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For Sale By Owner Online: Who Gets the Saved Commission?

By Xiao-Bai Li, Luvai Motiwalla

Communications of the ACM, Vol. 52 No. 2, Pages 110-114
10.1145/1461928.1461957


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The evolution of the internet in commerce has dramatically reshaped many industries such as security trading, retail banking, and travel agency industry. It had been widely predicted that the Internet would also present a serious threat to real estate agent as a market intermediary. Recent studies show, however, that in spite of growing use of the Internet by home buyers and sellers in the last several years, there is no sign of disintermediation in the real estate brokerage business. There can be many reasons why the predicted change did not occur. This study investigates the impact of the Internet on housing prices and its implications on real estate brokerage industry. We conducted an empirical study to identify the difference in price between the houses sold with a broker and those sold by the owner using the Internet. The findings of our study are interesting and provide a rationale for the continued strong presence of the realtors in housing industry.

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The Internet and Real Estate Industry

Over the last decade, the Internet has experienced a phenomenal growth in attracting consumers and businesses. While Internet technology has penetrated virtually every geographical area of the globe,9 electronic-commerce (EC) has flourished in a variety of industries, including the real estate industry. It is argued by many that the EC economic model is much more efficient at the transaction cost level due to elimination of the middleman in the distribution channel.4 This economic efficiency has led to radical development of disinter-mediation in industries such as security trading, computer retailing, and travel agency industry. The real estate industry has seen similar sign of revolutionary changes. A number of for-sale-by-owner Web portals, such as ForSaleByOwner. com, Owners.com, Zillow.com, and ISoldMyHouse.com, have emerged and thrived in the recent years. At the same time, numerous online discount brokers like ZipRealty.com, HelpUSell. com, Assist2Sell.com and Redfin.com, have come into sight and gained some momentum. In early 2006, the Internet giants Google and Craigslist decided to enter the market, offering listing services for home buyers and sellers.6 These developments have brought enormous challenges to the National Association of Realtors (NAR), the industry's dominant power, which has already provided the Multiple Listing Services (MLS) on its official Web site, Realtor.com. Can the EC model efficiency emerge from the real estate transactions? Will the realtor be disintermediated in the near future? These have been some of the questions asked by experts and researchers following this industry.

According to NAR's 2005 Profile of Home Buyers and Sellers, the number of buyers who use the Internet to search for a home has increased from only 2% of buyers in 1995 to 77% in 2005; and 24% of buyers first learned about the home purchased via the Internet. On the other hand, the number of real estate brokers using the Internet for business has risen 25% to 62% since 1996. Another study reported that in 2004 more than 70% of home buyers shop online before purchasing a home, up from 41% in 2001.8 This increased use of the Internet has put the realtors in fierce competition with cyberintermediaries and individual buyers and sellers. Fearing about loosing hold on home selling, the realtors are fighting back by persuading state governments to pass laws discouraging price competition, and excluding homes carried by discounters and sellers from being listed on the MLS database run by NAR. This has resulted in a series of legal actions taken by the U.S. Justice Department and the Federal Trade Commission (FTC) against NAR.6

Not too long ago, experts have touted that advances in technology would reduce or even eliminate the traditional role of the realtors in property transaction. Bill Gates of Microsoft had once identified real estate as one of the industries that will be "revolutionized" by technological changes.5 Academia had also predicted dramatic changes on the real estate brokerage industry thanks to the Internet.1, 12 According to them, many functions of the broker, such as property listing, property search, and financing assistance, can be performed by the buyer and seller using the Internet. Consequently, Internet technologies would facilitate more "for sale by owner" activities thereby reducing the need for the realtors. Information is power in this industry and once the realtors loose their control over this information, consumers would become free to buy and sell their homes without them.

In a follow-up study, however, Muhanna and Wolf7 found that the dire predictions of the earlier studies did not materialize. Realtors are still a necessary component of the home sale transaction and have thrived with the recent surge in demand for homes in the U.S. The disintermediation of the realtor has not occurred, according to them, for various reasons such as the nature of the real estate product, the realtor's position in the value-chain of the transaction, and intense lobbying by NAR with local and state governments. Homes are an expensive product that many consumers buy once in a lifetime. Beyond information sharing, the realtor provides other professional services such as price negotiation, open-house, closing assistance and other troubleshooting equally important to buyer and seller. Neither the disintermediation issue nor transaction cost arguments have interfered, thus far, with the growth of the real estate industry.

In this research, we take a novel approach to the problem. We conducted an empirical study to identify the difference in price between the houses sold with a broker and those sold by the owner using the Internet. The results of our study indicate that there is little or no price difference between these two types of selling methods, which implies that there is virtually no financial benefit for buying houses online. This lack of monetary incentive for buying online provides a rationale for the continued strong presence of the realtors in housing industry.

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Does E-Commerce Lower the House Price?

In this article, a house is said to be "sold and bought online" (SBOL) if the buyer finds sale information directly from the seller's listing posted on the Internet, and the sale and purchase transaction is made directly between the seller and the buyer, without a broker involved. Note that, unlike other EC activities, SBOL does not require the transaction to be processed online, which is rather unrealistic.

As described earlier, it is widely believed that the price of an SBOL house should be lower than that of an equivalent house carried by a broker. The basic rationale for this position is that the information and transaction cost of an SBOL house is lower than that of a house sold through a broker. A competing position is that the price of an SBOL house is not necessarily lower than that of an equivalent house sold through a broker. One can argue for this position because buyers do not pay broker's commission and they do not necessarily expect the price of an SBOL house to be lower. Furthermore, when assessing the price of an SBOL house, buyers and sellers tend to use the prices of other similar houses as reference for comparison, while the majority of those houses were sold with a broker involved.

We examined these two competing positions using an economic pricing method called hedonic pricing model. In this model, a house is viewed as a bundle of attributes, rather than as a single commodity. The house price represents the collected value of the various attributes. The marginal value of an attribute of a house can be estimated by examining how house price changes as related attributes change. The hedonic pricing method has been used in numerous studies to determine the relationships between house price and various characteristics, including road traffic noise, air quality, flooding, and other environmental attributes.3 Following the approach taken in these studies, we examined the impact of the Internet on house price by regressing the house price on a set of housing related attributes, including the use of the Internet.

A sample set of housing data was taken from three towns in Massachusetts. A few selection criteria were used in choosing sample homes. First, we focused on those homes sold in 2003 and 2004, instead of a longer period of time, because housing price had increased dramatically during late 1990s and early 2000s in these areas and it is difficult to make a reasonable comparison of house prices if the chosen time period is too long. Second, we restricted our sample to those existing homes built in the last two decades, because there were more online listings for these homes than for the others (otherwise, the proportion of SBOL homes will be too small). Third, in order to make comparisons more meaningful, subdivisions were selected such that there were always some SBOL homes in each subdivision.

Housing data was collected from property records maintained in the three town halls. Most of the housing attribute values can be found in individual property records in a town hall. These records, however, often do not provide detailed information on whether a house was bought and sold by owner online or through a broker. To find out this information, we first surveyed the home owners through a questionnaire. For those who did not respond, we verified this information by phone. When this information could not be verified, the record was removed from the sample. This process was extremely time-consuming and prevented us from getting a larger sample of homes for this study.

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A total of 188 house records were used in this study; 45 of them were SBOL houses, while the remainders were sold via brokers. The mean house price for the 188 houses is $559,270. The housing related attributes selected for this study are listed in Table 1. Initially, we also considered some other housing attributes. They were later excluded due to either a statistically insignificant relationship with house price, or a high correlation with some of the more basic attributes listed in Table 1. Each of the attributes was plotted against house price to check whether the pair-wise relationship was linear. A log transformation was taken for an attribute if necessary. As a result, the hedonic price model is specified as shown in equation 1.

The results of parameter estimates for regression equation (1) are reported in Table 2(a). Overall, the regression model fits the data well. The model explains approximately 76.5% of the variation in house prices, which is relatively high. The coefficients of the housing attributes all have expected sign and are all statistically significant except for SBOL. The results indicate that an increase in the age of a house will cause a decrease in the house price, holding other things constant. In contrast, an increase in lot size, living area, number of bedrooms, number of bathrooms, basement area, or capacity of garages, will result in a higher house price. Also, the same house was more expensive in 2004 than in 2003. In addition, three dummy variables for the three towns are all significant; but the results are not reported since they are not the interest of this study. The attribute of primary interest, SBOL, has a negative sign. This suggests that an SBOL house has a lower sale price than an equivalent house sold via a broker. However, this attribute is statistically insignificant (p-value = 0.5569). An immediate interpretation of this outcome is that there is no significant price difference between an SBOL house and an equivalent house sold with a broker.

In order to minimize the bias caused by incorrect model specification, we also performed regression analyses using different model specifications. The results from these alternative models differ slightly, but they all lead to the same conclusion that there is no significant price difference between an SBOL house and an equivalent house sold with a broker. Table 2(b) shows the results based on the regression model specified below, which assumes a straightforward linear relationship between house price and the set of housing attributes see equation 2.

This model does not fit the data as well as equation (1). However, since there is no log-transformation involved, it is easier to observe the impact of each attribute on house price. The coefficient of SBOL suggests that the price of an SBOL house is on average $6,045 lower than an equivalent house sold via a broker. However, this attribute is again statistically insignificant. Even if significant, this amount is only about 1% of the average house price ($559,270), much smaller than commission amount normally charged by a realtor.

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Discussions and Conclusions

We were somewhat surprised by the findings of our empirical study. In real estate brokerage business, it is a common practice that when the transaction of a property involves both buyer's and seller's agents, the two agents share the commission equally. This implies that the transaction cost is about the same for buying and selling a property. However, what we have seen here is an asymmetric distribution of benefits: a substantially larger part of the cost savings derived from SBOL go to the seller. In order to explain what could cause this somewhat unexpected outcome, we conducted a follow-up survey study.

A survey questionnaire concerning individual's opinion about buying and selling homes using the Internet were sent out to home owners in our sample regions. Out of 276 home owners surveyed, 33 responded, yielding a 12.0% response rate. Seven of the 33 home owners (21.2%) who responded bought their current home directly from the seller using the Internet. Since the sample size is somewhat small, it is difficult to perform a formal statistical analysis based on the survey data. Nevertheless, useful insights can be gained from the results of the survey.

Table 3 shows survey results for two questions closely related to our earlier findings. As expected, the majority of the respondents indicated that the commission saved in an SBOL transaction should be shared equally between buyer and seller. Nevertheless, those who believed that the seller should get a bigger share significantly outnumbered those who thought conversely, which is consistent with our findings. In response to the second question, more people preferred to use the Internet for home buying, as compared to home selling. This suggests that it is easier to use the Internet for buying than for selling a home.

The survey results suggest that most home owners believe that home searching, which is perhaps the most important function of a buyer's agent, can be easily performed using the Internet by a buyer (even an inexperienced one), while disseminating for-sale home information to potential buyers, which may be the most important function of a seller's agent, might not be as easily done by a seller using the Internet. This asymmetric view for professional expertise can be explained by the "economy of scale" theory. A real estate agent gains its professional expertise in both buying and selling of homes by representing a large number of both buyers and sellers, and by being involved in many buying and selling activities. Individuals will only buy or sell their homes once or a few times in their lifetime, and they will not have as many opportunities as brokers have to acquire knowledge and gain professional expertise in either buying or selling of homes. However, it is relatively easier for an individual buyer to learn the required knowledge for buying a home since the buyer can visit a large number of homes in market and gain expertise by repeated experiences. On the contrary, it may be difficult for an individual seller to adopt this "expertise by experience" practice, since the seller's experience will be limited to the seller's home only.

The asymmetric distribution of cost savings can also be attributed to the structural characteristics of real estate market. It is argued by many researchers that disintermediation is more likely to occur in a monopoly or oligopoly market than in a market with many suppliers.4 Airline industry is a typical example of the former case, while real estate industry is clearly the latter. With Internet technology, an airline company can easily bypass a travel agency by selling flight tickets on its own Web site. Individual home sellers, on the other hand, do not have such a market power to force disintermediation. More likely, the Internet will facilitate creating reintermediaries like discount brokers, or cyberintermediaries like those for-sale-by-owner Web portals.2, 4 Until today, these cyberintermediaries and reintermediaries are not powerful enough to compete with traditional realtors. The majority of the properties for sale are still listed exclusively in MLS run by NAR. Due to concerns about lack of exposure to potential buyers, most sellers are reluctant to use cyberintermediaries or reintermediaries. Indeed, 80% of the respondents who sold their last homes via brokers indicated that they did so in order to have their ad reach as many buyers as possible. On the other hand, it is much easier for a buyer to at least first try home search on the Internet. When there are more buyers than sellers who are willing to use the Internet to perform the agent functions, the market will create an asymmetric distribution of the cost savings in favor of the sellers.

The asymmetric distribution of commission savings implies that there is little or no additional financial benefit on the buyer side. In other industries, such as travel agency, security trading and computer retailing, e-commerce implementations have caused significant reductions in transaction cost for both seller and buyer. The buyers have strong financial incentive to go online, which eventually led to disintermediation or cyberintermediation in these industries. If the financial benefit is not sufficient to compensate the cost of going online, then it is logical for buyers to use traditional brokers. This is perhaps a major reason for the continued strong presence of the realtors in real estate industry.

This study suggests that cyberintermediaries (that is, for-sale-by-owner Web services) and reintermediaries (i.e., online discount brokers) must focus on establishing a sufficiently large base of home listings, in order to win the competition. Many of them have indeed realized this and created innovative ideas to gain market ground. One of the most exciting examples is the approach taken by Zillow. com, which provides a free instant estimate, as well as image and descriptive data for each individual home in its repository of 50 million homes in the U.S. (the site attracted such a huge number of visitors that it crashed for four hours soon after it launched on February 8, 2006). However, the installed bases of consumers for these real estate cyberintermediaries and reintermediaries are in general too small for them to operate efficiently and to compete effectively. ISoldMyHouse.com, for example, is the largest for-sale-by-owner Web portal serving Massachusetts area. Based on our observations over the last four years, the number of home listings posted on ISoldMyHouse.com is on average only about 10% to 25% of that posted on Realtor.com (NAR's official Web site) for the Massachusetts area. It is vital for the real estate cyberintermediaries to establish a sufficiently large customer base in order to be successful.

Realtors will find this study useful in assessing competitive environment and making strategic decisions. As discussed earlier, they have benefited from lack of strong competitors, especially in terms of home listings. However, this advantage cannot be taken for granted. Recent moves by the Internet giants Google and Craigslist could potentially change the equation of the race. An obvious approach for the realtors to stay dominant is to fight for continued control over the listing information, as they have been doing thus far. However, their recent lost to FTC in a set of related cases is an indication that this effort might not work out as they hoped for. It has been predicted that the Internet will eventually make all of the listings publicly available.6 A better strategy for realtors would be to join forces with the online cyberintermediaries with emphasis on value-added agency functions such as community networking, pricing, negotiation, and closing assistance that are hard to be replaced by the Internet.10, 11 Realtors could focus more on the back-end fulfillment functions that are essential for the success of cyberintermediaries like Zillow.com or ISoldMyHouse. com. While these sites can do a good job on listing homes, they do not have the infrastructure to successfully close the deal. Another approach would be to use the business model that LendingTree.com uses for mortgage lending and AutoByTel.com for automobile marketing, where a customer registers online searching for the product or service and local agents compete for their business. This approach has worked well in resolving channel conflicts and could work well for real estate business too. Realtors will find unbundling or re-bundling of existing full-commissioned service, perhaps through the use of the Internet, to be a viable alternative to compete and success.

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References

1. Baen, J. S. and Glittery, R. S. The coming downsizing of real estate: implications of technology. Journal of Real Estate Portfolio Management 3, 1 (1997), 118.

2. Fan, M., Stallaert, J. and Whinston, A. B. The Internet and the future of financial markets. Communications of the ACM 43, 11 (Nov. 2000), 8388.

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5. Gilon, P. and Cardenas, C. Appraisers and cyberspace: an introduction to the Internet. The Appraisal Journal 63, 4(1995), 469481.

6. Hagerty, J. R. and Delaney, K. J. Google, Craigslist expand into real estate. Wall Street Journal, April 6, 2006, Dl.

7. Muhanna, W. A. and Wolf, J. R. The impact of e-commerce on the real estate industry: Baen and Guttery revisited. Journal of Real Estate Portfolio Management 8, 2 (2002), 141152.

8. Mullaney, T. J. Real estate's new reality. Business Week, May 10, 2004.

9. Press, L., Burkhart, G., Foster, W., Goodman, S., Wolcott, P. and Woodard, J. An Internet diffusion framework. Communications of the ACM 41,10 (1998), 2126.

10. Sawyer, S., Crowston, K., Wigand, R.T. and Allbritton, M. The social embeddedness of transactions: Evidence from the residential realestate industry. The Information Society 19, 2 (2003), 135154.

11. Sawyer, S., Wigand, R.T. and Crowston, K. Redefining access: Uses and roles of information and communication technologies in the US residential real estate industry from 1995 to 2005. Journal of Information Technology 20, 4 (2005), 213233.

12. Tuccillo, J. A. Technology and the housing markets. Business Economics 32, 3 (1997), 1720.

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Authors

Xiao-Bai Li ([email protected]) is an associate professor of information systems in the Department of Operations and Information Systems at the University of Massachusetts Lowell.

Luvai Motiwalla ([email protected]) is a professor of information systems and the chairperson of the Department of Operations and Information Systems at the University of Massachusetts Lowell.

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Footnotes

DOI: http://doi.acm.org/10.1145/1461928.1461957

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Tables

T1Table 1. Attributes for estimating house price.

T2Table 2. Parameter estimates for registration analysis.

T3Table 3. Survey results reguarding the use of the Internet for home buying and selling.

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