Determinants of International Commercial Real †MyAssignmenthelp.com

Question: Discuss about the Determinants of International Commercial Real. Answer: Introduction Real estate business is a business involving the sale of land, buildings on that land and any other natural resources available in that piece of land (Leiser Groh, 2014). It is one of the booming businesses in most parts of the world due to appreciating value of pieces of land with time. In most cases, other commodities in the market might experience the fall in prices but for the real estate business the prices seem to ever been escalating (Cherif Grant, 2014). There are variety of factors to consider before engaging in this type of business just like in other business sectors. One who ought to engage in real estate business need to have high negotiation skills so that when negotiating the prices with the customers or the willing buyers, you dont overstate the prices or rather understate the prices and end up selling at a very low price (Abatecola et al, 2013). Population of a place have been associated with better business performance. In the densely populated areas, the business es tend to perform better than in less dense populated areas. As a result of this therefore, the geographical location of the real estate business should focus to be in well populated region. In this our case, we choose to pick on urban areas since more people tend to migrate to the urban places in search for jobs from the rural areas. The specific locations that were studied for the operation of our business were Belton, Domaine, Hills, Mount and Terrata. Data were collected from the aforementioned cities where information such as the number of rooms contained in a house in each city was recorded, the amount that was listed against each house from various cities for variety of houses of different number of rooms and even the prices for which the houses were finally sold. The startup of real estate business requires relatively large capital due to continuous increase in the real estate products and the maintenance cost that is involved in the process. Renovation skills is among the skills the owners of the real estate are expected to have (Brounen Koning, 2013). This is fundamental because it will ensure that houses before they are put and advertised for sale are in good and admirable condition. Experts in the sector put all due efforts in preparation of the real estate property ready for sale. The services would include buying of pieces of land, building houses, leasing or renting houses and selling some of the houses. In order to reach the wider market, marketing strategies that are put in place for use in reaching the willing buyers would be through the social media and other means such as the magazines, newspapers and the media broadcasting houses (Soroca $ Karasic, 2012). This will ensure that the business is made well known including the servic es offered by the business with the aim of boosting the sales of the business for its betterment and survival in the market. Acquired data from the cities of business operation will be used to predict the future performance of the business and some other factors that could lead to the increase or decrease in the price of the houses for either leasing, renting or selling. Research questions In order to meet the objectives set by business research, research questions will offer the guide through the answers provided that are directed towards achieving the objectives. Research questions are made from the center of interest in business with the aim of providing solutions to some of the business problems from the available answers. In this research, both qualitative and quantitative variables were involved. The qualitative variable was city names while the rest such as house number, number of bedrooms, bathrooms, all rooms, listed amount, final sale and advertising expenditure were quantitative. With these variables, we can be able to exhaust any kind of information that could be hidden through constructing questions that would be directed towards solving the problems. Growth of business is always one of the experience the business owners wish to have and enjoy. As a result, there was need to check for the mean difference among the sampled cities. Due to this therefore, the research ought to have the question; Is there mean difference in the final sale of the houses in the various sampled five cities? this question was important in helping to know if there was a variation of the final sales in the five cities. Probably, answering this question was useful in identifying the city that recorded the highest sales by considering mean of final sales. Development of a business enterprise depends on the imposed efforts by the marketing department among other issues including production of quality products. The marketing department incorporated advertisement service providers to help in selling the image of the business (Wahid Ahmed, 2011). The amount incurred in the advertisement was another major factor of concern since it falls under the business expenses, the business was most likely to go for the least charging advertising company in order to minimize the expenses. Research question arose in this section was; How do the cities compare in terms of the amount incurred on advertisement of the houses? Advertisement is examined due to its associated importance in business. One of the importance of advertisement is to create promotional perspective since our real estate business deals directly with the customers. Further, advertisement is carried out for the purpose of creating awareness among the customers about the products handle d by the business. This will help loyal customers and other prospective customers to stay aware about what the real estate business offers. The expense that is involved in the advertisement of the real estate products across the cities will help in future planning of the business so that adjustments can be made in case the allocated amount was less or much in a certain city. Still on advertising expenditure, we shall tend to answer the question; What is the relationship between all rooms of the houses and their advertising expenses? With this question, we shall be able to know how the number of rooms a house has affect the advertisement of the houses. Selected statistical methods Data summaries was important in the description of measures of central tendency and the measure of variability. These helped to determine some characteristics that were important in giving the description of the data in dataset. The data set was made up of house number, city, bedrooms, bathrooms, all rooms, listed, final sale and advertising expenditure. Apart from city, all the remaining variables were quantitative variables. As a result, quantitative statistical techniques were applied in the description of data and some relevant tests conducted. For the measure of central tendency, mean was one of the descriptive statistics that were applied to examine the characteristics of the dataset. Mean is the value calculated by dividing the sum of all the available observation and the number of observations (Englander, 2012). The formula that was applied was; Mean = where i= 1, 2, 3, . And n is the number of observations in the sample. Another measure of central tendency that was used was the standard deviation. This is the value that is more often used to examine how much the observations in the sample or a population are from the sample mean statistic or the population mean parameter. It is calculated using the formula below; Variance (S2) = for a sample, and the standard deviation is the square root of the variance and it is denoted by S for a sample. In order to be able to identify the type of relationship that exist between variables, scatter plot was used i.e. when determining the relationship type between all rooms variable and advertising expenditure variable. The scatter plot was important to help in identification of whether the relationship between the two variables was positive or negative. Also, the correlation coefficient (r) was calculated, Shape and symmetry of the data in the dataset was another issue of concern that resulted to the incorporation of the box plot. This helped to show how data was distributed in the dataset for various variables and whether or not there were outliers that would give deceptive information. Requirement for the construction of the box plot, five point measures were required. They included; the minimum, maximum, first quartile, median and third quartile (Ghasemi Zahediasl, 2012). Mean of the final sales for various cities was an issue of concern that was also asked for in the research questions. To solve this and have the question adequately answered, t-test was used and therefore the hypothesis had to be formulated and tested otherwise. T-test was used to test for the mean difference of the final sale for the five cities. Technical analysis Table 1: T-Test: Final sale Mean difference between Belton and Domaine Column1 Belton Domaine Mean 422.8 1603.45455 Variance 26873.73 71615.2727 Observations 10 11 df 19 t Stat -12.0337 P(T=t) one-tail 1.24E-10 t Critical one-tail 1.729133 P(T=t) two-tail 2.47E-10 t Critical two-tail 2.093024 P-value one tail is less than .05, we then reject the null hypothesis that there was no mean difference for Finale sale between Belton and Domaine since there was significant mean difference. Table 2: T-Test: Final sale Mean difference between Belton and Hills Column1 Belton Hills Mean 422.8 137.5 Variance 26873.73 480.5 Observations 10 10 df 18 t Stat 5.454934 P(T=t) one-tail 1.75E-05 t Critical one-tail 1.734064 P(T=t) two-tail 3.51E-05 t Critical two-tail 2.100922 P-value one tail is less than .05, we therefore reject the null hypothesis that there was no mean difference for Finale sale between Belton and Domaine since mean difference was significant. Table 3: T-Test: Final sale Mean difference between Belton and Mount Column1 Belton Mount Mean 422.8 445.714286 Variance 26873.73 42128.5714 Observations 10 7 df 15 t Stat -0.25606 P(T=t) one-tail 0.400693 t Critical one-tail 1.75305 P(T=t) two-tail 0.801386 t Critical two-tail 2.13145 P-value in this case is greater than .05, we then fail to reject the null hypothesis and conclude that there was no final sale mean difference between Belton and Mount. Table 4: T-Test: Final sale Mean difference between Belton and Terrata Column1 Belton Terrata Mean 422.8 727 Variance 26873.73 80956.6667 Observations 10 10 df 18 t Stat -2.92947 P(T=t) one-tail 0.004478 t Critical one-tail 1.734064 P(T=t) two-tail 0.008956 t Critical two-tail 2.100922 P-value one tail is less than .05, we therefore reject the null hypothesis that there was no mean difference for Finale sale between Belton and Terrata since the difference was significant. Table 5: T-Test: Final sale Mean difference between Domaine and Hills Column1 Domaine Hills Mean 1603.45455 137.5 Variance 71615.2727 480.5 Observations 11 10 df 19 t Stat 17.2295475 P(T=t) one-tail 2.3495E-13 t Critical one-tail 1.72913281 P(T=t) two-tail 4.699E-13 t Critical two-tail 2.09302405 We reject the null hypothesis that there was no final sale mean difference between Domaine and Hills since calculated P-value is less than .05. Table 6: T-Test: Final sale Mean difference between Domaine and Mount Column1 Domaine Mount Mean 1603.45455 445.714286 Variance 71615.2727 42128.5714 Observations 11 7 df 16 t Stat 9.73050423 P(T=t) one-tail 2.0059E-08 t Critical one-tail 1.74588368 P(T=t) two-tail 4.0117E-08 t Critical two-tail 2.1199053 We reject the null hypothesis that there was no final sale mean difference between Domaine and Mount since calculated P-value is less than .05. Able 7: T-Test: Final sale Mean difference between Domaine and Terrata Column1 Domaine Terrata Mean 1603.45455 727 Variance 71615.2727 80956.6667 Observations 11 10 df 19 t Stat 7.2743573 P(T=t) one-tail 3.3387E-07 t Critical one-tail 1.72913281 P(T=t) two-tail 6.6775E-07 t Critical two-tail 2.09302405 We reject the null hypothesis that there was no final sale mean difference between Domaine and Terrata since calculated P-value is less than .05. Table 8: T-Test: Final sale Mean difference between Hills and Mount Column1 Hills Mount Mean 137.5 445.714286 Variance 480.5 42128.5714 Observations 10 7 df 15 t Stat -4.77722123 P(T=t) one-tail 0.00012229 t Critical one-tail 1.75305036 P(T=t) two-tail 0.00024458 t Critical two-tail 2.13144955 We reject the null hypothesis that there was no final sale mean difference between Hills and Mount since calculated P-value is less than .05. Table 9: T-Test: Final sale Mean difference between Hills and Terrata Column1 Hills Terrata Mean 137.5 727 Variance 480.5 80956.6667 Observations 10 10 df 18 t Stat -6.53239566 P(T=t) one-tail 1.9295E-06 t Critical one-tail 1.73406361 P(T=t) two-tail 3.859E-06 t Critical two-tail 2.10092204 We reject the null hypothesis that there was no final sale mean difference between Hills and Terrata since calculated P-value is less than .05. Table 10: T-Test: Final sale Mean difference between Mount and Terrata Column1 Mount Terrata Mean 445.714286 727 Variance 42128.5714 80956.6667 Observations 7 10 df 15 t Stat -2.23151057 P(T=t) one-tail 0.02066422 t Critical one-tail 1.75305036 P(T=t) two-tail 0.04132845 t Critical two-tail 2.13144955 We reject the null hypothesis that there was no final sale mean difference between Mount and Terrata since calculated P-value is less than .05. Table 11: Cities mean advertising expenditure Belton 25.8 Domaine 77.63636364 Hills 9.8 Mount 32.14285714 Terrata 37.4 Domaine showed high advertisement expenditure of $77.6, Terrata $37.4, Mount $32.1, Belton $25.8 and the least advertisement expense was incurred on Hills city $9.8. All rooms Listed ($000) Final Sale ($000) Advertising expenditure ($000) All rooms 1 Listed ($000) 0.954659343 1 Final Sale ($000) 0.950100617 0.980465121 1 Advertising expenditure ($000) 0.726354923 0.765129663 0.757145917 1 Correlation coefficient (r) for All rooms and Advertising expenditure was 0.726354923 Coefficient of determination (R2) confirmed that the plots were 53% concentrated around the trend line and there was strong positive correlation. Listed values in the dataset were positively skewed as shown by box and whiskers above, final sale was as well skewed to the right and lastly, advertising expenditure was also skewed to the right showing that the data not normally distributed. Results and discussions T-tests were conducted to test for the final sale mean difference between various cities as sampled. Through the tests, we had the first question answered. In regard to that therefore, we would conclude that most cities showed final sale mean difference except for two towns i.e. Belton and Mount that had their mean final sale too close to each other (422.8 and 445.714286) that there was no significant difference could be detected by the test. From this therefore, the top business officials are supposed to put different efforts to venture and maximize the profits from those cities. In response to the second question, mean advertising expenditure was calculated and Domaine showed to have consumed the business a lot on advertisement as $77.6 was used. The amount incurred on advertisement varied while we had some cities like Hills having the business spending as low as $9.8. This variation can be considered to be as a result of internal or other external factors of the business that were not determined by this research analysis. The research was also interested in the relationship that would be existing between All rooms variable and Advertising expenditure. To determine the type of relationship, correlation analysis was conducted that revealed that there was strong positive correlation between the two variables since the correlation coefficient (r) was 0.726354923. The relationship is shown in the scatter plot above where its strength is at 53% as given by R-squared. The relationship meant that more number of rooms a house has as indicated in all rooms will have an equally higher advertisement expense incurred and when there are less rooms, low expenses will be incurred on advertisements. Tested variables from the sample showed that the data in the dataset were not normally distributed but skewed. All variables that were plotted in the box and whisker showed that they were all skewed to the right hand side thus positively skewed. Reference Abatecola, G., Caputo, A., Mari, M., Poggesi, S. (2013). Real estate management: past, present, and future research directions.International Journal of Globalisation and Small Business,5(1-2), 98-113. Brounen, D., de Koning, S. (2013). 50 years of real estate investment trusts: an international examination of the rise and performance of REITs. Journal of Real Estate Literature,20(2), 197-223. Cherif, E., Grant, D. (2014). Analysis of e-business models in real estate. Electronic Commerce Research,14(1), 25-50. Englander, M. (2012). The interview: Data collection in descriptive phenomenological human scientific research.Journal of Phenomenological Psychology,43(1), 13-35. Ghasemi, A., Zahediasl, S. (2012). Normality tests for statistical analysis: a guide for non-statisticians.International journal of endocrinology and metabolism,10(2), 486. Lieser, K., Groh, A. P. (2014). The determinants of international commercial real estate investment.The Journal of Real Estate Finance and Economics,48(4), 611-659. Soroca, A., Karasic, N. J. (2012).U.S. Patent No. 8,302,030. Washington, DC: U.S. Patent and Trademark Office. Wahid, N. A., Ahmed, M. (2011). The effect of attitude toward advertisement on Yemeni female consumers' attitude toward brand and purchase intention.Global Business and Management Research,3(1), 21.