REVEALING INFORMATION DIFFERENTIALS: FRANCHISING IN RESIDENTIAL BROKERAGE

Transcription

1 REVEALING INFORMATION DIFFERENTIALS: FRANCHISING IN RESIDENTIAL BROKERAGE John D. Benjamin Kogod School of Business American University 4400 Massachusetts Ave., NW Washington, DC (202) Peter Chinloy Kogod School of Business American University 4400 Massachusetts Ave., NW Washington, DC (202) G. Donald Jud Bryan School of Business and Economics University of North Carolina at Greensboro Greensboro, NC (336) Daniel T. Winkler Bryan School of Business and Economics University of North Carolina at Greensboro Greensboro, NC (336) March 2004 We are grateful to the National Association of Realtors for providing access to the data.

2 REVEALING INFORMATION DIFFERENTIALS: FRANCHISING IN RESIDENTIAL BROKERAGE Abstract This paper compares the value of information across brokerage firms. Some highly productive firms obtain specialized information from tight local client networks. Other firms lack this client information and find acquiring a clientele to be costly. The uninformed firm must select among strategies including paying a larger share of commission revenue to successful salespeople or purchasing widely available information such as is provided by a franchise. A franchise offers marketing strategies, economies of scale, and a brand. Consequently, there is self-selection among residential brokerage firms. Less-informed firms are revealed as the purchasers of franchises. A database of over 1,700 observations from the National Association of Realtors 2001 survey of real estate firms allows an investigation of franchising on residential real estate brokerage firms. Net margins between revenues received and commissions paid to the sales force are lower for firms with franchises. These results support the hypothesis that franchised firms are less informed. Franchises are profitable to those self-selected firms that use them. Franchising does not cover all the gap between that would make a firm become highly informed. Keywords: Franchise, Brokerage, Marketing, Productivity, Listings, Profitability, Self-selection 2

3 REVEALING INFORMATION DIFFERENTIALS: FRANCHISING IN RESIDENTIAL BROKERAGE I. Introduction This paper examines a common problem of competing firms in any industry. Firms differ in their information about customers. Firms with better-quality information can exploit their customer relationships as an additional factor of production to increase output and profits. Firms with lower-quality information are obliged to turn to outside information providers. Since these information providers are available to all firms, there is self-selection. Less-informed firms purchase the commonly available outside information such as a franchise while more-informed firms do not need to do so. Purchasing franchises is widespread in the real estate industry, particularly in residential brokerage and in hotels. Lewis and Anderson (1999) find that a franchise has lower costs than an independent brokerage firm. Anderson, Lewis, and Zumpano (2000) show franchised firms are more efficient than their non-franchised counterparts, but franchises are not necessarily more profitable. 12 In residential real estate brokerage, there are some firms with extensive knowledge of clients and a contact network to clients. A firm not having this information or network either accepts lower output and revenues or seeks alternative sources of clients. One such source 1 Other studies have examined economies of scale or scope in the real estate brokerage industry. Zumpano, Elder, and Crellin (1993) find modest economies of scale except for very large residential brokerage firms. Interestingly, they also report that larger brokerage firms do not have a competitive advantage over smaller firms regarding unit costs. Zumpano and Elder (1994) find that economies of scope (resulting from a balanced mix of listings and sales) minimize costs because specializing in either listings or sales may be suboptimal. Anderson, Fok, Zumpano, and Elder (1998) use a national sample of real estate brokerage firms along with a classical stochastic frontier to measure X-inefficiencies. The average brokerage firm operates close to its efficient frontier, indicating that real estate brokerage firms are relatively efficient. 2 Our focus is on the marketing and communications benefits of franchise membership. Many residential brokerage firms, however, use other customer generating tools such as technology, extensive print media, and traditional direct marketing such as mailings to gain clients. 3

4 available to all firms is a franchise. In exchange for receiving a proportion of revenue, such as the 8% charged by Cendant to Century 21 franchisees, the franchisor acts as a source of information. 3 This information includes referrals, marketing, branding, and a network. Unless the franchiser controls specific customer traffic, such as hotels with a reservation system, the services sold are general and common to all purchasers or franchisees. 4 This paper contains six sections. Section II examines prior research on franchising, both generally and in the real estate industry. Real estate firms have some characteristics including local information specificity that do not apply to typical franchising contracts. Section III presents the model. The self-selection conditions are developed. In addition, higher proportional fees on gross revenue cause an accelerating requirement for the franchise to perform. This rising returncost tradeoff is applicable to any contract where compensation is proportional to revenue, including asset and property management. A testable hypothesis is that while having a franchise increases the gross margin, it will reduce the net margin. In a cross-section of all firms, firms with franchises will be observed as self-selected into the less-informed group needing to purchase information rather than having an internal client network. Section IV provides model specification and discusses the sample of 1,700 residential brokerage firms in 2001 from the National Association of Realtors survey. The self-selection is confirmed by the findings in Section V. Firms having a franchise have higher gross profits, but lower net profits. Although franchises are profitable for franchisees, owning a franchise reduces 3 This fee is the sum of 6% of gross revenues for a royalty and 2% for national marketing. 4 The informed firms are those that network to local buyers and sellers, and a franchise for them has little value. Hence, only uninformed firms use a franchise. The exception is when the potential buyers are national, as with recreational property. In such cases, the residential brokerages are more likely to be franchises. For hotel franchises, the owners operate in local markets, acting either as owner-operators or franchisees, but the demand comes from outside the local market and probably across the country. The local owner cannot easily access the national market, and there is often non-repeat guest contact. A potential guest cannot test a distant market and relies on the hotel company to enforce franchise standards. 4

5 but does not eliminate the information differential with other, more highly-informed firms. National franchise branding cannot fully substitute for local information. Measured variables do not capture the self-selection, indicating that less-informed firms cannot become more informed by hiring seasoned brokers, for example. Those brokers will capture any unexploited economic rent. However, franchised firms tend to have a greater use of technology and relocation networks, as in Sirmans and Macpherson (2001). Section VI contains concluding remarks. II. Franchising and Residential Brokerage There has been support for the notion that on a gross basis, holding a franchise pays off and supports positive fees. 5 Jud, Rogers, and Crellin (1994) show that having a franchise affiliation results in a 9% increase in net revenue. 6 Even though there is a difference in performance across franchisees, and within the same unit over time, the fees levied by franchisors are relatively constant and not performance-based (Lafontaine and Shaw (1999)). Another area of academic study in franchising is information differences across franchisees. Signaling information about the quality of the franchisee is not available initially, but becomes observed over time (Gallini and Lutz (1992)). Franchisees that survive have better reputations, and therefore should qualify for lower fees (Mathewson and Winter (1985)). 5 If two firms are similarly informed and if the franchise adds value, then there may be a payoff. In the residential brokerage business, some markets are not strictly local but depend on a wider market for their customers. The Outer Banks of North Carolina has two relatively thinly-populated counties, but its market for purchases is regional, and captures Charlotte, the twin triangles of North Carolina, Richmond, and Washington DC. It is more efficient to reach a larger market by affiliating with a franchise. Conversely, the clients in these dispersed markets can rely on the reputation of the franchises. 6 Jud, Rogers, and Crellin (1994) estimate that franchise affiliation yields this average 9% increase in net revenues after subtracting the royalties, fees, and other charges associated with franchise affiliation. Other researchers provide further support for positive benefits associated with franchise affiliation. Frew and Jud (1986) find that franchise affiliation has a positive effect on brokerage firm sales and house prices, while Colwell and Marshall (1986) find that the presence of a franchise, the size of the sales force, and the quantity of display advertising positively affect market share of listings and sales for brokerage firms. Richins, Black, and Sirmans (1987) also show that franchise affiliation has a positive effect on market performance. 5

6 Lafontaine and Shaw (1999) indicate that information differences across franchisees are small and that imposing a plethora of separate fees leads to management costs. They find that the royalty or percentage fee and the fixed or franchise fee are not negatively correlated, as suggested by the equilibrium in (1), but instead are positively correlated. 7 In the conventional industrial organization analysis, franchising is a method of extracting effort by reallocating incentives. The royalty is the percentage payment from income to the franchisor, and the franchise fee is the fixed up-front cost. Let R be the percentage of revenue paid as the royalty and F be the franchise fee. The amount of broker labor input for a nonfranchised firm is B N while WN is the wage or dollar split of the commission revenue paid to sales agents. With no franchise membership, the profit for the firm is Y( K, B ) W B where N N N K represents fixed inputs. In franchising, labor input and effort are contingent on the form of organization of the firm. As an employee, the manager puts in less input and effort than if awarded a share of the profit. If the same firm is organized as a franchise, then maximizing profit of the franchisor subject to a profit-making condition of the franchisee yields (1) max (1 RY ) ( K, B) F WB Here W is the compensation for all employees except the owners, such as the sales force. There are several problems with application of this conventional franchise model to the real estate market. In real estate markets, the franchise fee F is usually negligible. That is not the case for a fast-food franchise where the buyer is purchasing a land parcel and improvements. 7 If both parties are risk-averse, then franchising creates a sharing of the risk (Martin (1988)). In real estate, the sharing is on revenue rather than net operating income, leading to incentives by the franchisor to increase expenses and reduce profits at the franchisee. 6

7 In that case, fast-food is not a franchise as defined by the literature. 8 By comparison, real estate brokerage firms are frequently liquidity-constrained with limited resources to pay a fixed fee. Also, there appear to be underlying network and clientele information differences between real estate brokerage firms. The characteristics of the real estate industry suggest that a separate modeling of information differentials and how they lead to franchising is required. III. A Model of Residential Real Estate Franchising Firms are arrayed in the real estate brokerage industry by knowledge and information. Their ability to secure listings, to have contacts with potential buyers, and to farm a territory involves specific skills. The more informed firms have knowledge and a contact base in their market. They have experience and goodwill with client relationships and access to a social network. Some firms have high levels of informationh while other firms have lower levels of information L. Firms have several choices to change themselves and become informed. One strategy is to hire some prominent salespeople. These brokers and agents will charge for their information, and the firm must pay the market differential cost to acquire them. Another strategy is to remain as a less-informed firm. The firm obtains profit margin m L < m H if the two types of groups are homogeneous in information internally. In the absence of any actions to affect this sorting on information, each firm determines the number of employees to hire. The work force is x including the salespeople and their 8 McDonald s has been cited as a firm where company and franchisee-owned stores coexist. A strategy by the firm appears to concentrate its company-owned stores in areas where real estate prices are high. In these markets, it can increase the number of stores as a pre-emptive strategy and to capture the Hotelling (1929) externalities. Franchisors have more national market information, and they want to own stores in markets where they can saturate and avoid franchisee complaints about turf. In declining or unsafe markets, conversely, they shift the business risk to the franchisee. The relatively high up-front franchise fee in the fast-food business comes from having to acquire the real estate (building and site). 7

8 support staff. All firms have access to the same production technology, but more-informed firms have a more skilled sales force. The production output is yx, () where y is the gross output generated by the firm. Output is increasing in the size of the work force and concave. Gross output is the total value of all transactions where the brokerage firm is involved, either as a listing or a sales transaction. Firms are compensated by receiving a percentage of commissions. That commission is split between listing and selling firms. If the split rate received by the firm is q, common to all firms regardless of information, then the gross revenue received is (2) g = qy( x) Employment can vary between the high- and low-information firms. The difference between high- and low-information firms is that even with the same number of salespeople x, gh That leads to the information-dominating conditions > g. L (3) g > g x = x, q = q H L H L H L While qh = q under all states of the world, the less-informed firm can achieve higher profits by L being larger and hiring more people. The high-information firm that operates in a smaller market niche would ultimately be a boutique firm. A less-informed firm such as a startup has a number of strategic alternatives. One is to become more informed by recruiting knowledgeable market insiders from existing firms. Such firms derive no net benefit. They are simply highly-informed firms paying a premium for talent. To the extent that the brokerage and sales force talent requires the complementary resources of insiders, there may be a waste of firm resources. A second strategy is to take no action. In this case, the firm remains uninformed. A third is to purchase information using a franchise strategy available to all. 8

9 Information is summarized by z in the technology g = qy( xz, ) that separates the high- from the low-information firms. High- information firms have z = z H > 0. Low-information firms have z = 0. A low-information firm cannot obtain the extensive and expensive experience without considerable cost. Instead, it attempts to narrow the gap by purchasing information that is available to all. The modeling is for franchisers, but could include any tradeoff where a firm pays a percentage of gross revenue in exchange for improved output or performance, including property and asset management as well as franchising. The franchise provides general market information z, where 0 < z z. H The firm seeking to buy this information has the gross revenue (4) gsz (, ) = (1 sqy ) ( xz, ), where s is the franchise royalty fee. Revenue of the firm declines with an increase percentage fee paid, as g = qy( xz, ) < 0. The return to the firm is the additional output, s or g = (1 sqy ) z( xz, ) > 0. The tradeoff between the return in added revenue and the fee paid is z z (1 sqy ) z ( xz, ) (5) w = = < 0. s qy( xz, ) g w The tradeoff is increasing in the fee paid or = qyz > 0. To be willing to pay a higher s franchise fee, the firm must obtain ever increasing revenue with respect to the fee. This condition applies to other fee-based compensation rules such as asset and property management. The above equation (5) models gross but not net revenue. Suppose all firms are competing for a similar sales force at compensation rate p < q. Here p is the proportion of the revenue received by the firm that is paid to the sales force. If the force is paid on a 50:50 split 9

10 q with the firm on revenue received, p =. In a 100% house such as ReMax, p = q. The highinformation firm has market attributes that yield productivity over and above that provided by its 2 sales force. It makes that sales force more productive, otherwise they would leave to join the low-information firms. Subtracting the expenses for px, the margin between revenue and costs is [ ] (6) msz (, ) = (1 s) qy( xz, ) px. The contract tradeoff on the net margin is z (1 sqy ) z( xz, ) (7) w = = < 0. s qy( xz, ) px m The net margin is lower than the gross revenue from adopting the franchise with its information. The tradeoff remains increasing in the franchise fee rate, as w qy (8) = z > 0. s qy( xz, ) px An increase in the franchise fee requires a larger net return in sales revenue per percentage point. This result implies that high franchise fees must justify accelerating productivity. The condition applies not only to franchise fees but also asset and property management fees levied as a proportion of gross revenue. Higher fees require ever-increasing added revenue per dollar of cost. While these are productivity benefits from employing a franchise, high-information firms do not need these services. Since z< z, the high-information firms have no reason to adopt (3)- H (7). This is particularly the case since the innovation is available to any firm in the market for a price. For an array of firms in quality order, the first group is uninformed z = 0 and the second 10

11 group has high informationz H. Even though the franchise package is available to all firms as ( sz, ) for the franchise fee and the projected return, only those with limited information will generally subscribe. The result is that in a sample of firms, those observed using a purchased technology that provides market information, such as franchising, will be self-selected. Only less-informed firms will be in the group adopting a franchise as reflected in equations (3)-(7). Prior to the arrival of the franchise innovation, the initial distribution of firms by quality f () z has two concentrations of density. Low-information or uninformed firms accumulate at zero where F(0) = f L > 0. High- information firms are at1 F( zh) = fh > 0. All participants with at least zh of information are assumed to be informed. The observation of a franchise will therefore be conditional on level of information. The distribution of gross and net margin for franchisees is (9) f ( g sz, ) = f ( g z< z ) H f ( m s, z) = f( m z < zh) Once a firm is observed to be a franchisee, it serves as a signal of relatively low information. The low- or uninformed firms at zero have another choice, that of acquiring the franchise and its information. There is a two-stage decision among all the firms. First, they know their level of information. Second, they make the decision to acquire a franchise. In a pooled data set of firms, some are franchisees and some are not. The observation of a firm as a franchise, if that is a signal of lower information, leads to lower net margins. Investment in the information is profitable, even if only to a self-selection of less-informed firms, leading to the conditions 11

12 (10) m(0) > msz (, ). gsz (, ) msz (, ) > 0 Contingent on the satisfaction of conditions (10), the operation of the franchise based on equations (3)-(7) can be estimated. IV. Specification and Data One method of separating the firms by information is the conventional self-selection model. If I is an index of preference intensity, then if I > I*,some threshold value then the firm chooses to be a franchisee. That probability choice depends on variables that are based on whether the firm has low information. Suppose those variables that are serving as a proxy the information are v 1 in zv ( 1). If the preference intensity is satisfied, then, (11) f = Pr( a= 1) = v1β 1 y = yxz (, ) = v2β2y + βsy S estimated sequentially where a = 1 where the firm is a franchisee and a = 0 otherwise. The coefficients of the measured variables v are β. Also S is the inverse Mills ratio or the selfselection variable. A significant positive coefficient β Sy > 0 confirms self-selection, but a positive value for the franchise. In the production technology yxz (, ), x represents observed and z unobserved inputs. The net margin from (6) is msz (, ) (1 s) [ qy( xz, ) px] force px and franchise royalty rate s. Substituting in (10) = after payments to the brokerage sales (12) f = Pr( a= 1) = v1β 1 msz (, ) = (1 s) [ qy( xz, ) px] = v2β2m + βsms. 12

13 Here the franchisee is a signal of lower information if the β Sm < 0. Having a franchise is profitable for the firms selecting it, but it becomes a measure of the information differential between firms as well as if (13) β > 0 β < 0. Sy Sm The testing in (11)-(13) may not lead to verification of the model. This uncertainty arises from the lack of accurate measurement of information in v 1. If these variables fail to capture the probability of obtaining a franchise, then the self-selection fails. This failure is possible, since if there were variables that accurately predicted high information or not using a franchise, then firms would obtain those levels. An alternative test is (where a is a dummy variable representing franchise affiliation): y= yxz (, ) = v2β2y + βaya (14) msz (, ) = (1 s) [ qy( xz, ) px] = v2β2m + βama βay > 0 βam < 0 Here the only information is on whether or not the firm is a franchisee. Conditional on that information, the firm is expected to have more or less information about the local market on average. There may be high-information firms that have franchises, if franchisors require referral affiliations or other national networks. The franchise investment is conditionally profitable for the typical firm. 9 The structures (12) and (14) therefore allow for testing of the model. The sample is a national survey of residential brokerage firms about their financial performance conducted by the National Association of Realtors. The survey was conducted of 9 In the empirical model, in addition to franchise affiliation, measured variables v include firm age, whether a firm is a member of a relocation network, firm size, use of technology and geographic location. Such variables have been employed in numerous studies including Frew and Jud (1986), Colwell and Marshall (1986), 13

14 9,321 firms in spring The response rate was 2,792 useable surveys or 30%. 10 If more than 50% of a respondent s business was from commercial brokerage, it was removed from the sample in order to obtain a sample of real estate firms that focus primarily on residential real estate. Missing responses to key variables and the 50% residential brokerage constraint reduced the sample to 1,792 useable observations. Table 1 presents the descriptive statistics for the 1,792 observations. There is a distribution between franchised and non-franchised firms. Of the sample, 26% are franchised with the remaining 74% non-franchised. Of all firms, 98% are members of a referral or relocation network. The average firm makes a net margin of 18.6% in profit on its sales, and has been in business for 21 years. This average business length creates a time to develop local information networks. The sample is distributed across the country, with 33% in the South, 23% in the Midwest, 26% in the West and the remaining 18% in the East. Of the sample 60% has only one office, and 56% of the firms have 10 or fewer brokers or agents. V. Empirical Results The first set of tests is on the probit specification (11)-(13), to determine whether there is information content in the decision to become a franchisee. The model is agnostic about this hypothesis since it relies on variables to predict the decision, and the results are presented in Table 2. Richins, Black and Sirmans (1987), and Jud, Rogers and Crellin (1994). See Benjamin, Jud and Sirmans (2000) for a review of factors influencing residential brokerage firm revenues and income. 10 Given that large residential real estate brokerage firms have historically lower response rates to NAR surveys, extra surveys were sent to larger sized firms (greater than 200 licensees) and medium sized firms ( licenses). These additional surveys were sent to ensure a representative response by firms. Responses were then weighted by firms as per the membership rolls included in the National Realtor Database System (NRDS). 14

15 The variables in the probit model include the size of firm, the number of relocation services subscribed to, the age of the firm, region and technology variables. 11 Although the hypothesis that all coefficients are zero can be rejected at the 1% level, firm size and one measure of technology are statistically significant. In the second stage, the independent variables v and the inverse Mills ratio S probabilities estimated from the probit model are included in the three regressions measuring financial performance. The self-selection variable coefficients are not statistically significant for any of these regressions with coefficients of (-0.99), (-1.40) and (-1.326) for the revenue, net income and net margin regressions, respectively, with t-statistics in parentheses. 12 The conclusion is that the sample selection test where information is unobservable does not distinguishing between types of firms. The alternative is the direct observation from (14). In addition to gross income y and margin m, the net operating income n is set as the dependent variable. Results are reported in Table 3. Each of the three financial performance models (revenue, net income, and net margin) is statistically significant at the 1% level or better, with model F-Values ranging from in the total revenue model to in the net income model. The adjusted R 2 varies from 0.25 in the total margin model to 0.13 in the net income model. 11 The technology variables include whether the firm uses a website, whether a firm s residential properties appear on the website, the number of third-party websites on which a firm s listings appear, and whether sales staff are encouraged to be accessible via ema il. 12 All of the dependent variables in these estimates are entered in logarithmic form, and these regressions are estimated using weighted least squares to correct for sample heteroskedasticity. The weights used in this procedure are the sample weights from the NAR survey. 15

16 All of the dependent variables appear in logarithmic form, and these regressions are estimated using weighted least squares to correct for sample heteroskedasticity. 13 The weights used in this procedure are the sample weights from the NAR survey, and they are designed to reflect the differential probability of firm and item non-response. 14 The results in Table 3 support the underlying information differential hypothesis of (13). In the total income y equation, the coefficient on the franchise variable β ay > 0 is positive and significant at the 1% level. The net margin regression results are shown in the last column of Table 3. Here the coefficient β am < 0, supporting the differential information. The franchise is profitable conditional of having lower information. It does not close the information gap between firms. Finally, the coefficient on the franchise for net operating income is zero from the second column of Table 3. This coefficient in the information differential model should be nonpositive. Otherwise, all firms would become franchises, or the franchise fee is underpriced. The net margin coefficient in Table 3 for β am < 0 is That indicates that a firm with a franchise has a margin that is 19.7% lower than a firm that does not have a franchise. At the sample mean margin of 18.6% of gross revenue, a firm with a franchise has a margin of about four percentage points lower, or 14.9%. This result is consistent with the cost of information asymmetry. If franchises are costly and all firms can acquire them, then only less-informed firms will pay these costs. 13 In matrix notation, let W be a diagonal matrix containing the sample weights w along the diagonal and zeros elsewhere, and let y and X be the usual matrices associated with the left- and right-hand side variables. The weighted least squares estimator is: b WLS = (X W WX) -1 X W Wy. See Greene (1990). 14 Historically, NAR s surveys of real estate brokerages had suffered from a biased response where smaller brokerages responded at a rate significantly higher than that of larger brokerages. For the 2001 survey, NAR stratified the brokerage industry s firms into four different groups. NAR then over sampled firms with 11 to 200 agents and those with more than 200 agents relative to firms with just one agent and those with 2 to 10 agents. These larger firms received the survey twice to induce a greater response. A weight was developed to control for both the over sampling of firms with 11 or more agents and for the different response rate for each of the four stratified groups. 16

17 In the gross income equation, among other variables, being a member of a relocation network increases sales revenues, although some of that increase is a double-counting from having a website. This finding supports the contention that firms that are part of a relocation network can increase their revenue, but that web impact reduces the benefit of a relocation service. The age variable results show that a firm obtains 2% more sales revenue with each year of age. The presence of being a part of a relocation network is advantageous, however, even though the benefits are reduced for firms that have extensive use of third-party websites to market their properties. The production function does not have negative returns in the realm of operations, since output and net income are increasing in the size of firm. Larger firms do have smaller margins as shown in column (3) of Table 3. Medium and large firms have coefficient values of and There are some additional regional effects, with margins being lower in the South and Midwest. The use of technology, as measured by the web impact factor is statistically significant and positive at the.01 level or better in each of the three financial performance equations. However, the negative interaction variable indicates that the benefits are somewhat less for firms that effectively use a relocation network. VI. Concluding Remarks If both parties are risk-averse, then franchising creates a sharing of the risk (Martin (1988)). In real estate, the sharing is on revenue rather than on net operating income, leading to incentives by the franchisor to increase expenses and reduce profits of the franchisee. When sharing is on the basis of net revenue, the problem is that the franchisee s input cannot be observed directly. 17

18 The information problem is more acute at the residential brokerage level. Inputs are not measured accurately, and the intensity of effort by the sales force is often unobservable to the office manager. Generally, the royalty will be lower when local inputs are greater, with more difficulty in monitoring and with marginal production of effort by the franchisee ((Stiglitz (1974), Lafontaine (1992)). That would typically be the case with residential brokerage, with more emphasis on fixed dollar fees. The problem is that residential brokerage firms are typically liquidity constrained, and cannot afford to pay these otherwise-optimal up-front franchise fees. Highly informed firms and salespeople have already networked the local community for clientele. It s not a question about whether the franchisor finds out more about the franchisee s quality over time or vice versa, but that firms differ in information about the local real estate market. 15 The ability to secure listings, to have contacts with potential buyers, and to farm a market area involves specific skills and knowledge. The more informed brokerage firms have customer knowledge and a contact base about their markets. They have experience, developed goodwill with client relationships, and can bring in business exogenously. Other firms must purchase such a relationship by being a franchise affiliate with access to marketing strategies, economies of scale, and a brand. The empirical results from the NAR 2001 member survey show that there are underlying information differences among firms in the real estate market. Purchasing a franchise acts as method of reducing but not eliminating that information gap. Actual franchise fees leave returns for the less-informed, but not excess returns. 15 By contrast, in the hotel market, reputation and information are communicated by the fixed cost or franchise fee. Hotel franchisors are arrayed by the capital expenses that they demand of their franchisees (Udell (1972)). Higher-end chains such as Marriott and Hilton have higher standards, so the franchise fee and royalty are positively correlated. 18

19 References Anderson, Randy, R. Fok, L. V. Zumpano, and H. W. Elder (1998) Measuring the Efficiency of Residential Real Estate Brokerage Firms, Journal of Real Estate Research, 16:2, Anderson, Randy, Danielle Lewis, and Leonard Zumpano (2000) Residential Real Estate Brokerage Efficiency from a Cost and Profit Perspective, Journal of Real Estate Finance and Economics, 20, Benjamin, J. D., G. D. Jud, and G. S. Sirmans (2000) What Do We Know About Real Estate Brokerage?, Journal of Real Estate Research 20:1/2, Colwell, P. F. and D. W. Marshall (1986) Market Share in the Real Estate Brokerage Industry, Journal of the American Real Estate & Urban Economics Association, 14:4, Frew, J. R. and G. D. Jud (1986) The Value of a Real Estate Franchise, Journal of the American Real Estate & Urban Economics Association, 14:2, Gallini, Nancy and Nancy Lutz (1992) Dual Distribution and Royalty Fees in Franchising, Journal of Law, Economics and Organization, 8, William H. Greene. Econometric Analysis (New York: MacMillan Co., 1996). Hotelling, Harold (1929) Stability in Competition, Economic Journal, 39, Jud, G. Donald, R. C. Rogers, and G. E. Crellin (1994) Franchising and Real Estate Brokerage, Journal of Real Estate Finance and Economics, 8:1, Lafontaine, Francine (1992) Agency Theory and Franchising: Some Empirical Results, Rand Journal of Economics, 23, Lafontaine, Francine and Kathryn Shaw (1999) The Dynamics of Franchise Contracting: Evidence from Panel Data, Journal of Political Economy, 107, Lewis, Danielle and Randy Anderson (1999) Residential Real Estate Brokerage Efficiency and the Implications of Franchising: A Bayesian Approach, Real Estate Economics, 27:3, Mathewson, Frank and Ralph Winter (1985) The Economics of Franchise Contracts, Journal of Law and Economics, 28, Martin, Robert (1988) Franchising and Risk Management, American Economic Review, 78,

20 Richins, M. L., W. C. Black, and C.F. Sirmans (1987) Strategic Orientation and Marketing Strategy: An Analysis of Residential Real Estate Brokerage Firms, Journal of Real Estate Research, 2:2, Sirmans, Stacy and David Macpherson (2001) Affinity Programs and the Real Estate Brokerage Industry, Journal of Real Estate Research, 22, Stiglitz, Joseph (1974) Incentives and Risk-Sharing in Sharecropping, Review of Economic Statistics, 41, Udell, Gerald (1972) The Franchise Contract, Cornell Hotel and Restaurant Quarterly, 13, Zumpano, L.V. and H. W. Elder (1994) Economies of Scope and Density in the Market for Real Estate Brokerage Services, Journal of the American Real Estate & Urban Economics Association, 22:3, Zumpano, L.V. H. W. Elder, and G. E. Crellin (1993) The Market for Residential Real Estate Brokerage Services: Costs of Production and Economies of Scale, Journal of Real Estate Finance and Economics, 6:3,

21 Table NAR Brokerage Firm Questionnaire - Summary Statistics Variable Description N Mean Standard Deviation Revenue ($m) Gross revenue 1, Lrev Natural log, gross revenue 1, Net Income ($m) Annual net operating income 1, Linc Natural log, net operating income 1, Net margin on gross revenue, percentage Net Margin (%) points 1, Lnetmargin Natural log, net margin 1, Reloc = 1 if firm is a member of a relocation network 1, Fran = 1 if firm is a franchisee 1, Age (in years) 1, Oneoff = 1 if firm has only one office 1, Mfirm Medium-sized firm, salespersons 1, Lfirm Large firm, more than 200 salespersons 1, West 1, South 1, Midwest 1, Website =1 if firm has a website 1, Weblist =1 if firm posts its listings on the web 1, = 1 if sales staff are accessible via 1, Numwebs Number of websites 1, Data Source: During Spring 2001, the economic Research Group of the National Association of Realtors sent a firm profile questionnaire to 9,321 real estate brokerage firms. Respondents returned 2,792 useable surveys, representing a 30% response rate. In order to obtain a sample of only residential real estate brokerage firms, if less than 50% of a respondent s business was from residential brokerage, it was removed from the sample. This restriction together with non-responses to particular variables reduced the sample to 1,792. Only 1,143 firms responded to the Net Margin question. Net income was defined as Net Income = Net Margin * Revenue.

22 Table 2. Analysis of Franchising as the Dependent Variable Probit Probit Marginal Effects Variable Coefficient Z-Statistic Coefficient Z-Statistic Intercept * * Reloc Age Oneoff Mfirm Lfirm * * Website Weblist Numbwebs * * West South Midwest N (Dep=0) 1324 N (Dep=1) 468 Total N 1792 Log Likelihood Restr. Log Lik Chi-Squared 60.74* * Indicates significance at.01 level, using a two-tailed test. ** Indicates significance at.05 level, using a two-tailed test. Notes: 1 Coefficient values shown in E -02 format. 22

23 Table 3. Brokerage Firm Financial Performance Ln(Revenue) Ln(Net Income) Ln(Net Margin) Variable Coefficient T-Value Coefficient T-Value Coefficient T-Value Constant * * * Fran βay * βan βam * Reloc ** * * Age * ** * Oneoff Mfirm * * * Lfirm * * ** Numbwebs * * * Reloc X Numbwebs * * West South * Midwest ** N Adjusted R Model F-Value 47.93* 16.58* 36.33* * Indicates significance at.01 level, using a two-tailed test. ** Indicates significance at.05 level, using a two-tailed test. 23