially when internet and mobile internet developed rapidly, the unstructured data and semistructured data exploded. For example, a bank could draw a conclusion by analyze unstructured data to find out why number of churn increased. Most definitions of big data all talk about the size of data. However, size, or volume, is not the only characteristic of big data. There are other two characteristics, variety and velocity. Variety means big data generates from several of sources. Data type was no longer connected to structured data. According to the EMC’s report, most of big data related to unstructured data. Velocity means the speed of data production. Data was no long structured data which was stored in the structured database. Data could come from anywhere and anytime: mobile, censors, devices, manufacturing machine etc. The stream of data generates in real time. This means company’s action should be taken with this speed. Structured data| Structured data is organized in structure. These data can be read and stored by computer. The form of structured data is structured data base that store specific data by methodology of columns and rows. | Unstructured data| Unstructured data refers to the data without identified structure. For example, video, audio, picture, text and so on. These data also called loosely structured data. | Semistructured data| Semistructured data organized in semantic entities. The data’s size and type in one group could be different. For example, XML and RSS feeds. This data try to reconcile the real world with computer based database.| Chart I. Three types of data. Big data analytics Big data analytics is not a technique. It is a terms that contains a lot of technologies (See EXHIBITION I). Based on enterprise’s different requirement, each program will use different technology to analyze data. However, with the big data’s development, some of these techniques become popular and useful. On the basis of the exhibition II, advanced analytics, visualization, real time, in-memory databases and unstructured data have strong-to-moderate commitment and strong potential growth. The traditional techniques, for example, OLAP tools and hand-coded SQL, have gradually lost their place. When a bank want to find the reason why the number of customer churn increased, or marketing department decide to push precise advertisement to their customer, they need to analyze customer behavior. These data from customer service emails, phone call records, sales interview reports, login data from mobile devices, and so on. Almost all of these data cannot be analyzed by traditional data analytic techniques. That’s why these new techniques development so rapid and fierce. How a company adopt big data analytics? According to the article Big Data, Analytics and the Path from Insights to Value† published on MIT Sloan Management Review, the author categorized the company who used big data analytics into three stages (See Exhibition II). For most companies, it is easy to establish an enterprise data warehouses (EDW’s). However, how to interpret these data and finding the business value from these data become the most crucial factor for companies. Besides, so many techniques and tools behind the term big data. For any company who decide to adopt big data analytics, the leading obstacle is lacking of understanding of how to use analytics to improve their business. From the article, the author gave 5 recommendation to any company who wanted to adopt big data analytics. 1. Think Big. Focus on the biggest and highest value opportunities. Narrow down the options. 2. Start in the Middle. Within each opportunity, start with questions, not data. Company prefer to collect data and information at first place. In fact, start with questions could help company continue to narrow down the scope and define the most valuable direction. 3. Make analytics come alive. When Problem was defined, company need to apply analytics. Choosing the propriety tools to analyze the data. 4. Add, dong detract. Use centralized analytics. Every analysis is connected. 5. Build the parts, plan the whole. Big data from everywhere. The data will become more and more big and complex. Building the data infrastructure is crucial for big data analytics. Big Data, Big Opportunity When company decide to concern big data, it means every department are involved. Big data is not IT department’s or analysts’ responsibility. In fact, big data analytics need information and help from sales, marketing, RD, IT and even external sources. Today, number of companies have entered into big data market. The following chart lists some big organizations who have adopted big data analytics. Besides, some of them provide big data services to other companies These organizations are just the tip of the iceberg. When big data converted from Blue Ocean to Red Ocean, some of these organizations have turned into services provider. This become a future trend in big data area. Big data needs expensive hardware and labor cost. Not every company can afford that. Besides, big data involved so many different computer technologies, not everyone understood all these techniques. For that matter, there will be more and more companies try to seek big data service from external environment. Using the external big data platform or tools could reduce the cost for building a totally new technique teams. What the companies need to do is finding the problem, narrow down the scope and sending the needs to services provider. When they get the analysis result, they could use the valued result to take the next action. Furthermore, these services provider will not only focus on big companies. The new fashion is to provide friendly interface and easy to use product to individual customer. What behind big data will be still mystery for people, however, the face or terminal of big data will become more and more friendly and simple. There is an example: Twithink. Twithink is a program invented by a MIT group. They provide customized twitter behavior analysis for customer. This program could draw some conclusion by analysis the unstructured information on Twitter. They collected the gender, location, time, key words, images, etc. from tweets. Then they analysis these data under certain arithmetic to draw conclusions. The last research was the Election in 2012. The latest research is Gun Control discussion which still in progress. Problem and threats. Although big data has many opportunity and advantage for enterprises, it still has some disadvantages. The first crucial problem is privacy invasion. After you searched one product on Amazon, the next time when you login to Amazon, you will find the products you may interested which was Amazon pushed to you. This is called precise advertisement. However, you even didn’t know when amazon collected your information. Another example was Google Analyst, company embedded code into their website to collect people’s internet behavior. These things happened every day and everywhere. It is hard to argue this action is right or wrong. Maybe some are good. However, if personal data is sold or published by someone, it will affect individual’s daily life. It will become a crucial problem. The Second problem is information’s validity. According to the article â€Å"With big data comes big responsibilities† points out that â€Å"big data sets are never complete†. If data is insufficient, the analysis result would be invalid or distorted. The invalid information would guide company to wrong direction and cause a big loss. Thus, big data also has two side. How to use it to create more value for company is the first consideration for all managers. Reference 1. Office 2013 Brings BI, Big Data to Windows 8 Tablets. ZDNet. N.p., n.d. Web. 25 Jan. 2013. 2. Big Recognition for IBM Big Data. Smarter Computing Blog Big Recognition for IBM Big Data Comments. N.p., n.d. Web. 25 Jan. 2013. 3. Big Data. Wikipedia. Wikimedia Foundation, 26 Jan. 2013. Web. 26 Jan. 2013. 4. Structured Data. Webopedia. N.p., n.d. Web. 26 Jan. 2013. 5. Unstructured Data. Webopedia. N.p., n.d. Web. 26 Jan. 2013. 6. Group of EMC. Big Data: Big Opportunities to Create Business Value. Rep. EMC, n.d. Web. 26 Jan. 2013. 7. Philip Russom. Big Data Analytics. N.p.: TDWI, 2011. Print. 8. Lavalle, Steve. Big Data, Analytics and the Path from Insights to Value. MIT Sloan Management Review Winter 2011: 21-31. Web. 9. Ã¥ ¤ §Ã¦â€¢ °Ã¦  ®Ã¥ · ²Ã¦Ë† Ã§ º ¢Ã¦ µ ·Ã¯ ¼Å¸Ã¯ ¼ Ã¥â€¦ ¨Ã§ Æ'Ã¥  Ã¥â€ºâ€ºÃ¤ ¸ ªÃ¥ ¤ §Ã¦â€¢ °Ã¦  ®Ã¥â€¦ ¬Ã¥  ¸Ã¥â€¦ ¨Ã©  ¢Ã§â€ºËœÃ§â€š ¹Ã¯ ¼ . N.p., n.d. Web. 26 Jan. 2013. 10. IBM InfoSphere Platform Big Data, Information Integration, Data Warehousing, Ma ster Data Management, Lifecycle Management Data Security. IBM InfoSphere Platform Big Data, Information Integration, Data Warehousing, Master Data Management, Lifecycle Management Data Security. N.p., n.d. Web. 26 Jan. 2013. 11. Amazon Web Services, Cloud Computing: Compute, Storage, Database. Amazon Web Services, Cloud Computing: Compute, Storage, Database. N.p., n.d. Web. 26 Jan. 2013. 12. Oracle Big Data Appliance. Oracle Big Data Appliance. N.p., n.d. Web. 26 Jan. 2013. 13. Google BigQuery Feedback on This Document. Google BigQuery. N.p., n.d. Web. 26 Jan. 2013. 14. EMC Greenplum Data Computing Appliance Data Warehousing, Data Analytics (FW).EMC Greenplum Data Computing Appliance Data Warehousing, Data Analytics (FW). N.p., n.d. Web. 26 Jan. 2013. 15. Teradata. Data Appliance, Data Warehouse, Business Intelligence à ¢Ã‚€Â“. N.p., n.d. Web. 26 Jan. 2013. 16. Twithinks. TwiThinks. N.p., n.d. Web. 26 Jan. 2013. 17. Eria Naone. With Big Data Comes Big Responsibilities. N.p.: MIT Technology Review, n.d. 2011.