The real estate industry is currently obsessed with controlling and protecting property listing data. Unfortunately for those engaging in this strategy, the real estate market is changing and real estate listings are becoming -- and will continue to become -- less and less controllable. How long has it been since you called a travel agent or the airline for information on flights and fares? How long will it be before detailed listing information is readily available to consumers? It already is in many markets. In the long run, attempts to keep listing information bottled-up and under the control of real estate brokers and associations will prove futile.
The irony is that the Realtor community in its frantic quest to protect the listing is overlooking a surefire winning strategy: go where they can't! Instead of fighting the losing battle of controlling online listings, set the listings free and take possession of the high ground. What is this "high ground?" It is consumer behavior and market behavior. This behavioral data, used as input to analytical models, can answer vital questions such as:
--What's the best price for this house?
--How long will it take to sell at that price?
--What's the marketability of this type of house in this neighborhood?
--Who are the hot clients and who are not?
--Is this neighborhood a good place to buy a house?
--What could I get for my house in today's market?
--When might a home buyer client be expected to make a purchase decision?
--When will my client be ready for a new house and what kind of house will it be?
--Where are the best opportunities for new construction and what segments are overbuilt?
--What are the likes and dislikes of each of my home buyer clients?
--What is the market appeal index of each of my seller's homes?
--Is it time to consider a price reduction or other marketing action?
--What are the seven habits of highly effective real estate agents?
--What are the seven habits of highly ineffective real estate agents?
Why can National Association of Realtors-sanctioned MLS associations do this when others cannot? The simple, but powerful, answer is that MLS associations are rules-driven, rules-enforcing organizations. Association membership requires strict adherence to data recording rules. Under these conditions, market behavior data is easy to collect, but it has to be used. This means actually developing predictive behavioral models the use the data as input. The model outputs are answers to the questions listed above.
What is the recipe for taking possession of this high ground?
Before describing the recipe, there are four requirements that must be met:
1. An information system that captures the data and serves the needs of every party -- consumer, agent, and others.
2. A data warehouse for storing and quickly accessing the extensive market and consumer behavior that will be captured.
3. Analytical and predictive information models that use market and consumer behavioral information as inputs.
4. Aggregated data to feed the information models and from which data can be mined to create additional and more effective models.
The winning recipe for technology developers:
1. Organize your "kitchen" with a browser-based framework into which an almost unlimited variety of functional components can be plugged. The infrastructure needed by any component is provided by the framework so that components are independent.
2. Stir in a variety of functional components in flavors to suit virtually any taste. The framework comes with a library of basic components, but this is only a starting place for third-party development. Extensive documentation accompanies the framework so that third parties can develop components for their own use or for resale.
3. Flavor the user experience your way. There is no standard recipe here! The framework is "skinnable" and customizable so both look-and-feel and navigation can be easily modified by a para-professional. This allows the user experience to be customized so that no two Web sites need be alike.
4. Throw in a large measure of collaboration. Spam, spam blockers, and e-mail jumble make e-mail an unreliable way to communicate. Avoid e-mail uncertainties by giving each party a personal Web space where they can do their thing and collaborate with each other.
5. Add copious quantities of consumer and market data. Good information requires good data, and an information system is only as good as the data it holds. Large data warehouses are needed for use in data mining operations, and as input to predictive and analytical information models.
6. Combine the ingredients into a state-of-the-art, customizable system. Package the framework and a basic set of components as open software, and provide an SDK (Software Developer's Kit) and documentation for anyone wishing to develop additional components or finished systems.
7. Bake it in a pan that has whatever shape you prefer. The framework and components are "roles-aware" so what each user (consumer, client, agent, appraiser, loan officer, etc.) can see and do is determined by the role or roles they are assigned. Do you want sellers to be able to see the Web-hit statistics on their property? Then allow listing agents to assign a seller-role to any or all of their listing clients.
8. Make your creation the piece de resistance with information models that provide answers to important questions relating to consumers and the market. This type of information goes way beyond a simple catalog of listings to create a win both for the consumer and the Realtor.
The secret ingredient: Shared aggregation
Every winning recipe has a secret ingredient and this one is no exception. The secret is shared aggregation. Realtor associations recognized this secret ingredient years ago when they agreed to share listings. But that was yesterday. Today's secret ingredient is the aggregation and sharing of data on consumer and market behavior, which includes answers to questions like:
--What are consumers saving as favorites?
--How are they ranking each of their saved favorites?
--What listings are they deleting from their favorites and new matches?
--What are they specifying in searches?
--What properties are they viewing online?
--How often do they visit their online Web space and what do they do while they are there?
--How do these behaviors relate to the time it takes to make a buy decision?
--When did the list price of each active listing change and by how much?
--On what date did each status change occur for each listing?
--How many new listings came on the market in any given time period and how many were sold during that same period?
The National Association of Realtors must lead the way to listing information that is freely shared between members and consumers. The goal should be to capture buying and selling consumers with comprehensive and complete listing information. Once captured, the goal must be to deliver value-added services -- many of which are made possible by information models driven by consumer and market data.
Read more: http://www.inman.com/inmannews.aspx?ID=51642