I’ve been looking at some of the “next generation” of consumer real estate search – beyond “natural language” search – this search includes commute time to jobs, cost of living preferences (insurance, entertainment, housing, utilities, etc.), distance to amenities (parks, grocery store, gyms, golf, airport, etc.), school information and performance, crime/safety information, and other demographics (own/rent, age, occupation type, education, income, family size), neighborhood and property foreclosure information, as well as all the traditionally searchable MLS fields. Consumer sites are also starting to get more sophisticated about suggesting homes that are similar to others the consumer has displayed an interest in and ordering the search results on relevance to all of the aforementioned lifestyle and demographic criteria and weighting as indicated by the consumer.
In contrast, MLS is still mostly focused on searching and displaying the listing characteristics, and prospect searches are often displayed in order of price rather than on the more complicated criteria that consumers use to select neighborhoods and homes. I reflected on this limitation to some degree in my earlier blog post, “Improving Prospecting Part 2 – Gesture and Intent and Beyond.”
Now, imagine the consumer goes through the effort of outlining their lifestyle and other non-listing-characteristic criteria on a web site and are presented with the carefully selected listings that match both their property characteristic criteria as well as all those other parameters. When they go to the real estate professional, that professional has no way of inputting any of that into their MLS for search – let alone having a way (say, via RETS) to have all of that preference information flow automatically into the MLS from the consumer’s search site(s) to generate a search for their new prospect.
MLSs can’t get complacent about new property search capabilities and leave them to consumer oriented websites alone to implement. As I’ve described above, there’s a relationship between consumer search and professional search that will necessitate, at the very least, following in the use of these capabilities and implementing the means for consumer preference data to flow from system to system. Or even better, real estate professional IT systems can lead those accessed by the consumer, allowing the professional to provide the consumer with additional professional-grade information and interpretation that helps maintain the real estate professional’s value.
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