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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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