47 Mid-American Journal of Business, Vol. 20, No. 1 Lussier Abstract The purpose of this study was to use the Lussier (1995) generic success versus failure (S/F) prediction model to de- velop a real estate industry specific model (S/F = ƒ [industry experience, age, advisors, planning, capital]). Using logistic regression analysis, the Lussier model (p = .028) and the real estate agency model (p = .001) are significant predictors of business success and failure. The Lussier model accurate- ly predicted 84 percent of the surveyed successful and failed matched pairs agencies as being successful or failed and the real estate model predicted 74 percent. The Lussier model explained 68 percent of the variance of contributing factors to success versus failure and the real estate model explained 56 percent. Implications are discussed. Introduction Small business is vital to the U.S. economy as over 99 percent of businesses are small and they create two out of every three new jobs (Williamson 2000). Yet, business fail- ure is both frequent and potentially damaging to the efficient operation of a market economy (Storey, Keasey, Watson and Wynarczyk 1987). Approximately 20 percent of busi- nesses close during their first year in business, 60 percent by the fifth year, and 75 percent by the tenth year (Nucci 1999). The important role of small business suggests that an understanding of why firms fail and succeed is crucial to the stability and health of the economy (Carter, Williams and Reynolds 1997; Cooper, Woo and Dunkelberg 1988; Gaskill, Van Auken and Manning 1993; Gimeno, Folta and Cooper 1997; Stockton 1989) and is of great interest to pub- lic policy makers concerned with economic development (Gilbert, Menon and Schwartz 1990; Ibrahim and Goodwin 1986). The real estate industry is critical to the economy. In 2001 the home ownership rate was 67.7 percent, and there were 5.3 million existing single-family homes and 746,000 condominiums and cooperatives sold (National Association of Realtors 2002). Commercial real estate provides busi- nesses with multidwelling houses, warehouses, factories, of- fices, and all types of stores based in three major categories: retail (malls), industrial (factories), and commercial (office buildings). Most real estate transactions are handled through real estate brokers and their agents, and some of them also provide property management services to businesses. Real estate volume is an economic indicator. If transactions are up, businesses are doing well and expanding, or visa versa, as realtors assist businesses to find the optimum location and facilities to maximize profits (Plotkin 2002). Seven percent, or one in fifteen adults, invest in start-ups (Zacharakis, Bygrave Shepherd 2000). Prospective busi- ness owners and those interested in new venture success face a critical question: what is the probability of success? However, there are few measures to help assess the chances of forming a successful enterprise (Dennis and Fernald 2001). Success versus failure prediction research benefits entrepreneurs, those who assist, train and advise them, those who provide capital for their ventures, suppliers, and public policy makers (Altman 1983; Gaskill, Van Auken and Kim 1994; Keats and Bracker 1988; Pech and Alistair 1993). Although the Lussier (1995) model does not estimate the chance or probability of failure, it can be used to predict the success or failure of a businesses. There are studies to better understand business success versus failure. However, as Gaskill et al. (1993) stated: there are many questions still to be resolved and warrant additional exploration... previous studies do not provide a comprehensive or unified explanation for small firm failure... comparisons are needed between successful and failed small business owners. According to Cochran (1981), A Success Versus Failure Prediction Model for the Real Estate Industry Robert N. Lussier, Springfield College This study develops and tests a nonfinan- cial model that predicts real estate busi- ness success or failure. The author would like to acknowledge Dr. Richard Divine, associate editor for the Mid-American Journal of Business, for his helpful input into this article.