The Relative Performance of Real Estate Marketing Platforms: MLS

The Relative Performance of Real Estate Marketing Platforms: MLS

NBER WORKING PAPER SERIES THE RELATIVE PERFORMANCE OF REAL ESTATE MARKETING PLATFORMS: MLS VERSUS FSBOMADISON.COM Igal Hendel Aviv Nevo François Orta...

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NBER WORKING PAPER SERIES

THE RELATIVE PERFORMANCE OF REAL ESTATE MARKETING PLATFORMS: MLS VERSUS FSBOMADISON.COM Igal Hendel Aviv Nevo François Ortalo-Magné Working Paper 13360 http://www.nber.org/papers/w13360

NATIONAL BUREAU OF ECONOMIC RESEARCH 1050 Massachusetts Avenue Cambridge, MA 02138 September 2007

We are grateful to the owners of FSBOMadison.com and the South-Central Wisconsin Realtors Association for providing us with their listing data. We thanks Geoff Ihle and James Robert for valuable research assistance, and Estelle Cantillon, Leemore Dafny, Morris Davis, Steve Levitt, and seminar participants for comments. Hendel and Nevo thank the Center for the Study of Industrial Organization and the Guthrie Center for Real Estate Research at Northwestern University. Ortalo-Magné acknowledges financial support from the Graduate School at UW--Madison. Hendel and Nevo are in the department of Economics at Northwestern University and NBER. Ortalo-Magné is in the department of Economics and the department of Real Estate and Urban Land Economics at the UW-Madison. Contact information: [email protected], [email protected], and [email protected] The views expressed herein are those of the author(s) and do not necessarily reflect the views of the National Bureau of Economic Research. © 2007 by Igal Hendel, Aviv Nevo, and François Ortalo-Magné. All rights reserved. Short sections of text, not to exceed two paragraphs, may be quoted without explicit permission provided that full credit, including © notice, is given to the source.

The Relative Performance of Real Estate Marketing Platforms: MLS versus FSBOMadison.com Igal Hendel, Aviv Nevo, and François Ortalo-Magné NBER Working Paper No. 13360 September 2007 JEL No. L14,L85,R31 ABSTRACT We compare outcomes obtained by sellers who listed their home on a newly developed For-Sale-By-Owner (FSBO) web site versus those who used an agent and the Multiple Listing Service (MLS). We do not find support for the hypothesis that listing on the MLS helps sellers obtain a significantly higher sale price. Listing on the MLS shortens the time it takes to sell a house. The diffusion of the new FSBO platform was quick, with the market share stabilizing after 2 years, suggesting it managed to gain a critical mass necessary to compete with the MLS. However, the lower effectiveness of FSBO (in terms of time to sell and probability of a sale) suggests that the increasing returns to network size are not fully exploited at its current size. We discuss the welfare implications of our findings. Igal Hendel Department of Economics Northwestern University 2001 Sheridan Road Evanston, IL 60208 and NBER [email protected] Aviv Nevo Department of Economics Northwestern University 2001 Sheridan Road Evanston, IL 60208-2600 and NBER [email protected]

François Ortalo-Magné School of Business University of Wisconsin, Madison 5259 Grainger Hall 975 University Avenue Madison, WI 53706-1323 [email protected]

1

Introduction

A large proportion of housing transactions are carried out with the help of realtors.1 Realtors provide expertise (on pricing, conditioning the house for sale and bargaining) and convenience (by showing the house, advertising and holding open houses and helping with the paperwork). Another advantage of working with a realtor is access to the Multiple Listing Service (MLS), a database that compiles information on all the properties listed by local realtors. For their services, sold almost exclusively as a bundle, realtors charge a commission at, or around, 6%. The commission rate has been stable over time and across regions and has been the subject of the scrutiny of antitrust authorities (see DOJ, 2007). The advent of the internet has a¤ected many markets. The real estate market is one of them. Direct marketing was always possible using newspapers, ‡yers and other forms of advertising. However, the internet o¤ers a cheaper and potentially more e¤ective platform that facilitates direct (by owner) marketing. Sellers can post detailed information, photos as well as virtual tours. For-Sale-By-Owner (FSBO) websites provide an alternative platform, or two-sided network, that competes directly with the MLS network. In this paper we study the performance of these two competing platforms: MLS and FSBO. The established platform o¤ering the bundle of services available from realtors, versus the newly established no-service platform. The actual cost of MLS transactions is the commission minus the price premium an MLS transaction might generate and the …nancial savings from a faster sale. The price premium may largely o¤set, or even more than make up for, the commission.2 We quantify the actual monetary cost of using an agent by comparing the performance of listings by owner to transactions with realtors. We also assess the platforms’e¤ectiveness, comparing measures like time on the market and the probability of sale within a time window. We focus on the city of Madison, Wisconsin, where a single website (FSBOmadison.com) has become the dominant for-sale-by-owner platform. With the cooperation of FSBOmadi1

Real estate agents are licensed by the state. A realtor is a real estate agent who is a member of the Realtor Association. 2 The National Association of Realtors website claims, based on the 2005 Home Buyer & Seller Survey that "the median home price for sellers who use an agent is 16.0 percent higher than a home sold directly by an owner; $230,000 vs. $198,200; there were no signi…cant di¤erences between the types of homes sold."

2

son.com we gained access to all FSBO listings since the start of the platform. We combined the FSBO data with data from two other sources. First, from the South-Central Wisconsin Realtors Association we got access to all MLS listings in the city. Second, we matched every listing with data from the city of Madison. The city of Madison assessor o¢ ce maintains a database with the full history of transactions on every property together with an exhaustive set of property characteristics. By merging these data sets we get a complete history of events that occurred for virtually every single family home for sale, over 15,000 observations, between January 1998 and December 2004. A history of a listing includes: date and platform of initial listing, moves across platforms, and outcome (sale date and price if sold, withdrawal date otherwise). After controlling for houses and seller heterogeneity, we …nd no support for the hypothesis that the MLS delivers a higher sale price than FSBO. Considering that realtors charge a 6% commission versus $150 for FSBO, FSBO sellers come ahead …nancially. The lack of a MLS premium does not mean realtors do not provide value to the seller. It means instead that the cost of the convenience provided by realtors seems to be the full commission. MLS does, however, lead to faster transactions. The longer time to sell on FSBO is driven by two factors. First, over 20% of FSBO listings do not sell on FSBO and have to list afresh on the MLS. Second, the probability of a quick sale is larger for houses initially listed on the MLS. Next, we consider the welfare implications of the results. From the quicker time to sell we conclude that the MLS is a more e¤ective matching platform. This suggests that FSBO’s current size does not fully exploit economies of scale in network size. In the context of homogenous platforms welfare would increase if all transactions were consolidated into a single network. The countervailing force, which calls for multiple platforms, is product di¤erentiation (Armstrong, 2006, and Rochet and Tirole, forthcoming). In this case, the platforms are di¤erentiated by the service level. Full service by agents and no frills by FSBO. Therefore, it might be e¢ cient for both platforms to coexist. The bundling of agents’services with the MLS, the source of di¤erentiation, is the current practice, but is not technologically dictated. It might be bene…cial to unbundle the platform from the additional services o¤ered

3

by agents.3 The raw price comparison shows that the average sale price of homes that sell on FSBO is higher than the average price of homes that sell with a realtor. The characteristics, reported in the city assessor’s database, of houses sold on the di¤erent platforms are somewhat di¤erent. However, after controlling for these observed characteristics a signi…cant price gap persists. Naturally, platform selection is the main suspect behind the persistent premium. We take several approaches to deal with selection. All the approaches support the same conclusion: MLS does not deliver a price premium. There are two concerns due to platform selection. First, there might be unobserved house characteristics that a¤ect both the decision to sell on FSBO and outcomes. For example, easier to sell homes (i.e., conform better to the taste of the population) may be more likely to be listed and sold through FSBO. At the same time these popular homes may sell at a premium. To deal with unobserved house heterogeneity we examine properties that sold multiple times. The inclusion of a house …xed e¤ect is essentially inconsequential. We therefore conclude that unobserved house heterogeneity, which is …xed over time, does not seem to be a problem. The second concern is the selection of sellers into FSBO. Sellers may di¤er, for example, in their patience or bargaining ability.4 More patient sellers are likely to get a better price, regardless of the platform they choose. At the same time they may be more prone to list on FSBO. In that case we will get a positive correlation between FSBO and sale price. We deal with the potential seller selection issue in several ways. First, we compare the houses that listed and sold on FSBO, to those that listed on FSBO, failed, and eventually sold on the MLS. These two groups of houses sell on di¤erent platforms but belong to the initial population that selected FSBO. If we think that the reason some sold while others did not is luck of the draw, then the di¤erence in price will give us the causal e¤ect of FSBO. Even if moving from FSBO to MLS depends on seller type the selection bias should be attenuated, as the group of FSBO listers is more homogenous than the population as a 3

Although there is a tendency –or attempts– in the direction of unbundling services, realtors are quite reluctant to do so (see Nadel (2007)). 4 For a descriptive study of bargaining patters using English data see Merlo and Ortalo-Magné (2004), and Merlo, Ortalo-Magné and Rust (2006) for a structural model of bargaining using the same data.

4

whole. The second approach to deal with seller heterogeneity is related to Levitt and Syverson (2006). They …nd –as we do in our data– a premium for realtors’ own properties sold on the MLS. They attribute this price gap to an incentive problem. We compare the realtors’ premium to the premium sellers get on FSBO. Both are by owner transactions; thus, do not su¤er from the agency problem identi…ed by Levitt and Syverson. They amount to by-owner transactions in di¤erent platforms. Since realtors are professionals this comparison should bound the impact of selection. Even if the homeowners who use FSBO are better bargainers than the typical homeowner, it is reasonable to assume they are no better at bargaining than professional realtors. We …nd that the FSBO premium is similar to the premium realtors obtain when selling their own homes. The third approach we take is to compare transactions of the same seller using di¤erent platforms. After matching seller names across transactions we …nd no price premium across platforms. Namely, the initial FSBO premium vanishes once we add a seller …xed e¤ect.5 One important caveat is that our data comes from a single city. We do not know how representative the results are of other markets. Similar FSBO websites exist in many other markets, mostly in medium size cities (see www.fsbopublishers.org). Madison is reasonably representative in measurable demographics, although it is unique in other dimensions (college town, state capital), it is unclear how this would impact our main …ndings. It would be useful to repeat the analysis for other markets. As far as we know this is the …rst paper that compares the performance of MLS to an internet based FSBO platform. A related study that complements our …ndings, by Bernheim and Meer (2007) compares non-MLS listings with and without agent. They look at sales of faculty and sta¤ homes on the Stanford University campus with and without an agent. They …nd, consistent with our …ndings, that brokers accelerate sales but do not deliver higher prices. They isolate the e¤ect of information from other broker services, since the Stanford Housing O¢ ce maintains a free listing service for eligible buyers they know the value of a 5

We examined various factors that impact the sellers decision to sell on FSBO as instrumental variables. For example, we used the fraction of previous sales on FSBO in the seller’s neighborhood. The point estimates we …nd are consistent with a FSBO premium. However, the instruments are very weak and the standard errors are very large.

5

broker does not reside in information di¤usion (i.e., the platform). Instead, brokers’value “is likely con…ned to pro-motional services, negotiations, and the interpretation of market data.” The rest of the paper is organized as follows. Section 2 presents the institutional background with special emphasis on Madison. Section 3 brie‡y describes a theoretical framework, borrowed from the labor literature, to think about platform selection. Section 4 presents the data and basic descriptive analysis. Section 5 presents the results. It starts with raw platform comparisons followed by several approaches to deal with selection. Finally, we present some welfare implications and concluding remarks.

2

Realtors and FSBOMadison.com

Historically, most real estate transactions have been performed using real estate agents. Homeowners wishing to sell their homes contract with a real estate agent (the listing agent) o¤ering the agent exclusivity for a limited period, usually 6 months, and agreeing to pay a commission, of usually 6% of the sale price, if the house is sold during the contract period.6 The commission is typically split between the listing agent and the selling agent, who is the agent that brings the buyer.7 When the same agent lists and sells the property, this agent gets the whole commission. Real estate agents are licensed by the state. In most states licensing requires a short course and passing an exam. A real estate agent becomes a realtor when s/he joins the realtor association and subscribes to its code of ethics. Joining the association provides the agent with several advantages; one of them is access to the MLS. Working with an agent, and agreeing to pay the commission, gives the homeowner access to a number of services. The National Association of Realtors (NAR) argues that Realtors provide valuable help with setting the listing price, preparing the house, checking potential buyers’ quali…cations, showing the house, bargaining the terms of the deal, and handling 6

For a discussion of the commissions charged by agents see DOJ (2007). Some states, for example, Wisconsin, also recognize the status of buyer agency. If a buyer agent is involved in the transaction, s/he deals with the listing agent to settle the terms of the transactions, and gets the selling agent commission. 7

6

the paperwork. Another advantage of working with a realtor is access to the MLS. In the market we examine this involves the ability to list on the South Central Wisconsin MLS, which costs a minimal fee, $10 as of 2007, but requires membership in the organization, and thus is available only to local realtors. In 1998 an alternative to the MLS was launched in Madison, Wisconsin: the website FSBOMadison.com. Christie Miller and Mary Clare Murphy recruited 9 listings from ads in the local newspaper, added Mrs. Murphy’s house and launched their website with 10 listings. From the get-go, the strategy of FSBOMadison.com was to provide a cheap no-frills service. In exchange for a fee of $75 initially, $150 for most of the period of our sample, homeowners can post their listing on the website (property characteristics, contact details and a few pictures). FSBO provides sellers with a yard sign similar to those provided by realtors but with the distinctive logo and color of FSBOMadison.com. Listings are kept active for 6 months, more if the fee is paid again. FSBOMadison.com has establishing itself as basically the only website for for-sale-by-owner properties in the city. Properties are removed from the site upon instruction of the homeowners. Typical events that trigger removal include sale of the property, withdrawal of the property from the market, or transfer of the property to the MLS platform. The sta¤ of FSBOMadison.com monitors listings on the MLS and extinguishes any listing from their website that ends up on the MLS. This is done primarily to avoid disputes with the MLS. Real estate agents are occasionally involved in FSBO sales when they represent the buyer and one of the parties to the transaction accepts to pay a buying agent commission, typically 3%. In such a case, a FSBO transaction only saves half the realtor commission. Recently, a number of limited-service brokers have emerged. In Madison, the dominant …rm appears to be Madcity Homes (www.madcityhomes.com). Madcity Homes charges $399 to list a house on the MLS for 6 months and also provides the seller with a yard sign. The homeowner gets no other service. Additional services are available for an extra fee upon request. The homeowner is responsible for paying the 3% commission to any realtor that sells the house, whether the realtor is under buyer agency agreement or not. No commission must be paid if the sale does not involve a realtor. By the end of 2004, when our sample ends, this …rm had too few listings for us to analyze the extent to which limited-service brokerage 7

yields di¤erent outcomes than full-service MLS listings or FSBOMadison.com listings.

3

Theoretical Framework

In this section we brie‡y describe a theoretical framework, borrowed from the labor literature that will help us think about platform selection and guide the empirical exercise. Coles and Muthoo (1998) present a stock-and-‡ow model of matching between unemployed workers and job vacancies.8 We adopt their stock-and-‡ow model, to platform choice. There are several relevant issues like: incomplete information, learning about market conditions or own property, which we abstract from. The basic idea of their model is as follows. There is a ‡ow of new buyers and sellers into the market (which in their case is a single platform) in every period. The ‡ow of entrants is immediately –and costlessly–put in touch with the stock of agents on the other side of the market. There is a probability

that there are gains from trade between each buyer and

seller (namely, that a house for sale meets the needs of each speci…c buyer). The parties that …nd a single match to trade with split their gains from trade. If instead a newcomer meets multiple counterparts, she receives simultaneous o¤ers generating a Bertrand-type game. Agents that trade leave the market. Incoming buyers (sellers) that do not …nd a match, or fail to trade, join the stock of buyers (sellers). They remain on the market waiting for newcomers (‡ow) to trade with. Coles and Muthoo show that in equilibrium matched players always trade (due to complete information). In equilibrium there is no trade among the stocks; if two members of the stock had gains from trading they would have traded already (upon arrival, namely, when one was part of the (in-)‡ow). Thus, in equilibrium the incoming ‡ow trades with the stock. We consider two variations: (i) the coexistence of two competing platforms, F and M , where agents can meet and, (ii) house and seller heterogeneity. The framework will help us think about platform selection as well as moves across platforms.9 8

See also Coles and Smith (1998), and Taylor (1995), and for a discussion of brokerage choice Salant (1991), Yavas and Colwell (1999) and Munneke and Yavas (2001). 9 We do not solve the full model. Solving the model with heterogeneity would be very hard, beyond the scope of this paper. We only intend to intuitively discuss plausible extensions of a more basic framework easier to solve.

8

Notice that in a stationary environment it would be hard to explain why sellers move across platforms. Coles and Muthoo’s framework, once we allow for multiple platforms, captures the idea of exploring a stock and then moving on to the other network’s unexplored stock. As we argue next, within the relevant environment of our application it provides a sharp prediction regarding platform moves, namely, one way moves across platforms. Heterogeneity We think of houses di¤ering in their degree of liquidity,

. Owners

of more liquid houses, may systematically opt for one of the platforms, and at the same time sell at a premium (as they generate more o¤ers). Sellers may also be heterogeneous, for example, in their patience or bargaining ability. Patience in this model will a¤ect both platform choice as well as transaction price given a platform. Platform Choice We make the following assumptions in order to capture the main practical di¤erences across platforms. First, we assume that only a proportion of agents know the existence of platform F .10 Only informed agents have a choice, uninformed ones trade on M:11 Second, we assume there is an asymmetry between buyers and sellers. While informed buyers can shop on both platforms, sellers choose a single platform. This exclusivity is required by the MLS. Third, listing on M , in addition to the exclusivity, involves a commitment to pay a transaction cost (or commission) C should the house sell within periods of listing. These assumptions make F a cheaper alternative, it involves no fees. At the same time F involves less exposure, thus a lower matching rate. Implications Only informed sellers face a choice, they have to chose an exclusive platform. Others parties (uninformed buyers and uniformed sellers) are either uniformed of F; or shop on both (informed buyers are not constrained by exclusivity). The trade o¤ faced by informed sellers is between an expensive and more e¤ective platform, M; and the non-fee F platform that o¤ers exposure to fewer buyers (only those informed of F ). An immediate implication is that for any speci…c property, the extra exposure leads to higher success rate. 10 Heterogeneity in the disutility of trading without a realtor can also drive platform choice. Some sellers are aware of the option of selling by owner but may …nd it too costly or unpleasant. 11 Although not necessary, it is reasonable to assume that the set of buyers aware of F is a subset of those aware of M. For example, out of town buyers are less likely to be familiar with FSBOmadison.com.

9

Claim 1 For given seller and house characteristics, on M we should observe shorter time to sell and higher success rate, holding time on the market …xed. The bene…t of listing on F is common to all sellers, however, the more patient the seller or liquid the property the less costly it is to try F …rst: Thus, the appeal of F depends on seller patience and liquidity of the property,

(see House and Ozdenoren (2007)):

Two implications are immediate. First, impatient sellers and non-liquid properties list on M . Second, sellers that fail to match on M have no incentive to move to F . The reason is that buyers on F are a subset of those that shop on M; thus after listing on M will not …nd in the stock of F any buyers who have not seen the property. Having explored all the stock of buyers (all buyers are present on M ), the seller has to wait for the ‡ow of new buyers. Since the ‡ow is larger on M; impatient sellers stay. In sum, we should not expect any ‡ow from M to F: In contrast, patient sellers and owners of liquid properties prefer to initially list on F: If they fail to match on F; they move to M to match with the rest of the stock of buyers (those that shop only on M ): Once they explored M , the whole stock of buyers has been exhausted, thus, they have no incentive to move back to F: The incentives just described can be summarized in the following claims. Claim 2 More patient sellers and owners of liquid houses list on F …rst. Claim 3 Sellers that fail to match on F move to M and stay (to match the ‡ow on M). There are no moves from M to F Given similar terms, buyers are indi¤erent between the platforms. Thus, as buyers become more patient they would not pay a platform premium. Claim 4 As frictions vanish (i.e., more buyers become patient and informed about F) prices across platforms tend to coincide In sum, the model suggests that patient sellers are likely to list on FSBO to expose their property to a subset of the stock of buyers. Should they fail to match, they move on to the MLS to expose their property to the rest of the stock, and the subsequent ‡ow of buyers. In contrast, no moves from MLS to FSBO are expected. 10

4

Data

We obtained data from FSBOMadison.com, the South-Central Wisconsin Realtors Association, the City of Madison and Dane County. We merged the data into a single database, organized by parcel numbers as designated by the City. We restrict our attention to single family homes because of lack of address details for condos in the FSBO and MLS records and incompatibility between the city and county database for condos records. MLS data The South-Central Wisconsin Realtors Association provided us with all listing activity on their Multiple Listing Service between 1/1/1998 and 5/23/2005. For each listing, we know the address of the property, its parcel number, the listing date, and the status of the listing. In addition, whenever relevant, each record contains the expiration date of the listing, the accepted o¤er date, the closing date and the sale price as recorder by realtors. We also know whether the listing realtor has an interest in the property. FSBO data The owners of the FSBOMadison.com website provided us with information on all the listings with their service since it started in 1998. For each listing, we know the address of the property, the last name of the seller, the date the property is put on the web and sometimes information about the outcome of the listing. We use data for the years 1998-2004, with an address in the city of Madison. City Data The city of Madison is located within Dane County. The city assessor database provides information on sale prices and a large set of property characteristics, about both the parcel and the buildings. In addition, the county maintains a county-wide database with location information for each parcel. We use this database to obtain spatial coordinates for each property. The county and the city do not use the same parcel numbers for condominium. Whenever there are such incompatibilities, we use Streetmap to locate the properties. Matching the three data sets we get 19,142 observations. An observation is a marketing history from initial listing, on one of the platforms, until sale or withdrawal from the market. Actual histories can be complicated, like listing with several agents. We exclude new 11

construction from the sample, 3007 observations. New units are generally sold by developers. The reason we exclude them is that we are interested in platform performance for the average non-professional seller. We exclude 149 houses that went though major renovations (we do not know their characteristics at the time they sold). We exclude 253 observations due to missing price or sales information. We include units between $50,000 and $1,000,000, which top censors 4 units and bottom censors 72 inexpensive units. After merging these data sets and excluding observations as described we get 15,606 listings, which represent 12,373 unique properties, in the period 1998 to 2004.

4.1

Descriptive Statistics

Table 1 summarizes platform usage over time. A row represents where the property was initially listed. The columns represent the eventual outcome of the listing, namely, whether it sold and how. The market share of FSBO in listings during the entire sample period is roughly 20%. We de…ne a non-sale as any listing that showed up on either MLS or FSBO but was not recorded later in the city data with a sale price. Approximately 87% of the properties eventually sell. Out of the properties that sell, 95% sell through the initial listing platform. The remaining 5% are almost completely switches from FSBO to MLS. Switches from MLS to FSBO are almost nonexistent, accounting for just 0.2% of the MLS listings. This is consistent with the predictions of the model (i.e., Claim 3) by which some sellers may try the cheaper platform …rst but they have no incentive to return. Moreover, should they prefer to list on M they would not move to F , as they have no additional stock to match on F once M was already explored. The market share of FSBO in properties sold is 14%, slightly below its listing share. Since FSBO was only introduced in 1998, these numbers somewhat underestimate the current FSBO market share. Therefore, in the rest of Table 1 we present the breakdown for every other year of the sample. FSBO’s share in listing and in outcome increases over time. By 2004, the last year of the sample, FSBO share in listing is almost 26%, and the share in sales is almost 19%.

12

In terms of di¤usion, it is interesting to point out how quickly FSBO came to maturity. While the …rst listings are in mid 1998, by 2000 FSBO’s market share basically plateaued. To judge the performance of each platform we look at the proportion of properties that sell through their initial listing platform. Of the 3,138 initial FSBO listings 2,153 or 68.6% sell on FSBO while 86.0% of initial MLS listings (10,725 out of 12,468) sell on MLS. While there is a clear trend in FSBO listing, increasing from 6% in 1998 to 25.6% in 2004, the success rate is more stable. The success rate in 2004, 72.6%, is higher than the rate in 1998, 55.8%. However, there is no clear trend in the intermediate years. Just as the penetration of FSBO increases over time it also di¤ers across neighborhoods. In Table 2 we present the FSBO penetration rate across di¤erent assessment areas. These areas are de…ned by the City of Madison for assessment purposes. We get similar variation if we look at elementary schools areas. The FSBO listing share varies between 7.9% and 43.6% The top FSBO share neighborhoods tend to be close to campus. Similar variation is present also in the FSBO share of sales. The success rate of FSBO listings also varies by neighborhood. For a neighborhood with at least ten FSBO listings the success rate ranges from 31% to 100% (with one outlier at 9%). The mean success rate is 66% and the standard deviation is 13.2%. There is a positive relation between the propensity to list on FSBO and the success rate, which can be seen through a linear regression. Using the estimated slope, one standard deviation increase in the success rate translates into 2 percentage points increase in the propensity to list on FSBO. In the analysis below we compare the performance of properties sold through FSBO and through MLS. A key question is whether these properties are comparable. In Table 3 we compare several property characteristics. The columns present the mean and standard deviation for properties listed initially through FSBO and MLS. The last two columns present the di¤erence between these means and the t-statistic of the di¤erence. The di¤erences in the means for most characteristics are small. However, because of the reasonably large sample size the di¤erences are signi…cant in some cases. For example, FSBO properties are somewhat older, tend to be on smaller lots and have smaller basements, but have somewhat newer roofs and furnaces.

13

5

Results

5.1

Outcomes by FSBO and MLS platforms

We now explore the di¤erences in outcomes for properties sold through FSBO and MLS. Tables 4-6 present the results from regressing sale price, time on the market and the probability of a sale, on a FSBO dummy variable and various controls. In Table 4 we display the e¤ect of platform on price. In the top panel of the table the dependent variable is the logarithm of price, while in the bottom panel we regress the price level on various controls. The sample in columns (i) through (iv) includes only properties that sold on the platform they were originally listed. In the …rst column we regress price on a dummy variable that equals one if the house was sold on FSBO (divided by 100). If listing platform is determined at random, and the seller cannot switch from the platform they were assigned then this regression measures the causal e¤ect of selling on FSBO. In the spirit of this ideal situation the sample includes only houses that sold on the platform they originally list. The results suggest that on average there is a large positive premium for selling on FSBO, roughly an 11 percent premium or 14,800 dollars. Since the dependent variable is the sale price, and not the sale price net of commission, this premium is on top of the saved commission. The magnitude of the premium is driven by the time trends that we saw in Table 1. Over time prices have gone up and so has the share of FSBO sales. Indeed, once we control for year and month time dummy variables and a linear time trend, in column (ii), the e¤ect goes down to roughly 4 percent, or 3,000 dollars, but is still statistically signi…cant. The numbers in Table 3 suggest that there is some di¤erence in the observed characteristics of houses sold through FSBO and MLS. If the houses sold on FSBO have more attractive characteristics, then the FSBO dummy variable will also capture the impact of these features, rather than the e¤ect of selling through FSBO. Furthermore, Table 2 suggests that FSBO has a higher share in some areas. If these areas are more attractive this will bias our estimates. In order to control for the di¤erences in houses we construct a hedonic model of prices. Column (iii) reports the results from this model. In the controls we include the characteristics 14

of the house, displayed in Table 3. The e¤ect of selling on FSBO is mostly unchanged and stays at roughly 4 percent. This is consistent with the numbers in Table 3 that suggested that while some characteristics were statistically di¤erent, the di¤erences are small. In column (iv) we also control for neighborhood characteristics by including neighborhood …xed e¤ects. The coe¢ cients on these controls are of no direct interest. However, the key is that we are able to explain 92.4 percent of the variation in the logarithm of price, and 89.3 percent of the variation in price. The impact of selling through FSBO goes down to approximately 3.2 percent. The regressions in columns (i) through (iv) focus on the impact of the platform through which the house was sold. In column (v) we explore the impact of the initial listing channel. There are two di¤erences relative to the results in column (iv). First, the sample now includes switchers: houses that initially listed on one platform but that sold through the other. These are mostly houses that listed on FSBO but ended up being sold through MLS. Second, now the FSBO dummy is de…ned as being initially listed on FSBO, as apposed to being sold through FSBO. This regression is of interest for a potential seller asking what is the expected impact on price if they list on FSBO, and then behave like the sellers in the sample (depending on how lucky they were with the FSBO stock of buyers), regardless of where they end up selling. The results suggest that the premium for listing on FSBO, which is estimated at 3.1 percent, is almost identical to the premium for selling through FSBO. To further explore the distinction between listing and selling on FSBO we also examine, in column (vi), the regression that includes both the initial listing platform and the sales channel. We see that there is a small additional premium of selling on FSBO of 0.7 percent. This premium is driven by the very small number of houses that initially listed on MLS, but were eventually sold on FSBO. In the last column we separate these houses. These houses command a large premium, over 6 percent relative to houses that listed and sold on MLS. Once we isolate the thirty properties that list on MLS but eventually sell on FSBO, we …nd that now the additional premium of selling on FSBO disappears. Overall the results in Table 4 deliver a surprising result. Sellers on FSBO are able to sell their houses at a premium relative to MLS. In addition, sellers that initially list their 15

houses on FSBO but then move to MLS also command a signi…cant premium relative to initial MLS listings. The causal interpretation of the results relies on random assignment to platform, or random success, conditional on time, house and neighborhood characteristics. Random assignment is a strong assumption in this context. We deal with selection in the next section. We also explored the FSBO premium by year. We ran the regression in column (v) by year. The estimated coe¢ cients (standard errors) from 1998 to 2004 are: 4.05 (1.01), 3.39 (0.82), 2.13 (0.72),3.40 (0.58), 3.02 (0.59),3.14 (0.52), and 2.69 (0.52). These numbers suggest that the FSBO premium was roughly stable through out the sample period. Finally, we used a quantile regression to estimate the e¤ect of listing on FSBO, the e¤ects were constant across quantiles and thus essentially identical to the e¤ects in the mean regression in Table 4. We now examine other outcomes. In Table 5 we focus on the total time to sell, de…ned as the time between the initial listing and the sale date as recorded in the city data. The dependent variable in all regressions is the total time to sell, and the controls follow a similar structure to Table 4. In columns (i) through (iv) we focus on the sample of houses that sold on the platform where they were initially listed. Without any additional controls, the results in column (i) suggest that total time to sell is 4 days shorter when selling on FSBO. Once we control for year and month dummies, and for house and neighborhood characteristics, the e¤ect of selling on FSBO is not statistically signi…cant. The additional controls change the R-squared very little, compared to the price regression where the house and neighborhood characteristics explained a large fraction of the variation.12 Notice that the lack of a statistical di¤erence in the time on the market does not imply that FSBO is as e¤ective a platform as the MLS. Quite the contrary, this suggests that the MLS is likely more e¤ective. While the average time to sell on the MLS re‡ects the whole population of houses listed on MLS, since there are few switches to FSBO, the FSBO average represents the average conditional on the house belonging to the 75% that sold on FSBO 12

Time on market is de…ned by the timing of closing which depends on considerations hard to predict, thus a lower explanatory power is expected.

16

before moving to MLS. Even absent unobserved heterogeneity the FSBO average represent the luckiest draws, in terms of time to sell, while MLS the whole population. In the last three columns we once again study the full sample of houses that sold, not just houses that sold on the platform originally listed. In column (v) we …nd that sellers who originally list on FSBO should expect to take 20 days longer to sell. This is largely driven by houses that originally listed on FSBO but then switch to MLS. The results in column (vii) allow us to separate the e¤ects in four groups. The base group is properties listed and sold on MLS. Relative to this group the properties listed and sold on FSBO take 1 day longer, the same result we found in column (iv). For houses that listed on FSBO but eventually sold on MLS the time to sell is almost 69 days longer. Finally, for houses that listed on MLS but that were sold through FSBO the expected time to sell is 115 days longer. To further characterize the di¤erences of outcomes between the two platforms we report, in Table 6, the e¤ect of platform on the probability of sale. In all cases we regress a dummy variable, which varies by column, on platform dummy variables, year and month dummy variables, a linear time trend, house and neighborhood characteristics. We start by examining in columns (i) and (ii) the probability of a sale. The dependent variable is equal to one if the property sold. A non-sale is de…ned if we do not observe a sale price in the city data. Overall in the sample 87 percent of the properties sold. The properties initially listed on FSBO tend to have a higher probability of eventually being sold, although some of them are eventually sold through MLS. In column (ii) we separate the properties into four groups depending on initial listing and …nal channel. If the property sold the …nal platform is the platform where it sold, otherwise it is the last platform used for listing. We …nd that relative to the base group –properties that listed and sold on MLS – properties that listed and sold on FSBO are roughly 1 percentage points more likely to sell, although the di¤erence is not statistically signi…cant. The properties that listed on FSBO but eventually switched to MLS are even more likely to sell. Relative to the base group they are roughly 4 percentage points more likely to sell. The properties that list MLS and switch to FSBO are less likely to sell, but this is an extremely small group and the e¤ect is not estimated precisely. In columns (iii)-(viii) we examine the probability of a sale, conditional on eventually being 17

sold, within a …xed number of days. We look at 180, 90 and 60 days. We …nd a pattern similar to what we saw in Table 5: the properties listed on FSBO tend to take longer to sell. Thus, within a …xed interval of time a FSBO property is less likely to sell. Although FSBO listings are somewhat more likely to eventually sell, their initial success is lower than MLS. This is mainly driven by the properties that start on FSBO and switch to MLS. In columns (iv), (vi) and (viii) we separate the properties into four groups. The FSBO listing that sold on FSBO are less likely to sell within 60 or 90 days. This is consistent with MLS exposing sellers to a bigger stock of buyers (as in Claim 1). The properties that start on either FSBO or MLS, and then switch, take an even longer time to sell and thus are much less likely to sell within a …xed time period.

5.2

Selection

In the previous section we documented the di¤erence in outcomes for properties listed on FSBO and MLS. A key issue in interpreting the results is selection. As suggested by Claim 2 there are two separate concerns. First, are properties sold on FSBO comparable to those sold on MLS? We control for a rich set of observed house characteristics, but it is still possible that there are unobserved di¤erences that are correlated with the platform choice. Second, even if the house unobserved characteristics are not correlated with the channel, the sellers attributes might be. We now discuss both of these issues in detail. 5.2.1

Unobserved House Characteristics

As we show in Table 2 there are some di¤erences in observed characteristics between the properties listed on FSBO and MLS. These di¤erences are not large but in some cases they are statistically signi…cant. Indeed, once we control for house and neighborhood characteristics, in the regressions we display in Tables 4-6, the results change somewhat. The di¤erences in the observed characteristics might suggest di¤erences in unobserved characteristics as well. To examine this issue we exploit properties that were sold multiple times in our sample using di¤erent platforms. As long as the unobserved characteristics are constant over time looking at properties that sold multiple times, then including a house …xed e¤ect will control for the

18

unobserved characteristic. Recall that we eliminated from our sample property that undergo a major renovation during our period of study (this is one of the characteristics reported by the city assessor). In our sample, there are 2,023 properties that sold more than once. The majority, 1,872, sold twice, with 146 and 5 selling three and four times. Together this yields 4,202 sales. Out of these sales 3,376 (or 80%) were listed and sold on MLS, 629 (15%) listed and sold on FSBO, 194(5%) listed on FSBO and sold on MLS, and only 3 listed on MLS but sold on FSBO. Out of the 2,023 properties that were sold multiple times we have 645 that were sold using di¤erent platforms at di¤erent times. In Table 7 we present results using this sample. Di¤erent columns focus on di¤erent outcome variables. In all regressions we include year and month dummy variables and a linear time trend. In almost all cases the results are similar to those we found in Tables 4-6, where we controlled for di¤erences across properties using the house and neighborhood characteristics. We also display in Table 7 regressions using the same sample, but dropping the …xed e¤ects and controlling for di¤erences using the house and neighborhood characteristics instead. The results are essentially identical. The motivation behind this regression is twofold. First, to highlight that the sample of houses that sell multiple times –used in this section– is representative, namely, that …ndings for those houses (without …xed e¤ects) are similar to those for the whole sample. Second, to show that controlling for house characteristics delivers similar …ndings as those rendered using …xed e¤ects. Together these results suggest that there is no bias in the estimates due to an unobserved house e¤ect that is …xed over time. This should not be surprising. The di¤erences in the observed characteristics were not large and controlling for them did not make a large di¤erence. Since most unobserved house characteristics, we can think of, seem (roughly) …xed over time we conclude that we should not be concerned over the impact of unobserved household characteristics on our estimates.

19

5.2.2

Seller Selection

If an unobserved seller type a¤ects both the outcome variable and platform choice our estimates will be biased. For example, some sellers might be better, or more patient, at bargaining and therefore able to get a higher price regardless of the platform they use. Being more patient, according to the model, they are also more likely to list on FSBO. Absent appropriate controls for seller type we will overestimate the e¤ect of selling on FSBO. We explore several ways to deal with this problem. Conditioning on Initial Listing The …rst approach is to compare the di¤erences in outcomes between those sellers who listed on FSBO and sold on FSBO and those who initially listed on FSBO but ended up switching to MLS. The results in Table 4 suggest that conditional on listing on FSBO there is a small, and not statistically signi…cant, increase in price from also selling on FSBO. If we believe that moves to MLS, after listing on FSBO, are purely driven by random forces then the estimates suggest that the two platforms deliver the same prices. There is no gain in the sale price from selling on MLS relative to FSBO. Even if moving to MLS depends on seller type the selection bias should be reduced, as the group of FSBO listers is more homogenous than the population as a whole. Namely, in the range of sellers, these observations belong to the set that self-selected into FSBO. Furthermore, it is not clear that the selection indeed dictates a bias. Consider selection on patience. Is it the more or the less patient seller who moves to MLS? A patient seller may stay longer on FSBO. On the other hand, moving to MLS entails a long wait (given the …ndings in the previous section), thus it might be that the more patient sellers are those that decide to move on to the MLS. In other words, there might be selection, but its relation to sales price is less clear.13 13

For the sample of movers (from FSBO to MLS) we regressed price, time on the market on the MLS, and probability of selling within the …rst 60 days after moving on the time the house spent on FSBO before changing platforms. We found that the time spent on FSBO has no explanatory power on any of those performance variables on the MLS. The lack of correlation between stay in FSBO and MLS performance seems to suggest as the decision to stay more or less on FSBO does not seem to re‡ect any systematic selection.

20

By-Owner Sales on MLS Our second approach to quantify the role of unobservable seller characteristics is to compare FSBO sales to realtors’ transactions of their own properties. These transactions provide us with a "sale by owner" using the MLS. Levitt and Syverson (2006) report that realtors are able to obtain better prices when they sell properties in which they have an ownership stake relative to properties, sold by the same realtors, where they are not owners. We assume that realtors are no worse at selling their own properties than non-agents. In other words, the e¤ect of realtors selling their own homes is an upper bound on the impact of seller selection. The results are presented in Table 8. The variable "Sold by Owner" is a dummy variable that equals one for all sales by either a realtor selling their own home on the MLS, or a sale on FSBO. The variable "Sold on FSBO" equals one for sales on FSBO, and therefore its coe¢ cient measures directly the di¤erence between the performance of FSBO sales and sales by owner/agents on MLS. The regressions in columns (i) and (iii) include only properties that sold on the platform where they were initially listed. The results in the other columns include all properties that sold. As in Levitt and Syverson we …nd that owners obtain a premium when selling properties in which they have an ownership share. However, for price, time to sell and probability of sale within 180 days there is no statistically signi…cant di¤erence between agent/owner and sales on FSBO. FSBO sales on the other hand are less likely to happen within 60 or 90 days. Seller Fixed E¤ects Our …nal approach is based on using multiple sales by the same seller. We use the observed multiple sales to control for unobserved seller heterogeneity. Matching names across transactions we identi…ed 265 sellers who listed properties using di¤erent platforms, these involved 744 sales.14 The results are presented in Table 9. In the …rst column we regress the logarithm of price on a dummy variable that equals one if the seller listed a property on FSBO any time during the sample, not necessarily at that observation. The sample includes all the sales in the sample and the regression includes the usual time, house and neighborhood controls. We see that most of the e¤ect of FSBO 14

There are two possible mistakes in matching names. Sellers that register transactions with somehow di¤erent names (e.g., with or without initials, or with spouse vs without) may be overlooked. We may miss those valid matches. On the other hand, we may misclassify as a match two sellers with identical names.

21

we saw in Table 4 can be explained by this dummy variable. This might not be too surprising since this coe¢ cient is a weighted average of the sellers that sold only once using FSBO and those that sold more than once and used FSBO at least once. Since the …rst group is larger they might explain most of the e¤ect. For that reason in column (ii) we run the same regression but for MLS transaction only. Since the sample includes exclusively MLS transactions the coe¢ cient on FSBO lister re‡ects the selection e¤ect and not a platform e¤ect. The results suggest that FSBO sellers are indeed likely to get a higher price even when selling through MLS. On average they get 0.9% more. Note, that they take slightly longer to sell, although the e¤ect is not statistically signi…cant, suggesting that they are more patient. All this points out to seller selection is indeed present. Consistent with Claim 2 selection is creating a positive correlation between price and propensity to list on FSBO. However, selection is not enough to fully reverse the result that MLS does not command a premium. In the last two columns in the table we restrict the sample to the properties sold by sellers who had multiple sales/listings. There are 216 di¤erent sellers that sold multiple properties using di¤erent platforms involving 608 sales. In column (iii) we report the result of regressing the log of price, and time to sell, of the properties sold by these sellers on a dummy variable that equals one if the property was listed on FSBO, and the usual controls. We include also …xed e¤ects for the sellers. The results suggest that when listing on FSBO these sellers get 1.65% higher price, but the e¤ect is not statistically signi…cant. On average it takes 16 days longer to sell the house when listing on FSBO. In column (iv) we repeat the analysis with a dummy variable that equals one if the property is listed and sold using FSBO. As in column (iii) we include seller …xed e¤ects. The results suggest that there is no statistical di¤erence in the price or the time to sell. We also examined instrumental variables regressions to control for the potential correlation between FSBO and the unobserved characteristics. In all these cases the impact of FSBO was not statistically di¤erent than zero. However, depending on the exact functional form, the standard errors were very large, which is consistent with the instrumental variables being only weakly correlated with the decision to use FSBO. Indeed the "…rst stage" veri…es this. The instruments we tried include the neighbors’propensity to list, or their success, on 22

FSBO. In summary, we explored various ways to control for seller selection in the decision to use FSBO. The results suggest that indeed selection is present. After controlling for selection we …nd that the FSBO premium disappears. We …nd no evidence that MLS provides any premium relative to FSBO.

6

Welfare Implications

We now discuss the welfare implications of the entry of a FSBO platform implied by our results. FSBO listings di¤er from MLS listings in two distinct ways: FSBO involves no commissions and the FSBO platform potentially delivers a di¤erent matching propensity (due to network size). Consider both elements. We argue the former represents a welfare neutral transfer, while the latter impacts total welfare (through slower matching, longer spells on the market, and perhaps poorer matches). Commissions, as any price, may impact total welfare through a change in the quantity of houses transacted. We assume that, in the relevant range of commissions, their impact on the number of transactions is negligible. Thus, commission avoidance represents redistribution from realtors to FSBO users. Sellers on FSBO enjoy a substantial reduction in the cost of transacting in the real estate market. While they have to put in more e¤ort, revealed preferences tell us FSBO users must be better o¤, at the expense of realtors who lose part of their rents. The lesson from the Madison case is that this welfare transfer can be achieved with a relatively small initial investment. The key to the success of the platform resides in coordination needed to generate a critical mass. Competition from independent alternative networks may preclude generating a critical mass. The second welfare consideration is that FSBO, or any competing platform, may preclude fully exploiting scale economies in network size. In the presence of network e¤ects, depending on the environment, only one platform can survive (Cantillon and Yin, 2006). However, the countervailing force, which calls for multiple platforms, is product di¤erentiation (Armstrong, 2006, and Rochet and Tirole, forthcoming). We evaluate the implications of our …ndings regarding these two determinants of the optimal market structure: di¤erentiation 23

and network e¤ects. We ask what our …ndings say about the welfare consequences of FSBO. The overall picture coming out of the …ndings presented in the previous sections is that although FSBO does not entail a price penalty, it is a less e¤ective platform. The larger network, the MLS, does better in terms of both expected time to sell and probability of a transaction within a time period. The e¤ectiveness of the larger platform suggests scale economies appear not to be fully achieved by FSBO. The …nding from our analysis that MLS is more e¤ective is a key for welfare. Absent di¤erentiation across platforms we could conclude that a single platform would lead to higher aggregate welfare. However, platform di¤erentiation may justify the coexistence of several platforms. What can we say about platform di¤erentiation? There are two dimensions in which the platforms di¤er. One is the matching e¤ectiveness of the platforms, which is a¤ected by the size of the network. The second di¤erence across platforms is the service provided by the agent (showing, pricing, conditioning the house). There is a sense in which both these dimensions are vertical (i.e., at equal prices all consumers prefer a more e¤ective platform and one that o¤ers a higher level of service). Sellers di¤er in their willingness to pay for these services. The key distinction between the two dimensions of di¤erentiation involves network e¤ects. Platform e¤ectiveness, involves network economies while the other services do not. Other things equal a larger platform bene…ts all traders. On the other hand, the other services involve no externality; no platform is required for their e¢ cient provision. If FSBO was di¤erentiated from MLS only in its e¤ectiveness, then its entry would decrease total welfare. However, FSBO is also di¤erentiated in the level of service. Therefore, it might be socially e¢ cient to have multiple platforms, o¤ering di¤erent service levels, catering to di¤erent types of sellers. Realtors have historically bundled their services with the MLS.15 But there is no technological reason for this bundling. In case of unbundling the additional services, and o¤ering di¤erent service levels, welfare gains can be accrued by having a single network. The single platform would exploit network economies, delivering higher welfare relative to the current allocation which involves a substantial fraction of the population listing in a less e¤ective 15

There are many reasons why realtors historically bundled their services. Regardless of the reason, they are quite reluctant to unbundle them (e.g., see foreclosure cases mentioned above). Unbundled services although becoming more common are still rarely o¤ered (see Nadel 2007).

24

platform. In addition, the add-on services can be separately marketed to those willing to pay for them, allowing to exploit network scale economies without imposing an ine¢ cient provision of services (too high for some). There is an additional dimension of e¢ ciency gains rendered by unbundling. As pointed out by Hsie and Moretti (2003) the …xed commission leads to: excess realtor entry (especially in expensive areas) and to rent dissipation through non-price competition among agents (see DOJ, 2007). Unbundling the services from the MLS could resolve both. Agent prices will be tied to the type and quality of the services they o¤er. Moreover, entry will not be tied to the rents generated by the access to the MLS.

7

Concluding Remarks

In this paper we examine the relative performance of two competing networks: MLS and FSBO. After controlling for di¤erences in house and seller characteristics we …nd that MLS delivers no price premium. MLS does, however, lead to faster transactions. The longer time to sell on FSBO is driven by two factors. First, a proportion of FSBO listings fails and have to move to the MLS. Second, the probability of a quick sell is larger for houses initially listed on the MLS. The lack of a MLS premium means that the cost of the commission charged by realtors is not compensated by a higher sale price, on average. The empirical …ndings are consistent with the stock and ‡ow matching framework discussed in Section 3. Consistent with Claim 1, matching is more e¤ective in the platform with the largest inventory, MLS. The type of selection we found in 5.2.2 is consistent with Claim 2, by which FSBO listing is associated with higher house prices (e.g., better bargainers or more patient sellers). Table 1 shows support for the prediction that FSBO listers move to MLS (and stay) in order to explore the bigger stock of MLS buyers, but MLS listers won’t move to FSBO (Claim 3). While over 22% of FSBO listers eventually move to the MLS only 0.2% of MLS listers move to FSBO. Finally, the lack of price di¤erences across platforms can be interpreted, in light of Claim 4, as lack of frictions. In a frictionless environment, with patient enough buyers shopping on both platforms, we should not expect a platform premium. In other words, it is the sellers for whom platform choice matters (due to service 25

and convenience) that will carry the burden of the commission. What do our results imply for market structure in the brokerage industry in Madison? If one believes that sellers are aware of the FSBO option, and know that there is no premium associated with MLS, then our results suggest that a large fraction of the population is willing to pay a signi…cant amount for the services provided by realtors. Thus, despite the 6% commission rate, realtors are going to continue to maintain a high market share. An alternative view is that FSBOMadison.com is still di¤using. As more people become aware of it, and realize that there might not be a price penalty associated with FSBO, its share of the market will increase. The data set we use in this paper comes from one market. We selected this market because of the availability of data and the willingness of FSBOMadison.com and the local realtors association to cooperate with us and share their data. At this point we cannot generalize beyond this market. Without further data and analysis we do not know if our results hold more broadly. As we show the penetration rates of FSBOMadison.com vary widely across neighborhoods. It is our impression, based on casual observation, that the penetration rates of FSBO vary across markets. Understanding what drives this variation and the forces behind the di¤usion of FSBO is key to understanding the broader implications of our …ndings. The data we analyzed so far end at 2004. It would be interesting to study a market during a more di¢ cult time, during a cooler housing market. We could see if the cost or, returns to, using a realtor vary with the cyclicality of the market.

26

References [1] Armstrong, Mark. ”Competition in Two-Sided Markets” Rand Journal of Economics, 2006. [2] Bernheim, Douglas and Jonathan Meer. 2007. “How Much Value do Real Estate Brokers Add: A Case Study”mimeo Stanford University. [3] Cantillon, Estelle and Pai-Ling Yin, 2006 ”How and When do Markets Tip? Lessons from the Battle of the Bund”mimeo ULB. [4] Coles, Melvyn and Abhinay Muthoo. “Strategic Bargaining and Competitive Bidding in a Dynamic Market Equilibrium”Review of Economic Studies 1998, 65, 235-60. [5] Coles, Melvyn and Eric Smith. “Marketplace and Matching” International Economic Review, 1998, Vol 39, No 1. [6] Department of Justice. “Competition in the Real Estate Brokerage Industry”A Report by the FTC and DOJ, April 2007. [7] House, Christopher and Emre Ozdenoren. 2007. “Durable Goods and Conformity” mimeo University of Michigan. [8] Hsieh, Chang Tai and Enrico Moretti. “Can Free Entry be Ine¢ cient? Fixed Commissions and Social Waste in the Real Estate Industry” Journal of Political Economy 111(5), 2003 [9] Levitt, Steven and Chad Syverson. “Market Distortions When Agents are Better Informed: The Value of Information in Real Estate Transactions”, University of Chicago mimeo. [10] Merlo, Antonio and François Ortalo-Magné. ”Bargaining over Residential Real Estate: Evidence from England”Journal of Urban Economics, September 2004, 192-216.

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[11] Merlo, Antonio, François Ortalo-Magné and John Rust. ”Models of Bargaining and Price Determination of Residential Real Estate: Theory and Evidence” 2006 mimeo University of Maryland. [12] Munneke, Henry and Abdullah Yavas. “Incentives and Performance in Real Estate Brokerage: Theory and Evidence” Journal of Real Estate Finance and Economics, 2001, 22, 5-21. [13] Nadel, Mark. 2007. “A Critical Assessment of the Standard Traditional Residential Real Estate Broker COmmission Rate Structure" Brookings Institute mimeo. [14] Rochet, Jean -Charles and Jean Tirole, ”Two sided markets: A progress report” forthcoming Rand Journal of Economics. [15] Salant, Stephen. “For Sale by Owner: When to Use a Broker and How to Price the House”The Journal of Real Estate Finance and Economics, 1991, 4, 157-173. [16] Taylor, Curtis. “The Long Side of the Market and The Short End of the Stick: bargaining Power and Price Formation in Buyers’, Sellers’ and Balanced Markets” The Quarterly Journal of Economics, August 1995, 837-855. [17] Yavas, Abdullah and Peter Colwell. “Buyer Brokerage: Incentive and E¢ ciency Implications”, Journal of Real Estate Finance and Economics, 1999, 18: 259-277

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Table 1: Properties by Initial Listing Platform and Outcome, By Year ListnOutcome MLS FSBO Unsold Total 1998 to 2004 MLS 10,725 (86.0%) 30 (0.2%) 1,713 (13.7%) 12,468 (79.9%) FSBO 697 (22.2%) 2,153 (68.6%) 288 (9.2%) 3,138 (20.1%) Total 11,422 (73.2%) 2,183 (14.0%) 2,001 (12.8%) 15,606 1998 MLS 1,806 (84.2%) 3 (0.1%) 336 (15.7%) 2,145 (94.0%) FSBO 43 (31.2%) 77 (55.8%) 18 (13.0%) 138 (6.0%) Total 1,849 (81.0%) 80 (3.5%) 354 (15.5%) 2,283 2000 MLS 1,285 (87.0%) 4 (0.3%) 187 (12.7%) 1,476 (80.3%) FSBO 106 (29.3%) 226 (62.4%) 30 (8.3%) 362 (19.7%) Total 1,391 (75.6%) 230 (12.5%) 217 (11.8) 1,838 2002 MLS 1,458 (86.9%) 3 (0.2%) 216 (12.9%) 1,677 (76.6%) FSBO 101 (19.7%) 381 (74.4%) 30 (5.9%) 512 (23.4%) Total 1,559 (71.2%) 384 (17.5%) 246 (11.2%) 2,189 2004 MLS 1,564 (81.3%) 9 (0.5%) 352 (18.3%) 1,925 (74.4%) FSBO 102 (15.4%) 480 (72.6%) 79 (12.0%) 661 (25.6%) Total 1,666 (64.4%) 489 (18.9%) 431 (16.7) 2,586 The year is de…ned by initial listing date. An unsold property is de…ned as not having a sales price in the city data.

29

Area Area Area Area Area Area Area Area Area Area Area Area Area

Table 2: FSBO Penetration Rates, By Area FSBO Listing Share (%) FSBO Outcome Share(%) Properties Sold 70 43.6 33.7 101 28 39.3 25.0 56 17 37.7 28.1 231 89 33.8 27.0 148 19 27.3 18.2 154 1 26.5 18.7 219 21 24.5 16.1 143 2 20.9 14.1 206 88 19.4 12.6 326 76 17.8 12.9 309 39 12.2 7.7 181 73 9.4 7.3 382 86 7.9 2.4 165 Overall 20.1 14.0 15,606

An area is de…ned by the City of Madison for assessment purposes. The above areas are a sample out of areas de…ned by the city.

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Table 3: Sample Property Characteristics by Listing Channel MLS FSBO Characteristic Mean Std. Dev. Mean Std. Dev. Di¤erence t-stat age (as of 2007) 46.32 24.37 48.27 26.46 1.95 3.73 # of bedrooms 3.07 0.72 3.05 0.68 -0.02 -1.47 # of full bath rooms 1.59 0.67 1.58 0.65 -0.01 -0.60 # of rooms 3.66 1.20 3.68 1.15 0.02 0.95 total sq footage 1,736.10 697.85 1,717.09 582.36 -19.00 -1.34 lot size 9,606.95 5,391.42 9,017.61 5,260.97 -586.50 -5.19 basement sq footage 998.46 381.79 959.40 329.74 -39.06 -4.99 inside condition 3.71 0.55 3.64 0.60 -0.07 -5.53 outside condition 3.75 0.49 3.75 0.51 -0.01 -0.92 roof age (as of 2007) 26.12 23.94 24.89 24.26 -1.23 -2.43 furnace age (as of 2007) 26.23 23.34 24.90 23.40 -1.33 -2.71 central air 0.81 0.39 0.82 0.38 0.01 1.61 quality class 4.79 1.16 4.84 1.07 0.05 1.88 street noise 16.12 26.90 15.35 26.47 -0.77 -1.37 water front 0.39 5.31 0.26 3.96 -0.13 -1.25 parcel view 2.03 0.20 2.02 0.18 -0.004 -0.96 The above characteristics are a sample of those available to us from the city data.

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Table 4: The E¤ect of platform on Price (i) (ii) (iii) (iv) (v) (vi) (vii) Dependent variable: logarithm of price Sold on FSBO/100 10.75 4.02 3.96 3.19 – 0.67 0.31 (0.92) (0.85) (0.35) (0.28) (0.45) (0.47) Initially Listed – – – – 3.07 2.59 2.87 on FSBO/100 (0.24) (0.40) (0.41) MLS Listing, – – – – – – 6.41 Sold on FSBO/100 (1.98) R2 = 0.015 0.190 0.870 0.924 0.925 0.925 0.925 Dependent variable: price (in 1000’s of dollars) Sold on FSBO 14.81 2.93 5.21 5.09 – 0.23 -0.64 (2.02) (1.93) (0.85) (0.71) (1.18) (1.08) Initially Listed – – – – 5.09 4.92 5.60 on FSBO (0.62) (1.05) (1.08) MLS Listing, 15.55 Sold on FSBO (5.15) R2 = 0.007 0.124 0.842 0.893 0.894 0.894 0.894 Time Controls no yes yes yes yes yes yes House Characteristics no no yes yes yes yes yes Neighborhood E¤ects no no no yes yes yes yes N = 12,878 12,878 12,878 12,878 13,605 13,605 13,605 All columns report results from OLS regressions. In columns (i)-(iv), the sample includes only properties that sold on the platform they originally listed. The sample in column (v) -(vii) also includes properties that sold on a di¤erent platform than originally listed. Time controls include year and month dummy variables and a linear time trend.

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Table 5: The E¤ect of platform on Time to Sell (i) (ii) (iii) (iv) (v) (vi) (vii) Sold on FSBO -3.83 -3.76 -0.16 1.42 – -61.08 -67.52 (1.68) (1.67) (1.63) (1.65) (2.83) (2.57) Initially Listed – – – – 20.07 63.92 68.95 on FSBO (1.51) (2.52) (2.90) MLS Listing, 115.13 Sold on FSBO (12.28) Time Controls no yes yes yes yes yes yes House Characteristics no no yes yes yes yes yes Neighborhood E¤ects no no no yes yes yes yes N = 12,877 12,877 12,877 12,877 13,604 13,604 13,604 R2 = 0.001 0.018 0.148 0.174 0.181 0.209 0.214 All columns report results from OLS regressions. The dependent variable is total time to sell, measured in days, from the date of the initial listing until the sale date, recorded in the city data. In columns (i)-(iv), the sample includes only houses that sold on the platform they originally listed. The sample in column (v) -(vii) also includes houses that sold on a di¤erent platform than originally listed. Time controls include year and month dummy variables.

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Table 6: The E¤ect of platform on Probability of Sale (i) (ii) (iii) (iv) (v) (vi) Dependent variable: dummy Conditional on sale, sold variable equal to 1 if: Sold 180 days 90 days Initially Listed 1.90 – -7.43 – -11.74 – on FSBO/100 (0.69) (0.73) (1.10) FSBO listing – 1.03 – 0.56 – -2.51 stayed on FSBO/100 (0.78) (0.81) (1.25) FSBO listing – 4.19 – -28.34 – -35.70 moved to MLS/100 (1.21) (1.23) (1.90) MLS listing – -4.31 – -18.21 – -5.72 moved to FSBO/100 (5.29) (5.75) (8.80) Mean of dependent variable(%) 87.1 87.5 53.0 N = 15,605 13,605 2 R = 0.132 0.134 0.132 0.160 0.118 0.134

(vii) (viii) within: 60 days -10.07 – (0.96) – -5.93 (1.09) – -20.91 (1.65) – -9.60 (7.68) 23.5 0.083

All columns report results from OLS regressions. The dependent variable is a dummy variable, which varies by column. In columns (i) and (ii), the sample includes properties that were not sold, while in columns (iii)-(viii) the sample is only properties that a sale was eventually observed. All regressions include year and month dummy variables, a linear time trend, house and neighborhood characteristics.

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0.088

Table 7: House Fixed E¤ects Regressions Dependent variable: log of price time Initially Listed 2.48 2.65 – – 25.33 19.73 on FSBO/100* (0.49) (0.41) (3.47) (2.48) FSBO listing – – 2.38 2.73 – – sold on FSBO/100* (0.55) (0.46) FSBO listing – – 2.83 2.43 – – moved to MLS/100* (0.86) (0.76) MLS listing – – 9.15 3.49 – – moved to FSBO/100* (4.24) (3.88) House Fixed E¤ects yes no yes no yes no House+Neighborhood Char no yes no yes no yes

to sell –

Dependent variable: dummy Conditional on sale, sold variable equal to 1 if: Sold 90 days Initially Listed 0.30 0.45 -14.45 – – -7.35 on FSBO/100 (0.34) (0.24) (2.79) (2.50) FSBO listing – – – -6.05 -1.28 sold on FSBO/100 (3.15) (2.23) FSBO listing – – – -36.42 -37.60 moved to MLS/100 (4.87) (3.65) MLS listing – – – -31.82 33.89 moved to FSBO/100 (24.14) (18.75) House Fixed E¤ects yes no yes yes no yes House+Neighborhood Char no yes no no yes no

9.50 3.65 (3.88) (2.70) 67.75 69.80 (5.99) (4.41) 24.31 51.81 (45.00) (34.01) yes no no yes within: 60 days –



-2.46 -1.45 (2.84) (2.01) -20.30 -22.44 (4.38) (3.30) 6.64 -0.93 (21.75) (16.94) yes no no yes

*In columns where the dependent variable is "time to sell" the independent variables are not divided by 100. All columns report results from OLS regressions. The sample includes properties where multiple sales were observed, there are 2023 such properties involving 4202 sales. In columns where "sold" is the dependent variable the sample also includes properties that were listed more than once, at di¤erent times even if they did not sell, there are 2728 such properties involving 4921 listings. All regressions include year and month dummy variables and a linear time trend.

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Table 8: FSBO versus Sales by Agent/Owner on MLS (i) (ii) (iii) (iv) (v) (vi) (vii) Dependent variable: log of price time to sell sold in 60 sold in 90 sold in 180 Sold by Owner/100 2.12 1.84 -0.95 -3.47 2.41 7.27 1.95 (0.70) (0.68) (4.19) (4.35) (2.74) (3.17) (2.19) Sold on FSBO/100 1.12 1.22 2.35 0.28 -6.75 -6.93 0.51 (0.74) (0.72) (4.41) (4.60) (2.89) (3.34) (2.19) N= 12,878 13,605 12,877 13,604 13,605 13,605 13,605 All columns report results from OLS regressions. In columns (i) and (iii), the sample includes only houses that sold on the platform they originally listed. The sample in columns (ii) and (iv) -(vii) also includes houses that sold on a di¤erent platform than originally listed. All regressions include year and month dummy variables, a linear time trend, house and neighborhood characteristics.

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Table 9: Controlling for Unobserved Seller Heterogeneity (i) (ii) (iii) (iv) Dependent variable: logarithm of price Initially Listed 1.65 on FSBO/100 (1.86) FSBO listing 1.96 sold on FSBO/100 (2.59) FSBO Seller/100 2.58 0.92 (0.25) (0.54) Dependent variable: time to sell Initially Listed 16.47 on FSBO (10.87) FSBO listing -16.66 sold on FSBO (15.38) FSBO Seller -2.65 4.33 (1.57) (3.32) Sample all sales MLS listings sellers w/ multiple listings/sales Fixed E¤ects no no yes yes N= 13,605 10,755 742 608 All columns report results from OLS regressions. In column (ii) the sample includes only properties there were listed on MLS. In columns (iii) and (iv) the samples include properties sold by sellers with multiple sales between 1998 and 2004, there are 265 sellers that sold properties listed using di¤erent platforms, involving 744 sales, 216 seller sold properties using di¤erent platforms, involving 608 sales. The regressions in columns (iii) and (iv) include seller …xed e¤ects. All regressions include year and month dummy variables. a linear time trend, house and neighborhood characteristics.

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