best homes inc forecasting case study

Best Homes: Forecasting Methods

Introduction, forecasting suggestions, forecast decomposition, regional sales projections role.

Best Homes is a company that builds and sells new houses in various areas of the US. Starting Business in 1945, they have been able to expand from the East Coast to Midwest, West Coast and even the south. Their pricing and housing quality ranges greatly, allowing all kinds of individuals to purchase housing that conforms to their income level. Housing is a field that directly deals with one of the most vital needs of individuals – living space. Despite the population numbers continuously expanding, construction companies have to consider both positive and negative changes in demand during their work. The ability of individuals to purchase homes, as well as their willingness to spend a considerable portion of their income on a home depends largely on present political and economic circumstances (Rodrigues et al., 2020). Furthermore, the quality, placement, and management of housing can affect the willingness of people to buy it.

As a result, construction companies have to consider and manage multiple factors of influence, many of which are outside of their immediate control. In a modern competitive landscape, organizations cannot afford to make mistakes, or lose profits. Failing to consider how outside factors influence one’s business means opening it up for potential instability, which can quickly wear down even the most profitable organizations. For this reason, companies in the housing business have to engage in forecasting. The term refers to the practice of predicting future company metrics and business-related events depending on existing evidence. Forecasting can concern a singular organization, an industry, or the entire economy. With the availability of data from Best Homes’ storied past, it is possible to use forecasting to enhance the organizations’ capabilities. This work will focus on discussing potentially effective forecasting methods for Best Homes, as well as applying some of them using the data presented in the case study.

The primary purpose of prediction methods is not to determine the exact demand, the number of products sold, or other indicators. Forecasting is always wrong; the probability of predicting anything in business up to the last digit is almost zero; in this regard, indicators of forecast error are introduced (Schroeder, 2020). Forecasts are based on the need to make various decisions that will have an effect on the future. According to these decisions, the degree of influence, and calculation methods, these methods are classified into several groups. Such a classification is needed to adapt forecasts for various applied problems.

The fundamental division of forecasting methods is quantitative and qualitative approaches. Quantitative ones consider analytical indicators with which it is possible to perform mathematical and statistical calculations. These approaches include the Best Homes method, which works with sales statistics for the last five years and by region to obtain an excellent forecast. Of the advantages of this method, straightforward solutions stand out, of the disadvantages – the need for interpretation and non-obvious dependence on external factors. Demand regulators in the real estate market can include various mechanisms from global economic situations to geopolitical situations that are not subject to the company’s influence (Kim et al., 2020; Gaca, 2019). However, at Best Homes, this quantitative method worked well, driving the company’s sales.

Qualitative methods are ranked according to the different tasks they are aimed at in organizations. In the case of Best Homes, the company needs to pay attention to these approaches since, at this stage, they are not yet implemented in the organization. These methods are more versatile and provide more flexible mechanisms for working with customers, sales, and products but cannot give accurate quantitative estimates (Schroeder, 2020). Best Homes may consider using the life-cycle analog, Delphi, and market surveys (Schroeder, 2020). The life-cycle analogy will allow each product to be viewed through the prize of its life cycle, making it possible to implement in historical sales data and data by region the most frequent factors influencing external and internal factors on the purchase of a house. In addition, this method is designed for a long sales cycle, which contains the construction and further sale of a house (Schroeder, 2020). Market surveys are more difficult to implement, but they can clarify such points as the solvency of the target client group, key aspects when choosing real estate, and the needs of various client groups for their further segmentation.

This approach will be appropriate within a specific region and sales period. Customers in surveys will be able to clarify the reasons for seasonal demand with the region’s specifics. In the long run, this approach loses its effectiveness (Schroeder, 2020). In this regard, Best Homes may implement a similar forecasting method before launching a new development project in a particular region. With data coming from customers, Best Homes can improve the life-cycle analog and have a practical but partial interpretation of quantitative forecasting methods. Finally, one of the longest-running but most detailed quantitative methods, Delphi, can help a company solve complex issues, usually related to external factors. Technological development has picked up a fast pace and now needs to match this pace to remain competitive in terms of resources such as quality and build time (Ullah et al., 2018). A group of experts within this approach can express an outside point of view, enabling Best Homes to find a solution to a problem or a vector for the nearest development. Implementing all three methods at once may be too resource-intensive for Best Homes. On the other hand, the company already has experience in implementing several forecasting methods at once to solve the internal problems of the organization.

Table 1. Decomposition of Best Homes Sales 2016

The decomposition method in sales forecasting is usually applied to monthly or quarterly data when the seasonal nature of demand is evident and when the manager wants to forecast sales for a year and smaller periods. It is essential to determine when the change in sales reflects general, fundamental processes and when it is associated with the seasonality of demand. Just as the demand for sunscreen increases significantly in the regions during the sunniest months, the real estate market has its mechanisms of seasonal demand. Table 1 provides Best Homes 2016 sales to find the average monthly demand for that year. As a rule, such an analysis is carried out for a more significant number of years to identify the seasonality factor to take into account its quantitative indicators for future years.

The nature of changes in the real estate market can be different. First, Best Homes’ trend of increasing sales contributes to a gradual and long-term growth rate. Secondly, seasonality reflects the fluctuations in the time series associated with the change of seasons. This factor usually appears the same every year, although the exact sales pattern may vary from year to year. Thirdly, cyclicity as a factor is not always present since this factor reflects ups and downs with the exclusion of seasonal and erratic fluctuations. These ups and downs usually occur over a long period, perhaps two to five years. New buildings are just included in the goods group that are subject to similar dynamics (Ionașcu et al., 2020). Finally, a random factor is singled out – a component that remains after excluding the trend, cyclicality, and seasonal factor.

These forecasts for the geographic location of new buildings may reflect the influence of factors that are difficult to consider or are not entirely taken into account in the sales statistics of previous years. These include the opening of a new production facility in the region and the influx of people looking for new housing. In addition, regional estimates can show the solvency of the target audience of customers in terms of the number of sales. Based on these data, the company can conclude the geographic location of the launch of a new project. In addition, the scale of such regional assessments is essential since even within the same city, depending on factors such as infrastructure and distance from the center, the price of equally equipped houses can change significantly. Finally, these forecasts will be combined with historical statistical data and the work of the HR department, which must seek and hire experts to build a house according to a particular established algorithm.

This paper provides an analysis of Best Homes in the context of business development planning. The main forecasting methods were considered, their pros and cons were given as part of the implementation to this organization. Regional differentiation and the decomposition method are used as tools for estimating future sales from existing information. As a result, data was obtained for forecasts that are specific to this business and, in particular, to Best Homes.

Gaca, R. (2019). Price as a measure of market value on the real estate market. Real Estate Management and Valuation, 26 (4), 68-77. Web.

Ionașcu, E., Mironiuc, M., Anghel, I., & Huian, M. C. (2020). The Involvement of Real Estate Companies in Sustainable Development—An Analysis from the SDGs Reporting Perspective. Sustainability, 12 (3), 798. Web.

Kim, Y., Choi, S., & Yi, M. Y. (2020). Applying comparable sales method to the automated estimation of real estate prices. Sustainability, 12 (14), 5679.

Rodrigues, P., Lourenço, R., & Hill, R. (2020). House price forecasting and uncertainty: Examining Portugal and Spain. Banco de Portugal . Web.

Schroeder, R. G. (2020). Operations management in the supply chain: decisions and cases . McGraw-Hill US Higher Ed USE. Web.

Ullah, F., Sepasgozar, S. M., & Wang, C. (2018). A systematic review of smart real estate technology: Drivers of, and barriers to, the use of digital disruptive technologies and online platforms . Sustainability, 10 (9), 3142. Web.

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Best Homes, Inc.: Forecasting Assignment

Best Homes, Inc.: Forecasting

Unit VI Case Study (material attached)

For this assignment, read the case study, “Best Homes, Inc.: Forecasting” on pages 447 – 448 of your textbook. Once you have read and reviewed the case scenario, respond to the following questions with thorough explanations and well-supported rationale:

• What forecasting methods should the company consider? Please justify.
• Use the classical decomposition method to forecast average demand for 2016 by month. What is your forecast of monthly average demand for 2016?
• Best Homes is also collecting sales projections from each of its regions for 2016. What role should these additional sales projections play, along with the forecast from question 2, in determining the final national forecast?

Your response must be a minimum of three -two pages, and APA style must be followed when writing your response. References must include your textbook plus a minimum of one additional credible reference. All sources used, including the textbook, must be referenced; paraphrased and quoted material must have accompanying in-text citations.

Information about accessing the Blackboard Grading Rubric for this assignment is provided below.

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Forecasting: Principles and Practice (2nd ed)

1.5 some case studies.

The following four cases are from our consulting practice and demonstrate different types of forecasting situations and the associated problems that often arise.

The client was a large company manufacturing disposable tableware such as napkins and paper plates. They needed forecasts of each of hundreds of items every month. The time series data showed a range of patterns, some with trends, some seasonal, and some with neither. At the time, they were using their own software, written in-house, but it often produced forecasts that did not seem sensible. The methods that were being used were the following:

• average of the last 12 months data;
• average of the last 6 months data;
• prediction from a straight line regression over the last 12 months;
• prediction from a straight line regression over the last 6 months;
• prediction obtained by a straight line through the last observation with slope equal to the average slope of the lines connecting last year’s and this year’s values;
• prediction obtained by a straight line through the last observation with slope equal to the average slope of the lines connecting last year’s and this year’s values, where the average is taken only over the last 6 months.

They required us to tell them what was going wrong and to modify the software to provide more accurate forecasts. The software was written in COBOL, making it difficult to do any sophisticated numerical computation.

In this case, the client was the Australian federal government, who needed to forecast the annual budget for the Pharmaceutical Benefit Scheme (PBS). The PBS provides a subsidy for many pharmaceutical products sold in Australia, and the expenditure depends on what people purchase during the year. The total expenditure was around A$7 billion in 2009, and had been underestimated by nearly $1 billion in each of the two years before we were asked to assist in developing a more accurate forecasting approach.

In order to forecast the total expenditure, it is necessary to forecast the sales volumes of hundreds of groups of pharmaceutical products using monthly data. Almost all of the groups have trends and seasonal patterns. The sales volumes for many groups have sudden jumps up or down due to changes in what drugs are subsidised. The expenditures for many groups also have sudden changes due to cheaper competitor drugs becoming available.

Thus we needed to find a forecasting method that allowed for trend and seasonality if they were present, and at the same time was robust to sudden changes in the underlying patterns. It also needed to be able to be applied automatically to a large number of time series.

A large car fleet company asked us to help them forecast vehicle re-sale values. They purchase new vehicles, lease them out for three years, and then sell them. Better forecasts of vehicle sales values would mean better control of profits; understanding what affects resale values may allow leasing and sales policies to be developed in order to maximise profits.

At the time, the resale values were being forecast by a group of specialists. Unfortunately, they saw any statistical model as a threat to their jobs, and were uncooperative in providing information. Nevertheless, the company provided a large amount of data on previous vehicles and their eventual resale values.

In this project, we needed to develop a model for forecasting weekly air passenger traffic on major domestic routes for one of Australia’s leading airlines. The company required forecasts of passenger numbers for each major domestic route and for each class of passenger (economy class, business class and first class). The company provided weekly traffic data from the previous six years.

Air passenger numbers are affected by school holidays, major sporting events, advertising campaigns, competition behaviour, etc. School holidays often do not coincide in different Australian cities, and sporting events sometimes move from one city to another. During the period of the historical data, there was a major pilots’ strike during which there was no traffic for several months. A new cut-price airline also launched and folded. Towards the end of the historical data, the airline had trialled a redistribution of some economy class seats to business class, and some business class seats to first class. After several months, however, the seat classifications reverted to the original distribution.