Patent Publication Number: US-2011078138-A1

Title: System for Matching Property Characteristics or Desired Property Characteristics to Real Estate Agent Experience

Description:
RELATED APPLICATIONS 
     The present application claims the benefit of U.S. Provisional Patent Application Ser. No. 61/230,979, filed Aug. 3, 2009, U.S. Provisional Patent Application Ser. No. 61/230,981, filed Aug. 3, 2009 and U.S. Provisional Patent Application Ser. No. 61/231,315, filed Aug. 4, 2009, each of which in incorporated herein by reference in its entirety. 
    
    
     FIELD OF THE INVENTION 
     The present invention relates to real estate listing services. More specifically, the present invention relates to a system and method matching property characteristics or other real estate characteristics with relevant real estate agents. 
     BACKGROUND 
     Choosing a real estate agent can be a daunting task. Currently agents are found through personal recommendations, associations with brokerages, advertising and internet searches and business networks. While each of these methods provides capability in reaching the agent&#39;s general target audience of buyers and sellers, the success in reaching the audience may not correlate well with selling success. 
     Attempts have been made to remedy this information gap. Some websites aggregate current house listings and associate the listings with an agent. Other websites assign a static rating, showing whether the agent is overall a “good” agent or “bad” agent. These overall ratings are then used to recommend agents to buyers or sellers. Thus, the websites suggest that an agent highly ranked overall will be an effective agent to the buyer or seller. 
     The problem with many rating systems is that they do not necessarily correlate with success in a particular market. For example, a real estate agent, who is successful at selling million dollar homes, may be much less effective at selling starter homes or condominiums. Also, a particular agent may have a good working knowledge of one part of a city and virtually no knowledge of pricing trends, amenities and other valuable information in an area even a few miles away or within the same ZIP code. 
     These results may be further manipulated by search engine optimization (SEO) specialists who hone in on specific search terms to bring a webpage&#39;s results higher in the SERP&#39;s. This may result in the SERP ranking elevation of a real-estate agent, who understands SEO, but does not necessarily understand how to sell a house in a particular area or with particular characteristics. In other words, the real estate agent may be able to rank well about houses in a neighborhood without actually having sales experience in that neighborhood, etc. 
     These deficits in the currently available information and search engines are very difficult to remedy for the common home owner or purchaser. Obtaining accurate information about a real estate agent&#39;s ability to sell homes in a particular geographic area or neighborhood and with particular characteristics is often near impossible. Yet, much evidence exists to suggest that a consumer receives the most benefit and value from a real estate agent who is well versed in handling transactions for properties that are similar in price, location, as well as other characteristics of the subject property. Thus, there is a significant need for a method and system which enables a home buyer or seller to obtain information about an agent that would be most desirable for their particular situation. 
     SUMMARY OF THE INVENTION 
     It is an object of the present invention to provide an improved service matching consumers with relevant real estate agents based on their desired home characteristics and each given agent&#39;s past experience. 
     According to one aspect of the invention, a web service or computer based program returns proposed agent matches based on home characteristics. (For ease of reference, the program or method of the present invention will be referred to as a “web service”, although a non-website based system is contemplated and within the scope of the invention). More specifically, the web service compares the details of a seller&#39;s home or of a buyer&#39;s desired home attributes against an agent&#39;s history of homes for sale or purchase, whether it be home price, location or other characteristics. The web service then returns proposed agent profiles ranked according to their experience as related to the home. These dynamic results provide a better estimate of agent experience with a neighborhood and home characteristics than a generic agent rating. 
     According to another aspect of the invention, the web service or system may rate the agent based on impartial outcome information from real estate closing data because the closing data is required to be placed into the web service such that the web service&#39;s owner may be paid its part of the commission and the customer may be paid their rebate. In other words, the web service or system may calculate how close a listing agent came to obtaining full list price on the property, or how much of a discount the buyer&#39;s agent was able to obtain for his or her clients. 
     Additionally, the web service or system may include and evaluate additional factors, including, whether each listing posted on the MLS by a given agent sold, what the sale price was relative to comparable sales, whether it was priced competitively, the number of days a listing sat on the market prior to selling relative to comparable properties, and the difference between list price and sold price, etc. 
     It is another object of the present invention to provide improved search engine result pages (SERP&#39;s) with relevant experienced real estate agents. 
     According to one aspect of the invention, a search engine returns agent matches in the SERP&#39;s based on areas of expertise. More specifically, the web service compares the search terms against an agent&#39;s history of homes for sale or purchase, whether it be home price, location or other characteristics. The web service then returns proposed agent results ranked according to their experience as related to the search terms. These results provide a better estimate of agent experience with a neighborhood or home characteristics than a generic agent website content result. 
     These and other aspects of the present invention are realized in a web service or system which matches home characteristics with relevant real estate agents as shown and described in the following figures and related description. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
       Various embodiments of the present invention are shown and described in reference to the numbered drawings wherein: 
         FIG. 1  shows a flow chart overview of the interaction between the web service subsystems; 
         FIG. 2  shows a flow chart of the home seller&#39;s portion of the web service system; 
         FIG. 3  shows a flow chart of the home buyer&#39;s portion of the web service system; 
         FIG. 4  shows a diagram of system interactions in the web service system; 
         FIG. 5  shows a diagram of the home characteristic to agent ranking module; 
         FIG. 6  shows a screenshot of the buyer invitation to use the home-agent match web service system; 
         FIG. 7  shows a screenshot of the seller information survey system; 
         FIG. 8  shows a screenshot of the agent recommendations and the interface to the vetting module on a display screen; 
         FIG. 9  shows a screenshot of the bid comparison interface to the customer bid review and acceptance module; 
         FIG. 10  shows a screenshot of the agent profile interface to the agent database module; 
         FIG. 11  shows a screenshot of the agent results interface tab to the buyer search module on a display screen; 
         FIG. 12  shows an alternate screenshot of the information survey system as seen in  FIG. 7 ; 
         FIG. 13  shows a screenshot of another embodiment of the agent recommendation interface and vetting module; 
         FIG. 14  shows a screenshot of another embodiment of the bid comparison interface to the customer bid review and acceptance module; 
         FIG. 15  shows a screenshot of the agent bid request interface; 
         FIG. 16  shows a screenshot of the agent submit seller proposal interface; 
         FIG. 17  shows a screenshot of the agent submit buyer proposal interface; 
         FIG. 18  shows a screenshot of the agent claiming interface; 
         FIG. 19  shows a screenshot of the manage client interface; 
         FIG. 20  shows a screenshot of the interface to the lead management subsystem; 
         FIG. 21  shows a screenshot of the auction proposal interface. 
         FIG. 22  shows a diagram of the interaction between the web search servers; 
         FIG. 23  shows a flow chart of the term-experience matching subsystem; 
         FIG. 24  shows a diagram of the professional experience to search term ranking subsystem; 
         FIG. 25  shows a search engine result page with organic experienced professional results; 
         FIG. 26  shows a search engine result page with profile enhanced organic experienced professional results; 
         FIG. 27  shows a search engine result page with organic experienced professional results and advertising that includes relevant certifications; 
         FIG. 28  shows a search engine result page with profile enhanced organic experienced professional results and advertising that includes relevant certifications; 
         FIG. 29  shows a search engine result page with professional results as supplemental to the organic result for the domain; 
         FIG. 30  shows a search engine result page with profile enhanced professional results as supplemental to the organic result for the domain with advertising that includes relevant certifications; 
         FIG. 31  shows a search engine result page with certified professional results as supplemental to the organic result for the domain; 
         FIG. 32  shows a diagram of a data collection server and its connection to other content servers. 
     
    
    
     It will be appreciated that the drawings are illustrative and not limiting of the scope of the invention which is defined by the appended claims. The embodiments shown accomplish various aspects and objects of the invention. It is appreciated that it is not possible to clearly show each element and aspect of the invention in a single figure, and as such, multiple figures are presented to separately illustrate the various details of the invention in greater clarity. Similarly, not every embodiment need accomplish all advantages of the present invention. 
     DETAILED DESCRIPTION 
     The invention and accompanying drawings will now be discussed in reference to the numerals provided therein so as to enable one skilled in the art to practice the present invention. The drawings and descriptions are exemplary of various aspects of the invention and are not intended to narrow the scope of the appended claims. 
     Turning now to  FIG. 1 , a flow chart is shown providing an overview of the interaction between the system, described herein as a web service system  10  and subsystems  20 ,  22 ,  24 . (It should also be recognized that while a web service is likely the most common use of the invention, it may also consist of a client-server program or other system. Therefore, while the system may be described in terms of a web service for clarity, other systems, such as client-server, are considered and may be substituted where appropriate.). 
     An information management subsystem  20  communicates with a customer subsystem  22  and agent subsystem  24 . These subsystems  20 ,  22 ,  24  interact with the purpose of matching characteristics of a home or desired home with an agent that has the most relevant experience and success in similar homes. This may include relevant experience such as home price, home location, home type, home size and other characteristics which may be important to home buyers. 
     As used herein, the term “customer” refers to an actual customer or a potential customer. 
     The information management subsystem  20  begins processing when it discovers a new data source and adds a database  30  (or ads to an existing database) for that source. Current data within the new database is mined  32  such that it may be associated with an agent&#39;s history in the agent database  34 . Until a request for an agent is received  36 , the information management subsystem  20  continues to monitor the data source and mine any new data  32  for association with an agent&#39;s database  34 . 
     Meanwhile, an agent may arrive at the web service interface  37  for the first time. If the agent&#39;s information is already in the database and the agent&#39;s profile exists  38 , the agent may claim the profile  40 . Otherwise, the agent will be required to create a new profile  42  for inclusion in the database. Once the agent has a profile, she may check back from time to time to review any current prospective customer requests and profiles (i.e. sales leads) or receive new sales leads  44 . Likewise, the agent may be notified of any new leads by a communication such as an email, text message, etc. 
     Meanwhile, a customer may arrive at the web service interface  46 . The customer inputs his desired or actual home criteria  48 , such as any variety of home characteristics and optionally specify agent characteristics, which may include gender, languages spoken, required years of experience, and whether the agent is an active real estate investor. The customer then submits the information to the information management subsystem  20  to request an agent  50 . 
     In one embodiment, the subsystems then begin interacting with the purpose of matching the house characteristic information and, optionally the agent characteristics, submitted in  48  to an agent&#39;s history mined in  32 . Each agent servicing the home area submitted in  48  is evaluated against the same home criteria  52 . The system returns one or more agents  54  and communicates the results to both the customer subsystem  22  and the agent subsystem  24 . 
     Both the customer in  56  and the agent in  44  receive the results of the request in  50 . The customer receives a portion of the agent information  56 ; the agent receives a portion of the customer information  44 . If the customer accepts the agent  62  and the agent accepts the customer  64 , the agent begins work on finalizing any terms necessary to acquire the customer as a client. After reaching an agreement, the agent then works with the customer to purchase or sell real estate. 
     Upon closing of a real estate deal, the information is input into the agent subsystem  66  along with payment information  68 , which is communicated to the information management system  20 . The information management subsystem  20  receives the payment  70  and rewards the customer with a rebate  72  of a portion of the payment received  70 . 
     In one embodiment, outcomes are monitored for the matched agent and customer. For a seller, a larger than expected price (per square foot or other home characteristics), properties they have closed as seller or buyer, re-list occurrences, property price fluctuations over time compared with the local, state and national markets and the amount of time on the market may be noted and stored as part of the agent&#39;s history with that sale. For a buyer&#39;s agent, similar statistics may be gathered and stored evidencing such characteristics as a lower than expected purchase price, quick closing or other desirable concessions. If no transaction is forthcoming, that failure may also be stored. Thus, the particular success of the agent may be dependent on performance of each real estate deal processed through the system. 
     Data attributes that may be used by the system to recommend agents to a customer include:
         Agent&#39;s state license status   Number of past complaints with the state department   Number of active complaints with the state or Better Business Bureau   Office location   Average number of submitted offers internally tracked   Number and percentage of repeat customers   Number, type, and location of properties an agent owns   Languages spoken   MLS membership status   Total number of active listings   Total number of referred customers over a given timeframe   Buyer&#39;s agent history or seller&#39;s agent history   Price per square foot relative to comps   Days on market relative to comps   Difference between ask vs. sale price   Number of active listings weighted by similarity to subject or desired property
           Location of active listings   
           Number of sales represented on buy and sale side, weighted by similarity to subject or desired property, during a given time frame
           Number of homes sold during the last 180 days, 1 year, 2 years individually for each the buy and sell side of the transaction.
               Location of sold homes   
               
           Number of inactive listings and similarity to subject or desired property
           Location of inactive listings   
           Ratio of properties sold vs. listed   Number of buyers represented during a given timeframe
           Location and characteristics of sales   
           Number of sellers represented during a given timeframe
           Location and characteristics of sales   
           Publicly and privately available 3rd party ratings
           Analysis of 3rd party reviews   
           Internally sourced ratings and reviews   Number of price drops—for each property listed and/or sold, how many listing price reductions were made with the MLS by the agent in a given time frame   Length of time as MLS member or actively listing properties in a given geographical location   Level of diligence—on average per listing;
           Number of open houses that they conduct in a given time frame   Number of property photos published   Level of detail of their property description submissions   Quality of their other property marketing materials   
               

     In-system performance may be monitored and scored as well. Negative reviews decrease match score, while positive reviews and frequent closing will increase match score. Work load and number of current leads may also be a factor used to spread work out and avoid a deluge of work on a specific agent in a hot market. 
     In another embodiment, the agent&#39;s history or “track record” is overlaid on a baseline or “hyper-local comparable market average” to evaluate the effectiveness of a realtor. For example, the price per square foot of the properties in each transaction that a realtor represented is compared with the geographically immediate comparable sales price per square foot (in last 6 months). The sale price versus listing price (“spread”) is evaluated relative to the same population and days on market (“DOM”). The system also evaluates whether the agent represented the buy or sell side compared with the current home characteristics request. The results of the comparisons are then aggregated such that a value may be given to an agent&#39;s history relative to the current home characteristics request. 
     For example, a buy side performance measure may compare a $212 price per square foot versus an average rate of $289 per square foot for comparable units in the immediate geographic surroundings, within the last six months, with market values trending upward during that period and give a positive match rating of +40 points. A seven percent list versus sell spread compared with a four percent average may give a match rating of +10 points. These points are then aggregated in a total match rating for the agent and compared with other agents that were relevant for the subject property&#39;s characteristics. 
     In another example, a sell side performance may compare 52 DOM versus an average of 92 DOM for the universe of comparable property sales in the immediate geographic area (1 mile radius, dependent on population density and number of qualifying transaction records returned) over the last 6 months and give +20 points match rating. A $252 price per square foot received versus a market average of $221 per square foot market average may give +50 points match rating. The system may also take into account recent market fluctuations and programmatically remove outliers and employ other techniques to remove anomalous transaction data, where applicable. These points are then aggregated in a total match rating for the agent compared with all other subject property qualified agents. 
     In one embodiment the match ratings are normalized such that the top match point value is given a rating of 99%. This communicates that the web service system believes the match to be the best agent for the job. 
     External data sources may be monitored and mined. These data sources may be from private or public bodies. The web service system may connect to external systems such as Multiple Listing Services (MLS) or information brokerages. The web service system may also connect to public data sources as well, including government sources, such as tax data, title data or even agent discipline data, as well as publicly and privately available websites. The data retrieved from these sources can then be used in the matching algorithm. For instance, an agent that turns up disciplinary violations may rank lower than a similar agent without those violations. 
     Turning now to  FIG. 2 , a flow chart of the home seller&#39;s interface portion  100  of the web service system  10  ( FIG. 1 ) is shown. The home seller&#39;s interface portion  100  may be used to perform actions  46 ,  48 ,  50 ,  56  and  62  of  FIG. 1  and effectively serves as part of the customer subsystem  22 . 
     A seller who wishes to find an agent navigates to the seller&#39;s page  110  of the web service system  10 . After creating an account  112 , the seller inputs the characteristics of the home to be sold  114 . The seller may then choose to have the system match the seller&#39;s home with the most highly relevant agent, a quick match, or may have a group of the most highly relevant agents bid to service the seller  116 . 
     Should the seller select the quick match, the web service  10  compares the home characteristics with the agent&#39;s experience and selects the best match of an agent with the home characteristics  118 . If the seller does not accept the match  120 , the process may be repeated by removing the previously selected agent from the results  121 . The next best match is then returned. Once the seller accepts the agent, the seller and the agent may begin their process of selling the house  122 . 
     If the seller selects to have the agents bid on the project, the web service  10  returns a listing of the top agents matched according to the home&#39;s characteristics and allows the seller to remove any of the matched agents  124 . The remaining agents may then be sent a request for a bid  126 . The agents may then review the bid request and any other competing bids  128 . If the seller has not already selected a bid  130 , the agent may submit or modify their bid  132 . Once the seller selects a bid  130 , the agent and seller may begin their process of selling the house  122 . Such a scenario enables agents to bid competitively for work which they find most appealing, and gives the seller the ability to obtain concessions such as larger rebates, waiver of exclusive listing agreements or other desirable terms. 
     Once the house has closed  134 , the agent has the duty to report the closing through the web service system  10 . The commission may be paid to the agent, who forwards the required portion to the web service owners A rebate incentive paid to the seller ensures that the seller has an interest in enforcing the agent&#39;s submission of the closing (including information and compensation) to the web service. 
     In addition, the system may be paid in different ways. The system may retrieve records that indicate that a sale occurred by a particular agent and at a particular address and therefore send an inquiry or invoice to a given agent who received a customer lead request. The system may also be named as the co-broker in a transaction, as the system may contain a licensed real estate brokerage, and its commission may be paid directly from escrow upon the close of a sale. The system may also utilize publicly available tax assessor and title data (transaction data) to compare against system activity data (allocated customer leads and their property criteria) to audit agent reporting activity in order to identify non-reported transactions. 
     Turning now to  FIG. 3 , a flow chart of the home buyer&#39;s portion  150  of the web services system  10  ( FIG. 1 ) as may be used to carry out steps of the customer subsystem  22  is shown. A buyer may either navigate to the web service by searching for a home  152  or by navigating to a request real estate agent page or interface  154 . The web service system then requests information about the buyer&#39;s desired home characteristics. This may be auto-populated by selecting a link from a home result page  156  or manually filled in by the buyer  158 . The buyer then may create an account  159  to access the web service system  10  if he does not have one already. 
     Once the buyer has completed the data entry phase, the buyer may select from three options  160 . If the buyer had previously selected a specific house, the buyer may choose to contact the seller&#39;s agent directly  162 . The buyer may alternately choose a quick match to an agent that best matches the desired home characteristics  164 , and, if important, be immediately available. The buyer may instead choose to request bids from a list of top matching agents relative to the desired home characteristics. Such bids may be limited to a given time period, for example 48 hours. 
     If the buyer selects a quick match, an agent is notified of the match  164 . If the customer does not accept the top matching agent  166 , that agent is removed from the results  168  and the request may be repeated  164 . Once the buyer accepts an agent, the agent and buyer may work together to find a desired home  167 . 
     If the buyer selects to have the agents bid on the representation, the web service system  10  returns a listing of the top agents according to the match and allows the buyer to remove any of the matched agents  169 . The remaining agents are then sent a request for a bid  170 . The agents may then review the bid request and any other competing bids  172 . If the buyer has not already selected a bid  174 , the agent may submit or modify their bid  176 . Once the seller selects a bid  174 , the agent and buyer may begin their process of buying the house  167 . 
     Once the house has closed  178 , the agent has the duty to report the closing through the web service system  10 . The commission may be paid  180  to the agent, who forwards the required portion to the web service system owners, or it may be paid directly to the system service owner as a licensed real estate brokerage. A rebate incentive paid to the buyer ensures that the buyer has an interest in enforcing the agent&#39;s submission of the closing into the web service system. 
     In an alternative embodiment, an online user can request information or schedule a viewing for a property, specifying that they would like to be contacted by a local expert. A system generated email will send the buyer&#39;s information, with exception of contact information, to a list of agents who have been filtered and selected using agent match. The first agent who “claims” the buyer and accepts the terms and conditions will receive the buyer&#39;s contact information. Once an agent claims the buyer, that agent will have exclusive access to the buyer&#39;s contact information. This alternative favors buyer response time as agents are encouraged to act on a first come first serve basis. 
     Turning now to  FIG. 4 , a diagram of system interactions in the system  10  as a web service is shown. A customer computer  200  may contact the web service system server  202  through the internet  204 . When a search is requested, the web service server  202  may request data from internal local or network database servers  206  to make the ranking decisions with the agent history compared with the customer submitted home characteristic data. Once an agent has been selected for contact, the agent may receive notification from a number of different sources including email on a personal computer  208 , a text message sent through a wireless carrier  210  and delivery to a mobile device  212 . The agent may also use similar methods to contact the web service server  202  to review any potential clients. 
     The web service system server may also be connected with other services. For instance, Multiple Listing Service (MLS)  214  data may be queried for data on the neighborhood, agent history, data verification and status of any pending closing. Other data sources may be similarly queried for useful data to be included in the database servers  206 . Should any problems be detected or customer requests received, the web service system server  202  may communicate with a customer support system  208  for follow up by a customer support representative  218 . 
     The web service system may require agent pre-approval based on an acceptance of terms of service from the agent or the agent&#39;s brokerage, including a co-cooperative brokerage agreement, and an experience and license status check. The pre-approval may further assure customers that poor agents have been vetted from the system and only transitionally relevant agents returned. It will also assure that any agent is contractually bound to pay the appropriate portion of the sales price to the owner of the web service system. 
     Turning now to  FIG. 5 , a diagram of the home characteristic to agent ranking module is shown. Using home characteristic data  250  entered by the potential customer, each data field is compared with the stable of homes sold by the agent. The advantage of the system is that an agent&#39;s experience is matched to the home characteristics. An agent who normally sells million dollar homes may be inexperienced in selling starter homes because the needs, desires, trade-offs, market conditions, and their existing buyer or seller customer and broker contacts are different. Consequently their ability to facilitate a transaction is likely to be limited. Likewise, an agent well versed in selling single family homes, may have little experience with condominiums and the like. Thus, in accordance with the present invention, it is desirable to improve the match between the customer&#39;s needs and the experience of the agent who will be working with the customers. 
     In one embodiment, the number of bedrooms  252 , bathrooms  254 , square footage  256 , lot size  258 , price range  260 , and/or neighborhood  262  are compared  263  against the number of homes sold by the agent with similar bedrooms  264 , bathrooms  266 , square footage  268 , lot size  270 , price range  272  and/or number of homes sold at or near the desired neighborhood  274 . This is referred to herein as an agent experience match system. 
     In an alternate embodiment, each home purchased or sold by the agent  275  is rated on the home data  250  provided by the customer. Each agent home rating is then aggregated to form a match rating. This is referred to herein as an agent home match system. 
     Each of the agent home criteria may be weighted such that inexact matches contribute a partial rating, such as a two bathroom house contributing a partial rating to a desired one and one half bathroom house, because of the close proximity to the desired result. These rating weights may be based on a selected importance by the customer or pre-set by the web service system. For example, one seller-user may give more weight to hastening and ensuring a transaction while another user is looking for the highest sale price or the lowest acquisition price, irrespective of timing. 
     Further, static data may also be included in the result matching system. For example, it has been noted that there is likely a correlation between response time to a customer request and a positive real estate outcome. Therefore, an agent may also be rated on her responsiveness to the customer over a series of interactions. Additionally, the system will use the conversion or transaction rate of a given agent as a ratio of the number of successful transaction relative to the total number of customer leads allocated over a given time period. 
     These static data points may be pre-compiled and stored along with the agent&#39;s other static data, such as contact information. When the home-agent matching system is requested by a customer, a portion of the agent rating may depend on such static data, but the rating overall is dynamic, based on the home characteristics submitted. 
     Overseeing the customer and agent experience may be a customer service subsystem. The customer service subsystem may monitor a customer&#39;s interaction from the website, including from the customer&#39;s first arrival on the web service system to the wrapping up of closing on a home. It may further monitor agent activities and interactions. By monitoring customer progression and agent activities, the customer service subsystem may be able to not only create reports of activity, but also detect potential problems and deploy customer service representatives to proactively resolve those problems. 
     In one embodiment, the customer service subsystem requires progress report submissions on a bi-monthly basis by the agent. In the event an agent misses progress report submissions multiple times, they may risk having their customer removed and given to another agent as well as a negative impact on the agent match score. 
     If the system detects a problem, such as lack of customer progression, a customer service representative may be notified. The customer service representative may be instructed by the system to start by sending a simple email to the customer or agent, or the system may automatically send an email in the customer service representative&#39;s name requesting further information or asking how the process is going. If the system or customer service representative does not receive a response, the system or customer service representative may escalate the attempt to a phone call. Should the system and customer service representative attempts to contact fail, the system or customer service representative may choose as a last resort to make the agent account inactive until further contact and to allocate the customer request to a new agent, based on system settings and company policy. 
     The purpose of the customer service subsystem may be to enable the customers and agents to work together to achieve the sale or purchase of real estate. The customer service subsystem may include frequently asked questions (FAQ) and online intervention forms for customer service representatives, which may intervene to attempt to resolve conflicts. 
     This system may also include the ability to privately message one another, leave a message on an online bulletin board as well as trigger emails and send messages to mobile devices. The agent or customer may search homes, select favorites or create a favorites list. The agent or customer may also compare properties on their attributes, schedule property viewings/tours, provide feedback on particular properties viewed and thereby actively curate the list to reflect the evolving preferences of the customer. 
     Turning now to  FIG. 6 , a screenshot of the buyer invitation to use the home-agent match web services is shown. The buyer may select from the three options as previously described: contact the seller&#39;s agent directly, use quick match or request bids to obtain buy side agent and brokerage representation. 
     Turning now to  FIG. 7 , a screenshot of the seller information survey system is shown. The seller is requested to fill out a survey of the home&#39;s characteristics for use in the home-agent matching system and other important data. This data may then be used to dynamically rate an agent&#39;s experience in selling similar homes. 
     Turning now to  FIG. 8 , a screenshot of the agent recommendations and the interface to the vetting module is shown. If a bid is requested by the customer, the top matched agents are shown. The customer has an opportunity to refuse any of the agents based on their personal biases. This vetting module is useful to prevent an agent from wasting time to bid on an opportunity when the customer would never select the agent based on some characteristic or bias. For example, some customers may never choose an agent who worked for a national firm or an unrecognized brokerage. Likewise, a customer may prefer to have an agent who is of the same gender, same age or other characteristic so that the customer may inquire of the agent about issues which may be important to the customer. Thus, for example, a 25 year old woman may select an agent of a similar age and gender to consult on local nightlife, the dating scene and the perceived safety of particular neighborhoods. In contrast, a retiree may seek a 60 year old plus agent to gain insights about the suitability of a neighborhood and local attractions for grandchildren, etc., or amenities for the elderly. Likewise, a person may prefer an agent that speaks his or her native language over those who do not. 
     Turning now to  FIG. 9 , a screenshot of the bid comparison interface to the customer bid review and acceptance module is shown. The agents, having submitted bids through the web service, compete to be selected by the buyer/seller. The bid comparison screen shown is a summary/comparison screen that allows the seller/buyer to compare key statistics related to the agent representation. The customer may further drill down into the specifics of the offered representation by clicking on a link. 
     Turning now to  FIG. 10 , a screenshot of the agent profile interface to the agent database module is shown. The agent profile interface displays a selection of the information collected about the agent by the web service system. This may include contact information, current listings, past listings, blog links/posts, customer reviews, answered questions, twitter posts, recommendations and other information gathered by the web service system. The information may be further condensed into summary statistics. For example, as seen in the screenshot, an agent&#39;s home data may be summarized into an average price, high and low sales prices, and a usual number of bedrooms in the home sold. 
     The agent profile may serve as a reinforcement of the home-agent matching system. After receiving a recommendation by the system, a customer may browse the agent&#39;s profile to gain confidence that the agent services similar homes. When the customer views the agent&#39;s data listings, listing history and sales history, the customer may feel more comfortable in the agent&#39;s knowledge. This may be further bolstered by other gathered data such as blog posts, twitter posts and answers. 
     Turning now to  FIG. 11 , a screenshot of the agent results interface tab to the buyer search module is shown. Based on a customer&#39;s search performed for houses in a neighborhood/area, the web service populates the area agents tab with agents ranked by their score which is dynamically created based on the home search criteria input by the customer. Should the customer choose to select one of the agents, the web service system may start the quick match process with the selected agent as the result. In another embodiment, the web service will ask whether the customer wishes to request a bid from that agent or from a group of agents. 
     Turning now to  FIG. 12 , an alternate screenshot of the information survey system as seen in  FIG. 7  is shown. After deciding to receive the agent match benefits, a seller is requested to fill out house characteristics that may be used by the agent match system. A seller may optionally specify a target time frame when they would like to sell their home. In one embodiment, the system may auto-populate the fields from previously stored data based on an address or MLS number. The client may then have the option to update any information that is out of date. 
     Turning now to  FIG. 13 , a screenshot of another embodiment of the agent recommendation interface and vetting module is shown. In one embodiment, after filling out the survey data, the client is presented with this screen on their display. Agent profiles are accessible from the agent name links, areas served are linked to area profiles, and the summary statistics of the agents are shown. The client may use any of this available data in vetting any agents from the bidding process based on their personal bias, desires or needs. 
     Turning now to  FIG. 14 , a screenshot of another embodiment of the bid comparison interface to the customer bid review and acceptance module is shown. After the customer has selected the potential agents from which to receive bids, the agents receive a message (such as in  FIG. 15 ) requesting a bid. Any agents who submit bids will be shown on this interface and associated with their bid. Once a satisfactory bid is made, the client may accept the desired bid. 
     Turning now to  FIG. 15 , a screenshot of the agent bid request interface is shown. After a client has requested selected agents to bid, an invitation to bid is sent to the selected agents. The agent may then choose to submit a proposal, decline involvement or, in some cases, request more information. 
     In one embodiment, the web service system also gives the client a rating. The web service system mines data about the client and gives them a rating. This mined customer data may include data from the survey, a credit report, or internal or external or other free or paid database requests. 
     Turning now to  FIG. 16 , a screenshot of the agent submit seller proposal interface is shown. After having received the invitation and selected submit proposal, the agent is then requested to fill out the proposal survey. The proposal survey may include several fields for computing summary data and an information box for other terms not included in the fields. Once submitted, the information may then show up on the client bid comparison interface as seen in  FIG. 14 . 
     Turning now to  FIG. 17 , a screenshot of the agent submit buyer proposal interface is shown. Similarly to  FIG. 16 , after having received the invitation and selected submit proposal, the agent is requested to fill out the proposal survey. This information is then relayed in a similar manner to the client. 
     While the screenshots show a web page, the system of the present invention may use other means of communication, such as SMS, XML, push notification, email and other technologies to ensure that the client and agent are notified of any changes in the bidding process. Similarly, the system may accept communications in modifying, submitting or accepting bids. 
     Turning now to  FIG. 18 , a screenshot of the agent claiming interface is shown. If the client has chosen the quick match feature of the web service system, once notified, the agent may accept or decline the invitation to become the agent for the client. If the agent declines, the web service system may exclude the agent from the results and give the next highest match an opportunity. 
     Turning now to  FIG. 19 , a screenshot of the manage client interface is shown. Once a relationship has been confirmed by both parties, the system may keep track of the interaction between them. In this screenshot, it may be noted that the relationship has progressed from looking in a general area (i.e. Oakland), to narrowing the search, to composing an offer, to the current status of waiting for a response. By tracking communications between the parties, current status and the history of status, the web service system may detect relationships in danger of souring and initiate alert or warning messages to the agent and customer service representative responsible for the particular customer. The customer service representative may then respond to both parties and ensure a satisfactory resolution. Further, by tracking the interaction, an agent may be rated on her attentiveness, time taken for each status step and other indicators of active client service. 
     Turning now to  FIG. 20 , a screenshot of the interface to a lead management subsystem is shown. An agent may actively manage their workload in the web service system by ensuring that current clients are recently contacted and by bidding or accepting potential leads when the current client list contains too few current clients. The information includes current status, such that the agent may remember the current goals and next steps for each client. 
     A lead management subsystem may direct the distribution of customers and monitor agent progress with the customers. In some cases, as above, the lead management subsystem will monitor reverse auctions. In other cases, as above, the lead management subsystem will monitor a quick match. However, the lead management subsystem may monitor other forms of customer acquisition including co-representation opportunities, leads and marketing responses. 
     In one embodiment, the lead management subsystem manages referral opportunities. Referral opportunities may come from people such as customers, clients or agents. After working within the web service system, agents may prefer to have the organization and tools provided within the web service system and therefore request their clients register for an account on the web service system. Other agents may not be familiar with or willing to represent a client and may therefore recommend the web service system. These referral opportunities may be tracked, and the number and frequency recorded, such that a frequent referring agent may be thanked and/or rewarded. 
     Once the referral opportunity has been given to the web service, the lead management subsystem may utilize an agent&#39;s match rating to inform the referrer of a good match, warn of a poor match or give recommendations based on the firm requested. Once informed, the customer or agent may then complete or abandon the referral. If completed, the system may then contact the selected agent and/or the customer to encourage the relationship to form and any terms and conditions to be accepted. 
     The match system may also be used to independently look up an agent that was recommended and give a match rating. 
     Turning now to  FIG. 21 , a screenshot of the auction proposal interface is shown. Once an agent has submitted a proposal, the proposal may be reviewed from this interface. The interface also contains other agent proposals for comparison. If the client or agents have specific questions or needs outside the bid, either party may make non-binding suggestions on the comments tab that may suggest important provisions that were not part of the original client submission. 
     While the previous discussion has centered on a customer searching for an agent, the match system may also be applied when a customer is searching for a house. Information contained in the search may be examined to create a request for a match from the information management subsystem. These agent match results may be shown in a sidebar along with the home results. 
     As used herein, the term “sidebar” refers to a location separated from primary content, including a location that may separate portions of primary content. For example, a sidebar may be located on the sides or top or bottom of a display area, or it may be located in between paragraphs of primary content. In the example above, the home search results may be the primary content and the agent match results may be the sidebar. 
     Search results may be examined in different ways to gather information to use in the request for a match. If the search uses home characteristics, those characteristics may be directly sent with the request for a match. If the request is for a neighborhood, the neighborhood may be examined for current characteristics and median or average values for the characteristics selected and sent with the request for a match. If the request is for a home, the home characteristics may be used or the broader scope of the neighborhood used and sent with the request for a match. Search history may also be used, where the results of the customer&#39;s search may be aggregated and used to determine the characteristics to be sent with the request to match. 
     These matches may then be used to pull relevant content from the agent information stored in the information management subsystem and display it to the user. Relevant information may include such things as agent profile photographs, home photographs, match rating, date the last home was sold, number of homes sold, average time on market and other statistics that may be tracked or derived from information in the information management subsystem. The click-through ratio may also be monitored such that the most relevant information for similar searches may be used. 
     The web service system may calculate areas of expertise for agents. An area of expertise is specific knowledge that may be attributed to an agent. The data in the information management subsystem may be used to calculate of such areas of expertise. Areas of expertise may include individual characteristics such as an individual area of expertise in a neighborhood, as evidenced by an agent&#39;s representation of numerous buyers or sellers in a neighborhood. Similarly, an agent may have an aggregate area of expertise in “First Time Homebuyers” which may be evidenced by individual areas of expertise in a price range, typical customer ages and FHA loans. These areas of expertise may be used to supplement information in the sidebar results related to searches or even appear on agent profiles. 
     Agent profiles may also benefit from match rating, areas of expertise and search results. When viewing a profile, the web service may review any searches performed by the customer and place them in a sidebar to the profile. The sidebar may then also include match ratings for the agent to the prior search results in the sidebar. Further, the search results may be examined by the web service and relevant areas of expertise may be placed on the agent&#39;s profile. For example, an agent who has subcharacteristics such as experience in a price range, typical customer ages and FHA loans may show the area of expertise in “First Time Homebuyers” to a customer who may be searching relevant houses to a “First Time Homebuyer.” However, to a customer that is not in the age nor price range, but has searched on terms relevant to FHA loans, the agent profile may display “FHA Experienced” in the place of “First Time Homebuyer.” 
     While the present invention can be used specifically for searching and executing the method within a web-based service or in a system which is run on individual computers, etc., aspects of the present invention can also be used in the context of a search engine to improve searching for agents and in particular for generating improved search engine results pages (SERPS), etc. While discussed below as a web search system based process, it will be appreciated that it could be performed on intranets or other computer systems which are capable of receiving a relaying the needed information. 
     Turning now to  FIG. 22 , a diagram is shown of internal interactions of a search system  1010  between an internet document server  1020 , an agent profile server  1030  and a result server  1040 . User  1012  through an internet connected device  1014  submits a search request to web search system servers  1016  through the Internet  1018  and receives results  1042  of the search. 
     In one embodiment, the search request is captured by dispatch server  1019 . Dispatch server  1019  may forward the request to internet document server  1020  and agent profile server  1030 . In the case that dispatch server  1019  has been enhanced, dispatch server  1019  may detect whether the search is appropriate for internet document server  1020 , agent profile server  1030  or both. Dispatch server  1019  with enhancements may then forward the search request to the appropriate servers. 
     Having received a request from dispatch server  1019 , internet document server  1020  may gather and rank relevant internet documents  1022 A,  1022 B and  1022 C based on the search request received. These documents and their associated ranking scores are then forwarded to result server  1040  for aggregation and sorting. 
     Agent profile server  1030  may also receive the search request from dispatch server  1019 . Search terms within the search request may be examined for professional areas of expertise. Using the areas of expertise and any modifying terms within the search request, relevant agent profiles  1032 A,  1032 B and  1032 C are gathered and ranked. The profiles and associated ranking scores are then forwarded to result server  1040  for aggregation and sorting. 
     Result server  1040  organizes results  1042  according to the rankings submitted for each result element  1044 A,  1044 B,  1044 C,  1044 D,  1044 E and  1044 F. The result server may then determine the final order  1046 A,  1046 B,  1046 C,  1046 D,  1046 E and  1046 F of the result elements. Results  1042  are then communicated to internet connected device  1014  and displayed to user  1012 . In one embodiment, result server  1040  compiles the results into a web page and sends the web page to internet connected device  1014 . 
     In some cases, there may be difficulty measuring the relevance of an internet document compared with an agent profile. Therefore, it may be useful to scale the rankings such that both sets of results may be combined. In one embodiment, the result server may normalize each result set such that each result set may have the same total result value, but individual results may vary. In another case, each server may normalize the result set to a maximum relevance value. When the sets are then combined, the result with the best relevance ranking will then be at the top of the search engine result page (SERP). 
     The SERP may be constructed on many different technologies. While SERP&#39;s are discussed as web pages for clarity, it should be recognized that many different technologies and communications may be used. For instance, the SERP may be constructed in or contained within HTML, XML, XHTML, RSS technologies, ATOM, AJAX, Document Object Model, email, text, PDF, SMS, push notification and other communications or documents that may hold the SERP result data. 
     An information management subsystem (see  FIG. 32  for more details) may keep the agent profile server data up to date. It may begin gathering when it discovers a new data source and adds a database for that source. Current data within the new database is mined such that it may associate current data with an agent&#39;s history and area of expertise in an agent database. Until a search for an agent is received, the information management subsystem may continue to monitor the data sources and mine any new data for association with an agent&#39;s database. 
     The information system may calculate areas of expertise for agents. An area of expertise is specific knowledge that may be attributed to an agent. The data in the information management subsystem may be used to calculate such areas of expertise. Areas of expertise may include individual characteristics such as an individual area of expertise for a real estate agent in a neighborhood, as evidenced by an agent&#39;s representation of numerous buyers or sellers in a neighborhood. Similarly, an agent may have an aggregate area of expertise such as “First Time Homebuyers” which may be evidenced by individual areas of expertise in a price range, typical customer ages and FHA loans. Discussed above are a large number of factors which may be used in ranking a particular real estate agent as being relevant to a particular home, neighborhood, house type, etc., and any of those factors may be used in compiling results regarding the expertise of an agent. 
     Turning now to  FIG. 23 , a flow chart of term-experience matching subsystem  1100  is shown. While the subsystem process is shown for agent profiles for clarity reasons, it should be recognized that the process is similar for other agents. It should also be recognized that search terms may be broken up into term units. Term units may be further broken down into other term units or combined to make new term units. Therefore, the available number of term units may be greater than the number of original search terms, depending on the analysis algorithm. 
     The search terms  1102  are received by term-experience matching subsystem  1100  and broken down into smaller term units, if possible. Each term unit is examined to see if they match an area of expertise  1104 . If the area of expertise is an exact match or found to be equivalent, the agent database retrieves agent profiles with the requested area of expertise  1106 . The agent profile results are stored for later examination  1108 . The next term unit is then requested  1110  if there are more term units  1112 . If more term units are available, the matching process begins again at whether the term unit matches an area of expertise  1104 . 
     If the term unit does not match an area of expertise  1104 , the term unit will be examined for a modifier  1114 . If the modifier is found, it will be stored for later use  1116 . In either case, whether the modifier is found or not, the system will move to the next term  1110 , if any are available  1112 . 
     A modifier may represent a specific need related to an area of interest. Simple modifiers may include “must,” “not,” and “never.” For instance, if a search requests an area of expertise “must” include farmland, agent profiles without farmland experience should be excluded. Modifiers may also include minimum, maximum, level of desire, level of importance or other ranges, cutoffs, or weights to include in the ranking algorithm. Modifiers may also be implicit in the term units because of order, punctuation, overall context, local context and expected default behavior. 
     Once the term units are exhausted  1112 , the term-experience matching subsystem  1100  checks to make sure that at least one area of expertise was found  1118 . If not, the term-experience matching subsystem  1100  returns a message signaling that there are no results  1120 . In this case, the system suggests that a more generic search should be performed. 
     If at least one area of expertise was found  1118 , term-experience matching subsystem  1100  also checks for modifiers that may alter the ranking algorithm  1122 . If no modifiers are found, then a default modifier for the ranking algorithm will be selected  1124 . The agent profiles are then ranked  1126  and returned with their ranks. 
     The ranking algorithm may result in a general relevance rating for use in comparison with other internet documents, or it may produce an intermediate ranking which may be scaled for relevance comparison with other internet documents. 
     Turning now to  FIG. 24 , a diagram of professional experience to search term ranking subsystem  1130  is shown. A typical agent profile may include years of experience, number of lawsuits pending or resolved, negative or average user reviews, average fees, locations of service, number of clients serviced, office location, licenses/certificates active or inactive, education, and active insurances. The agent to home buyer/seller system, also known as a professional experience to search term ranking subsystem  1130 , may be called a result matching system. 
     Using search terms, area of expertise data  1150  is extracted by term-experience matching subsystem  1100  that was entered by the user  1012  ( FIG. 22 ). These may include, the size and characteristics of homes sold based on bedrooms, bathroom, overall size, lot size cost, neighborhood, etc. Each extracted area of expertise  1152 ,  1154 ,  1156 ,  1158 ,  1160  and  1162  is compared with the stable of homes sold by the agent. The advantage of the matching system is that the agent&#39;s experience is matched to the home characteristics. An agent that normally sells million dollar homes may be inexperienced in selling starter homes because the needs, desires and trade-offs are different. Likewise, an agent well versed in selling single family homes, may have little experience with condominiums and the like. Thus, in accordance with the present invention, it is desirable to improve the match between the customer&#39;s needs and the experience of the agent who will be working with the customers. 
     In one embodiment, the number of bedrooms  1152 , bathrooms  1154 , square footage  1156 , lot size  1158 , price range  1160 , and/or building type or neighborhood  1162  sought by the searcher  1012  are compared  1163  against the number of homes sold by the agent with similar bedrooms  1164 , bathrooms  1166 , square footage  1168 , lot size  1170 , price range  1172  and/or number of homes/condominiums (etc.) sold at or near the neighborhood desired  1174 . This is referred to herein as an agent experience match system (or sometimes as a professional experience match system). 
     In an alternate embodiment, each home purchased or sold by the agent  1175  is rated on the home data  1150  provided by the customer. Each agent home rating is then aggregated to form a match rating. This is referred to herein as an agent home match system. 
     Each of the agent home criteria may be weighted such that inexact matches contribute a partial rating, such as a two bathroom house contributing a partial rating to a desired one and one half bathroom house, because of the close proximity to the desired result. These rating weights may be based on a selected importance by the customer or pre-set by the result matching system. 
     Further, static data may also be included in the result matching system. For example, it has been noted that there is likely a correlation between response time to a customer request and a positive real estate outcome. Therefore, an agent may also be rated on his or her responsiveness to the customer. 
     These static data points may be pre-compiled and stored along with the agent&#39;s other static data, such as contact information. When the result matching system is requested by a customer, a portion of the agent rating may depend on such static data, but the rating overall is dynamic, based on the areas of expertise extracted. 
     In one embodiment, outcomes are monitored for agents and/or customers. For a seller, a larger than expected price (per square foot or other home characteristics), properties they have closed as seller or buyer, if a re-list occurred, property price fluctuations over time compared with the local, state and national markets and the amount of time on the market may be noted and stored as part of the agent&#39;s history with that sale. For a buyer&#39;s agent, similar statistics may be gathered and stored evidencing such characteristics as a lower than expected purchase price, quick closing or other desirable concessions. If no transaction is forthcoming, a failure may also be stored. Customer performance metrics may be monitored and scored as well. Negative reviews decrease match score, while positive reviews and frequent closing will increase match score. These data points may be internally or externally gathered. Thus, the particular success of the agent may be dependent on performance of each real estate deal. 
     In another embodiment, the agent&#39;s history or “track record” is overlaid on a baseline or “hyper-local comparable market average” to evaluate the effectiveness of a realtor. For example, the price per square foot of the properties in each transaction that a realtor represented is compared with the geographically immediate comparable sales price per square foot (in last 6 months). The sale price versus listing price (“spread”) is evaluated relative to the same population and days on market (“DOM”). The system also evaluates whether the agent represented the buy or sell side compared with the current home characteristics request. The results of the comparisons are then aggregated such that a value may be given to agent&#39;s history relative to the current home characteristics request. 
     For example, a buy side performance measure may compare a $212 price per square foot versus an average market of $289 per square foot and give a positive match rating of +40 points. A seven percent list versus sell spread compared with a four percent average may give a match rating of +10 points. These points are then aggregated in a total match rating for the agent compared with the house characteristics. 
     In another example, a sell side performance may compare 52 DOM versus an average of 92 DOM in the market and give +20 points match rating. A $252 price per square foot received versus a market average of $221 per square foot market average may give +50 points match rating. These points are then aggregated in a total match rating for the agent compared with the house characteristics. 
     In one embodiment the match ratings are normalized such that the top match point value is given a rating of 99%. This communicates that the result matching system believes the match to be the best agent for the job, but that there may be uncertainties. 
     External data sources may be monitored and mined. These data sources may be from private or public bodies. The result matching system may connect to external systems such as Multiple Listing Services (MLS) or information brokerages. The result matching system may also connect to public data sources as well, including government sources, such as tax data, title data or even agent discipline data. These sources can then be used in the matching algorithm. For instance, an agent that turns up disciplinary violations may rank lower than a similar agent without those violations. 
     Turning now to  FIG. 25 , a search engine result page (SERP)  1178  with organic experienced agent results is shown. A user  1012  (not shown) may type in search terms  1180  within a search box  1182  and initiate a search such as a click on activate a search button  1184 . Resulting SERP  1178  may be shown in response to such request. 
     In one embodiment, the SERP may contain both standard results  1186 A and  1186 B in combination with expertise results  1188 A and  1188 B. All of the results are organic, meaning non-paid SERP results. The advantage is that the user is presented with both classical ranking documents with expertise based profiles to present relevant results based on the search terms entered. 
     More specifically, the term-experience matching subsystem and the result matching system recognized that 1600 Pennsylvania Ave in Washington D.C. has specific spotlights of an “estimated value of $300 Million”  190 , “16 bedrooms”  192  and “35 bathrooms”  1194 . The system may have been able to gather other facts that are implicit in the search such as the typical age of the residents of the neighborhood and family composition of the neighborhood residents. The result matching system may then compile and rank the agents according to their areas of expertise relative to the search terms. The SERP result for the agents may then contain descriptions relating to which area or areas of expertise were significant in their rating for the search terms. In this embodiment, the result matching system decided that J. Cardella&#39;s price range  1196  was significant, while J Thompson&#39;s experience in similar number of bedrooms and bathrooms were significant. 
     Having decided that the results were significant, the system then combines the expertise results  1188 A and  1188 B with standard results  1186 A and  1186 B. The result ordering is based off an overall relevance rating that compares a normalized expertise rating with classical result ratings. The expertise results use the agent profile information to compose expertise results  1188 A and  1188 B to appear as classical results  1186 A and  1186 B in the SERP. In one embodiment, it pulls the agent name  1198 A and  1198 B and agent profile link  1200 A and  1200 B. Because the SERP looks the same, the users are already familiar with its functionality, but are now given relevant results that may be more relevant than before. 
     These areas of expertise spotlighted in  1190 ,  1192 ,  1194  and  1196  are beneficial because they increase the confidence of the user in the relevance of the results. For instance, a user may be looking at a specific property for a number of different reasons. A user looking at a price range may be attracted to price range spotlight  1196 . Another user may care about bedrooms and view bedroom spotlight  1192  as more relevant. Thus, in either case, the user is given information that may direct her to the desired information. 
     Turning now to  FIG. 26 , a search engine result page  1178  with profile enhanced organic experienced agent results  1202 A and  1202 B is shown. Expertise results may be further enhanced or differentiated from classical results by the addition of profile information. This profile information may include profile pictures  1204 A and  1204 B, certifications, areas of expertise, related areas to consider and other information available in the profile data. The profile information may stem from single data entries such as a profile link or be combinations of data entries such as an average house price or area of expertise. 
     The enhanced organic experienced agent results  1202 A and  1202 B may be enhanced with the same or similar information, or further differentiated by selecting to display different profile information. In one embodiment, the enhanced organic experienced agent results  1202 A and  1202 B are enhanced with similar information to have a consistent user experience and allow for direct comparison. In another embodiment, the enhanced organic experienced agent results  1202 A and  1202 B vary the information presented such that a user who may not find one result relevant may find relevant information in another result. For example, one person may not find the price range spotlight  1196  relevant (or of higher importance), but may find the bedroom spotlight  1192  and bathroom spotlight  194  relevant (or of higher importance). 
     Having expertise results does not mean that portions of the agent profile website will not receive classical result ranking. In fact, profile aggregation page  1206  may rank high in a classical ranking algorithm because it contains relevant information related to the search terms. 
     Turning now to  FIG. 27 , a search engine result page with organic experienced agent results and advertising that includes relevant certifications is shown. In addition to the organic results seen by  1186 A,  1202 A,  1206  and  1202 B, search system  1010  (not shown) may also include a sidebar  1208 . The sidebar  1208  may include advertising or further information for the user to consider. 
     The search system  1010  may show a sidebar  1208  populated with agent listings  1210 A,  1210 B,  1210 C and  1210 D. Agent listings  1210 A,  1210 B,  1210 C and  1210 D may be ranked or displayed based on factors that may include their relevance to the search terms. The listings may include profile information, including names of agents  1212 A,  1212 B,  1212 C and  1212 D. The profile may further be enhanced by certifications, which may include certifications of areas of expertise  1214 A,  1214 B,  1214 C and  1214 D by the professional experience matching subsystem or information management subsystem. 
     The certifications may be from individual certifications or aggregate certifications. For example, an agent who has certifications such as experience in a price range, typical customer ages and FHA loans may show the aggregate certification in “First Time Homebuyers” to a customer who may be searching relevant houses to a “First Time Homebuyer.” However, to a customer that is not in the age nor price range, but has searched on terms relevant to FHA loans, the agent profile may display the individual certification of “FHA Experienced” in the place of “First Time Homebuyer.” 
     Agent listings  1210 A,  1210 B,  1210 C and  1210 D may be selected for display in the sidebar based on a separate ranking system for the organic results. In one embodiment, the sidebar  1208  comprises paid advertisements. The payment and relevance may then be used to decide if the agent listings  1210 A,  1210 B,  1210 C and  1210 D will show in the sidebar  1208 . 
     In another embodiment, the agent listings  1210 A,  1210 B,  1210 C and  1210 D are supplemental results that are selected for sidebar display. These results may or may not have been included in the organic results. These results may include a diverse selection of certifications or other information presented by the professional experience matching subsystem. 
     In fact, the sidebar may be used in cases where the search engine does not include agent results within the organic results because the results did not rank high enough or the search engine has not included agent results within the organic results. The sidebar may therefore add useful information to current search engines, without much change to their current system. 
     Turning now to  FIG. 28 , a search engine result page with profile enhanced organic experienced agent results and advertising that includes relevant certifications is shown. In one embodiment enhanced organic experienced agent results  1202 A and  1202 B are used in conjunction with the sidebar  1208 . 
     Turning now to  FIG. 29 , a search engine result page with agent results as supplemental to the organic result for the domain is shown. Instead of being shown as their own result, agent result  1216 A and  1216 B may be shown as supplements to the organic result of a classically ranked page such as profile aggregation page  1206 . These supplemental results preserve the classical ranking algorithm of the search engine because they attach to the rank of their parent result, but provide useful results to the user based on their search terms. 
     Turning now to  FIG. 30 , a search engine result page with profile enhanced agent results as supplemental to the organic result for the domain with advertising that includes relevant certifications is shown. The system may combine enhanced supplemental agent listings  1218 A and  1218 B with a sidebar  1208  and other features previously discussed. 
     Turning now to  FIG. 31 , a search engine result page with certified agent results as supplemental to the organic result for the domain is shown. Supplemental results may have different formats, which may include information available from the profile data or information subsystem. These formats may be altered to suit the search engine style. In one embodiment, the supplement results may be supplemental certified results  1220 A,  1220 B,  1220 C and  1220 D which include profile pictures, names and links to the profile pages. 
     A website search with certified agent results related to the search and website may also be prepared. Searches may also be based on contextual information. In one embodiment, a user performs a search on a brokerage website. In addition to classical results, the system may use the contextual information to return certified agent results relating to the context of the website, or in this case, agents within the brokerage. In one embodiment, these results are placed under an organic listing for the agent rating service result  1228 . The results may contain certifications relating to the context of the website. 
     The context may be predetermined from an aggregate search of the website or from a view of the current search page. In one embodiment, the search code is sent information about the website to give the search engine context. The information may be contained in an invisible box in the search form or even part of a JavaScript code. 
     Turning now to  FIG. 32 , a diagram of data collection server and its connection to other content servers is shown. Search system  1010  may comprise an information management subsystem  1232  with a data collection server  1234  connected to information storage  1236 . The data collection server may query database server  1238  to collect information from its various databases  1240 A and  1240 B. The data collection server may also request documents  1242  from document server  1244 . The requests and transmissions may be serviced by a network including the internet  1018 . 
     The documents  1242  and databases  1240 A and  1240 B may be public or private, paid or unpaid information. By aggregating the data, information management subsystem  1232  may provide agent data and statistics for the rating of agents. 
     While multiple servers and subsystems have been mentioned, it should be understood that the servers and subsystems may reside on a single machine or a single server or subsystem may span multiple machines. 
     While the focus of the discussion has been on residential real-estate, it should be appreciated that commercial real estate or other forms of real estate agent matching would be similar. While specific data fields may be different, the desired fields may be compared against the agent&#39;s past real estate transaction fields to prepare a valuation of that agent. 
     There is thus disclosed an improved system for matching property characteristics or desired property characteristics to real estate agent experience. It will be appreciated that numerous changes may be made to the present invention without departing from the scope of the claims.