Forecasting and Business Analysis - Adelaide Market - Case Study Example

Name : xxxxxxxxxxx Institution : xxxxxxxxxxx Course : xxxxxxxxxxx Title : Forecasting and Business Analysis Tutor : xxxxxxxxxxx @2010 Forecasting and Business Analysis Summary The key purpose of this survey is to offer economic indicator for Ourvees which is an international company that wants to venture within Adelaide market. The information that was used within the survey includes the one availed by Australian Bureau of Statistics regarding Adelaide’s RGRT as well as unemployment rate, average weekly spending and consumer price index within Adelaide. After the price index was calculated and its significance examined, the potentiality of the consumers was arrived at as well as the competition that is likely to be present within Adelaide. This information would be very important in helping Ourvees which is an international retail company to establish if the environment is favorable for it to venture in that location. Moreover, this information would be valuable in estimating the approximate profitability of the Ourvees Company, should it venture in Adelaide. Introduction Background to the survey The survey is designed to offer short term economic indicator for an international retail company known as Ourvees. Furthermore, the information will be used to compile the retail trade sector of quarterly national account on the production part as well as in compiling the recreation good consumption expenditure on the expenditure part. Population This survey targets population within every geographical unit operating in Adelaide market within Australia that is categorized on Australian bureau of statistics. Sales trend The total sum of sales trend has reduced from January 2008 and has dropped with a 3.5% from then. The drop rate was highest between September 2008 and January 2009 when it standardized 0.5 per month but from January 2009, the rate has dropped to 0.1% per month. The key retail sales trend has been increasing from September 1995, at a standard rate of 0.4% monthly. Within the last two years, the rate rise slowed to 0.1% monthly. Before April 2007, the slowest phase within the key trend also slowed to 0.1% monthly (Jobst 2007). (Shiawassee 2009). Retailing Retailing sales dropped by 5.1% within June 2009, the largest dollar decrease in any industry within June 2009. The drop is general since it spreads allover every retail activity within this industry. After attaining a turning point within February 2008, the development within retailing sales has increased with 12.5% (International Statistical Institute 2009). Saturation point The saturation point within the commercial property market was computed within square meters per 1000 residents. The following factors were considered: The broad saturation point utilizing the spaces within a shopping center in a given area. Saturation point was computed in accordance to the different kinds of shopping area. Shopping centers were separated into kinds in accordance with the Urban Land Institute categorization. The amount of space in every kind was established within the given area. It then became apparent; the location of niches and the type of likelihoods there are for competitors to develop a business or a real estate (Robert 1998). Table 1 CONSUMERS’ EXPENDITURE IN A LOCAL MARKET FOR GOODS’ CATEGORY. Groups of customers according to income Expenditure for one person on goods and services, rubles Share from the given income Number of the population Expenditure on non-food products Expenditure on the product Min. Max. Min. Max. Min. Max. Up to 7000 rubles 3 915 5 481 9,7% 28 373 30 213 308 42 298 631 4 078 797 5 710 315 From 7000 to 15 000 rubles 5 481 11 745 41,6% 121 680 181 404 438 388 723 795 24 489 599 52 477 712 From 15 000 to 25 000 rubles 11 745 19 575 37,5% 109688 350412075 584 020 125 47 305 630 78 842 717 More than 25, 000 rubles 19 575 39 150 11,2% 32 760 174 427 344 34 8854 688 23 547 691 47 095 383 Total in a month     100% 292 500 736 457 165 1 363 897 239 99 421 717 184 126 127 Total in a year         8 837 485 975 16 366 766 869 1 193 060 607 2 209 513 527 (National Technical Information Service 2006) Table 2 THE SATURATION POINT USING SPACES IN SHOPPING CENTRES (ACCORDING TO TYPES OF SHOPPING CENTRES). THREE SHOPPING CENTRES RENTED SPACE IN SQUARE METRES Regional and super-regional shopping centers by 2009 – 407 699 square meters  in total Functional (November 2007)   0 Projects in the planning stage (possibility of opening more than 80%)  380 600 Projects in the planning stage (possibility of opening less than 80%) 220 000 District level shopping centers by 2009 – 264 485 square meters in total Functional (November 2007) Out of them: 264 485 Having a large anchor hypermarket 85 570 Having an anchor food supermarket 111 115 Not having a food anchor 67 800 Projects within the planning point (possibility of opening more than 80%) 86 000 Local shopping centers by 2009 – 155 176 square meters total Functional (November 2007) Out of them: 128 176 With an anchor hypermarket with a food supermarket 51 500 Without a food anchor 76 676 Projects in the planning point (Probability of opening more than 80%) 27 000 “Fashion” centers and department stores by 2009 – 68 591 square meters total Specialized shopping centers by 2008 – 220 608 square meters in Buildings of up to 4500 square meters for rent which don’t match to the modern form of shopping centre 52 200 square meters total Rented area of 2000 to 4500 square meters 32 500 Rented area of 2000 square meters 19 700 (British Information Services 2007). After the calculation of the index, its significance was appropriately evaluated. This included the number of clients as well as the client’s income. In a number of towns within Australia with a population of about a million, the significance within 2009 was within a band of 450-700 square meters for thousand residents; the towns having population of 300-700 thousand individuals within limitation of 150-300 square meters per thousand residents (Harvey 2010). Therefore, it is anticipated that by 2010, if the planned projects occur, the total space within shopping centers will be twice within several towns with a population of 300,000 to one million and thus the saturation point index will go beyond 800 square meters of hired space per 1000 residents (National Library of Australia 2009). This implies that there will be very stiff competition as well as the competition for the tenants and the customers will increase. Within provinces they regularly familiarize themselves on Adelaide in regard to the quantity of space and trade outlets and do not take into consideration the population size. Nevertheless, within these megalopolises which have huge quantities of residents, migrants as well as visitors to the city, the saturation point is relatively low (International Statistical Institute 2009). For instance, by 2010 the saturation point for spaces within shopping centers per 1000 residents is anticipated to be about 40% higher than in Adelaide and more than thrice higher within Austria. This has been estimated through the comparison of consumers’ income. Practice indicates that within local mini-markets within Australia as well as multi-functional complexes for numerous flats and cottages, 4000 square meters of space within shopping centers can function efficiently. Specifically, areas like Adelaide would generate income for square kilometer if there are a considerably high number of clients (Conrad 2004). Conclusion It is anticipated that with an increase within the saturation point of spaces within trade outlets the key competition within shopping centers shall be between light industry goods. This implies that a big drop in profitability within this area when compared to otter areas where the profitability will not drop significantly (National Technical Information Service 2006). To start with, light industry good require minimal resources for entrepreneurs in comparison to food products, technically complex goods and materials. Again, there are lesser demands on the premises while managing the sale of light industry goods. This is because modern technology and variety of clothes or even shoes can be put to sale within any space with much ease. Lastly, light goods are normally sold within the central area of towns and departments with goods which are well known to consumers can find their place within street retail, for example, on the first floor of shopping-office-hotel complexes and not operate efficiently within district level centers on the town’s outskirts. Appendix Sample selection The sample is reselected every month to make sure that the sample illustrates changes taking place within the retailing population. Measurement errors Errors within the survey are grouped into two categories: Non-sampling error Non-sampling error comprises of errors that occur as a result of biases within the models of response and non-response, erroneousness within reporting by respondents and also errors that occur during the recording and coding of the information. The amount of these errors is hard to quantify. Statistics can be revised in case major errors are detected within succeeding quarters. Sampling error This is the measure of the variability that takes place by chance due to the sample and not the whole population is surveyed. Usage and limitation of the data The key limitations include the unavailability of a commodity breakdown, sample error as well as the privacy concerns with comprehensive data release. Sales are categorized by industry and definite sales by commodity are unavailable. Sample errors in addition to privacy can limit the availability of data at detailed categories. Collection unit Geographic unit Population and Sample Size Around 3,500 enterprises have been chosen within the postal sample from the complete population and just about 37,000 enterprises have their data formed from tax data. Output variables These are entity data that can be gotten from survey or output data. Regularly, output variables may be cross tabulated with other output variables, for instance sales by industry categorization. This table indicates present and past output variables which have been released for this survey. Variable Name Description Sales Derived variable. This is the value of sales of goods and services for the month or quarter. Sales are discharged by industry in real, seasonally adjusted, steady price, and per capita terms and by region within real and seasonally accustomed terms. A quarterly trend sequence which eliminates serial and uneven effects is also generated. The sales value for all industries is computed through adding all the weighted sales values for all units within the industry. The weighted value for all units is calculated through multiplying the sales value for all units by the weight on the unit record dataset. . Stocks Derived variable. Trading stocks, finished goods and work within development, materials and many others on hand at the end of the quarter. Stocks are released through industry. The stocks value for every industry is calculated through adding all the weighted stocks values for all units within the industry. The weighted value for all units is calculated through multiplying the stocks value for all units with weight. Deflators Derived Variable Retail trade deflators are retained for every industry and are availed quarterly. Retail trade deflators are founded on subgroups of the consumer’s price index. Industry Classification Variable The Retail Trade Survey is categorized by 24 industries. Bibliography British Information Services, 2007, Survey of British and Commonwealth affairs, HMSO, California. Conrad, J., 2004, Computing Retail Turn over, Sage, New Jersey. Harvey, M., 2010, Pedestrian malls, streetscapes, and urban spaces, John Wiley and Sons, New York. Jobst, C., 2007, Environmental management in European companies: success stories and evaluation, CRC Press, Sydney. National Library of Australia, 2009, Australian national bibliography, National Library Australia, Austria. National Technical Information Service, 2006, U.S. government research & development reports, National Technical Information Service, Minnesota. International Statistical Institute, 2009, Proceedings of the session, Part 2, International Statistical Institute, Michigan. Robert, E., 1998, How to prepare a feasibility study: a step-by-step guide including 3 models Prentice-Hall, Minnesota. Shiawassee, C., 2009, Citizens' survey of Shiawassee-Clinton County Vocational-Technical feasibility study, Bureau of School Services, Michigan. Read More