An Investigation of Correlations between Management Practices and Profitability in Production Home Building Companies

Introduction

In 2005, annual revenues of $649.8 billion were generated from the building of new residential construction within the United States, dwarfing all other categories of construction (ENR, 2005). Small-volume home building companies producing 25 or fewer units per year historically make up the majority of home building firms in the United State. However, based on information provided by the director of the Data Service Center of the National Association of Home Builders (NAHB), larger home building companies producing more than twenty-five homes per year were responsible for the majority of new home construction in the United States (Bajwa, 2002).

According to a recognized researcher and construction-industry consultant who has identified common management errors that often lead to company failure, “A contracting business has only three primary functions: getting the work, doing the work, and accounting for the work, or put in other terms, marketing, operation, and administration” (Schleifer, 1990). It has been proposed by a number of researchers that identifying and implementing specific management practices in a construction company can be one of the most influential factors contributing to the ongoing success of that firm (Strischek, 1998; Lussier, 1995; Schleifer, 1990; Adrian, 1976). In fact, small business failures, including those of home building companies, typically seem to be characterized by a lack of management skill and experience (Flahvin, 1985; Gaskill, Van Auken, & Manning, 1993). By identifying and benchmarking important management practices identified in production home building companies, a measuring stick can be provided against which owners of these types of firms can compare their own business practices.

Business owners that experience failure sometimes try to place blame on external influences, but this argument has been rejected by studies which report that some businesses, including home builders, can make record profits even when times are tough. In fact, it has been determined that in some cases superior management skills and abilities allow owners to enjoy success, even during bad economic periods (Evans, 1992; Flahvin, 1985). Because of the many administrative activities and management requirements placed upon home builders, one of the most serious obstacles to success is seen as the implementation of questionable management systems and controls in a company; or maybe worse, the complete lack of any established control systems within a company (Flahvin, 1985; Gaskill, et al, 1993; Gill, 1968; Strischek, 1998). Like any other business, home building companies need management systems and procedure.

To date, much research has been published regarding how to identify the current health and profitability of businesses, including home building companies, by analyzing their financial ratios and comparing them to established industry standards. However, another approach to assessing the vitality and health of a business is to evaluate how a company’s management practices relate to its profitability.

By designing a scientific study using a representative sample of production home builders nationwide, it should be possible to measure the frequency of use of specific management practices and to identify those practices that are common to highly profitable companies. In addition, the study should be designed to differentiate between management practices that characterize successful companies and those that characterize unsuccessful companies.

The Statement of the Problem and Subproblems

The purpose of this research was to determine which, if any, of the important management practices identified in production home building companies were significant indicators of the current level of profitability of those companies.

The first subproblem was to generate a list of management practices considered by industry experts to be the most important ones for production home builders. The second subproblem was to develop a survey instrument that would effectively measure the level of use of each of the selected management practices in the companies participating in this study. The third subproblem was to determine the profit margins of each of the companies surveyed. The fourth subproblem was to determine the relationship between companies’ management practices and their profit margins and to identify which practices, if any, were highly correlated to the profitability of production home building companies.

The Hypothesis

The main purpose of this study was to determine which, if any, of the management practices identified were significant predictors of profitability for production home builders. The hypothesis for this study follows:

H_o:  There are no differences in the management practices used by successful and unsuccessful

        production home building companies.

H_a:  There are differences in the management practices used by successful and unsuccessful

        production home building companies.

Limitations and Delimitations

This research was intended to explain some of the most important management practices which contributed to the success of production home building companies; however, it was not intended to explain all possible factors which might influence their success. The aim of this study was to identify the profit margins of responding companies as dependent variables for the statistical tests described below in order to determine which companies were successful and which companies were unsuccessful. This study was also structured to identify how management practices were implemented in the production home building companies surveyed and how these management practices were correlated to profit margins in the identified population of companies, which are home building companies producing 51 to 150 homes per year that subscribed to Professional Builder magazine at the time of this research. It is possible that the same findings that apply to these companies also apply to other production home builders that did not subscribe to Professional Builder, but this cannot be concluded from the results of this study.

Research Methodology

Development of the Survey Instrument

In an effort to describe current management practices of production home builders, a nationwide survey was conducted by mailing written questionnaires to owners and managers of 474 companies that subscribed to Professional Builder in the summer of 2004. The first step in creating this sample was to list all subscribing home builders that reported building 51 to 150 new homes per year according to their zip codes. Next, every nth name was selected in order to stratify the sample across the United States. When the list was submitted to the researchers by Professional Builder’s data research department, a number of the companies listed did not appear to be production home building companies. Telephone calls were made to each of these companies, and many of them were eliminated from the original sample.

To collect data describing the current management practices of production home building companies, a written questionnaire was sent to the owner(s) of each company selected to participate in this study. There were at least two major reasons for utilizing questionnaires in this research. First, it would be financially impractical to conduct personal or telephone interviews with so many owners; and second, in order to answer questions regarding some of the management practices addressed, respondents might find it necessary to access records not immediately available during a personal interview (Leedy & Ormrod, 2005). As an added incentive for respondents, two one-dollar bills were included in each questionnaire. A second copy of the questionnaire was also mailed to those companies not responding to the first mailing. Finally, telephone contact was attempted for all companies that did not respond to either the first or second round of mailings.

The questionnaire used in this study was designed to identify the use of approximately 50 critical management practices in production home building companies. In addition to obtaining information regarding the companies’ management practices, the questionnaire was also designed so that companies would report revenues, costs of construction, and overhead expenses, allowing the researchers to derive both gross and net profit margins.

The questionnaire was developed by utilizing a three-fold approach. First, literature related to management practices employed by home builders was reviewed; second, owners of home building companies, accountants, and university faculty whose areas of research addressed issues in construction company management were interviewed; and third, questionnaires from similar studies conducted previously were utilized. These questionnaires had been developed over a period of several years, relying on input from a subcommittee of builder-members of the Business Management and Information Technology Committee of the National Association of Home Builders (NAHB).  This subcommittee included home builders, accountants and businessmen with extensive experience in owning and operating residential construction companies (Hutchings & Christofferson, 2004).

Analysis of the Data

Response Rate and Description of Companies Responding to the Survey

The total number of questionnaires returned was 129, resulting in an initial response rate of 27.2 percent (129/474). This represented a response rate about three times as high as typical financial studies conducted previously by such organizations as the NAHB. In those studies that yielded lower response rates, financial incentives were seldom, if ever, included; and second-round mailings of questionnaires did not normally occur.

A number of interesting factors surfaced from the information provided by the companies that responded to the survey. For example, it was discovered that the average company had been in business for a little over 21 years. Production home builders responding to the survey reported average annual revenues of just under $22 million per company. With a mean sales price of approximately $246,000 per home, this translated to an average of 89 homes sold per company during the year. Most respondents indicated that at the time of this study their local economies were excellent or very good.

The study was originally structured to derive both gross profit margins and net profit margins. However, even with the financial incentive ($2) included in the questionnaires and with follow-up phone calls, few respondents provided enough reliable information to determine net profit margins. Because of this, a decision was made to use gross profit margins as the only dependent variable upon which inferential statistical tests were performed. In order to try to correlate the independent variables -- represented by the management practices addressed in the questionnaire -- to the dependent variable of gross profit margin, it was first necessary to determine which companies actually provided gross profit margin figures that were reasonable.

Of the 129 returned questionnaires, 74 provided enough information to derive gross profit margins. According to financial studies targeting production home builders published by the NAHB and other groups interested in these ratios, gross profit margins typically average somewhere between approximately 15 and 30 percent, with gross profit margins as high as 35 or 40 percent not uncommon. Because it is unlikely that home building companies would enjoy gross profit margins higher than 50 percent, except in exceptional cases, a decision was made to use the data from all responding companies with gross profit margins of less than 50 percent. Nineteen of the of the 74 surveys were rejected because gross profit margins did not fall below the 50 percent ceiling or because they were missing values on at least one question in the survey. Gross profit margins in the final sample ranged from 7 to 43 percent. After applying the filtering requirements for gross profit margins discussed above, the final sample size for inferential statistical tests consisted of 55 companies. The authors are assuming that the 55 respondents are representative of the population, recognizing at the same time that there may be some unquantifiable non-response bias.

Applying Inferential Statistical Tools

The data collected from this study were analyzed using two different statistical methods. First, correlation coefficient (Pearson’s) tests were intended to find any variable that was significantly related to gross-profit margins. Second, step-wise regression was used to identify the group of variables that best explains the variability in gross-profit margin. All data were analyzed by using SAS statistical software, Version 8.

Correlation Coefficients

Pearson’s correlation is a statistic that looks for relationships between variables. Because of the normality of the data, a parametric approach was selected for the correlation analysis performed, using gross profit margin as the dependent variable in all cases.

In the context of this study, gross profit margin is the dependent variable. Each of the independent variables was compared to the dependent variable in order to determine whether there was a significant correlation between any one of the independent variables and the dependent variable. Because so many factors were tested, it was necessary to divide the desired overall alpha value (p < .1) by the number of tests performed on all variables to determine which variables were truly significant. The resulting level of significance for testing purposes of individual variables was p < .001.

The research hypothesis for the Pearson’s correlation statistic can be stated as follows:

H_o:  There are no relationships between gross profit margin and each of the

        variables tested.

H_a:  There is a relationship between gross profit margin and each of the variables

        tested.

Step-Wise Regression

The second test that was performed was step-wise regression. Step-wise regression in this study attempted to identify the best set of independent variables that predict the dependent variable, gross-profit margin. Theoretically, information is built up on the dependent variable as terms are added to the model. When one independent variable is introduced into the model, it may significantly explain variations in the dependent variable. But when other independent variables, in addition to the first variable, are introduced into the model, the first variable may not prove to be as significant as it was when it stood alone. Step-wise regression seeks to find a model, a group of independent variables, that best predict the response of the dependent variable, which in this study is gross profit margin (Draper & Smith, 1981).

Data Analysis and Results

This section discusses the findings from the inferential statistical tests discussed above. The results provide information regarding relationships between the management practices tested and the profitability of production home building companies.

Correlation Coefficients

A Pearson’s Correlation Coefficient between the independent variables and the dependent variable, gross profit margin, were computed and tested at an alpha level of .001. No variables seemed to have a definite relationship to profitability for production home building companies producing 50 to 150 homes per year. Four variables had p-values < .01, but because of the number of tests that were run, it cannot be stated with certainty that they had a significant relationship to profitability. The following table lists the results of the single-variable correlations for production home building companies that had p-values < .01, along with a description of the question, the actual p-value, and the Pearson’s correlation coefficient. The variables are listed in order according to p-value, beginning with the most significant. Note the positive or negative correlation for each variable described.

Conclusions for Correlation Coefficients

In conclusion, although all the management practices listed above were correlated in some way to profitability, it is impossible to state that these independent variables, with p-values > .001 are statistically significant. From a statistical point of view, this is not surprising, given the number of tests performed. It appears that profitability is most likely a function of many variables. Although all the management practices listed above were correlated in some way to profitability, none were statistically significant practices that related positively to profitability.

Step-Wise Regression

A step-wise regression test was run using the 55 questionnaires that provided usable gross-profit margins for production home building companies. Three computer-generated random variables, which were not correlated in any way with the dependent variable, were included with the other independent variables. By using these random variables it was less likely that the model would be over fit. In most cases, when the first random variable enters the model through the step-wise regression, none of the future variables as they enter the model will significantly improve the prediction of the dependent variable.

Table 2 below summarizes the findings for the regression model for companies in the sample. The regression analysis brought in 6 variables prior to including one of the random variables. As will be explained later, all of the variables in the model make some sense. The R-squared value of .6852 indicates that 68 percent of the variability in final gross profit margin is explained by these variables in the model (Lawson & Erjavec, 2001). All the variables that entered the model were yes/no questions except for the question regarding variance purchase orders, which was a Lickert-scale question.

Because this study is interested in which factors affect profitability, for purposes of this discussion, the important findings from the regression model have to do with directionality of the factor effects (positive or negative correlations). For example, in Table 2, notice that half of the variables accepted into the model had positive effects on profitability of the companies tested. The other 3 variables -- Sales commissions are 5 percent or greater; Company uses no formal scheduling procedures; and New employees are given more than 8 hours of safety training -- all yielded negative effects. Note, however, that the only one that doesn’t seem logical on the surface is the fact that new employees were given more than 8 hours of safety training. Even though this question entered the model as the fourth variable, it was only based on positive responses given by 3 of the 55 companies. For the remaining 5 variables that entered the model there were enough companies that answered yes to the question that the level of significance is reliable.

Conclusions for Step-wise Regression

Based on the researchers’ personal experience in the home building industry, all of the variables in the identified model made sense. In addition, based on the fitted model’s R-squared value, approximately 68 percent of the variability in the final gross profit margin was explained by these variables.

Conclusions

Based on the inferential statistical tests described in this study, only three management practices were significantly correlated to profitability. There was a positive relationship between profitability and companies that used variance purchase orders, reviewed job-cost variances at least once a month, and whose employees used portable electronic equipment, such as PDA’s, laptops, etc. Negative correlations to profitability were indicated when companies paid more than five percent in commissions for new home sales, did not use formal scheduling procedures, and provided new employees with more than eight hours of safety training. Remember, however, that very few companies responded positively to the practice of providing more than eight hours of safety training.

It should be noted that this has been an investigative study into an important, but not thoroughly researched topic in the home building industry. It appears from the findings that the problem of determining profitability in production home building companies based on non-financial predictors is very dynamic. To say for sure that one or even a combination of management practices is solely responsible for profitability does not seem possible, given the evidence of this study, especially with such a limited sample. However, it can be said that new information has been discovered and reported regarding the management practices of production home builders in the United States. Another important note is, that according to the information provided by the respondents, this research was performed when the economy was generally very strong. It is possible that the results may differ during a less robust economy.

List of References

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Bajwa, B. (2002, October 22, 2002). Director, Data Service Center, Information Technology & Telecommunications, National Association of Home Builders. Washington, D.C.

 [e-mail correspondence to D. M. Hutchings].

Draper, N. R., & Smith, H. (1981). Aplied regression analysis.. New York: John Wiley and Sons.

ENR Engineering News-Record (2005, November 21). A rebound in nonresidential building markets keeps growth going. November 21, 2005.

Evans, L. S. (1992). Readings in Management of the Home Building Business ( Lee S. Evans, Ed.) (p. 195 pages). Nederland, Colorado: Lee S. Evans and Associates, Inc.

Flahvin, A. (1985, October). Why small businesses fail. The Australian Accountant, pp. 17-20.

Gaskill, L. R., Van Auken, H. E., & Manning, R. A. (1993, October). A factor analytic study of the perceived causes of small business failure. Journal of Small Business Management, pp. 18-31.

Gill, P. G. (1968). Systems management techniques for builders and contractors. New York, NY: McGraw-Hill, Inc.

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Lawson, J., & Erjavec, J. (2001). Modern statistics for engineering and quality improvement. Pacific Grove, California: Duxbury, Thomson Learning.

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Schleifer, T. C. (1990). Construction contractors’ survival guide. Ohn Wiley & Sons, Inc., New York. (page xi)

Strischek, D. (1998, July). Red warning flags of contractor failure. Journal of Lending & Credit Risk Management, 80(11), 40-47.